xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

9dadcf359a8cb4d146fb46a1fbc9d8cd9138f7b0

9,257

py

Python

Machine-Learning-in-Production/00-Course-Overview.py

databricks-academy/ml-in-production

1fd6713e18cfc36357f3a98d75fedc8ffbf9eedc

[ "CC0-1.0" ]

14

2021-09-21T19:48:02.000Z

2022-03-09T19:22:39.000Z

Machine-Learning-in-Production/00-Course-Overview.py

databricks-academy/ml-in-production

1fd6713e18cfc36357f3a98d75fedc8ffbf9eedc

[ "CC0-1.0" ]

null

Machine-Learning-in-Production/00-Course-Overview.py

databricks-academy/ml-in-production

1fd6713e18cfc36357f3a98d75fedc8ffbf9eedc

[ "CC0-1.0" ]

5

2021-08-22T12:12:49.000Z

2022-02-28T15:47:43.000Z

# 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 # Machine Learning in Production # MAGIC ### Managing the Complete Machine Learning Lifecycle with MLflow, Deployment and CI/CD # MAGIC # MAGIC In this 1-day course, machine learning engineers, data engineers, and data scientists learn the best practices for managing the complete machine learning lifecycle from experimentation and model management through various deployment modalities and production issues. Students begin with end-to-end reproducibility of machine learning models using MLflow including data management, experiment tracking, and model management before deploying models with batch, streaming, and real time as well as addressing related monitoring, alerting, and CI/CD issues. Sample code accompanies all modules and theoretical concepts. # MAGIC # MAGIC First, this course explores managing the experimentation process using MLflow with a focus on end-to-end reproducibility including data, model, and experiment tracking. Second, students operationalize their models by integrating with various downstream deployment tools including saving models to the MLflow model registry, managing artifacts and environments, and automating the testing of their models. Third, students implement batch, streaming, and real time deployment options. Finally, additional production issues including continuous integration, continuous deployment are covered as well as monitoring and alerting. # MAGIC # MAGIC By the end of this course, you will have built an end-to-end pipeline to log, deploy, and monitor machine learning models. This course is taught entirely in Python. # MAGIC # MAGIC ## Lessons # MAGIC # MAGIC | Time | Lesson                                                                       | Description                                                                                                                                                                                                                                                   | # MAGIC |:----:|-------|-------------| # MAGIC | 30m | **Introductions & Setup** | *Registration, Courseware & Q&As* | # MAGIC | 30m | **ML in Production Overview** | Introducing the full end-to-end ML lifecycle | # MAGIC | 10m | **Break** || # MAGIC | 20m | **[Experimentation - Data Management]($./01-Experimentation)** | [Manage data with Delta & Databricks Feature Store]($./01-Experimentation/01-Data-Management) | # MAGIC | 40m | **[Experimentation - Experiment Tracking & Lab]($./01-Experimentation)** | [Track ML experiment with MLflow]($./01-Experimentation/02-Experiment-Tracking) [Experiment Tracking Lab]($./01-Experimentation/Labs/02-Experiment-Tracking-Lab) | # MAGIC | 10m | **Break** || # MAGIC | 30m | **[Experimentation - Advanced Experiment Tracking & Lab]($./01-Experimentation)** | [Advanced Experiment Tracking]($./01-Experimentation/03-Advanced-Experiment-Tracking) [Advanced Experiment Tracking Lab (Optional)]($./01-Experimentation/Labs/03-Advanced-Experiment-Tracking-Lab) | # MAGIC | 30m | **[Model Management - MLflow Models & Lab]($./02-Model-Management)** | [Model management with MLflow]($./02-Model-Management/01-Model-Management) [Model managment lab]($./02-Model-Management/Labs/01-Model-Management-Lab) | # MAGIC | | **Break** || # MAGIC | 35m | **[Model Management - Model Registry]($./02-Model-Management)** | [Register, version, and deploy models with MLflow]($./02-Model-Management/02-Model-Registry) | # MAGIC | 25m | **[Model Management - Webhooks]($./02-Model-Management)** | [Create a testing job and a webhook of registered model]($./02-Model-Management/03a-Webhooks-and-Testing) [Automated Testing]($./02-Model-Management/03b-Webhooks-Job-Demo)| # MAGIC | 10m | **Break** || # MAGIC | 60m |**[Deployment Paradigms]($./03-Deployment-Paradigms)** | [Batch]($./03-Deployment-Paradigms/01-Batch) [Real time]($./03-Deployment-Paradigms/02-Real-Time) [Streaming (Reference)]($./Reference/03-Streaming-Deployment) [Labs]($./03-Deployment-Paradigms/Labs)| # MAGIC | 10m | **Break** || # MAGIC | 60m | **[Production]($./04-Production)** | [Monitoring]($./04-Production/01-Monitoring) [Monitoring Lab]($./04-Production/Labs/01-Monitoring-Lab)[Alerting (Reference)]($./Reference/02-Alerting) [Pipeline Example (Reference)]($./Reference/04-Pipeline-Example/00-Orchestrate)| # MAGIC # MAGIC # MAGIC ## Prerequisites # MAGIC - Experience with Python (`pandas`, `sklearn`, `numpy`) # MAGIC - Background in machine learning and data science # MAGIC # MAGIC ## Cluster Requirements # MAGIC - See your instructor for specific requirements # MAGIC # MAGIC <img src="https://files.training.databricks.com/images/icon_warn_24.png"/> **Certain features used in this course, such as the notebooks API and model registry, are only available to paid or trial subscription users of Databricks.** # MAGIC If you are using the Databricks Community Edition, click the `Upgrade` button on the landing page <a href="https://accounts.cloud.databricks.com/registration.html#login" target="_blank">or navigate here</a> to start a free trial. # COMMAND ---------- # MAGIC %md # MAGIC ## ![Spark Logo Tiny](https://files.training.databricks.com/images/105/logo_spark_tiny.png) Classroom-Setup # MAGIC # MAGIC For each lesson to execute correctly, please make sure to run the **`Classroom-Setup`** cell at the start of each lesson (see the next cell). # COMMAND ---------- # MAGIC %run ./Includes/Classroom-Setup # COMMAND ---------- # MAGIC %md ### Agile Data Science # MAGIC # MAGIC Deploying machine learning models into production comes with a wide array of challenges, distinct from those data scientists face when they're initially training models. Teams often solve these challenges with custom, in-house solutions that are often brittle, monolithic, time consuming, and difficult to maintain. # MAGIC # MAGIC A systematic approach to the deployment of machine learning models results in an agile solution that minimizes developer time and maximizes the business value derived from data science. To achieve this, data scientists and data engineers need to navigate various deployment solutions as well as have a system in place for monitoring and alerting once a model is out in production. # MAGIC # MAGIC The main deployment paradigms are as follows: # MAGIC # MAGIC 1. **Batch:** predictions are created and stored for later use, such as a database that can be queried in real time in a web application # MAGIC 2. **Streaming:** data streams are transformed where the prediction is needed soon after it arrives in a data pipeline but not immediately # MAGIC 3. **Real time:** normally implemented with a REST endpoint, a prediction is needed on the fly with low latency # MAGIC 4. **Mobile/Embedded:** entails embedding machine learning solutions in mobile or IoT devices and is outside the scope of this course # MAGIC # MAGIC Once a model is deployed in one of these paradigms, it needs to be monitored for performance with regards to the quality of predictions, latency, throughput, and other production considerations. When performance starts to slip, this is an indication that the model needs to be retrained, more resources need to be allocated to serving the model, or any number of improvements are needed. An alerting infrastructure needs to be in place to capture these performance issues. # COMMAND ---------- # MAGIC %md-sandbox # MAGIC © 2021 Databricks, Inc. All rights reserved. # MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the <a href="http://www.apache.org/">Apache Software Foundation</a>. # MAGIC # 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="http://help.databricks.com/">Support</a>

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171

bzl

Python

dotnet/private/deps/gen/base.bzl

nolen777/rules_mono

b49c210478c2240fcc7be655c9fc37d751610fb1

[ "Apache-2.0" ]

null

dotnet/private/deps/gen/base.bzl

nolen777/rules_mono

b49c210478c2240fcc7be655c9fc37d751610fb1

[ "Apache-2.0" ]

null

dotnet/private/deps/gen/base.bzl

nolen777/rules_mono

b49c210478c2240fcc7be655c9fc37d751610fb1

[ "Apache-2.0" ]

null

load("@rules_mono//dotnet/private:rules/nuget.bzl", "nuget_package") def dotnet_repositories_nuget(): ### Generated by the tool ### End of generated by the tool

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py

Python

tests/TestGoods.py

DaveTCode/tradingsim

4e7fe5389d9af9a0a34ca23b9e42e7e366a71966

[ "MIT" ]

null

tests/TestGoods.py

DaveTCode/tradingsim

4e7fe5389d9af9a0a34ca23b9e42e7e366a71966

[ "MIT" ]

null

tests/TestGoods.py

DaveTCode/tradingsim

4e7fe5389d9af9a0a34ca23b9e42e7e366a71966

[ "MIT" ]

null

import random import unittest from tradingsim.simulation.goods import Goods class GoodsTests(unittest.TestCase): def test_str(self): good = Goods("test_good", 1, 1, 1, 1) self.assertEqual(str(good), "test_good") def test_purchase_cost_of_one(self): good = Goods("test_good", 1, 1, 1, 1) self.assertAlmostEqual(good.purchase_cost_of_one(1), 1) good = Goods("a", 10, 8, 100, 20) self.assertAlmostEqual(good.purchase_cost_of_one(0), 10) self.assertAlmostEqual(good.purchase_cost_of_one(15), 10) # Check that the max_cost_amount works self.assertAlmostEqual(good.purchase_cost_of_one(20), 10) # Check that the max_cost_amount works on boundary self.assertAlmostEqual(good.purchase_cost_of_one(100), 8) self.assertAlmostEqual(good.purchase_cost_of_one(200), 8) self.assertAlmostEqual(good.purchase_cost_of_one(60), 9) self.assertAlmostEqual(good.purchase_cost_of_one(41), 9) self.assertAlmostEqual(good.purchase_cost_of_one(39), 8) self.assertAlmostEqual(good.purchase_cost_of_one(81), 10) self.assertAlmostEqual(good.purchase_cost_of_one(79), 9) def test_purchase_cost(self): good = Goods("a", 10, 8, 100, 20) self.assertAlmostEqual(good.purchase_cost(20, 20), 10 * 20) self.assertAlmostEqual(good.purchase_cost(60, 1), 9) self.assertAlmostEqual(good.purchase_cost(100, 80), 719) self.assertAlmostEqual(good.purchase_cost(60, 2), 18) self.assertAlmostEqual(good.purchase_cost(45, 10), 5 * 9 + 5 * 8) def test_sale_cost_of_one(self): good = Goods("test_good", 1, 1, 1, 1) self.assertAlmostEqual(good.sale_cost_of_one(1), 1) good = Goods("a", 10, 8, 100, 20) self.assertAlmostEqual(good.sale_cost_of_one(0), 10) self.assertAlmostEqual(good.sale_cost_of_one(100), 8) self.assertAlmostEqual(good.sale_cost_of_one(60), 9) self.assertAlmostEqual(good.sale_cost_of_one(41), 9) self.assertAlmostEqual(good.sale_cost_of_one(39), 10) self.assertAlmostEqual(good.sale_cost_of_one(81), 8) self.assertAlmostEqual(good.sale_cost_of_one(79), 9) def test_sale_cost(self): good = Goods("a", 10, 8, 100, 20) self.assertAlmostEqual(good.sale_cost(20, 20), 10 * 20) self.assertAlmostEqual(good.sale_cost(60, 1), 9) self.assertAlmostEqual(good.sale_cost(20, 80), 721) # Shouldn't this be 719? Rounding issues maybe. self.assertAlmostEqual(good.sale_cost(60, 2), 18) self.assertAlmostEqual(good.sale_cost(45, 10), 5 * 9 + 5 * 10) def test_sale_less_than_purchase(self): for i in range(100): g = Goods("A", random.randint(10, 200), random.randint(0, 10), random.randint(5, 10), random.randint(1, 4)) self.assertEqual(g.sale_cost(10, 2), g.purchase_cost(8, 2))

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d1baae2a3a62c041521eaf4781454470ad51b69a

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py

Python

amocrm_asterisk_ng/scenario/impl/classic/functions/core/__init__.py

iqtek/amocrn_asterisk_ng

429a8d0823b951c855a49c1d44ab0e05263c54dc

[ "MIT" ]

null

amocrm_asterisk_ng/scenario/impl/classic/functions/core/__init__.py

iqtek/amocrn_asterisk_ng

429a8d0823b951c855a49c1d44ab0e05263c54dc

[ "MIT" ]

null

amocrm_asterisk_ng/scenario/impl/classic/functions/core/__init__.py

iqtek/amocrn_asterisk_ng

429a8d0823b951c855a49c1d44ab0e05263c54dc

[ "MIT" ]

null

from .IGetCallDirectionFunction import IGetCallDirectionFunction from .IsInternalNumberFunction import IsInternalNumberFunction

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Python

Packs/TaniumThreatResponse/Integrations/TaniumThreatResponse/TaniumThreatResponse_test.py

diCagri/content

c532c50b213e6dddb8ae6a378d6d09198e08fc9f

[ "MIT" ]

799

2016-08-02T06:43:14.000Z

2022-03-31T11:10:11.000Z

Packs/TaniumThreatResponse/Integrations/TaniumThreatResponse/TaniumThreatResponse_test.py

diCagri/content

c532c50b213e6dddb8ae6a378d6d09198e08fc9f

[ "MIT" ]

9,317

2016-08-07T19:00:51.000Z

2022-03-31T21:56:04.000Z

Packs/TaniumThreatResponse/Integrations/TaniumThreatResponse/TaniumThreatResponse_test.py

diCagri/content

c532c50b213e6dddb8ae6a378d6d09198e08fc9f

[ "MIT" ]

1,297

2016-08-04T13:59:00.000Z

2022-03-31T23:43:06.000Z

PROCESS_TREE_RAW = [ { "id": 3, "ptid": 3, "pid": 1, "name": "1: <Pruned Process>", "parent": "4: System", "children": [ { "id": 44, "ptid": 44, "pid": 4236, "name": "4236: mmc.exe", "parent": "1: <Pruned Process>", "children": [] }, { "id": 45, "ptid": 45, "pid": 4840, "name": "4840: cmd.exe", "parent": "1: <Pruned Process>", "children": [] } ] } ] PROCESS_TREE_TWO_GENERATIONS_RAW = [ { "id": 3, "ptid": 3, "pid": 1, "name": "1: <Pruned Process>", "parent": "4: System", "children": [ { "id": 44, "ptid": 44, "pid": 4236, "name": "4236: mmc.exe", "parent": "1: <Pruned Process>", "children": [ { "id": 420, "ptid": 44, "pid": 4236, "name": "4236: mmc.exe", "parent": "1: <Pruned Process>", "children": [] } ] } ] } ] PROCESS_TREE_ITEM_RES = { "ID": 3, "PTID": 3, "PID": 1, "Name": "1: <Pruned Process>", "Parent": "4: System", "Children": [ { "ID": 44, "PTID": 44, "PID": 4236, "Name": "4236: mmc.exe", "Parent": "1: <Pruned Process>", "Children": [] }, { "ID": 45, "PTID": 45, "PID": 4840, "Name": "4840: cmd.exe", "Parent": "1: <Pruned Process>", "Children": [] } ] } PROCESS_TREE_ITEM_TWO_GENERATIONS_RES = { "ID": 3, "PTID": 3, "PID": 1, "Name": "1: <Pruned Process>", "Parent": "4: System", "Children": [ { "ID": 44, "PTID": 44, "PID": 4236, "Name": "4236: mmc.exe", "Parent": "1: <Pruned Process>", "Children": [ { "id": 420, "ptid": 44, "pid": 4236, "name": "4236: mmc.exe", "parent": "1: <Pruned Process>", "children": [] } ] } ] } PROCESS_TREE_READABLE_RES = { "ID": 3, "PTID": 3, "PID": 1, "Name": "1: <Pruned Process>", "Parent": "4: System", "Children": [ { "ID": 44, "PTID": 44, "PID": 4236, "Name": "4236: mmc.exe", "Parent": "1: <Pruned Process>", "ChildrenCount": 0 }, { "ID": 45, "PTID": 45, "PID": 4840, "Name": "4840: cmd.exe", "Parent": "1: <Pruned Process>", "ChildrenCount": 0 } ] } PROCESS_TREE_TWO_GENERATIONS_READABLE_RES = { "ID": 3, "PTID": 3, "PID": 1, "Name": "1: <Pruned Process>", "Parent": "4: System", "Children": [ { "ID": 44, "PTID": 44, "PID": 4236, "Name": "4236: mmc.exe", "Parent": "1: <Pruned Process>", "ChildrenCount": 1 } ] } def test_get_process_tree_item(): from TaniumThreatResponse import get_process_tree_item tree, readable_output = get_process_tree_item(PROCESS_TREE_RAW[0], 0) assert tree == PROCESS_TREE_ITEM_RES assert readable_output == PROCESS_TREE_READABLE_RES def test_get_process_tree_item_two_generations(): from TaniumThreatResponse import get_process_tree_item tree, readable_output = get_process_tree_item(PROCESS_TREE_TWO_GENERATIONS_RAW[0], 0) assert tree == PROCESS_TREE_ITEM_TWO_GENERATIONS_RES assert readable_output == PROCESS_TREE_TWO_GENERATIONS_READABLE_RES

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py

Python

trax/trax/exceptions.py

christianlupus/trax

85af6f908cbf55584f74856207ae3f6530728ccb

[ "MIT" ]

4

2021-01-19T16:12:24.000Z

2021-08-05T07:25:44.000Z

trax/trax/exceptions.py

christianlupus/trax

85af6f908cbf55584f74856207ae3f6530728ccb

[ "MIT" ]

1

2021-03-18T20:44:01.000Z

trax/trax/exceptions.py

christianlupus/trax

85af6f908cbf55584f74856207ae3f6530728ccb

[ "MIT" ]

1

2021-08-16T01:10:52.000Z

from django.forms import ValidationError class HandleError(ValidationError): pass

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py

Python

src/processor.py

RaymondLZhou/job-scraper

9de07729051357276a5b5c9d2892bb763ff61330

[ "MIT" ]

null

src/processor.py

RaymondLZhou/job-scraper

9de07729051357276a5b5c9d2892bb763ff61330

[ "MIT" ]

null

src/processor.py

RaymondLZhou/job-scraper

9de07729051357276a5b5c9d2892bb763ff61330

[ "MIT" ]

null

import linker # Cleans and appends results from Monster based on page HTML layout def processDataMonster(job_elems, source, jobList, titles, companies, locations, times, links, sources): for job_elem in job_elems: title_elem = job_elem.find("h2", class_="title") company_elem = job_elem.find("div", class_="company") location_elem = job_elem.find("div", class_="location") time_elem = job_elem.find("div", class_="meta flex-col") # Clean results title, company, location, time = linker.verifyData(title_elem, company_elem, location_elem, time_elem) # Ignore empty results if(title == "" and company == "" and location == "" and time == ""): continue time = time.split('\n')[0] link = job_elem.find("a")["href"] # Append data to lists linker.appendData(title, company, location, time, link, source, jobList, titles, companies, locations, times, links, sources) # Cleans and appends results from Indeed based on page HTML layout def processDataIndeed(job_elems, source, jobList, titles, companies, locations, times, links, sources): for job_elem in job_elems: title_elem = job_elem.find('h2', class_='title') company_elem = job_elem.find('span', class_='company') location_elem = job_elem.find('div', class_='location') time_elem = job_elem.find("span", class_="date") if(location_elem is None): location_elem = job_elem.find('span', class_='location') # Clean results title, company, location, time = linker.verifyData(title_elem, company_elem, location_elem, time_elem) # Ignore empty and irrelevant results if(title == "" and company == "" and location == "" and time == ""): continue if(company == "The Sydney Call Centre"): continue link = "https://ca.indeed.com" + job_elem.find("a")["href"] # Append data to lists linker.appendData(title, company, location, time, link, source, jobList, titles, companies, locations, times, links, sources)

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py

Python

module.py

Eve-AI/package-template

c079e2da9e50f73a0e68d38dc836be4247dbfc5e

[ "MIT" ]

null

module.py

Eve-AI/package-template

c079e2da9e50f73a0e68d38dc836be4247dbfc5e

[ "MIT" ]

null

module.py

Eve-AI/package-template

c079e2da9e50f73a0e68d38dc836be4247dbfc5e

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding:utf-8 -*- import utils def run(string, entities): # utils.output('end', 'intent', utils.translate('intent')) pass

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6

ee3ed8072b4fa02938e3adbc5e83178be3ac350f

12,241

py

Python

pydlm/access/dlmAccessMod.py

onnheimm/pydlm

4693af6e621e3b75feda7ca15327b69a4ca622a7

[ "BSD-3-Clause" ]

423

2016-09-15T06:45:26.000Z

2022-03-29T08:41:11.000Z

pydlm/access/dlmAccessMod.py

onnheimm/pydlm

4693af6e621e3b75feda7ca15327b69a4ca622a7

[ "BSD-3-Clause" ]

50

2016-09-14T19:45:49.000Z

2021-07-26T17:04:10.000Z

pydlm/access/dlmAccessMod.py

onnheimm/pydlm

4693af6e621e3b75feda7ca15327b69a4ca622a7

[ "BSD-3-Clause" ]

99

2016-09-19T08:08:41.000Z

2022-03-07T13:47:36.000Z

from copy import deepcopy from pydlm.base.tools import getInterval from pydlm.access._dlmGet import _dlmGet class dlmAccessModule(_dlmGet): """ A dlm module for all the access methods """ def getAll(self): """ get all the _result class which contains all results Returns: The @result object containing all computed results. """ return deepcopy(self.result) def getMean(self, filterType='forwardFilter', name='main'): """ get mean for data or component. If the working dates are not (0, self.n - 1), then a warning will prompt stating the actual filtered dates. Args: filterType: the type of mean to be returned. Could be 'forwardFilter', 'backwardSmoother', and 'predict'. Default to 'forwardFilter'. name: the component to get mean. When name = 'main', then it returns the filtered mean for the time series. When name = some component's name, then it returns the filtered mean for that component. Default to 'main'. Returns: A list of the time series observations based on the choice """ # get the working date start, end = self._checkAndGetWorkingDates(filterType=filterType) end += 1 # To get the result for the last date. # get the mean for the fitlered data if name == 'main': # get out of the matrix form if filterType == 'forwardFilter': return self._1DmatrixToArray( self.result.filteredObs[start:end]) elif filterType == 'backwardSmoother': return self._1DmatrixToArray( self.result.smoothedObs[start:end]) elif filterType == 'predict': return self._1DmatrixToArray( self.result.predictedObs[start:end]) else: raise NameError('Incorrect filter type.') # get the mean for the component self._checkComponent(name) return self._getComponentMean(name=name, filterType=filterType, start=start, end=(end - 1)) def getVar(self, filterType='forwardFilter', name='main'): """ get the variance for data or component. If the filtered dates are not (0, self.n - 1), then a warning will prompt stating the actual filtered dates. Args: filterType: the type of variance to be returned. Could be 'forwardFilter', 'backwardSmoother', and 'predict'. Default to 'forwardFilter'. name: the component to get variance. When name = 'main', then it returns the filtered variance for the time series. When name = some component's name, then it returns the filtered variance for that component. Default to 'main'. Returns: A list of the filtered variances based on the choice. """ # get the working date start, end = self._checkAndGetWorkingDates(filterType=filterType) end += 1 # get the variance for the time series data if name == 'main': # get out of the matrix form if filterType == 'forwardFilter': return self._1DmatrixToArray( self.result.filteredObsVar[start:end]) elif filterType == 'backwardSmoother': return self._1DmatrixToArray( self.result.smoothedObsVar[start:end]) elif filterType == 'predict': return self._1DmatrixToArray( self.result.predictedObsVar[start:end]) else: raise NameError('Incorrect filter type.') # get the variance for the component self._checkComponent(name) return self._getComponentVar(name=name, filterType=filterType, start=start, end=(end - 1)) def getResidual(self, filterType='forwardFilter'): """ get the residuals for data after filtering or smoothing. If the working dates are not (0, self.n - 1), then a warning will prompt stating the actual filtered dates. Args: filterType: the type of residuals to be returned. Could be 'forwardFilter', 'backwardSmoother', and 'predict'. Default to 'forwardFilter'. Returns: A list of residuals based on the choice """ # get the working date start, end = self._checkAndGetWorkingDates(filterType=filterType) end += 1 # To get the result for the last date. # get the mean for the fitlered data # get out of the matrix form if filterType == 'forwardFilter': return self._1DmatrixToArray( [self.data[i] - self.result.filteredObs[i] for i in range(start, end)]) elif filterType == 'backwardSmoother': return self._1DmatrixToArray( [self.data[i] - self.result.smoothedObs[i] for i in range(start, end)]) elif filterType == 'predict': return self._1DmatrixToArray( [self.data[i] - self.result.predictedObs[i] for i in range(start, end)]) else: raise NameError('Incorrect filter type.') def getInterval(self, p=0.95, filterType='forwardFilter', name='main'): """ get the confidence interval for data or component. If the filtered dates are not (0, self.n - 1), then a warning will prompt stating the actual filtered dates. Args: p: The confidence level. filterType: the type of CI to be returned. Could be 'forwardFilter', 'backwardSmoother', and 'predict'. Default to 'forwardFilter'. name: the component to get CI. When name = 'main', then it returns the confidence interval for the time series. When name = some component's name, then it returns the confidence interval for that component. Default to 'main'. Returns: A tuple with the first element being a list of upper bounds and the second being a list of the lower bounds. """ # get the working date start, end = self._checkAndGetWorkingDates(filterType=filterType) end += 1 # get the mean and the variance for the time series data if name == 'main': # get out of the matrix form if filterType == 'forwardFilter': compMean = self._1DmatrixToArray( self.result.filteredObs[start:end]) compVar = self._1DmatrixToArray( self.result.filteredObsVar[start:end]) elif filterType == 'backwardSmoother': compMean = self._1DmatrixToArray( self.result.smoothedObs[start:end]) compVar = self._1DmatrixToArray( self.result.smoothedObsVar[start:end]) elif filterType == 'predict': compMean = self._1DmatrixToArray( self.result.predictedObs[start:end]) compVar = self._1DmatrixToArray( self.result.predictedObsVar[start:end]) else: raise NameError('Incorrect filter type.') # get the mean and variance for the component else: self._checkComponent(name) compMean = self._getComponentMean(name=name, filterType=filterType, start=start, end=(end - 1)) compVar = self._getComponentVar(name=name, filterType=filterType, start=start, end=(end - 1)) # get the upper and lower bound upper, lower = getInterval(compMean, compVar, p) return (upper, lower) def getLatentState(self, filterType='forwardFilter', name='all'): """ get the latent states for different components and filters. If the filtered dates are not (0, self.n - 1), then a warning will prompt stating the actual filtered dates. Args: filterType: the type of latent states to be returned. Could be 'forwardFilter', 'backwardSmoother', and 'predict'. Default to 'forwardFilter'. name: the component to get latent state. When name = 'all', then it returns the latent states for the time series. When name = some component's name, then it returns the latent states for that component. Default to 'all'. Returns: A list of lists, standing for the latent states given the different choices. """ # get the working dates start, end = self._checkAndGetWorkingDates(filterType=filterType) end += 1 # to return the full latent states if name == 'all': if filterType == 'forwardFilter': return list(map(lambda x: x if x is None else self._1DmatrixToArray(x), self.result.filteredState[start:end])) elif filterType == 'backwardSmoother': return list(map(lambda x: x if x is None else self._1DmatrixToArray(x), self.result.smoothedState[start:end])) elif filterType == 'predict': return list(map(lambda x: x if x is None else self._1DmatrixToArray(x), self.result.smoothedState[start:end])) else: raise NameError('Incorrect filter type.') # to return the latent state for a given component self._checkComponent(name) return list(map(lambda x: x if x is None else self._1DmatrixToArray(x), self._getLatentState(name=name, filterType=filterType, start=start, end=(end - 1)))) def getLatentCov(self, filterType='forwardFilter', name='all'): """ get the error covariance for different components and filters. If the filtered dates are not (0, self.n - 1), then a warning will prompt stating the actual filtered dates. Args: filterType: the type of latent covariance to be returned. Could be 'forwardFilter', 'backwardSmoother', and 'predict'. Default to 'forwardFilter'. name: the component to get latent cov. When name = 'all', then it returns the latent covariance for the time series. When name = some component's name, then it returns the latent covariance for that component. Default to 'all'. Returns: A list of numpy matrices, standing for the filtered latent covariance. """ # get the working dates start, end = self._checkAndGetWorkingDates(filterType=filterType) end += 1 # to return the full latent covariance if name == 'all': if filterType == 'forwardFilter': return self.result.filteredCov[start:end] elif filterType == 'backwardSmoother': return self.result.smoothedCov[start:end] elif filterType == 'predict': return self.result.smoothedCov[start:end] else: raise NameError('Incorrect filter type.') # to return the latent covariance for a given component self._checkComponent(name) return self._getLatentCov(name=name, filterType=filterType, start=start, end=(end - 1))

41.921233

79

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12,241

5.342455

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0.05049

0.050929

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0.714474

0.65052

0.622128

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0.00592

0.365248

12,241

291

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42.065292

0.873488

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false

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1

0

1

0

1

0

null

0

6

ee5239b4cbd2330ff1090d2d0cbb2da76c8e9e1b

154

py

Python

mysite/posts/admin.py

2021fallCMPUT404/group-cmput404-project

985b76dc6c554caf77e7cf5788355cca22a26e74

[ "Apache-2.0" ]

2

2021-12-06T06:42:41.000Z

2022-03-29T21:40:14.000Z

mysite/posts/admin.py

2021fallCMPUT404/group-cmput404-project

985b76dc6c554caf77e7cf5788355cca22a26e74

[ "Apache-2.0" ]

7

2021-10-29T20:31:44.000Z

2021-12-05T06:55:58.000Z

mysite/posts/admin.py

2021fallCMPUT404/group-cmput404-project

985b76dc6c554caf77e7cf5788355cca22a26e74

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import Post,Comment, Node admin.site.register(Post) admin.site.register(Comment) admin.site.register(Node)

22

38

0.811688

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5.434783

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1

0

null

0

1

0

1

0

6

ee79190229147b7ee80f21ac9adff2c9059053c3

4,548

py

Python

run_cifar_train.py

qinwei-hfut/LDAM-DRW

c93ebbfa82912fa9778723bce8da9ee1425597dc

[ "MIT" ]

null

run_cifar_train.py

qinwei-hfut/LDAM-DRW

c93ebbfa82912fa9778723bce8da9ee1425597dc

[ "MIT" ]

null

run_cifar_train.py

qinwei-hfut/LDAM-DRW

c93ebbfa82912fa9778723bce8da9ee1425597dc

[ "MIT" ]

null

import os def run_exp(gpu,imb_type,imb_factor,loss_type,train_rule,exp_str,normalize_type,dataset): # python cifar_train.py --gpu 0 --imb_type exp --imb_factor 0.01 --loss_type LDAM --train_rule DRW the_command = "python cifar_train.py " \ + " --gpu="+str(gpu) \ + " --imb_type="+imb_type \ + " --imb_factor="+str(imb_factor) \ + " --loss_type="+loss_type \ + " --train_rule="+train_rule \ + " --exp_str="+exp_str \ + " --normalize_type="+normalize_type \ + " --dataset="+dataset \ print(the_command) os.system(the_command) dataset = "cifar100" # run_exp(gpu=0,imb_type='exp',imb_factor=0.02,loss_type="LDAM",train_rule="DRW",normalize_type="logit_normalization",exp_str='ln_1') # run_exp(gpu=0,imb_type='exp',imb_factor=0.02,loss_type="LDAM",train_rule="DRW",normalize_type="prob_normalization",exp_str='pn_1') # run_exp(gpu=0,imb_type='exp',imb_factor=0.02,loss_type="LDAM",train_rule="DRW",normalize_type="logit_normalization",exp_str='ln_2') # run_exp(gpu=0,imb_type='exp',imb_factor=0.02,loss_type="LDAM",train_rule="DRW",normalize_type="logit_normalization",exp_str='ln_3') # run_exp(gpu=0,imb_type='exp',imb_factor=0.02,loss_type="LDAM",train_rule="DRW",normalize_type="prob_normalization",exp_str='pn_1') # run_exp(gpu=0,imb_type='exp',imb_factor=0.02,loss_type="LDAM",train_rule="DRW",normalize_type="prob_normalization",exp_str='pn_2') # run_exp(gpu=0,imb_type='exp',imb_factor=0.02,loss_type="LDAM",train_rule="DRW",normalize_type="prob_normalization",exp_str='pn_3') # run_exp(gpu=0,imb_type='exp',imb_factor=0.02,loss_type="LDAM",train_rule="DRW",normalize_type="logit_standardization",exp_str='ls_1') # run_exp(gpu=0,imb_type='exp',imb_factor=0.02,loss_type="LDAM",train_rule="DRW",normalize_type="prob_standardization",exp_str='ps_1') # run_exp(gpu=0,imb_type='exp',imb_factor=0.02,loss_type="LDAM",train_rule="DRW",normalize_type="logit_standardization",exp_str='ls_2') # run_exp(gpu=0,imb_type='exp',imb_factor=0.02,loss_type="LDAM",train_rule="DRW",normalize_type="logit_standardization",exp_str='ls_3') # run_exp(gpu=0,imb_type='exp',imb_factor=0.02,loss_type="LDAM",train_rule="DRW",normalize_type="prob_standardization",exp_str='ps_2') # run_exp(gpu=0,imb_type='exp',imb_factor=0.02,loss_type="LDAM",train_rule="DRW",normalize_type="prob_standardization",exp_str='ps_3') # run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="none",exp_str='none_4',dataset=dataset) # run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="prob_division",exp_str='pd_7_10k',dataset=dataset) # run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="none",exp_str='none_5',dataset=dataset) # run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="none",exp_str='none_6',dataset=dataset) # run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="prob_division",exp_str='pd_8_10k',dataset=dataset) # run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="prob_division",exp_str='pd_9_10k',dataset=dataset) # run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="uniform",exp_str='uniform_test',dataset=dataset) # run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="gaussian",exp_str='gau_2',dataset=dataset) # run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="gaussian",exp_str='gau_3',dataset=dataset) # run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="dy_gaussian",exp_str='dg_1',dataset=dataset) # run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="dy_gaussian",exp_str='dg_2',dataset=dataset) # run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="dy_gaussian",exp_str='dg_3',dataset=dataset) run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="static_gaussian",exp_str='sg_1',dataset=dataset) run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="static_gaussian",exp_str='sg_2',dataset=dataset) run_exp(gpu=0,imb_type='exp',imb_factor=0.005,loss_type="LDAM",train_rule="DRW",normalize_type="static_gaussian",exp_str='sg_3',dataset=dataset)

73.354839

148

0.757696

810

4,548

3.912346

0.071605

0.070685

0.08236

0.100663

0.901862

0.889555

0.881982

0.874408

0

0.038541

0.047274

4,548

62

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73.354839

0.692823

0.772208

0

0.214851

0

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false

0

0.058824

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0.058824

0

null

0

1

0

null

0

6

ee8b902e6672c8e09e777111eb4396dcaed3e477

538

py

Python

fib.py

Housebear/python-learning

6a6bb50a3151f75f0855879d2e1cb036cc8bef77

[ "MIT" ]

null

fib.py

Housebear/python-learning

6a6bb50a3151f75f0855879d2e1cb036cc8bef77

[ "MIT" ]

null

fib.py

Housebear/python-learning

6a6bb50a3151f75f0855879d2e1cb036cc8bef77

[ "MIT" ]

null

#!/usr/bin/python3 # -*- coding:utf-8 -*- # File Name: fib.py # Author: Lipsum # Mail: niuleipeng@gmail.com # Created Time: 2016-05-11 21:58:41 #!/usr/bin/python3 # -*- coding:utf-8 -*- # File Name: fib.py # Author: Lipsum # Mail: niuleipeng@gmail.com # Created Time: 2020-10-02 15:38:07 def fib(max): n, a, b = 0, 0, 1 while n < max: yield b a, b = b, a + b n = n + 1 # f = fib(5) # print(next(f)) # print(next(f)) # print(next(f)) # print(next(f)) # print(next(f)) for x in fib(10): print(x)

15.371429

35

0.557621

95

538

3.157895

0.484211

0.15

0.166667

0.2

0.706667

0

0.095588

0.241636

538

35

36

15.371429

0.639706

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0

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0

null

0

1

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1

0

null

0

6

c9fadc76f036b6023590f9b8808db4b52b2d0978

8,886

py

Python

CGATReport/DataTypes.py

IanSudbery/sphinx-report

34492b24d3df9261e9c74c3c3d6d4493c258aeac

[ "MIT" ]

9

2015-02-14T16:53:58.000Z

2022-01-03T20:22:42.000Z

CGATReport/DataTypes.py

IanSudbery/sphinx-report

34492b24d3df9261e9c74c3c3d6d4493c258aeac

[ "MIT" ]

26

2015-01-29T15:39:02.000Z

2018-02-14T09:04:21.000Z

CGATReport/DataTypes.py

IanSudbery/sphinx-report

34492b24d3df9261e9c74c3c3d6d4493c258aeac

[ "MIT" ]

4

2015-11-25T17:11:11.000Z

2022-01-03T20:22:45.000Z

import copy class DataSimple(object): """Base class for data types. Derived classes enforce consistency checks on data. """ __slots__ = ["_instance", "_data", "_fn"] def __init__(self, fn): """Store data returned by function.""" object.__setattr__(self, "_fn", fn) object.__setattr__(self, "_data", None) object.__setattr__(self, "_instance", None) def __call__(self, *args, **kwargs): """call the function and return a clone of one-self. """ # Decorators will use the same object for each decoration # and data will get overwritten in successive calls to the same function. # Thus clone oneself before storing the data and return # the clone. clone = copy.copy(self) setattr(clone, "_data", self._fn(self._instance, *args, **kwargs)) clone.__check__() return clone def __len__(self): return len(self._data) def __get__(self, instance, cls=None): object.__setattr__(self, "_instance", instance) return self def __getstate__(self): # previously used deepcoy, but not necessary return {"_data": self._data} def __setstate__(self, dict): for key, val in list(dict.items()): object.__setattr__(self, key, val) def __iter__(self): return self._data.__iter__() def __getitem__(self, *args, **kwargs): return self._data.__getitem__(*args, **kwargs) def __setslice__(self, *args, **kwargs): return self._data.__getslice__(*args, **kwargs) def __contains__(self): return self._data.__contains__(*args, **kwargs) def __copy__(self): return self.__class__(self) # def __getattr__(self, name): # return getattr(self._data, name) # def __setattr__(self, name, value): # setattr(self._data, name, value) class Data(object): """Base class for data types. Derived classes enforce consistency checks on data. """ __slots__ = ["_data"] def __init__(self, data): """Store data returned by function.""" object.__setattr__(self, "_data", data) if data: self.__check__() def __len__(self): return len(self._data) def __getstate__(self): # previously used deepcoy, but not necessary return {"_data": self._data} def __setstate__(self, dict): for key, val in list(dict.items()): object.__setattr__(self, key, val) def __iter__(self): return self._data.__iter__() def __getitem__(self, *args, **kwargs): return self._data.__getitem__(*args, **kwargs) def __setslice__(self, *args, **kwargs): return self._data.__getslice__(*args, **kwargs) def __contains__(self): return self._data.__contains__(*args, **kwargs) def __copy__(self): return self.__class__(self) # def __getattr__(self, name): # return getattr(self._data, name) # def __setattr__(self, name, value): # setattr(self._data, name, value) class SingleColumn(Data): """Single column. The data can be any scalar type. Example: (1,2,"a") """ def __init__(self, fn): Data.__init__(self, fn) def __check__(self): assert type(self._data) in ContainerTypes, "returned type is not a collection: %s" % ( type(self._data)) for x in self._data: assert type(x) in NumberTypes, "value %s is not a number: type=%s" % ( str(x), type(x)) class SingleColumnData(Data): """Single column data. All data are numerical values. Example: (1,2,3) """ def __init__(self, fn): Data.__init__(self, fn) def __check__(self): assert type(self._data) in ContainerTypes, "returned type is not a collection: %s" % ( type(self._data)) for x in self._data: assert type(x) in NumberTypes, "value %s is not a number: type=%s" % ( str(x), type(x)) class MultipleColumns(Data): """Multiple column data The data can be any scalar type. All columns have the same length. Example: (("column1", "column2"), (("val1",2,3), ("val2",2,3))) """ def __init__(self, fn): Data.__init__(self, fn) def __check__(self): assert type(self._data) in ContainerTypes, "returned type is not a collection: %s" % ( type(self._data)) assert type(self._data[0]) in ContainerTypes, "first column is not a collection: %s" % ( type(self._data[0])) assert type(self._data[1]) in ContainerTypes, "second column is not a collection: %s" % ( type(self._data[1])) for c in self._data[1]: assert type( c) in ContainerTypes, "column is not a collection: %s" % (type(c)) try: assert min([len(c) for c in self._data[1]]) == max([len(c) for c in self._data[1]]), \ "data columns have not the same length: %i != %i." %\ (min([len(c) for c in self._data[1]]), max([len(c) for c in self._data[1]])) except ValueError as msg: # ignore errors due to empty sequences pass class MultipleColumnData(Data): """Multiple column data All data are numerical values. Example: (("column1", "column2"), ((1,2,3), (1,2,3))) """ def __init__(self, fn): Data.__init__(self, fn) def __check__(self): assert type(self._data) in ContainerTypes, "returned type is not a collection: %s" % ( type(self._data)) assert type(self._data[0]) in ContainerTypes, "first field is not a collection: %s" % ( type(self._data[0])) assert type(self._data[1]) in ContainerTypes, "second field is not a collection: %s" % ( type(self._data[1])) for c in self._data[1]: assert type( c) in ContainerTypes, "column is not a collection: %s" % (type(c)) for x in c: assert type(x) in NumberTypes, "value %s is not a number: type=%s" % ( str(x), type(x)) assert min([len(c) for c in self._data[1]]) == max( [len(c) for c in self._data[1]]), "data columns have not the same length." class LabeledData(Data): """Labeled data points. Data can be of any type. There is only one value per label. Example: (("column1", 1), ("column2",2)) """ def __init__(self, fn): Data.__init__(self, fn) def __check__(self): assert type( self._data) in ContainerTypes, "returned type is not a collection: %s" % (self._data) for x in self._data: assert type( x) in ContainerTypes, "row is not a collection: %s" % str(x) assert len( x) == 2, "data is not a column, value tuple: %s" % str(x) def returnLabeledValue(Data): """decorator for Trackers returning:class:`LabeledValue`.""" def wrapped_f(*args, **kwargs): return LabeledValue(f(*args, **kwargs)) return wrapped_f def returnSingleColumn(f): """decorator for Trackers returning:class:`SingleColumn`.""" def wrapped_f(*args, **kwargs): return SingleColumn(f(*args, **kwargs)) return wrapped_f def returnSingleColumnData(f): """decorator for Trackers returning:class:`SingleColumnData`.""" def wrapped_f(*args, **kwargs): return SingleColumnData(f(*args, **kwargs)) return wrapped_f def returnMultipleColumns(f): """decorator for Trackers returning:class:`MultipleColumn`.""" def wrapped_f(*args, **kwargs): return MultipleColumns(f(*args, **kwargs)) return wrapped_f def returnMultipleColumnData(f): """decorator for Trackers returning:class:`MultipleColumnData`.""" def wrapped_f(*args, **kwargs): return MultipleColumnData(f(*args, **kwargs)) return wrapped_f def returnLabeledData(f): """decorator for Trackers returning:class:`LabeledData`.""" def wrapped_f(*args, **kwargs): return LabeledData(f(*args, **kwargs)) return wrapped_f # def returnSingleColumn(f): # """decorator for Trackers returning:class:`SingleColumn`.""" # return SingleColumn(f) # def returnSingleColumnData(f): # """decorator for Trackers returning:class:`SingleColumnData`.""" # return SingleColumnData(f) # def returnMultipleColumns(f): # """decorator for Trackers returning:class:`MultipleColumn`.""" # return MultipleColumns(f) # def returnMultipleColumnData(f): # """decorator for Trackers returning:class:`MultipleColumnData`.""" # return MultipleColumnData(f) # def returnLabeledData(f): # """decorator for Trackers returning:class:`LabeledData`.""" # return LabeledData(f)

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a0176ded4c6d5a2fd3b7b2f2b8873dc3161d6c53

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py

Python

llama/__init__.py

HEPonHPC/llama

6a46c395d84e6ac824fbebb1b108e68699ca64ab

[ "MIT" ]

null

llama/__init__.py

HEPonHPC/llama

6a46c395d84e6ac824fbebb1b108e68699ca64ab

[ "MIT" ]

null

llama/__init__.py

HEPonHPC/llama

6a46c395d84e6ac824fbebb1b108e68699ca64ab

[ "MIT" ]

null

from .llama import *

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6

a018bd6f5a659860894977ac4726329dbd8a5748

6,488

py

Python

tests/pytests/unit/states/postgresql/test_extension.py

babs/salt

c536ea716d5308880b244e7980f4b659d86fc104

[ "Apache-2.0" ]

1

2021-02-11T16:55:00.000Z

tests/pytests/unit/states/postgresql/test_extension.py

babs/salt

c536ea716d5308880b244e7980f4b659d86fc104

[ "Apache-2.0" ]

9

2021-03-31T20:25:25.000Z

2021-07-04T05:33:46.000Z

tests/pytests/unit/states/postgresql/test_extension.py

babs/salt

c536ea716d5308880b244e7980f4b659d86fc104

[ "Apache-2.0" ]

1

2020-06-02T14:15:24.000Z

import pytest import salt.modules.postgres as postgresmod import salt.states.postgres_extension as postgres_extension from tests.support.mock import Mock, patch @pytest.fixture def configure_loader_modules(): return { postgres_extension: { "__grains__": {"os_family": "linux"}, "__salt__": { "config.option": Mock(), "cmd.run_all": Mock(), "file.chown": Mock(), "file.remove": Mock(), }, "__opts__": {"test": False}, }, } def test_present_failed(): """ scenario of creating upgrading extensions with possible schema and version specifications """ with patch.dict( postgres_extension.__salt__, { "postgres.create_metadata": Mock( side_effect=[ [postgresmod._EXTENSION_NOT_INSTALLED], [postgresmod._EXTENSION_TO_MOVE, postgresmod._EXTENSION_INSTALLED], ] ), "postgres.create_extension": Mock(side_effect=[False, False]), }, ): ret = postgres_extension.present("foo") assert ret == { "comment": "Failed to install extension foo", "changes": {}, "name": "foo", "result": False, } ret = postgres_extension.present("foo") assert ret == { "comment": "Failed to upgrade extension foo", "changes": {}, "name": "foo", "result": False, } def test_present(): """ scenario of creating upgrading extensions with possible schema and version specifications """ with patch.dict( postgres_extension.__salt__, { "postgres.create_metadata": Mock( side_effect=[ [postgresmod._EXTENSION_NOT_INSTALLED], [postgresmod._EXTENSION_INSTALLED], [postgresmod._EXTENSION_TO_MOVE, postgresmod._EXTENSION_INSTALLED], ] ), "postgres.create_extension": Mock(side_effect=[True, True, True]), }, ): ret = postgres_extension.present("foo") assert ret == { "comment": "The extension foo has been installed", "changes": {"foo": "Installed"}, "name": "foo", "result": True, } ret = postgres_extension.present("foo") assert ret == { "comment": "Extension foo is already present", "changes": {}, "name": "foo", "result": True, } ret = postgres_extension.present("foo") assert ret == { "comment": "The extension foo has been upgraded", "changes": {"foo": "Upgraded"}, "name": "foo", "result": True, } def test_presenttest(): """ scenario of creating upgrading extensions with possible schema and version specifications """ with patch.dict( postgres_extension.__salt__, { "postgres.create_metadata": Mock( side_effect=[ [postgresmod._EXTENSION_NOT_INSTALLED], [postgresmod._EXTENSION_INSTALLED], [postgresmod._EXTENSION_TO_MOVE, postgresmod._EXTENSION_INSTALLED], ] ), "postgres.create_extension": Mock(side_effect=[True, True, True]), }, ): with patch.dict(postgres_extension.__opts__, {"test": True}): ret = postgres_extension.present("foo") assert ret == { "comment": "Extension foo is set to be installed", "changes": {}, "name": "foo", "result": None, } ret = postgres_extension.present("foo") assert ret == { "comment": "Extension foo is already present", "changes": {}, "name": "foo", "result": True, } ret = postgres_extension.present("foo") assert ret == { "comment": "Extension foo is set to be upgraded", "changes": {}, "name": "foo", "result": None, } def test_absent(): """ scenario of creating upgrading extensions with possible schema and version specifications """ with patch.dict( postgres_extension.__salt__, { "postgres.is_installed_extension": Mock(side_effect=[True, False]), "postgres.drop_extension": Mock(side_effect=[True, True]), }, ): ret = postgres_extension.absent("foo") assert ret == { "comment": "Extension foo has been removed", "changes": {"foo": "Absent"}, "name": "foo", "result": True, } ret = postgres_extension.absent("foo") assert ret == { "comment": ("Extension foo is not present, so it cannot be removed"), "changes": {}, "name": "foo", "result": True, } def test_absent_failed(): """ scenario of creating upgrading extensions with possible schema and version specifications """ with patch.dict(postgres_extension.__opts__, {"test": False}): with patch.dict( postgres_extension.__salt__, { "postgres.is_installed_extension": Mock(side_effect=[True, True]), "postgres.drop_extension": Mock(side_effect=[False, False]), }, ): ret = postgres_extension.absent("foo") assert ret == { "comment": "Extension foo failed to be removed", "changes": {}, "name": "foo", "result": False, } def test_absent_failedtest(): with patch.dict( postgres_extension.__salt__, { "postgres.is_installed_extension": Mock(side_effect=[True, True]), "postgres.drop_extension": Mock(side_effect=[False, False]), }, ): with patch.dict(postgres_extension.__opts__, {"test": True}): ret = postgres_extension.absent("foo") assert ret == { "comment": "Extension foo is set to be removed", "changes": {}, "name": "foo", "result": None, }

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1

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null

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6

4e53bf9dcf9b269fd27554d237dfc2a89b7654ee

29

py

Python

pyqt_foldable_window/__init__.py

yjg30737/pyqt-foldable-window

ab7e77b2a517532fa1e5ecb57733d787f971b962

[ "MIT" ]

1

2022-02-02T10:33:32.000Z

pyqt_foldable_window/__init__.py

yjg30737/pyqt-foldable-window

ab7e77b2a517532fa1e5ecb57733d787f971b962

[ "MIT" ]

null

pyqt_foldable_window/__init__.py

yjg30737/pyqt-foldable-window

ab7e77b2a517532fa1e5ecb57733d787f971b962

[ "MIT" ]

null

from .foldableWindow import *

29

0.827586

3

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1

0

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29

1

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0.923077

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1

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1

0

null

0

1

0

1

0

1

0

6

09635738f5b2205db060dba86992dd9ec6813d1d

93

py

Python

thethings/endpoints/__init__.py

VekotinVerstas/DjangoHttpBroker-TheThing

9ba2f02b397b132945177554da0f3aaf094e5b22

[ "MIT" ]

null

thethings/endpoints/__init__.py

VekotinVerstas/DjangoHttpBroker-TheThing

9ba2f02b397b132945177554da0f3aaf094e5b22

[ "MIT" ]

null

thethings/endpoints/__init__.py

VekotinVerstas/DjangoHttpBroker-TheThing

9ba2f02b397b132945177554da0f3aaf094e5b22

[ "MIT" ]

2

2020-05-05T12:57:47.000Z

2020-08-14T13:33:56.000Z

from broker.providers.endpoint import import_endpoints import_endpoints(__file__, __name__)

23.25

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93

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null

0

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0

1

0

1

0

6

11ef3126b2d613cd0c944e40fd5c1955764dcd3f

68

py

Python

double3/double3sdk/imu/imu.py

CLOMING/winter2021_double

9b920baaeb3736a785a6505310b972c49b5b21e9

[ "Apache-2.0" ]

null

double3/double3sdk/imu/imu.py

CLOMING/winter2021_double

9b920baaeb3736a785a6505310b972c49b5b21e9

[ "Apache-2.0" ]

null

double3/double3sdk/imu/imu.py

CLOMING/winter2021_double

9b920baaeb3736a785a6505310b972c49b5b21e9

[ "Apache-2.0" ]

null

from double3sdk.double_api import _DoubleAPI class _Imu: pass

11.333333

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0

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1

0

6

11f6ada767d0d96f897868f64e56840998521ca8

7,347

py

Python

tests/test_core.py

jakirkham/mplview

0847e4ccf3c4247cb72f35600b7f5f553b429c2d

[ "BSD-3-Clause" ]

2

2018-05-30T18:53:19.000Z

2018-06-11T17:32:54.000Z

tests/test_core.py

jakirkham/mplview

0847e4ccf3c4247cb72f35600b7f5f553b429c2d

[ "BSD-3-Clause" ]

15

2016-11-01T12:54:03.000Z

2019-02-28T18:16:48.000Z

tests/test_core.py

jakirkham/mplview

0847e4ccf3c4247cb72f35600b7f5f553b429c2d

[ "BSD-3-Clause" ]

null

__author__ = "John Kirkham <kirkhamj@janelia.hhmi.org>" __date__ = "$Nov 01, 2016 9:19$" import unittest import numpy import matplotlib matplotlib.use('Agg') import matplotlib.pyplot import mplview import mplview.core class TestMatplotlibViewer(unittest.TestCase): def setUp(self): self.mplv = matplotlib.pyplot.figure( FigureClass=mplview.core.MatplotlibViewer ) def test_state(self): self.assertIsInstance(self.mplv, matplotlib.figure.Figure) self.assertIsNotNone(getattr(self.mplv, "viewer", None)) def test_init_image(self): img = numpy.arange(12.0).reshape(3,4) self.mplv.set_images(img) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img, cur_img)) def test_init_image_matshow(self): img = numpy.arange(12.0).reshape(3,4) self.mplv.set_images(img, use_matshow=True) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img, cur_img)) def test_init_image_stack(self): img = numpy.arange(60.0).reshape(5,3,4) self.mplv.set_images(img) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[0], cur_img)) cur_img = self.mplv.get_image(1) self.assertTrue(numpy.array_equal(img[1], cur_img)) cur_img = self.mplv.get_image(-1) self.assertTrue(numpy.array_equal(img[-1], cur_img)) def test_image_stack_retry(self): img = numpy.arange(60.0).reshape(5,3,4) self.mplv.set_images(img) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[0], cur_img)) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[0], cur_img)) cur_img = self.mplv.get_image(2) self.assertTrue(numpy.array_equal(img[2], cur_img)) cur_img = self.mplv.get_image(2) self.assertTrue(numpy.array_equal(img[2], cur_img)) cur_img = self.mplv.get_image(-1) self.assertTrue(numpy.array_equal(img[-1], cur_img)) cur_img = self.mplv.get_image(-1) self.assertTrue(numpy.array_equal(img[-1], cur_img)) def test_init_too_big(self): img = numpy.arange(60.0).reshape(1,5,3,4) with self.assertRaises(ValueError) as e: self.mplv.set_images(img) def test_format_coord(self): img = numpy.arange(12.0).reshape(3,4) self.mplv.set_images(img) exp_str = 'x=0.0000, y=0.0000, z=0.0000' self.assertEqual(exp_str, self.mplv.format_coord(0.0, 0.0)) exp_str = 'x=0.2000, y=0.0000, z=0.0000' self.assertEqual(exp_str, self.mplv.format_coord(0.2, 0.0)) exp_str = 'x=0.0000, y=0.2000, z=0.0000' self.assertEqual(exp_str, self.mplv.format_coord(0.0, 0.2)) exp_str = 'x=0.2000, y=0.2000, z=0.0000' self.assertEqual(exp_str, self.mplv.format_coord(0.2, 0.2)) exp_str = 'x=0.8000, y=0.2000, z=1.0000' self.assertEqual(exp_str, self.mplv.format_coord(0.8, 0.2)) exp_str = 'x=0.2000, y=0.8000, z=4.0000' self.assertEqual(exp_str, self.mplv.format_coord(0.2, 0.8)) exp_str = 'x=0.8000, y=0.8000, z=5.0000' self.assertEqual(exp_str, self.mplv.format_coord(0.8, 0.8)) exp_str = 'x=4.0000, y=5.0000' self.assertEqual(exp_str, self.mplv.format_coord(4.0, 5.0)) def test_image_color_range(self): img = numpy.linspace(0, 1, 12).reshape(3,4) self.mplv.set_images(img, vmin=0.0, vmax=1.0) self.assertEqual(self.mplv.vmin, 0.0) self.assertEqual(self.mplv.vmax, 1.0) self.assertEqual(self.mplv.svmin, 0.0) self.assertEqual(self.mplv.svmax, 1.0) self.mplv.color_range_update(0.0, 1.0) self.assertEqual(self.mplv.svmin, 0.0) self.assertEqual(self.mplv.svmax, 1.0) self.mplv.color_range_update(0.1, 0.9) self.assertEqual(self.mplv.svmin, 0.1) self.assertEqual(self.mplv.svmax, 0.9) self.mplv.color_range_update(0.25, 0.75) self.assertAlmostEqual(self.mplv.svmin, 0.3) self.assertAlmostEqual(self.mplv.svmax, 0.7) self.mplv.color_range_update(0.5, 0.5) self.assertEqual(self.mplv.svmin, 0.0) self.assertEqual(self.mplv.svmax, 1.0) def test_navigator_callback(self): img = numpy.arange(60.0).reshape(5,3,4) self.mplv.set_images(img) v = [0] def callback(v=v): v[0] += 1 self.assertEqual(v[0], 0) cid = self.mplv.time_nav.on_time_update(callback) self.assertEqual(v[0], 0) self.mplv.time_nav.time_update(2) self.assertEqual(v[0], 1) self.mplv.time_nav.time_update(2) self.assertEqual(v[0], 1) self.mplv.time_nav.time_update(4) self.assertEqual(v[0], 2) self.mplv.time_nav.disconnect(cid) self.mplv.time_nav.time_update(1) self.assertEqual(v[0], 2) def test_image_stack_nav_pos(self): img = numpy.arange(60.0).reshape(5,3,4) self.mplv.set_images(img) self.mplv.time_nav.time_update(2) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[2], cur_img)) self.mplv.time_nav.time_update(-1) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[0], cur_img)) self.mplv.time_nav.time_update(10) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[-1], cur_img)) def test_image_stack_nav_ends(self): img = numpy.arange(60.0).reshape(5,3,4) self.mplv.set_images(img) self.mplv.time_nav.begin_time(None) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[0], cur_img)) self.mplv.time_nav.begin_time(None) self.mplv.time_nav.prev_time(None) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[0], cur_img)) self.mplv.time_nav.end_time(None) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[-1], cur_img)) self.mplv.time_nav.end_time(None) self.mplv.time_nav.next_time(None) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[-1], cur_img)) def test_image_stack_nav_step(self): img = numpy.arange(60.0).reshape(5,3,4) self.mplv.set_images(img) self.mplv.time_nav.begin_time(None) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[0], cur_img)) self.mplv.time_nav.next_time(None) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[1], cur_img)) self.mplv.time_nav.prev_time(None) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[0], cur_img)) self.mplv.time_nav.end_time(None) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[-1], cur_img)) self.mplv.time_nav.prev_time(None) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[-2], cur_img)) self.mplv.time_nav.next_time(None) cur_img = self.mplv.get_image() self.assertTrue(numpy.array_equal(img[-1], cur_img)) def tearDown(self): del self.mplv

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null

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1

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null

0

6

ee9b8834ac3bd09bb93d15b005b944d34f1c87c5

210

py

Python

sqlalchemy_model_builder/__init__.py

aminalaee/fastapi-admin

15206af6b223f778cbe64d1e72d4200289e72eba

[ "MIT" ]

2

2021-06-04T17:33:49.000Z

2022-03-23T19:22:35.000Z

sqlalchemy_model_builder/__init__.py

aminalaee/sqlalchemy-model-builder

15206af6b223f778cbe64d1e72d4200289e72eba

[ "MIT" ]

10

2021-06-09T06:02:05.000Z

2021-08-08T16:22:34.000Z

sqlalchemy_model_builder/__init__.py

aminalaee/sqlalchemy-model-builder

15206af6b223f778cbe64d1e72d4200289e72eba

[ "MIT" ]

null

__version__ = "0.0.6" from sqlalchemy_model_builder.exceptions import ModelBuilderException from sqlalchemy_model_builder.model_builder import ModelBuilder __all__ = ["ModelBuilder", "ModelBuilderException"]

30

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Python

3.7.0/lldb-3.7.0.src/test/tools/lldb-mi/variable/TestMiGdbSetShowPrint.py

androm3da/clang_sles

2ba6d0711546ad681883c42dfb8661b842806695

[ "MIT" ]

3

2016-02-10T14:18:40.000Z

2018-02-05T03:15:56.000Z

3.7.0/lldb-3.7.0.src/test/tools/lldb-mi/variable/TestMiGdbSetShowPrint.py

androm3da/clang_sles

2ba6d0711546ad681883c42dfb8661b842806695

[ "MIT" ]

1

2016-02-10T15:40:03.000Z

3.7.0/lldb-3.7.0.src/test/tools/lldb-mi/variable/TestMiGdbSetShowPrint.py

androm3da/clang_sles

2ba6d0711546ad681883c42dfb8661b842806695

[ "MIT" ]

null

""" Test lldb-mi -gdb-set and -gdb-show commands for 'print option-name'. """ import lldbmi_testcase from lldbtest import * import unittest2 class MiGdbSetShowTestCase(lldbmi_testcase.MiTestCaseBase): mydir = TestBase.compute_mydir(__file__) @lldbmi_test @expectedFailureWindows("llvm.org/pr22274: need a pexpect replacement for windows") @skipIfFreeBSD # llvm.org/pr22411: Failure presumably due to known thread races @skipIfLinux # llvm.org/pr22841: lldb-mi tests fail on all Linux buildbots def test_lldbmi_gdb_set_show_print_char_array_as_string(self): """Test that 'lldb-mi --interpreter' can print array of chars as string.""" self.spawnLldbMi(args = None) # Load executable self.runCmd("-file-exec-and-symbols %s" % self.myexe) self.expect("\^done") # Run to BP_gdb_set_show_print_char_array_as_string_test line = line_number('main.cpp', '// BP_gdb_set_show_print_char_array_as_string_test') self.runCmd("-break-insert main.cpp:%d" % line) self.expect("\^done,bkpt={number=\"1\"") self.runCmd("-exec-run") self.expect("\^running") self.expect("\*stopped,reason=\"breakpoint-hit\"") # Test that default print char-array-as-string value is "off" self.runCmd("-gdb-show print char-array-as-string") self.expect("\^done,value=\"off\"") # Test that an char* is expanded to string when print char-array-as-string is "off" self.runCmd("-var-create - * cp") self.expect("\^done,name=\"var\d+\",numchild=\"1\",value=\"0x[0-9a-f]+ \\\\\\\"hello\\\\\\\"\",type=\"const char \*\",thread-id=\"1\",has_more=\"0\"") # Test that an char[] isn't expanded to string when print char-array-as-string is "off" self.runCmd("-var-create - * ca") self.expect("\^done,name=\"var\d+\",numchild=\"6\",value=\"\[6\]\",type=\"const char \[6\]\",thread-id=\"1\",has_more=\"0\"") # Test that an char16_t* is expanded to string when print char-array-as-string is "off" self.runCmd("-var-create - * u16p") self.expect("\^done,name=\"var\d+\",numchild=\"1\",value=\"0x[0-9a-f]+ u\\\\\\\"hello\\\\\\\"\",type=\"const char16_t \*\",thread-id=\"1\",has_more=\"0\"") # Test that an char16_t[] isn't expanded to string when print char-array-as-string is "off" self.runCmd("-var-create - * u16a") self.expect("\^done,name=\"var\d+\",numchild=\"6\",value=\"\[6\]\",type=\"const char16_t \[6\]\",thread-id=\"1\",has_more=\"0\"") # Test that an char32_t* is expanded to string when print char-array-as-string is "off" self.runCmd("-var-create - * u32p") self.expect("\^done,name=\"var\d+\",numchild=\"1\",value=\"0x[0-9a-f]+ U\\\\\\\"hello\\\\\\\"\",type=\"const char32_t \*\",thread-id=\"1\",has_more=\"0\"") # Test that an char32_t[] isn't expanded to string when print char-array-as-string is "off" self.runCmd("-var-create - * u32a") self.expect("\^done,name=\"var\d+\",numchild=\"6\",value=\"\[6\]\",type=\"const char32_t \[6\]\",thread-id=\"1\",has_more=\"0\"") # Test that -gdb-set can set print char-array-as-string flag self.runCmd("-gdb-set print char-array-as-string on") self.expect("\^done") self.runCmd("-gdb-set print char-array-as-string 1") self.expect("\^done") self.runCmd("-gdb-show print char-array-as-string") self.expect("\^done,value=\"on\"") # Test that an char* is expanded to string when print char-array-as-string is "on" self.runCmd("-var-create - * cp") self.expect("\^done,name=\"var\d+\",numchild=\"1\",value=\"0x[0-9a-f]+ \\\\\\\"hello\\\\\\\"\",type=\"const char \*\",thread-id=\"1\",has_more=\"0\"") # Test that an char[] isn't expanded to string when print char-array-as-string is "on" self.runCmd("-var-create - * ca") self.expect("\^done,name=\"var\d+\",numchild=\"6\",value=\"\\\\\\\"hello\\\\\\\"\",type=\"const char \[6\]\",thread-id=\"1\",has_more=\"0\"") # Test that an char16_t* is expanded to string when print char-array-as-string is "on" self.runCmd("-var-create - * u16p") self.expect("\^done,name=\"var\d+\",numchild=\"1\",value=\"0x[0-9a-f]+ u\\\\\\\"hello\\\\\\\"\",type=\"const char16_t \*\",thread-id=\"1\",has_more=\"0\"") # Test that an char16_t[] isn't expanded to string when print char-array-as-string is "on" self.runCmd("-var-create - * u16a") self.expect("\^done,name=\"var\d+\",numchild=\"6\",value=\"u\\\\\\\"hello\\\\\\\"\",type=\"const char16_t \[6\]\",thread-id=\"1\",has_more=\"0\"") # Test that an char32_t* is expanded to string when print char-array-as-string is "on" self.runCmd("-var-create - * u32p") self.expect("\^done,name=\"var\d+\",numchild=\"1\",value=\"0x[0-9a-f]+ U\\\\\\\"hello\\\\\\\"\",type=\"const char32_t \*\",thread-id=\"1\",has_more=\"0\"") # Test that an char32_t[] isn't expanded to string when print char-array-as-string is "on" self.runCmd("-var-create - * u32a") self.expect("\^done,name=\"var\d+\",numchild=\"6\",value=\"U\\\\\\\"hello\\\\\\\"\",type=\"const char32_t \[6\]\",thread-id=\"1\",has_more=\"0\"") # Test that -gdb-set print char-array-as-string fails if "on"/"off" isn't specified self.runCmd("-gdb-set print char-array-as-string") self.expect("\^error,msg=\"The request ''print' expects option-name and \"on\" or \"off\"' failed.\"") # Test that -gdb-set print char-array-as-string fails when option is unknown self.runCmd("-gdb-set print char-array-as-string unknown") self.expect("\^error,msg=\"The request ''print' expects option-name and \"on\" or \"off\"' failed.\"") @lldbmi_test @expectedFailureWindows("llvm.org/pr22274: need a pexpect replacement for windows") @expectedFailureGcc("https://llvm.org/bugs/show_bug.cgi?id=23357") @skipIfFreeBSD # llvm.org/pr22411: Failure presumably due to known thread races def test_lldbmi_gdb_set_show_print_expand_aggregates(self): """Test that 'lldb-mi --interpreter' can expand aggregates everywhere.""" self.spawnLldbMi(args = None) # Load executable self.runCmd("-file-exec-and-symbols %s" % self.myexe) self.expect("\^done") # Run to BP_gdb_set_show_print_expand_aggregates line = line_number('main.cpp', '// BP_gdb_set_show_print_expand_aggregates') self.runCmd("-break-insert main.cpp:%d" % line) self.expect("\^done,bkpt={number=\"1\"") self.runCmd("-exec-run") self.expect("\^running") self.expect("\*stopped,reason=\"breakpoint-hit\"") # Test that default print expand-aggregates value is "off" self.runCmd("-gdb-show print expand-aggregates") self.expect("\^done,value=\"off\"") # Test that composite type isn't expanded when print expand-aggregates is "off" self.runCmd("-var-create var1 * complx") self.expect("\^done,name=\"var1\",numchild=\"3\",value=\"{\.\.\.}\",type=\"complex_type\",thread-id=\"1\",has_more=\"0\"") # Test that composite type[] isn't expanded when print expand-aggregates is "off" self.runCmd("-var-create var2 * complx_array") self.expect("\^done,name=\"var2\",numchild=\"2\",value=\"\[2\]\",type=\"complex_type \[2\]\",thread-id=\"1\",has_more=\"0\"") # Test that -gdb-set can set print expand-aggregates flag self.runCmd("-gdb-set print expand-aggregates on") self.expect("\^done") self.runCmd("-gdb-set print expand-aggregates 1") self.expect("\^done") self.runCmd("-gdb-show print expand-aggregates") self.expect("\^done,value=\"on\"") # Test that composite type is expanded when print expand-aggregates is "on" self.runCmd("-var-create var3 * complx") self.expect("\^done,name=\"var3\",numchild=\"3\",value=\"{i = 3, inner = {l = 3}, complex_ptr = 0x[0-9a-f]+}\",type=\"complex_type\",thread-id=\"1\",has_more=\"0\"") # Test that composite type[] is expanded when print expand-aggregates is "on" self.runCmd("-var-create var4 * complx_array") self.expect("\^done,name=\"var4\",numchild=\"2\",value=\"{\[0\] = {i = 4, inner = {l = 4}, complex_ptr = 0x[0-9a-f]+}, \[1\] = {i = 5, inner = {l = 5}, complex_ptr = 0x[0-9a-f]+}}\",type=\"complex_type \[2\]\",thread-id=\"1\",has_more=\"0\"") # Test that -gdb-set print expand-aggregates fails if "on"/"off" isn't specified self.runCmd("-gdb-set print expand-aggregates") self.expect("\^error,msg=\"The request ''print' expects option-name and \"on\" or \"off\"' failed.\"") # Test that -gdb-set print expand-aggregates fails when option is unknown self.runCmd("-gdb-set print expand-aggregates unknown") self.expect("\^error,msg=\"The request ''print' expects option-name and \"on\" or \"off\"' failed.\"") @lldbmi_test @expectedFailureWindows("llvm.org/pr22274: need a pexpect replacement for windows") @expectedFailureGcc("https://llvm.org/bugs/show_bug.cgi?id=23357") @skipIfFreeBSD # llvm.org/pr22411: Failure presumably due to known thread races def test_lldbmi_gdb_set_show_print_aggregate_field_names(self): """Test that 'lldb-mi --interpreter' can expand aggregates everywhere.""" self.spawnLldbMi(args = None) # Load executable self.runCmd("-file-exec-and-symbols %s" % self.myexe) self.expect("\^done") # Run to BP_gdb_set_show_print_aggregate_field_names line = line_number('main.cpp', '// BP_gdb_set_show_print_aggregate_field_names') self.runCmd("-break-insert main.cpp:%d" % line) self.expect("\^done,bkpt={number=\"1\"") self.runCmd("-exec-run") self.expect("\^running") self.expect("\*stopped,reason=\"breakpoint-hit\"") # Test that default print aggregatep-field-names value is "on" self.runCmd("-gdb-show print aggregate-field-names") self.expect("\^done,value=\"on\"") # Set print expand-aggregates flag to "on" self.runCmd("-gdb-set print expand-aggregates on") self.expect("\^done") # Test that composite type is expanded with field name when print aggregate-field-names is "on" self.runCmd("-var-create var1 * complx") self.expect("\^done,name=\"var1\",numchild=\"3\",value=\"{i = 3, inner = {l = 3}, complex_ptr = 0x[0-9a-f]+}\",type=\"complex_type\",thread-id=\"1\",has_more=\"0\"") # Test that composite type[] is expanded with field name when print aggregate-field-names is "on" self.runCmd("-var-create var2 * complx_array") self.expect("\^done,name=\"var2\",numchild=\"2\",value=\"{\[0\] = {i = 4, inner = {l = 4}, complex_ptr = 0x[0-9a-f]+}, \[1\] = {i = 5, inner = {l = 5}, complex_ptr = 0x[0-9a-f]+}}\",type=\"complex_type \[2\]\",thread-id=\"1\",has_more=\"0\"") # Test that -gdb-set can set print aggregate-field-names flag self.runCmd("-gdb-set print aggregate-field-names off") self.expect("\^done") self.runCmd("-gdb-set print aggregate-field-names 0") self.expect("\^done") self.runCmd("-gdb-show print aggregate-field-names") self.expect("\^done,value=\"off\"") # Test that composite type is expanded without field name when print aggregate-field-names is "off" self.runCmd("-var-create var3 * complx") self.expect("\^done,name=\"var3\",numchild=\"3\",value=\"{3,\{3\},0x[0-9a-f]+}\",type=\"complex_type\",thread-id=\"1\",has_more=\"0\"") # Test that composite type[] is expanded without field name when print aggregate-field-names is "off" self.runCmd("-var-create var4 * complx_array") self.expect("\^done,name=\"var4\",numchild=\"2\",value=\"{{4,\{4\},0x[0-9a-f]+},{5,\{5\},0x[0-9a-f]+}}\",type=\"complex_type \[2\]\",thread-id=\"1\",has_more=\"0\"") # Test that -gdb-set print aggregate-field-names fails if "on"/"off" isn't specified self.runCmd("-gdb-set print aggregate-field-names") self.expect("\^error,msg=\"The request ''print' expects option-name and \"on\" or \"off\"' failed.\"") # Test that -gdb-set print aggregate-field-names fails when option is unknown self.runCmd("-gdb-set print aggregate-field-names unknown") self.expect("\^error,msg=\"The request ''print' expects option-name and \"on\" or \"off\"' failed.\"") if __name__ == '__main__': unittest2.main()

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6

011d0931e1200ee171a4f47a8c9c0803c928ffd0

174

py

Python

data/__init__.py

isamu-isozaki/hidden-networks

7dcb96a7de43b65ffde176d771f88b5ecedb84ab

[ "Apache-2.0" ]

132

2019-12-03T19:02:36.000Z

2022-03-27T15:56:43.000Z

data/__init__.py

isamu-isozaki/hidden-networks

7dcb96a7de43b65ffde176d771f88b5ecedb84ab

[ "Apache-2.0" ]

9

2019-12-05T16:28:33.000Z

2022-02-21T21:49:13.000Z

data/__init__.py

isamu-isozaki/hidden-networks

7dcb96a7de43b65ffde176d771f88b5ecedb84ab

[ "Apache-2.0" ]

45

2019-12-04T00:11:53.000Z

2022-03-30T21:07:37.000Z

from data.cifar import CIFAR10 from data.imagenet import ImageNet from data.tinyimagenet import TinyImageNet from data.mnist import MNIST from data.bigcifar import BigCIFAR10

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0

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011d79b29a8da8afdb960917fc2da1c69648d032

11,106

py

Python

loss.py

memmelma/Mode_Collapse

c06a4e769933ebece9f0bdeb150f9d8b61077f85

[ "MIT" ]

14

2020-06-22T12:56:10.000Z

2022-03-31T10:23:00.000Z

loss.py

memmelma/Mode_Collapse

c06a4e769933ebece9f0bdeb150f9d8b61077f85

[ "MIT" ]

null

loss.py

memmelma/Mode_Collapse

c06a4e769933ebece9f0bdeb150f9d8b61077f85

[ "MIT" ]

2

2022-01-21T01:22:23.000Z

2022-02-13T18:08:08.000Z

from typing import Optional import torch import torch.nn as nn import torch.nn.functional as F class GANLossGenerator(nn.Module): """ This class implements the standard generator GAN loss proposed in: https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf """ def __init__(self) -> None: """ Constructor method. """ # Call super constructor super(GANLossGenerator, self).__init__() def forward(self, discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor: """ Forward pass. :param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples :return: (torch.Tensor) Standard generator GAN loss """ # Loss can be computed by utilizing the softplus function since softplus combines both sigmoid and log return - F.softplus(discriminator_prediction_fake).mean() class GANLossDiscriminator(nn.Module): """ This class implements the standard discriminator GAN loss proposed in: https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf """ def __init__(self) -> None: """ Constructor method. """ # Call super constructor super(GANLossDiscriminator, self).__init__() def forward(self, discriminator_prediction_real: torch.Tensor, discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor: """ Forward pass. :param discriminator_prediction_real: (torch.Tensor) Raw discriminator prediction for real samples :param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples :return: (torch.Tensor) Standard discriminator GAN loss """ # Loss can be computed by utilizing the softplus function since softplus combines both sigmoid and log return F.softplus(- discriminator_prediction_real).mean() \ + F.softplus(discriminator_prediction_fake).mean() class NSGANLossGenerator(nn.Module): """ This class implements the non-saturating generator GAN loss proposed in: https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf """ def __init__(self) -> None: """ Constructor method. """ # Call super constructor super(NSGANLossGenerator, self).__init__() def forward(self, discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor: """ Forward pass. :param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples :return: (torch.Tensor) Non-saturating generator GAN loss """ # Loss can be computed by utilizing the softplus function since softplus combines both sigmoid and log return F.softplus(- discriminator_prediction_fake).mean() class NSGANLossDiscriminator(GANLossDiscriminator): """ This class implements the non-saturating discriminator GAN loss proposed in: https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf """ def __init__(self) -> None: """ Constructor method. """ # Call super constructor super(NSGANLossDiscriminator, self).__init__() class WassersteinGANLossGenerator(nn.Module): """ This class implements the Wasserstein generator GAN loss proposed in: http://proceedings.mlr.press/v70/arjovsky17a/arjovsky17a.pdf """ def __index__(self) -> None: """ Constructor method. """ # Call super constructor super(WassersteinGANLossGenerator, self).__index__() def forward(self, discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor: """ Forward pass. :param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples :return: (torch.Tensor) Wasserstein Generator GAN loss with gradient """ return - discriminator_prediction_fake.mean() class WassersteinGANLossDiscriminator(nn.Module): """ This class implements the Wasserstein generator GAN loss proposed in: http://proceedings.mlr.press/v70/arjovsky17a/arjovsky17a.pdf """ def __init__(self) -> None: """ Constructor method. """ # Call super constructor super(WassersteinGANLossDiscriminator, self).__init__() def forward(self, discriminator_prediction_real: torch.Tensor, discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor: """ Forward pass. :param discriminator_prediction_real: (torch.Tensor) Raw discriminator prediction for real samples :param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples :return: (torch.Tensor) Wasserstein generator GAN loss with gradient penalty """ return - discriminator_prediction_real.mean() \ + discriminator_prediction_fake.mean() class WassersteinGANLossGPGenerator(WassersteinGANLossGenerator): """ This class implements the Wasserstein generator GAN loss proposed in: https://proceedings.neurips.cc/paper/2017/file/892c3b1c6dccd52936e27cbd0ff683d6-Paper.pdf """ def __index__(self) -> None: """ Constructor method. """ # Call super constructor super(WassersteinGANLossGPGenerator, self).__index__() class WassersteinGANLossGPDiscriminator(nn.Module): """ This class implements the Wasserstein generator GAN loss proposed in: https://proceedings.neurips.cc/paper/2017/file/892c3b1c6dccd52936e27cbd0ff683d6-Paper.pdf """ def __init__(self) -> None: """ Constructor method. """ # Call super constructor super(WassersteinGANLossGPDiscriminator, self).__init__() def forward(self, discriminator_prediction_real: torch.Tensor, discriminator_prediction_fake: torch.Tensor, discriminator: nn.Module, real_samples: torch.Tensor, fake_samples: torch.Tensor, lambda_gradient_penalty: Optional[float] = 2., **kwargs) -> torch.Tensor: """ Forward pass. :param discriminator_prediction_real: (torch.Tensor) Raw discriminator prediction for real samples :param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples :return: (torch.Tensor) Wasserstein discriminator GAN loss with gradient penalty """ # Generate random alpha for interpolation alpha = torch.rand((real_samples.shape[0], 1), device=real_samples.device) # Make interpolated samples samples_interpolated = (alpha * real_samples + (1. - alpha) * fake_samples) samples_interpolated.requires_grad = True # Make discriminator prediction discriminator_prediction_interpolated = discriminator(samples_interpolated) # Calc gradients gradients = torch.autograd.grad(outputs=discriminator_prediction_interpolated.sum(), inputs=samples_interpolated, create_graph=True, retain_graph=True)[0] # Calc gradient penalty gradient_penalty = (gradients.view(gradients.shape[0], -1).norm(dim=1) - 1.).pow(2).mean() return - discriminator_prediction_real.mean() \ + discriminator_prediction_fake.mean() \ + lambda_gradient_penalty * gradient_penalty class LSGANLossGenerator(nn.Module): """ This class implements the least squares generator GAN loss proposed in: https://openaccess.thecvf.com/content_ICCV_2017/papers/Mao_Least_Squares_Generative_ICCV_2017_paper.pdf """ def __init__(self) -> None: """ Constructor method. """ # Call super constructor super(LSGANLossGenerator, self).__init__() def forward(self, discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor: """ Forward pass. :param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples :return: (torch.Tensor) Generator LSGAN loss """ return - 0.5 * (discriminator_prediction_fake - 1.).pow(2).mean() class LSGANLossDiscriminator(nn.Module): """ This class implements the least squares discriminator GAN loss proposed in: https://openaccess.thecvf.com/content_ICCV_2017/papers/Mao_Least_Squares_Generative_ICCV_2017_paper.pdf """ def __init__(self) -> None: """ Constructor method. """ # Call super constructor super(LSGANLossDiscriminator, self).__init__() def forward(self, discriminator_prediction_real: torch.Tensor, discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor: """ Forward pass. :param discriminator_prediction_real: (torch.Tensor) Raw discriminator prediction for real samples :param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples :return: (torch.Tensor) Discriminator LSGAN loss """ return 0.5 * ((- discriminator_prediction_real - 1.).pow(2).mean() + discriminator_prediction_fake.pow(2).mean()) class HingeGANLossGenerator(WassersteinGANLossGenerator): """ This class implements the Hinge generator GAN loss proposed in: https://arxiv.org/pdf/1705.02894.pdf """ def __init__(self) -> None: """ Constructor method. """ # Call super constructor super(HingeGANLossGenerator, self).__init__() class HingeGANLossDiscriminator(nn.Module): """ This class implements the Hinge discriminator GAN loss proposed in: https://arxiv.org/pdf/1705.02894.pdf """ def __init__(self) -> None: """ Constructor method. """ # Call super constructor super(HingeGANLossDiscriminator, self).__init__() def forward(self, discriminator_prediction_real: torch.Tensor, discriminator_prediction_fake: torch.Tensor, **kwargs) -> torch.Tensor: """ Forward pass. :param discriminator_prediction_real: (torch.Tensor) Raw discriminator prediction for real samples :param discriminator_prediction_fake: (torch.Tensor) Raw discriminator predictions for fake samples :return: (torch.Tensor) Hinge discriminator GAN loss """ return - torch.minimum(torch.tensor(0., dtype=torch.float, device=discriminator_prediction_real.device), discriminator_prediction_real - 1.).mean() \ - torch.minimum(torch.tensor(0., dtype=torch.float, device=discriminator_prediction_fake.device), - discriminator_prediction_fake - 1.).mean()

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297

py

Python

paradigm/QuestionGeneration/Crossword.py

Paradigm-shift-AI/paradigm-brain

5347a91dbb45b1352534a256968ce7f6ff6bb299

[ "MIT" ]

null

paradigm/QuestionGeneration/Crossword.py

Paradigm-shift-AI/paradigm-brain

5347a91dbb45b1352534a256968ce7f6ff6bb299

[ "MIT" ]

null

paradigm/QuestionGeneration/Crossword.py

Paradigm-shift-AI/paradigm-brain

5347a91dbb45b1352534a256968ce7f6ff6bb299

[ "MIT" ]

null

class Crossword: def __init__(self, processed_transcript: dict): self.processed_transcript = processed_transcript self.question = [] def __generate_question(self): return 1 def questions(self): self.__generate_question() return self.question

22.846154

56

0.670034

30

297

6.2

0.433333

0.306452

0.247312

0

0.004525

0.255892

297

12

57

24.75

0.837104

0

1

0

1

0.333333

false

0

0.111111

0.666667

0

null

1

0

1

0

null

0

1

0

1

0

6

019b2e8343a699dc4ca783aa4b763c92c6920e49

39

py

Python

Python/Hello_Git_Hacktoberfest.py

PRONAY24/Hello-world

2e4e5a97837ec74effe674e23e2347a03b8c6573

[ "MIT" ]

null

Python/Hello_Git_Hacktoberfest.py

PRONAY24/Hello-world

2e4e5a97837ec74effe674e23e2347a03b8c6573

[ "MIT" ]

null

Python/Hello_Git_Hacktoberfest.py

PRONAY24/Hello-world

2e4e5a97837ec74effe674e23e2347a03b8c6573

[ "MIT" ]

null

print("Hello GitHub Hacktoberfest.!!")

19.5

38

0.74359

4

39

7.25

1

0

0.076923

39

1

39

0.805556

0

0.74359

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

6d85bf327e48c563e0d5cee6bd34370dfc1d949f

118

py

Python

app/pitch/__init__.py

jonodrew/graduate-rotator

0039033257a53ffa67edae288c9b7c37b50086a3

[ "MIT" ]

1

2021-09-10T13:55:25.000Z

app/pitch/__init__.py

jonodrew/graduate-rotator

0039033257a53ffa67edae288c9b7c37b50086a3

[ "MIT" ]

59

2020-06-29T21:50:14.000Z

2022-03-31T13:11:15.000Z

app/pitch/__init__.py

jonodrew/graduate-rotator

0039033257a53ffa67edae288c9b7c37b50086a3

[ "MIT" ]

null

from flask import Blueprint pitch_bp = Blueprint("pitch", __name__) from app.pitch import routes # noqa: E402,F401

19.666667

47

0.762712

17

118

5

0.705882

0.329412

0

0.06

0.152542

118

5

48

23.6

0.79

0.127119

0

0.049505

0

1

0

false

0

0.666667

0

0.666667

1

0

null

1

0

1

0

null

0

1

0

1

0

6

6dc59f37b5aa510a8b6f3e79eaefc13ee14d1f1d

981

py

Python

nlp/lstm_gru.py

mikuh/models-tf2

1541aa4e6355e20da67d0ae562b4daa8c0823b6f

[ "MIT" ]

null

nlp/lstm_gru.py

mikuh/models-tf2

1541aa4e6355e20da67d0ae562b4daa8c0823b6f

[ "MIT" ]

null

nlp/lstm_gru.py

mikuh/models-tf2

1541aa4e6355e20da67d0ae562b4daa8c0823b6f

[ "MIT" ]

null

from tensorflow import keras input_length = 100 def lstm_model(): model = keras.Sequential([ keras.layers.Embedding(input_dim=30000, output_dim=100, input_length=input_length), keras.layers.LSTM(32, return_sequences=True), keras.layers.LSTM(1, activation='sigmoid', return_sequences=False) ]) model.compile(optimizer=keras.optimizers.Adam(), loss=keras.losses.BinaryCrossentropy(), metrics=['accuracy']) return model def gru_model(): model = keras.Sequential([ keras.layers.Embedding(input_dim=30000, output_dim=32, input_length=input_length), keras.layers.LSTM(32, return_sequences=True), keras.layers.LSTM(1, activation='sigmoid', return_sequences=False) ]) model.compile(optimizer=keras.optimizers.Adam(), loss=keras.losses.BinaryCrossentropy(), metrics=['accuracy']) return model if __name__ == '__main__': lstm = lstm_model() lstm.summary() gru = gru_model() gru.summary()

31.645161

114

0.704383

118

981

5.644068

0.322034

0.099099

0.09009

0.075075

0.831832

0

0.029233

0.163099

981

31

115

31.645161

0.781973

0

0.521739

0

0.038697

0

1

0.086957

false

0

0.043478

0

0.217391

0

null

0

1

0

null

0

6

6dedd43790eb0c3aeedaadb6857d49dbcd7e65ee

39

py

Python

tests/test_basic.py

normcyr/comptage_velo_mtl

42014b52b4aa19df6ef694b818c0b258c789146e

[ "MIT" ]

null

tests/test_basic.py

normcyr/comptage_velo_mtl

42014b52b4aa19df6ef694b818c0b258c789146e

[ "MIT" ]

null

tests/test_basic.py

normcyr/comptage_velo_mtl

42014b52b4aa19df6ef694b818c0b258c789146e

[ "MIT" ]

null

from .context import comptage_velo_mtl

19.5

38

0.871795

6

39

5.333333

1

0

0.102564

39

1

39

0.914286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

a307667074b16d05f42cdfed465e6d3e024d6e20

31

py

Python

twitterwall/__init__.py

johnTheSloth/TwitterWall

a0938b466f72caaf8e742bea7713f87fc59d1743

[ "CC0-1.0" ]

null

twitterwall/__init__.py

johnTheSloth/TwitterWall

a0938b466f72caaf8e742bea7713f87fc59d1743

[ "CC0-1.0" ]

null

twitterwall/__init__.py

johnTheSloth/TwitterWall

a0938b466f72caaf8e742bea7713f87fc59d1743

[ "CC0-1.0" ]

null

from .twitterwallCore import *

15.5

30

0.806452

3

31

8.333333

1

0

0.129032

31

1

31

0.925926

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

0967a9fd1e7532cdb3b93bab07a57565aebebbf7

151

py

Python

test.py

SalehAghajani/Casimir_programming

396e33fdff771e1bc80ed3f72ed7f7360ff4bbf9

[ "MIT" ]

null

test.py

SalehAghajani/Casimir_programming

396e33fdff771e1bc80ed3f72ed7f7360ff4bbf9

[ "MIT" ]

null

test.py

SalehAghajani/Casimir_programming

396e33fdff771e1bc80ed3f72ed7f7360ff4bbf9

[ "MIT" ]

null

print('Hello World') import numpy as np def circumstance_circle(radius): return 2*np.pi*raius def area_circle(radius): return np.pi*(radius**2)

12.583333

32

0.735099

25

151

4.36

0.64

0.220183

0.330275

0

0.015385

0.139073

151

11

33

13.727273

0.823077

0

0.073826

0

1

0.333333

false

0

0.166667

0.333333

0.833333

0.166667

1

0

null

1

0

null

0

1

0

1

0

6

096931808fc4e9da200e83cef2b22af5af63b4cd

48,802

py

Python

quarkchain/cluster/tests/test_shard_state.py

chenhuan14/pyquarkchain

4fc28dd89947af1263fdeeeba57218515cd0ee77

[ "MIT" ]

4

2018-11-07T14:58:52.000Z

2019-12-22T20:35:00.000Z

quarkchain/cluster/tests/test_shard_state.py

chenhuan14/pyquarkchain

4fc28dd89947af1263fdeeeba57218515cd0ee77

[ "MIT" ]

null

quarkchain/cluster/tests/test_shard_state.py

chenhuan14/pyquarkchain

4fc28dd89947af1263fdeeeba57218515cd0ee77

[ "MIT" ]

3

2019-01-02T11:15:00.000Z

2019-12-22T20:32:28.000Z

import random import unittest from quarkchain.cluster.shard_state import ShardState from quarkchain.cluster.tests.test_utils import ( get_test_env, create_transfer_transaction, ) from quarkchain.core import CrossShardTransactionDeposit, CrossShardTransactionList from quarkchain.core import Identity, Address from quarkchain.diff import EthDifficultyCalculator from quarkchain.evm import opcodes from quarkchain.genesis import GenesisManager def create_default_shard_state(env, shard_id=0, diff_calc=None): genesis_manager = GenesisManager(env.quark_chain_config) shard_state = ShardState(env=env, shard_id=shard_id, diff_calc=diff_calc) shard_state.init_genesis_state(genesis_manager.create_root_block()) return shard_state class TestShardState(unittest.TestCase): def test_shard_state_simple(self): env = get_test_env() state = create_default_shard_state(env) self.assertEqual(state.root_tip.height, 0) self.assertEqual(state.header_tip.height, 0) def test_gas_price(self): id_list = [Identity.create_random_identity() for _ in range(5)] acc_list = [Address.create_from_identity(i, full_shard_id=0) for i in id_list] env = get_test_env(genesis_account=acc_list[0], genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) # 5 tx per block, make 3 blocks for _ in range(3): for j in range(5): state.add_tx( create_transfer_transaction( shard_state=state, key=id_list[j].get_key(), from_address=acc_list[j], to_address=random.choice(acc_list), value=0, gas_price=42 if j == 0 else 0, ) ) b = state.create_block_to_mine(address=acc_list[1]) state.finalize_and_add_block(b) # for testing purposes, update percentile to take max gas price state.gas_price_suggestion_oracle.percentile = 100 gas_price = state.gas_price() self.assertEqual(gas_price, 42) # results should be cached (same header). updating oracle shouldn't take effect state.gas_price_suggestion_oracle.percentile = 50 gas_price = state.gas_price() self.assertEqual(gas_price, 42) def test_estimate_gas(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_random_account(full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) tx_gen = lambda data: create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=12345, data=data, ) tx = tx_gen(b"") estimate = state.estimate_gas(tx, acc1) self.assertEqual(estimate, 21000) tx = tx_gen(b"12123478123412348125936583475758") estimate = state.estimate_gas(tx, acc1) self.assertEqual(estimate, 23176) def test_execute_tx(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_random_account(full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=12345, ) # adding this line to make sure `execute_tx` would reset `gas_used` state.evm_state.gas_used = state.evm_state.gas_limit res = state.execute_tx(tx, acc1) self.assertEqual(res, b"") def test_add_tx_incorrect_from_shard_id(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=1) acc2 = Address.create_random_account(full_shard_id=1) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) # state is shard 0 but tx from shard 1 tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=12345, ) self.assertFalse(state.add_tx(tx)) self.assertIsNone(state.execute_tx(tx, acc1)) def test_one_tx(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_random_account(full_shard_id=0) acc3 = Address.create_random_account(full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=12345, gas=50000, ) state.evm_state.gas_used = state.evm_state.gas_limit self.assertTrue(state.add_tx(tx)) block, i = state.get_transaction_by_hash(tx.get_hash()) self.assertEqual(block.tx_list[0], tx) self.assertEqual(block.header.create_time, 0) self.assertEqual(i, 0) # tx claims to use more gas than the limit and thus not included b1 = state.create_block_to_mine(address=acc3, gas_limit=49999) self.assertEqual(len(b1.tx_list), 0) b1 = state.create_block_to_mine(address=acc3, gas_limit=50000) self.assertEqual(len(b1.tx_list), 1) # Should succeed state.finalize_and_add_block(b1) self.assertEqual(state.header_tip, b1.header) self.assertEqual( state.get_balance(id1.recipient), 10000000 - opcodes.GTXCOST - 12345 ) self.assertEqual(state.get_balance(acc2.recipient), 12345) self.assertEqual(state.get_balance(acc3.recipient), opcodes.GTXCOST // 2) # Check receipts self.assertEqual(len(state.evm_state.receipts), 1) self.assertEqual(state.evm_state.receipts[0].state_root, b"\x01") self.assertEqual(state.evm_state.receipts[0].gas_used, 21000) block, i = state.get_transaction_by_hash(tx.get_hash()) self.assertEqual(block, b1) self.assertEqual(i, 0) # Check receipts in storage resp = state.get_transaction_receipt(tx.get_hash()) self.assertIsNotNone(resp) block, i, r = resp self.assertEqual(block, b1) self.assertEqual(i, 0) self.assertEqual(r.success, b"\x01") self.assertEqual(r.gas_used, 21000) # Check Account has full_shard_id self.assertEqual( state.evm_state.get_full_shard_id(acc2.recipient), acc2.full_shard_id ) tx_list, _ = state.db.get_transactions_by_address(acc1) self.assertEqual(tx_list[0].value, 12345) tx_list, _ = state.db.get_transactions_by_address(acc2) self.assertEqual(tx_list[0].value, 12345) def test_duplicated_tx(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_random_account(full_shard_id=0) acc3 = Address.create_random_account(full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=12345, ) self.assertTrue(state.add_tx(tx)) self.assertFalse(state.add_tx(tx)) # already in tx_queue self.assertEqual(len(state.tx_queue), 1) self.assertEqual(len(state.tx_dict), 1) block, i = state.get_transaction_by_hash(tx.get_hash()) self.assertEqual(len(block.tx_list), 1) self.assertEqual(block.tx_list[0], tx) self.assertEqual(block.header.create_time, 0) self.assertEqual(i, 0) b1 = state.create_block_to_mine(address=acc3) self.assertEqual(len(b1.tx_list), 1) # Should succeed state.finalize_and_add_block(b1) self.assertEqual(state.header_tip, b1.header) self.assertEqual( state.get_balance(id1.recipient), 10000000 - opcodes.GTXCOST - 12345 ) self.assertEqual(state.get_balance(acc2.recipient), 12345) self.assertEqual(state.get_balance(acc3.recipient), opcodes.GTXCOST // 2) # Check receipts self.assertEqual(len(state.evm_state.receipts), 1) self.assertEqual(state.evm_state.receipts[0].state_root, b"\x01") self.assertEqual(state.evm_state.receipts[0].gas_used, 21000) block, i = state.get_transaction_by_hash(tx.get_hash()) self.assertEqual(block, b1) self.assertEqual(i, 0) # tx already confirmed self.assertTrue(state.db.contain_transaction_hash(tx.get_hash())) self.assertFalse(state.add_tx(tx)) def test_add_invalid_tx_fail(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_random_account(full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=999999999999999999999, # insane ) self.assertFalse(state.add_tx(tx)) self.assertEqual(len(state.tx_queue), 0) def test_add_non_neighbor_tx_fail(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_random_account(full_shard_id=3) # not acc1's neighbor acc3 = Address.create_random_account(full_shard_id=8) # acc1's neighbor env = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) state = create_default_shard_state(env=env) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=0, gas=1000000, ) self.assertFalse(state.add_tx(tx)) self.assertEqual(len(state.tx_queue), 0) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc3, value=0, gas=1000000, ) self.assertTrue(state.add_tx(tx)) self.assertEqual(len(state.tx_queue), 1) def test_exceeding_xshard_limit(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_random_account(full_shard_id=1) acc3 = Address.create_random_account(full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) # a huge number to make xshard tx limit become 0 so that no xshard tx can be # included in the block env.quark_chain_config.MAX_NEIGHBORS = 10 ** 18 state = create_default_shard_state(env=env) # xshard tx tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=12345, gas=50000, ) self.assertTrue(state.add_tx(tx)) b1 = state.create_block_to_mine(address=acc3) self.assertEqual(len(b1.tx_list), 0) # inshard tx tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc3, value=12345, gas=50000, ) self.assertTrue(state.add_tx(tx)) b1 = state.create_block_to_mine(address=acc3) self.assertEqual(len(b1.tx_list), 1) def test_two_tx_in_one_block(self): id1 = Identity.create_random_identity() id2 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_from_identity(id2, full_shard_id=0) acc3 = Address.create_random_account(full_shard_id=0) env = get_test_env( genesis_account=acc1, genesis_minor_quarkash=2000000 + opcodes.GTXCOST ) state = create_default_shard_state(env=env) state.add_tx( create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=1000000, ) ) b0 = state.create_block_to_mine(address=acc3) state.finalize_and_add_block(b0) self.assertEqual(state.get_balance(id1.recipient), 1000000) self.assertEqual(state.get_balance(acc2.recipient), 1000000) self.assertEqual(state.get_balance(acc3.recipient), opcodes.GTXCOST // 2) # Check Account has full_shard_id self.assertEqual( state.evm_state.get_full_shard_id(acc2.recipient), acc2.full_shard_id ) state.add_tx( create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=Address( acc2.recipient, acc2.full_shard_id + 2 ), # set a different full shard id value=12345, gas=50000, ) ) state.add_tx( create_transfer_transaction( shard_state=state, key=id2.get_key(), from_address=acc2, to_address=acc1, value=54321, gas=40000, ) ) b1 = state.create_block_to_mine(address=acc3, gas_limit=40000) self.assertEqual(len(b1.tx_list), 1) b1 = state.create_block_to_mine(address=acc3, gas_limit=90000) self.assertEqual(len(b1.tx_list), 2) # Should succeed state.finalize_and_add_block(b1) self.assertEqual(state.header_tip, b1.header) self.assertEqual( state.get_balance(id1.recipient), 1000000 - opcodes.GTXCOST - 12345 + 54321 ) self.assertEqual( state.get_balance(acc2.recipient), 1000000 - opcodes.GTXCOST + 12345 - 54321 ) self.assertEqual(state.get_balance(acc3.recipient), opcodes.GTXCOST * 1.5) # Check receipts self.assertEqual(len(state.evm_state.receipts), 2) self.assertEqual(state.evm_state.receipts[0].state_root, b"\x01") self.assertEqual(state.evm_state.receipts[0].gas_used, 21000) self.assertEqual(state.evm_state.receipts[1].state_root, b"\x01") self.assertEqual(state.evm_state.receipts[1].gas_used, 42000) block, i = state.get_transaction_by_hash(b1.tx_list[0].get_hash()) self.assertEqual(block, b1) self.assertEqual(i, 0) block, i = state.get_transaction_by_hash(b1.tx_list[1].get_hash()) self.assertEqual(block, b1) self.assertEqual(i, 1) # Check acc2 full_shard_id doesn't change self.assertEqual( state.evm_state.get_full_shard_id(acc2.recipient), acc2.full_shard_id ) def test_fork_does_not_confirm_tx(self): """Tx should only be confirmed and removed from tx queue by the best chain""" id1 = Identity.create_random_identity() id2 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_from_identity(id2, full_shard_id=0) acc3 = Address.create_random_account(full_shard_id=0) env = get_test_env( genesis_account=acc1, genesis_minor_quarkash=2000000 + opcodes.GTXCOST ) state = create_default_shard_state(env=env) state.add_tx( create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=1000000, ) ) b0 = state.create_block_to_mine(address=acc3) b1 = state.create_block_to_mine(address=acc3) b0.tx_list = [] # make b0 empty state.finalize_and_add_block(b0) self.assertEqual(len(state.tx_queue), 1) self.assertEqual(len(b1.tx_list), 1) state.finalize_and_add_block(b1) # b1 is a fork and does not remove the tx from queue self.assertEqual(len(state.tx_queue), 1) b2 = state.create_block_to_mine(address=acc3) state.finalize_and_add_block(b2) self.assertEqual(len(state.tx_queue), 0) def test_revert_fork_put_tx_back_to_queue(self): """Tx in the reverted chain should be put back to the queue""" id1 = Identity.create_random_identity() id2 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_from_identity(id2, full_shard_id=0) acc3 = Address.create_random_account(full_shard_id=0) env = get_test_env( genesis_account=acc1, genesis_minor_quarkash=2000000 + opcodes.GTXCOST ) state = create_default_shard_state(env=env) state.add_tx( create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=1000000, ) ) b0 = state.create_block_to_mine(address=acc3) b1 = state.create_block_to_mine(address=acc3) state.finalize_and_add_block(b0) self.assertEqual(len(state.tx_queue), 0) b1.tx_list = [] # make b1 empty state.finalize_and_add_block(b1) self.assertEqual(len(state.tx_queue), 0) b2 = b1.create_block_to_append() state.finalize_and_add_block(b2) # now b1-b2 becomes the best chain and we expect b0 to be reverted and put the tx back to queue self.assertEqual(len(state.tx_queue), 1) b3 = b0.create_block_to_append() state.finalize_and_add_block(b3) self.assertEqual(len(state.tx_queue), 1) b4 = b3.create_block_to_append() state.finalize_and_add_block(b4) # b0-b3-b4 becomes the best chain self.assertEqual(len(state.tx_queue), 0) def test_stale_block_count(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc3 = Address.create_random_account(full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) b1 = state.create_block_to_mine(address=acc3) b2 = state.create_block_to_mine(address=acc3) b2.header.create_time += 1 state.finalize_and_add_block(b1) self.assertEqual(state.db.get_block_count_by_height(1), 1) state.finalize_and_add_block(b2) self.assertEqual(state.db.get_block_count_by_height(1), 2) def test_xshard_tx_sent(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_from_identity(id1, full_shard_id=1) acc3 = Address.create_random_account(full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) env1 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state1 = create_default_shard_state(env=env1, shard_id=1) # Add a root block to update block gas limit so that xshard tx can be included root_block = ( state.root_tip.create_block_to_append() .add_minor_block_header(state.header_tip) .add_minor_block_header(state1.header_tip) .finalize() ) state.add_root_block(root_block) tx = create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=888888, gas=opcodes.GTXXSHARDCOST + opcodes.GTXCOST, ) state.add_tx(tx) b1 = state.create_block_to_mine(address=acc3) self.assertEqual(len(b1.tx_list), 1) self.assertEqual(state.evm_state.gas_used, 0) # Should succeed state.finalize_and_add_block(b1) self.assertEqual(len(state.evm_state.xshard_list), 1) self.assertEqual( state.evm_state.xshard_list[0], CrossShardTransactionDeposit( tx_hash=tx.get_hash(), from_address=acc1, to_address=acc2, value=888888, gas_price=1, ), ) self.assertEqual( state.get_balance(id1.recipient), 10000000 - 888888 - opcodes.GTXCOST - opcodes.GTXXSHARDCOST, ) # Make sure the xshard gas is not used by local block self.assertEqual( state.evm_state.gas_used, opcodes.GTXCOST + opcodes.GTXXSHARDCOST ) # GTXXSHARDCOST is consumed by remote shard self.assertEqual(state.get_balance(acc3.recipient), opcodes.GTXCOST // 2) def test_xshard_tx_insufficient_gas(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_from_identity(id1, full_shard_id=1) acc3 = Address.create_random_account(full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) state.add_tx( create_transfer_transaction( shard_state=state, key=id1.get_key(), from_address=acc1, to_address=acc2, value=888888, gas=opcodes.GTXCOST, ) ) b1 = state.create_block_to_mine(address=acc3) self.assertEqual(len(b1.tx_list), 0) self.assertEqual(len(state.tx_queue), 0) def test_xshard_tx_received(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_from_identity(id1, full_shard_id=16) acc3 = Address.create_random_account(full_shard_id=0) env0 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) env1 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) state0 = create_default_shard_state(env=env0, shard_id=0) state1 = create_default_shard_state(env=env1, shard_id=16) # Add a root block to allow later minor blocks referencing this root block to # be broadcasted root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(state0.header_tip) .add_minor_block_header(state1.header_tip) .finalize() ) state0.add_root_block(root_block) state1.add_root_block(root_block) # Add one block in shard 0 b0 = state0.create_block_to_mine() state0.finalize_and_add_block(b0) b1 = state1.get_tip().create_block_to_append() b1.header.hash_prev_root_block = root_block.header.get_hash() tx = create_transfer_transaction( shard_state=state1, key=id1.get_key(), from_address=acc2, to_address=acc1, value=888888, gas=opcodes.GTXXSHARDCOST + opcodes.GTXCOST, gas_price=2, ) b1.add_tx(tx) # Add a x-shard tx from remote peer state0.add_cross_shard_tx_list_by_minor_block_hash( h=b1.header.get_hash(), tx_list=CrossShardTransactionList( tx_list=[ CrossShardTransactionDeposit( tx_hash=tx.get_hash(), from_address=acc2, to_address=acc1, value=888888, gas_price=2, ) ] ), ) # Create a root block containing the block with the x-shard tx root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(b0.header) .add_minor_block_header(b1.header) .finalize() ) state0.add_root_block(root_block) # Add b0 and make sure all x-shard tx's are added b2 = state0.create_block_to_mine(address=acc3) state0.finalize_and_add_block(b2) self.assertEqual(state0.get_balance(acc1.recipient), 10000000 + 888888) # Half collected by root self.assertEqual( state0.get_balance(acc3.recipient), opcodes.GTXXSHARDCOST * 2 // 2 ) # X-shard gas used evmState0 = state0.evm_state self.assertEqual(evmState0.xshard_receive_gas_used, opcodes.GTXXSHARDCOST) def test_xshard_tx_received_exclude_non_neighbor(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_from_identity(id1, full_shard_id=3) acc3 = Address.create_random_account(full_shard_id=0) env0 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) env1 = get_test_env( genesis_account=acc1, genesis_minor_quarkash=10000000, shard_size=64 ) state0 = create_default_shard_state(env=env0, shard_id=0) state1 = create_default_shard_state(env=env1, shard_id=3) # Add one block in shard 0 b0 = state0.create_block_to_mine() state0.finalize_and_add_block(b0) b1 = state1.get_tip().create_block_to_append() tx = create_transfer_transaction( shard_state=state1, key=id1.get_key(), from_address=acc2, to_address=acc1, value=888888, gas=opcodes.GTXXSHARDCOST + opcodes.GTXCOST, gas_price=2, ) b1.add_tx(tx) # Add a x-shard tx from remote peer state0.add_cross_shard_tx_list_by_minor_block_hash( h=b1.header.get_hash(), tx_list=CrossShardTransactionList( tx_list=[ CrossShardTransactionDeposit( tx_hash=tx.get_hash(), from_address=acc2, to_address=acc1, value=888888, gas_price=2, ) ] ), ) # Create a root block containing the block with the x-shard tx root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(b0.header) .add_minor_block_header(b1.header) .finalize() ) state0.add_root_block(root_block) # Add b0 and make sure all x-shard tx's are added b2 = state0.create_block_to_mine(address=acc3) state0.finalize_and_add_block(b2) self.assertEqual(state0.get_balance(acc1.recipient), 10000000) # Half collected by root self.assertEqual(state0.get_balance(acc3.recipient), 0) # X-shard gas used evmState0 = state0.evm_state self.assertEqual(evmState0.xshard_receive_gas_used, 0) def test_xshard_for_two_root_blocks(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) acc2 = Address.create_from_identity(id1, full_shard_id=1) acc3 = Address.create_random_account(full_shard_id=0) env0 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) env1 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state0 = create_default_shard_state(env=env0, shard_id=0) state1 = create_default_shard_state(env=env1, shard_id=1) # Add a root block to allow later minor blocks referencing this root block to # be broadcasted root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(state0.header_tip) .add_minor_block_header(state1.header_tip) .finalize() ) state0.add_root_block(root_block) state1.add_root_block(root_block) # Add one block in shard 0 b0 = state0.create_block_to_mine() state0.finalize_and_add_block(b0) b1 = state1.get_tip().create_block_to_append() b1.header.hash_prev_root_block = root_block.header.get_hash() tx = create_transfer_transaction( shard_state=state1, key=id1.get_key(), from_address=acc2, to_address=acc1, value=888888, gas=opcodes.GTXXSHARDCOST + opcodes.GTXCOST, ) b1.add_tx(tx) # Add a x-shard tx from state1 state0.add_cross_shard_tx_list_by_minor_block_hash( h=b1.header.get_hash(), tx_list=CrossShardTransactionList( tx_list=[ CrossShardTransactionDeposit( tx_hash=tx.get_hash(), from_address=acc2, to_address=acc1, value=888888, gas_price=2, ) ] ), ) # Create a root block containing the block with the x-shard tx root_block0 = ( state0.root_tip.create_block_to_append() .add_minor_block_header(b0.header) .add_minor_block_header(b1.header) .finalize() ) state0.add_root_block(root_block0) b2 = state0.get_tip().create_block_to_append() state0.finalize_and_add_block(b2) b3 = b1.create_block_to_append() b3.header.hash_prev_root_block = root_block.header.get_hash() # Add a x-shard tx from state1 state0.add_cross_shard_tx_list_by_minor_block_hash( h=b3.header.get_hash(), tx_list=CrossShardTransactionList( tx_list=[ CrossShardTransactionDeposit( tx_hash=bytes(32), from_address=acc2, to_address=acc1, value=385723, gas_price=3, ) ] ), ) root_block1 = ( state0.root_tip.create_block_to_append() .add_minor_block_header(b2.header) .add_minor_block_header(b3.header) .finalize() ) state0.add_root_block(root_block1) # Test x-shard gas limit when create_block_to_mine b5 = state0.create_block_to_mine(address=acc3, gas_limit=0) # Current algorithm allows at least one root block to be included self.assertEqual(b5.header.hash_prev_root_block, root_block0.header.get_hash()) b6 = state0.create_block_to_mine(address=acc3, gas_limit=opcodes.GTXXSHARDCOST) self.assertEqual(b6.header.hash_prev_root_block, root_block0.header.get_hash()) # There are two x-shard txs: one is root block coinbase with zero gas, and anonther is from shard 1 b7 = state0.create_block_to_mine( address=acc3, gas_limit=2 * opcodes.GTXXSHARDCOST ) self.assertEqual(b7.header.hash_prev_root_block, root_block1.header.get_hash()) b8 = state0.create_block_to_mine( address=acc3, gas_limit=3 * opcodes.GTXXSHARDCOST ) self.assertEqual(b8.header.hash_prev_root_block, root_block1.header.get_hash()) # Add b0 and make sure all x-shard tx's are added b4 = state0.create_block_to_mine(address=acc3) self.assertEqual(b4.header.hash_prev_root_block, root_block1.header.get_hash()) state0.finalize_and_add_block(b4) self.assertEqual(state0.get_balance(acc1.recipient), 10000000 + 888888 + 385723) # Half collected by root self.assertEqual( state0.get_balance(acc3.recipient), opcodes.GTXXSHARDCOST * (2 + 3) // 2 ) # Check gas used for receiving x-shard tx self.assertEqual(state0.evm_state.gas_used, 18000) self.assertEqual(state0.evm_state.xshard_receive_gas_used, 18000) def test_fork_resolve(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) b0 = state.get_tip().create_block_to_append() b1 = state.get_tip().create_block_to_append() state.finalize_and_add_block(b0) self.assertEqual(state.header_tip, b0.header) # Fork happens, first come first serve state.finalize_and_add_block(b1) self.assertEqual(state.header_tip, b0.header) # Longer fork happens, override existing one b2 = b1.create_block_to_append() state.finalize_and_add_block(b2) self.assertEqual(state.header_tip, b2.header) def test_root_chain_first_consensus(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) env0 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) env1 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state0 = create_default_shard_state(env=env0, shard_id=0) state1 = create_default_shard_state(env=env1, shard_id=1) # Add one block and prepare a fork b0 = state0.get_tip().create_block_to_append(address=acc1) b2 = state0.get_tip().create_block_to_append( address=Address.create_empty_account() ) state0.finalize_and_add_block(b0) state0.finalize_and_add_block(b2) b1 = state1.get_tip().create_block_to_append() b1.finalize(evm_state=state1.run_block(b1)) # Create a root block containing the block with the x-shard tx state0.add_cross_shard_tx_list_by_minor_block_hash( h=b1.header.get_hash(), tx_list=CrossShardTransactionList(tx_list=[]) ) root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(b0.header) .add_minor_block_header(b1.header) .finalize() ) state0.add_root_block(root_block) b00 = b0.create_block_to_append() state0.finalize_and_add_block(b00) self.assertEqual(state0.header_tip, b00.header) # Create another fork that is much longer (however not confirmed by root_block) b3 = b2.create_block_to_append() state0.finalize_and_add_block(b3) b4 = b3.create_block_to_append() state0.finalize_and_add_block(b4) self.assertGreater(b4.header.height, b00.header.height) self.assertEqual(state0.header_tip, b00.header) def test_shard_state_add_root_block(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) env0 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) env1 = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state0 = create_default_shard_state(env=env0, shard_id=0) state1 = create_default_shard_state(env=env1, shard_id=1) # Add one block and prepare a fork b0 = state0.get_tip().create_block_to_append(address=acc1) b2 = state0.get_tip().create_block_to_append( address=Address.create_empty_account() ) state0.finalize_and_add_block(b0) state0.finalize_and_add_block(b2) b1 = state1.get_tip().create_block_to_append() b1.finalize(evm_state=state1.run_block(b1)) # Create a root block containing the block with the x-shard tx state0.add_cross_shard_tx_list_by_minor_block_hash( h=b1.header.get_hash(), tx_list=CrossShardTransactionList(tx_list=[]) ) root_block = ( state0.root_tip.create_block_to_append() .add_minor_block_header(b0.header) .add_minor_block_header(b1.header) .finalize() ) root_block1 = ( state0.root_tip.create_block_to_append() .add_minor_block_header(b2.header) .add_minor_block_header(b1.header) .finalize() ) state0.add_root_block(root_block) b00 = b0.create_block_to_append() state0.finalize_and_add_block(b00) self.assertEqual(state0.header_tip, b00.header) # Create another fork that is much longer (however not confirmed by root_block) b3 = b2.create_block_to_append() state0.finalize_and_add_block(b3) b4 = b3.create_block_to_append() state0.finalize_and_add_block(b4) self.assertEqual(state0.header_tip, b00.header) self.assertEqual(state0.db.get_minor_block_by_height(2), b00) self.assertIsNone(state0.db.get_minor_block_by_height(3)) b5 = b1.create_block_to_append() state0.add_cross_shard_tx_list_by_minor_block_hash( h=b5.header.get_hash(), tx_list=CrossShardTransactionList(tx_list=[]) ) root_block2 = ( root_block1.create_block_to_append() .add_minor_block_header(b3.header) .add_minor_block_header(b4.header) .add_minor_block_header(b5.header) .finalize() ) self.assertFalse(state0.add_root_block(root_block1)) self.assertTrue(state0.add_root_block(root_block2)) self.assertEqual(state0.header_tip, b4.header) self.assertEqual(state0.meta_tip, b4.meta) self.assertEqual(state0.root_tip, root_block2.header) self.assertEqual(state0.db.get_minor_block_by_height(2), b3) self.assertEqual(state0.db.get_minor_block_by_height(3), b4) def test_shard_state_fork_resolve_with_higher_root_chain(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) b0 = state.get_tip().create_block_to_append() state.finalize_and_add_block(b0) root_block = ( state.root_tip.create_block_to_append() .add_minor_block_header(b0.header) .finalize() ) self.assertEqual(state.header_tip, b0.header) self.assertTrue(state.add_root_block(root_block)) b1 = state.get_tip().create_block_to_append() b2 = state.get_tip().create_block_to_append(nonce=1) b2.header.hash_prev_root_block = root_block.header.get_hash() b3 = state.get_tip().create_block_to_append(nonce=2) b3.header.hash_prev_root_block = root_block.header.get_hash() state.finalize_and_add_block(b1) self.assertEqual(state.header_tip, b1.header) # Fork happens, although they have the same height, b2 survives since it confirms root block state.finalize_and_add_block(b2) self.assertEqual(state.header_tip, b2.header) # b3 confirms the same root block as b2, so it will not override b2 state.finalize_and_add_block(b3) self.assertEqual(state.header_tip, b2.header) def test_shard_state_difficulty(self): env = get_test_env() for shard in env.quark_chain_config.SHARD_LIST: shard.GENESIS.DIFFICULTY = 10000 env.quark_chain_config.SKIP_MINOR_DIFFICULTY_CHECK = False diff_calc = EthDifficultyCalculator(cutoff=9, diff_factor=2048, minimum_diff=1) env.quark_chain_config.NETWORK_ID = ( 1 ) # other network ids will skip difficulty check state = create_default_shard_state(env=env, shard_id=0, diff_calc=diff_calc) # Check new difficulty b0 = state.create_block_to_mine(state.header_tip.create_time + 8) self.assertEqual( b0.header.difficulty, state.header_tip.difficulty // 2048 + state.header_tip.difficulty, ) b0 = state.create_block_to_mine(state.header_tip.create_time + 9) self.assertEqual(b0.header.difficulty, state.header_tip.difficulty) b0 = state.create_block_to_mine(state.header_tip.create_time + 17) self.assertEqual(b0.header.difficulty, state.header_tip.difficulty) b0 = state.create_block_to_mine(state.header_tip.create_time + 24) self.assertEqual( b0.header.difficulty, state.header_tip.difficulty - state.header_tip.difficulty // 2048, ) b0 = state.create_block_to_mine(state.header_tip.create_time + 35) self.assertEqual( b0.header.difficulty, state.header_tip.difficulty - state.header_tip.difficulty // 2048 * 2, ) def test_shard_state_recovery_from_root_block(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) blockHeaders = [] blockMetas = [] for i in range(12): b = state.get_tip().create_block_to_append(address=acc1) state.finalize_and_add_block(b) blockHeaders.append(b.header) blockMetas.append(b.meta) # add a fork b1 = state.db.get_minor_block_by_height(3) b1.header.create_time += 1 state.finalize_and_add_block(b1) self.assertEqual(state.db.get_minor_block_by_hash(b1.header.get_hash()), b1) root_block = state.root_tip.create_block_to_append() root_block.minor_block_header_list = blockHeaders[:5] root_block.finalize() state.add_root_block(root_block) recoveredState = ShardState(env=env, shard_id=0) recoveredState.init_from_root_block(root_block) # forks are pruned self.assertIsNone( recoveredState.db.get_minor_block_by_hash(b1.header.get_hash()) ) self.assertEqual( recoveredState.db.get_minor_block_by_hash( b1.header.get_hash(), consistency_check=False ), b1, ) self.assertEqual(recoveredState.root_tip, root_block.header) self.assertEqual(recoveredState.header_tip, blockHeaders[4]) self.assertEqual(recoveredState.confirmed_header_tip, blockHeaders[4]) self.assertEqual(recoveredState.meta_tip, blockMetas[4]) self.assertEqual( recoveredState.evm_state.trie.root_hash, blockMetas[4].hash_evm_state_root ) def test_add_block_receipt_root_not_match(self): id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1) acc3 = Address.create_random_account(full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env) b1 = state.create_block_to_mine(address=acc3) # Should succeed state.finalize_and_add_block(b1) b1.finalize(evm_state=state.run_block(b1)) b1.meta.hash_evm_receipt_root = b"00" * 32 self.assertRaises(ValueError, state.add_block(b1)) def test_not_update_tip_on_root_fork(self): """ block's hash_prev_root_block must be on the same chain with root_tip to update tip. +--+ a. |r1| /+--+ / | +--+ / +--+ +--+ |r0|<----|m1|<---|m2| c. +--+ \ +--+ +--+ \ | | \+--+ | b. |r2|<----+ +--+ Initial state: r0 <- m1 Then adding r1, r2, m2 should not make m2 the tip because r1 is the root tip and r2 and r1 are not on the same root chain. """ id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) m1 = state.get_tip().create_block_to_append(address=acc1) state.finalize_and_add_block(m1) r1 = state.root_tip.create_block_to_append() r2 = state.root_tip.create_block_to_append() r1.minor_block_header_list.append(m1.header) r1.finalize() state.add_root_block(r1) r2.minor_block_header_list.append(m1.header) r2.header.create_time = r1.header.create_time + 1 # make r2, r1 different r2.finalize() self.assertNotEqual(r1.header.get_hash(), r2.header.get_hash()) state.add_root_block(r2) self.assertEqual(state.root_tip, r1.header) m2 = m1.create_block_to_append(address=acc1) m2.header.hash_prev_root_block = r2.header.get_hash() state.finalize_and_add_block(m2) # m2 is added self.assertEqual(state.db.get_minor_block_by_hash(m2.header.get_hash()), m2) # but m1 should still be the tip self.assertEqual(state.header_tip, m1.header) def test_add_root_block_revert_header_tip(self): """ block's hash_prev_root_block must be on the same chain with root_tip to update tip. +--+ |r1|<-------------+ /+--+ | / | | +--+ / +--+ +--+ +--+ |r0|<----|m1|<---|m2| <---|m3| +--+ \ +--+ +--+ +--+ \ | \ \+--+. +--+ |r2|<-----|r3| (r3 includes m2) +--+ +--+ Initial state: r0 <- m1 <- m2 Adding r1, r2, m3 makes r1 the root_tip, m3 the header_tip Adding r3 should change the root_tip to r3, header_tip to m2 """ id1 = Identity.create_random_identity() acc1 = Address.create_from_identity(id1, full_shard_id=0) env = get_test_env(genesis_account=acc1, genesis_minor_quarkash=10000000) state = create_default_shard_state(env=env, shard_id=0) m1 = state.get_tip().create_block_to_append(address=acc1) state.finalize_and_add_block(m1) m2 = state.get_tip().create_block_to_append(address=acc1) state.finalize_and_add_block(m2) r1 = state.root_tip.create_block_to_append() r2 = state.root_tip.create_block_to_append() r1.minor_block_header_list.append(m1.header) r1.finalize() state.add_root_block(r1) r2.minor_block_header_list.append(m1.header) r2.header.create_time = r1.header.create_time + 1 # make r2, r1 different r2.finalize() self.assertNotEqual(r1.header.get_hash(), r2.header.get_hash()) state.add_root_block(r2) self.assertEqual(state.root_tip, r1.header) m3 = state.create_block_to_mine(address=acc1) self.assertEqual(m3.header.hash_prev_root_block, r1.header.get_hash()) state.finalize_and_add_block(m3) r3 = r2.create_block_to_append(address=acc1) r3.add_minor_block_header(m2.header) r3.finalize() state.add_root_block(r3) self.assertEqual(state.root_tip, r3.header) self.assertEqual(state.header_tip, m2.header)

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null

0

1

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null

0

6

09741a01e28308c48f26f7897b62450946a0dc81

5,399

py

Python

test/test_telluric.py

cylammarco/ASPIRED

d4d04a5dfdf7c4719f9d22feea6d4035bfee8345

[ "BSD-3-Clause" ]

13

2019-12-22T08:44:57.000Z

2022-03-23T18:09:14.000Z

test/test_telluric.py

cylammarco/ASPIRED

d4d04a5dfdf7c4719f9d22feea6d4035bfee8345

[ "BSD-3-Clause" ]

71

2019-09-13T20:58:28.000Z

2022-03-27T13:40:56.000Z

test/test_telluric.py

cylammarco/ASPIRED

d4d04a5dfdf7c4719f9d22feea6d4035bfee8345

[ "BSD-3-Clause" ]

null

import copy import numpy as np from aspired import spectral_reduction from aspired.flux_calibration import FluxCalibration ''' onedspec = spectral_reduction.OneDSpec(log_file_name=None) onedspec.science_spectrum_list[0].add_wavelength(wave) onedspec.science_spectrum_list[0].add_flux(flux_sci, None, None) onedspec.science_spectrum_list[0].add_flux_continuum(flux_sci_continuum) fluxcal = FluxCalibration(log_file_name=None) telluric_func = fluxcal.get_telluric_profile(wave, flux_std, flux_std_continuum, mask_range=[[495, 551], [700, 753], [848, 960]], return_function=True) fluxcal.inspect_telluric_profile() onedspec.add_telluric_function(telluric_func) onedspec.get_telluric_profile() onedspec.inspect_telluric_profile( display=True) onedspec.apply_telluric_correction() std_wave = np.load('test/test_data/std_wave.npy') std_flux = np.load('test/test_data/std_flux.npy') std_flux_continuum = np.load('test/test_data/std_flux_continuum.npy') sci_wave = np.load('test/test_data/sci_wave.npy') sci_flux = np.load('test/test_data/sci_flux.npy') sci_flux_continuum = np.load('test/test_data/sci_flux_continuum.npy') onedspec = spectral_reduction.OneDSpec(log_file_name=None) onedspec.science_spectrum_list[0].add_wavelength(sci_wave) onedspec.science_spectrum_list[0].add_flux(sci_flux, None, None) onedspec.science_spectrum_list[0].add_flux_continuum(sci_flux_continuum) fluxcal = FluxCalibration(log_file_name=None) telluric_func = fluxcal.get_telluric_profile(std_wave, std_flux, std_flux_continuum, return_function=True) onedspec.add_telluric_function(telluric_func) onedspec.get_telluric_profile(auto_apply=False) onedspec.inspect_telluric_profile( display=True) ''' def test_telluric_square_wave(): wave = np.arange(1000.) flux_sci = np.ones(1000) * 5. flux_std = np.ones(1000) * 100. flux_sci_continuum = copy.deepcopy(flux_sci) flux_std_continuum = copy.deepcopy(flux_std) flux_sci[500:550] *= 0.01 flux_sci[700:750] *= 0.001 flux_sci[850:950] *= 0.1 flux_std[500:550] *= 0.01 flux_std[700:750] *= 0.001 flux_std[850:950] *= 0.1 # Get the telluric profile fluxcal = FluxCalibration(log_file_name=None) telluric_func = fluxcal.get_telluric_profile(wave, flux_std, flux_std_continuum, mask_range=[[495, 551], [700, 753], [848, 960]], return_function=True) onedspec = spectral_reduction.OneDSpec(log_file_name=None) onedspec.science_spectrum_list[0].add_wavelength(wave) onedspec.science_spectrum_list[0].add_flux(flux_sci, None, None) onedspec.science_spectrum_list[0].add_flux_continuum(flux_sci_continuum) onedspec.add_telluric_function(telluric_func) onedspec.get_telluric_profile() onedspec.apply_telluric_correction() assert np.isclose(np.nansum(onedspec.science_spectrum_list[0].flux), np.nansum(flux_sci_continuum), rtol=1e-2) onedspec.inspect_telluric_profile( display=False, return_jsonstring=True, save_fig=True, fig_type='iframe+jpg+png+svg+pdf', filename='test/test_output/test_telluric') def test_telluric_real_data(): std_wave = np.load('test/test_data/std_wave.npy') std_flux = np.load('test/test_data/std_flux.npy') std_flux_continuum = np.load('test/test_data/std_flux_continuum.npy') sci_wave = np.load('test/test_data/sci_wave.npy') sci_flux = np.load('test/test_data/sci_flux.npy') sci_flux_continuum = np.load('test/test_data/sci_flux_continuum.npy') # Get the telluric profile fluxcal = FluxCalibration(log_file_name=None) telluric_func = fluxcal.get_telluric_profile(std_wave, std_flux, std_flux_continuum, return_function=True) onedspec = spectral_reduction.OneDSpec(log_file_name=None) onedspec.science_spectrum_list[0].add_wavelength(sci_wave) onedspec.science_spectrum_list[0].add_flux(sci_flux, None, None) onedspec.science_spectrum_list[0].add_flux_continuum(sci_flux_continuum) onedspec.add_telluric_function(telluric_func) onedspec.get_telluric_profile() onedspec.apply_telluric_correction() assert np.isclose(np.nansum(onedspec.science_spectrum_list[0].flux), np.nansum(sci_flux_continuum), rtol=1e-2) onedspec.inspect_telluric_profile( display=False, return_jsonstring=True, save_fig=True, fig_type='iframe+jpg+png+svg+pdf', filename='test/test_output/test_telluric')

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5,399

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0

null

0

1

0

null

0

6

09749554017eb56bbfcb7b1f0a5bfc2b91658755

3,118

py

Python

music/analysis_merge.py

rkneusel9/SwarmOptimization

5445b6f90ab49339ca0fdb71e98d44e6827c95a8

[ "MIT" ]

2

2022-01-11T17:14:14.000Z

2022-03-07T10:22:32.000Z

music/analysis_merge.py

rkneusel9/SwarmOptimization

5445b6f90ab49339ca0fdb71e98d44e6827c95a8

[ "MIT" ]

null

music/analysis_merge.py

rkneusel9/SwarmOptimization

5445b6f90ab49339ca0fdb71e98d44e6827c95a8

[ "MIT" ]

1

2021-11-24T01:11:49.000Z

# # file: analysis_merge.py # # Plots for melody merge results # # RTK, 27-Oct-2020 # Last update: 27-Oct-2020 # ################################################################ import numpy as np import matplotlib.pylab as plt import pickle def Plot(giter, gbest, sym, lbl): d = np.zeros(10000) for i in range(len(giter)): d[giter[i]:] = gbest[i] plt.plot(range(10000)[::800], d[::800], marker=sym, linestyle='none', color='k', label=lbl) plt.plot(range(10000), d, linewidth=1, color='k') # alpha = 0.5 p = pickle.load(open("results/merge/mary_ode_alpha_0.5_DE/results.pkl","rb")) Plot(p["giter"],p["gbest"], "o", "DE") p = pickle.load(open("results/merge/mary_ode_alpha_0.5_RO/results.pkl","rb")) Plot(p["giter"],p["gbest"], "s", "RO") p = pickle.load(open("results/merge/mary_ode_alpha_0.5_GA/results.pkl","rb")) Plot(p["giter"],p["gbest"], "X", "GA") p = pickle.load(open("results/merge/mary_ode_alpha_0.5_PSO/results.pkl","rb")) Plot(p["giter"],p["gbest"], "<", "PSO") p = pickle.load(open("results/merge/mary_ode_alpha_0.5_GWO/results.pkl","rb")) Plot(p["giter"],p["gbest"], ">", "GWO") p = pickle.load(open("results/merge/mary_ode_alpha_0.5_JAYA/results.pkl","rb")) Plot(p["giter"],p["gbest"], "*", "Jaya") plt.legend(loc="upper right") plt.tight_layout(pad=0, w_pad=0, h_pad=0) plt.savefig("merge_0.5_plot.png", dpi=300) plt.show() plt.close() # alpha = 0.1 p = pickle.load(open("results/merge/mary_ode_alpha_0.1_DE/results.pkl","rb")) Plot(p["giter"],p["gbest"], "o", "DE") p = pickle.load(open("results/merge/mary_ode_alpha_0.1_RO/results.pkl","rb")) Plot(p["giter"],p["gbest"], "s", "RO") p = pickle.load(open("results/merge/mary_ode_alpha_0.1_GA/results.pkl","rb")) Plot(p["giter"],p["gbest"], "X", "GA") p = pickle.load(open("results/merge/mary_ode_alpha_0.1_PSO/results.pkl","rb")) Plot(p["giter"],p["gbest"], "<", "PSO") p = pickle.load(open("results/merge/mary_ode_alpha_0.1_GWO/results.pkl","rb")) Plot(p["giter"],p["gbest"], ">", "GWO") p = pickle.load(open("results/merge/mary_ode_alpha_0.1_JAYA/results.pkl","rb")) Plot(p["giter"],p["gbest"], "*", "Jaya") plt.legend(loc="upper right") plt.tight_layout(pad=0, w_pad=0, h_pad=0) plt.savefig("merge_0.1_plot.png", dpi=300) plt.show() plt.close() # alpha = 0.9 p = pickle.load(open("results/merge/mary_ode_alpha_0.9_DE/results.pkl","rb")) Plot(p["giter"],p["gbest"], "o", "DE") p = pickle.load(open("results/merge/mary_ode_alpha_0.9_RO/results.pkl","rb")) Plot(p["giter"],p["gbest"], "s", "RO") p = pickle.load(open("results/merge/mary_ode_alpha_0.9_GA/results.pkl","rb")) Plot(p["giter"],p["gbest"], "X", "GA") p = pickle.load(open("results/merge/mary_ode_alpha_0.9_PSO/results.pkl","rb")) Plot(p["giter"],p["gbest"], "<", "PSO") p = pickle.load(open("results/merge/mary_ode_alpha_0.9_GWO/results.pkl","rb")) Plot(p["giter"],p["gbest"], ">", "GWO") p = pickle.load(open("results/merge/mary_ode_alpha_0.9_JAYA/results.pkl","rb")) Plot(p["giter"],p["gbest"], "*", "Jaya") plt.legend(loc="upper right") plt.tight_layout(pad=0, w_pad=0, h_pad=0) plt.savefig("merge_0.9_plot.png", dpi=300) plt.show() plt.close()

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0.138889

0.844136

0.829733

0

0.034807

0.078576

3,118

81

96

38.493827

0.641838

0.044901

0

0.5

0

0.425172

0.295862

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1

0.016667

false

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0.05

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0.066667

0

null

0

1

0

null

0

6

09d11e9b43e16e7e7cb5c47d400f2aa13354dbc0

31

py

Python

FlowNet2_src/flownet2/__init__.py

TopGun666/FlowVO

382bf31e9acc49dcb448713cb8e7e79eb4ae9e8b

[ "MIT" ]

2

2020-07-08T08:23:05.000Z

2020-10-21T01:46:06.000Z

FlowNet2_src/flownet2/__init__.py

TopGun666/FlowVO

382bf31e9acc49dcb448713cb8e7e79eb4ae9e8b

[ "MIT" ]

4

2020-03-29T03:02:09.000Z

2020-11-29T22:56:59.000Z

FlowNet2_src/flownet2/__init__.py

TopGun666/FlowVO

382bf31e9acc49dcb448713cb8e7e79eb4ae9e8b

[ "MIT" ]

3

2020-11-17T23:48:29.000Z

2022-03-16T12:44:44.000Z

from .flownet2 import FlowNet2

15.5

30

0.83871

4

31

6.5

0.75

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0.074074

0.129032

31

1

31

0.888889

0

1

0

true

0

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1

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0

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0

1

0

null

0

1

0

1

0

1

0

6

61eb4c1c85c94de28b0cca6a936f4841099aca13

1,682

py

Python

back/REST/flask/rating.py

lvvanegas10/Historio

a0a7bf27eb78ca0ac3ca9893c0dae88e17235b15

[ "MIT" ]

1

2020-06-11T18:06:34.000Z

back/REST/flask/rating.py

lvvanegas10/Historio

a0a7bf27eb78ca0ac3ca9893c0dae88e17235b15

[ "MIT" ]

null

back/REST/flask/rating.py

lvvanegas10/Historio

a0a7bf27eb78ca0ac3ca9893c0dae88e17235b15

[ "MIT" ]

2

2018-09-18T12:39:33.000Z

2019-02-28T07:06:01.000Z

from flask import Blueprint from aux import * rating = Blueprint("rating", __name__) @rating.route('/', methods=["POST"]) @authorExists def createRating(): incoming = request.json story = incoming["story"] author = incoming["author"]["username"] rating = incoming["rating"] resp = query( "match (a:Author) match (s:Story) where a.username = $uname and id(s) = $sid merge (s)<-[r:Rating ]-(a) on create set r.rating = $rating, r.comment = $comment, r.date = $date return r", {"uname": author, "sid": story, "rating": rating["rating"], "comment": rating["comment"], "date": rating["date"]}) return Response(json.dumps(resp), content_type='application/json') @rating.route('/', methods=["PUT"]) def updateRating(): incoming = request.json story = incoming["story"] author = incoming["author"]["username"] rating = incoming["rating"] resp = query( " match (s:Story)<-[r:Rating ]-(a:Author) where a.username = $uname and id(s) = $sid set r.rating = $rating, r.comment = $comment, r.date = $date return r", {"uname": author, "sid": story, "rating": rating["rating"], "comment": rating["comment"], "date": rating["date"]}) return Response(json.dumps(resp), content_type='application/json') @rating.route('/', methods=["DELETE"]) def deleteRating(): incoming = request.json story = incoming["story"] author = incoming["author"]["username"] resp = query("match (s:Story)<-[r:Rating ]-(a:Author) where a.username = $uname and id(s) = $sid delete r", {"uname": author, "sid": story}) return Response(json.dumps(resp), content_type='application/json')

40.047619

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1,682

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0.767176

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1,682

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0.058824

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null

0

1

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null

0

6

11094b8bd229f65dea928b6ec8f190db607367ba

111

py

Python

src/ceploy/__init__.py

qadahtm/cloud-deployer

b4ba3b594bccaa7e4c563b3adca6fc43673f1a6c

[ "Apache-2.0" ]

null

src/ceploy/__init__.py

qadahtm/cloud-deployer

b4ba3b594bccaa7e4c563b3adca6fc43673f1a6c

[ "Apache-2.0" ]

null

src/ceploy/__init__.py

qadahtm/cloud-deployer

b4ba3b594bccaa7e4c563b3adca6fc43673f1a6c

[ "Apache-2.0" ]

null

from .constants import Provider from .cloud import Cloud from .cloud import VmInstance from .utils import Utils

27.75

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0.828829

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111

5.75

0.4375

0.195652

0.326087

0

0.135135

111

4

32

27.75

0.958333

0

1

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true

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null

0

1

0

1

0

1

0

6

1114a1441ff03a2c7e9113d1a53e9fac39bc1493

3,609

py

Python

tfrecords/write_concentrations.py

Aremaki/MscProjectNMR

5bb8fb129d5fe326aa73b56cb7c5b01a17aebb0d

[ "MIT" ]

null

tfrecords/write_concentrations.py

Aremaki/MscProjectNMR

5bb8fb129d5fe326aa73b56cb7c5b01a17aebb0d

[ "MIT" ]

null

tfrecords/write_concentrations.py

Aremaki/MscProjectNMR

5bb8fb129d5fe326aa73b56cb7c5b01a17aebb0d

[ "MIT" ]

1

2021-07-28T11:18:00.000Z

import tensorflow as tf from tfrecords.write import _bytes_feature, serialize_array def _float_feature(value): """Returns a float_list from a float / double.""" return tf.train.Feature(float_list=tf.train.FloatList(value=[value])) def serialize_example_concentrations(X, Y): """ Creates a tf.train.Example message ready to be written to a file. """ # Create a dictionary mapping the feature name to the tf.train.Example-compatible # data type. feature = { 'X': _bytes_feature(serialize_array(X)), 'Y': _bytes_feature(serialize_array(Y)), } # Create a Features message using tf.train.Example. example_proto = tf.train.Example(features=tf.train.Features(feature=feature)) return example_proto.SerializeToString() def serialize_example_concentrations_single(X, y): """ Creates a tf.train.Example message ready to be written to a file. """ # Create a dictionary mapping the feature name to the tf.train.Example-compatible # data type. feature = { 'X': _bytes_feature(serialize_array(X)), 'y': _float_feature(y), } # Create a Features message using tf.train.Example. example_proto = tf.train.Example(features=tf.train.Features(feature=feature)) return example_proto.SerializeToString() def tf_serialize_example_concentrations(X, Y): tf_string = tf.py_function(serialize_example_concentrations, (X, Y), tf.string) return tf.reshape(tf_string, ()) # The result is a scalar def tf_serialize_example_concentrations_single(X, y): tf_string = tf.py_function(serialize_example_concentrations_single, (X, y), tf.string) return tf.reshape(tf_string, ()) # The result is a scalar def write_tfrecords_concentrations(path, dataset=None, size=1000, number=None): """ :param dataset: tf.data object containing the examples (X: array(tf.float32), Y: array(tf.float32)) :param size: Number of example in each tfrecord file :param number: Number of files :return: Create the tfrecord files """ serialized_dataset = dataset.map(tf_serialize_example_concentrations) if number: #The number has priority over the size size = len(serialized_dataset) // number else: number = len(serialized_dataset) // size if len(serialized_dataset) % number >= 1: number += 1 for i in range(number): filename = path + '/data_{}.tfrecord'.format(i) data_to_write = serialized_dataset.take(size) serialized_dataset = serialized_dataset.skip(size) writer = tf.data.experimental.TFRecordWriter(filename) writer.write(data_to_write) def write_tfrecords_concentrations_single(path, dataset=None, size=1000, number=None): """ :param dataset: tf.data object containing the examples (X: array(tf.float32), Y: array(tf.float32)) :param size: Number of example in each tfrecord file :param number: Number of files :return: Create the tfrecord files """ serialized_dataset = dataset.map(tf_serialize_example_concentrations_single) if number: #The number has priority over the size size = len(serialized_dataset) // number else: number = len(serialized_dataset) // size if len(serialized_dataset) % number >= 1: number += 1 for i in range(number): filename = path + '/data_{}.tfrecord'.format(i) data_to_write = serialized_dataset.take(size) serialized_dataset = serialized_dataset.skip(size) writer = tf.data.experimental.TFRecordWriter(filename) writer.write(data_to_write)

36.09

103

0.700748

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3,609

5.208511

0.185106

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0.042484

0.883987

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3,609

100

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0.269231

0

null

0

1

0

null

0

6

115792ab6602036bb26ac8846eb27f23ccfb1ffe

100

py

Python

iniscrapec/modules/__init__.py

riccardopaltrinieri/inipec-scraper

70ca30cf543553a6296d97046b37990283ef3f05

[ "MIT" ]

null

iniscrapec/modules/__init__.py

riccardopaltrinieri/inipec-scraper

70ca30cf543553a6296d97046b37990283ef3f05

[ "MIT" ]

null

iniscrapec/modules/__init__.py

riccardopaltrinieri/inipec-scraper

70ca30cf543553a6296d97046b37990283ef3f05

[ "MIT" ]

null

from modules.dao import Dao from modules.gui import RootWindow from modules.scraper import find_pec

25

36

0.85

16

100

5.25

0.5625

0.392857

0

0.12

100

3

37

33.333333

0.954545

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

11590668af011ae6da6438ac162f9072248fb965

128

py

Python

articles/admin.py

Uncensored-Developer/django-elastic-drf-example

bddd5bc2c869425eef4c940228e7a8a122aa5500

[ "MIT" ]

2

2020-02-23T11:17:39.000Z

2021-01-11T13:20:47.000Z

articles/admin.py

Uncensored-Developer/django-elastic-drf-example

bddd5bc2c869425eef4c940228e7a8a122aa5500

[ "MIT" ]

2

2019-12-05T14:03:53.000Z

articles/admin.py

Uncensored-Developer/django-elastic-drf-example

bddd5bc2c869425eef4c940228e7a8a122aa5500

[ "MIT" ]

null

from django.contrib import admin from articles import models as articles_models admin.site.register(articles_models.Article)

18.285714

46

0.84375

18

128

5.888889

0.611111

0.264151

0

0.109375

128

6

47

21.333333

0.929825

0

1

0

true

0

0.666667

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0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

feca2ef199ff9760ea084933d9527e793a07a090

187

py

Python

sleekxmpp/plugins/xep_0065/__init__.py

E-Tahta/sleekxmpp

ed067c9412835c5fe44bf203936262bcec09ced4

[ "BSD-3-Clause" ]

499

2015-01-04T21:45:16.000Z

2022-02-14T13:04:08.000Z

sleekxmpp/plugins/xep_0065/__init__.py

numanturle/SleekXMPP

1aeefd88accf45947c6376e9fac3abae9cbba8aa

[ "BSD-3-Clause" ]

159

2015-01-02T19:09:47.000Z

2020-02-12T08:29:54.000Z

sleekxmpp/plugins/xep_0065/__init__.py

numanturle/SleekXMPP

1aeefd88accf45947c6376e9fac3abae9cbba8aa

[ "BSD-3-Clause" ]

209

2015-01-07T16:23:16.000Z

2022-01-26T13:02:20.000Z

from sleekxmpp.plugins.base import register_plugin from sleekxmpp.plugins.xep_0065.stanza import Socks5 from sleekxmpp.plugins.xep_0065.proxy import XEP_0065 register_plugin(XEP_0065)

23.375

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28

187

5.535714

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0.180645

0.387097

0.296774

0.348387

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0.085562

187

7

54

26.714286

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true

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null

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null

0

1

0

1

0

6

3a50f373708e2afd1a214a3c3f9a885b9c4b301e

18

py

Python

model_zoo/east/__init__.py

saicoco/gluon-east

9597bf4fe20a971940fbd5e72c221040ecacb5b7

[ "MIT" ]

2

2019-01-05T02:40:06.000Z

2019-03-20T18:00:05.000Z

model_zoo/east/__init__.py

saicoco/gluon-east

9597bf4fe20a971940fbd5e72c221040ecacb5b7

[ "MIT" ]

null

model_zoo/east/__init__.py

saicoco/gluon-east

9597bf4fe20a971940fbd5e72c221040ecacb5b7

[ "MIT" ]

null

from east import *

18

0.777778

3

18

4.666667

1

0

0.166667

18

1

18

0.933333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

28a56e0f50d127a53e0210503144a6c51bab59ae

37

py

Python

cffi_ext/__init__.py

gwangyi/cffi_ext

10be829f94065a80d5db2912096d82aafb08ccd2

[ "MIT" ]

null

cffi_ext/__init__.py

gwangyi/cffi_ext

10be829f94065a80d5db2912096d82aafb08ccd2

[ "MIT" ]

null

cffi_ext/__init__.py

gwangyi/cffi_ext

10be829f94065a80d5db2912096d82aafb08ccd2

[ "MIT" ]

null

from .extractor import cdef_extract

12.333333

35

0.837838

5

37

6

1

0

0.135135

37

2

36

18.5

0.9375

0

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true

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1

0

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null

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null

0

1

0

1

0

1

0

6

28b020f4b93b4bc09c260e143089f4a3c6a84a95

59

py

Python

__init__.py

vedant-jad99/Snipping-Tool

312daf66c5e594e2cb139e1f5944e842801d42bf

[ "MIT" ]

1

2021-02-11T14:54:42.000Z

__init__.py

vedant-jad99/Snipper

312daf66c5e594e2cb139e1f5944e842801d42bf

[ "MIT" ]

null

__init__.py

vedant-jad99/Snipper

312daf66c5e594e2cb139e1f5944e842801d42bf

[ "MIT" ]

null

import tools.tools import tools.toolTip import main_app.app

19.666667

20

0.864407

10

59

5

0.5

0.44

0

0.084746

59

3

21

19.666667

0.925926

0

1

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true

0

1

0

1

0

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null

1

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1

0

null

0

1

0

1

0

1

0

6

28be229fb7cb93e1ecdc1c71cf6480fe93461b80

42

py

Python

ome/dumping/__init__.py

aebrahim/ome

f3ba928e2df41bffb91ac921693ca3a1d73ce956

[ "MIT" ]

null

ome/dumping/__init__.py

aebrahim/ome

f3ba928e2df41bffb91ac921693ca3a1d73ce956

[ "MIT" ]

null

ome/dumping/__init__.py

aebrahim/ome

f3ba928e2df41bffb91ac921693ca3a1d73ce956

[ "MIT" ]

null

from model_dumping.dump import dump_model

21

41

0.880952

7

42

5

0.714286

0

0.095238

42

1

42

0.921053

0

1

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true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

e90fdd364300ac40566f0a840b6babf3ada001fa

117

py

Python

Project/speak_auth/app/api/__init__.py

joexu01/speak_auth

05b2a10761463d45d926ff0d8b865f9f6ab86757

[ "MIT" ]

null

Project/speak_auth/app/api/__init__.py

joexu01/speak_auth

05b2a10761463d45d926ff0d8b865f9f6ab86757

[ "MIT" ]

3

2020-03-24T17:15:01.000Z

2020-03-31T04:38:24.000Z

Project/speak_auth/app/api/__init__.py

joexu01/speak_auth

05b2a10761463d45d926ff0d8b865f9f6ab86757

[ "MIT" ]

null

# -*- coding: UTF-8 -*- from flask import Blueprint api = Blueprint('api', __name__) from . import errors, person

14.625

32

0.675214

15

117

5

0.733333

0.32

0

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117

7

33

16.714286

0.770833

0.179487

0

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false

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0

1

0

null

0

1

0

1

0

6

3a7049c5ca7fee8be06e46b2304d641d6a046c69

12,428

py

Python

test/crude_algorithm.py

Danderson123/Masters_Project

ef9e2fbadda3626a244dfdae42729bd007752d45

[ "CC0-1.0" ]

null

test/crude_algorithm.py

Danderson123/Masters_Project

ef9e2fbadda3626a244dfdae42729bd007752d45

[ "CC0-1.0" ]

null

test/crude_algorithm.py

Danderson123/Masters_Project

ef9e2fbadda3626a244dfdae42729bd007752d45

[ "CC0-1.0" ]

1

2020-11-18T12:14:40.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Initial testing script for crude_annotate.py """ string = "TGATGCTGAGACTGACGCACTTGTTGATGCTGAAGCTGACGCACTGGTTGATGCGGATTCAGAAGCTGATGTGCTTGCTGAGGCTGACGCACTCGTTGATGCTGAGACTGACGCACTTGTTGACGCTGAGGCTGAGGCACTTGTCGACGCTGAAGCTGACGCACTCGTTGATGCTGAAGCCGAGGCACTGGTTGACGCTGAAGCTGAAGCACTGGTTGACGCTGAGGCTGACACACTCGTTGATGCTGAGGCTGACGCACTGGTACTTGCTGAAGCCGAGGCGCTAGTGCTTGCTG" genes = [] start_indexes = [] end_indexes = [] for x in range(len(string)): gene = '' index = 0 if string[x:x+3] == "ATG": start = x #start is the index of ATG gene = string[x:x+3] #gene is ATG cut = string[x:] #ATG to end of string end = 0 for y in range(len(cut)): # for character in the cut if not cut[y:y+3] == "CTA": #if the character:character + 3 not CTA gene += cut[y:y+3] #append the codon to the gene end = y+2 #new y becomes y + 3 print(end) else: genes.append(gene + "CTA") start_indexes.append(start+1) end_of_cut = start + y end_indexes.append(y + 7) continue genes = [] start_indexes = [] end_indexes = [] cuts = [] strands = [] for x in range(len(string)): index = x if string[index:index+3] == "ATG": gene = '' end_codon = ["TAG", "TAA", "TGA"] gene_strand = "+" start = index #start is the index of ATG start_indexes.append(start + 1) cut = string[index:] #ATG to end of string cuts.append(cut) for y in range(len(cut)): # for character in the cut if (y + 3) % 3 ==0: gene+=cut[y:y+3] if any(cut[y:y+3] == motif for motif in end_codon): genes.append(gene) strands.append(gene_strand) end_indexes.append(start + (y+3)) elif any(string[index:index+3] == rev_stop for rev_stop in ["CTA", "TTA", "TCA"]): gene = '' gene_strand = "-" start = index #start is the index of ATG start_indexes.append(start + 1) cut = string[index:] #ATG to end of string cuts.append(cut) for y in range(len(cut)): # for character in the cut if (y + 3) % 3 ==0: gene+=cut[y:y+3] if cut[y:y+3] == "CAT": genes.append(gene) strands.append(gene_strand) end_indexes.append(start + (y+3)) for x in tqdm(range(len(split))): locus_number = 0 title = split[x].split(" ")[0] fasta_split = split[x].split("\n") fasta_header = ">" + fasta_split[0] sequence = "".join(fasta_split[1:]) sequences_all_regions.append(fasta_header) sequences_all_regions.append(sequence) #region_titles.append("##sequence-region " + title + " 1 " + str(len(sequence))) annotations = set() for regex in regexList: if re.search(regex, sequence): some_list= re.finditer(regex,sequence) for result in some_list: if (result.end() - result.start()) % 3 == 0: #codon = regex.split("(")[0] start_codon = regex[regex.find("=")+1:regex.find(")")] print(start_codon) if start_codon == "ATG": gene_strand = "+" elif start_codon == "CTA" or start_codon == "TTA" or start_codon == "TCA": gene_strand = "-" else: gene_strand = "" annotation = title + "\tCRUDE\tCDS\t" + str(result.start() - 2) + "\t" + str(result.end() + 3) + "\t.\t" + gene_strand + "\t0\tID=" + title.split(".")[0] + "_" + str(locus_number) + ";product=putative_protein_region" annotations.add(annotation) locus_number += 1 annotation_all_regions += ["##sequence-region " + title + " 1 " + str(len(sequence))] annotation_all_regions += sorted(list(annotations), key=lambda x: x.split('\t')[3]) for x in tqdm(range(len(split))): locus_number = 0 title = split[x].split(" ")[0] fasta_split = split[x].split("\n") fasta_header = ">" + fasta_split[0] sequence = "".join(fasta_split[1:]) sequences_all_regions.append(fasta_header) sequences_all_regions.append(sequence) #region_titles.append("##sequence-region " + title + " 1 " + str(len(sequence))) annotations = set() for base in range(len(sequence)): index = base if string[index:index+3] == "ATG": gene = '' end_codon = ["TAG", "TAA", "TGA"] gene_strand = "+" start = index #start is the index of ATG cut = string[index:] #ATG to end of string for y in range(len(cut)): # for character in the cut if (y + 3) % 3 ==0: gene+=cut[y:y+3] if any(cut[y:y+3] == motif for motif in end_codon): annotation = title + "\tCRUDE\tCDS\t" + str(start + 1) + "\t" + str(start + (y+3)) + "\t.\t" + gene_strand + "\t0\tID=" + title.split(".")[0] + "_" + str(locus_number) + ";product=putative_protein_region" annotations.add(annotation) locus_number += 1 elif any(string[index:index+3] == rev_stop for rev_stop in ["CTA", "TTA", "TCA"]): gene = '' gene_strand = "-" start = index #start is the index of ATG cut = string[index:] #ATG to end of string for y in range(len(cut)): # for character in the cut if (y + 3) % 3 ==0: gene+=cut[y:y+3] if cut[y:y+3] == "CAT": annotation = title + "\tCRUDE\tCDS\t" + str(start + 1) + "\t" + str(start + (y+3)) + "\t.\t" + gene_strand + "\t0\tID=" + title.split(".")[0] + "_" + str(locus_number) + ";product=putative_protein_region" annotations.add(annotation) locus_number += 1 annotation_all_regions += ["##sequence-region " + title + " 1 " + str(len(sequence))] annotation_all_regions += sorted(list(annotations), key=lambda x: x.split('\t')[3]) region_titles = [] annotation_all_regions = [] sequences_all_regions = ["##FASTA"] import re from tqdm import tqdm regex1 = r"ATG(.*)(?=TAG)" regex2 = r"ATG)(.*)(?=TAA)" regex3 = r"ATG)(.*)(?=TGA)" regex4 = r"CTA)(.*)(?=CAT)" regex5 = r"TTA)(.*)(?=CAT)" regex6 = r"TCA)(.*)(?=CAT)" stop1 = r"(?<=ATG)(.*)TAG" stop2 = r"(?<=ATG)(.*)TAA" stop3 = r"(?<=ATG)(.*)TGA" stop4 = r"(?<=CTA)(.*)CAT" stop5 = r"(?<=TTA)(.*)CAT" stop6 = r"(?<=TCA)(.*)CAT" regexList = [regex1, regex2, regex3, regex4, regex5, regex6] stopList = [stop1, stop2, stop3, stop4, stop5, stop6] for x in tqdm(range(len(split))): locus_number = 0 title = split[x].split(" ")[0] fasta_split = split[x].split("\n") fasta_header = ">" + fasta_split[0] sequence = "".join(fasta_split[1:]) sequences_all_regions.append(fasta_header) sequences_all_regions.append(sequence) #region_titles.append("##sequence-region " + title + " 1 " + str(len(sequence))) annotations = set() for regex in regexList: if re.search(regex, sequence): start_list = re.finditer(regex,sequence) for stop_regex in stopList: if re.search(stop_regex, sequence): stop_codon_list = re.finditer(stop_regex,sequence) for result in start_list: for stop_result in stop_codon_list: if (stop_result.end() - result.start()) % 3 == 0: codon = regex.split("(")[0] start_codon = regex[regex.find("=")+1:regex.find(")")] print(start_codon) if start_codon == "ATG": gene_strand = "+" elif start_codon == "CTA" or start_codon == "TTA" or start_codon == "TCA": gene_strand = "-" else: gene_strand = "" annotation = title + "\tCRUDE\tCDS\t" + str(result.start() - 2) + "\t" + str(stop_result.end() + 3) + "\t.\t" + gene_strand + "\t0\tID=" + title.split(".")[0] + "_" + str(locus_number) + ";product=putative_protein_region" annotations.add(annotation) locus_number += 1 annotation_all_regions += ["##sequence-region " + title + " 1 " + str(len(sequence))] annotation_all_regions += sorted(list(annotations), key=lambda x: x.split('\t')[3]) for x in tqdm(range(len(split))): locus_number = 0 title = split[x].split(" ")[0] fasta_split = split[x].split("\n") fasta_header = ">" + fasta_split[0] sequence = "".join(fasta_split[1:]) sequences_all_regions.append(fasta_header) sequences_all_regions.append(sequence) #region_titles.append("##sequence-region " + title + " 1 " + str(len(sequence))) annotations = set() for base in range(len(sequence)): index = base if sequence[index:index+3] == "ATG": gene = '' end_codon = ["TAG", "TAA", "TGA"] gene_strand = "+" start = index #start is the index of ATG cut = sequence[index:] #ATG to end of string for y in range(len(cut)): # for character in the cut if (y + 3) % 3 ==0: gene+=cut[y:y+3] if any(cut[y:y+3] == motif for motif in end_codon): annotation = title + "\tCRUDE\tCDS\t" + str(start + 1) + "\t" + str(start + (y+3)) + "\t.\t" + gene_strand + "\t0\tID=" + title.split(".")[0] + "_" + str(locus_number) + ";product=putative_protein_region" annotations.add(annotation) locus_number += 1 else: pass elif any(sequence[index:index+3] == rev_stop for rev_stop in ["CTA", "TTA", "TCA"]): gene = '' gene_strand = "-" start = index #start is the index of ATG cut = sequence[index:] #ATG to end of string for y in range(len(cut)): # for character in the cut if (y + 3) % 3 ==0: gene+=cut[y:y+3] if cut[y:y+3] == "CAT": annotation = title + "\tCRUDE\tCDS\t" + str(start + 1) + "\t" + str(start + (y+3)) + "\t.\t" + gene_strand + "\t0\tID=" + title.split(".")[0] + "_" + str(locus_number) + ";product=putative_protein_region" annotations.add(annotation) locus_number += 1 else: pass annotation_all_regions += ["##sequence-region " + title + " 1 " + str(len(sequence))] annotation_all_regions += sorted(list(annotations), key=lambda x: x.split('\t')[3]) #gff_file = "\n".join(region_titles + annotation_all_regions + sequences_all_regions) gff_file = "\n".join(list(annotation_all_regions)) fasta_out = "\n".join(list(sequences_all_regions)) total_genes += annotation_all_regions #outfile_name = os.path.basename(header).split(".fna")[0] + ".gff" outfile_name = os.path.basename(header).split(".fna")[0] outfile_fasta = open("crudely_annotated/" + outfile_name + ".fna", "w") outfile_fasta.write(fasta_out) outfile_fasta.close() outfile_gff = open("crudely_annotated/" + outfile_name + ".gff", "w") outfile_gff.write(gff_file) outfile_gff.close() end_time = time.time() print(str(end_time - start_time) + " seconds" ) print(len(total_genes))

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1

0

null

0

6

3a9cc633da6f8d12e0eb7ec8e7212a26c42f5288

93

py

Python

ci/test_fs.py

lukemassa/readfs

a83a972f82333c584ac345ac6730a72e350fe653

[ "MIT" ]

null

ci/test_fs.py

lukemassa/readfs

a83a972f82333c584ac345ac6730a72e350fe653

[ "MIT" ]

null

ci/test_fs.py

lukemassa/readfs

a83a972f82333c584ac345ac6730a72e350fe653

[ "MIT" ]

null

#!/usr/bin/python3 import unittest import fs class TestBlock(unittest.TestCase): pass

10.333333

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93

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0.012821

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93

8

36

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null

0

1

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null

0

1

0

1

0

6

3ab4d8712bf58460b2b07147c59aa7da2ab57df4

46

py

Python

atomate/feff/fireworks/__init__.py

Zhuoying/atomate

067023f0f740d3abac47b7ae7743c1c31eff8a06

[ "BSD-3-Clause-LBNL" ]

null

atomate/feff/fireworks/__init__.py

Zhuoying/atomate

067023f0f740d3abac47b7ae7743c1c31eff8a06

[ "BSD-3-Clause-LBNL" ]

null

atomate/feff/fireworks/__init__.py

Zhuoying/atomate

067023f0f740d3abac47b7ae7743c1c31eff8a06

[ "BSD-3-Clause-LBNL" ]

null

from .core import EELSFW, XASFW, EXAFSPathsFW

23

45

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46

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0

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1

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1

0

null

0

1

0

1

0

1

0

6

3ae4344ffb1b578b19a7819f261c44e8516b8edf

417

py

Python

trac/Scripts/check-trac-subtickets-script.py

thinkbase/PortableTrac

9ea0210f6b88f135ef73f370b48127af0495b2d7

[ "BSD-3-Clause" ]

2

2015-08-06T04:19:21.000Z

2020-04-29T23:52:10.000Z

trac/Scripts/check-trac-subtickets-script.py

thinkbase/PortableTrac

9ea0210f6b88f135ef73f370b48127af0495b2d7

[ "BSD-3-Clause" ]

null

trac/Scripts/check-trac-subtickets-script.py

thinkbase/PortableTrac

9ea0210f6b88f135ef73f370b48127af0495b2d7

[ "BSD-3-Clause" ]

null

#!"E:\PortableTrac\Portable Python 2.7.3.1\App\python.exe" # EASY-INSTALL-ENTRY-SCRIPT: 'tracsubticketsplugin==0.2.0.dev-20121107','console_scripts','check-trac-subtickets' __requires__ = 'tracsubticketsplugin==0.2.0.dev-20121107' import sys from pkg_resources import load_entry_point sys.exit( load_entry_point('tracsubticketsplugin==0.2.0.dev-20121107', 'console_scripts', 'check-trac-subtickets')() )

41.7

114

0.76259

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417

5.293103

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0.224756

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0.436482

0

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417

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null

1

0

1

0

1

null

0

1

0

6

c96d70ee433c05888567a1181dbbfcb9eb83e88d

92

py

Python

boa3_test/test_sc/native_test/stdlib/AtoiMismatchedType.py

OnBlockIO/neo3-boa

cb317292a67532a52ed26f2b0f0f7d0b10ac5f5f

[ "Apache-2.0" ]

25

2020-07-22T19:37:43.000Z

2022-03-08T03:23:55.000Z

boa3_test/test_sc/native_test/stdlib/AtoiMismatchedType.py

OnBlockIO/neo3-boa

cb317292a67532a52ed26f2b0f0f7d0b10ac5f5f

[ "Apache-2.0" ]

419

2020-04-23T17:48:14.000Z

2022-03-31T13:17:45.000Z

boa3_test/test_sc/native_test/stdlib/AtoiMismatchedType.py

OnBlockIO/neo3-boa

cb317292a67532a52ed26f2b0f0f7d0b10ac5f5f

[ "Apache-2.0" ]

15

2020-05-21T21:54:24.000Z

2021-11-18T06:17:24.000Z

from boa3.builtin.nativecontract.stdlib import StdLib def main(): StdLib.atoi(10, 10)

15.333333

53

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92

5.230769

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92

5

54

18.4

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1

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true

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null

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1

0

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6

c976e3ba1fd05cc2e3f0bca0ed40f7557b4defcc

552

py

Python

src/api/domain/dashboard/GetDataOperationJobExecutionWidget/GetDataOperationJobExecutionWidgetQuery.py

PythonDataIntegrator/pythondataintegrator

6167778c36c2295e36199ac0d4d256a4a0c28d7a

[ "MIT" ]

14

2020-12-19T15:06:13.000Z

2022-01-12T19:52:17.000Z

src/api/domain/dashboard/GetDataOperationJobExecutionWidget/GetDataOperationJobExecutionWidgetQuery.py

PythonDataIntegrator/pythondataintegrator

6167778c36c2295e36199ac0d4d256a4a0c28d7a

[ "MIT" ]

43

2021-01-06T22:05:22.000Z

2022-03-10T10:30:30.000Z

src/api/domain/dashboard/GetDataOperationJobExecutionWidget/GetDataOperationJobExecutionWidgetQuery.py

PythonDataIntegrator/pythondataintegrator

6167778c36c2295e36199ac0d4d256a4a0c28d7a

[ "MIT" ]

4

2020-12-18T23:10:09.000Z

2021-04-02T13:03:12.000Z

from dataclasses import dataclass from infrastructure.cqrs.IQuery import IQuery from domain.dashboard.GetDataOperationJobExecutionWidget.GetDataOperationJobExecutionWidgetRequest import GetDataOperationJobExecutionWidgetRequest from domain.dashboard.GetDataOperationJobExecutionWidget.GetDataOperationJobExecutionWidgetResponse import GetDataOperationJobExecutionWidgetResponse @dataclass class GetDataOperationJobExecutionWidgetQuery(IQuery[GetDataOperationJobExecutionWidgetResponse]): request: GetDataOperationJobExecutionWidgetRequest = None

55.2

149

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552

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0.5

0.039448

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0.052536

552

9

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61.333333

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null

0

1

0

1

0

1

0

6

c97e9d379697b646ae57fa85967a85d1a7568e53

194

py

Python

zalgolib/__init__.py

jivanyatra/zalgolibpy

7f0d054d370e90c031c1e5d6637503cb1ad38e6f

[ "MIT" ]

1

2022-03-06T16:43:13.000Z

zalgolib/__init__.py

jivanyatra/zalgolib

7f0d054d370e90c031c1e5d6637503cb1ad38e6f

[ "MIT" ]

null

zalgolib/__init__.py

jivanyatra/zalgolib

7f0d054d370e90c031c1e5d6637503cb1ad38e6f

[ "MIT" ]

null

from .zalgolib import enzalgofy, dezalgofy from .diacritics import DOWN_MARKS, DOWN_LEN from .diacritics import MID_MARKS, MID_LEN from .diacritics import UP_MARKS, UP_LEN __version__ = "0.2.0"

32.333333

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4.933333

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0.405405

0.310811

0

0.017544

0.118557

194

6

45

32.333333

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0.025641

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1

0

false

0

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1

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null

0

1

0

1

0

6

c9840bc19bbaeea29a9cacb76f6370359d37d1d1

23

py

Python

fairways/io/__init__.py

dan-win/fairways_py

771623c6f9ec40e8016b5cebb7951613d01e31f7

[ "Apache-2.0" ]

103

2015-02-12T20:21:53.000Z

2022-03-29T15:30:47.000Z

cyvlfeat/generic/__init__.py

samousavizade/cyvlfeat

03297e4d1a6924920a7cf2df9d558c93a8445b9f

[ "BSD-2-Clause" ]

49

2015-05-05T03:48:37.000Z

2022-03-09T13:54:24.000Z

cyvlfeat/generic/__init__.py

samousavizade/cyvlfeat

03297e4d1a6924920a7cf2df9d558c93a8445b9f

[ "BSD-2-Clause" ]

68

2015-02-11T10:33:11.000Z

2022-02-08T09:26:34.000Z

from .generic import *

11.5

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0.73913

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23

5.666667

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1

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null

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1

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6

a384e1102599eebadaa51b8e403d86000021b3dc

15,510

py

Python

cca_zoo/test/test_deepmodels.py

jameschapman19/cca_zoo

45c38f0164a324e8fcc33a480814842e747d86c3

[ "MIT" ]

96

2020-11-10T11:16:55.000Z

2022-03-31T08:34:59.000Z

cca_zoo/test/test_deepmodels.py

jameschapman19/cca_zoo

45c38f0164a324e8fcc33a480814842e747d86c3

[ "MIT" ]

76

2020-11-25T00:47:43.000Z

2022-03-30T13:58:45.000Z

cca_zoo/test/test_deepmodels.py

jameschapman19/cca_zoo

45c38f0164a324e8fcc33a480814842e747d86c3

[ "MIT" ]

24

2020-10-26T06:12:37.000Z

2022-03-03T08:00:00.000Z

import numpy as np from sklearn.utils.validation import check_random_state from torch import optim, manual_seed from torch.utils.data import Subset from cca_zoo import data from cca_zoo.data import Noisy_MNIST_Dataset from cca_zoo.deepmodels import DCCA, DCCAE, DVCCA, DCCA_NOI, DTCCA, SplitAE, DeepWrapper from cca_zoo.deepmodels import objectives, architectures from cca_zoo.models import CCA manual_seed(0) rng = check_random_state(0) X = rng.rand(200, 10) Y = rng.rand(200, 10) Z = rng.rand(200, 10) X_conv = rng.rand(100, 1, 16, 16) Y_conv = rng.rand(100, 1, 16, 16) train_dataset = data.CCA_Dataset([X, Y]) def test_input_types(): latent_dims = 2 device = "cpu" encoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) encoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) # DCCA dcca_model = DCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], objective=objectives.CCA, ) dcca_model = DeepWrapper(dcca_model, device=device) dcca_model.fit(train_dataset, epochs=3) dcca_model.fit(train_dataset, val_dataset=train_dataset, epochs=3) dcca_model.fit((X, Y), val_dataset=(X, Y), epochs=3) dcca_model.fit((X, Y), val_split=0.2, epochs=3) def tutorial_test(): # Load MNIST Data N = 500 latent_dims = 2 dataset = Noisy_MNIST_Dataset(mnist_type="FashionMNIST", train=True) ids = np.arange(min(2 * N, len(dataset))) np.random.shuffle(ids) train_ids, val_ids = np.array_split(ids, 2) val_dataset = Subset(dataset, val_ids) train_dataset = Subset(dataset, train_ids) test_dataset = Noisy_MNIST_Dataset(mnist_type="FashionMNIST", train=False) test_ids = np.arange(min(N, len(test_dataset))) np.random.shuffle(test_ids) test_dataset = Subset(test_dataset, test_ids) print("DCCA") encoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=784) encoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=784) dcca_model = DCCA(latent_dims=latent_dims, encoders=[encoder_1, encoder_2]) dcca_model = DeepWrapper(dcca_model) dcca_model.fit(train_dataset, val_dataset=val_dataset, epochs=2) dcca_results = np.stack( (dcca_model.score(train_dataset), dcca_model.correlations(test_dataset)[0, 1]) ) def test_large_p(): large_p = 256 X = rng.rand(2000, large_p) Y = rng.rand(2000, large_p) latent_dims = 32 device = "cpu" encoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=large_p) encoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=large_p) dcca_model = DCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], objective=objectives.MCCA, eps=1e-3, ) optimizer = optim.Adam(dcca_model.parameters(), lr=1e-4) dcca_model = DeepWrapper(dcca_model, device=device, optimizer=optimizer) dcca_model.fit((X, Y), epochs=100) def test_DCCA_methods_cpu(): latent_dims = 4 cca_model = CCA(latent_dims=latent_dims).fit((X, Y)) device = "cpu" epochs = 100 # DCCA encoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) encoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) dcca_model = DCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], objective=objectives.CCA, ) optimizer = optim.SGD(dcca_model.parameters(), lr=1e-2) dcca_model = DeepWrapper(dcca_model, device=device, optimizer=optimizer) dcca_model.fit((X, Y), epochs=epochs) assert ( np.testing.assert_array_less( cca_model.score((X, Y)).sum(), dcca_model.score((X, Y)).sum() ) is None ) # DGCCA encoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) encoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) dgcca_model = DCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], objective=objectives.GCCA, ) optimizer = optim.SGD(dgcca_model.parameters(), lr=1e-2) dgcca_model = DeepWrapper(dgcca_model, device=device, optimizer=optimizer) dgcca_model.fit((X, Y), epochs=epochs) assert ( np.testing.assert_array_less( cca_model.score((X, Y)).sum(), dgcca_model.score((X, Y)).sum() ) is None ) # DMCCA encoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) encoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) dmcca_model = DCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], objective=objectives.MCCA, ) optimizer = optim.SGD(dmcca_model.parameters(), lr=1e-2) dmcca_model = DeepWrapper(dmcca_model, device=device, optimizer=optimizer) dmcca_model.fit((X, Y), epochs=epochs) assert ( np.testing.assert_array_less( cca_model.score((X, Y)).sum(), dmcca_model.score((X, Y)).sum() ) is None ) # DCCA_NOI encoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) encoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) dcca_noi_model = DCCA_NOI( latent_dims, X.shape[0], encoders=[encoder_1, encoder_2], rho=0 ) optimizer = optim.Adam(dcca_noi_model.parameters(), lr=1e-2) dcca_noi_model = DeepWrapper(dcca_noi_model, device=device, optimizer=optimizer) dcca_noi_model.fit((X, Y), epochs=epochs) assert ( np.testing.assert_array_less( cca_model.score((X, Y)).sum(), dcca_noi_model.score((X, Y)).sum() ) is None ) def test_DTCCA_methods_cpu(): latent_dims = 2 device = "cpu" encoder_1 = architectures.Encoder(latent_dims=10, feature_size=10) encoder_2 = architectures.Encoder(latent_dims=10, feature_size=10) encoder_3 = architectures.Encoder(latent_dims=10, feature_size=10) # DTCCA dtcca_model = DTCCA(latent_dims=latent_dims, encoders=[encoder_1, encoder_2]) dtcca_model = DeepWrapper(dtcca_model, device=device) dtcca_model.fit((X, Y), epochs=20) # DCCA dcca_model = DCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], objective=objectives.GCCA, ) dcca_model = DeepWrapper(dcca_model, device=device) dcca_model.fit((X, Y), epochs=20) def test_scheduler(): latent_dims = 2 device = "cpu" encoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) encoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) # DCCA dcca_model = DCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], objective=objectives.CCA, ) optimizer = optim.Adam(dcca_model.parameters(), lr=1e-4) scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, 1) dcca_model = DeepWrapper( dcca_model, device=device, optimizer=optimizer, scheduler=scheduler ) dcca_model.fit((X, Y), epochs=20) def test_DGCCA_methods_cpu(): latent_dims = 2 device = "cpu" encoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) encoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) encoder_3 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) # DTCCA dtcca_model = DTCCA(latent_dims=latent_dims, encoders=[encoder_1, encoder_2]) dtcca_model = DeepWrapper(dtcca_model, device=device) dtcca_model.fit((X, Y, Z)) # DGCCA dgcca_model = DCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2, encoder_3], objective=objectives.GCCA, ) dgcca_model = DeepWrapper(dgcca_model, device=device) dgcca_model.fit((X, Y, Z)) # DMCCA dmcca_model = DCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2, encoder_3], objective=objectives.MCCA, ) dmcca_model = DeepWrapper(dmcca_model, device=device) dmcca_model.fit((X, Y, Z)) def test_DCCAE_methods_cpu(): latent_dims = 2 device = "cpu" encoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) encoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) decoder_1 = architectures.Decoder(latent_dims=latent_dims, feature_size=10) decoder_2 = architectures.Decoder(latent_dims=latent_dims, feature_size=10) # DCCAE dccae_model = DCCAE( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], decoders=[decoder_1, decoder_2], ) dccae_model = DeepWrapper(dccae_model, device=device) dccae_model.fit((X, Y), epochs=20) # SplitAE splitae_model = SplitAE( latent_dims=latent_dims, encoder=encoder_1, decoders=[decoder_1, decoder_2] ) splitae_model = DeepWrapper(splitae_model, device=device) splitae_model.fit((X, Y), epochs=10) def test_DCCAEconv_methods_cpu(): latent_dims = 2 device = "cpu" encoder_1 = architectures.CNNEncoder(latent_dims=latent_dims, feature_size=[16, 16]) encoder_2 = architectures.CNNEncoder(latent_dims=latent_dims, feature_size=[16, 16]) decoder_1 = architectures.CNNDecoder(latent_dims=latent_dims, feature_size=[16, 16]) decoder_2 = architectures.CNNDecoder(latent_dims=latent_dims, feature_size=[16, 16]) # DCCAE dccae_model = DCCAE( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], decoders=[decoder_1, decoder_2], ) dccae_model = DeepWrapper(dccae_model, device=device) dccae_model.fit((X_conv, Y_conv)) def test_DVCCA_methods_cpu(): latent_dims = 2 device = "cpu" encoder_1 = architectures.Encoder( latent_dims=latent_dims, feature_size=10, variational=True ) encoder_2 = architectures.Encoder( latent_dims=latent_dims, feature_size=10, variational=True ) decoder_1 = architectures.Decoder( latent_dims=latent_dims, feature_size=10, norm_output=True ) decoder_2 = architectures.Decoder( latent_dims=latent_dims, feature_size=10, norm_output=True ) # DVCCA dvcca_model = DVCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], decoders=[decoder_1, decoder_2], ) dvcca_model = DeepWrapper(dvcca_model, device=device) dvcca_model.fit((X, Y)) def test_DVCCA_p_methods_cpu(): latent_dims = 2 device = "cpu" encoder_1 = architectures.Encoder( latent_dims=latent_dims, feature_size=10, variational=True ) encoder_2 = architectures.Encoder( latent_dims=latent_dims, feature_size=10, variational=True ) private_encoder_1 = architectures.Encoder( latent_dims=latent_dims, feature_size=10, variational=True ) private_encoder_2 = architectures.Encoder( latent_dims=latent_dims, feature_size=10, variational=True ) decoder_1 = architectures.Decoder( latent_dims=2 * latent_dims, feature_size=10, norm_output=True ) decoder_2 = architectures.Decoder( latent_dims=2 * latent_dims, feature_size=10, norm_output=True ) # DVCCA dvcca_model = DVCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], decoders=[decoder_1, decoder_2], private_encoders=[private_encoder_1, private_encoder_2], ) dvcca_model = DeepWrapper(dvcca_model, device=device) dvcca_model.fit((X, Y)) def test_DCCA_methods_gpu(): latent_dims = 2 device = "cuda" encoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) encoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) # DCCA dcca_model = DCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], objective=objectives.CCA, ) dcca_model = DeepWrapper(dcca_model, device=device) dcca_model.fit((X, Y)) # DGCCA dgcca_model = DCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], objective=objectives.GCCA, ) dgcca_model = DeepWrapper(dgcca_model, device=device) dgcca_model.fit((X, Y)) # DMCCA dmcca_model = DCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], objective=objectives.MCCA, ) dmcca_model = DeepWrapper(dmcca_model, device=device) dmcca_model.fit((X, Y)) # DCCA_NOI dcca_noi_model = DCCA_NOI( latent_dims, X.shape[0], encoders=[encoder_1, encoder_2], rho=0 ) dcca_noi_model = DeepWrapper(dcca_noi_model, device=device) dcca_noi_model.fit((X, Y)) def test_DGCCA_methods_gpu(): latent_dims = 2 device = "cuda" encoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) encoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) encoder_3 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) # DGCCA dgcca_model = DCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2, encoder_3], objective=objectives.GCCA, ) dgcca_model = DeepWrapper(dgcca_model, device=device) dgcca_model.fit((X, Y, Z)) # DMCCA dmcca_model = DCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2, encoder_3], objective=objectives.MCCA, ) dmcca_model = DeepWrapper(dmcca_model, device=device) dmcca_model.fit((X, Y, Z)) def test_DCCAE_methods_gpu(): latent_dims = 2 device = "cuda" encoder_1 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) encoder_2 = architectures.Encoder(latent_dims=latent_dims, feature_size=10) decoder_1 = architectures.Decoder(latent_dims=latent_dims, feature_size=10) decoder_2 = architectures.Decoder(latent_dims=latent_dims, feature_size=10) # DCCAE dccae_model = DCCAE( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], decoders=[decoder_1, decoder_2], ) dccae_model = DeepWrapper(dccae_model, device=device) dccae_model.fit((X, Y)) def test_DVCCA_methods_gpu(): latent_dims = 2 device = "cuda" encoder_1 = architectures.Encoder( latent_dims=latent_dims, feature_size=10, variational=True ) encoder_2 = architectures.Encoder( latent_dims=latent_dims, feature_size=10, variational=True ) decoder_1 = architectures.Decoder( latent_dims=latent_dims, feature_size=10, norm_output=True ) decoder_2 = architectures.Decoder( latent_dims=latent_dims, feature_size=10, norm_output=True ) # DVCCA dvcca_model = DVCCA( latent_dims=latent_dims, encoders=[encoder_1, encoder_2], decoders=[decoder_1, decoder_2], ) dvcca_model = DeepWrapper(dvcca_model, device=device) dvcca_model.fit((X, Y)) def test_linear(): encoder_1 = architectures.LinearEncoder(latent_dims=1, feature_size=10) encoder_2 = architectures.LinearEncoder(latent_dims=1, feature_size=10) dcca_model = DCCA(latent_dims=1, encoders=[encoder_1, encoder_2]) dcca_model = DeepWrapper(dcca_model).fit((X, Y), epochs=35) cca = CCA().fit((X, Y)) # check linear encoder with SGD matches vanilla linear CCA assert ( np.testing.assert_array_almost_equal( cca.score((X, Y)), dcca_model.score((X, Y)), decimal=2 ) is None )

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6

6e6240d05f3b3a50eeaa37d19e7929506c84dd0e

5,494

py

Python

veripro/config/veripro.py

Suganyabalasubramanian/veriPRO

289c2193ee54d3d7fed113b2bbc86300416a05c5

[ "MIT" ]

null

veripro/config/veripro.py

Suganyabalasubramanian/veriPRO

289c2193ee54d3d7fed113b2bbc86300416a05c5

[ "MIT" ]

null

veripro/config/veripro.py

Suganyabalasubramanian/veriPRO

289c2193ee54d3d7fed113b2bbc86300416a05c5

[ "MIT" ]

null

from frappe import _ def get_data(): return [ { "label": _("General"), "items": [ { "type": "doctype", "name": "Checks", "description": _("checks"), "onboard": 1, }, { "type":"doctype", "name": "Check Package", "description": _("check_package"), "onboard": 1, }, { "type": "doctype", "name": "Batch", "description": _("batch"), "onboard": 1, }, { "type": "doctype", "name": "Case", "description": _("Case"), "onboard": 1, }, # { # "type": "doctype", # "name": "Case", # "description": _("Invoice"), # "onboard": 1, # }, ] }, { "label": _("Entry"), "items": [ { "type": "doctype", "name": "Address Check", "description": _("checks"), "onboard": 1, }, { "type": "doctype", "name": "Education Check", "description": _("veripro"), "onboard": 1, }, { "type": "doctype", "name": "Employment", "description": _("Applicants"), "onboard": 1, }, { "type": "doctype", "name": "Identity Check", "description": _("Invoice"), "onboard": 1, }, { "type": "doctype", "name": "Criminal Check", "description": _("Invoice"), "onboard": 1, }, ] }, { "label": _("Execution"), "items": [ { "type": "doctype", "name": "Verify Address Check", "description": _("checks"), "onboard": 1, }, { "type": "doctype", "name": "Verify Education Check", "description": _("veripro"), "onboard": 1, }, { "type": "doctype", "name": "Verify Employment Check", "description": _("Applicants"), "onboard": 1, }, { "type": "doctype", "name": "Verify Identity Check", "description": _("Invoice"), "onboard": 1, }, { "type": "doctype", "name": "Verify Criminal Check", "description": _("Invoice"), "onboard": 1, }, ] } # { # "module_name": "veriPRO", # "color": "grey", # "icon": "fa fa-star", # "type": "module", # "label": _("General"), # "items": [ # { # "type": "doctype", # "name": "Check Package", # "icon": "fa fa-star", # "label": _("Check Package"), # "description": _("Check Package"), # }, # { # "type": "doctype", # "name": "Applicant", # "icon": "fa fa-star", # "label": _("Applicant"), # "description": _("Applicant"), # }, # { # "type": "doctype", # "name": "veriPRO Batch", # "icon": "fa fa-star", # "label": _("veriPRO Batch"), # "description": _("veriPRO Batch"), # }, # { # "type": "doctype", # "name": "Sales Invoice", # "icon": "fa fa-star", # "label": _("veriPRO Batch"), # "description": _("veriPRO Batch"), # }, # ], # }, # { # "module_name": "veriPRO", # "color": "grey", # "icon": "fa fa-star", # "type": "module", # "label": _("Entry"), # "items": [ # { # "type": "doctype", # "name": "Check Package", # "icon": "fa fa-star", # "label": _("Check Package"), # "description": _("Check Package"), # }, # { # "type": "doctype", # "name": "Applicant", # "icon": "fa fa-star", # "label": _("Applicant"), # "description": _("Applicant"), # }, # { # "type": "doctype", # "name": "veriPRO Batch", # "icon": "fa fa-star", # "label": _("veriPRO Batch"), # "description": _("veriPRO Batch"), # }, # { # "type": "doctype", # "name": "Sales Invoice", # "icon": "fa fa-star", # "label": _("veriPRO Batch"), # "description": _("veriPRO Batch"), # }, # ], # } ]

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1

0

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null

0

1

0

6

6e7771f57a487e2580a86213a56177435c7ae8fb

2,161

py

Python

brain_image_text/flags.py

cvsubmittemp/BraVL

784e3df6a8b6cf9f19bd28d0e0a2d21eede47ebd

[ "MIT" ]

null

brain_image_text/flags.py

cvsubmittemp/BraVL

784e3df6a8b6cf9f19bd28d0e0a2d21eede47ebd

[ "MIT" ]

null

brain_image_text/flags.py

cvsubmittemp/BraVL

784e3df6a8b6cf9f19bd28d0e0a2d21eede47ebd

[ "MIT" ]

1

2022-03-28T10:29:35.000Z

from utils.BaseFlags import parser as parser # DATASET NAME parser.add_argument('--dataset', type=str, default='Brain_Image_Text', help="name of the dataset") # DATA DEPENDENT # to be set by experiments themselves parser.add_argument('--style_m1_dim', type=int, default=0, help="dimension of varying factor latent space") parser.add_argument('--style_m2_dim', type=int, default=0, help="dimension of varying factor latent space") parser.add_argument('--style_m3_dim', type=int, default=0, help="dimension of varying factor latent space") parser.add_argument('--num_hidden_layers', type=int, default=2, help="number of channels in images") parser.add_argument('--likelihood_m1', type=str, default='laplace', help="output distribution") parser.add_argument('--likelihood_m2', type=str, default='laplace', help="output distribution") parser.add_argument('--likelihood_m3', type=str, default='laplace', help="output distribution") # LOSS TERM WEIGHTS parser.add_argument('--beta_m1_style', type=float, default=1.0, help="default weight divergence term style modality 1") parser.add_argument('--beta_m2_style', type=float, default=1.0, help="default weight divergence term style modality 2") parser.add_argument('--beta_m3_style', type=float, default=1.0, help="default weight divergence term style modality 3") parser.add_argument('--beta_m1_rec', type=float, default=1.0, help="default weight reconstruction modality 1") parser.add_argument('--beta_m2_rec', type=float, default=1.0, help="default weight reconstruction modality 2") parser.add_argument('--beta_m3_rec', type=float, default=1.0, help="default weight reconstruction modality 3") parser.add_argument('--div_weight_m1_content', type=float, default=0.25, help="default weight divergence term content modality 1") parser.add_argument('--div_weight_m2_content', type=float, default=0.25, help="default weight divergence term content modality 2") parser.add_argument('--div_weight_m3_content', type=float, default=0.25, help="default weight divergence term content modality 2") parser.add_argument('--div_weight_uniform_content', type=float, default=0.25, help="default weight divergence term prior") #

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6

6ebab96c4a6b6e0edf79a43f4c0c8544dc9db4ab

137

py

Python

demo-project/src/demo_project/pipelines/feature_engineering/__init__.py

admariner/kedro-viz

371f7f943ee1c9a69545ed6aa1cd468deafed7f0

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

125

2022-01-10T14:18:32.000Z

2022-03-31T16:08:29.000Z

demo-project/src/demo_project/pipelines/feature_engineering/__init__.py

kedro-org/kedro-viz

627aeca40c543412afb988a8ddc64e86dcaf27ec

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

81

2022-01-10T15:14:24.000Z

2022-03-31T16:20:59.000Z

demo-project/src/demo_project/pipelines/feature_engineering/__init__.py

admariner/kedro-viz

371f7f943ee1c9a69545ed6aa1cd468deafed7f0

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

11

2022-01-12T14:57:54.000Z

2022-03-07T06:48:30.000Z

""" This is a boilerplate pipeline 'feature_engineering' generated using Kedro 0.18.1 """ from .pipeline import create_pipeline # NOQA

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0

null

0

1

0

1

0

1

0

6

6ebfdc9706090e2a6caa03ce8a224dc942d9f753

97

py

Python

QuantTorch/LogLinNet.py

Enderdead/BinaryConnect_PyTorch

990e970b1fbd299ff88200db21a9cc3fe44706d3

[ "MIT" ]

75

2019-03-19T07:36:56.000Z

2021-12-23T02:34:59.000Z

QuantTorch/LogLinNet.py

Enderdead/BinaryConnect_PyTorch

990e970b1fbd299ff88200db21a9cc3fe44706d3

[ "MIT" ]

10

2019-03-19T21:16:56.000Z

2019-04-16T15:05:37.000Z

QuantTorch/LogLinNet.py

Enderdead/BinaryConnect_PyTorch

990e970b1fbd299ff88200db21a9cc3fe44706d3

[ "MIT" ]

9

2019-08-12T10:33:55.000Z

2021-07-23T02:10:06.000Z

from QuantTorch.functions.log_lin_connect import * from QuantTorch.layers.log_lin_layers import *

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5.714286

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0

1

0

1

0

6

2829feebbad655b17c9c4d26e91272dd5e02226a

4,368

py

Python

cogs/profile.py

nemesio65/Discord-Bot

b576f0c220aaf5f1bc2373821efee47531b275ff

[ "MIT" ]

null

cogs/profile.py

nemesio65/Discord-Bot

b576f0c220aaf5f1bc2373821efee47531b275ff

[ "MIT" ]

null

cogs/profile.py

nemesio65/Discord-Bot

b576f0c220aaf5f1bc2373821efee47531b275ff

[ "MIT" ]

null

import random as r import sqlite3 import discord from discord.ext import commands import asyncio import datetime import math class ProfileCog(commands.Cog, name='Profile'): def __init__(self, bot): self.bot = bot @commands.command() async def biosetup(self, ctx, *, content:str): db = sqlite3.connect('bot.db') cursor = db.cursor() cursor.execute(f"SELECT profiles.user_id, profiles.guild_id FROM profiles JOIN levels ON profiles.user_id = levels.user_id and profiles.guild_id = levels.guild_id WHERE levels.guild_id = '{ctx.message.author.guild.id}' and levels.user_id = '{ctx.message.author.id}'") result = cursor.fetchone() if result is None: sql = ("INSERT INTO profiles(guild_id, user_id, bio, twitch) VALUES(?,?,?,?)") val = (ctx.message.author.guild.id, ctx.message.author.id, str(content), None) cursor.execute(sql, val) db.commit() await ctx.send('Your bio has been updated.') else: cursor.execute(f"SELECT user_id, bio FROM profiles WHERE guild_id = '{ctx.message.author.guild.id}' and user_id = '{ctx.message.author.id}'") result1 = cursor.fetchone() sql = ("UPDATE profiles SET bio = ? WHERE guild_id = ? and user_id = ?") val = (str(content), str(ctx.message.guild.id), str(ctx.message.author.id)) cursor.execute(sql, val) db.commit() await ctx.send(f"your bio has been updated.") @commands.command() async def twitchsetup(self, ctx, *, content:str): db = sqlite3.connect('bot.db') cursor = db.cursor() cursor.execute(f"SELECT profiles.user_id, profiles.guild_id FROM profiles JOIN levels ON profiles.user_id = levels.user_id and profiles.guild_id = levels.guild_id WHERE levels.guild_id = '{ctx.message.author.guild.id}' and levels.user_id = '{ctx.message.author.id}'") result = cursor.fetchone() if result is None: sql = ("INSERT INTO profiles(guild_id, user_id, bio, twitch) VALUES(?,?,?,?)") val = (ctx.message.author.guild.id, ctx.message.author.id, None, str(content)) cursor.execute(sql, val) db.commit() await ctx.send('Your twitch has been updated.') else: cursor.execute(f"SELECT user_id, twitch FROM profiles WHERE guild_id = '{ctx.message.author.guild.id}' and user_id = '{ctx.message.author.id}'") result1 = cursor.fetchone() sql = ("UPDATE profiles SET twitch = ? WHERE guild_id = ? and user_id = ?") val = (str(content), str(ctx.message.guild.id), str(ctx.message.author.id)) cursor.execute(sql, val) db.commit() await ctx.send(f"your twitch has been updated.") @commands.command() async def bio(self, ctx, user:discord.User=None): if user is not None: db = sqlite3.connect('bot.db') cursor = db.cursor() cursor.execute( f"SELECT user_id, bio, twitch FROM profiles WHERE guild_id = '{ctx.message.author.guild.id}' and user_id = '{user.id}'") result = cursor.fetchone() if result is None: await ctx.send('That user has no bio.') else: await ctx.send(f"{user.name} bio: '{str(result[1])}' twitch: '{str(result[2])}' .") cursor.close() db.close() elif user is None: db = sqlite3.connect('bot.db') cursor = db.cursor() cursor.execute( f"SELECT user_id, bio, twitch FROM profiles WHERE guild_id = '{ctx.message.guild.id}' and user_id = '{ctx.message.author.id}'") result = cursor.fetchone() if result is None: await ctx.send('That user has no bio.') else: await ctx.send( f"{ctx.message.author.name} bio: '{str(result[1])}' twitch: '{str(result[2])}' .") cursor.close() db.close() def setup(bot): bot.add_cog(ProfileCog(bot)) db = sqlite3.connect('bot.db') cursor = db.cursor() cursor.executescript(''' CREATE TABLE IF NOT EXISTS profiles ( guild_id TEXT, user_id TEXT, bio TEXT, twitch TEXT ) ''') print('Profiles is loaded')

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0.011111

0

null

0

1

0

null

0

6

288215dd458aab4e832606cd84d8e15289afa4c1

24

py

Python

pyjs/transport/__init__.py

vulcan-coalition/pyjs

cfafea13269ac04988478e107941b8c9f3147af4

[ "Apache-2.0" ]

null

pyjs/transport/__init__.py

vulcan-coalition/pyjs

cfafea13269ac04988478e107941b8c9f3147af4

[ "Apache-2.0" ]

null

pyjs/transport/__init__.py

vulcan-coalition/pyjs

cfafea13269ac04988478e107941b8c9f3147af4

[ "Apache-2.0" ]

null

from . import websocket

12

23

0.791667

3

24

6.333333

1

0

0.166667

24

1

24

0.95

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

955e59c2ac9cb825b31f8910f61471cb2c725361

29

py

Python

aggregate_extensions/__init__.py

mynl/aggregate_extensions

514aff89e95ba74f34848b324f3986654db3a2cb

[ "BSD-3-Clause" ]

null

aggregate_extensions/__init__.py

mynl/aggregate_extensions

514aff89e95ba74f34848b324f3986654db3a2cb

[ "BSD-3-Clause" ]

null

aggregate_extensions/__init__.py

mynl/aggregate_extensions

514aff89e95ba74f34848b324f3986654db3a2cb

[ "BSD-3-Clause" ]

null

from . allocation import *

7.25

26

0.689655

3

29

6.666667

1

0

0.241379

29

3

27

9.666667

0.909091

0

1

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true

0

1

0

1

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1

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null

0

1

0

null

0

1

0

1

0

1

0

6

95cd131ab757776a7600b159afbdd2191be3ca37

37

py

Python

rpn/__init__.py

bradleysawler/python-rpn

727dda890d106aced9c07035aa4034178394cc05

[ "MIT" ]

2

2016-11-09T14:46:29.000Z

2019-12-24T18:13:25.000Z

rpn/__init__.py

bradleysawler/python-rpn

727dda890d106aced9c07035aa4034178394cc05

[ "MIT" ]

1

2020-04-22T06:26:50.000Z

rpn/__init__.py

bradleysawler/python-rpn

727dda890d106aced9c07035aa4034178394cc05

[ "MIT" ]

1

2020-03-07T05:34:46.000Z

from .rpn import solve_rpn, RPNError

18.5

36

0.810811

6

37

4.833333

0.833333

0

0.135135

37

1

37

0.90625

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true

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1

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null

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null

0

1

0

1

0

1

0

6

95d616ca5d2b521674b7eb24ba0515626041dea3

7,774

py

Python

geoalchemy2/tests/test_types.py

fredj/geoalchemy2

9f26714e8d181440ac03d7295d34d615cac11d02

[ "MIT" ]

null

geoalchemy2/tests/test_types.py

fredj/geoalchemy2

9f26714e8d181440ac03d7295d34d615cac11d02

[ "MIT" ]

null

geoalchemy2/tests/test_types.py

fredj/geoalchemy2

9f26714e8d181440ac03d7295d34d615cac11d02

[ "MIT" ]

null

import unittest import re from nose.tools import eq_, raises def eq_sql(a, b, msg=None): a = re.sub(r'[\n\t]', '', str(a)) eq_(a, b, msg) def _create_geometry_table(): from sqlalchemy import Table, MetaData, Column from geoalchemy2.types import Geometry table = Table('table', MetaData(), Column('geom', Geometry)) return table def _create_geography_table(): from sqlalchemy import Table, MetaData, Column from geoalchemy2.types import Geography table = Table('table', MetaData(), Column('geom', Geography)) return table class TestGeometry(unittest.TestCase): def test_get_col_spec(self): from geoalchemy2 import Geometry g = Geometry(srid=900913) eq_(g.get_col_spec(), 'geometry(GEOMETRY,900913)') def test_column_expression(self): from sqlalchemy.sql import select table = _create_geometry_table() s = select([table.c.geom]) eq_sql(s, 'SELECT ST_AsBinary("table".geom) AS geom FROM "table"') def test_select_bind_expression(self): from sqlalchemy.sql import select table = _create_geometry_table() s = select(['foo']).where(table.c.geom == 'POINT(1 2)') eq_sql(s, 'SELECT foo FROM "table" WHERE ' '"table".geom = ST_GeomFromText(:geom_1)') eq_(s.compile().params, {'geom_1': 'POINT(1 2)'}) def test_insert_bind_expression(self): from sqlalchemy.sql import insert table = _create_geometry_table() i = insert(table).values(geom='POINT(1 2)') eq_sql(i, 'INSERT INTO "table" (geom) VALUES (ST_GeomFromText(:geom))') eq_(i.compile().params, {'geom': 'POINT(1 2)'}) def test_function_call(self): from sqlalchemy.sql import select table = _create_geometry_table() s = select([table.c.geom.ST_Buffer(2)]) eq_sql(s, 'SELECT ST_AsBinary(ST_Buffer("table".geom, :param_1)) ' 'AS "ST_Buffer_1" FROM "table"') @raises(AttributeError) def test_non_ST_function_call(self): table = _create_geometry_table() table.c.geom.Buffer(2) def test_subquery(self): # test for geometry columns not delivered to the result # http://hg.sqlalchemy.org/sqlalchemy/rev/f1efb20c6d61 from sqlalchemy.sql import select table = _create_geometry_table() s = select([table]).alias('name').select() eq_sql(s, 'SELECT ST_AsBinary(name.geom) AS geom FROM ' '(SELECT "table".geom AS geom FROM "table") AS name') class TestGeography(unittest.TestCase): def test_get_col_spec(self): from geoalchemy2 import Geography g = Geography(srid=900913) eq_(g.get_col_spec(), 'geography(GEOMETRY,900913)') def test_column_expression(self): from sqlalchemy.sql import select table = _create_geography_table() s = select([table.c.geom]) eq_sql(s, 'SELECT ST_AsBinary("table".geom) AS geom FROM "table"') def test_select_bind_expression(self): from sqlalchemy.sql import select table = _create_geography_table() s = select(['foo']).where(table.c.geom == 'POINT(1 2)') eq_sql(s, 'SELECT foo FROM "table" WHERE ' '"table".geom = ST_GeogFromText(:geom_1)') eq_(s.compile().params, {'geom_1': 'POINT(1 2)'}) def test_insert_bind_expression(self): from sqlalchemy.sql import insert table = _create_geography_table() i = insert(table).values(geom='POINT(1 2)') eq_sql(i, 'INSERT INTO "table" (geom) VALUES (ST_GeogFromText(:geom))') eq_(i.compile().params, {'geom': 'POINT(1 2)'}) def test_function_call(self): from sqlalchemy.sql import select table = _create_geography_table() s = select([table.c.geom.ST_Buffer(2)]) eq_sql(s, 'SELECT ST_AsBinary(ST_Buffer("table".geom, :param_1)) ' 'AS "ST_Buffer_1" FROM "table"') @raises(AttributeError) def test_non_ST_function_call(self): table = _create_geography_table() table.c.geom.Buffer(2) def test_subquery(self): # test for geography columns not delivered to the result # http://hg.sqlalchemy.org/sqlalchemy/rev/f1efb20c6d61 from sqlalchemy.sql import select table = _create_geography_table() s = select([table]).alias('name').select() eq_sql(s, 'SELECT ST_AsBinary(name.geom) AS geom FROM ' '(SELECT "table".geom AS geom FROM "table") AS name') class TestPoint(unittest.TestCase): def test_get_col_spec(self): from geoalchemy2.types import Geometry g = Geometry(geometry_type='POINT', srid=900913) eq_(g.get_col_spec(), 'geometry(POINT,900913)') class TestCurve(unittest.TestCase): def test_get_col_spec(self): from geoalchemy2.types import Geometry g = Geometry(geometry_type='CURVE', srid=900913) eq_(g.get_col_spec(), 'geometry(CURVE,900913)') class TestLineString(unittest.TestCase): def test_get_col_spec(self): from geoalchemy2.types import Geometry g = Geometry(geometry_type='LINESTRING', srid=900913) eq_(g.get_col_spec(), 'geometry(LINESTRING,900913)') class TestPolygon(unittest.TestCase): def test_get_col_spec(self): from geoalchemy2.types import Geometry g = Geometry(geometry_type='POLYGON', srid=900913) eq_(g.get_col_spec(), 'geometry(POLYGON,900913)') class TestMultiPoint(unittest.TestCase): def test_get_col_spec(self): from geoalchemy2.types import Geometry g = Geometry(geometry_type='MULTIPOINT', srid=900913) eq_(g.get_col_spec(), 'geometry(MULTIPOINT,900913)') class TestMultiLineString(unittest.TestCase): def test_get_col_spec(self): from geoalchemy2.types import Geometry g = Geometry(geometry_type='MULTILINESTRING', srid=900913) eq_(g.get_col_spec(), 'geometry(MULTILINESTRING,900913)') class TestMultiPolygon(unittest.TestCase): def test_get_col_spec(self): from geoalchemy2.types import Geometry g = Geometry(geometry_type='MULTIPOLYGON', srid=900913) eq_(g.get_col_spec(), 'geometry(MULTIPOLYGON,900913)') class TestGeometryCollection(unittest.TestCase): def test_get_col_spec(self): from geoalchemy2.types import Geometry g = Geometry(geometry_type='GEOMETRYCOLLECTION', srid=900913) eq_(g.get_col_spec(), 'geometry(GEOMETRYCOLLECTION,900913)') class TestFunction(unittest.TestCase): def test_ST_Equal_WKTElement_WKTElement(self): from sqlalchemy import func from geoalchemy2.elements import WKTElement expr = func.ST_Equals(WKTElement('POINT(1 2)'), WKTElement('POINT(1 2)')) eq_sql(expr, 'ST_Equals(' 'ST_GeomFromText(:ST_GeomFromText_1, :ST_GeomFromText_2), ' 'ST_GeomFromText(:ST_GeomFromText_3, :ST_GeomFromText_4))') eq_(expr.compile().params, {u'ST_GeomFromText_1': 'POINT(1 2)', u'ST_GeomFromText_2': -1, u'ST_GeomFromText_3': 'POINT(1 2)', u'ST_GeomFromText_4': -1}) def test_ST_Equal_Column_WKTElement(self): from sqlalchemy import func from geoalchemy2.elements import WKTElement table = _create_geometry_table() expr = func.ST_Equals(table.c.geom, WKTElement('POINT(1 2)')) eq_sql(expr, 'ST_Equals("table".geom, ' 'ST_GeomFromText(:ST_GeomFromText_1, :ST_GeomFromText_2))') eq_(expr.compile().params, {u'ST_GeomFromText_1': 'POINT(1 2)', u'ST_GeomFromText_2': -1})

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79

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980

7,774

4.903061

0.112245

0.034964

0.041623

0.052653

0.805411

0.803746

0.785432

0.780645

0.704266

0.690114

0

0.033736

0.233599

7,774

219

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35.497717

0.772743

0.027528

0

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0

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0.086962

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false

0

0.191358

0

0.438272

0

null

0

1

0

null

0

6

c262c8c923e69e417011da1b888a9e0a2fb35768

83

py

Python

kino/draw/__init__.py

BrancoLab/Kino

0e914e3d65fdf76e4efa95b9848cb30da3653f3d

[ "MIT" ]

1

2021-12-09T09:19:25.000Z

kino/draw/__init__.py

BrancoLab/Kino

0e914e3d65fdf76e4efa95b9848cb30da3653f3d

[ "MIT" ]

null

kino/draw/__init__.py

BrancoLab/Kino

0e914e3d65fdf76e4efa95b9848cb30da3653f3d

[ "MIT" ]

null

# from kino.draw.animal import DrawAnimal # from kino.draw.kinematics import Steps

27.666667

41

0.807229

12

83

5.583333

0.666667

0.238806

0.358209

0

0.120482

83

2

42

41.5

0.917808

0.939759

0

null

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null

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1

null

true

0

null

1

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null

1

0

1

0

1

0

null

0

1

0

6

c2ed9e0c3cb9b54965dabc6c38469e32c8923ddb

37,860

py

Python

instances/passenger_demand/pas-20210421-2109-int6000000000000001e-1/90.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210421-2109-int6000000000000001e-1/90.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210421-2109-int6000000000000001e-1/90.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 1304 passenger_arriving = ( (2, 2, 5, 3, 1, 0, 2, 3, 2, 1, 3, 0), # 0 (4, 6, 3, 3, 0, 0, 3, 4, 1, 5, 2, 0), # 1 (1, 1, 3, 1, 4, 0, 2, 3, 3, 3, 0, 0), # 2 (1, 6, 0, 2, 0, 0, 3, 1, 0, 1, 0, 0), # 3 (2, 5, 7, 1, 2, 0, 4, 2, 0, 0, 0, 0), # 4 (4, 0, 2, 2, 2, 0, 2, 2, 2, 1, 0, 0), # 5 (1, 6, 2, 2, 1, 0, 5, 2, 1, 3, 0, 0), # 6 (1, 5, 1, 1, 1, 0, 8, 2, 4, 1, 1, 0), # 7 (2, 1, 3, 1, 0, 0, 1, 3, 2, 4, 0, 0), # 8 (0, 6, 3, 0, 0, 0, 3, 7, 2, 1, 2, 0), # 9 (1, 2, 1, 5, 2, 0, 2, 4, 2, 1, 0, 0), # 10 (1, 4, 4, 1, 0, 0, 4, 3, 1, 1, 1, 0), # 11 (0, 3, 7, 1, 0, 0, 2, 6, 4, 2, 0, 0), # 12 (1, 2, 1, 1, 0, 0, 4, 3, 5, 3, 2, 0), # 13 (1, 3, 0, 3, 2, 0, 6, 6, 4, 1, 0, 0), # 14 (1, 3, 5, 1, 3, 0, 2, 1, 2, 4, 1, 0), # 15 (1, 3, 0, 2, 1, 0, 1, 3, 3, 5, 2, 0), # 16 (1, 3, 2, 0, 0, 0, 1, 6, 3, 1, 1, 0), # 17 (3, 2, 0, 0, 1, 0, 3, 9, 0, 2, 3, 0), # 18 (4, 4, 6, 4, 0, 0, 5, 0, 2, 4, 0, 0), # 19 (5, 2, 3, 0, 3, 0, 3, 4, 2, 3, 0, 0), # 20 (1, 1, 2, 0, 0, 0, 2, 2, 3, 2, 0, 0), # 21 (2, 3, 2, 1, 0, 0, 4, 4, 7, 3, 0, 0), # 22 (2, 3, 1, 2, 1, 0, 2, 5, 2, 0, 2, 0), # 23 (3, 6, 1, 1, 2, 0, 1, 3, 4, 2, 1, 0), # 24 (3, 2, 6, 2, 1, 0, 0, 7, 4, 3, 1, 0), # 25 (1, 4, 1, 2, 0, 0, 4, 5, 4, 2, 0, 0), # 26 (2, 3, 4, 0, 1, 0, 0, 4, 2, 2, 2, 0), # 27 (2, 1, 7, 1, 0, 0, 1, 2, 1, 3, 1, 0), # 28 (2, 4, 4, 0, 2, 0, 1, 4, 2, 2, 0, 0), # 29 (0, 3, 2, 0, 1, 0, 3, 5, 4, 3, 1, 0), # 30 (3, 6, 3, 0, 0, 0, 2, 2, 3, 0, 0, 0), # 31 (0, 9, 4, 2, 0, 0, 5, 5, 1, 1, 0, 0), # 32 (2, 6, 4, 3, 2, 0, 2, 4, 1, 3, 2, 0), # 33 (1, 4, 3, 2, 0, 0, 4, 3, 5, 1, 0, 0), # 34 (5, 3, 2, 1, 2, 0, 1, 3, 2, 1, 0, 0), # 35 (1, 6, 8, 0, 1, 0, 1, 2, 1, 3, 3, 0), # 36 (2, 1, 6, 0, 0, 0, 1, 5, 2, 2, 3, 0), # 37 (0, 2, 4, 3, 0, 0, 3, 4, 3, 1, 2, 0), # 38 (3, 5, 2, 1, 0, 0, 2, 1, 3, 4, 1, 0), # 39 (2, 4, 1, 2, 1, 0, 4, 1, 3, 1, 2, 0), # 40 (1, 7, 0, 0, 2, 0, 2, 4, 3, 1, 0, 0), # 41 (3, 4, 2, 2, 4, 0, 2, 2, 4, 1, 2, 0), # 42 (3, 4, 2, 2, 1, 0, 2, 5, 3, 4, 1, 0), # 43 (4, 6, 4, 0, 1, 0, 5, 2, 3, 0, 1, 0), # 44 (1, 3, 4, 0, 1, 0, 1, 4, 2, 2, 0, 0), # 45 (2, 3, 5, 1, 2, 0, 3, 6, 1, 4, 3, 0), # 46 (1, 3, 2, 1, 0, 0, 6, 3, 3, 2, 0, 0), # 47 (2, 2, 3, 3, 1, 0, 2, 4, 2, 2, 2, 0), # 48 (3, 0, 5, 0, 1, 0, 4, 1, 1, 1, 0, 0), # 49 (1, 2, 3, 2, 1, 0, 0, 4, 1, 0, 0, 0), # 50 (3, 7, 2, 0, 1, 0, 2, 4, 1, 1, 3, 0), # 51 (3, 1, 2, 0, 0, 0, 3, 5, 4, 3, 0, 0), # 52 (2, 1, 1, 1, 0, 0, 2, 4, 3, 3, 1, 0), # 53 (2, 3, 4, 2, 1, 0, 0, 6, 2, 5, 2, 0), # 54 (3, 2, 1, 0, 1, 0, 4, 0, 3, 4, 2, 0), # 55 (3, 3, 3, 1, 1, 0, 3, 5, 0, 2, 0, 0), # 56 (1, 2, 2, 2, 1, 0, 2, 1, 2, 2, 1, 0), # 57 (1, 4, 3, 3, 0, 0, 0, 2, 4, 0, 0, 0), # 58 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 59 ) station_arriving_intensity = ( (1.5897909350307289, 4.077876420454546, 4.7965416131105405, 3.8017663043478263, 4.285817307692308, 2.8540760869565225), # 0 (1.6047132060286802, 4.123224959227694, 4.822449322514998, 3.8229386322463776, 4.317939903846154, 2.853103283514493), # 1 (1.6194650863330406, 4.167900841750842, 4.84774207369323, 3.8436449275362325, 4.349384615384616, 2.8521007246376815), # 2 (1.6340340539947322, 4.211855859375001, 4.8724013897814915, 3.863867527173913, 4.380122596153847, 2.851068546195653), # 3 (1.6484075870646768, 4.25504180345118, 4.896408793916025, 3.883588768115943, 4.410125000000001, 2.850006884057971), # 4 (1.662573163593796, 4.297410465330389, 4.919745809233077, 3.902790987318841, 4.439362980769231, 2.848915874094203), # 5 (1.6765182616330119, 4.338913636363637, 4.942393958868896, 3.9214565217391315, 4.467807692307693, 2.8477956521739136), # 6 (1.690230359233246, 4.379503107901936, 4.964334765959726, 3.939567708333334, 4.49543028846154, 2.8466463541666673), # 7 (1.7036969344454203, 4.419130671296296, 4.985549753641818, 3.9571068840579713, 4.522201923076924, 2.845468115942029), # 8 (1.7169054653204567, 4.457748117897728, 5.006020445051415, 3.974056385869566, 4.5480937500000005, 2.8442610733695655), # 9 (1.7298434299092773, 4.49530723905724, 5.025728363324765, 3.9903985507246387, 4.573076923076924, 2.8430253623188406), # 10 (1.7424983062628039, 4.5317598261258425, 5.044655031598115, 4.0061157155797105, 4.597122596153847, 2.841761118659421), # 11 (1.7548575724319582, 4.567057670454545, 5.062781973007713, 4.021190217391305, 4.620201923076923, 2.8404684782608696), # 12 (1.7669087064676616, 4.601152563394361, 5.080090710689803, 4.035604393115943, 4.642286057692309, 2.839147576992754), # 13 (1.7786391864208373, 4.6339962962962975, 5.096562767780633, 4.049340579710145, 4.663346153846154, 2.8377985507246377), # 14 (1.7900364903424055, 4.665540660511364, 5.112179667416452, 4.062381114130435, 4.683353365384616, 2.8364215353260875), # 15 (1.8010880962832896, 4.695737447390573, 5.126922932733506, 4.074708333333334, 4.702278846153847, 2.835016666666667), # 16 (1.8117814822944105, 4.724538448284933, 5.1407740868680385, 4.0863045742753625, 4.720093750000001, 2.833584080615943), # 17 (1.8221041264266904, 4.751895454545455, 5.1537146529563, 4.097152173913044, 4.736769230769233, 2.8321239130434788), # 18 (1.8320435067310508, 4.777760257523148, 5.165726154134534, 4.107233469202899, 4.752276442307693, 2.830636299818841), # 19 (1.841587101258414, 4.802084648569023, 5.176790113538988, 4.11653079710145, 4.76658653846154, 2.8291213768115946), # 20 (1.850722388059702, 4.82482041903409, 5.186888054305914, 4.125026494565218, 4.779670673076923, 2.827579279891305), # 21 (1.8594368451858356, 4.845919360269361, 5.196001499571551, 4.1327028985507255, 4.7915, 2.8260101449275368), # 22 (1.867717950687738, 4.865333263625843, 5.204111972472152, 4.139542346014493, 4.802045673076924, 2.8244141077898557), # 23 (1.8755531826163303, 4.8830139204545455, 5.211200996143959, 4.145527173913044, 4.811278846153846, 2.8227913043478265), # 24 (1.8829300190225344, 4.898913122106482, 5.217250093723223, 4.150639719202899, 4.819170673076923, 2.8211418704710147), # 25 (1.8898359379572727, 4.91298265993266, 5.222240788346188, 4.15486231884058, 4.825692307692308, 2.819465942028986), # 26 (1.8962584174714663, 4.9251743252840905, 5.2261546031491, 4.15817730978261, 4.830814903846155, 2.817763654891305), # 27 (1.9021849356160379, 4.935439909511785, 5.22897306126821, 4.160567028985508, 4.834509615384616, 2.8160351449275365), # 28 (1.9076029704419084, 4.943731203966752, 5.23067768583976, 4.162013813405798, 4.836747596153847, 2.814280548007247), # 29 (1.9125000000000003, 4.950000000000001, 5.231250000000001, 4.1625000000000005, 4.8375, 2.8125000000000004), # 30 (1.9170822170716115, 4.955207279829545, 5.230820969202899, 4.162412193627452, 4.837226196808512, 2.8100257558720645), # 31 (1.92156550511509, 4.960345738636365, 5.229546014492754, 4.162150490196079, 4.836410638297873, 2.8062148550724646), # 32 (1.925951878196931, 4.96541473721591, 5.227443342391306, 4.161717463235295, 4.83506210106383, 2.8011046101949026), # 33 (1.9302433503836318, 4.970413636363637, 5.22453115942029, 4.161115686274511, 4.833189361702129, 2.794732333833084), # 34 (1.9344419357416882, 4.975341796875, 5.22082767210145, 4.160347732843138, 4.830801196808512, 2.78713533858071), # 35 (1.9385496483375964, 4.980198579545456, 5.216351086956522, 4.1594161764705895, 4.827906382978725, 2.7783509370314845), # 36 (1.9425685022378518, 4.9849833451704555, 5.211119610507247, 4.158323590686275, 4.824513696808511, 2.768416441779111), # 37 (1.9465005115089515, 4.989695454545455, 5.2051514492753626, 4.157072549019608, 4.820631914893617, 2.757369165417291), # 38 (1.9503476902173915, 4.994334268465909, 5.1984648097826085, 4.155665625000001, 4.816269813829788, 2.7452464205397304), # 39 (1.9541120524296678, 4.998899147727274, 5.191077898550725, 4.154105392156863, 4.811436170212766, 2.73208551974013), # 40 (1.9577956122122764, 5.003389453125, 5.18300892210145, 4.152394424019608, 4.806139760638298, 2.717923775612195), # 41 (1.9614003836317138, 5.0078045454545475, 5.174276086956523, 4.150535294117647, 4.800389361702129, 2.702798500749626), # 42 (1.9649283807544762, 5.0121437855113635, 5.164897599637682, 4.148530575980393, 4.794193750000001, 2.6867470077461273), # 43 (1.968381617647059, 5.01640653409091, 5.154891666666668, 4.146382843137255, 4.78756170212766, 2.6698066091954025), # 44 (1.971762108375959, 5.0205921519886365, 5.144276494565219, 4.1440946691176475, 4.780501994680852, 2.652014617691155), # 45 (1.9750718670076732, 5.024700000000001, 5.133070289855074, 4.141668627450981, 4.77302340425532, 2.633408345827087), # 46 (1.978312907608696, 5.028729438920456, 5.121291259057971, 4.139107291666667, 4.765134707446809, 2.6140251061969018), # 47 (1.981487244245525, 5.032679829545455, 5.108957608695652, 4.136413235294118, 4.7568446808510645, 2.5939022113943038), # 48 (1.9845968909846547, 5.0365505326704545, 5.096087545289856, 4.133589031862746, 4.748162101063831, 2.5730769740129937), # 49 (1.9876438618925836, 5.040340909090909, 5.0826992753623195, 4.130637254901962, 4.739095744680852, 2.551586706646677), # 50 (1.990630171035806, 5.044050319602273, 5.0688110054347835, 4.127560477941177, 4.729654388297873, 2.5294687218890557), # 51 (1.9935578324808187, 5.047678125000001, 5.054440942028986, 4.124361274509805, 4.719846808510639, 2.5067603323338337), # 52 (1.996428860294118, 5.051223686079546, 5.039607291666667, 4.121042218137255, 4.709681781914894, 2.483498850574713), # 53 (1.9992452685422, 5.054686363636364, 5.024328260869566, 4.117605882352942, 4.6991680851063835, 2.4597215892053974), # 54 (2.0020090712915604, 5.058065518465909, 5.00862205615942, 4.114054840686276, 4.688314494680852, 2.4354658608195905), # 55 (2.0047222826086957, 5.061360511363636, 4.992506884057971, 4.110391666666667, 4.677129787234043, 2.410768978010995), # 56 (2.007386916560103, 5.064570703125002, 4.976000951086957, 4.10661893382353, 4.6656227393617025, 2.3856682533733133), # 57 (2.0100049872122767, 5.067695454545454, 4.959122463768116, 4.102739215686276, 4.653802127659575, 2.3602009995002504), # 58 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59 ) passenger_arriving_acc = ( (2, 2, 5, 3, 1, 0, 2, 3, 2, 1, 3, 0), # 0 (6, 8, 8, 6, 1, 0, 5, 7, 3, 6, 5, 0), # 1 (7, 9, 11, 7, 5, 0, 7, 10, 6, 9, 5, 0), # 2 (8, 15, 11, 9, 5, 0, 10, 11, 6, 10, 5, 0), # 3 (10, 20, 18, 10, 7, 0, 14, 13, 6, 10, 5, 0), # 4 (14, 20, 20, 12, 9, 0, 16, 15, 8, 11, 5, 0), # 5 (15, 26, 22, 14, 10, 0, 21, 17, 9, 14, 5, 0), # 6 (16, 31, 23, 15, 11, 0, 29, 19, 13, 15, 6, 0), # 7 (18, 32, 26, 16, 11, 0, 30, 22, 15, 19, 6, 0), # 8 (18, 38, 29, 16, 11, 0, 33, 29, 17, 20, 8, 0), # 9 (19, 40, 30, 21, 13, 0, 35, 33, 19, 21, 8, 0), # 10 (20, 44, 34, 22, 13, 0, 39, 36, 20, 22, 9, 0), # 11 (20, 47, 41, 23, 13, 0, 41, 42, 24, 24, 9, 0), # 12 (21, 49, 42, 24, 13, 0, 45, 45, 29, 27, 11, 0), # 13 (22, 52, 42, 27, 15, 0, 51, 51, 33, 28, 11, 0), # 14 (23, 55, 47, 28, 18, 0, 53, 52, 35, 32, 12, 0), # 15 (24, 58, 47, 30, 19, 0, 54, 55, 38, 37, 14, 0), # 16 (25, 61, 49, 30, 19, 0, 55, 61, 41, 38, 15, 0), # 17 (28, 63, 49, 30, 20, 0, 58, 70, 41, 40, 18, 0), # 18 (32, 67, 55, 34, 20, 0, 63, 70, 43, 44, 18, 0), # 19 (37, 69, 58, 34, 23, 0, 66, 74, 45, 47, 18, 0), # 20 (38, 70, 60, 34, 23, 0, 68, 76, 48, 49, 18, 0), # 21 (40, 73, 62, 35, 23, 0, 72, 80, 55, 52, 18, 0), # 22 (42, 76, 63, 37, 24, 0, 74, 85, 57, 52, 20, 0), # 23 (45, 82, 64, 38, 26, 0, 75, 88, 61, 54, 21, 0), # 24 (48, 84, 70, 40, 27, 0, 75, 95, 65, 57, 22, 0), # 25 (49, 88, 71, 42, 27, 0, 79, 100, 69, 59, 22, 0), # 26 (51, 91, 75, 42, 28, 0, 79, 104, 71, 61, 24, 0), # 27 (53, 92, 82, 43, 28, 0, 80, 106, 72, 64, 25, 0), # 28 (55, 96, 86, 43, 30, 0, 81, 110, 74, 66, 25, 0), # 29 (55, 99, 88, 43, 31, 0, 84, 115, 78, 69, 26, 0), # 30 (58, 105, 91, 43, 31, 0, 86, 117, 81, 69, 26, 0), # 31 (58, 114, 95, 45, 31, 0, 91, 122, 82, 70, 26, 0), # 32 (60, 120, 99, 48, 33, 0, 93, 126, 83, 73, 28, 0), # 33 (61, 124, 102, 50, 33, 0, 97, 129, 88, 74, 28, 0), # 34 (66, 127, 104, 51, 35, 0, 98, 132, 90, 75, 28, 0), # 35 (67, 133, 112, 51, 36, 0, 99, 134, 91, 78, 31, 0), # 36 (69, 134, 118, 51, 36, 0, 100, 139, 93, 80, 34, 0), # 37 (69, 136, 122, 54, 36, 0, 103, 143, 96, 81, 36, 0), # 38 (72, 141, 124, 55, 36, 0, 105, 144, 99, 85, 37, 0), # 39 (74, 145, 125, 57, 37, 0, 109, 145, 102, 86, 39, 0), # 40 (75, 152, 125, 57, 39, 0, 111, 149, 105, 87, 39, 0), # 41 (78, 156, 127, 59, 43, 0, 113, 151, 109, 88, 41, 0), # 42 (81, 160, 129, 61, 44, 0, 115, 156, 112, 92, 42, 0), # 43 (85, 166, 133, 61, 45, 0, 120, 158, 115, 92, 43, 0), # 44 (86, 169, 137, 61, 46, 0, 121, 162, 117, 94, 43, 0), # 45 (88, 172, 142, 62, 48, 0, 124, 168, 118, 98, 46, 0), # 46 (89, 175, 144, 63, 48, 0, 130, 171, 121, 100, 46, 0), # 47 (91, 177, 147, 66, 49, 0, 132, 175, 123, 102, 48, 0), # 48 (94, 177, 152, 66, 50, 0, 136, 176, 124, 103, 48, 0), # 49 (95, 179, 155, 68, 51, 0, 136, 180, 125, 103, 48, 0), # 50 (98, 186, 157, 68, 52, 0, 138, 184, 126, 104, 51, 0), # 51 (101, 187, 159, 68, 52, 0, 141, 189, 130, 107, 51, 0), # 52 (103, 188, 160, 69, 52, 0, 143, 193, 133, 110, 52, 0), # 53 (105, 191, 164, 71, 53, 0, 143, 199, 135, 115, 54, 0), # 54 (108, 193, 165, 71, 54, 0, 147, 199, 138, 119, 56, 0), # 55 (111, 196, 168, 72, 55, 0, 150, 204, 138, 121, 56, 0), # 56 (112, 198, 170, 74, 56, 0, 152, 205, 140, 123, 57, 0), # 57 (113, 202, 173, 77, 56, 0, 152, 207, 144, 123, 57, 0), # 58 (113, 202, 173, 77, 56, 0, 152, 207, 144, 123, 57, 0), # 59 ) passenger_arriving_rate = ( (1.5897909350307289, 3.2623011363636363, 2.877924967866324, 1.5207065217391305, 0.8571634615384615, 0.0, 2.8540760869565225, 3.428653846153846, 2.2810597826086956, 1.918616645244216, 0.8155752840909091, 0.0), # 0 (1.6047132060286802, 3.298579967382155, 2.8934695935089985, 1.5291754528985508, 0.8635879807692308, 0.0, 2.853103283514493, 3.4543519230769233, 2.2937631793478266, 1.928979729005999, 0.8246449918455387, 0.0), # 1 (1.6194650863330406, 3.3343206734006734, 2.908645244215938, 1.5374579710144929, 0.8698769230769231, 0.0, 2.8521007246376815, 3.4795076923076924, 2.3061869565217394, 1.939096829477292, 0.8335801683501683, 0.0), # 2 (1.6340340539947322, 3.369484687500001, 2.9234408338688946, 1.545547010869565, 0.8760245192307694, 0.0, 2.851068546195653, 3.5040980769230776, 2.3183205163043477, 1.9489605559125964, 0.8423711718750002, 0.0), # 3 (1.6484075870646768, 3.4040334427609436, 2.9378452763496146, 1.553435507246377, 0.8820250000000001, 0.0, 2.850006884057971, 3.5281000000000002, 2.3301532608695656, 1.9585635175664096, 0.8510083606902359, 0.0), # 4 (1.662573163593796, 3.437928372264311, 2.951847485539846, 1.5611163949275362, 0.8878725961538462, 0.0, 2.848915874094203, 3.5514903846153847, 2.3416745923913043, 1.9678983236932306, 0.8594820930660777, 0.0), # 5 (1.6765182616330119, 3.4711309090909093, 2.9654363753213375, 1.5685826086956525, 0.8935615384615384, 0.0, 2.8477956521739136, 3.5742461538461536, 2.352873913043479, 1.9769575835475584, 0.8677827272727273, 0.0), # 6 (1.690230359233246, 3.5036024863215487, 2.9786008595758355, 1.5758270833333334, 0.8990860576923079, 0.0, 2.8466463541666673, 3.5963442307692315, 2.363740625, 1.9857339063838901, 0.8759006215803872, 0.0), # 7 (1.7036969344454203, 3.5353045370370366, 2.991329852185091, 1.5828427536231884, 0.9044403846153848, 0.0, 2.845468115942029, 3.617761538461539, 2.3742641304347827, 1.994219901456727, 0.8838261342592592, 0.0), # 8 (1.7169054653204567, 3.566198494318182, 3.003612267030849, 1.5896225543478264, 0.90961875, 0.0, 2.8442610733695655, 3.638475, 2.3844338315217395, 2.002408178020566, 0.8915496235795455, 0.0), # 9 (1.7298434299092773, 3.5962457912457917, 3.015437017994859, 1.5961594202898552, 0.9146153846153847, 0.0, 2.8430253623188406, 3.658461538461539, 2.394239130434783, 2.0102913453299056, 0.8990614478114479, 0.0), # 10 (1.7424983062628039, 3.625407860900674, 3.0267930189588688, 1.602446286231884, 0.9194245192307693, 0.0, 2.841761118659421, 3.677698076923077, 2.403669429347826, 2.0178620126392457, 0.9063519652251685, 0.0), # 11 (1.7548575724319582, 3.653646136363636, 3.0376691838046277, 1.6084760869565218, 0.9240403846153845, 0.0, 2.8404684782608696, 3.696161538461538, 2.4127141304347828, 2.025112789203085, 0.913411534090909, 0.0), # 12 (1.7669087064676616, 3.680922050715489, 3.048054426413882, 1.614241757246377, 0.9284572115384617, 0.0, 2.839147576992754, 3.713828846153847, 2.4213626358695657, 2.032036284275921, 0.9202305126788722, 0.0), # 13 (1.7786391864208373, 3.7071970370370377, 3.05793766066838, 1.6197362318840578, 0.9326692307692308, 0.0, 2.8377985507246377, 3.7306769230769232, 2.429604347826087, 2.038625107112253, 0.9267992592592594, 0.0), # 14 (1.7900364903424055, 3.732432528409091, 3.0673078004498713, 1.624952445652174, 0.9366706730769232, 0.0, 2.8364215353260875, 3.746682692307693, 2.437428668478261, 2.044871866966581, 0.9331081321022727, 0.0), # 15 (1.8010880962832896, 3.7565899579124578, 3.0761537596401034, 1.6298833333333334, 0.9404557692307693, 0.0, 2.835016666666667, 3.7618230769230774, 2.4448250000000002, 2.050769173093402, 0.9391474894781144, 0.0), # 16 (1.8117814822944105, 3.779630758627946, 3.084464452120823, 1.634521829710145, 0.9440187500000001, 0.0, 2.833584080615943, 3.7760750000000005, 2.4517827445652176, 2.0563096347472154, 0.9449076896569865, 0.0), # 17 (1.8221041264266904, 3.8015163636363636, 3.09222879177378, 1.6388608695652176, 0.9473538461538464, 0.0, 2.8321239130434788, 3.7894153846153857, 2.4582913043478265, 2.0614858611825198, 0.9503790909090909, 0.0), # 18 (1.8320435067310508, 3.8222082060185185, 3.09943569248072, 1.6428933876811593, 0.9504552884615385, 0.0, 2.830636299818841, 3.801821153846154, 2.464340081521739, 2.0662904616538134, 0.9555520515046296, 0.0), # 19 (1.841587101258414, 3.841667718855218, 3.106074068123393, 1.6466123188405797, 0.9533173076923078, 0.0, 2.8291213768115946, 3.8132692307692313, 2.46991847826087, 2.0707160454155953, 0.9604169297138045, 0.0), # 20 (1.850722388059702, 3.8598563352272715, 3.1121328325835482, 1.650010597826087, 0.9559341346153846, 0.0, 2.827579279891305, 3.8237365384615383, 2.475015896739131, 2.0747552217223655, 0.9649640838068179, 0.0), # 21 (1.8594368451858356, 3.8767354882154885, 3.1176008997429308, 1.65308115942029, 0.9582999999999999, 0.0, 2.8260101449275368, 3.8331999999999997, 2.4796217391304354, 2.0784005998286204, 0.9691838720538721, 0.0), # 22 (1.867717950687738, 3.892266610900674, 3.122467183483291, 1.655816938405797, 0.9604091346153847, 0.0, 2.8244141077898557, 3.8416365384615387, 2.483725407608696, 2.0816447889888607, 0.9730666527251685, 0.0), # 23 (1.8755531826163303, 3.9064111363636362, 3.1267205976863752, 1.6582108695652176, 0.9622557692307692, 0.0, 2.8227913043478265, 3.8490230769230767, 2.4873163043478264, 2.0844803984575835, 0.9766027840909091, 0.0), # 24 (1.8829300190225344, 3.919130497685185, 3.1303500562339335, 1.6602558876811595, 0.9638341346153845, 0.0, 2.8211418704710147, 3.855336538461538, 2.4903838315217395, 2.086900037489289, 0.9797826244212963, 0.0), # 25 (1.8898359379572727, 3.930386127946128, 3.1333444730077127, 1.661944927536232, 0.9651384615384615, 0.0, 2.819465942028986, 3.860553846153846, 2.492917391304348, 2.088896315338475, 0.982596531986532, 0.0), # 26 (1.8962584174714663, 3.940139460227272, 3.1356927618894597, 1.6632709239130437, 0.966162980769231, 0.0, 2.817763654891305, 3.864651923076924, 2.4949063858695655, 2.0904618412596396, 0.985034865056818, 0.0), # 27 (1.9021849356160379, 3.948351927609427, 3.1373838367609257, 1.664226811594203, 0.9669019230769231, 0.0, 2.8160351449275365, 3.8676076923076925, 2.4963402173913045, 2.091589224507284, 0.9870879819023568, 0.0), # 28 (1.9076029704419084, 3.954984963173401, 3.138406611503856, 1.664805525362319, 0.9673495192307693, 0.0, 2.814280548007247, 3.869398076923077, 2.4972082880434785, 2.092271074335904, 0.9887462407933503, 0.0), # 29 (1.9125000000000003, 3.9600000000000004, 3.1387500000000004, 1.665, 0.9675, 0.0, 2.8125000000000004, 3.87, 2.4975, 2.0925000000000002, 0.9900000000000001, 0.0), # 30 (1.9170822170716115, 3.9641658238636355, 3.138492581521739, 1.6649648774509804, 0.9674452393617023, 0.0, 2.8100257558720645, 3.8697809574468094, 2.497447316176471, 2.0923283876811594, 0.9910414559659089, 0.0), # 31 (1.92156550511509, 3.9682765909090914, 3.137727608695652, 1.6648601960784315, 0.9672821276595746, 0.0, 2.8062148550724646, 3.869128510638298, 2.497290294117647, 2.091818405797101, 0.9920691477272728, 0.0), # 32 (1.925951878196931, 3.9723317897727273, 3.1364660054347833, 1.6646869852941177, 0.9670124202127659, 0.0, 2.8011046101949026, 3.8680496808510636, 2.497030477941177, 2.090977336956522, 0.9930829474431818, 0.0), # 33 (1.9302433503836318, 3.976330909090909, 3.134718695652174, 1.664446274509804, 0.9666378723404256, 0.0, 2.794732333833084, 3.8665514893617026, 2.496669411764706, 2.0898124637681157, 0.9940827272727273, 0.0), # 34 (1.9344419357416882, 3.9802734374999997, 3.13249660326087, 1.664139093137255, 0.9661602393617023, 0.0, 2.78713533858071, 3.864640957446809, 2.4962086397058827, 2.0883310688405796, 0.9950683593749999, 0.0), # 35 (1.9385496483375964, 3.984158863636364, 3.1298106521739135, 1.6637664705882356, 0.9655812765957449, 0.0, 2.7783509370314845, 3.8623251063829795, 2.4956497058823537, 2.086540434782609, 0.996039715909091, 0.0), # 36 (1.9425685022378518, 3.987986676136364, 3.126671766304348, 1.66332943627451, 0.9649027393617021, 0.0, 2.768416441779111, 3.8596109574468085, 2.494994154411765, 2.0844478442028986, 0.996996669034091, 0.0), # 37 (1.9465005115089515, 3.9917563636363633, 3.1230908695652175, 1.662829019607843, 0.9641263829787234, 0.0, 2.757369165417291, 3.8565055319148938, 2.4942435294117646, 2.082060579710145, 0.9979390909090908, 0.0), # 38 (1.9503476902173915, 3.995467414772727, 3.119078885869565, 1.6622662500000003, 0.9632539627659574, 0.0, 2.7452464205397304, 3.8530158510638297, 2.4933993750000005, 2.079385923913043, 0.9988668536931817, 0.0), # 39 (1.9541120524296678, 3.9991193181818185, 3.114646739130435, 1.661642156862745, 0.9622872340425531, 0.0, 2.73208551974013, 3.8491489361702125, 2.492463235294118, 2.0764311594202898, 0.9997798295454546, 0.0), # 40 (1.9577956122122764, 4.0027115625, 3.10980535326087, 1.660957769607843, 0.9612279521276595, 0.0, 2.717923775612195, 3.844911808510638, 2.4914366544117645, 2.07320356884058, 1.000677890625, 0.0), # 41 (1.9614003836317138, 4.006243636363638, 3.1045656521739136, 1.6602141176470588, 0.9600778723404256, 0.0, 2.702798500749626, 3.8403114893617025, 2.490321176470588, 2.0697104347826087, 1.0015609090909094, 0.0), # 42 (1.9649283807544762, 4.00971502840909, 3.0989385597826087, 1.6594122303921572, 0.9588387500000001, 0.0, 2.6867470077461273, 3.8353550000000003, 2.4891183455882357, 2.0659590398550725, 1.0024287571022725, 0.0), # 43 (1.968381617647059, 4.013125227272727, 3.0929350000000007, 1.6585531372549018, 0.9575123404255319, 0.0, 2.6698066091954025, 3.8300493617021276, 2.487829705882353, 2.061956666666667, 1.0032813068181818, 0.0), # 44 (1.971762108375959, 4.016473721590909, 3.086565896739131, 1.657637867647059, 0.9561003989361703, 0.0, 2.652014617691155, 3.824401595744681, 2.4864568014705886, 2.0577105978260875, 1.0041184303977273, 0.0), # 45 (1.9750718670076732, 4.019760000000001, 3.0798421739130446, 1.6566674509803923, 0.954604680851064, 0.0, 2.633408345827087, 3.818418723404256, 2.4850011764705884, 2.0532281159420296, 1.0049400000000002, 0.0), # 46 (1.978312907608696, 4.022983551136364, 3.0727747554347826, 1.6556429166666666, 0.9530269414893617, 0.0, 2.6140251061969018, 3.812107765957447, 2.483464375, 2.0485165036231883, 1.005745887784091, 0.0), # 47 (1.981487244245525, 4.026143863636364, 3.0653745652173914, 1.654565294117647, 0.9513689361702128, 0.0, 2.5939022113943038, 3.805475744680851, 2.4818479411764707, 2.043583043478261, 1.006535965909091, 0.0), # 48 (1.9845968909846547, 4.029240426136363, 3.0576525271739134, 1.6534356127450982, 0.9496324202127661, 0.0, 2.5730769740129937, 3.7985296808510642, 2.4801534191176473, 2.0384350181159423, 1.0073101065340908, 0.0), # 49 (1.9876438618925836, 4.032272727272726, 3.0496195652173914, 1.6522549019607846, 0.9478191489361703, 0.0, 2.551586706646677, 3.791276595744681, 2.478382352941177, 2.0330797101449276, 1.0080681818181816, 0.0), # 50 (1.990630171035806, 4.035240255681818, 3.04128660326087, 1.6510241911764707, 0.9459308776595745, 0.0, 2.5294687218890557, 3.783723510638298, 2.476536286764706, 2.0275244021739134, 1.0088100639204545, 0.0), # 51 (1.9935578324808187, 4.0381425, 3.0326645652173916, 1.6497445098039216, 0.9439693617021278, 0.0, 2.5067603323338337, 3.775877446808511, 2.4746167647058828, 2.0217763768115944, 1.009535625, 0.0), # 52 (1.996428860294118, 4.0409789488636365, 3.0237643750000003, 1.6484168872549019, 0.9419363563829788, 0.0, 2.483498850574713, 3.767745425531915, 2.472625330882353, 2.0158429166666667, 1.0102447372159091, 0.0), # 53 (1.9992452685422, 4.043749090909091, 3.014596956521739, 1.6470423529411766, 0.9398336170212767, 0.0, 2.4597215892053974, 3.7593344680851066, 2.470563529411765, 2.009731304347826, 1.0109372727272727, 0.0), # 54 (2.0020090712915604, 4.046452414772727, 3.005173233695652, 1.6456219362745101, 0.9376628989361703, 0.0, 2.4354658608195905, 3.750651595744681, 2.4684329044117654, 2.003448822463768, 1.0116131036931817, 0.0), # 55 (2.0047222826086957, 4.049088409090909, 2.995504130434783, 1.6441566666666665, 0.9354259574468086, 0.0, 2.410768978010995, 3.7417038297872343, 2.4662349999999997, 1.9970027536231885, 1.0122721022727272, 0.0), # 56 (2.007386916560103, 4.051656562500001, 2.9856005706521738, 1.6426475735294117, 0.9331245478723404, 0.0, 2.3856682533733133, 3.732498191489362, 2.4639713602941176, 1.9904003804347825, 1.0129141406250002, 0.0), # 57 (2.0100049872122767, 4.054156363636363, 2.9754734782608696, 1.64109568627451, 0.930760425531915, 0.0, 2.3602009995002504, 3.72304170212766, 2.461643529411765, 1.9836489855072463, 1.0135390909090907, 0.0), # 58 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59 ) passenger_allighting_rate = ( (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 0 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 1 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 2 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 3 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 4 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 5 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 6 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 7 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 8 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 9 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 10 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 11 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 12 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 13 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 14 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 15 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 16 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 17 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 18 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 19 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 20 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 21 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 22 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 23 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 24 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 25 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 26 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 27 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 28 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 29 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 30 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 31 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 32 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 33 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 34 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 35 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 36 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 37 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 38 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 39 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 40 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 41 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 42 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 43 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 44 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 45 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 46 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 47 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 48 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 49 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 50 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 51 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 52 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 53 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 54 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 55 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 56 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 57 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 58 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 59 ) """ parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html """ #initial entropy entropy = 258194110137029475889902652135037600173 #index for seed sequence child child_seed_index = ( 1, # 0 89, # 1 )

113.014925

218

0.728896

5,147

37,860

5.359433

0.202059

0.313214

0.247961

0.469821

0.336088

0.330143

0.329019

0.328004

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0.818869

0.119229

37,860

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1

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1

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null

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6

6c23ec2583140bf984576a6351f3963680033c1b

36

py

Python

pymedphys/_mudensity/delivery/__init__.py

pymedphys/pymedphys-archive-2019

6bb7c8d0da2e93ff56469bb47e65b15ece2ea25e

[ "Apache-2.0" ]

1

2020-12-20T14:13:56.000Z

pymedphys/_mudensity/delivery/__init__.py

pymedphys/pymedphys-archive-2019

6bb7c8d0da2e93ff56469bb47e65b15ece2ea25e

[ "Apache-2.0" ]

6

2020-10-06T15:36:46.000Z

2022-02-27T05:15:17.000Z

pymedphys/_mudensity/delivery/__init__.py

cpbhatt/pymedphys

177b3db8e2a6e83c44835d0007d1d5c7a420fd99

[ "Apache-2.0" ]

1

2020-12-20T14:14:00.000Z

from .core import DeliveryMuDensity

18

35

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36

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0

1

0

1

0

1

0

6

6c627c6b39fa381c23371035b3fed6453ffb65d9

177

py

Python

mcore/enum.py

eruvanos/mcore

7d890d19dbbb382b0ca28d863555832002f38382

[ "MIT" ]

null

mcore/enum.py

eruvanos/mcore

7d890d19dbbb382b0ca28d863555832002f38382

[ "MIT" ]

null

mcore/enum.py

eruvanos/mcore

7d890d19dbbb382b0ca28d863555832002f38382

[ "MIT" ]

null

from enum import Enum class AutoNameEnum(Enum): def _generate_next_value_(self, start, count, last): return self def __repr__(self): return self.value

19.666667

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0.683616

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177

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19.666667

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null

0

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null

0

1

0

1

0

6

6c7b6a1a077e42e46c23c3b1d7a1c7e89812b8ec

5,936

py

Python

filtering_class3/test_assignment3.py

eriksonJAguiar/imageproc_SCC5830_icmc

f9419cee35dbcf811e059d98788ce66097ee43c4

[ "MIT" ]

null

filtering_class3/test_assignment3.py

eriksonJAguiar/imageproc_SCC5830_icmc

f9419cee35dbcf811e059d98788ce66097ee43c4

[ "MIT" ]

null

filtering_class3/test_assignment3.py

eriksonJAguiar/imageproc_SCC5830_icmc

f9419cee35dbcf811e059d98788ce66097ee43c4

[ "MIT" ]

null

import unittest from assignment3 import * from imageio import imread from os import path from matplotlib import pyplot as plt import numpy as np def read_in_out(): in_ = list() out_ = list() path = './CasosDeTeste/' for f in listdir(path): if f.endswith('.in'): i = open(path+f).read().splitlines() in_.append(i) elif f.endswith('.out'): o = open(path+f).read().splitlines() out_.append(o[0]) return (in_, out_) class TestAssignment(unittest.TestCase): def test_filter_1d(self): imgref = imageio.imread('arara.png', as_gray=True) w = np.array([-2, -1, 0, 1, 2]) img_hat = filter_1d(imgref, w) plt.figure(figsize=(12, 12)) plt.subplot(121) plt.imshow(imgref, cmap="gray", vmin=0, vmax=255) plt.title('Original') plt.axis('off') plt.colorbar() plt.subplot(122) plt.imshow(img_hat, cmap="gray", vmin=0, vmax=255) plt.title('Filtered') plt.axis('off') plt.colorbar() plt.show() print(root_mean_square_error(imgref, img_hat)) def test_filter_2d(self): imgref = imageio.imread('image02_quant.png') w = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) img_hat = filter_2d(imgref, w) plt.figure(figsize=(12, 12)) plt.subplot(121) plt.imshow(imgref, cmap="gray", vmin=0, vmax=255) plt.title('Original') plt.axis('off') plt.colorbar() plt.subplot(122) plt.imshow(img_hat, cmap="gray", vmin=0, vmax=255) plt.title('Filtered') plt.axis('off') plt.colorbar() plt.show() print(root_mean_square_error(imgref, img_hat)) def test_filter_median(self): imgref = imageio.imread('image02_salted.png').astype(np.uint8) img_hat = median_filter(imgref, 5) #img_hat = ndimage.median_filter(imgref, 5) plt.figure(figsize=(12, 12)) plt.subplot(121) plt.imshow(imgref, cmap="gray", vmin=0, vmax=255) plt.colorbar() plt.title('Original') plt.axis('off') plt.subplot(122) plt.imshow(img_hat, cmap="gray", vmin=0, vmax=255) plt.title('Filtered') plt.colorbar() plt.axis('off') plt.show() print(root_mean_square_error(imgref, img_hat)) def test_integrated_case1(self): in_, out_ = read_in_out() i,o = in_[0], out_[0] method = int(i[1]) imgref = imageio.imread(i[0], as_gray=True) w = np.array(i[3].split(' ')).astype(int) rmse = select_method(imgref, method, w) print('rmse: %f; real: %f' % (rmse, float(o))) self.assertTrue(rmse >= (float(o)-5) and rmse <= (float(o)+5)) def test_integrated_case2(self): in_, out_ = read_in_out() i,o = in_[1], out_[1] method = int(i[1]) imgref = imageio.imread(i[0], as_gray=True) w = int(i[2]) rmse = select_method(imgref, method, w) print('rmse: %f; real: %f' % (rmse, float(o))) self.assertTrue(rmse >= (float(o)-5) and rmse <= (float(o)+5)) def test_integrated_case3(self): in_, out_ = read_in_out() i,o = in_[2], out_[2] method = int(i[1]) imgref = imageio.imread(i[0], as_gray=True) w_aux = [] for row in range(3,len(i)): w_aux.append(i[row].split(' ')) w = np.array(w_aux).astype(int) rmse = select_method(imgref, method, w) print('rmse: %f; real: %f' % (rmse, float(o))) self.assertTrue(rmse >= (float(o)-5) and rmse <= (float(o)+5)) def test_integrated_case4(self): in_, out_ = read_in_out() i,o = in_[3], out_[3] method = int(i[1]) imgref = imageio.imread(i[0], as_gray=True) w = np.array(i[3].split(' ')).astype(int) rmse = select_method(imgref, method, w) print('rmse: %f; real: %f' % (rmse, float(o))) self.assertTrue(rmse >= (float(o)-5) and rmse <= (float(o)+5)) def test_integrated_case5(self): in_, out_ = read_in_out() i,o = in_[4], out_[4] method = int(i[1]) imgref = imageio.imread(i[0], as_gray=True) w = int(i[2]) rmse = select_method(imgref, method, w) print('rmse: %f; real: %f' % (rmse, float(o))) self.assertTrue(rmse >= (float(o)-5) and rmse <= (float(o)+5)) def test_integrated_case6(self): in_, out_ = read_in_out() i,o = in_[5], out_[5] method = int(i[1]) imgref = imageio.imread(i[0], as_gray=True) w_aux = [] for row in range(3,len(i)): w_aux.append(i[row].split(' ')) w = np.array(w_aux).astype(int) rmse = select_method(imgref, method, w) print('rmse: %f; real: %f' % (rmse, float(o))) self.assertTrue(rmse >= (float(o)-5) and rmse <= (float(o)+5)) def test_integrated_case7(self): in_, out_ = read_in_out() i,o = in_[6], out_[6] method = int(i[1]) imgref = imageio.imread(i[0], as_gray=True) w_aux = [] for row in range(3,len(i)): w_aux.append(i[row].split(' ')) w = np.array(w_aux).astype(int) rmse = select_method(imgref, method, w) print('rmse: %f; real: %f' % (rmse, float(o))) self.assertTrue(rmse >= (float(o)-5) and rmse <= (float(o)+5)) def test_integrated_case8(self): in_, out_ = read_in_out() i,o = in_[7], out_[7] method = int(i[1]) imgref = imageio.imread(i[0], as_gray=True) w = int(i[2]) rmse = select_method(imgref, method, w) print('rmse: %f; real: %f' % (rmse, float(o))) self.assertTrue(rmse >= (float(o)-5) and rmse <= (float(o)+5)) if __name__ == '__main__': unittest.main()

32.977778

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5,936

3.634752

0.131206

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0.078049

0.057236

0.803577

0.769106

0.763252

0.754797

0.697561

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0.034385

0.289589

5,936

179

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33.162011

0.694807

0.007075

0

0.675325

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0.053114

0

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0.077922

false

0

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0

null

0

1

0

null

0

6

66b0819fd8c0a4fa082e481813c8b2de94087cd8

3,631

py

Python

test/layer/test_conv2d.py

ControlNet/tensorneko

70dfb2f6395e1703dbdf5d5adcfed7b1334efb8f

[ "MIT" ]

9

2021-05-23T17:38:09.000Z

2021-12-30T19:12:12.000Z

test/layer/test_conv2d.py

ControlNet/tensorneko

70dfb2f6395e1703dbdf5d5adcfed7b1334efb8f

[ "MIT" ]

null

test/layer/test_conv2d.py

ControlNet/tensorneko

70dfb2f6395e1703dbdf5d5adcfed7b1334efb8f

[ "MIT" ]

null

import unittest from torch import Tensor from tensorneko.layer import Conv2d from fn import F import torch.nn as nn import torch class TestConv2d(unittest.TestCase): """The test class for :class:`tensorneko.layer.Conv2d`.""" @property def b(self): return 8 @property def h(self): return 32 @property def w(self): return 32 @property def c(self): return self.in_features @property def in_features(self): return 3 @property def out_features(self): return 64 @property def kernel_size(self): return 3, 3 @property def stride(self): return 1, 1 @property def padding(self): return 1, 1 @property def activation_factory(self): return nn.ReLU @property def normalization_factory(self): return F(nn.BatchNorm2d, self.out_features) def test_single_layer(self): """The a single layer without batch normalization and activation""" # Create a batch of size 8 x = torch.rand(self.b, self.c, self.h, self.w) # Create a single layer neko_layer = Conv2d(self.in_features, self.out_features, self.kernel_size, self.stride, self.padding) torch_layer = nn.Sequential(neko_layer.conv) # Forward prop neko_result: Tensor = neko_layer(x) pytorch_result: Tensor = torch_layer(x) # Check the output self.assertTrue((neko_result == pytorch_result).all()) def test_single_layer_with_activation(self): """The a single layer with activation""" # Create a batch of size 8 x = torch.rand(self.b, self.c, self.h, self.w) # Create a single layer neko_layer = Conv2d(self.in_features, self.out_features, self.kernel_size, self.stride, self.padding, build_activation=self.activation_factory) torch_layer = nn.Sequential(neko_layer.conv, neko_layer.activation) # Forward prop neko_result: Tensor = neko_layer(x) pytorch_result: Tensor = torch_layer(x) # Check the output self.assertTrue((neko_result == pytorch_result).all()) def test_single_layer_with_normalization(self): """The a single layer with batch normalization""" # Create a batch of size 8 x = torch.rand(self.b, self.c, self.h, self.w) # Create a single layer neko_layer = Conv2d(self.in_features, self.out_features, self.kernel_size, self.stride, self.padding, build_normalization=self.normalization_factory) torch_layer = nn.Sequential(neko_layer.conv, neko_layer.normalization) # Forward prop neko_result: Tensor = neko_layer(x) pytorch_result: Tensor = torch_layer(x) # Check the output self.assertTrue((neko_result == pytorch_result).all()) def test_single_layer_with_normalization_and_activation(self): """The a single layer with batch normalization and activation""" # Create a batch of size 8 x = torch.rand(self.b, self.c, self.h, self.w) # Create a single layer neko_layer = Conv2d(self.in_features, self.out_features, self.kernel_size, self.stride, self.padding, build_activation=self.activation_factory, build_normalization=self.normalization_factory) torch_layer = nn.Sequential(neko_layer.conv, neko_layer.normalization, neko_layer.activation) # Forward prop neko_result: Tensor = neko_layer(x) pytorch_result: Tensor = torch_layer(x) # Check the output self.assertTrue((neko_result == pytorch_result).all())

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66361d397d0611ed5f883e4ab9658e85af52ccc4

233

py

Python

feeds/managers/base.py

kbase/feeds

a2ed4cb88120aeb10a295919cb0fba85e13d462d

[ "MIT" ]

null

feeds/managers/base.py

kbase/feeds

a2ed4cb88120aeb10a295919cb0fba85e13d462d

[ "MIT" ]

48

2018-10-15T23:36:50.000Z

2022-01-19T02:49:30.000Z

feeds/managers/base.py

kbase/feeds

a2ed4cb88120aeb10a295919cb0fba85e13d462d

[ "MIT" ]

3

2018-10-03T20:37:41.000Z

2019-01-16T15:03:19.000Z

class BaseManager(object): def __init__(self): pass def get_target_users(self, activity): """ TODO: Abstract some basic functionality here for the generic activity type. """ return []

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py

Python

statsmodels/tsa/statespace/tests/test_prediction.py

CCHiggins/statsmodels

300b6fba90c65c8e94b4f83e04f7ae1b0ceeac2e

[ "BSD-3-Clause" ]

1

2022-01-26T19:37:11.000Z

statsmodels/tsa/statespace/tests/test_prediction.py

CCHiggins/statsmodels

300b6fba90c65c8e94b4f83e04f7ae1b0ceeac2e

[ "BSD-3-Clause" ]

1

2022-01-20T05:49:29.000Z

2022-01-25T00:01:31.000Z

statsmodels/tsa/statespace/tests/test_prediction.py

CCHiggins/statsmodels

300b6fba90c65c8e94b4f83e04f7ae1b0ceeac2e

[ "BSD-3-Clause" ]

3

2017-01-23T02:31:56.000Z

2020-11-18T04:14:44.000Z

""" Tests for prediction of state space models Author: Chad Fulton License: Simplified-BSD """ import pytest import numpy as np import pandas as pd from numpy.testing import assert_equal, assert_raises, assert_allclose, assert_ from statsmodels import datasets from statsmodels.tsa.statespace import sarimax, varmax from statsmodels.tsa.statespace.tests.test_impulse_responses import TVSS dta = datasets.macrodata.load_pandas().data dta.index = pd.period_range(start='1959Q1', end='2009Q3', freq='Q') def test_predict_dates(): index = pd.date_range(start='1950-01-01', periods=11, freq='D') np.random.seed(324328) endog = pd.Series(np.random.normal(size=10), index=index[:-1]) # Basic test mod = sarimax.SARIMAX(endog, order=(1, 0, 0)) res = mod.filter(mod.start_params) # In-sample prediction should have the same index pred = res.predict() assert_equal(len(pred), mod.nobs) assert_equal(pred.index.values, index[:-1].values) # Out-of-sample forecasting should extend the index appropriately fcast = res.forecast() assert_equal(fcast.index[0], index[-1]) # Simple differencing in the SARIMAX model should eliminate dates of # series eliminated due to differencing mod = sarimax.SARIMAX(endog, order=(1, 1, 0), simple_differencing=True) res = mod.filter(mod.start_params) pred = res.predict() # In-sample prediction should lose the first index value assert_equal(mod.nobs, endog.shape[0] - 1) assert_equal(len(pred), mod.nobs) assert_equal(pred.index.values, index[1:-1].values) # Out-of-sample forecasting should still extend the index appropriately fcast = res.forecast() assert_equal(fcast.index[0], index[-1]) # Simple differencing again, this time with a more complex differencing # structure mod = sarimax.SARIMAX(endog, order=(1, 2, 0), seasonal_order=(0, 1, 0, 4), simple_differencing=True) res = mod.filter(mod.start_params) pred = res.predict() # In-sample prediction should lose the first 6 index values assert_equal(mod.nobs, endog.shape[0] - (4 + 2)) assert_equal(len(pred), mod.nobs) assert_equal(pred.index.values, index[4 + 2:-1].values) # Out-of-sample forecasting should still extend the index appropriately fcast = res.forecast() assert_equal(fcast.index[0], index[-1]) def test_memory_no_predicted(): # Tests for forecasts with memory_no_predicted is set endog = [0.5, 1.2, 0.4, 0.6] mod = sarimax.SARIMAX(endog, order=(1, 0, 0)) res1 = mod.filter([0.5, 1.]) mod.ssm.memory_no_predicted = True res2 = mod.filter([0.5, 1.]) # Make sure we really didn't store all of the values in res2 assert_equal(res1.predicted_state.shape, (1, 5)) assert_(res2.predicted_state is None) assert_equal(res1.predicted_state_cov.shape, (1, 1, 5)) assert_(res2.predicted_state_cov is None) # Check that we can't do dynamic in-sample prediction assert_raises(ValueError, res2.predict, dynamic=True) assert_raises(ValueError, res2.get_prediction, dynamic=True) # Make sure the point forecasts are the same assert_allclose(res1.forecast(10), res2.forecast(10)) # Make sure the confidence intervals are the same fcast1 = res1.get_forecast(10) fcast2 = res1.get_forecast(10) assert_allclose(fcast1.summary_frame(), fcast2.summary_frame()) @pytest.mark.parametrize('use_exog', [True, False]) @pytest.mark.parametrize('trend', ['n', 'c', 't']) def test_concatenated_predict_sarimax(use_exog, trend): endog = np.arange(100).reshape(100, 1) * 1.0 exog = np.ones(100) if use_exog else None if use_exog: exog[10:30] = 2. trend_params = [0.1] ar_params = [0.5] exog_params = [1.2] var_params = [1.] params = [] if trend in ['c', 't']: params += trend_params params += ar_params if use_exog: params += exog_params params += var_params y1 = endog.copy() y1[-50:] = np.nan mod1 = sarimax.SARIMAX(y1, order=(1, 1, 0), trend=trend, exog=exog) res1 = mod1.smooth(params) p1 = res1.get_prediction() pr1 = p1.prediction_results x2 = exog[:50] if use_exog else None mod2 = sarimax.SARIMAX(endog[:50], order=(1, 1, 0), trend=trend, exog=x2) res2 = mod2.smooth(params) x2f = exog[50:] if use_exog else None p2 = res2.get_prediction(start=0, end=99, exog=x2f) pr2 = p2.prediction_results attrs = ( pr1.representation_attributes + pr1.filter_attributes + pr1.smoother_attributes) for key in attrs: assert_allclose(getattr(pr2, key), getattr(pr1, key)) @pytest.mark.parametrize('use_exog', [True, False]) @pytest.mark.parametrize('trend', ['n', 'c', 't']) def test_concatenated_predict_varmax(use_exog, trend): endog = np.arange(200).reshape(100, 2) * 1.0 exog = np.ones(100) if use_exog else None trend_params = [0.1, 0.2] var_params = [0.5, -0.1, 0.0, 0.2] exog_params = [1., 2.] cov_params = [1., 0., 1.] params = [] if trend in ['c', 't']: params += trend_params params += var_params if use_exog: params += exog_params params += cov_params y1 = endog.copy() y1[-50:] = np.nan mod1 = varmax.VARMAX(y1, order=(1, 0), trend=trend, exog=exog) res1 = mod1.smooth(params) p1 = res1.get_prediction() pr1 = p1.prediction_results x2 = exog[:50] if use_exog else None mod2 = varmax.VARMAX(endog[:50], order=(1, 0), trend=trend, exog=x2) res2 = mod2.smooth(params) x2f = exog[50:] if use_exog else None p2 = res2.get_prediction(start=0, end=99, exog=x2f) pr2 = p2.prediction_results attrs = ( pr1.representation_attributes + pr1.filter_attributes + pr1.smoother_attributes) for key in attrs: assert_allclose(getattr(pr2, key), getattr(pr1, key)) @pytest.mark.parametrize('use_exog', [True, False]) @pytest.mark.parametrize('trend', ['n', 'c', 't']) def test_predicted_filtered_smoothed_with_nans(use_exog, trend): # In this test, we construct a model with only NaN values for `endog`, so # that predicted, filtered, and smoothed forecasts should all be the # same endog = np.zeros(200).reshape(100, 2) * np.nan exog = np.ones(100) if use_exog else None trend_params = [0.1, 0.2] var_params = [0.5, -0.1, 0.0, 0.2] exog_params = [1., 2.] cov_params = [1., 0., 1.] params = [] if trend in ['c', 't']: params += trend_params params += var_params if use_exog: params += exog_params params += cov_params x_fit = exog[:50] if use_exog else None mod = varmax.VARMAX(endog[:50], order=(1, 0), trend=trend, exog=x_fit) res = mod.smooth(params) x_fcast = exog[50:61] if use_exog else None p_pred = res.get_prediction( start=0, end=60, information_set='predicted', exog=x_fcast) f_pred = res.get_prediction( start=0, end=60, information_set='filtered', exog=x_fcast) s_pred = res.get_prediction( start=0, end=60, information_set='smoothed', exog=x_fcast) # Test forecasts assert_allclose(s_pred.predicted_mean, p_pred.predicted_mean) assert_allclose(s_pred.var_pred_mean, p_pred.var_pred_mean) assert_allclose(f_pred.predicted_mean, p_pred.predicted_mean) assert_allclose(f_pred.var_pred_mean, p_pred.var_pred_mean) assert_allclose(p_pred.predicted_mean[:50], res.fittedvalues) assert_allclose(p_pred.var_pred_mean[:50].T, res.forecasts_error_cov) p_signal = res.get_prediction( start=0, end=60, information_set='predicted', signal_only=True, exog=x_fcast) f_signal = res.get_prediction( start=0, end=60, information_set='filtered', signal_only=True, exog=x_fcast) s_signal = res.get_prediction( start=0, end=60, information_set='smoothed', signal_only=True, exog=x_fcast) # Test signal predictions assert_allclose(s_signal.predicted_mean, p_signal.predicted_mean) assert_allclose(s_signal.var_pred_mean, p_signal.var_pred_mean) assert_allclose(f_signal.predicted_mean, p_signal.predicted_mean) assert_allclose(f_signal.var_pred_mean, p_signal.var_pred_mean) if use_exog is False and trend == 'n': assert_allclose(p_signal.predicted_mean[:50], res.fittedvalues) assert_allclose(p_signal.var_pred_mean[:50].T, res.forecasts_error_cov) else: assert_allclose(p_signal.predicted_mean[:50] + mod['obs_intercept'], res.fittedvalues) assert_allclose((p_signal.var_pred_mean[:50] + mod['obs_cov']).T, res.forecasts_error_cov) def test_predicted_filtered_smoothed_with_nans_TVSS(reset_randomstate): mod = TVSS(np.zeros((50, 2)) * np.nan) mod.ssm.initialize_known([1.2, 0.8], np.eye(2)) res = mod.smooth([]) mod_oos = TVSS(np.zeros((11, 2)) * np.nan) kwargs = {key: mod_oos[key] for key in [ 'obs_intercept', 'design', 'obs_cov', 'transition', 'selection', 'state_cov']} p_pred = res.get_prediction( start=0, end=60, information_set='predicted', **kwargs) f_pred = res.get_prediction( start=0, end=60, information_set='filtered', **kwargs) s_pred = res.get_prediction( start=0, end=60, information_set='smoothed', **kwargs) # Test forecasts assert_allclose(s_pred.predicted_mean, p_pred.predicted_mean) assert_allclose(s_pred.var_pred_mean, p_pred.var_pred_mean) assert_allclose(f_pred.predicted_mean, p_pred.predicted_mean) assert_allclose(f_pred.var_pred_mean, p_pred.var_pred_mean) assert_allclose(p_pred.predicted_mean[:50], res.fittedvalues) assert_allclose(p_pred.var_pred_mean[:50].T, res.forecasts_error_cov) p_signal = res.get_prediction( start=0, end=60, information_set='predicted', signal_only=True, **kwargs) f_signal = res.get_prediction( start=0, end=60, information_set='filtered', signal_only=True, **kwargs) s_signal = res.get_prediction( start=0, end=60, information_set='smoothed', signal_only=True, **kwargs) # Test signal predictions assert_allclose(s_signal.predicted_mean, p_signal.predicted_mean) assert_allclose(s_signal.var_pred_mean, p_signal.var_pred_mean) assert_allclose(f_signal.predicted_mean, p_signal.predicted_mean) assert_allclose(f_signal.var_pred_mean, p_signal.var_pred_mean) assert_allclose(p_signal.predicted_mean[:50] + mod['obs_intercept'].T, res.fittedvalues) assert_allclose((p_signal.var_pred_mean[:50] + mod['obs_cov'].T).T, res.forecasts_error_cov) @pytest.mark.parametrize('use_exog', [True, False]) @pytest.mark.parametrize('trend', ['n', 'c', 't']) def test_predicted_filtered_smoothed_varmax(use_exog, trend): endog = np.log(dta[['realgdp', 'cpi']]) if trend in ['n', 'c']: endog = endog.diff().iloc[1:] * 100 if trend == 'n': endog -= endog.mean() exog = np.ones(100) if use_exog else None if use_exog: exog[20:40] = 2. trend_params = [0.1, 0.2] var_params = [0.5, -0.1, 0.0, 0.2] exog_params = [1., 2.] cov_params = [1., 0., 1.] params = [] if trend in ['c', 't']: params += trend_params params += var_params if use_exog: params += exog_params params += cov_params x_fit = exog[:50] if use_exog else None mod = varmax.VARMAX(endog[:50], order=(1, 0), trend=trend, exog=x_fit) # Add in an obs_intercept and obs_cov to make the test more comprehensive mod['obs_intercept'] = [5, -2.] mod['obs_cov'] = np.array([[1.2, 0.3], [0.3, 3.4]]) res = mod.smooth(params) x_fcast = exog[50:61] if use_exog else None p_pred = res.get_prediction( start=0, end=60, information_set='predicted', exog=x_fcast) f_pred = res.get_prediction( start=0, end=60, information_set='filtered', exog=x_fcast) s_pred = res.get_prediction( start=0, end=60, information_set='smoothed', exog=x_fcast) # Test forecasts fcast = res.get_forecast(11, exog=x_fcast) d = mod['obs_intercept'][:, None] Z = mod['design'] H = mod['obs_cov'][:, :, None] desired_s_signal = Z @ res.smoothed_state desired_f_signal = Z @ res.filtered_state desired_p_signal = Z @ res.predicted_state[..., :-1] assert_allclose(s_pred.predicted_mean[:50], (d + desired_s_signal).T) assert_allclose(s_pred.predicted_mean[50:], fcast.predicted_mean) assert_allclose(f_pred.predicted_mean[:50], (d + desired_f_signal).T) assert_allclose(f_pred.predicted_mean[50:], fcast.predicted_mean) assert_allclose(p_pred.predicted_mean[:50], (d + desired_p_signal).T) assert_allclose(p_pred.predicted_mean[50:], fcast.predicted_mean) desired_s_signal_cov = ( Z[None, :, :] @ res.smoothed_state_cov.T @ Z.T[None, :, :]) desired_f_signal_cov = ( Z[None, :, :] @ res.filtered_state_cov.T @ Z.T[None, :, :]) desired_p_signal_cov = ( Z[None, :, :] @ res.predicted_state_cov[..., :-1].T @ Z.T[None, :, :]) assert_allclose(s_pred.var_pred_mean[:50], (desired_s_signal_cov.T + H).T) assert_allclose(s_pred.var_pred_mean[50:], fcast.var_pred_mean) assert_allclose(f_pred.var_pred_mean[:50], (desired_f_signal_cov.T + H).T) assert_allclose(f_pred.var_pred_mean[50:], fcast.var_pred_mean) assert_allclose(p_pred.var_pred_mean[:50], (desired_p_signal_cov.T + H).T) assert_allclose(p_pred.var_pred_mean[50:], fcast.var_pred_mean) p_signal = res.get_prediction( start=0, end=60, information_set='predicted', signal_only=True, exog=x_fcast) f_signal = res.get_prediction( start=0, end=60, information_set='filtered', signal_only=True, exog=x_fcast) s_signal = res.get_prediction( start=0, end=60, information_set='smoothed', signal_only=True, exog=x_fcast) # Test signal predictions fcast_signal = fcast.predicted_mean - d.T fcast_signal_cov = (fcast.var_pred_mean.T - H).T assert_allclose(s_signal.predicted_mean[:50], desired_s_signal.T) assert_allclose(s_signal.predicted_mean[50:], fcast_signal) assert_allclose(f_signal.predicted_mean[:50], desired_f_signal.T) assert_allclose(f_signal.predicted_mean[50:], fcast_signal) assert_allclose(p_signal.predicted_mean[:50], desired_p_signal.T) assert_allclose(p_signal.predicted_mean[50:], fcast_signal) assert_allclose(s_signal.var_pred_mean[:50], desired_s_signal_cov) assert_allclose(s_signal.var_pred_mean[50:], fcast_signal_cov) assert_allclose(f_signal.var_pred_mean[:50], desired_f_signal_cov) assert_allclose(f_signal.var_pred_mean[50:], fcast_signal_cov) assert_allclose(p_signal.var_pred_mean[:50], desired_p_signal_cov) assert_allclose(p_signal.var_pred_mean[50:], fcast_signal_cov) def test_predicted_filtered_smoothed_TVSS(reset_randomstate): mod = TVSS(np.zeros((50, 2))) mod.ssm.initialize_known([1.2, 0.8], np.eye(2)) res = mod.smooth([]) mod_oos = TVSS(np.zeros((11, 2)) * np.nan) kwargs = {key: mod_oos[key] for key in [ 'obs_intercept', 'design', 'obs_cov', 'transition', 'selection', 'state_cov']} p_pred = res.get_prediction( start=0, end=60, information_set='predicted', **kwargs) f_pred = res.get_prediction( start=0, end=60, information_set='filtered', **kwargs) s_pred = res.get_prediction( start=0, end=60, information_set='smoothed', **kwargs) p_signal = res.get_prediction( start=0, end=60, information_set='predicted', signal_only=True, **kwargs) f_signal = res.get_prediction( start=0, end=60, information_set='filtered', signal_only=True, **kwargs) s_signal = res.get_prediction( start=0, end=60, information_set='smoothed', signal_only=True, **kwargs) # Test forecasts and signals d = mod['obs_intercept'].transpose(1, 0)[:, :, None] Z = mod['design'].transpose(2, 0, 1) H = mod['obs_cov'].transpose(2, 0, 1) fcast = res.get_forecast(11, **kwargs) fcast_signal = fcast.predicted_mean - mod_oos['obs_intercept'].T fcast_signal_cov = fcast.var_pred_mean - mod_oos['obs_cov'].T desired_s_signal = Z @ res.smoothed_state.T[:, :, None] desired_f_signal = Z @ res.filtered_state.T[:, :, None] desired_p_signal = Z @ res.predicted_state.T[:-1, :, None] assert_allclose(s_pred.predicted_mean[:50], (d + desired_s_signal)[..., 0]) assert_allclose(s_pred.predicted_mean[50:], fcast.predicted_mean) assert_allclose(f_pred.predicted_mean[:50], (d + desired_f_signal)[..., 0]) assert_allclose(f_pred.predicted_mean[50:], fcast.predicted_mean) assert_allclose(p_pred.predicted_mean[:50], (d + desired_p_signal)[..., 0]) assert_allclose(p_pred.predicted_mean[50:], fcast.predicted_mean) assert_allclose(s_signal.predicted_mean[:50], desired_s_signal[..., 0]) assert_allclose(s_signal.predicted_mean[50:], fcast_signal) assert_allclose(f_signal.predicted_mean[:50], desired_f_signal[..., 0]) assert_allclose(f_signal.predicted_mean[50:], fcast_signal) assert_allclose(p_signal.predicted_mean[:50], desired_p_signal[..., 0]) assert_allclose(p_signal.predicted_mean[50:], fcast_signal) for t in range(mod.nobs): assert_allclose(s_pred.var_pred_mean[t], Z[t] @ res.smoothed_state_cov[..., t] @ Z[t].T + H[t]) assert_allclose(f_pred.var_pred_mean[t], Z[t] @ res.filtered_state_cov[..., t] @ Z[t].T + H[t]) assert_allclose(p_pred.var_pred_mean[t], Z[t] @ res.predicted_state_cov[..., t] @ Z[t].T + H[t]) assert_allclose(s_signal.var_pred_mean[t], Z[t] @ res.smoothed_state_cov[..., t] @ Z[t].T) assert_allclose(f_signal.var_pred_mean[t], Z[t] @ res.filtered_state_cov[..., t] @ Z[t].T) assert_allclose(p_signal.var_pred_mean[t], Z[t] @ res.predicted_state_cov[..., t] @ Z[t].T) assert_allclose(s_pred.var_pred_mean[50:], fcast.var_pred_mean) assert_allclose(f_pred.var_pred_mean[50:], fcast.var_pred_mean) assert_allclose(p_pred.var_pred_mean[50:], fcast.var_pred_mean) assert_allclose(s_signal.var_pred_mean[50:], fcast_signal_cov) assert_allclose(f_signal.var_pred_mean[50:], fcast_signal_cov) assert_allclose(p_signal.var_pred_mean[50:], fcast_signal_cov) @pytest.mark.parametrize('use_exog', [False, True]) @pytest.mark.parametrize('trend', ['n', 'c', 't']) def test_predicted_filtered_dynamic_varmax(use_exog, trend): endog = np.log(dta[['realgdp', 'cpi']]) if trend in ['n', 'c']: endog = endog.diff().iloc[1:] * 100 if trend == 'n': endog -= endog.mean() exog = np.ones(100) if use_exog else None if use_exog: exog[20:40] = 2. trend_params = [0.1, 0.2] var_params = [0.5, -0.1, 0.0, 0.2] exog_params = [1., 2.] cov_params = [1., 0., 1.] params = [] if trend in ['c', 't']: params += trend_params params += var_params if use_exog: params += exog_params params += cov_params # Compute basic model with 50 observations x_fit1 = exog[:50] if use_exog else None x_fcast1 = exog[50:61] if use_exog else None mod1 = varmax.VARMAX(endog[:50], order=(1, 0), trend=trend, exog=x_fit1) res1 = mod1.filter(params) # Compute basic model with only 20 observations x_fit2 = exog[:20] if use_exog else None x_fcast2 = exog[20:61] if use_exog else None mod2 = varmax.VARMAX(endog[:20], order=(1, 0), trend=trend, exog=x_fit2) res2 = mod2.filter(params) # Test predictions p1 = res1.get_prediction(start=0, dynamic=20, end=60, exog=x_fcast1) p2 = res2.get_prediction(start=0, end=60, exog=x_fcast2) assert_allclose(p1.predicted_mean, p2.predicted_mean) assert_allclose(p1.var_pred_mean, p2.var_pred_mean) p1 = res1.get_prediction(start=2, dynamic=18, end=60, exog=x_fcast1) p2 = res2.get_prediction(start=2, end=60, exog=x_fcast2) assert_allclose(p1.predicted_mean, p2.predicted_mean) assert_allclose(p1.var_pred_mean, p2.var_pred_mean) p1 = res1.get_prediction(start=20, dynamic=True, end=60, exog=x_fcast1) p2 = res2.get_prediction(start=20, end=60, exog=x_fcast2) assert_allclose(p1.predicted_mean, p2.predicted_mean) assert_allclose(p1.var_pred_mean, p2.var_pred_mean) # Test predictions, filtered p1 = res1.get_prediction(start=0, dynamic=20, end=60, exog=x_fcast1, information_set='filtered') p2 = res2.get_prediction(start=0, end=60, exog=x_fcast2, information_set='filtered') assert_allclose(p1.predicted_mean, p2.predicted_mean) assert_allclose(p1.var_pred_mean, p2.var_pred_mean) p1 = res1.get_prediction(start=2, dynamic=18, end=60, exog=x_fcast1, information_set='filtered') p2 = res2.get_prediction(start=2, end=60, exog=x_fcast2, information_set='filtered') assert_allclose(p1.predicted_mean, p2.predicted_mean) assert_allclose(p1.var_pred_mean, p2.var_pred_mean) p1 = res1.get_prediction(start=20, dynamic=True, end=60, exog=x_fcast1, information_set='filtered') p2 = res2.get_prediction(start=20, end=60, exog=x_fcast2, information_set='filtered') assert_allclose(p1.predicted_mean, p2.predicted_mean) assert_allclose(p1.var_pred_mean, p2.var_pred_mean) # Test signals p1 = res1.get_prediction(start=0, dynamic=20, end=60, exog=x_fcast1, signal_only=True) p2 = res2.get_prediction(start=0, end=60, exog=x_fcast2, signal_only=True) assert_allclose(p1.predicted_mean, p2.predicted_mean) assert_allclose(p1.var_pred_mean, p2.var_pred_mean) p1 = res1.get_prediction(start=2, dynamic=18, end=60, exog=x_fcast1, signal_only=True) p2 = res2.get_prediction(start=2, end=60, exog=x_fcast2, signal_only=True) assert_allclose(p1.predicted_mean, p2.predicted_mean) assert_allclose(p1.var_pred_mean, p2.var_pred_mean) p1 = res1.get_prediction(start=20, dynamic=True, end=60, exog=x_fcast1, signal_only=True) p2 = res2.get_prediction(start=20, end=60, exog=x_fcast2, signal_only=True) assert_allclose(p1.predicted_mean, p2.predicted_mean) assert_allclose(p1.var_pred_mean, p2.var_pred_mean) # Test signal, filtered p1 = res1.get_prediction(start=0, dynamic=20, end=60, exog=x_fcast1, signal_only=True, information_set='filtered') p2 = res2.get_prediction(start=0, end=60, exog=x_fcast2, signal_only=True, information_set='filtered') assert_allclose(p1.predicted_mean, p2.predicted_mean) assert_allclose(p1.var_pred_mean, p2.var_pred_mean) p1 = res1.get_prediction(start=2, dynamic=18, end=60, exog=x_fcast1, signal_only=True, information_set='filtered') p2 = res2.get_prediction(start=2, end=60, exog=x_fcast2, signal_only=True, information_set='filtered') assert_allclose(p1.predicted_mean, p2.predicted_mean) assert_allclose(p1.var_pred_mean, p2.var_pred_mean) p1 = res1.get_prediction(start=20, dynamic=True, end=60, exog=x_fcast1, signal_only=True, information_set='filtered') p2 = res2.get_prediction(start=20, end=60, exog=x_fcast2, signal_only=True, information_set='filtered') assert_allclose(p1.predicted_mean, p2.predicted_mean) assert_allclose(p1.var_pred_mean, p2.var_pred_mean)

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image_classification_pytorch/__init__.py

denred0/image-classification-pytorch

9b4e43c3f01e28a6ae63a2a4abb75d9add6b690a

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2021-05-18T08:30:43.000Z

2021-08-13T16:54:30.000Z

image_classification_pytorch/__init__.py

denred0/image_classification_pytorch

9b4e43c3f01e28a6ae63a2a4abb75d9add6b690a

[ "MIT" ]

null

image_classification_pytorch/__init__.py

denred0/image_classification_pytorch

9b4e43c3f01e28a6ae63a2a4abb75d9add6b690a

[ "MIT" ]

null

from image_classification_pytorch.model import ICPModel from image_classification_pytorch.datamodule import ICPDataModule from image_classification_pytorch.dataset import ICPDataset from image_classification_pytorch.train import ICPTrainer from image_classification_pytorch.inference import ICPInference from image_classification_pytorch.schedulers import * from image_classification_pytorch.optimizers import * from image_classification_pytorch.dict import *

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plugins/cjk-auto-spacing/__init__.py

mohnjahoney/website_source

edc86a869b90ae604f32e736d9d5ecd918088e6a

[ "MIT" ]

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2020-01-27T09:02:25.000Z

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plugins/cjk-auto-spacing/__init__.py

mohnjahoney/website_source

edc86a869b90ae604f32e736d9d5ecd918088e6a

[ "MIT" ]

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2020-03-22T06:57:57.000Z

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plugins/cjk-auto-spacing/__init__.py

mohnjahoney/website_source

edc86a869b90ae604f32e736d9d5ecd918088e6a

[ "MIT" ]

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2020-07-10T00:13:30.000Z

2022-01-26T08:22:33.000Z

from .cjk_auto_spacing import *

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pygsm/growing_string_methods/__init__.py

stenczelt/pyGSM

48e7a710744ec768e2c4a0f4d8dc1f9ffd948ce1

[ "MIT" ]

null

pygsm/growing_string_methods/__init__.py

stenczelt/pyGSM

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[ "MIT" ]

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2021-05-29T13:04:31.000Z

2021-05-30T11:05:41.000Z

pygsm/growing_string_methods/__init__.py

stenczelt/pyGSM

48e7a710744ec768e2c4a0f4d8dc1f9ffd948ce1

[ "MIT" ]

null

from .de_gsm import DE_GSM from .gsm import GSM from .se_cross import SE_Cross from .se_gsm import SE_GSM

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terrascript/nsxt/__init__.py

bkez322/python-terrascript

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2022-02-07T21:08:14.000Z

2022-03-03T04:41:28.000Z

terrascript/nsxt/__init__.py

bkez322/python-terrascript

7779a9d0c65b7f4b463746c84a4f181dd895a849

[ "BSD-2-Clause" ]

null

terrascript/nsxt/__init__.py

bkez322/python-terrascript

7779a9d0c65b7f4b463746c84a4f181dd895a849

[ "BSD-2-Clause" ]

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2022-02-06T01:49:42.000Z

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# terrascript/nsxt/__init__.py import terrascript class nsxt(terrascript.Provider): pass

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deprecated/origin_stgcn_repo/tools/utils/__init__.py

fserracant/mmskeleton

44008bdef3dd6354a17c220fac8bcd8cd08ed201

[ "Apache-2.0" ]

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2022-03-31T12:24:55.000Z

deprecated/origin_stgcn_repo/tools/utils/__init__.py

fserracant/mmskeleton

44008bdef3dd6354a17c220fac8bcd8cd08ed201

[ "Apache-2.0" ]

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2019-08-24T15:36:11.000Z

2022-03-23T06:57:02.000Z

deprecated/origin_stgcn_repo/tools/utils/__init__.py

fserracant/mmskeleton

44008bdef3dd6354a17c220fac8bcd8cd08ed201

[ "Apache-2.0" ]

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2018-01-24T00:56:54.000Z

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from . import video from . import openpose from . import visualization

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seq_conv/__init__.py

shobrook/lstm-graph-conv

23db8adb967290700cb39a8a35d74e3e2b239cc4

[ "MIT" ]

8

2020-07-23T06:34:41.000Z

2022-01-15T15:48:17.000Z

seq_conv/__init__.py

shobrook/lstm-graph-conv

23db8adb967290700cb39a8a35d74e3e2b239cc4

[ "MIT" ]

2

2021-04-12T15:22:44.000Z

2021-04-16T10:36:50.000Z

seq_conv/__init__.py

shobrook/SeqConv

23db8adb967290700cb39a8a35d74e3e2b239cc4

[ "MIT" ]

null

from .seq_conv import SeqConv

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Python

tests/directories_helper.py

captainhunt/sysrsync

ee3faadd82ac23eead97336d4ab930bc9da35d4e

[ "MIT" ]

36

2019-01-23T15:14:00.000Z

2022-03-30T20:53:11.000Z

tests/directories_helper.py

captainhunt/sysrsync

ee3faadd82ac23eead97336d4ab930bc9da35d4e

[ "MIT" ]

18

2019-12-10T04:09:30.000Z

2022-02-14T08:37:34.000Z

tests/directories_helper.py

captainhunt/sysrsync

ee3faadd82ac23eead97336d4ab930bc9da35d4e

[ "MIT" ]

11

2019-05-24T12:15:42.000Z

2022-02-20T16:16:43.000Z

from sysrsync.helpers import directories from nose.tools import eq_ def test_strip_trailing_slash(): """test strip trailing slash""" test_dir = '/a/' expect = '/a' result = directories.strip_trailing_slash(test_dir) eq_(expect, result) def test_skip_strip_trailing_slash(): """test skip strip trailing slash when not necessary""" test_dir = '/a' result = directories.strip_trailing_slash(test_dir) eq_(result, test_dir) def test_add_trailing_slash(): """test add trailing slash""" test_dir = '/a' expect = '/a/' result = directories.add_trailing_slash(test_dir) eq_(expect, result) def test_skip_add_trailing_slash(): """test skip add trailing slash when not necessary""" test_dir = '/a/' result = directories.add_trailing_slash(test_dir) eq_(result, test_dir) def test_sanitize_trailing_slash(): """test sanitize trailing slash when syncing source contents""" source, target = '/a', '/b/' expect_source, expect_target = '/a/', '/b' result_source, result_target = directories.sanitize_trailing_slash( source, target) eq_(expect_source, result_source) eq_(expect_target, result_target) def test_sanitize_trailing_slash_no_action_needed(): """test sanitize trailing slash when syncing source contents when already sanitized""" source, target = '/a/', '/b' expect_source, expect_target = '/a/', '/b' result_source, result_target = directories.sanitize_trailing_slash( source, target) eq_(expect_source, result_source) eq_(expect_target, result_target) def test_sanitize_trailing_slash_whole_source(): """test sanitize trailing slash when syncing whole source""" source, target = '/a/', '/b/' expect_source, expect_target = '/a', '/b' result_source, result_target = directories.sanitize_trailing_slash( source, target, sync_sourcedir_contents=False) eq_(expect_source, result_source) eq_(expect_target, result_target) def test_sanitize_trailing_slash_whole_source_no_action_needed(): """test sanitize trailing slash when syncing whole source when already sanitized""" source, target = '/a', '/b/' expect_source, expect_target = '/a', '/b' result_source, result_target = directories.sanitize_trailing_slash( source, target, sync_sourcedir_contents=False) eq_(expect_source, result_source) eq_(expect_target, result_target) def test_dir_with_ssh(): """should compose string with ssh for rsync connection""" directory = '/a' ssh = 'host' expect = 'host:/a' result = directories.get_directory_with_ssh(directory, ssh) eq_(result, expect) def test_dir_without_ssh(): """should return directory when ssh is None""" directory = '/a' ssh = None result = directories.get_directory_with_ssh(directory, ssh) eq_(result, directory)

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af5421c895bf0254c42c939d6d953e738256d2a9

27,418

py

Python

TinyDCU_Net_16-master/speech_data.py

scofir/AEC-Challenge

d0df780f428dd73dc40ef9cd917c089d2f1894fa

[ "MIT" ]

1

2021-08-03T03:57:31.000Z

TinyDCU_Net_16-master/speech_data.py

scofir/AEC-Challenge

d0df780f428dd73dc40ef9cd917c089d2f1894fa

[ "MIT" ]

null

TinyDCU_Net_16-master/speech_data.py

scofir/AEC-Challenge

d0df780f428dd73dc40ef9cd917c089d2f1894fa

[ "MIT" ]

null

import os import sys import logging import traceback import json import librosa import random import time import threading import torch import torch.nn as nn import numpy as np from utils.signalprocess import * from utils.tools import * from utils.istft import ISTFT try: from Queue import Queue except ImportError: from queue import Queue class Producer(threading.Thread): def __init__(self, reader): threading.Thread.__init__(self) self.reader = reader self.exitcode = 0 self.stop_flag = False def run(self): try: min_queue_size = self.reader.cfg.min_queue_size while not self.stop_flag: idx = self.reader.next_produce_idx if idx < len(self.reader.clean_wav_list): if self.reader.batch_queue.qsize() < min_queue_size: group_list = self.reader.load_one_batch() for batch in group_list: self.reader.batch_queue.put(batch) else: time.sleep(1) else: time.sleep(1) except Exception as e: logging.warning("producer exception: %s" % e) self.exitcode = 1 traceback.print_exc() def stop(self): self.stop_flag = True def get_file_line(file_list, cfg=None): line_list = [] with open(file_list, 'r') as f: for line in f.readlines(): line = line.strip().split()[0] if cfg is not None: sig_data = audio_read(line, samp_rate=cfg.sample_rate) if len(sig_data) >= cfg.chunk_length: line_list.append(line) else: line_list.append(line) return line_list def compute_reverberation(config, clean_sig, impulse_sig): fftsize = 16384 if len(impulse_sig) < fftsize: impulse_sig = np.concatenate((impulse_sig, np.zeros((fftsize - len(impulse_sig),)))) erir_sig = np.zeros((fftsize,)) lrir_sig = np.zeros((fftsize,)) erir_sig[:1024] = impulse_sig[:1024] lrir_sig[:fftsize] = impulse_sig[:fftsize] prhk = rfft(lrir_sig) erhk = rfft(erir_sig) frames = samples_to_stft_frames(len(clean_sig), size=config.frame_size, shift=config.frame_shift) rfrm = np.zeros((fftsize,)) efrm = np.zeros((fftsize,)) reverb_sig = np.zeros((len(clean_sig),)) earlyr_sig = np.zeros((len(clean_sig),)) for i in range(frames): xinp = np.zeros((fftsize,)) xinp[:config.frame_shift] = clean_sig[i * config.frame_shift:(i + 1) * config.frame_shift] xink = rfft(xinp) rink = xink * prhk eink = xink * erhk time_signal = irfft(rink) rfrm[:(fftsize - config.frame_shift)] = time_signal[:(fftsize - config.frame_shift)] \ + rfrm[config.frame_shift:fftsize] rfrm[(fftsize - config.frame_shift):fftsize] = time_signal[(fftsize - config.frame_shift):fftsize] time_signal = irfft(eink) efrm[:(fftsize - config.frame_shift)] = time_signal[:(fftsize - config.frame_shift)] \ + efrm[config.frame_shift:fftsize] efrm[(fftsize - config.frame_shift):fftsize] = time_signal[(fftsize - config.frame_shift):fftsize] reverb_sig[i * config.frame_shift:(i + 1) * config.frame_shift] = rfrm[:config.frame_shift] earlyr_sig[i * config.frame_shift:(i + 1) * config.frame_shift] = efrm[:config.frame_shift] return earlyr_sig, reverb_sig def compute_features(config, noisy_sig, clean_sig): frames = samples_to_stft_frames(len(clean_sig), size=config.frame_size, shift=config.frame_shift) sent_width = frames // 16 sent_width = sent_width * 16 sent_height = config.frame_size / 2 + 1 clean_stft = stft_analysis(clean_sig, size=config.frame_size, shift=config.frame_shift) noisy_stft = stft_analysis(noisy_sig, size=config.frame_size, shift=config.frame_shift) clean_stft = clean_stft[:sent_width, :sent_height] noisy_stft = noisy_stft[:sent_width, :sent_height] frames = clean_stft.shape[0] frebin = clean_stft.shape[1] clean_feat = np.vstack((np.real(clean_stft), np.imag(clean_stft))) noisy_feat = np.vstack((np.real(noisy_stft), np.imag(noisy_stft))) noisy_feat = np.reshape(noisy_feat, [1, 2, frames, frebin]) clean_feat = np.reshape(clean_feat, [1, 2, frames, frebin]) return noisy_feat, clean_feat def compute_reverb_features(config, noise_sig, reverb_sig, early_sig): reverb_power = np.mean(np.square(reverb_sig)) noise_power = np.mean(np.square(noise_sig)) snr = 12 * (np.random.rand() + 0.25) scale = np.sqrt(reverb_power / noise_power) * 10 ** (-snr / 10) # get mixture sig if len(reverb_sig) >= len(noise_sig): repeat_num = np.ceil(len(reverb_sig) / len(noise_sig)).astype(np.int32) repeat_noise_sig = np.tile(scale * noise_sig, repeat_num) else: repeat_idx = np.random.randint(len(noise_sig) - len(reverb_sig)) repeat_noise_sig = scale * noise_sig[repeat_idx:repeat_idx + len(reverb_sig)] noisy_sig = reverb_sig + repeat_noise_sig[:len(reverb_sig)] normAmp = np.random.rand() sent_height = config.frame_size / 2 + 1 # normAmp = np.sqrt(len(clean_sig) / np.sum(clean_sig ** 2.0)) if normAmp < 0.1: normAmp = 0.1 early_sig = early_sig * normAmp noisy_sig = noisy_sig * normAmp clean_stft = stft_analysis(early_sig, size=config.frame_size, shift=config.frame_shift) noisy_stft = stft_analysis(noisy_sig, size=config.frame_size, shift=config.frame_shift) clean_stft = clean_stft[:, :sent_height] noisy_stft = noisy_stft[:, :sent_height] frames = samples_to_stft_frames(len(early_sig), size=config.frame_size, shift=config.frame_shift) sent_width = frames // 16 sent_width = 16 * sent_width if frames > sent_width: start_idx = np.random.randint(frames - sent_width) else: start_idx = 0 clean_stft = clean_stft[start_idx:start_idx + sent_width, :sent_height] noisy_stft = noisy_stft[start_idx:start_idx + sent_width, :sent_height] clean_feat = np.vstack((np.real(clean_stft), np.imag(clean_stft))) noisy_feat = np.vstack((np.real(noisy_stft), np.imag(noisy_stft))) noisy_feats = np.reshape(noisy_feat, [1, 2, sent_width, sent_height]) clean_feats = np.reshape(clean_feat, [1, 2, sent_width, sent_height]) return noisy_feats, clean_feats class SpeechReader(object): def __init__(self, config, job_type, clean_list=None, noisy_list=None, impulse_list=None): self.cfg = config self.job_type = job_type if clean_list is not None and noisy_list is not None: self.clean_wav_list = get_file_line(clean_list, config) self.noisy_wav_list = get_file_line(noisy_list, config) else: if job_type is not None: json_path = os.path.join(config.json_dir, self.job_type, 'files.json') with open(json_path, 'r') as f: json_list = json.load(f) random.shuffle(json_list) self.clean_wav_list = [] self.noisy_wav_list = [] for wav_file_name in json_list: clean_wav_file_path = os.path.join(config.dataset_dir, self.job_type, 'clean', wav_file_name) noisy_wav_file_path = os.path.join(config.dataset_dir, self.job_type, 'mix', wav_file_name) self.clean_wav_list.append(clean_wav_file_path) self.noisy_wav_list.append(noisy_wav_file_path) if impulse_list is not None: self.impulse_wav_list = get_file_line(impulse_list) self.next_produce_idx = 0 self.next_consume_idx = 0 self.running_out_flag = 0 self.batch_count = 0 self.narray_window = analysis_window(config.frame_size, config.frame_shift) def __getitem__(self, index): """Reads an wave file and preprocesses it and returns.""" print(index) clean_file = self.clean_wav_list[index] # clean_sig = audio_read(clean_file, samp_rate=self.cfg.sample_rate) # noise_file = self.noise_wav_list[np.random.randint(len(self.noise_wav_list))] # noise_sig = audio_read(noise_file, samp_rate=self.cfg.sample_rate) # noisy_feat, clean_feat, noisy_sig = compute_features(self.cfg, noise_sig, clean_sig) noisy_feat, clean_feat, noisy_sig, clean_sig = 0, 0, 0, 0 return noisy_feat, clean_feat, noisy_sig, clean_sig def __len__(self): """Returns the total number of clean files.""" return len(self.clean_wav_list) def start(self): self.next_produce_idx = 0 self.next_consume_idx = 0 self.running_out_flag = 0 def reset(self): self.next_produce_idx = 0 self.next_consume_idx = 0 self.running_out_flag = 0 self.batch_count = 0 json_path = os.path.join(self.cfg.json_dir, self.job_type, 'files.json') with open(json_path, 'r') as f: json_list = json.load(f) random.shuffle(json_list) self.clean_wav_list = [] self.noisy_wav_list = [] for wav_file_name in json_list: clean_wav_file_path = os.path.join(self.cfg.dataset_dir, self.job_type, 'clean', wav_file_name) noisy_wav_file_path = os.path.join(self.cfg.dataset_dir, self.job_type, 'mix', wav_file_name) self.clean_wav_list.append(clean_wav_file_path) self.noisy_wav_list.append(noisy_wav_file_path) def shuffle_data_list(self): random.shuffle(self.noisy_wav_list) def load_samples(self, noisy_name): noisy_sig = audio_read(noisy_name, samp_rate=self.cfg.sample_rate) noisy_stft = stft_analysis(noisy_sig, size=self.cfg.frame_size, shift=self.cfg.frame_shift) frames = samples_to_stft_frames(len(noisy_sig), size=self.cfg.frame_size, shift=self.cfg.frame_shift) return noisy_sig, noisy_stft, frames def load_one_mixture(self): """Reads an wave file and preprocesses it and returns.""" clean_file = self.clean_wav_list[np.random.randint(len(self.clean_wav_list))] noise_file = self.noisy_wav_list[np.random.randint(len(self.noisy_wav_list))] clean_sig = audio_read(clean_file, samp_rate=self.cfg.sample_rate) noise_sig = audio_read(noise_file, samp_rate=self.cfg.sample_rate) clean_power = np.mean(np.square(clean_sig)) noise_power = np.mean(np.square(noise_sig)) snr = 20 * (np.random.rand() - 0.25) scale = np.sqrt(clean_power / noise_power) * 10 ** (-snr / 10) # get mixture sig if len(clean_sig) >= len(noise_sig): repeat_num = np.ceil(len(clean_sig) / len(noise_sig)).astype(np.int32) repeat_noise_sig = np.tile(scale * noise_sig, repeat_num) else: repeat_idx = np.random.randint(len(noise_sig) - len(clean_sig)) repeat_noise_sig = scale * noise_sig[repeat_idx:repeat_idx + len(clean_sig)] noisy_sig = clean_sig + repeat_noise_sig[:len(clean_sig)] # dump wav file # audio_write('./wav/clean.wav', clean_sig, 16000) # audio_write('./wav/noisy.wav', noisy_sig, 16000) frames = samples_to_stft_frames(len(noisy_sig), size=self.cfg.frame_size, shift=self.cfg.frame_shift) clean_stft = stft_analysis(clean_sig, size=self.cfg.frame_size, shift=self.cfg.frame_shift) noisy_stft = stft_analysis(noisy_sig, size=self.cfg.frame_size, shift=self.cfg.frame_shift) clean_magn = np.abs(clean_stft) noisy_magn = np.abs(noisy_stft) return noisy_sig, noisy_stft, noisy_magn, clean_sig, clean_stft, clean_magn, frames def load_one_item(self): """Reads an wave file and preprocesses it and returns.""" clean_file = self.clean_wav_list[self.next_consume_idx] clean_sig = audio_read(clean_file, samp_rate=self.cfg.sample_rate) noisy_file = self.noisy_wav_list[self.next_consume_idx] noisy_sig = audio_read(noisy_file, samp_rate=self.cfg.sample_rate) clean_sig = np.reshape(clean_sig, [1, len(clean_sig)]) noisy_sig = np.reshape(noisy_sig, [1, len(noisy_sig)]) self.next_consume_idx = min(self.next_consume_idx + 1, len(self.clean_wav_list)) return noisy_sig, clean_sig def load_one_batch(self): """Reads an wave file and preprocesses it and returns.""" noisy_list, clean_list, frame_list = [], [], [] for i in range(self.cfg.batch_size): next_consume_idx = self.next_consume_idx % len(self.clean_wav_list) clean_file = self.clean_wav_list[next_consume_idx] clean_sig = audio_read(clean_file, samp_rate=self.cfg.sample_rate) noisy_file = self.noisy_wav_list[next_consume_idx] noisy_sig = audio_read(noisy_file, samp_rate=self.cfg.sample_rate) # clean_sig = np.reshape(clean_sig, [1, len(clean_sig)]) # noisy_sig = np.reshape(noisy_sig, [1, len(noisy_sig)]) clean_sig = torch.FloatTensor(clean_sig) noisy_sig = torch.FloatTensor(noisy_sig) if len(clean_sig) > self.cfg.chunk_length: wav_start = random.randint(0, len(clean_sig) - self.cfg.chunk_length) clean_sig = clean_sig[wav_start:wav_start + self.cfg.chunk_length] noisy_sig = noisy_sig[wav_start:wav_start + self.cfg.chunk_length] frame_num = len(clean_sig) // self.cfg.frame_shift + 1 clean_list.append(clean_sig) noisy_list.append(noisy_sig) frame_list.append(frame_num) self.next_consume_idx = self.next_consume_idx + 1 if self.next_consume_idx >= len(self.clean_wav_list): self.running_out_flag = 1 clean_list = nn.utils.rnn.pad_sequence(clean_list, batch_first=True) noisy_list = nn.utils.rnn.pad_sequence(noisy_list, batch_first=True) # print('self.next_produce_idx: ' + str(self.next_produce_idx)) return noisy_list, clean_list, frame_list def load_one_norm_norm_batch(self): """Reads an wave file and preprocesses it and returns.""" noisy_feat_list, clean_feat_list, frame_list = [], [], [] for i in range(self.cfg.batch_size): next_consume_idx = self.next_consume_idx % len(self.clean_wav_list) clean_file = self.clean_wav_list[next_consume_idx] clean_sig = audio_read(clean_file, samp_rate=self.cfg.sample_rate) noisy_file = self.noisy_wav_list[next_consume_idx] noisy_sig = audio_read(noisy_file, samp_rate=self.cfg.sample_rate) if len(clean_sig) > self.cfg.chunk_length: wav_start = random.randint(0, len(clean_sig) - self.cfg.chunk_length) clean_sig = clean_sig[wav_start:wav_start + self.cfg.chunk_length] noisy_sig = noisy_sig[wav_start:wav_start + self.cfg.chunk_length] frame_num = len(clean_sig) // self.cfg.frame_shift + 1 noisy_feat = librosa.stft(noisy_sig, n_fft=self.cfg.frame_size, hop_length=self.cfg.frame_shift, window=self.narray_window, pad_mode='constant').T clean_feat = librosa.stft(clean_sig, n_fft=self.cfg.frame_size, hop_length=self.cfg.frame_shift, window=self.narray_window, pad_mode='constant').T noisy_feat, clean_feat = noisy_feat[0:frame_num, :], clean_feat[0:frame_num, :] noisy_real, noisy_imag = np.real(noisy_feat), np.imag(noisy_feat) clean_real, clean_imag = np.real(clean_feat), np.imag(clean_feat) noisy_feat = torch.FloatTensor(np.concatenate( (noisy_real[:, :, np.newaxis].astype(np.float32), noisy_imag[:, :, np.newaxis].astype(np.float32)), axis=-1)) clean_feat = torch.FloatTensor(np.concatenate( (clean_real[:, :, np.newaxis].astype(np.float32), clean_imag[:, :, np.newaxis].astype(np.float32)), axis=-1)) noisy_feat_list.append(noisy_feat) clean_feat_list.append(clean_feat) frame_list.append(frame_num) self.next_consume_idx = self.next_consume_idx + 1 if self.next_consume_idx >= len(self.clean_wav_list): self.running_out_flag = 1 self.batch_count = self.batch_count + 1 noisy_feat_list = nn.utils.rnn.pad_sequence(noisy_feat_list, batch_first=True) clean_feat_list = nn.utils.rnn.pad_sequence(clean_feat_list, batch_first=True) noisy_feat_list = noisy_feat_list.permute(0, 3, 1, 2).contiguous() clean_feat_list = clean_feat_list.permute(0, 3, 1, 2).contiguous() # print('self.next_produce_idx: ' + str(self.next_produce_idx)) return noisy_feat_list, clean_feat_list, frame_list def load_one_comp_norm_batch(self): """Reads an wave file and preprocesses it and returns.""" noisy_feat_list, clean_feat_list, frame_list = [], [], [] for i in range(self.cfg.batch_size): next_consume_idx = self.next_consume_idx % len(self.clean_wav_list) clean_file = self.clean_wav_list[next_consume_idx] clean_sig = audio_read(clean_file, samp_rate=self.cfg.sample_rate) noisy_file = self.noisy_wav_list[next_consume_idx] noisy_sig = audio_read(noisy_file, samp_rate=self.cfg.sample_rate) if len(clean_sig) > self.cfg.chunk_length: wav_start = random.randint(0, len(clean_sig) - self.cfg.chunk_length) clean_sig = clean_sig[wav_start:wav_start + self.cfg.chunk_length] noisy_sig = noisy_sig[wav_start:wav_start + self.cfg.chunk_length] frame_num = len(clean_sig) // self.cfg.frame_shift + 1 noisy_feat = librosa.stft(noisy_sig, n_fft=self.cfg.frame_size, hop_length=self.cfg.frame_shift, window=self.narray_window, pad_mode='constant').T clean_feat = librosa.stft(clean_sig, n_fft=self.cfg.frame_size, hop_length=self.cfg.frame_shift, window=self.narray_window, pad_mode='constant').T noisy_feat, clean_feat = noisy_feat[0:frame_num, :], clean_feat[0:frame_num, :] noisy_mag, noisy_phase = np.abs(noisy_feat), np.angle(noisy_feat) noisy_mag_com = np.sqrt(noisy_mag) noisy_real, noisy_imag = noisy_mag_com * np.cos(noisy_phase), noisy_mag_com * np.sin(noisy_phase) clean_real, clean_imag = np.real(clean_feat), np.imag(clean_feat) noisy_feat = torch.FloatTensor(np.concatenate( (noisy_real[:, :, np.newaxis].astype(np.float32), noisy_imag[:, :, np.newaxis].astype(np.float32)), axis=-1)) clean_feat = torch.FloatTensor(np.concatenate( (clean_real[:, :, np.newaxis].astype(np.float32), clean_imag[:, :, np.newaxis].astype(np.float32)), axis=-1)) noisy_feat_list.append(noisy_feat) clean_feat_list.append(clean_feat) frame_list.append(frame_num) self.next_consume_idx = self.next_consume_idx + 1 if self.next_consume_idx >= len(self.clean_wav_list): self.running_out_flag = 1 self.batch_count = self.batch_count + 1 noisy_feat_list = nn.utils.rnn.pad_sequence(noisy_feat_list, batch_first=True) clean_feat_list = nn.utils.rnn.pad_sequence(clean_feat_list, batch_first=True) noisy_feat_list = noisy_feat_list.permute(0, 3, 1, 2).contiguous() clean_feat_list = clean_feat_list.permute(0, 3, 1, 2).contiguous() # print('self.next_produce_idx: ' + str(self.next_produce_idx)) return noisy_feat_list, clean_feat_list, frame_list def load_one_norm_comp_batch(self): """Reads an wave file and preprocesses it and returns.""" noisy_feat_list, clean_feat_list, frame_list = [], [], [] for i in range(self.cfg.batch_size): next_consume_idx = self.next_consume_idx % len(self.clean_wav_list) clean_file = self.clean_wav_list[next_consume_idx] clean_sig = audio_read(clean_file, samp_rate=self.cfg.sample_rate) noisy_file = self.noisy_wav_list[next_consume_idx] noisy_sig = audio_read(noisy_file, samp_rate=self.cfg.sample_rate) if len(clean_sig) > self.cfg.chunk_length: wav_start = random.randint(0, len(clean_sig) - self.cfg.chunk_length) clean_sig = clean_sig[wav_start:wav_start + self.cfg.chunk_length] noisy_sig = noisy_sig[wav_start:wav_start + self.cfg.chunk_length] frame_num = len(clean_sig) // self.cfg.frame_shift + 1 noisy_feat = librosa.stft(noisy_sig, n_fft=self.cfg.frame_size, hop_length=self.cfg.frame_shift, window=self.narray_window, pad_mode='constant').T clean_feat = librosa.stft(clean_sig, n_fft=self.cfg.frame_size, hop_length=self.cfg.frame_shift, window=self.narray_window, pad_mode='constant').T noisy_feat, clean_feat = noisy_feat[0:frame_num, :], clean_feat[0:frame_num, :] clean_mag, clean_phase = np.abs(clean_feat), np.angle(clean_feat) clean_mag_com = np.sqrt(clean_mag) noisy_real, noisy_imag = np.real(noisy_feat), np.imag(noisy_feat) clean_real, clean_imag = clean_mag_com * np.cos(clean_phase), clean_mag_com * np.sin(clean_phase) noisy_feat = torch.FloatTensor(np.concatenate( (noisy_real[:, :, np.newaxis].astype(np.float32), noisy_imag[:, :, np.newaxis].astype(np.float32)), axis=-1)) clean_feat = torch.FloatTensor(np.concatenate( (clean_real[:, :, np.newaxis].astype(np.float32), clean_imag[:, :, np.newaxis].astype(np.float32)), axis=-1)) noisy_feat_list.append(noisy_feat) clean_feat_list.append(clean_feat) frame_list.append(frame_num) self.next_consume_idx = self.next_consume_idx + 1 if self.next_consume_idx >= len(self.clean_wav_list): self.running_out_flag = 1 self.batch_count = self.batch_count + 1 noisy_feat_list = nn.utils.rnn.pad_sequence(noisy_feat_list, batch_first=True) clean_feat_list = nn.utils.rnn.pad_sequence(clean_feat_list, batch_first=True) noisy_feat_list = noisy_feat_list.permute(0, 3, 1, 2).contiguous() clean_feat_list = clean_feat_list.permute(0, 3, 1, 2).contiguous() # print('self.next_produce_idx: ' + str(self.next_produce_idx)) return noisy_feat_list, clean_feat_list, frame_list def load_one_comp_comp_batch(self): """Reads an wave file and preprocesses it and returns.""" noisy_feat_list, clean_feat_list, frame_list = [], [], [] for i in range(self.cfg.batch_size): next_consume_idx = self.next_consume_idx % len(self.clean_wav_list) clean_file = self.clean_wav_list[next_consume_idx] clean_sig = audio_read(clean_file, samp_rate=self.cfg.sample_rate) noisy_file = self.noisy_wav_list[next_consume_idx] noisy_sig = audio_read(noisy_file, samp_rate=self.cfg.sample_rate) if len(clean_sig) > self.cfg.chunk_length: wav_start = random.randint(0, len(clean_sig) - self.cfg.chunk_length) clean_sig = clean_sig[wav_start:wav_start + self.cfg.chunk_length] noisy_sig = noisy_sig[wav_start:wav_start + self.cfg.chunk_length] frame_num = len(clean_sig) // self.cfg.frame_shift + 1 noisy_feat = librosa.stft(noisy_sig, n_fft=self.cfg.frame_size, hop_length=self.cfg.frame_shift, window=self.narray_window, pad_mode='constant').T clean_feat = librosa.stft(clean_sig, n_fft=self.cfg.frame_size, hop_length=self.cfg.frame_shift, window=self.narray_window, pad_mode='constant').T noisy_feat, clean_feat = noisy_feat[0:frame_num, :], clean_feat[0:frame_num, :] noisy_mag, noisy_phase = np.abs(noisy_feat), np.angle(noisy_feat) clean_mag, clean_phase = np.abs(clean_feat), np.angle(clean_feat) noisy_mag_com, clean_mag_com = np.sqrt(noisy_mag), np.sqrt(clean_mag) noisy_real, noisy_imag = noisy_mag_com * np.cos(noisy_phase), noisy_mag_com * np.sin(noisy_phase) clean_real, clean_imag = clean_mag_com * np.cos(clean_phase), clean_mag_com * np.sin(clean_phase) noisy_feat = torch.FloatTensor(np.concatenate( (noisy_real[:, :, np.newaxis].astype(np.float32), noisy_imag[:, :, np.newaxis].astype(np.float32)), axis=-1)) clean_feat = torch.FloatTensor(np.concatenate( (clean_real[:, :, np.newaxis].astype(np.float32), clean_imag[:, :, np.newaxis].astype(np.float32)), axis=-1)) noisy_feat_list.append(noisy_feat) clean_feat_list.append(clean_feat) frame_list.append(frame_num) self.next_consume_idx = self.next_consume_idx + 1 if self.next_consume_idx >= len(self.clean_wav_list): self.running_out_flag = 1 self.batch_count = self.batch_count + 1 noisy_feat_list = nn.utils.rnn.pad_sequence(noisy_feat_list, batch_first=True) clean_feat_list = nn.utils.rnn.pad_sequence(clean_feat_list, batch_first=True) noisy_feat_list = noisy_feat_list.permute(0, 3, 1, 2).contiguous() clean_feat_list = clean_feat_list.permute(0, 3, 1, 2).contiguous() # print('self.next_produce_idx: ' + str(self.next_produce_idx)) return noisy_feat_list, clean_feat_list, frame_list def is_running_out(self): if self.running_out_flag == 1: return True else: return False def next_batch(self): while self.producer.exitcode == 0: try: if self.batch_queue.qsize() > 0: batch_data = self.batch_queue.get(block=False) self.next_consume_idx = min(self.next_consume_idx + 1, len(self.clean_wav_list)) print('self.next_consume_idx: ' + str(self.next_consume_idx)) return batch_data else: time.sleep(0.5) except Exception as e: time.sleep(3) def get_reader(config, job_type=None, clean_list=None, noisy_list=None, impulse_list=None): data_reader = SpeechReader(config, job_type, clean_list, noisy_list, impulse_list) return data_reader

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af5663f8e8250faf95f324d3ba927cc94fb8eaba

238

py

Python

monolith_filemanager/console_commands/install_tensorflow.py

MonolithAILtd/monolith-filemanager

2369e244e4d8a48890f55d00419a83001a5c6c40

[ "Apache-2.0" ]

3

2021-06-02T09:45:00.000Z

2022-02-01T14:30:01.000Z

monolith_filemanager/console_commands/install_tensorflow.py

MonolithAILtd/monolith-filemanager

2369e244e4d8a48890f55d00419a83001a5c6c40

[ "Apache-2.0" ]

3

2021-05-26T11:46:28.000Z

2021-11-04T10:14:42.000Z

monolith_filemanager/console_commands/install_tensorflow.py

MonolithAILtd/monolith-filemanager

2369e244e4d8a48890f55d00419a83001a5c6c40

[ "Apache-2.0" ]

2

2021-06-04T15:02:14.000Z

2021-09-03T09:26:45.000Z

import subprocess import sys def install_tensorflow(): """ Installs the tensorflow requirement for the package. Returns: None """ subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'tensorflow>=2.1.0'])

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1

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6

afa3fa16ff275b693b0d6d8a4ceb7f459ac86b99

31

py

Python

face_network/__init__.py

DongChengdongHangZhou/Siamese-Network-tiff

aaf923ad59301af1b3237e605964341a90dc414b

[ "MIT" ]

null

face_network/__init__.py

DongChengdongHangZhou/Siamese-Network-tiff

aaf923ad59301af1b3237e605964341a90dc414b

[ "MIT" ]

null

face_network/__init__.py

DongChengdongHangZhou/Siamese-Network-tiff

aaf923ad59301af1b3237e605964341a90dc414b

[ "MIT" ]

null

from .Network import simpleCNN

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6

afc8a068cc19889eca49229381594cb34ebcf6fe

35

py

Python

sysidentpy/parameter_estimation/__init__.py

armandobs14/sysidentpy

108bd9ec6f0bc30652f915861b6eaeee08bad330

[ "BSD-3-Clause" ]

107

2020-05-19T12:59:56.000Z

2022-03-29T05:25:27.000Z

sysidentpy/parameter_estimation/__init__.py

armandobs14/sysidentpy

108bd9ec6f0bc30652f915861b6eaeee08bad330

[ "BSD-3-Clause" ]

20

2020-05-24T15:56:15.000Z

2022-03-05T19:54:02.000Z

sysidentpy/parameter_estimation/__init__.py

armandobs14/sysidentpy

108bd9ec6f0bc30652f915861b6eaeee08bad330

[ "BSD-3-Clause" ]

25

2020-05-19T14:02:17.000Z

2022-03-15T20:17:58.000Z

from .estimators import Estimators

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6

afd2b1b81829c178873b1a47303af127587353a2

42

py

Python

netbox_api/__init__.py

zinic/netbox_api

ace6cb2b60edd93f4a37f7a29e8d262a1c8e1fc4

[ "MIT" ]

2

2018-04-10T09:28:48.000Z

2019-08-10T22:48:51.000Z

netbox_api/__init__.py

zinic/netbox_api

ace6cb2b60edd93f4a37f7a29e8d262a1c8e1fc4

[ "MIT" ]

null

netbox_api/__init__.py

zinic/netbox_api

ace6cb2b60edd93f4a37f7a29e8d262a1c8e1fc4

[ "MIT" ]

null

from netbox_api.api import new_api_client

21

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6

bb6e43eb0eeb61735417edaf9d2326c4673f1c95

47

py

Python

models/thresholds/__init__.py

YashYash/advanced-lane-lines

236d410a1220ea266f105794a8c3ead7da6bc0f9

[ "MIT" ]

null

models/thresholds/__init__.py

YashYash/advanced-lane-lines

236d410a1220ea266f105794a8c3ead7da6bc0f9

[ "MIT" ]

null

models/thresholds/__init__.py

YashYash/advanced-lane-lines

236d410a1220ea266f105794a8c3ead7da6bc0f9

[ "MIT" ]

null

from models.thresholds.model import Thresholds

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6

bbbcd0b733df8417578eb37746026f76c7997a52

119

py

Python

src/nodes/corenodes/transform/__init__.py

Correct-Syntax/GimelStudio

db6e2db35730e11bcb25f5ba82823e68b86003f1

[ "Apache-2.0" ]

1

2022-01-16T01:15:24.000Z

src/nodes/corenodes/transform/__init__.py

Correct-Syntax/GimelStudio

db6e2db35730e11bcb25f5ba82823e68b86003f1

[ "Apache-2.0" ]

null

src/nodes/corenodes/transform/__init__.py

Correct-Syntax/GimelStudio

db6e2db35730e11bcb25f5ba82823e68b86003f1

[ "Apache-2.0" ]

null

from .flip_node import FlipNode from .rotate_node import RotateNode from .circular_shift_node import CircularShiftNode

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6

bbcb9e0696b7d8e6b5c67b2146329636121bc224

208

py

Python

english_teaching_app/blueprint.py

SebastianDix/english_teaching_app

10b32b3459a5936f95d382630c7e9123a920a925

[ "Apache-2.0" ]

null

english_teaching_app/blueprint.py

SebastianDix/english_teaching_app

10b32b3459a5936f95d382630c7e9123a920a925

[ "Apache-2.0" ]

null

english_teaching_app/blueprint.py

SebastianDix/english_teaching_app

10b32b3459a5936f95d382630c7e9123a920a925

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 from flask import Blueprint sebastian_blueprint = Blueprint('sebastian',__name__) @sebastian_blueprint.route('/<string:name>') def home(name): return f"You shall prevail, {name}!"

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a541a8ab7ff7a710e46b6fffc92952ff9a4f1d93

231

py

Python

great_expectations/datasource/data_connector/sorter/__init__.py

vanderGoes/great_expectations

9790cd992a8a4de672c640e89ddd7278a0ca0889

[ "Apache-2.0" ]

6,451

2017-09-11T16:32:53.000Z

2022-03-31T23:27:49.000Z

great_expectations/datasource/data_connector/sorter/__init__.py

vanderGoes/great_expectations

9790cd992a8a4de672c640e89ddd7278a0ca0889

[ "Apache-2.0" ]

3,892

2017-09-08T18:57:50.000Z

2022-03-31T23:15:20.000Z

great_expectations/datasource/data_connector/sorter/__init__.py

vanderGoes/great_expectations

9790cd992a8a4de672c640e89ddd7278a0ca0889

[ "Apache-2.0" ]

1,023

2017-09-08T15:22:05.000Z

2022-03-31T21:17:08.000Z

from .sorter import Sorter # isort:skip from .custom_list_sorter import CustomListSorter from .date_time_sorter import DateTimeSorter from .lexicographic_sorter import LexicographicSorter from .numeric_sorter import NumericSorter

38.5

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6

a562b7945e913468db367fd3adfe21a53848976e

174

py

Python

Cartwheel/cartwheel-3d/Python/App/__init__.py

MontyThibault/centre-of-mass-awareness

58778f148e65749e1dfc443043e9fc054ca3ff4d

[ "MIT" ]

null

Cartwheel/cartwheel-3d/Python/App/__init__.py

MontyThibault/centre-of-mass-awareness

58778f148e65749e1dfc443043e9fc054ca3ff4d

[ "MIT" ]

null

Cartwheel/cartwheel-3d/Python/App/__init__.py

MontyThibault/centre-of-mass-awareness

58778f148e65749e1dfc443043e9fc054ca3ff4d

[ "MIT" ]

null

from SNMApp import SNMApp from SnapshotTree import SnapshotBranch, Snapshot from ObservableList import ObservableList import Scenario import InstantChar import KeyframeEditor

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6

a584521c0c10dcd37673b86c6ad111c93c93a10a

12,212

py

Python

spytest/tests/routing/BGP/test_bgp_rr_traffic.py

mykolaf/sonic-mgmt

de77268526173c5e3a345f3f3703b56eb40c5eed

[ "Apache-2.0" ]

1

2021-09-15T17:09:13.000Z

spytest/tests/routing/BGP/test_bgp_rr_traffic.py

mykolaf/sonic-mgmt

de77268526173c5e3a345f3f3703b56eb40c5eed

[ "Apache-2.0" ]

1

2020-02-05T16:51:53.000Z

spytest/tests/routing/BGP/test_bgp_rr_traffic.py

mykolaf/sonic-mgmt

de77268526173c5e3a345f3f3703b56eb40c5eed

[ "Apache-2.0" ]

null

import pytest from spytest import st, tgapi from spytest.utils import poll_wait import apis.routing.ip as ipapi import apis.routing.bgp as bgpapi import BGP.bgplib as bgplib bgp_cli_type = 'vtysh' @pytest.fixture(scope="module", autouse=True) def bgp_module_hooks(request): st.ensure_min_topology('D1D2:1', 'D1T1:1', 'D2T1:1') bgplib.init_resource_data(st.get_testbed_vars()) bgp_pre_config() yield bgp_pre_config_cleanup() # bgp module level pre config function def bgp_pre_config(): global topo st.banner("BGP MODULE CONFIG - START") # loopback config bgplib.l3tc_vrfipv4v6_address_leafspine_loopback_config_unconfig(config='yes', config_type='all') # TG Configuration bgplib.l3tc_vrfipv4v6_address_leafspine_tg_config_unconfig(config='yes', config_type='all') st.banner("BGP MODULE CONFIG - END") # bgp module level pre config cleanup function def bgp_pre_config_cleanup(): st.banner("BGP MODULE CONFIG CLEANUP - START") # loopback unconfig bgplib.l3tc_vrfipv4v6_address_leafspine_loopback_config_unconfig(config='no') # TG uconfiguration bgplib.l3tc_vrfipv4v6_address_leafspine_tg_config_unconfig(config='no') st.banner("BGP MODULE CONFIG CLEANUP - END") @pytest.fixture(scope="function") def bgp_func_hooks(request): yield ################################################################################ # BGP Route Reflector with traffic fixture, class and test cases - START def bgp_rr_traffic_pre_config(): global topo st.banner("BGP RR WITH TRAFFIC CLASS CONFIG - START") # underlay config - configure physical interfaces bgplib.l3tc_underlay_config_unconfig(config='yes') # config ip on underlay interface bgplib.l3tc_vrfipv4v6_address_leafspine_config_unconfig(config='yes', config_type='all') # Ping Verification if not bgplib.l3tc_vrfipv4v6_address_leafspine_ping_test(config_type='all', ping_count=3): st.error("Ping failed in between Spine - Leaf") st.report_fail('test_case_failed') ibgp_as = bgplib.data['spine_as'] bgplib.l3tc_vrfipv4v6_address_leafspine_rr_tg_bgp_config(config='yes', rr_enable='true') bgplib.l3tc_vrfipv4v6_address_leafspine_bgp_config(config='yes', rr_enable='true') # BGP Neighbor Verification if not poll_wait(bgplib.l3tc_vrfipv4v6_address_leafspine_bgp_check, 10, config_type='all'): st.error("Neighbour is failed to Establish between Spine - Leaf") st.report_fail('test_case_failed') st.log("Getting all topology info related to connectivity / TG and other parameters between duts") topo = bgplib.get_leaf_spine_topology_info() st.banner("BGP RR WITH TRAFFIC CLASS CONFIG - END") def bgp_rr_traffic_pre_config_cleanup(): st.banner("BGP RR WITH TRAFFIC CLASS CONFIG CLEANUP - START") ibgp_as = bgplib.data['spine_as'] bgplib.l3tc_vrfipv4v6_address_leafspine_bgp_config(config='no', rr_enable='true') bgplib.l3tc_vrfipv4v6_address_leafspine_config_unconfig(config='no') bgpapi.cleanup_router_bgp(st.get_dut_names()) ipapi.clear_ip_configuration(st.get_dut_names(), family='all', thread=True) bgplib.l3tc_underlay_config_unconfig(config='no') bgplib.l3tc_vrfipv4v6_address_leafspine_rr_tg_bgp_config(config='no', rr_enable='true') st.banner("BGP RR WITH TRAFFIC CLASS CONFIG CLEANUP - END") @pytest.fixture(scope='class') def bgp_rr_traffic_class_hook(request): bgp_rr_traffic_pre_config() yield bgp_rr_traffic_pre_config_cleanup() # Route Reflector with traffic Class @pytest.mark.usefixtures('bgp_rr_traffic_class_hook') class TestBGPRrTraffic(): @pytest.mark.bgp_rr_traffic def test_ft_bgp_rr_traffic_check(self): TG_D1 = topo.tg_dut_list_name[0] TG_D2 = topo.tg_dut_list_name[1] tg_ob = topo['T1{}P1_tg_obj'.format(TG_D1)] bgp_handle = topo['T1{}P1_ipv4_tg_bh'.format(TG_D1)] tc_fail_flag = 0 spine_as = int(bgplib.data['spine_as']) bgp_ctrl = tg_ob.tg_emulation_bgp_control(handle=bgp_handle['handle'], mode='stop') st.wait(10) st.log("Advertising Routes from one of the Leaf Router") bgp_route = tg_ob.tg_emulation_bgp_route_config(handle=bgp_handle['handle'], mode='add', num_routes='100', prefix='121.1.1.0', as_path='as_seq:1') bgp_ctrl = tg_ob.tg_emulation_bgp_control(handle=bgp_handle['handle'], mode='start') # Sleep for some time and the check the route count in neighbour st.wait(10) bgp_summary = bgpapi.show_bgp_ipv4_summary(topo.dut_list[1]) rib_entries = bgp_summary[0]['ribentries'] st.log('RIB Entries : {}'.format(rib_entries)) # when route-reflector is not configured at server(spine), we should not learn anything at # route-reflector-client (leaf node), ideally, route count should be 0. if int(rib_entries) > 10: st.error('iBGP Routes are advertised to iBGP peer DUT, even when route-reflector-client is not configured') tc_fail_flag = 1 # now configure route-reflector-client at spine node result = bgpapi.create_bgp_route_reflector_client(topo.dut_list[0], spine_as, 'ipv4', 'spine_leaf', 'yes') if not result: st.log("Configuring client reflection on {} {} bgp {} Failed".format(topo.dut_list[0], 'ipv4', spine_as)) tc_fail_flag = 1 bgpapi.create_bgp_next_hop_self(topo.dut_list[0], spine_as, 'ipv4', 'spine_leaf', 'yes', 'yes',cli_type=bgp_cli_type) st.wait(15) bgp_summary = bgpapi.show_bgp_ipv4_summary(topo.dut_list[1]) rib_entries = bgp_summary[0]['ribentries'] st.log('RIB Entries : {}'.format(rib_entries)) if int(rib_entries) < 100: st.error('iBGP Routes are not advertised to route-reflector-client') tc_fail_flag = 1 st.log("Initiating the Ipv4 traffic for those Routes from another Leaf Router") src_handle = 'handle' dst_handle = 'handles' if tg_ob.tg_type == 'ixia': src_handle = 'ipv4_handle' dst_handle = 'handle' tr1 = tg_ob.tg_traffic_config(port_handle=topo['T1{}P1_ipv4_tg_ph'.format(TG_D2)], emulation_src_handle=topo['T1{}P1_ipv4_tg_ih'.format(TG_D2)][src_handle], emulation_dst_handle=bgp_route[dst_handle], circuit_endpoint_type='ipv4', mode='create', transmit_mode='single_burst', pkts_per_burst='2000', length_mode='fixed', rate_pps=1000) stream_id1 = tr1['stream_id'] tg_ob.tg_traffic_control(action='run', handle=stream_id1) st.wait(20) tg1_stats = tgapi.get_traffic_stats(tg_ob, port_handle=topo["T1{}P1_ipv4_tg_ph".format(TG_D1)]) tg2_stats = tgapi.get_traffic_stats(tg_ob, port_handle=topo["T1{}P1_ipv4_tg_ph".format(TG_D2)]) if not (int(tg2_stats.tx.total_packets) and int(tg1_stats.rx.total_packets)): st.error('Received ZERO stats.') tc_fail_flag = 1 else: percent_rx = float(int(tg1_stats.rx.total_packets) - int(tg2_stats.tx.total_packets)) / int( tg2_stats.tx.total_packets) * 100 st.log('tg1_stats.rx.total_packets : {}'.format(tg1_stats.rx.total_packets)) st.log('tg2_stats.tx.total_packets : {}'.format(tg2_stats.tx.total_packets)) st.log('percent_rx : {}'.format(percent_rx)) if percent_rx > 0.5: tc_fail_flag = 1 tg_ob.tg_emulation_bgp_control(handle=bgp_handle['handle'], mode='stop') if tc_fail_flag: st.report_fail("traffic_verification_failed") st.report_pass('test_case_passed') @pytest.mark.bgp6_rr_traffic def test_ft_bgp6_rr_traffic_check(self): TG_D1 = topo.tg_dut_list_name[0] TG_D2 = topo.tg_dut_list_name[1] tg_ob = topo['T1{}P1_tg_obj'.format(TG_D1)] bgp_handle = topo['T1{}P1_ipv6_tg_bh'.format(TG_D1)] tc_fail_flag = 0 spine_as = int(bgplib.data['spine_as']) st.log("Advertising Routes from one of the Leaf Router") bgp_route = tg_ob.tg_emulation_bgp_route_config(handle=bgp_handle['handle'], mode='add', ip_version='6', num_routes='100', prefix='1001::1', as_path='as_seq:1') bgp_ctrl = tg_ob.tg_emulation_bgp_control(handle=bgp_handle['handle'], mode='start') # Sleep for some time and the check the route count in neighbour st.wait(10) bgp_summary = bgpapi.show_bgp_ipv6_summary(topo.dut_list[1]) rib_entries = bgp_summary[0]['ribentries'] st.log('RIB Entries : {}'.format(rib_entries)) # when route-reflector is not configured at server(spine), we should not learn anything at # route-reflector-client (leaf node), ideally, route count should be 0. if int(rib_entries) > 10: st.error('iBGP Routes are advertised to iBGP peer DUT, even when route-reflector-client is not configured') tc_fail_flag = 1 # now configure route-reflector-client at spine node result = bgpapi.create_bgp_route_reflector_client(topo.dut_list[0], spine_as, 'ipv6', 'spine_leaf6', 'yes') if not result: st.log("Configuring client reflection on {} {} bgp {} Failed".format(topo.dut_list[0], 'ipv6', spine_as)) tc_fail_flag = 1 bgpapi.create_bgp_next_hop_self(topo.dut_list[0], spine_as, 'ipv6', 'spine_leaf6', 'yes', 'yes',cli_type=bgp_cli_type) st.wait(15) bgp_summary = bgpapi.show_bgp_ipv6_summary(topo.dut_list[1]) rib_entries = bgp_summary[0]['ribentries'] st.log('RIB Entries : {}'.format(rib_entries)) if int(rib_entries) < 100: st.error('iBGP Routes are not advertised to route-reflector-client') tc_fail_flag = 1 st.log("Initiating the Ipv6 traffic for those Routes from another Leaf Router") src_handle = 'handle' dst_handle = 'handles' if tg_ob.tg_type == 'ixia': src_handle = 'ipv6_handle' dst_handle = 'handle' tr1 = tg_ob.tg_traffic_config(port_handle=topo['T1{}P1_ipv6_tg_ph'.format(TG_D2)], emulation_src_handle=topo['T1{}P1_ipv6_tg_ih'.format(TG_D2)][src_handle], emulation_dst_handle=bgp_route[dst_handle], circuit_endpoint_type='ipv6', mode='create', transmit_mode='single_burst', pkts_per_burst='2000', length_mode='fixed', rate_pps=1000) stream_id1 = tr1['stream_id'] tg_ob.tg_traffic_control(action='run', handle=stream_id1) st.wait(20) tg1_stats = tgapi.get_traffic_stats(tg_ob, port_handle=topo["T1{}P1_ipv6_tg_ph".format(TG_D1)]) tg2_stats = tgapi.get_traffic_stats(tg_ob, port_handle=topo["T1{}P1_ipv6_tg_ph".format(TG_D2)]) if not (int(tg2_stats.tx.total_packets) and int(tg1_stats.rx.total_packets)): st.error('Received ZERO stats.') tc_fail_flag = 1 else: percent_rx = float(int(tg2_stats.tx.total_packets) - int(tg1_stats.rx.total_packets)) / int( tg2_stats.tx.total_packets) * 100 st.log('tg1_stats.rx.total_packets : {}'.format(tg1_stats.rx.total_packets)) st.log('tg2_stats.tx.total_packets : {}'.format(tg2_stats.tx.total_packets)) st.log('percent_rx : {}'.format(percent_rx)) if percent_rx > 0.5: tc_fail_flag = 1 tg_ob.tg_emulation_bgp_control(handle=bgp_handle['handle'], mode='stop') if tc_fail_flag: st.report_fail("traffic_verification_failed") st.report_pass('test_case_passed') # BGP Neighbor In L3 Over LAG fixture, class and test cases - END ################################################################################

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Python

spotdl/types/__init__.py

phcreery/spotdl-v4

3bd3768de10ae80b5e1ba3bbe6b792f7fc9f8dfc

[ "MIT" ]

10

2022-01-03T15:00:34.000Z

2022-03-18T19:55:37.000Z

spotdl/types/__init__.py

phcreery/spotdl-v4

3bd3768de10ae80b5e1ba3bbe6b792f7fc9f8dfc

[ "MIT" ]

9

2022-01-15T05:43:35.000Z

2022-03-16T17:57:47.000Z

spotdl/types/__init__.py

phcreery/spotdl-v4

3bd3768de10ae80b5e1ba3bbe6b792f7fc9f8dfc

[ "MIT" ]

11

2022-01-03T15:00:22.000Z

2022-03-27T19:27:05.000Z

""" Types for the spotdl package. """ from spotdl.types.song import Song from spotdl.types.playlist import Playlist from spotdl.types.album import Album from spotdl.types.artist import Artist from spotdl.types.saved import Saved

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py

Python

libs/yowsup/yowsup/yowsup/layers/interface/__init__.py

akshitpradhan/TomHack

837226e7b38de1140c19bc2d478eeb9e379ed1fd

[ "MIT" ]

22

2017-07-14T20:01:17.000Z

2022-03-08T14:22:39.000Z

libs/yowsup/yowsup/yowsup/layers/interface/__init__.py

akshitpradhan/TomHack

837226e7b38de1140c19bc2d478eeb9e379ed1fd

[ "MIT" ]

6

2017-07-14T21:03:50.000Z

2021-06-10T19:08:32.000Z

libs/yowsup/yowsup/yowsup/layers/interface/__init__.py

akshitpradhan/TomHack

837226e7b38de1140c19bc2d478eeb9e379ed1fd

[ "MIT" ]

13

2017-07-14T20:13:14.000Z

2020-11-12T08:06:05.000Z

from .interface import YowInterfaceLayer, ProtocolEntityCallback

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py

Python

terraformspawner/__init__.py

sodre/terraformspawner

d1369371a861680311659b1a5dcea7b6f0b136db

[ "BSD-3-Clause" ]

null

terraformspawner/__init__.py

sodre/terraformspawner

d1369371a861680311659b1a5dcea7b6f0b136db

[ "BSD-3-Clause" ]

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2019-04-18T06:04:19.000Z

terraformspawner/__init__.py

sodre/terraformspawner

d1369371a861680311659b1a5dcea7b6f0b136db

[ "BSD-3-Clause" ]

null

from .terraformspawner import TerraformSpawner

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Python

build/lib.linux-x86_64-2.7/bucket/__init__.py

ibtesamlatif2997/python-GCS

dd535d4ddf76da4d24b2afb9031242e8b386539e

[ "MIT" ]

null

build/lib.linux-x86_64-2.7/bucket/__init__.py

ibtesamlatif2997/python-GCS

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[ "MIT" ]

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2021-03-19T10:14:40.000Z

2021-06-10T19:54:46.000Z

build/lib.linux-x86_64-2.7/bucket/__init__.py

ibtesamlatif2997/python-GCS

dd535d4ddf76da4d24b2afb9031242e8b386539e

[ "MIT" ]

null

from .gcpbucket import UploadorDownloadFile

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py

Python

report/__init__.py

tonygalmiche/is_gestion_lot

ed27f6874443cc29ec2e7fe0c5a187a1dd9d5037

[ "MIT" ]

null

report/__init__.py

tonygalmiche/is_gestion_lot

ed27f6874443cc29ec2e7fe0c5a187a1dd9d5037

[ "MIT" ]

null

report/__init__.py

tonygalmiche/is_gestion_lot

ed27f6874443cc29ec2e7fe0c5a187a1dd9d5037

[ "MIT" ]

null

# -*- coding: utf-8 -*- import stock_bloquer_lot import stock_debloquer_lot import stock_change_location_lot import stock_rebut_lot # vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:

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py

Python

thor/utils/__init__.py

B612-Asteroid-Institute/thor

d3d1dcbe86f67a62c90b4cde3fc577e414825cf2

[ "BSD-3-Clause" ]

null

thor/utils/__init__.py

B612-Asteroid-Institute/thor

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null

thor/utils/__init__.py

B612-Asteroid-Institute/thor

d3d1dcbe86f67a62c90b4cde3fc577e414825cf2

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null

from .io import * from .spice import * from .astropy import * from .horizons import * from .mpc import * from .ades import *

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Python

tf_crnn/parse_args.py

sunmengnan/city_brain

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[ "MIT" ]

null

tf_crnn/parse_args.py

sunmengnan/city_brain

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[ "MIT" ]

null

tf_crnn/parse_args.py

sunmengnan/city_brain

478f0b974f4491b4201956f37b83ce6860712bc8

[ "MIT" ]

null

#!/usr/env/bin python3 import argparse import os import datetime from libs.utils import check_dir_exist from libs.config import load_config OUTPUT_DIR = os.getenv("OUTPUT_DIR") CURRENT_DIR = os.path.abspath(os.path.dirname(__file__)) def save_flags(args, save_dir): """ Save flags into file, use date as file name :param args: tf.app.flags :param save_dir: dir to save flags file """ filename = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S") + ".txt" if not os.path.exists(save_dir): os.makedirs(save_dir) filepath = os.path.join(save_dir, filename) print("Save flags to %s" % filepath) cfg = load_config(args.cfg_name) with open(filepath, mode="w", encoding="utf-8") as f: d = vars(args) for k, v in d.items(): f.write("%s: %s\n" % (k, v)) print("=" * 30) for k, v in cfg.items(): f.write('%s: %s\n' % (k, v)) def parse_infer_args(infer = True): if OUTPUT_DIR is None: output_dir = os.path.join(CURRENT_DIR, 'output') else: output_dir = OUTPUT_DIR parser = argparse.ArgumentParser() parser.add_argument('--gpu', action='store_true', default=False) # 如果是使用 TFrecord 模式，则会加载相应数据目录下的所有 tf_record 文件 # 如果是 JPG 模式，则会去加载相应目录下二级目录中的 jpg 图片和 labels.txt #parser.add_argument('--train_dir', required=True) #parser.add_argument('--train_file_format', required=True, choices=['TF', 'JPG']) #parser.add_argument('--val_dir', required=True) #parser.add_argument('--val_file_format', required=True, choices=['TF', 'JPG']) #parser.add_argument('--test_dir', default=None, help='test 只支持小文件图片格式') #parser.add_argument('--restore', action='store_true', help='Whether to resotre checkpoint from ckpt_dir') #parser.add_argument('--restore_step', action='store_true', help='如果 restore step，lr 会减小') parser.add_argument('--tag', default='default', help='Subdirectory to create in checkpoint_dir/log_dir/result_dir') parser.add_argument('--ckpt_dir', default=os.path.join(output_dir, 'checkpoint'), help='Directory to save tensorflow checkpoint') #parser.add_argument('--log_dir', default=os.path.join(output_dir, 'output/log'), # help='Directory to save tensorboard logs') parser.add_argument('--result_dir', default=os.path.join(output_dir, 'output/result'), help='Directory to save val/test result') parser.add_argument('--chars_file', default=os.path.join(CURRENT_DIR, 'data/ocr_chars/chn.txt'), help='Chars file to load') parser.add_argument('--cfg_name', default='raw', help="raw / squeeze/ dense / resnet / simple") parser.add_argument('--val_step', type=int, default=5000, help='Steps to do val.test and save checkpoint') parser.add_argument('--log_step', type=int, default=50, help='Steps save tensorboard summary') parser.add_argument('--display_step', type=int, default=10, help='Steps print loss to stdout') # Only for inference parser.add_argument('--infer_dir', default='./data/demo', help='Directory store infer images and labels') parser.add_argument('--infer_data_ordered', action='store_true', help='ground truth 存在 labels.txt 文件中') parser.add_argument('--load_sub_infer_dir', action='store_true', help='对 infer_dir 中的子目录进行测试') parser.add_argument('--infer_copy_failed', action='store_true', help='拷贝结果错误的测试数据图片到特定目录') parser.add_argument('--infer_batch_size', type=int, default=1) args, _ = parser.parse_known_args() #if (not infer) and (not os.path.exists(args.train_dir)): # parser.error('train_dir not exist') #if (args.val_dir is not None) and (not os.path.exists(args.val_dir)): # parser.error('val_dir not exist') #if (args.test_dir is not None) and (not os.path.exists(args.test_dir)): # parser.error('test_dir not exist') if infer and (not os.path.exists(args.infer_dir)): parser.error('infer_dir not exist') args.ckpt_dir = os.path.join(args.ckpt_dir, args.tag) args.best_test_ckpt_dir = os.path.join(args.ckpt_dir, 'best_test_ckpt') args.flags_dir = os.path.join(args.ckpt_dir, "flags") #args.log_dir = os.path.join(args.log_dir, args.tag) args.result_dir = os.path.join(args.result_dir, args.tag) check_dir_exist(args.ckpt_dir) check_dir_exist(args.best_test_ckpt_dir) check_dir_exist(args.flags_dir) #check_dir_exist(args.log_dir) check_dir_exist(args.result_dir) save_flags(args, args.flags_dir) print('Use %s as base net' % args.cfg_name) return args def parse_args(infer=False): if OUTPUT_DIR is None: output_dir = os.path.join(CURRENT_DIR, 'output') else: output_dir = OUTPUT_DIR parser = argparse.ArgumentParser() parser.add_argument('--gpu', action='store_true', default=False) # 如果是使用 TFrecord 模式，则会加载相应数据目录下的所有 tf_record 文件 # 如果是 JPG 模式，则会去加载相应目录下二级目录中的 jpg 图片和 labels.txt parser.add_argument('--train_dir', required=True) parser.add_argument('--train_file_format', required=True, choices=['TF', 'JPG']) parser.add_argument('--val_dir', required=True) parser.add_argument('--val_file_format', required=True, choices=['TF', 'JPG']) parser.add_argument('--test_dir', default=None, help='test 只支持小文件图片格式') parser.add_argument('--restore', action='store_true', help='Whether to resotre checkpoint from ckpt_dir') parser.add_argument('--restore_step', action='store_true', help='如果 restore step，lr 会减小') parser.add_argument('--tag', default='default', help='Subdirectory to create in checkpoint_dir/log_dir/result_dir') parser.add_argument('--ckpt_dir', default=os.path.join(output_dir, 'checkpoint'), help='Directory to save tensorflow checkpoint') parser.add_argument('--log_dir', default=os.path.join(output_dir, 'output/log'), help='Directory to save tensorboard logs') parser.add_argument('--result_dir', default=os.path.join(output_dir, 'output/result'), help='Directory to save val/test result') parser.add_argument('--chars_file', default=os.path.join(CURRENT_DIR, 'data/ocr_chars/chn.txt'), help='Chars file to load') parser.add_argument('--cfg_name', default='raw', help="raw / squeeze/ dense / resnet / simple") parser.add_argument('--val_step', type=int, default=5000, help='Steps to do val.test and save checkpoint') parser.add_argument('--log_step', type=int, default=50, help='Steps save tensorboard summary') parser.add_argument('--display_step', type=int, default=10, help='Steps print loss to stdout') # Only for inference parser.add_argument('--infer_dir', default='./data/demo', help='Directory store infer images and labels') parser.add_argument('--infer_data_ordered', action='store_true', help='ground truth 存在 labels.txt 文件中') parser.add_argument('--load_sub_infer_dir', action='store_true', help='对 infer_dir 中的子目录进行测试') parser.add_argument('--infer_copy_failed', action='store_true', help='拷贝结果错误的测试数据图片到特定目录') parser.add_argument('--infer_batch_size', type=int, default=1) args, _ = parser.parse_known_args() if (not infer) and (not os.path.exists(args.train_dir)): parser.error('train_dir not exist') if (args.val_dir is not None) and (not os.path.exists(args.val_dir)): parser.error('val_dir not exist') if (args.test_dir is not None) and (not os.path.exists(args.test_dir)): parser.error('test_dir not exist') if infer and (not os.path.exists(args.infer_dir)): parser.error('infer_dir not exist') args.ckpt_dir = os.path.join(args.ckpt_dir, args.tag) args.best_test_ckpt_dir = os.path.join(args.ckpt_dir, 'best_test_ckpt') args.flags_dir = os.path.join(args.ckpt_dir, "flags") args.log_dir = os.path.join(args.log_dir, args.tag) args.result_dir = os.path.join(args.result_dir, args.tag) check_dir_exist(args.ckpt_dir) check_dir_exist(args.best_test_ckpt_dir) check_dir_exist(args.flags_dir) check_dir_exist(args.log_dir) check_dir_exist(args.result_dir) save_flags(args, args.flags_dir) print('Use %s as base net' % args.cfg_name) return args if __name__ == '__main__': args = parse_args()

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null

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null

0

6

b1d08507f193d3613adccdce2fa72ba603ae18e4

73

py

Python

MobuToMaya/MobuToMayaTools/__init__.py

jazzboysc/SERiggingTools

41289589b88bc812240f6f87359456dbc1a209cd

[ "MIT" ]

4

2020-06-10T07:54:47.000Z

2021-04-22T01:57:02.000Z

MobuToMaya/MobuToMayaTools/__init__.py

jazzboysc/SERiggingTools

41289589b88bc812240f6f87359456dbc1a209cd

[ "MIT" ]

null

MobuToMaya/MobuToMayaTools/__init__.py

jazzboysc/SERiggingTools

41289589b88bc812240f6f87359456dbc1a209cd

[ "MIT" ]

null

import MobuServer2020 import SendAnimationToMayaTool import SendToMayaUI

18.25

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73

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0.666667

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0.059701

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1

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1

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6

b1d9dee0f67766888eb055365298f1b2e9982fed

34

py

Python

groundhog/datasets/__init__.py

wavelets/GroundHog

7ca23c9d741d3b10912c71c9a9ac883e86e70f17

[ "BSD-3-Clause" ]

1

2015-10-06T22:03:06.000Z

groundhog/datasets/__init__.py

wavelets/GroundHog

7ca23c9d741d3b10912c71c9a9ac883e86e70f17

[ "BSD-3-Clause" ]

null

groundhog/datasets/__init__.py

wavelets/GroundHog

7ca23c9d741d3b10912c71c9a9ac883e86e70f17

[ "BSD-3-Clause" ]

null

from LM_dataset import LMIterator

17

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b1e449451e4fb0993af8a57d3c072e5c996dc132

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py

Python

causal_hdf5_runner.py

mbchang/multiagent-particle-envs

9535594effd62c7682b7d6c41c2acbcdd9d56d64

[ "MIT" ]

null

causal_hdf5_runner.py

mbchang/multiagent-particle-envs

9535594effd62c7682b7d6c41c2acbcdd9d56d64

[ "MIT" ]

null

causal_hdf5_runner.py

mbchang/multiagent-particle-envs

9535594effd62c7682b7d6c41c2acbcdd9d56d64

[ "MIT" ]

null

import argparse from collections import OrderedDict import numpy as np import os import itertools import time parser = argparse.ArgumentParser() parser.add_argument('--for-real', action='store_true') args = parser.parse_args() def product_dict(**kwargs): keys = kwargs.keys() vals = kwargs.values() for instance in itertools.product(*vals): yield dict(zip(keys, instance)) class Runner(): def __init__(self, command='python3 information_economy/scratch/vickrey.py', gpus=[]): self.gpus = gpus self.command = command self.flags = {} def add_flag(self, flag_name, flag_values=''): self.flags[flag_name] = flag_values def append_flags_to_command(self, command, flag_dict): for flag_name, flag_value in flag_dict.items(): if type(flag_value) == bool: if flag_value == True: command += ' --{}'.format(flag_name) else: command += ' --{} {}'.format(flag_name, flag_value) return command def command_prefix(self, i): prefix = 'CUDA_VISIBLE_DEVICES={} DISPLAY=:0 '.format(self.gpus[i]) if len(self.gpus) > 0 else 'DISPLAY=:0 ' command = prefix+self.command return command def command_suffix(self, command): # if len(self.gpus) == 0: # command += ' --cpu' # command += ' --printf' command += ' &' return command def generate_commands(self, execute=False): i = 0 j = 0 for flag_dict in product_dict(**self.flags): command = self.command_prefix(i) command = self.append_flags_to_command(command, flag_dict) command = self.command_suffix(command) print(command) if execute: os.system(command) if len(self.gpus) > 0: i = (i + 1) % len(self.gpus) j += 1 print('Launched {} jobs'.format(j)) class RunnerWithIDs(Runner): def __init__(self, command, gpus): Runner.__init__(self, command, gpus) def product_dict(self, **kwargs): ordered_kwargs_dict = OrderedDict() for k, v in kwargs.items(): if not k == 'seed': ordered_kwargs_dict[k] = v keys = ordered_kwargs_dict.keys() vals = ordered_kwargs_dict.values() for instance in itertools.product(*vals): yield dict(zip(keys, instance)) def generate_commands(self, execute=False): if 'seed' not in self.flags: Runner.generate_commands(self, execute) else: i = 0 j = 0 for flag_dict in self.product_dict(**self.flags): command = self.command_prefix() command = self.append_flags_to_command(command, flag_dict) # add exp_id: one exp_id for each flag_dict. exp_id = ''.join(str(s) for s in np.random.randint(10, size=7)) command += ' --expid {}'.format(exp_id) # command doesn't get modified from here on for seed in self.flags['seed']: seeded_command = command seeded_command += ' --seed {}'.format(seed) seeded_command = self.command_suffix(seeded_command) print(seeded_command) if execute: os.system(seeded_command) if len(self.gpus) > 0: i = (i + 1) % len(self.gpus) j += 1 print('Launched {} jobs'.format(j)) def all_counterfactuals_draft1_7_6_2021(): """ for laptop """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['20']) r.add_flag('max_episode_length', ['8']) r.add_flag('t_intervene', ['4']) r.add_flag('intervention_type', ['displacement', 'addition', 'removal', 'force']) r.add_flag('data_root', ['intervenable_bouncing']) r.generate_commands(execute=args.for_real) def all_counterfactuals_geb_7_6_2021(): """ for geb """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['10000']) r.add_flag('max_episode_length', ['8']) r.add_flag('t_intervene', ['4']) r.add_flag('intervention_type', ['displacement', 'addition', 'removal', 'force']) r.add_flag('data_root', ['intervenable_bouncing']) r.generate_commands(execute=args.for_real) def all_counterfactuals_earlier_geb_7_7_2021(): """ for geb intervention step at 2 T = 8 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['10000']) r.add_flag('max_episode_length', ['8']) r.add_flag('t_intervene', ['2']) r.add_flag('intervention_type', ['displacement', 'addition', 'removal', 'force']) r.add_flag('data_root', ['intervenable_bouncing_s2_t8']) r.generate_commands(execute=args.for_real) def all_counterfactuals_earlier_baobab_7_21_2021(): """ for geb intervention step at 2 T = 8 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['20']) r.add_flag('max_episode_length', ['8']) r.add_flag('t_intervene', ['2']) r.add_flag('intervention_type', ['displacement', 'addition', 'removal', 'force']) r.add_flag('data_root', ['intervenable_bouncing_s2_t8']) r.generate_commands(execute=args.for_real) def testing_colors_baobab_7_22_2021(): """ for geb intervention step at 2 T = 8 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['20']) r.add_flag('max_episode_length', ['8']) r.add_flag('t_intervene', ['2']) r.add_flag('intervention_type', ['displacement', 'addition', 'removal', 'force']) r.add_flag('data_root', ['intervenable_bouncing_s2_t8_colors']) r.generate_commands(execute=args.for_real) def colors_geb_7_22_2021(): """ for geb intervention step at 2 T = 8 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['10000']) r.add_flag('max_episode_length', ['8']) r.add_flag('t_intervene', ['2']) r.add_flag('intervention_type', ['displacement', 'addition', 'removal', 'force']) r.add_flag('data_root', ['intervenable_bouncing_s2_t8_colors']) r.generate_commands(execute=args.for_real) def horizon20_geb_8_27_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['1000']) r.add_flag('max_episode_length', ['20']) r.add_flag('t_intervene', ['5']) r.add_flag('intervention_type', ['displacement', 'addition', 'removal', 'force']) r.add_flag('data_root', ['intervenable_bouncing_s5_t20_colors']) r.generate_commands(execute=args.for_real) def horizon20_geb_8_27_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['1000']) r.add_flag('max_episode_length', ['20']) r.add_flag('t_intervene', ['5']) r.add_flag('intervention_type', ['displacement', 'addition', 'removal', 'force']) r.add_flag('data_root', ['intervenable_bouncing_s5_t20_colors']) r.generate_commands(execute=args.for_real) def horizon20_baobab_8_27_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['10']) r.add_flag('max_episode_length', ['20']) r.add_flag('t_intervene', ['5']) r.add_flag('intervention_type', ['displacement']) r.add_flag('data_root', ['intervenable_bouncing_s5_t20_colors']) r.generate_commands(execute=args.for_real) def n8_s5_t20_baobab_9_5_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['10']) r.add_flag('max_episode_length', ['10']) r.add_flag('t_intervene', ['5']) r.add_flag('num_entities', ['8']) r.add_flag('intervention_type', ['displacement']) r.add_flag('data_root', ['intervenable_bouncing_k8_s5_t10']) r.generate_commands(execute=args.for_real) def displacement_debug_baobab_9_16_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['10']) r.add_flag('max_episode_length', ['10']) r.add_flag('t_intervene', ['0', '5']) r.add_flag('num_entities', ['4', '8']) r.add_flag('intervention_type', ['displacement']) r.add_flag('data_root', ['displacement_debug']) r.generate_commands(execute=args.for_real) def displacement_geb_9_16_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['2000']) r.add_flag('max_episode_length', ['10']) r.add_flag('t_intervene', ['0', '1', '2', '3', '4',' 5']) r.add_flag('num_entities', ['4', '8']) r.add_flag('intervention_type', ['displacement']) r.add_flag('data_root', ['displacement']) r.generate_commands(execute=args.for_real) def distshift_debug_baobab_9_21_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['10']) r.add_flag('max_episode_length', ['10']) r.add_flag('t_intervene', ['0']) r.add_flag('num_entities', ['4']) r.add_flag('intervention_type', ['displacement']) r.add_flag('data_root', ['distshift_debug']) r.generate_commands(execute=args.for_real) def distshift_debug_baobab_9_21_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['10']) r.add_flag('max_episode_length', ['10']) r.add_flag('t_intervene', ['0']) r.add_flag('num_entities', ['4']) r.add_flag('intervention_type', ['displacement']) r.add_flag('data_root', ['distshift_debug']) r.generate_commands(execute=args.for_real) def distshift_geb_9_21_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['2000']) r.add_flag('max_episode_length', ['10']) r.add_flag('t_intervene', ['0']) r.add_flag('num_entities', ['4']) r.add_flag('intervention_type', ['displacement']) r.add_flag('color_dist', [ 'uniform_k20', 'context_swap_k4_4505_a', 'context_swap_k4_4505_b', 'context_swap_k4_5000_a', 'context_swap_k4_5000_b', 'multiplicity_k20', 'fcontext_swap_k4_752500_a', 'fcontext_swap_k4_752500_b', ]) r.add_flag('data_root', ['distshift']) r.generate_commands(execute=args.for_real) def distshift_baobab_9_21_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing.py', gpus=[]) r.add_flag('num_episodes', ['100']) r.add_flag('max_episode_length', ['10']) r.add_flag('t_intervene', ['0']) r.add_flag('num_entities', ['4']) r.add_flag('intervention_type', ['displacement']) r.add_flag('color_dist', [ # 'uniform_k20', # 'context_swap_k4_4505_a', # 'context_swap_k4_4505_b', # 'context_swap_k4_5000_a', # 'context_swap_k4_5000_b', # 'multiplicity_k20', 'fcontext_swap_k4_752500_a', 'fcontext_swap_k4_752500_b', ]) r.add_flag('data_root', ['distshift']) r.generate_commands(execute=args.for_real) def whiteball_push_baobab_9_24_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing_white_action.py', gpus=[]) r.add_flag('num_episodes', ['10']) r.add_flag('max_episode_length', ['10']) r.add_flag('t_intervene', ['0']) r.add_flag('num_entities', ['4']) r.add_flag('color_dist', ['uniform_k20']) r.add_flag('intervention_type', ['displacement']) r.add_flag('data_root', ['whiteballpush']) r.generate_commands(execute=args.for_real) def whiteball_push_geb_9_24_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing_white_action.py', gpus=[]) r.add_flag('num_episodes', ['2000']) r.add_flag('max_episode_length', ['10']) r.add_flag('t_intervene', ['0']) r.add_flag('num_entities', ['4']) r.add_flag('color_dist', ['uniform_k20']) r.add_flag('intervention_type', ['displacement']) r.add_flag('data_root', ['whiteballpush']) r.generate_commands(execute=args.for_real) def whiteball_push_baobab_9_27_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing_white_action.py', gpus=[]) r.add_flag('num_episodes', ['10']) r.add_flag('max_episode_length', ['10']) r.add_flag('t_intervene', ['0']) r.add_flag('num_entities', ['1']) r.add_flag('color_dist', ['uniform_k20']) r.add_flag('intervention_type', ['displacement']) r.add_flag('data_root', ['singlewhiteballpush']) r.generate_commands(execute=args.for_real) def whiteball_push_geb_9_27_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing_white_action.py', gpus=[]) r.add_flag('num_episodes', ['2000']) r.add_flag('max_episode_length', ['10']) r.add_flag('t_intervene', ['0']) r.add_flag('num_entities', ['1']) r.add_flag('color_dist', ['uniform_k20']) r.add_flag('intervention_type', ['displacement']) r.add_flag('data_root', ['singlewhiteballpush']) r.generate_commands(execute=args.for_real) def whiteball_push_gauss1_10_25_2021(): """ t = 20 """ r = RunnerWithIDs(command='python bin/counterfactual_hdf5.py --scenario intervenable_bouncing_white_action.py', gpus=[]) r.add_flag('num_episodes', ['2000']) r.add_flag('max_episode_length', ['10']) r.add_flag('t_intervene', ['0']) r.add_flag('num_entities', ['4']) r.add_flag('color_dist', ['uniform_k20']) r.add_flag('intervention_type', ['displacement']) r.add_flag('data_root', ['singlewhiteballpush']) r.generate_commands(execute=args.for_real) if __name__ == '__main__': # all_counterfactuals_draft1_7_6_2021() # all_counterfactuals_geb_7_6_2021() # all_counterfactuals_earlier_geb_7_7_2021() # all_counterfactuals_earlier_baobab_7_21_2021() # testing_colors_baobab_7_22_2021() # colors_geb_7_22_2021() # horizon20_geb_8_27_2021() # horizon20_baobab_8_27_2021() # n8_s5_t20_baobab_9_5_2021() # displacement_debug_baobab_9_16_2021() # displacement_geb_9_16_2021() # distshift_debug_baobab_9_21_2021() # distshift_geb_9_21_2021() # distshift_baobab_9_21_2021() # whiteball_push_baobab_9_24_2021() # whiteball_push_geb_9_24_2021() # whiteball_push_baobab_9_27_2021() # whiteball_push_geb_9_27_2021() whiteball_push_gauss1_10_25_2021()

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593917995a6282d6ab8ae02cf31f0ebd523d9fba

240

py

Python

modules/rdpConfig.py

mygalus/RemoteConnexionOrganizers

29a6e881313b696c2f0ab2a1bce56233c54b8c22

[ "MIT" ]

1

2019-09-12T01:47:02.000Z

modules/rdpConfig.py

mygalus/RemoteConnexionOrganizers

29a6e881313b696c2f0ab2a1bce56233c54b8c22

[ "MIT" ]

null

modules/rdpConfig.py

mygalus/RemoteConnexionOrganizers

29a6e881313b696c2f0ab2a1bce56233c54b8c22

[ "MIT" ]

1

2020-04-05T07:23:30.000Z

class RdpConfig: def __init__(self, prog): self.prog = prog pass def getDefaultCmd(self): return (self.prog + ' --plugin cliprdr --ntlm --composition -x m -u Administrator -p "xxxxxxxx" -g 1920x1000 xxxxxxx')

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