xszheng2020/the_stack_dedup_python_hits_1 · Datasets at Hugging Face

a7ea14ccf7f41c0614b8f95c605b3bd30018a21b

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py

Python

example_project/blog/migrations/0001_initial.py

allran/djangorestframework-appapi

5e843b70910ccd55d787096ee08eb85315c80000

[ "BSD-2-Clause" ]

4

2019-10-15T06:47:29.000Z

2019-11-11T13:16:15.000Z

example_project/blog/migrations/0001_initial.py

allran/djangorestframework-appapi

5e843b70910ccd55d787096ee08eb85315c80000

[ "BSD-2-Clause" ]

null

example_project/blog/migrations/0001_initial.py

allran/djangorestframework-appapi

5e843b70910ccd55d787096ee08eb85315c80000

[ "BSD-2-Clause" ]

null

# Generated by Django 2.2.6 on 2019-10-16 02:53 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Author', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ('name', models.CharField(max_length=50)), ('email', models.EmailField(max_length=254)), ], options={ 'ordering': ['id'], }, ), migrations.CreateModel( name='Blog', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ('title', models.CharField(blank=True, max_length=255, null=True, verbose_name='title')), ('content', models.TextField(blank=True, null=True)), ('author', models.ForeignKey(blank=True, help_text='作者id', null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='author', to='blog.Author', verbose_name='作者')), ], options={ 'ordering': ['id'], }, ), migrations.CreateModel( name='UserFavorite', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ('blog', models.ForeignKey(blank=True, help_text='博客id', null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='blog', to='blog.Blog', verbose_name='博客')), ('user', models.ForeignKey(help_text='收藏人id', null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL, verbose_name='用户')), ], options={ 'verbose_name': '用户收藏', 'verbose_name_plural': '用户收藏', 'ordering': ['id'], 'unique_together': {('user', 'blog')}, }, ), ]

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a7f5cbeb6c6ac6730e6541d991681e7c83554dd8

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py

Python

fun.py

Krishna-Aaseri/Python_Logical_Questions

c0f025a56dbbf85426142adb423b25fa7b034adb

[ "MIT" ]

null

fun.py

Krishna-Aaseri/Python_Logical_Questions

c0f025a56dbbf85426142adb423b25fa7b034adb

[ "MIT" ]

null

fun.py

Krishna-Aaseri/Python_Logical_Questions

c0f025a56dbbf85426142adb423b25fa7b034adb

[ "MIT" ]

null

#def add(num,num1): # add1=num+num1 # print add1 #add(6,7) #def welcome(): # print "python kaisa lagta h aapko" # print "but please reply na kare aap" #welcome() user = int(raw_input("enter a number")) i = 0 new = [] while i < (user): user1 = int(raw_input("enter a number")) new.append(user1) i = i + 1 print new print "**********************************************" i = 0 new_list = [] while i < len(new): if new[i]%2 == 0: new_list.append(new) else: new_list.append(new) i = i + 1 print new_list

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a7faceab673a31a756534245b8aaabc503d661d6

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py

Python

docs/demos/theme_explorer/list_group.py

sthagen/facultyai-dash-bootstrap-components

2dd5eaf1c1494b2077bcee82eb7968ec2e23af46

[ "Apache-2.0" ]

50

2018-09-23T08:57:28.000Z

2019-02-02T19:59:35.000Z

docs/demos/theme_explorer/list_group.py

sthagen/dash-bootstrap-components

d79ad7f8fdf4c26165038e6989e24f2ac17663b1

[ "Apache-2.0" ]

99

2018-09-21T11:06:29.000Z

2019-02-04T09:04:07.000Z

docs/demos/theme_explorer/list_group.py

sthagen/dash-bootstrap-components

d79ad7f8fdf4c26165038e6989e24f2ac17663b1

[ "Apache-2.0" ]

3

2018-09-25T02:16:24.000Z

2018-12-22T20:56:31.000Z

import dash_bootstrap_components as dbc from dash import html from .util import make_subheading list_group = html.Div( [ make_subheading("ListGroup", "list_group"), dbc.ListGroup( [ dbc.ListGroupItem("No color applied"), dbc.ListGroupItem("The primary item", color="primary"), dbc.ListGroupItem("A secondary item", color="secondary"), dbc.ListGroupItem("A successful item", color="success"), dbc.ListGroupItem("A warning item", color="warning"), dbc.ListGroupItem("A dangerous item", color="danger"), dbc.ListGroupItem("An informative item", color="info"), dbc.ListGroupItem("A light item", color="light"), dbc.ListGroupItem("A dark item", color="dark"), dbc.ListGroupItem("An action item", action=True), dbc.ListGroupItem("An active item", active=True), dbc.ListGroupItem( [ html.H5("Item 4 heading"), html.P("Item 4 text"), ] ), ] ), ], className="mb-4", )

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c5041849eb6e20166cf188e490e80a877301469d

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py

Python

download-from-web/govori.py

miroslavradojevic/python-snippets

753e1c15dc077d3bcf5de4fd5d3a675daf0da27c

[ "MIT" ]

null

download-from-web/govori.py

miroslavradojevic/python-snippets

753e1c15dc077d3bcf5de4fd5d3a675daf0da27c

[ "MIT" ]

null

download-from-web/govori.py

miroslavradojevic/python-snippets

753e1c15dc077d3bcf5de4fd5d3a675daf0da27c

[ "MIT" ]

null

#!/usr/bin/env python # Download .mp3 podcast files of Radio Belgrade show Govori da bih te video (Speak so that I can see you) # grab all mp3s and save them with parsed name and date to the output folder import requests import os import time import xml.dom.minidom from urllib.parse import urlparse url = "https://www.rts.rs/page/radio/sr/podcast/5433/govori-da-bih-te-video/audio.html" # url results with xml that is further parsed timestamp = time.strftime("%Y%m%d-%H%M%S") out_dir = os.path.join("govori_" + timestamp) doc_path = "govori_" + timestamp + ".xml" if not os.path.exists(out_dir): os.makedirs(out_dir) try: req = requests.get(url) req.raise_for_status() doc = xml.dom.minidom.parseString(req.text) # TODO check if it is valid XML items = doc.getElementsByTagName("item") print("found ", len(items), " items") for item in items: # titles = item.getElementsByTagName("title") # if len(titles) > 0: # print(titles[0].firstChild.data) links = item.getElementsByTagName("link") if len(links) > 0: print(links[0].firstChild.data) # read element data value # get only filename of the .html https://bit.ly/2ZnqwK7 a = urlparse(links[0].firstChild.data) out_fname_pname = os.path.basename(a.path).replace('.html', '') else: out_fname_pname = "NA" enclosures = item.getElementsByTagName("enclosure") if len(enclosures) > 0: url_value = enclosures[0].attributes["url"].value # read attribute value print(url_value) if url_value.endswith('.mp3'): url_elements = urlparse(url_value).path.split('/') if len(url_elements) >= 5: out_fname_date = ''.join(url_elements[-5:-2]) # https://bit.ly/3e6mXMk else: out_fname_date = "NA" out_file = out_fname_date + "_" + out_fname_pname + ".mp3" print("saved to " + os.path.join(out_dir, out_file)) # save mp3 file from url_value to out_file # https://dzone.com/articles/simple-examples-of-downloading-files-using-python print("saving... ", end='') try: req = requests.get(url_value) req.raise_for_status() open(os.path.join(out_dir, out_file), 'wb').write(req.content) print("saved to " + os.path.join(out_dir, out_file)) except requests.exceptions.HTTPError as err: print(err) # raise SystemExit(err) print("") # save rss xml with open(os.path.join(out_dir, doc_path), "w", encoding="utf-8") as f: f.write(doc.toprettyxml()) print(os.path.join(out_dir, doc_path)) except requests.exceptions.HTTPError as err: print(err) # raise SystemExit(err)

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c506aceeb7ea06c9672cd06b35d80f96cd51d00c

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py

Python

setup.py

uhlerlab/conditional_independence

aa4b5117b6f24bf39433d427d490312864e9bd69

[ "BSD-3-Clause" ]

4

2021-01-29T20:27:31.000Z

2022-02-01T11:55:33.000Z

setup.py

uhlerlab/conditional_independence

aa4b5117b6f24bf39433d427d490312864e9bd69

[ "BSD-3-Clause" ]

null

setup.py

uhlerlab/conditional_independence

aa4b5117b6f24bf39433d427d490312864e9bd69

[ "BSD-3-Clause" ]

1

2021-09-12T13:41:21.000Z

import setuptools setuptools.setup( name='conditional_independence', version='0.1a.4', description='Parametric and non-parametric conditional independence tests.', long_description='', author='Chandler Squires', author_email='chandlersquires18@gmail.com', packages=setuptools.find_packages(exclude=['tests']), python_requires='>3.5.0', zip_safe=False, classifiers=[ 'Topic :: Scientific/Engineering', 'Topic :: Scientific/Engineering :: Mathematics', 'Topic :: Scientific/Engineering :: Artificial Intelligence', ], install_requires=[ 'scipy', 'dataclasses', 'numpy', # 'scikit_sparse', 'numexpr', 'scikit_learn', 'typing', 'pygam', 'tqdm', # 'numba', 'ipdb', ] )

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c509a2151c61ed3015af0423248b9cd0ce672927

1,975

py

Python

examples/ecs/server_interface.py

wangrui1121/huaweicloud-sdk-python

240abe00288760115d1791012d4e3c4592d77ad1

[ "Apache-2.0" ]

43

2018-12-19T08:39:15.000Z

2021-07-21T02:45:43.000Z

examples/ecs/server_interface.py

wangrui1121/huaweicloud-sdk-python

240abe00288760115d1791012d4e3c4592d77ad1

[ "Apache-2.0" ]

11

2019-03-17T13:28:56.000Z

2020-09-23T23:57:50.000Z

examples/ecs/server_interface.py

wangrui1121/huaweicloud-sdk-python

240abe00288760115d1791012d4e3c4592d77ad1

[ "Apache-2.0" ]

47

2018-12-19T05:14:25.000Z

2022-03-19T15:28:30.000Z

# -*-coding:utf-8 -*- from openstack import connection # create connection username = "xxxxxx" password = "xxxxxx" projectId = "xxxxxxxxxxxxxxxxxxxxxxxxxxxx" # tenant ID userDomainId = "xxxxxxxxxxxxxxxxxxxxxxxxxxxx" # user account ID auth_url = "xxxxxxxxxxxxxxxxxxxxxxxxxxxx" # endpoint url conn = connection.Connection(auth_url=auth_url, user_domain_id=userDomainId, project_id=projectId, username=username, password=password) # create server interface def create_server_interface(server_id, net_id=None, port_id=None, fixed_ip=None): attrs = {"net_id": net_id, "port_id": port_id, "fixed_ip": fixed_ip} kwargs = {} for key in attrs: if attrs[key]: kwargs[key] = attrs[key] print(kwargs) if kwargs == {}: message = "Parameter error" raise exceptions.SDKException(message) server = conn.compute.create_server_interface(server_id, **kwargs) print(server) return server # delete interface def delete_server_interface(server_interface, servr_id): conn.compute.delete_server_interface(server_interface, server=servr_id) # show interface detail def get_server_interface(server_interface, servr_id): server_ifa = conn.compute.get_server_interface(server_interface, server=servr_id) print(server_ifa) # get list of interface def server_interfaces(server_id): server_ifas = conn.compute.server_interfaces(server_id) for ifa in server_ifas: print(ifa) if __name__ == "__main__": server_id = "8700184b-79ff-414b-ab8e-11ed01bd3d3d" net_id = "e2103034-dcf3-4ac3-b551-6d5dd8fadb6e" server = create_server_interface(server_id, net_id) get_server_interface(server.id, server_id) server_interfaces(server_id) delete_server_interface(server.id, server_id)

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c50a6cdccc88ffe721b0e07a35e407563cda966e

9,060

py

Python

sdk/python/pulumi_google_native/dlp/v2/stored_info_type.py

AaronFriel/pulumi-google-native

75d1cda425e33d4610348972cd70bddf35f1770d

[ "Apache-2.0" ]

44

2021-04-18T23:00:48.000Z

2022-02-14T17:43:15.000Z

sdk/python/pulumi_google_native/dlp/v2/stored_info_type.py

AaronFriel/pulumi-google-native

75d1cda425e33d4610348972cd70bddf35f1770d

[ "Apache-2.0" ]

354

2021-04-16T16:48:39.000Z

2022-03-31T17:16:39.000Z

sdk/python/pulumi_google_native/dlp/v2/stored_info_type.py

AaronFriel/pulumi-google-native

75d1cda425e33d4610348972cd70bddf35f1770d

[ "Apache-2.0" ]

8

2021-04-24T17:46:51.000Z

2022-01-05T10:40:21.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from . import outputs from ._inputs import * __all__ = ['StoredInfoTypeArgs', 'StoredInfoType'] @pulumi.input_type class StoredInfoTypeArgs: def __init__(__self__, *, config: pulumi.Input['GooglePrivacyDlpV2StoredInfoTypeConfigArgs'], location: Optional[pulumi.Input[str]] = None, project: Optional[pulumi.Input[str]] = None, stored_info_type_id: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a StoredInfoType resource. :param pulumi.Input['GooglePrivacyDlpV2StoredInfoTypeConfigArgs'] config: Configuration of the storedInfoType to create. :param pulumi.Input[str] stored_info_type_id: The storedInfoType ID can contain uppercase and lowercase letters, numbers, and hyphens; that is, it must match the regular expression: `[a-zA-Z\d-_]+`. The maximum length is 100 characters. Can be empty to allow the system to generate one. """ pulumi.set(__self__, "config", config) if location is not None: pulumi.set(__self__, "location", location) if project is not None: pulumi.set(__self__, "project", project) if stored_info_type_id is not None: pulumi.set(__self__, "stored_info_type_id", stored_info_type_id) @property @pulumi.getter def config(self) -> pulumi.Input['GooglePrivacyDlpV2StoredInfoTypeConfigArgs']: """ Configuration of the storedInfoType to create. """ return pulumi.get(self, "config") @config.setter def config(self, value: pulumi.Input['GooglePrivacyDlpV2StoredInfoTypeConfigArgs']): pulumi.set(self, "config", value) @property @pulumi.getter def location(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "location") @location.setter def location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "location", value) @property @pulumi.getter def project(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "project") @project.setter def project(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "project", value) @property @pulumi.getter(name="storedInfoTypeId") def stored_info_type_id(self) -> Optional[pulumi.Input[str]]: """ The storedInfoType ID can contain uppercase and lowercase letters, numbers, and hyphens; that is, it must match the regular expression: `[a-zA-Z\d-_]+`. The maximum length is 100 characters. Can be empty to allow the system to generate one. """ return pulumi.get(self, "stored_info_type_id") @stored_info_type_id.setter def stored_info_type_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "stored_info_type_id", value) class StoredInfoType(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, config: Optional[pulumi.Input[pulumi.InputType['GooglePrivacyDlpV2StoredInfoTypeConfigArgs']]] = None, location: Optional[pulumi.Input[str]] = None, project: Optional[pulumi.Input[str]] = None, stored_info_type_id: Optional[pulumi.Input[str]] = None, __props__=None): """ Creates a pre-built stored infoType to be used for inspection. See https://cloud.google.com/dlp/docs/creating-stored-infotypes to learn more. Auto-naming is currently not supported for this resource. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[pulumi.InputType['GooglePrivacyDlpV2StoredInfoTypeConfigArgs']] config: Configuration of the storedInfoType to create. :param pulumi.Input[str] stored_info_type_id: The storedInfoType ID can contain uppercase and lowercase letters, numbers, and hyphens; that is, it must match the regular expression: `[a-zA-Z\d-_]+`. The maximum length is 100 characters. Can be empty to allow the system to generate one. """ ... @overload def __init__(__self__, resource_name: str, args: StoredInfoTypeArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Creates a pre-built stored infoType to be used for inspection. See https://cloud.google.com/dlp/docs/creating-stored-infotypes to learn more. Auto-naming is currently not supported for this resource. :param str resource_name: The name of the resource. :param StoredInfoTypeArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(StoredInfoTypeArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, config: Optional[pulumi.Input[pulumi.InputType['GooglePrivacyDlpV2StoredInfoTypeConfigArgs']]] = None, location: Optional[pulumi.Input[str]] = None, project: Optional[pulumi.Input[str]] = None, stored_info_type_id: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = StoredInfoTypeArgs.__new__(StoredInfoTypeArgs) if config is None and not opts.urn: raise TypeError("Missing required property 'config'") __props__.__dict__["config"] = config __props__.__dict__["location"] = location __props__.__dict__["project"] = project __props__.__dict__["stored_info_type_id"] = stored_info_type_id __props__.__dict__["current_version"] = None __props__.__dict__["name"] = None __props__.__dict__["pending_versions"] = None super(StoredInfoType, __self__).__init__( 'google-native:dlp/v2:StoredInfoType', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'StoredInfoType': """ Get an existing StoredInfoType resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = StoredInfoTypeArgs.__new__(StoredInfoTypeArgs) __props__.__dict__["current_version"] = None __props__.__dict__["name"] = None __props__.__dict__["pending_versions"] = None return StoredInfoType(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="currentVersion") def current_version(self) -> pulumi.Output['outputs.GooglePrivacyDlpV2StoredInfoTypeVersionResponse']: """ Current version of the stored info type. """ return pulumi.get(self, "current_version") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Resource name. """ return pulumi.get(self, "name") @property @pulumi.getter(name="pendingVersions") def pending_versions(self) -> pulumi.Output[Sequence['outputs.GooglePrivacyDlpV2StoredInfoTypeVersionResponse']]: """ Pending versions of the stored info type. Empty if no versions are pending. """ return pulumi.get(self, "pending_versions")

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0

null

0

1

0

null

0

1

c50d8c67882d7ef410bf79b36de881a95ed1d06e

631

py

Python

python/cw/letterfreq2.py

vesche/snippets

7a9d598df99c26c4e0c63669f9f95a94eeed0d08

[ "Unlicense" ]

7

2016-01-03T19:42:07.000Z

2018-10-23T14:03:12.000Z

python/cw/letterfreq2.py

vesche/snippets

7a9d598df99c26c4e0c63669f9f95a94eeed0d08

[ "Unlicense" ]

null

python/cw/letterfreq2.py

vesche/snippets

7a9d598df99c26c4e0c63669f9f95a94eeed0d08

[ "Unlicense" ]

1

2018-03-09T08:52:01.000Z

#!/usr/bin/env python from __future__ import division import sys from string import ascii_lowercase with open(sys.argv[1]) as f: data = f.read().splitlines() d = {} for line in data: for letter in line: letter = letter.lower() if letter not in ascii_lowercase+' ': continue if letter not in d: d[letter] = 1 else: d[letter] += 1 total = 0 for k,v in d.iteritems(): total += v for k,v in d.iteritems(): d[k] = float('{:.2%}'.format(v/total)[:-1]) for k,v in sorted(d.items(), key=lambda(k,v): (-v, k)): print "'{}' {}%".format(k, str(v))

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c5118009a2cf132e4b87f2f696c2abdd36248815

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py

Python

Coursework_02/Q3/airport/scenarios.py

eBe02/COMP0037-21_22

c0872548ff4b653e3f786734666838813db2149a

[ "Apache-2.0" ]

null

Coursework_02/Q3/airport/scenarios.py

eBe02/COMP0037-21_22

c0872548ff4b653e3f786734666838813db2149a

[ "Apache-2.0" ]

null

Coursework_02/Q3/airport/scenarios.py

eBe02/COMP0037-21_22

c0872548ff4b653e3f786734666838813db2149a

[ "Apache-2.0" ]

null

''' Created on 25 Jan 2022 @author: ucacsjj ''' from .airport_map import MapCellType from .airport_map import AirportMap # This file contains a set of functions which build different maps. Only # two of these are needed for the coursework. Others are ones which were # used for developing and testing the algorithms and might be of use. # Helper function which fills sets the type of all cells in a rectangular # region to have the same type. def _set_block_to_single_type(airport_map, cell_type, start_coords, end_coords): for x in range(start_coords[0], end_coords[0] + 1): for y in range(start_coords[1], end_coords[1] + 1): airport_map.set_cell_type(x, y, cell_type) # This scenario can be used to test the different traversability costs def test_traversability_costs_scenario(): airport_map = AirportMap("Test Traversabilty Map", 15, 15) for x in range(0, 14): airport_map.set_wall(x, 7) airport_map.add_secret_door(7, 7) return airport_map, 200 def one_row_scenario(): airport_map = AirportMap("One Row Scenario", 15, 1) airport_map.add_robot_end_station(14, 0, 100) return airport_map, 200 def two_row_scenario(): airport_map = AirportMap("Two Row Scenario", 15, 2) airport_map.add_robot_end_station(14, 0, 0) return airport_map, 200 def two_2x2_scenario(): airport_map = AirportMap("2x2 Scenario", 2, 2) airport_map.add_robot_end_station(0, 1, 100) return airport_map, 800 def test_3x3_scenario(): airport_map = AirportMap("3x3 Scenario", 3, 3) airport_map.add_robot_end_station(0, 2, 100) return airport_map, 800 def three_row_scenario(): airport_map = AirportMap("Three Row Scenario", 15, 3) airport_map.set_cell_type(2, 1, MapCellType.WALL) airport_map.add_robot_end_station(14, 0, 0) return airport_map, 200 def corridor_scenario(): airport_map = AirportMap("Three Row Scenario", 20, 7) _set_block_to_single_type(airport_map, MapCellType.WALL, (0, 0), (19, 0)) _set_block_to_single_type(airport_map, MapCellType.WALL, (0, 6), (19, 6)) _set_block_to_single_type(airport_map, MapCellType.CHAIR, (2, 1), (5, 1)) for y in range(3,7): airport_map.add_robot_end_station(19, y, 100) #_set_block_to_single_type(airport_map, MapCellType.ROBOT_END_STATION, (19, 0), (19, 6)) return airport_map, 450 def mini_scenario(): # Create the map airport_map = AirportMap("Mini Scenario", 15, 15) # Create the wall on either side and the customs area for x in range(0, 15): airport_map.set_wall(x, 7) for x in range(5, 7): airport_map.set_customs_area(x, 7) airport_map.add_charging_station(4, 4, 1, 1) airport_map.add_secret_door(14, 7) airport_map.add_toilet(4, 1) airport_map.add_robot_end_station(0, 14, 100) return airport_map, 800 def full_scenario(): airport_map = AirportMap("Full Scenario", 60, 40) # The wall separating the two areas, including the customs area # and the secret door _set_block_to_single_type(airport_map, MapCellType.WALL, (0, 18), (59, 20)) _set_block_to_single_type(airport_map, MapCellType.CUSTOMS_AREA, (25, 18), (35, 20)) _set_block_to_single_type(airport_map, MapCellType.SECRET_DOOR, (59, 18), (59, 20)) # The reclaim areas airport_map.add_rubbish_bin(2, 33) _set_block_to_single_type(airport_map, MapCellType.BAGGAGE_CLAIM, (5, 30), (8, 36)) airport_map.add_rubbish_bin(11, 33) _set_block_to_single_type(airport_map, MapCellType.BAGGAGE_CLAIM, (15, 28), (18, 39)) airport_map.add_rubbish_bin(22, 38) _set_block_to_single_type(airport_map, MapCellType.BAGGAGE_CLAIM, (25, 28), (28, 39)) airport_map.add_rubbish_bin(31, 38) _set_block_to_single_type(airport_map, MapCellType.BAGGAGE_CLAIM, (35, 28), (38, 39)) airport_map.add_rubbish_bin(41, 38) _set_block_to_single_type(airport_map, MapCellType.BAGGAGE_CLAIM, (45, 28), (48, 39)) airport_map.add_rubbish_bin(51, 33) _set_block_to_single_type(airport_map, MapCellType.BAGGAGE_CLAIM, (55, 30), (58, 36)) # The bins in the reclaim areas # Add the horizontal chairs with bins at either end for i in range(5): y_coord = 2 + i * 3 _set_block_to_single_type(airport_map, MapCellType.CHAIR, (5, y_coord), (18, y_coord)) airport_map.add_rubbish_bin(4, y_coord) airport_map.add_rubbish_bin(19, y_coord) # Add the vertical chairs with bins at either end for i in range(5): x_coord = 42 + i * 3 _set_block_to_single_type(airport_map, MapCellType.CHAIR, (x_coord, 2), (x_coord, 14)) airport_map.add_rubbish_bin(x_coord, 1) airport_map.add_rubbish_bin(x_coord, 15) # The toilets. These generate rubbish to be collected airport_map.add_toilet(0, 21) airport_map.add_toilet(0, 17) airport_map.add_toilet(38, 0) airport_map.add_toilet(58, 21) # These charge the robot back up again airport_map.add_charging_station(1, 38, 15, 1) airport_map.add_charging_station(58, 38, 15, 1) airport_map.add_charging_station(36, 0, 30, 1) airport_map.add_charging_station(59, 0, 40, 1) airport_map.add_robot_end_station(1, 21, 50) return airport_map, 800

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null

1

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null

0

1

c511d2974df6ea839e2f08eec91ae6a38dd211bf

332

py

Python

setup.py

abkfenris/adm_locations

266915ab7e7559bd4c66d4090bcd69a2a93ab563

[ "MIT" ]

null

setup.py

abkfenris/adm_locations

266915ab7e7559bd4c66d4090bcd69a2a93ab563

[ "MIT" ]

null

setup.py

abkfenris/adm_locations

266915ab7e7559bd4c66d4090bcd69a2a93ab563

[ "MIT" ]

null

from setuptools import setup setup( name='csv_locate', version='0.1', py_modules=['csv_to_json'], install_requires=[ 'click', 'colorama', 'geocoder', 'geojson', 'jinja2', ], entry_points=''' [console_scripts] csv_locate=csv_to_json:convert ''', )

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0

null

0

null

0

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1

c5198d8481c8a0970f981fde506e8ae0b90aab1f

1,763

py

Python

bin/wls_users.py

rstyczynski/wls-tools

292a39a3f7af7b9d7d4c4849618d6789daae9b58

[ "Apache-2.0" ]

null

bin/wls_users.py

rstyczynski/wls-tools

292a39a3f7af7b9d7d4c4849618d6789daae9b58

[ "Apache-2.0" ]

null

bin/wls_users.py

rstyczynski/wls-tools

292a39a3f7af7b9d7d4c4849618d6789daae9b58

[ "Apache-2.0" ]

null

#!$BEA_HOME/oracle_common/common/bin/wlst.sh # default values admin_name = 'AdminServer' admin_address = 'localhost' admin_port = 7001 admin_protocol = 't3' admin_url = admin_protocol + "://" + admin_address + ":" + str(admin_port) def usage(): print "dump_users [-s|--server -p|--port] [-u|--url] [-d|--delimiter]" try: opts, args = getopt.getopt( sys.argv[1:], 's:p:u::d:h', ['server=','port=','url=','delimiter='] ) except getopt.GetoptError, err: print str(err) usage() sys.exit(2) for opt, arg in opts: if opt in ('--help'): usage() sys.exit(2) elif opt in ('-s', '--server'): admin_name = arg elif opt in ('-p', '--port'): admin_port = arg admin_url = admin_protocol + "://" + admin_address + ":" + str(admin_port) elif opt in ('-u', '--url'): admin_url = arg elif opt in ('-d', '--delimiter'): delimiter = arg else: usage() sys.exit(2) connect(url=admin_url, adminServerName=admin_name) # do work from weblogic.management.security.authentication import UserReaderMBean from weblogic.management.security.authentication import GroupReaderMBean realmName=cmo.getSecurityConfiguration().getDefaultRealm() authProvider = realmName.getAuthenticationProviders() print 'admin_url,group,user' for i in authProvider: if isinstance(i,GroupReaderMBean): groupReader = i cursor = i.listGroups("*",0) while groupReader.haveCurrent(cursor): group = groupReader.getCurrentName(cursor) usergroup = i.listAllUsersInGroup(group,"*",0) for user in usergroup: print '%s,%s,%s' % (admin_url,group,user) groupReader.advance(cursor) groupReader.close(cursor) # disconnect() exit()

27.546875

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1

c5206e72ad25192f5a2ed7316aa7ced0c3105161

436

py

Python

tests/test_calculate_branch.py

ivergara/python-abc

b5bb87b80315f8e5ecd2d6f35b7208f0a7df9c3a

[ "Unlicense" ]

2

2021-07-25T20:12:21.000Z

2021-07-25T21:19:23.000Z

tests/test_calculate_branch.py

ivergara/python-abc

b5bb87b80315f8e5ecd2d6f35b7208f0a7df9c3a

[ "Unlicense" ]

1

2021-12-28T22:07:05.000Z

tests/test_calculate_branch.py

ivergara/python-abc

b5bb87b80315f8e5ecd2d6f35b7208f0a7df9c3a

[ "Unlicense" ]

1

2021-12-07T19:53:45.000Z

import pytest from tests import assert_source_returns_expected BRANCH_CASES = [ # Call ('print("hello world")', 'b | print("hello world")'), # Await ("await noop()", "b | await noop()"), # Class instantiation ("Noop()", "b | Noop()"), ] @pytest.mark.parametrize("source,expected", BRANCH_CASES) def test_branch(capsys, source, expected): assert_source_returns_expected(capsys, source, expected) is True

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null

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null

0

1

c53b92a47fb947f6f8b829b01647aa8c055f8973

644

py

Python

character/migrations/0004_alter_character_alignment.py

scottBowles/dnd

a1ef333f1a865d51b5426dc4b3493e8437584565

[ "MIT" ]

null

character/migrations/0004_alter_character_alignment.py

scottBowles/dnd

a1ef333f1a865d51b5426dc4b3493e8437584565

[ "MIT" ]

null

character/migrations/0004_alter_character_alignment.py

scottBowles/dnd

a1ef333f1a865d51b5426dc4b3493e8437584565

[ "MIT" ]

null

# Generated by Django 3.2.5 on 2021-08-12 02:00 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('character', '0003_alter_character_id'), ] operations = [ migrations.AlterField( model_name='character', name='alignment', field=models.CharField(blank=True, choices=[('LG', 'Lawful Good'), ('NG', 'Neutral Good'), ('CG', 'Chaotic Good'), ('LN', 'Lawful Neutral'), ('N', 'True Neutral'), ('CN', 'Chaotic Neutral'), ('LE', 'Lawful Evil'), ('NE', 'Neutral Evil'), ('CE', 'Chaotic Evil')], max_length=2, null=True), ), ]

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1

c53bcf309d42be5b0611b4932b04593b5fb3c79b

818

py

Python

graphs_trees/check_balance/test_check_balance.py

filippovitale/interactive-coding-challenges

8380a7aa98618c3cc9c0271c30bd320937d431ad

[ "Apache-2.0" ]

null

graphs_trees/check_balance/test_check_balance.py

filippovitale/interactive-coding-challenges

8380a7aa98618c3cc9c0271c30bd320937d431ad

[ "Apache-2.0" ]

null

graphs_trees/check_balance/test_check_balance.py

filippovitale/interactive-coding-challenges

8380a7aa98618c3cc9c0271c30bd320937d431ad

[ "Apache-2.0" ]

1

2019-12-13T12:57:44.000Z

from nose.tools import assert_equal class TestCheckBalance(object): def test_check_balance(self): node = Node(5) insert(node, 3) insert(node, 8) insert(node, 1) insert(node, 4) assert_equal(check_balance(node), True) node = Node(5) insert(node, 3) insert(node, 8) insert(node, 9) insert(node, 10) assert_equal(check_balance(node), False) node = Node(3) insert(node, 2) insert(node, 1) insert(node, 5) insert(node, 4) insert(node, 6) insert(node, 7) assert_equal(check_balance(node), False) print('Success: test_check_balance') def main(): test = TestCheckBalance() test.test_check_balance() if __name__ == '__main__': main()

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null

1

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null

0

1

c53d9c366f6302c3f4189f86bcaf5a05f084763e

19,136

py

Python

src_RealData/Nets/ObjectOriented.py

XYZsake/DRFNS

73fc5683db5e9f860846e22c8c0daf73b7103082

[ "MIT" ]

42

2018-10-07T08:19:01.000Z

2022-02-08T17:41:24.000Z

src_RealData/Nets/ObjectOriented.py

XYZsake/DRFNS

73fc5683db5e9f860846e22c8c0daf73b7103082

[ "MIT" ]

11

2018-12-22T00:15:46.000Z

2021-12-03T10:29:32.000Z

src_RealData/Nets/ObjectOriented.py

XYZsake/DRFNS

73fc5683db5e9f860846e22c8c0daf73b7103082

[ "MIT" ]

14

2018-08-26T06:47:06.000Z

2021-07-24T11:52:58.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import tensorflow as tf import numpy as np import os from sklearn.metrics import confusion_matrix from datetime import datetime class ConvolutionalNeuralNetwork: """ Generic object for create DNN models. This class instinciates all functions needed for DNN operations. """ def __init__( self, LEARNING_RATE=0.01, K=0.96, BATCH_SIZE=1, IMAGE_SIZE=28, NUM_LABELS=10, NUM_CHANNELS=1, NUM_TEST=10000, STEPS=2000, LRSTEP=200, DECAY_EMA=0.9999, N_PRINT = 100, LOG="/tmp/net", SEED=42, DEBUG=True, WEIGHT_DECAY=0.00005, LOSS_FUNC=tf.nn.l2_loss, N_FEATURES=16): self.LEARNING_RATE = LEARNING_RATE self.K = K self.BATCH_SIZE = BATCH_SIZE self.IMAGE_SIZE = IMAGE_SIZE self.NUM_LABELS = NUM_LABELS self.NUM_CHANNELS = NUM_CHANNELS self.N_FEATURES = N_FEATURES # self.NUM_TEST = NUM_TEST self.STEPS = STEPS self.N_PRINT = N_PRINT self.LRSTEP = LRSTEP self.DECAY_EMA = DECAY_EMA self.LOG = LOG self.SEED = SEED self.sess = tf.InteractiveSession() self.sess.as_default() self.var_to_reg = [] self.var_to_sum = [] self.init_vars() self.init_model_architecture() self.init_training_graph() self.Saver() self.DEBUG = DEBUG self.loss_func = LOSS_FUNC self.weight_decay = WEIGHT_DECAY def regularize_model(self): """ Adds regularization to parameters of the model given LOSS_FUNC """ if self.DEBUG: for var in self.var_to_sum + self.var_to_reg: self.add_to_summary(var) self.WritteSummaryImages() for var in self.var_to_reg: self.add_to_regularization(var) def add_to_summary(self, var): """ Adds histogram for each parameter in var """ if var is not None: tf.summary.histogram(var.op.name, var) def add_to_regularization(self, var): """ Combines loss with regularization loss """ if var is not None: self.loss = self.loss + self.weight_decay * self.loss_func(var) def add_activation_summary(self, var): """ Add activation summary with information about sparsity """ if var is not None: tf.summary.histogram(var.op.name + "/activation", var) tf.summary.scalar(var.op.name + "/sparsity", tf.nn.zero_fraction(var)) def add_gradient_summary(self, grad, var): """ Add gradiant summary to summary """ if grad is not None: tf.summary.histogram(var.op.name + "/gradient", grad) def input_node_f(self): """ Input node, called when initialising the network """ return tf.placeholder( tf.float32, shape=(self.BATCH_SIZE, self.IMAGE_SIZE, self.IMAGE_SIZE, self.NUM_CHANNELS)) def label_node_f(self): """ Label node, called when initialising the network """ return tf.placeholder( tf.float32, shape=(self.BATCH_SIZE, self.IMAGE_SIZE, self.IMAGE_SIZE, 1)) def conv_layer_f(self, i_layer, w_var, strides, scope_name, padding="SAME"): """ Defining convolution layer """ with tf.name_scope(scope_name): return tf.nn.conv2d(i_layer, w_var, strides=strides, padding=padding) def relu_layer_f(self, i_layer, biases, scope_name): """ Defining relu layer """ with tf.name_scope(scope_name): act = tf.nn.relu(tf.nn.bias_add(i_layer, biases)) self.var_to_sum.append(act) return act def weight_const_f(self, ks, inchannels, outchannels, stddev, scope_name, name="W", reg="True"): """ Defining parameter to give to a convolution layer """ with tf.name_scope(scope_name): K = tf.Variable(tf.truncated_normal([ks, ks, inchannels, outchannels], # 5x5 filter, depth 32. stddev=stddev, seed=self.SEED)) self.var_to_reg.append(K) self.var_to_sum.append(K) return K def weight_xavier(self, ks, inchannels, outchannels, scope_name, name="W"): """ Initialises a convolution kernel for a convolution layer with Xavier initialising """ xavier_std = np.sqrt( 1. / float(ks * ks * inchannels) ) return self.weight_const_f(ks, inchannels, outchannels, xavier_std, scope_name, name=name) def biases_const_f(self, const, shape, scope_name, name="B"): """ Initialises biais """ with tf.name_scope(scope_name): b = tf.Variable(tf.constant(const, shape=[shape]), name=name) self.var_to_sum.append(b) return b def max_pool(self, i_layer, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME", name="MaxPool"): """ Performs max pool operation """ return tf.nn.max_pool(i_layer, ksize=ksize, strides=strides, padding=padding, name=name) def BatchNorm(self, Input, n_out, phase_train, scope='bn', decay=0.9, eps=1e-5): """ Performs batch normalisation. Code taken from http://stackoverflow.com/a/34634291/2267819 """ with tf.name_scope(scope): init_beta = tf.constant(0.0, shape=[n_out]) beta = tf.Variable(init_beta, name="beta") init_gamma = tf.random_normal([n_out], 1.0, 0.02) gamma = tf.Variable(init_gamma) batch_mean, batch_var = tf.nn.moments(Input, [0, 1, 2], name='moments') ema = tf.train.ExponentialMovingAverage(decay=decay) def mean_var_with_update(): ema_apply_op = ema.apply([batch_mean, batch_var]) with tf.control_dependencies([ema_apply_op]): return tf.identity(batch_mean), tf.identity(batch_var) mean, var = tf.cond(phase_train, mean_var_with_update, lambda: (ema.average(batch_mean), ema.average(batch_var))) normed = tf.nn.batch_normalization(Input, mean, var, beta, gamma, eps) return normed def DropOutLayer(self, Input, scope="DropOut"): """ Performs drop out on the input layer """ with tf.name_scope(scope): return tf.nn.dropout(Input, self.keep_prob) ##keep prob has to be defined in init_var def init_vars(self): """ Initialises variables for the graph """ self.input_node = self.input_node_f() self.train_labels_node = self.label_node_f() self.conv1_weights = self.weight_xavier(5, self.NUM_CHANNELS, 8, "conv1/") self.conv1_biases = self.biases_const_f(0.1, 8, "conv1/") self.conv2_weights = self.weight_xavier(5, 8, 8, "conv2/") self.conv2_biases = self.biases_const_f(0.1, 8, "conv2/") self.conv3_weights = self.weight_xavier(5, 8, 8, "conv3/") self.conv3_biases = self.biases_const_f(0.1, 8, "conv3/") self.logits_weight = self.weight_xavier(1, 8, self.NUM_LABELS, "logits/") self.logits_biases = self.biases_const_f(0.1, self.NUM_LABELS, "logits/") self.keep_prob = tf.Variable(0.5, name="dropout_prob") print('Model variables initialised') def WritteSummaryImages(self): """ Image summary to add to the summary """ tf.summary.image("Input", self.input_node, max_outputs=4) tf.summary.image("Label", self.train_labels_node, max_outputs=4) tf.summary.image("Pred", tf.expand_dims(tf.cast(self.predictions, tf.float32), dim=3), max_outputs=4) def init_model_architecture(self): """ Graph structure for the model """ self.conv1 = self.conv_layer_f(self.input_node, self.conv1_weights, [1,1,1,1], "conv1/") self.relu1 = self.relu_layer_f(self.conv1, self.conv1_biases, "conv1/") self.conv2 = self.conv_layer_f(self.relu1, self.conv2_weights, [1,1,1,1], "conv2/") self.relu2 = self.relu_layer_f(self.conv2, self.conv2_biases, "conv2/") self.conv3 = self.conv_layer_f(self.relu2, self.conv3_weights, [1,1,1,1], "conv3/") self.relu3 = self.relu_layer_f(self.conv3, self.conv3_biases, "conv3/") self.last = self.relu3 print('Model architecture initialised') def init_training_graph(self): """ Graph optimization part, here we define the loss and how the model is evaluated """ with tf.name_scope('Evaluation'): self.logits = self.conv_layer_f(self.last, self.logits_weight, strides=[1,1,1,1], scope_name="logits/") self.predictions = tf.argmax(self.logits, axis=3) with tf.name_scope('Loss'): self.loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.logits, labels=tf.squeeze(tf.cast(self.train_labels_node, tf.int32), squeeze_dims=[3]), name="entropy"))) tf.summary.scalar("entropy", self.loss) with tf.name_scope('Accuracy'): LabelInt = tf.squeeze(tf.cast(self.train_labels_node, tf.int64), squeeze_dims=[3]) CorrectPrediction = tf.equal(self.predictions, LabelInt) self.accuracy = tf.reduce_mean(tf.cast(CorrectPrediction, tf.float32)) tf.summary.scalar("accuracy", self.accuracy) with tf.name_scope('Prediction'): self.TP = tf.count_nonzero(self.predictions * LabelInt) self.TN = tf.count_nonzero((self.predictions - 1) * (LabelInt - 1)) self.FP = tf.count_nonzero(self.predictions * (LabelInt - 1)) self.FN = tf.count_nonzero((self.predictions - 1) * LabelInt) with tf.name_scope('Precision'): self.precision = tf.divide(self.TP, tf.add(self.TP, self.FP)) tf.summary.scalar('Precision', self.precision) with tf.name_scope('Recall'): self.recall = tf.divide(self.TP, tf.add(self.TP, self.FN)) tf.summary.scalar('Recall', self.recall) with tf.name_scope('F1'): num = tf.multiply(self.precision, self.recall) dem = tf.add(self.precision, self.recall) self.F1 = tf.scalar_mul(2, tf.divide(num, dem)) tf.summary.scalar('F1', self.F1) with tf.name_scope('MeanAccuracy'): Nprecision = tf.divide(self.TN, tf.add(self.TN, self.FN)) self.MeanAcc = tf.divide(tf.add(self.precision, Nprecision) ,2) tf.summary.scalar('Performance', self.MeanAcc) #self.batch = tf.Variable(0, name = "batch_iterator") self.train_prediction = tf.nn.softmax(self.logits) self.test_prediction = tf.nn.softmax(self.logits) tf.global_variables_initializer().run() print('Computational graph initialised') def error_rate(self, predictions, labels, iter): """ Operations to perform on the training prediction every N_PRINT iterations. These values are printed to screen. """ predictions = np.argmax(predictions, 3) labels = labels[:,:,:,0] cm = confusion_matrix(labels.flatten(), predictions.flatten(), labels=[0, 1]).astype(np.float) b, x, y = predictions.shape total = b * x * y TP = cm[1, 1] TN = cm[0, 0] FN = cm[0, 1] FP = cm[1, 0] acc = (TP + TN) / (TP + TN + FN + FP) * 100 precision = TP / (TP + FP) acc1 = np.mean([precision, TN / (TN + FN)]) * 100 recall = TP / (TP + FN) F1 = 2 * precision * recall / (recall + precision) error = 100 - acc return error, acc, acc1, recall * 100, precision * 100, F1 * 100 def optimization(self, var_list): """ Defining the optimization method to solve the task """ with tf.name_scope('optimizer'): optimizer = tf.train.AdamOptimizer(self.learning_rate) grads = optimizer.compute_gradients(self.loss, var_list=var_list) if self.DEBUG: for grad, var in grads: self.add_gradient_summary(grad, var) self.optimizer = optimizer.apply_gradients(grads, global_step=self.global_step) def LearningRateSchedule(self, lr, k, epoch): """ Defines the learning rate """ with tf.name_scope('LearningRateSchedule'): self.global_step = tf.Variable(0., trainable=False) tf.add_to_collection('global_step', self.global_step) if self.LRSTEP == "epoch/2": decay_step = float(epoch) / (2 * self.BATCH_SIZE) elif "epoch" in self.LRSTEP: num = int(self.LRSTEP[:-5]) decay_step = float(num) * float(epoch) / self.BATCH_SIZE else: decay_step = float(self.LRSTEP) self.learning_rate = tf.train.exponential_decay( lr, self.global_step, decay_step, k, staircase=True) tf.summary.scalar("learning_rate", self.learning_rate) def Validation(self, DG_TEST, step): """ How the models validates on the test set. """ n_test = DG_TEST.length n_batch = int(np.ceil(float(n_test) / self.BATCH_SIZE)) l, acc, F1, recall, precision, meanacc = 0., 0., 0., 0., 0., 0. for i in range(n_batch): Xval, Yval = DG_TEST.Batch(0, self.BATCH_SIZE) feed_dict = {self.input_node: Xval, self.train_labels_node: Yval} l_tmp, acc_tmp, F1_tmp, recall_tmp, precision_tmp, meanacc_tmp, pred = self.sess.run([self.loss, self.accuracy, self.F1, self.recall, self.precision, self.MeanAcc, self.predictions], feed_dict=feed_dict) l += l_tmp acc += acc_tmp F1 += F1_tmp recall += recall_tmp precision += precision_tmp meanacc += meanacc_tmp l, acc, F1, recall, precision, meanacc = np.array([l, acc, F1, recall, precision, meanacc]) / n_batch summary = tf.Summary() summary.value.add(tag="Test/Accuracy", simple_value=acc) summary.value.add(tag="Test/Loss", simple_value=l) summary.value.add(tag="Test/F1", simple_value=F1) summary.value.add(tag="Test/Recall", simple_value=recall) summary.value.add(tag="Test/Precision", simple_value=precision) summary.value.add(tag="Test/Performance", simple_value=meanacc) self.summary_test_writer.add_summary(summary, step) print(' Validation loss: %.1f' % l) print(' Accuracy: %1.f%% \n acc1: %.1f%% \n recall: %1.f%% \n prec: %1.f%% \n f1 : %1.f%% \n' % (acc * 100, meanacc * 100, recall * 100, precision * 100, F1 * 100)) self.saver.save(self.sess, self.LOG + '/' + "model.ckpt", global_step=self.global_step) def Saver(self): """ Defining the saver, it will load if possible. """ print("Setting up Saver...") self.saver = tf.train.Saver() ckpt = tf.train.get_checkpoint_state(self.LOG) if ckpt and ckpt.model_checkpoint_path: self.saver.restore(self.sess, ckpt.model_checkpoint_path) print("Model restored...") def ExponentialMovingAverage(self, var_list, decay=0.9999): """ Adding exponential moving average to increase performance. This aggregates parameters from different steps in order to have a more robust classifier. """ with tf.name_scope('ExponentialMovingAverage'): ema = tf.train.ExponentialMovingAverage(decay=decay) maintain_averages_op = ema.apply(var_list) # Create an op that will update the moving averages after each training # step. This is what we will use in place of the usual training op. with tf.control_dependencies([self.optimizer]): self.training_op = tf.group(maintain_averages_op) def train(self, DGTrain, DGTest, saver=True): """ How the model should train. """ epoch = DGTrain.length self.LearningRateSchedule(self.LEARNING_RATE, self.K, epoch) trainable_var = tf.trainable_variables() self.regularize_model() self.optimization(trainable_var) self.ExponentialMovingAverage(trainable_var, self.DECAY_EMA) tf.global_variables_initializer().run() tf.local_variables_initializer().run() self.summary_test_writer = tf.summary.FileWriter(self.LOG + '/test', graph=self.sess.graph) self.summary_writer = tf.summary.FileWriter(self.LOG + '/train', graph=self.sess.graph) merged_summary = tf.summary.merge_all() steps = self.STEPS for step in range(steps): batch_data, batch_labels = DGTrain.Batch(0, self.BATCH_SIZE) feed_dict = {self.input_node: batch_data, self.train_labels_node: batch_labels} # self.optimizer is replaced by self.training_op for the exponential moving decay _, l, lr, predictions, s = self.sess.run( [self.training_op, self.loss, self.learning_rate, self.train_prediction, merged_summary], feed_dict=feed_dict) if step % self.N_PRINT == 0: i = datetime.now() print i.strftime('%Y/%m/%d %H:%M:%S: \n ') self.summary_writer.add_summary(s, step) error, acc, acc1, recall, prec, f1 = self.error_rate(predictions, batch_labels, step) print(' Step %d of %d' % (step, steps)) print(' Learning rate: %.5f \n') % lr print(' Mini-batch loss: %.5f \n Accuracy: %.1f%% \n acc1: %.1f%% \n recall: %1.f%% \n prec: %1.f%% \n f1 : %1.f%% \n' % (l, acc, acc1, recall, prec, f1)) self.Validation(DGTest, step)

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0.570861

2,351

19,136

4.482773

0.171416

0.010817

0.016131

0.024196

0.217668

0.155233

0.098491

0.071828

0.057121

0.04137

0

0.022297

0.315635

19,136

500

216

38.272

0.782453

0.021112

0

0.061889

0

0.006515

0.057297

0.001441

0

null

0

0.016287

null

0.035831

0

null

0

null

0

1

0

1

c53f7e729f7148ea37a06ebe087c005b16755a1d

25,133

py

Python

maintest.py

thorsilver/ABM-for-social-care

3a47868d2881799980a3f9f24b78c66a31eda194

[ "MIT" ]

null

maintest.py

thorsilver/ABM-for-social-care

3a47868d2881799980a3f9f24b78c66a31eda194

[ "MIT" ]

null

maintest.py

thorsilver/ABM-for-social-care

3a47868d2881799980a3f9f24b78c66a31eda194

[ "MIT" ]

1

2018-01-05T15:42:40.000Z

from sim import Sim import os import cProfile import pylab import math import matplotlib.pyplot as plt import argparse import json import decimal import numpy as np def init_params(): """Set up the simulation parameters.""" p = {} ## The basics: starting population and year, etc. p['initialPop'] = 750 p['startYear'] = 1860 p['endYear'] = 2050 p['thePresent'] = 2012 p['statsCollectFrom'] = 1960 p['minStartAge'] = 20 p['maxStartAge'] = 40 p['verboseDebugging'] = False p['singleRunGraphs'] = True p['favouriteSeed'] = None p['numRepeats'] = 1 p['loadFromFile'] = False ## Mortality statistics p['baseDieProb'] = 0.0001 p['babyDieProb'] = 0.005 p['maleAgeScaling'] = 14.0 p['maleAgeDieProb'] = 0.00021 p['femaleAgeScaling'] = 15.5 p['femaleAgeDieProb'] = 0.00019 p['num5YearAgeClasses'] = 28 ## Transitions to care statistics p['baseCareProb'] = 0.0002 p['personCareProb'] = 0.0008 ##p['maleAgeCareProb'] = 0.0008 p['maleAgeCareScaling'] = 18.0 ##p['femaleAgeCareProb'] = 0.0008 p['femaleAgeCareScaling'] = 19.0 p['numCareLevels'] = 5 p['cdfCareTransition'] = [ 0.7, 0.9, 0.95, 1.0 ] p['careLevelNames'] = ['none','low','moderate','substantial','critical'] p['careDemandInHours'] = [ 0.0, 8.0, 16.0, 30.0, 80.0 ] ## Availability of care statistics p['childHours'] = 5.0 p['homeAdultHours'] = 30.0 p['workingAdultHours'] = 25.0 p['retiredHours'] = 60.0 p['lowCareHandicap'] = 0.5 p['hourlyCostOfCare'] = 20.0 ## Fertility statistics p['growingPopBirthProb'] = 0.215 p['steadyPopBirthProb'] = 0.13 p['transitionYear'] = 1965 p['minPregnancyAge'] = 17 p['maxPregnancyAge'] = 42 ## Class and employment statistics p['numOccupationClasses'] = 3 p['occupationClasses'] = ['lower','intermediate','higher'] p['cdfOccupationClasses'] = [ 0.6, 0.9, 1.0 ] ## Age transition statistics p['ageOfAdulthood'] = 17 p['ageOfRetirement'] = 65 ## Marriage and divorce statistics (partnerships really) p['basicFemaleMarriageProb'] = 0.25 p['femaleMarriageModifierByDecade'] = [ 0.0, 0.5, 1.0, 1.0, 1.0, 0.6, 0.5, 0.4, 0.1, 0.01, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0 ] p['basicMaleMarriageProb'] = 0.3 p['maleMarriageModifierByDecade'] = [ 0.0, 0.16, 0.5, 1.0, 0.8, 0.7, 0.66, 0.5, 0.4, 0.2, 0.1, 0.05, 0.01, 0.0, 0.0, 0.0 ] p['basicDivorceRate'] = 0.06 p['variableDivorce'] = 0.06 p['divorceModifierByDecade'] = [ 0.0, 1.0, 0.9, 0.5, 0.4, 0.2, 0.1, 0.03, 0.01, 0.001, 0.001, 0.001, 0.0, 0.0, 0.0, 0.0 ] ## Leaving home and moving around statistics p['probApartWillMoveTogether'] = 0.3 p['coupleMovesToExistingHousehold'] = 0.3 p['basicProbAdultMoveOut'] = 0.22 p['probAdultMoveOutModifierByDecade'] = [ 0.0, 0.2, 1.0, 0.6, 0.3, 0.15, 0.03, 0.03, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] p['basicProbSingleMove'] = 0.05 p['probSingleMoveModifierByDecade'] = [ 0.0, 1.0, 1.0, 0.8, 0.4, 0.06, 0.04, 0.02, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ] p['basicProbFamilyMove'] = 0.03 p['probFamilyMoveModifierByDecade'] = [ 0.0, 0.5, 0.8, 0.5, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 ] p['agingParentsMoveInWithKids'] = 0.1 p['variableMoveBack'] = 0.1 ## Description of the map, towns, and houses p['mapGridXDimension'] = 8 p['mapGridYDimension'] = 12 p['townGridDimension'] = 40 p['numHouseClasses'] = 3 p['houseClasses'] = ['small','medium','large'] p['cdfHouseClasses'] = [ 0.6, 0.9, 5.0 ] p['ukMap'] = [ [ 0.0, 0.1, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0 ], [ 0.1, 0.1, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0 ], [ 0.0, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.2, 1.0, 0.5, 0.0, 0.0, 0.0, 0.0 ], [ 0.4, 0.0, 0.2, 0.2, 0.4, 0.0, 0.0, 0.0 ], [ 0.6, 0.0, 0.0, 0.3, 0.8, 0.2, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.6, 0.8, 0.4, 0.0, 0.0 ], [ 0.0, 0.0, 0.2, 1.0, 0.8, 0.6, 0.1, 0.0 ], [ 0.0, 0.0, 0.1, 0.2, 1.0, 0.6, 0.3, 0.4 ], [ 0.0, 0.0, 0.5, 0.7, 0.5, 1.0, 1.0, 0.0 ], [ 0.0, 0.0, 0.2, 0.4, 0.6, 1.0, 1.0, 0.0 ], [ 0.0, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0 ] ] p['mapDensityModifier'] = 0.6 p['ukClassBias'] = [ [ 0.0, -0.05, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ], [ -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, -0.05, -0.05, 0.05, 0.0, 0.0, 0.0, 0.0 ], [ -0.05, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ], [ -0.05, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ], [ 0.0, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, -0.05, 0.0, -0.05, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, -0.05, 0.0, 0.2, 0.15, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.1, 0.2, 0.15, 0.0 ], [ 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0 ] ] ## Graphical interface details p['interactiveGraphics'] = True p['delayTime'] = 0.0 p['screenWidth'] = 1300 p['screenHeight'] = 700 p['bgColour'] = 'black' p['mainFont'] = 'Helvetica 18' p['fontColour'] = 'white' p['dateX'] = 70 p['dateY'] = 20 p['popX'] = 70 p['popY'] = 50 p['pixelsInPopPyramid'] = 2000 p['careLevelColour'] = ['blue','green','yellow','orange','red'] p['houseSizeColour'] = ['brown','purple','yellow'] p['pixelsPerTown'] = 56 p['maxTextUpdateList'] = 22 return p p = init_params() ####################################################### ## A basic single run def basicRun(p): s = Sim(p) tax = s.run() ####################################################### ## Batch run (no graphics) def batchRun(num): p['interactiveGraphics'] = False dataFile = open('batchRunData.txt','w') for i in range ( 0, num ): print "Doing batch run: ", i taxList = [] s = Sim(p) tax = s.run() taxList.append(tax) print "Social care cost per taxpayer: ", tax dataFile.write(str(i) + "\t" + str(tax) + "\n") dataFile.close() ####################################################### ## Retirement age run (no graphics) def retireRun(reps): taxMeans = [] taxSEs = [] p['verboseDebugging'] = False p['singleRunGraphs'] = False p['interactiveGraphics'] = False dataFile = open('retirementAgeData2.txt','w') #p['ageingParentList'] = [50, 55, 65, 70, 75, 80] for variableCare in p['ageingParentList']: p['ageOfRetirement'] = variableCare print "Trying retirement age: ", variableCare taxList = [] for i in range ( 0, reps ): print i, s = Sim(p) tax = s.run() taxList.append(tax) print tax dataFile.write(str(variableCare) + "\t" + str(i) + "\t" + str(tax) + "\n") taxMeans.append(pylab.mean(taxList)) taxSEs.append(pylab.std(taxList) / math.sqrt(reps)) dataFile.close() indices1 = pylab.arange(len(p['ageingParentList'])) taxFig = pylab.figure() taxBar = taxFig.add_subplot(1,1,1) taxBar.bar(indices1, taxMeans, facecolor='red', align='center', yerr=taxSEs, ecolor='black') taxBar.set_ylabel('Mean social care cost per taxpayer') taxBar.set_xlabel('Age of retirement') taxBar.set_xticks(indices1) taxBar.set_xticklabels(p['ageingParentList']) pylab.savefig('retirementAgeRunSet1.pdf') pylab.show() ####################################################### ##runs for sensitivity analysis using GEM-SA def gemRun(reps): taxMeans = [] taxSEs = [] p['verboseDebugging'] = False p['singleRunGraphs'] = False p['interactiveGraphics'] = False dataFile = open('GEMSA data new.txt','a') meansFile = open('GEMSA means new.txt', 'a') outFile = open('GEMSA outputs new.txt', 'a') # agingParentList = [ 0.0, 0.1, 0.2, 0.4 ] # careProbList = [ 0.0004, 0.0008, 0.0012, 0.0016 ] # retiredHoursList = [ 20.0, 30.0, 40.0, 60.0 ] # retiredAgeList = [ 60.0 ] # ageingParentList = [ 0.0, 0.1 ] # careProbList = [ 0.0004 ] # retiredHoursList = [ 20.0 ] # retiredAgeList = [ 60.0 ] for variableCare in p['ageingParentList']: for variableProb in p['careProbList']: for variableRetired in p['retiredHoursList']: for variableAge in p['retiredAgeList']: p['agingParentsMoveInWithKids'] = variableCare p['personCareProb'] = variableProb p['retiredHours'] = variableRetired p['ageOfRetirement'] = variableAge print "Trying parents-moving-in probability: ", variableCare print "Trying person care probability: ", variableProb print "Trying retired hours: ", variableRetired print "Trying retirement age: ", variableAge taxList = [] taxSum = 0.0 meansFile.write(str(variableCare) + "\t" + str(variableProb) + "\t" + str(variableRetired) + "\t" + str(variableAge) + "\n") for i in range ( 0, reps ): print i, s = Sim(p) tax, seed = s.run() taxList.append(tax) taxSum += tax print tax dataFile.write(str(seed) + "\t" + str(variableCare) + "\t" + str(variableProb) + "\t" + str(variableRetired) + "\t" + str(variableAge) + "\t" + str(tax) + "\n") taxMeans.append(pylab.mean(taxList)) outFile.write(str(taxSum/reps) + "\n") taxSEs.append(pylab.std(taxList) / math.sqrt(reps)) dataFile.close() meansFile.close() outFile.close() ####################################################### ##runs for sensitivity analysis using GEM-SA - LPtau and Maximin LH def sensitivityRun(runtype, ageingList, careList, retiredHList, retiredAList, reps): taxMeans = [] taxSEs = [] p['verboseDebugging'] = False p['singleRunGraphs'] = False p['interactiveGraphics'] = False dataFile = open(runtype + ' GEMSA data.txt','a') meansFile = open(runtype + ' GEMSA means.txt', 'a') outFile = open(runtype + ' GEMSA outputs.txt', 'a') # agingParentList = [ 0.0, 0.1, 0.2, 0.4 ] # careProbList = [ 0.0004, 0.0008, 0.0012, 0.0016 ] # retiredHoursList = [ 20.0, 30.0, 40.0, 60.0 ] # retiredAgeList = [ 60.0 ] # ageingParentList = [ 0.0, 0.1 ] # careProbList = [ 0.0004 ] # retiredHoursList = [ 20.0 ] # retiredAgeList = [ 60.0 ] for run in xrange(len(ageingList)): p['agingParentsMoveInWithKids'] = ageingList[run] p['personCareProb'] = careList[run] p['retiredHours'] = retiredHList[run] p['ageOfRetirement'] = retiredAList[run] print "Trying parents-moving-in probability: ", ageingList[run] print "Trying person care probability: ", careList[run] print "Trying retired hours: ", retiredHList[run] print "Trying retirement age: ", retiredAList[run] taxList = [] taxSum = 0.0 meansFile.write(str(ageingList[run]) + "\t" + str(careList[run]) + "\t" + str(retiredHList[run]) + "\t" + str(retiredAList[run]) + "\n") for i in range ( 0, reps ): print i, s = Sim(p) tax, seed = s.run() taxList.append(tax) taxSum += tax print tax dataFile.write(str(seed) + "\t" + str(ageingList[run]) + "\t" + str(careList[run]) + "\t" + str(retiredHList[run]) + "\t" + str(retiredAList[run]) + "\t" + str(tax) + "\n") taxMeans.append(pylab.mean(taxList)) outFile.write(str(taxSum/reps) + "\n") taxSEs.append(pylab.std(taxList) / math.sqrt(reps)) dataFile.close() meansFile.close() outFile.close() ####################################################### ##runs for sensitivity analysis using GEM-SA - LPtau and Maximin LH # def sensitivityLarge(runtype, ageingList, careList, retiredHList, retiredAList, baseDieList, babyDieList, personCareList, maleCareList, femaleCareList, \ # childHoursList, homeAdultList, workingAdultList, lowCareList, growingBirthList, basicDivorceList, variableDivorceList, basicMaleMarriageList, \ # basicFemaleMarriageList, probMoveList, moveHouseholdList, probMoveOutList, probMoveBackList, reps): def sensitivityLarge(runtype, input_list, reps): taxMeans = [] taxSEs = [] p['verboseDebugging'] = False p['singleRunGraphs'] = False p['interactiveGraphics'] = False outFile = open(runtype + ' GEMSA outputs large.txt', 'a') for run in xrange(len(input_list[0])): print("Running simulation number {}...".format(run)) print("Number of reps: {}".format(reps)) sim_list = np.array(input_list) print(sim_list) p['agingParentsMoveInWithKids'] = sim_list[0,run] print(p['agingParentsMoveInWithKids']) p['personCareProb'] = sim_list[1,run] p['retiredHours'] = sim_list[2,run] p['ageOfRetirement'] = sim_list[3,run] p['baseDieProb'] = sim_list[4,run] p['babyDieProb'] = sim_list[5,run] p['personCareProb'] = sim_list[6,run] p['maleAgeCareScaling'] = sim_list[7,run] p['femaleAgeCareScaling'] = sim_list[8,run] p['childHours'] = sim_list[9,run] p['homeAdultHours'] = sim_list[10,run] p['workingAdultHours'] = sim_list[11,run] p['lowCareHandicap'] = sim_list[12,run] p['growingPopBirthProb'] = sim_list[13,run] p['basicDivorceRate'] = sim_list[14,run] p['variableDivorce'] = sim_list[15,run] p['basicMaleMarriageProb'] = sim_list[16,run] p['basicFemaleMarriageProb'] = sim_list[17,run] p['probApartWillMoveTogether'] = sim_list[18,run] p['coupleMovesToExistingHousehold'] = sim_list[19,run] p['basicProbAdultMoveOut'] = sim_list[20,run] p['variableMoveBack'] = sim_list[21,run] taxList = [] taxSum = 0.0 for i in range ( 0, reps ): print i, s = Sim(p) tax, seed = s.run() taxList.append(tax) taxSum += tax print tax taxMeans.append(pylab.mean(taxList)) outFile.write(str(taxSum/reps) + "\n" + str(seed) + "\n") taxSEs.append(pylab.std(taxList) / math.sqrt(reps)) outFile.close() ####################################################### ##runs for sensitivity analysis using GEM-SA - LPtau and Maximin LH, 10 params def sensitivityTenParams(runtype, input_list, reps): taxMeans = [] taxSEs = [] p['verboseDebugging'] = False p['singleRunGraphs'] = False p['interactiveGraphics'] = False outFile = open(runtype + ' GEMSA outputs.txt', 'a') for run in xrange(len(input_list[0])): print("Running simulation number {}...".format(run)) print("Number of reps: {}".format(reps)) sim_list = np.array(input_list) print(sim_list) p['agingParentsMoveInWithKids'] = sim_list[0,run] p['baseCareProb'] = sim_list[1,run] p['retiredHours'] = sim_list[2,run] p['ageOfRetirement'] = sim_list[3,run] p['personCareProb'] = sim_list[4,run] p['maleAgeCareScaling'] = sim_list[5,run] p['femaleAgeCareScaling'] = sim_list[6,run] p['childHours'] = sim_list[7,run] p['homeAdultHours'] = sim_list[8,run] p['workingAdultHours'] = sim_list[9,run] taxList = [] taxSum = 0.0 for i in range ( 0, reps ): print i, s = Sim(p) tax, seed = s.run() taxList.append(tax) taxSum += tax print tax taxMeans.append(pylab.mean(taxList)) outFile.write(str(taxSum/reps) + "\t" + str(seed) + "\n") taxSEs.append(pylab.std(taxList) / math.sqrt(reps)) outFile.close() ####################################################### # Recurrent neural network experiments -- 10 params, outputs recorded per year def RNNOutputScenario(runtype, input_list, reps): taxMeans = [] taxSEs = [] p['verboseDebugging'] = False p['singleRunGraphs'] = False p['interactiveGraphics'] = False outFile = open(runtype + ' GEMSA outputs.txt', 'a') outFile2 = open(runtype + ' yearly outputs.txt', 'a') for run in xrange(len(input_list[0])): print("Running simulation number {}...".format(run)) print("Number of reps: {}".format(reps)) sim_list = np.array(input_list) #print(sim_list) p['agingParentsMoveInWithKids'] = sim_list[0, run] p['baseCareProb'] = sim_list[1, run] p['retiredHours'] = sim_list[2, run] p['ageOfRetirement'] = sim_list[3, run] p['personCareProb'] = sim_list[4, run] p['maleAgeCareScaling'] = sim_list[5, run] p['femaleAgeCareScaling'] = sim_list[6, run] p['childHours'] = sim_list[7, run] p['homeAdultHours'] = sim_list[8, run] p['workingAdultHours'] = sim_list[9, run] taxList = [] taxSum = 0.0 for i in range(0, reps): print i, s = Sim(p) tax, seed, carecost = s.run() taxList.append(tax) taxSum += tax print tax taxMeans.append(pylab.mean(taxList)) outFile.write(str(taxSum / reps) + "\t" + str(seed) + "\n") outFile2.write(str(carecost) + "\n") taxSEs.append(pylab.std(taxList) / math.sqrt(reps)) outFile.close() outFile2.close() ####################################################### ## A profiling run; use import pstats then p = pstats.Stats('profile.txt') then p.sort_stats('time').print_stats(10) #cProfile.run('s.run()','profile.txt') ####################################################### ## Parse command line arguments def loadParamFile(file, dict): """ Given a JSON filename and a dictionary, return the dictionary with the file's fields merged into it. Example: if the initial dictionary is dict['bobAge'] = 90 and dict['samAge']=20 and the JSON data is {'age':{'bob':40, 'fred':35}} the returned dictionary contains the following data values: dict['bobAge'] = 40, dict['fredAge'] = 35, dict['samAge'] = 20 """ json_data = open(file).read() data = json.loads(json_data) for group in data: fields = data.get(group) if type({}) == type(fields): # Group of fields - create name from item and group for item in fields: name = item + group[:1].upper() + group[1:] value = data [group][item] dict [name] = value else: # Single data value - naming is assumed to be correct case dict [group] = fields return dict def loadCommandLine(dict): """Process the command line, loading params file (if required). The dict argument will be augmented with data from the user-specified parameters file (if required), otherwise will return the dict argument unchanged""" parser = argparse.ArgumentParser( description='lives v1.0: complex social behaviour simulation.', epilog='Example: "maintest.py -f test.json -n 3" --- run 3 sims with test.json\'s params', formatter_class=argparse.RawTextHelpFormatter, prog='lives', usage='use "%(prog)s -h" for more information') group = parser.add_mutually_exclusive_group() parser.add_argument( '-f', '--file', help='parameters file in JSON format e.g. soylent.json') group.add_argument( '-n', '--num', metavar='N', type=int, default=0, help='number of runs to carry out.') group.add_argument('-r', '--retire', metavar='R', type=int, default=0, help='retirement batch, number of iterations.') group.add_argument('-g', '--gem', metavar='G', type=int, default=0, help='GEM-SA batch for sensitivity analysis, number of iterations.') group.add_argument('-l', '--lptau', metavar='L', type=int, default=0, help='sensitivity analysis batch with LPtau sampling.') group.add_argument('-m', '--maximin', metavar='M', type=int, default=0, help='sensitivity analysis batch with maximin latin hypercube sampling.') group.add_argument('-b', '--bigly', metavar='B', type=int, default=0, help='bigly sensitivity analysis batch with maximin latin hypercube sampling.') group.add_argument('-t', '--tenparams', metavar='T', type=int, default=0, help='10 parameter sensitivity analysis batch with maximin latin hypercube sampling.') group.add_argument('-c', '--recurrent', metavar='C', type=int, default=0, help='10 parameter time-series run for RNN.') args = parser.parse_args() print("~ Filename: {}".format(args.file)) print("~ Number: {}".format(args.num)) print("~ Retire: {}".format(args.retire)) print("~ GEM-SA: {}".format(args.gem)) print("~ LPtau: {}".format(args.lptau)) print("~ Maximin: {}".format(args.maximin)) print("~ Big SA: {}".format(args.bigly)) print("~ Ten Params: {}".format(args.tenparams)) print("~ Ten Params RNN: {}".format(args.recurrent)) if args.file: #agingParentList = json.load(retireList, parse_float=decimal.Decimal) res = loadParamFile (args.file, dict) print ("p = {}".format(dict)) basicRun(dict) elif args.num >= 1: batchRun(args.num) elif args.retire: p['ageingParentList'] = [] res = loadParamFile('retire.json', dict) print("List = {}".format(dict)) retireRun(args.retire) elif args.gem: p['ageingParentList'] = [] p['careProbList'] = [] p['retiredHoursList'] = [] p['retiredAgeList'] = [] res = loadParamFile('gem.json', dict) print("List = {}".format(dict)) gemRun(args.gem) elif args.lptau: sim_array = np.genfromtxt('lptau-4params.txt', delimiter=' ') sim_list = list(sim_array.T) # print(sim_list) ageingParentSettings = sim_list[0] careProbSettings = sim_list[1] retiredHoursSettings = sim_list[2] retiredAgeSettings = sim_list[3] # print(ageingParentSettings) # print(careProbSettings) # print(retiredHoursSettings) # print(retiredAgeSettings) sensitivityRun('LPtau', ageingParentSettings, careProbSettings, retiredHoursSettings, retiredAgeSettings, args.lptau) elif args.maximin: sim_array = np.genfromtxt('latinhypercube-4params.txt', delimiter=' ') sim_list = list(sim_array.T) # print(sim_list) ageingParentSettings = sim_list[0] careProbSettings = sim_list[1] retiredHoursSettings = sim_list[2] retiredAgeSettings = sim_list[3] # print(ageingParentSettings) # print(careProbSettings) # print(retiredHoursSettings) # print(retiredAgeSettings) sensitivityRun('Maximin', ageingParentSettings, careProbSettings, retiredHoursSettings, retiredAgeSettings, args.maximin) elif args.bigly: sim_array = np.genfromtxt('latinhypercube-22params.txt', delimiter=' ') sim_list = list(sim_array.T) #print(sim_list) np.savetxt('hypercube22_GEMSA_inputs.txt', sim_array, fmt='%1.8f', delimiter='\t', newline='\n') sensitivityLarge('hypercube22', sim_list, args.bigly) elif args.tenparams: sim_array = np.genfromtxt('LPtau-10params.txt', delimiter=' ') sim_list = list(sim_array.T) #print(sim_list) np.savetxt('lptau10_GEMSA_inputs.txt', sim_array, fmt='%1.8f', delimiter='\t', newline='\n') sensitivityTenParams('lptau10', sim_list, args.tenparams) elif args.recurrent: sim_array = np.genfromtxt('lptau10round2_GEMSA_inputs.csv', delimiter=',') sim_list = list(sim_array.T) print(sim_list) np.savetxt('lptau10_recurrent_inputs.txt', sim_array, fmt='%1.8f', delimiter='\t', newline='\n') RNNOutputScenario('LPtauRNN', sim_list, args.recurrent) else: basicRun(p) return dict # Load the default values, overwriting and adding to the initial p values loadParamFile("default.json", p) # Load values based upon the command line file passed (if any). loadCommandLine (p) #print ("p = {}".format(p))

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null

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null

0

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0

1

c542862715caa74d2fd3f0e9e9fcab1cbbe24d4a

284

py

Python

syncless/wscherry.py

irr/python-labs

43bb3a528c151653b2be832c7ff13240a10e18a4

[ "Apache-2.0" ]

4

2015-11-25T09:06:44.000Z

2019-12-11T21:35:21.000Z

syncless/wscherry.py

irr/python-labs

43bb3a528c151653b2be832c7ff13240a10e18a4

[ "Apache-2.0" ]

null

syncless/wscherry.py

irr/python-labs

43bb3a528c151653b2be832c7ff13240a10e18a4

[ "Apache-2.0" ]

2

2015-11-25T09:19:38.000Z

2016-02-26T03:54:06.000Z

import sys sys.path.append("/usr/lib/python2.7/site-packages") import redis _r = redis.Redis(host='localhost', port=6379, db=0) import cherrypy class Test(object): def index(self): _r.incr("/") return "OK!" index.exposed = True cherrypy.quickstart(Test())

17.75

51

0.661972

40

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4.65

0.775

0

0.030043

0.179577

284

15

52

18.933333

0.76824

0

0.158451

0.112676

0

1

0.090909

false

0

0.272727

0

0.545455

0

null

0

null

0

1

0

1

c544eb603d7c0e4860f104e7e494d3ae3bdfe615

538

py

Python

server.py

celinekeisja/jobmonitorservice

aaf56dd198c1275439a0f5ed27617fb458f715ac

[ "MIT" ]

null

server.py

celinekeisja/jobmonitorservice

aaf56dd198c1275439a0f5ed27617fb458f715ac

[ "MIT" ]

null

server.py

celinekeisja/jobmonitorservice

aaf56dd198c1275439a0f5ed27617fb458f715ac

[ "MIT" ]

1

2019-11-11T10:26:42.000Z

from flask_script import Manager from flask_migrate import Migrate, MigrateCommand from config import db import config app = config.connex_app app.add_api('swagger.yml') @app.route('/') def home(): return 'homepage here' @app.route("/job") @app.route("/job/<string:job_id>") def job(job_id=""): return 'result of job_id' migrate = Migrate(app=app, db=db) manager = Manager(app=app) manager.add_command('db', MigrateCommand) if __name__ == "__main__": manager.run() # app.run(host='localhost', port=5000, debug=True)

20.692308

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0.049046

0.059946

0

0.008602

0.135688

538

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54

20.692308

0.780645

0.089219

0

0.153374

0

1

0.111111

false

0

0.222222

0.111111

0.444444

0

null

0

null

0

1

0

1

c54c0437171dca7cbeb276eabca7979dd5dce208

2,202

py

Python

src/python/compressao_huffman.py

willisnou/Algoritmos-e-Estruturas-de-Dados

b70a2f692ccae948576177560e3628b9dece5aee

[ "MIT" ]

653

2015-06-07T14:45:40.000Z

2022-03-25T17:31:58.000Z

src/python/compressao_huffman.py

willisnou/Algoritmos-e-Estruturas-de-Dados

b70a2f692ccae948576177560e3628b9dece5aee

[ "MIT" ]

64

2017-10-29T10:53:37.000Z

2022-03-14T23:49:18.000Z

src/python/compressao_huffman.py

willisnou/Algoritmos-e-Estruturas-de-Dados

b70a2f692ccae948576177560e3628b9dece5aee

[ "MIT" ]

224

2015-06-07T14:46:00.000Z

2022-03-25T17:36:46.000Z

# Árvore Huffman class node: def __init__(self, freq, symbol, left=None, right=None): # Frequência do Símbolo self.freq = freq # Símbolo (caracter) self.symbol = symbol # nó à esquerda do nó atual self.left = left # nó à direita do nó atual self.right = right # direção da árvore (0/1) self.huff = '' # Função utilitária para imprimir # códigos huffman para todos os símbolos # na nova árvore huffman que sera criada def printNodes(node, val=''): # código huffman para o nó atual newVal = val + str(node.huff) # se o nó não pertence á ponta da # árvore então caminha dentro do mesmo # até a ponta if(node.left): printNodes(node.left, newVal) if(node.right): printNodes(node.right, newVal) # Se o nó estiver na ponta da árore # então exibe o código huffman if(not node.left and not node.right): print(f"{node.symbol} -> {newVal}") # caracteres para à árvore huffman chars = ['a', 'b', 'c', 'd', 'e', 'f'] # frequência dos caracteres freq = [5, 9, 12, 13, 16, 45] # lista contendo os nós não utilizados nodes = [] if __name__ == '__main__': # convertendo caracteres e frequência em # nós da árvore huffman for x in range(len(chars)): nodes.append(node(freq[x], chars[x])) while len(nodes) > 1: # Ordena todos os nós de forma ascendente # baseado em sua frequência nodes = sorted(nodes, key=lambda x: x.freq) # Seleciona os dois nós menores left = nodes[0] right = nodes[1] # Atribui um valor direcional à estes nós # (direita ou esquerda) left.huff = 0 right.huff = 1 # Combina os 2 nós menores para um novo nó pai # para eles. newNode = node( left.freq + right.freq, left.symbol + right.symbol, left, right) # remove os 2 nós e adiciona o nó pai # como um novo só sobre os outros nodes.remove(left) nodes.remove(right) nodes.append(newNode) # Árvore Huffman pronta! printNodes(nodes[0])

24.741573

60

0.584469

297

2,202

4.292929

0.417508

0.05098

0.014118

0.020392

0

0.013432

0.323797

2,202

88

61

25.022727

0.842848

0.398274

0

0.030139

0

0.011364

0

1

0.052632

false

0

0.078947

0.131579

0

null

0

null

0

1

0

1

c55176ac699f36bb549a798358fd9868f0da10c3

7,649

py

Python

getnear/tseries.py

edwardspeyer/getnear

746f3cedc1aed6166423f54d32e208017f660b38

[ "MIT" ]

null

getnear/tseries.py

edwardspeyer/getnear

746f3cedc1aed6166423f54d32e208017f660b38

[ "MIT" ]

null

getnear/tseries.py

edwardspeyer/getnear

746f3cedc1aed6166423f54d32e208017f660b38

[ "MIT" ]

null

from getnear.config import Tagged, Untagged, Ignore from getnear.logging import info from lxml import etree import re import requests import telnetlib def connect(hostname, *args, **kwargs): url = f'http://{hostname}/' html = requests.get(url).text doc = etree.HTML(html) for title in doc.xpath('//title'): if re.match('NETGEAR GS\d+T', title.text): return TSeries(hostname, *args, **kwargs) class TSeries: def __init__(self, hostname, password='password', old_password='password', debug=False): info('connecting') self.t = telnetlib.Telnet(hostname, 60000) if debug: self.t.set_debuglevel(2) info('entering admin mode') self.admin_mode() info('logging in') if self.login(password): return else: info('trying old password') self.admin_mode() if self.login(old_password): info('changing password') self.change_password(old_password, password) else: raise Exception('login failed') def admin_mode(self): self.t.read_until(b'please wait ...') self.t.write(b'admin\n') def login(self, password): self.t.read_until(b'Password:') self.t.write(password.encode('ascii')) self.t.write(b'\n') _, _, match = self.t.expect([b'>', b'Applying']) if b'Applying' in match: return False self.t.write(b'enable\n\n') self.t.read_until(b'#') return True def exit(self): # Leave "enable" mode self.t.write(b'exit\n') self.t.read_until(b'>') self.t.write(b'logout\n') def get_current_config(self): # (ports, pvids, {vlan_id -> {U, T, _, _...}) ports_pvids = dict(self.get_port_pvids()) ports = tuple(sorted(ports_pvids)) pvids = tuple(ports_pvids[p] for p in ports) vlans = {} vlan_ids = set(pvids) | set(self.get_vlan_ids()) for vlan_id in vlan_ids: port_map = dict(self.get_vlan(vlan_id)) membership = tuple(port_map[p] for p in ports) vlans[vlan_id] = membership return (ports, pvids, vlans) def get_vlan_ids(self): self.t.write(b'show vlan brief\n') output = self.page().decode(errors='ignore') for line in output.splitlines(): fields = line.split() if fields and fields[0].isnumeric(): yield int(fields[0]) def get_vlan(self, vlan_id): self.t.write(f'show vlan {vlan_id}\n'.encode()) for line in self.paged_table_body(): fields = line.split(maxsplit=3) interface_port, current = fields[0:2] interface, port = map(int, interface_port.split('/')) if interface == 0: port = int(interface_port.split('/')[1]) is_included = current == 'Include' is_tagged = 'Tagged' in line if is_tagged: state = Tagged elif is_included: state = Untagged else: state = Ignore yield port, state def get_port_pvids(self): self.t.write(b'show vlan port all\n') for line in self.paged_table_body(): fields = line.split() interface_port, pvid_s = fields[0:2] interface, port = map(int, interface_port.split('/')) if interface == 0: pvid = int(pvid_s) yield port, pvid def set_port_pvid(self, port, vlan_id): self.do_configure_interface(port, f'vlan pvid {vlan_id}') def set_port_vlan_tagging(self, port, vlan_id, is_tagged): if is_tagged: command = f'vlan tagging {vlan_id}' else: command = f'no vlan tagging {vlan_id}' self.do_configure_interface(port, command) def set_port_vlan_participation(self, port, vlan_id, is_included): if is_included: command = f'vlan participation include {vlan_id}' else: command = f'vlan participation exclude {vlan_id}' self.do_configure_interface(port, command) def add_vlan(self, vlan_id): self.do_vlan_database(f'vlan {vlan_id}') def delete_vlan(self, vlan_id): self.do_vlan_database(f'no vlan {vlan_id}') def do_configure_interface(self, port, command): self.t.write(b'configure\n') self.t.read_until(b'#') self.t.write(f'interface 0/{port}\n'.encode()) self.t.read_until(b'#') self.t.write((command + '\n').encode()) self.t.read_until(b'#') self.t.write(b'exit\n') self.t.read_until(b'#') self.t.write(b'exit\n') self.t.read_until(b'#') def do_vlan_database(self, command): self.t.write(b'vlan database\n') self.t.read_until(b'#') self.t.write((command + '\n').encode()) self.t.read_until(b'#') self.t.write(b'exit\n') self.t.read_until(b'#') def change_password(self, password_old, password_new): # TODO For this to work, we have to leave "enable" mode. It would be # better if all other commands entererd enable mode instead. More # verbose, but less confusing. Maybe have a cursor to remember which # mode we are in? self.t.write(b'exit\n') self.t.read_until(b'>') self.t.write(b'passwd\n') self.t.read_until(b'Enter old password:') self.t.write((password_old + '\n').encode()) self.t.read_until(b'Enter new password:') self.t.write((password_new + '\n').encode()) self.t.read_until(b'Confirm new password:') self.t.write((password_new + '\n').encode()) self.t.read_until(b'Password Changed!') self.t.write(b'enable\n') # Double newline self.t.read_until(b'#') def paged_table_body(self): output = self.page().decode(errors='ignore') in_body = False for line in output.splitlines(): if line.strip() == '': in_body = False if in_body: yield line if line and line[0:4] == '----': in_body = True def page(self): result = b'' while True: index, _, output = self.t.expect([ b'--More-- or $q$uit', b'#' ]) result += output if index == 0: self.t.write(b'\n') else: break return result def sync(self, config): ports, pvids, vlans = config vlan_ids = set(pvids) | set(vlans) for vlan_id in sorted(vlan_ids): info(f'adding vlan {vlan_id}') self.add_vlan(vlan_id) for port, pvid in zip(ports, pvids): info(f'setting port {port} to PVID {pvid}') self.set_port_pvid(port, pvid) for vlan_id, membership in vlans.items(): info(f'vlan {vlan_id}') for port, status in zip(ports, membership): if status == Ignore: info(f' port {port} off') self.set_port_vlan_participation(port, vlan_id, False) else: is_tagged = status == Tagged symbol = 'T' if is_tagged else 'U' info(f' port {port} {symbol}') self.set_port_vlan_participation(port, vlan_id, True) self.set_port_vlan_tagging(port, vlan_id, is_tagged)

34.61086

92

0.552098

998

7,649

4.079158

0.182365

0.056497

0.058954

0.061901

0.378531

0.293785

0.246868

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0.192827

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0.003854

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7,649

220

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34.768182

0.78054

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0.177419

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null

0

null

0

1

0

1

c552f157bcec716a7f87d20bd21cf1b7b813d8da

211

py

Python

models/dl-weights.py

diegoinacio/object-detection-flask-opencv

bc012e884138e9ead04115b8550e833bed134074

[ "MIT" ]

16

2020-03-01T07:35:35.000Z

2022-02-01T16:34:24.000Z

models/dl-weights.py

girish008/Real-Time-Object-Detection-Using-YOLOv3-OpenCV

6af4c550f6128768b646f5923af87c2f654cd1bd

[ "MIT" ]

6

2020-02-13T12:50:24.000Z

2022-02-02T03:22:30.000Z

models/dl-weights.py

girish008/Real-Time-Object-Detection-Using-YOLOv3-OpenCV

6af4c550f6128768b646f5923af87c2f654cd1bd

[ "MIT" ]

8

2020-06-22T10:23:58.000Z

2022-01-14T21:17:50.000Z

""" This script downloads the weight file """ import requests URL = "https://pjreddie.com/media/files/yolov3.weights" r = requests.get(URL, allow_redirects=True) open('yolov3_t.weights', 'wb').write(r.content)

23.444444

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0.739336

31

211

4.967742

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0.094787

211

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0.25

0

0.25

0

null

0

1

0

null

0

1

c55ca719e407ecd982eeb52d8e27fa9690f85669

420

py

Python

iis/tests/test_e2e.py

tcpatterson/integrations-core

3692601de09f8db60f42612b0d623509415bbb53

[ "BSD-3-Clause" ]

null

iis/tests/test_e2e.py

tcpatterson/integrations-core

3692601de09f8db60f42612b0d623509415bbb53

[ "BSD-3-Clause" ]

null

iis/tests/test_e2e.py

tcpatterson/integrations-core

3692601de09f8db60f42612b0d623509415bbb53

[ "BSD-3-Clause" ]

null

# (C) Datadog, Inc. 2022-present # All rights reserved # Licensed under Simplified BSD License (see LICENSE) import pytest from datadog_checks.dev.testing import requires_py3 from datadog_checks.iis import IIS @pytest.mark.e2e @requires_py3 def test_e2e_py3(dd_agent_check, aggregator, instance): aggregator = dd_agent_check(instance) aggregator.assert_service_check('iis.windows.perf.health', IIS.CRITICAL)

26.25

76

0.797619

60

420

5.383333

0.633333

0.068111

0.105263

0

0.024324

0.119048

420

15

77

28

0.848649

0.242857

0

0.073248

0

0.125

1

0.125

false

0

0.375

0

0.5

0

null

0

null

0

1

0

1

c5681f32ba0443d6943fe18106423ebafc204c78

12,733

py

Python

epgrefresh/src/plugin.py

builder08/enigma2-plugins_2

f8f08b947e23c1c86b011492a7323125774c3482

[ "OLDAP-2.3" ]

null

epgrefresh/src/plugin.py

builder08/enigma2-plugins_2

f8f08b947e23c1c86b011492a7323125774c3482

[ "OLDAP-2.3" ]

null

epgrefresh/src/plugin.py

builder08/enigma2-plugins_2

f8f08b947e23c1c86b011492a7323125774c3482

[ "OLDAP-2.3" ]

null

from __future__ import print_function # for localized messages from . import _, NOTIFICATIONDOMAIN # Config from Components.config import config, ConfigYesNo, ConfigNumber, ConfigSelection, \ ConfigSubsection, ConfigClock, ConfigYesNo, ConfigInteger, NoSave from Screens.MessageBox import MessageBox from Screens.Standby import TryQuitMainloop from Tools.BoundFunction import boundFunction from boxbranding import getImageDistro from Components.SystemInfo import SystemInfo from Components.NimManager import nimmanager # Error-print from traceback import print_exc from sys import stdout # Calculate default begin/end from time import time, localtime, mktime now = localtime() begin = mktime(( now.tm_year, now.tm_mon, now.tm_mday, 07, 30, 0, now.tm_wday, now.tm_yday, now.tm_isdst) ) end = mktime(( now.tm_year, now.tm_mon, now.tm_mday, 20, 00, 0, now.tm_wday, now.tm_yday, now.tm_isdst) ) #Configuration config.plugins.epgrefresh = ConfigSubsection() config.plugins.epgrefresh.enabled = ConfigYesNo(default=False) config.plugins.epgrefresh.begin = ConfigClock(default=int(begin)) config.plugins.epgrefresh.end = ConfigClock(default=int(end)) config.plugins.epgrefresh.interval_seconds = ConfigNumber(default=120) config.plugins.epgrefresh.delay_standby = ConfigNumber(default=10) config.plugins.epgrefresh.inherit_autotimer = ConfigYesNo(default=False) config.plugins.epgrefresh.afterevent = ConfigYesNo(default=False) config.plugins.epgrefresh.force = ConfigYesNo(default=False) config.plugins.epgrefresh.skipProtectedServices = ConfigSelection(choices=[ ("bg_only", _("Background only")), ("always", _("Foreground also")), ], default="bg_only" ) config.plugins.epgrefresh.enablemessage = ConfigYesNo(default=True) config.plugins.epgrefresh.wakeup = ConfigYesNo(default=False) config.plugins.epgrefresh.lastscan = ConfigNumber(default=0) config.plugins.epgrefresh.parse_autotimer = ConfigSelection(choices=[ ("always", _("Yes")), ("never", _("No")), ("bg_only", _("Background only")), ("ask_yes", _("Ask default Yes")), ("ask_no", _("Ask default No")), ], default="never" ) config.plugins.epgrefresh.erase = ConfigYesNo(default=False) adapter_choices = [("main", _("Main Picture"))] if SystemInfo.get("NumVideoDecoders", 1) > 1: adapter_choices.append(("pip", _("Picture in Picture"))) adapter_choices.append(("pip_hidden", _("Picture in Picture (hidden)"))) if len(nimmanager.nim_slots) > 1: adapter_choices.append(("record", _("Fake recording"))) config.plugins.epgrefresh.adapter = ConfigSelection(choices=adapter_choices, default="main") config.plugins.epgrefresh.show_in_extensionsmenu = ConfigYesNo(default=False) config.plugins.epgrefresh.show_run_in_extensionsmenu = ConfigYesNo(default=True) if getImageDistro() in ("openatv", "openvix",): config.plugins.epgrefresh.show_in_plugins = ConfigYesNo(default=False) else: config.plugins.epgrefresh.show_in_plugins = ConfigYesNo(default=True) config.plugins.epgrefresh.show_help = ConfigYesNo(default=True) config.plugins.epgrefresh.wakeup_time = ConfigInteger(default=-1) config.plugins.epgrefresh.showadvancedoptions = NoSave(ConfigYesNo(default=False)) # convert previous parameters config.plugins.epgrefresh.background = ConfigYesNo(default=False) if config.plugins.epgrefresh.background.value: config.plugins.epgrefresh.adapter.value = "pip_hidden" config.plugins.epgrefresh.background.value = False config.plugins.epgrefresh.save() config.plugins.epgrefresh.interval = ConfigNumber(default=2) if config.plugins.epgrefresh.interval.value != 2: config.plugins.epgrefresh.interval_seconds.value = config.plugins.epgrefresh.interval.value * 60 config.plugins.epgrefresh.interval.value = 2 config.plugins.epgrefresh.save() #pragma mark - Help try: from Components.Language import language from Plugins.SystemPlugins.MPHelp import registerHelp, XMLHelpReader from Tools.Directories import resolveFilename, SCOPE_PLUGINS, fileExists lang = language.getLanguage()[:2] HELPPATH = resolveFilename(SCOPE_PLUGINS, "Extensions/EPGRefresh") if fileExists(HELPPATH + "/locale/" + str(lang) + "/mphelp.xml"): helpfile = HELPPATH + "/locale/" + str(lang) + "/mphelp.xml" else: helpfile = HELPPATH + "/mphelp.xml" reader = XMLHelpReader(helpfile) epgrefreshHelp = registerHelp(*reader) except Exception as e: print("[EPGRefresh] Unable to initialize MPHelp:", e, "- Help not available!") epgrefreshHelp = None #pragma mark - # Notification-Domain # Q: Do we really need this or can we do this better? from Tools import Notifications try: Notifications.notificationQueue.registerDomain(NOTIFICATIONDOMAIN, _("EPGREFRESH_NOTIFICATION_DOMAIN"), deferred_callable=True) except Exception as e: EPGRefreshNotificationKey = "" #print("[EPGRefresh] Error registering Notification-Domain:", e) # Plugin from EPGRefresh import epgrefresh from EPGRefreshService import EPGRefreshService # Plugins from Components.PluginComponent import plugins from Plugins.Plugin import PluginDescriptor #pragma mark - Workaround for unset clock from enigma import eDVBLocalTimeHandler def timeCallback(isCallback=True): """Time Callback/Autostart management.""" thInstance = eDVBLocalTimeHandler.getInstance() if isCallback: # NOTE: this assumes the clock is actually ready when called back # this may not be true, but we prefer silently dying to waiting forever thInstance.m_timeUpdated.get().remove(timeCallback) elif not thInstance.ready(): thInstance.m_timeUpdated.get().append(timeCallback) return epgrefresh.start() # Autostart def autostart(reason, **kwargs): if reason == 0 and "session" in kwargs: session = kwargs["session"] epgrefresh.session = session if config.plugins.epgrefresh.enabled.value: # check if box was woken up by a timer, if so, check if epgrefresh set this timer if session.nav.wasTimerWakeup() and abs(config.plugins.epgrefresh.wakeup_time.getValue() - time()) <= 360: # if box is not in idle mode, do that from Screens.Standby import Standby, inStandby if not inStandby: from Tools import Notifications Notifications.AddNotificationWithID("Standby", Standby) timeCallback(isCallback=False) elif reason == 1: epgrefresh.stop() def getNextWakeup(): # Return invalid time if not automatically refreshing if not config.plugins.epgrefresh.enabled.value or \ not config.plugins.epgrefresh.wakeup.value: setConfigWakeupTime(-1) return -1 now = localtime() begin = int(mktime( (now.tm_year, now.tm_mon, now.tm_mday, config.plugins.epgrefresh.begin.value[0], config.plugins.epgrefresh.begin.value[1], 0, now.tm_wday, now.tm_yday, now.tm_isdst) )) # todays timespan has not yet begun if begin > time(): setConfigWakeupTime(begin) return begin # otherwise add 1 day setConfigWakeupTime(begin + 86400) return begin + 86400 def setConfigWakeupTime(value): config.plugins.epgrefresh.wakeup_time.value = value config.plugins.epgrefresh.save() # Mainfunction def main(session, **kwargs): try: from EPGRefreshConfiguration import EPGRefreshConfiguration session.openWithCallback( doneConfiguring, EPGRefreshConfiguration ) except: print("[EPGRefresh] Error while Opening EPGRefreshConfiguration") print_exc(file=stdout) def forceRefresh(session, **kwargs): epgrefresh.forceRefresh(session) def stopRunningRefresh(session, **kwargs): epgrefresh.stopRunningRefresh(session) def showPendingServices(session, **kwargs): epgrefresh.showPendingServices(session) def doneConfiguring(session, needsRestart): if needsRestart: session.openWithCallback(boundFunction(restartGUICB, session), MessageBox, _("To apply your Changes the GUI has to be restarted.\nDo you want to restart the GUI now?"), MessageBox.TYPE_YESNO, timeout=30) else: _startAfterConfig(session) def restartGUICB(session, answer): if answer is True: session.open(TryQuitMainloop, 3) else: _startAfterConfig(session) def _startAfterConfig(session): if config.plugins.epgrefresh.enabled.value: if not epgrefresh.isRunning(): epgrefresh.start(session) # Eventinfo def eventinfo(session, servicelist, **kwargs): ref = session.nav.getCurrentlyPlayingServiceReference() if not ref: return sref = ref.toString() # strip all after last : pos = sref.rfind(':') if pos != -1: sref = sref[:pos + 1] epgrefresh.services[0].add(EPGRefreshService(str(sref), None)) # XXX: we need this helper function to identify the descriptor # Extensions menu def extensionsmenu(session, **kwargs): main(session, **kwargs) extSetupDescriptor = PluginDescriptor(_("EPG-Refresh_SetUp"), description=_("Automatically refresh EPG"), where=PluginDescriptor.WHERE_EXTENSIONSMENU, fnc=extensionsmenu, needsRestart=False) extRunDescriptor = PluginDescriptor(_("EPG-Refresh_Refresh now"), description=_("Start EPGrefresh immediately"), where=PluginDescriptor.WHERE_EXTENSIONSMENU, fnc=forceRefresh, needsRestart=False) extStopDescriptor = PluginDescriptor(_("EPG-Refresh_Stop Refresh"), description=_("Stop Running EPG-refresh"), where=PluginDescriptor.WHERE_EXTENSIONSMENU, fnc=stopRunningRefresh, needsRestart=False) extPendingServDescriptor = PluginDescriptor(_("EPG-Refresh_Pending Services"), description=_("Show the pending Services for refresh"), where=PluginDescriptor.WHERE_EXTENSIONSMENU, fnc=showPendingServices, needsRestart=False) extPluginDescriptor = PluginDescriptor( name=_("EPGRefresh"), description=_("Automatically refresh EPG"), where=PluginDescriptor.WHERE_PLUGINMENU, fnc=main, icon="EPGRefresh.png", needsRestart=False) def AdjustExtensionsmenu(enable, PlugDescriptor): if enable: if PlugDescriptor not in plugins.getPlugins(PlugDescriptor.where): plugins.addPlugin(PlugDescriptor) else: try: plugins.removePlugin(PlugDescriptor) except ValueError as ve: if PlugDescriptor != extRunDescriptor: print("[EPGRefresh] AdjustExtensionsmenu got confused, tried to remove non-existant plugin entry... ignoring.") def housekeepingExtensionsmenu(configentry, force=False): if force or (epgrefresh != None and not epgrefresh.isRunning()): PlugDescriptor = None if configentry == config.plugins.epgrefresh.show_in_plugins: PlugDescriptor = extPluginDescriptor elif configentry == config.plugins.epgrefresh.show_in_extensionsmenu: PlugDescriptor = extSetupDescriptor elif configentry == config.plugins.epgrefresh.show_run_in_extensionsmenu: PlugDescriptor = extRunDescriptor #if PlugDescriptor != None: if PlugDescriptor is not None: AdjustExtensionsmenu(configentry.value, PlugDescriptor) config.plugins.epgrefresh.show_in_plugins.addNotifier(housekeepingExtensionsmenu, initial_call=False, immediate_feedback=True) config.plugins.epgrefresh.show_in_extensionsmenu.addNotifier(housekeepingExtensionsmenu, initial_call=False, immediate_feedback=True) config.plugins.epgrefresh.show_run_in_extensionsmenu.addNotifier(housekeepingExtensionsmenu, initial_call=False, immediate_feedback=True) def menu_main(menuid, **kwargs): if getImageDistro() in ("openvix", "openatv", "openspa", "openhdf"): if menuid != "epg": return [] else: return [] return [(_("EPGRefresh"), main, "epgrefresh", None)] def Plugins(**kwargs): # NOTE: this might be a little odd to check this, but a user might expect # the plugin to resume normal operation if installed during runtime, but # this is not given if the plugin is supposed to run in background (as we # won't be handed the session which we need to zap). So in turn we require # a restart if-and only if-we're installed during runtime AND running in # background. To improve the user experience in this situation, we hide # all references to this plugin. needsRestart = config.plugins.epgrefresh.enabled.value and not plugins.firstRun list = [ PluginDescriptor( name="EPGRefresh", where=[ PluginDescriptor.WHERE_AUTOSTART, PluginDescriptor.WHERE_SESSIONSTART ], fnc=autostart, wakeupfnc=getNextWakeup, needsRestart=needsRestart, ), PluginDescriptor( name=_("add to EPGRefresh"), where=PluginDescriptor.WHERE_EVENTINFO, fnc=eventinfo, needsRestart=needsRestart, ), ] list.append(PluginDescriptor(name=_("EPGRefresh"), description=_("Automatically refresh EPG"), where=PluginDescriptor.WHERE_MENU, fnc=menu_main)) if config.plugins.epgrefresh.show_in_extensionsmenu.value: extSetupDescriptor.needsRestart = needsRestart list.append(extSetupDescriptor) if config.plugins.epgrefresh.show_run_in_extensionsmenu.value: extRunDescriptor.needsRestart = needsRestart list.append(extRunDescriptor) if config.plugins.epgrefresh.show_in_plugins.value: extPluginDescriptor.needsRestart = needsRestart list.append(extPluginDescriptor) return list

35.766854

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0

null

0

null

0

1

0

1

c56aa8051395c03cfefdb6b4c31ba197b3b0d2c8

1,876

py

Python

examples/server.py

zaibon/tcprouter

7e9d2590e1b1d9d984ac742bd82fcbcf3d42b3ef

[ "BSD-3-Clause" ]

5

2019-05-30T23:36:05.000Z

2019-10-10T21:37:53.000Z

examples/server.py

zaibon/tcprouter

7e9d2590e1b1d9d984ac742bd82fcbcf3d42b3ef

[ "BSD-3-Clause" ]

7

2019-06-12T11:55:46.000Z

2019-11-18T22:53:06.000Z

examples/server.py

xmonader/eltcprouter

b3435733d102c2435e9f62aa469d34c475cc31bd

[ "BSD-3-Clause" ]

1

2021-01-05T20:09:51.000Z

from gevent import monkey; monkey.patch_all() import logging from gevent.server import StreamServer logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) class Receiver(object): """ Interface for a receiver - mimics Twisted's protocols """ def __init__(self): self.socket = None self.address = None def connection_made(self, socket, address): self.socket = socket self.address = address def connection_lost(self): pass def line_received(self, line): pass def send_line(self, line): self.socket.sendall(line + b'\n') class EchoReceiver(Receiver): """ A basic implementation of a receiver which echoes back every line it receives. """ def line_received(self, line): self.send_line(line) def Handler(receiver_class): """ A basic connection handler that applies a receiver object to each connection. """ def handle(socket, address): logger.info('Client (%s) connected', address) receiver = receiver_class() receiver.connection_made(socket, address) try: f = socket.makefile() while True: line = f.readline().strip() if line == "": break logger.info('Received line from client: %s', line) receiver.line_received(line.encode()) logger.info('Client (%s) disconnected.', address) except Exception as e: logger.exception(e) finally: try: f.close() receiver.connection_lost() except: pass return handle server = StreamServer(('0.0.0.0', 9092), Handler(EchoReceiver), keyfile='server.key', certfile='server.crt') logger.info('Server running') server.serve_forever()

25.351351

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0

1

3d6fef82415cc33c1f679313aef262f6b3b670a9

17,848

py

Python

sbvat/utils.py

thudzj/BVAT

2c7073cb7967583035eece7f4819821b313d73e6

[ "MIT" ]

3

2019-08-04T03:05:51.000Z

2021-04-24T02:35:05.000Z

sbvat/utils.py

thudzj/BVAT

2c7073cb7967583035eece7f4819821b313d73e6

[ "MIT" ]

null

sbvat/utils.py

thudzj/BVAT

2c7073cb7967583035eece7f4819821b313d73e6

[ "MIT" ]

1

2019-12-29T13:49:22.000Z

import numpy as np import pickle as pkl import networkx as nx import scipy.sparse as sp from scipy.sparse.linalg.eigen.arpack import eigsh import sys import tensorflow as tf import os import time import json from networkx.readwrite import json_graph from sklearn.metrics import f1_score import multiprocessing def parse_index_file(filename): """Parse index file.""" index = [] for line in open(filename): index.append(int(line.strip())) return index def sample_mask(idx, l): """Create mask.""" mask = np.zeros(l) mask[idx] = 1 return np.array(mask, dtype=np.bool) def save_sparse_csr(filename,array): np.savez(filename,data = array.data ,indices=array.indices, indptr =array.indptr, shape=array.shape ) def load_sparse_csr(filename): loader = np.load(filename) return sp.csr_matrix(( loader['data'], loader['indices'], loader['indptr']), shape = loader['shape']) def starfind_4o_nbrs(args): return find_4o_nbrs(*args) def find_4o_nbrs(adj, li): nbrs = [] for i in li: print(i) tmp = adj[i] for ii in np.nonzero(adj[i])[1]: tmp += adj[ii] for iii in np.nonzero(adj[ii])[1]: tmp += adj[iii] tmp += adj[np.nonzero(adj[iii])[1]].sum(0) nbrs.append(np.nonzero(tmp)[1]) return nbrs def load_data(dataset_str, is_sparse): if dataset_str == "ppi": return load_graphsage_data('data/ppi/ppi', is_sparse) """Load data.""" if dataset_str != 'nell': names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph'] objects = [] for i in range(len(names)): with open("data/ind.{}.{}".format(dataset_str, names[i]), 'rb') as f: if sys.version_info > (3, 0): objects.append(pkl.load(f, encoding='latin1')) else: objects.append(pkl.load(f)) x, y, tx, ty, allx, ally, graph = tuple(objects) test_idx_reorder = parse_index_file("data/ind.{}.test.index".format(dataset_str)) test_idx_range = np.sort(test_idx_reorder) if dataset_str == 'citeseer': # Fix citeseer dataset (there are some isolated nodes in the graph) # Find isolated nodes, add them as zero-vecs into the right position test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1) tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1])) tx_extended[test_idx_range-min(test_idx_range), :] = tx tx = tx_extended ty_extended = np.zeros((len(test_idx_range_full), y.shape[1])) ty_extended[test_idx_range-min(test_idx_range), :] = ty ty = ty_extended features = sp.vstack((allx, tx)).tolil() features[test_idx_reorder, :] = features[test_idx_range, :] features = preprocess_features(features, is_sparse) adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph)) support = preprocess_adj(adj) labels = np.vstack((ally, ty)) labels[test_idx_reorder, :] = labels[test_idx_range, :] idx_test = test_idx_range.tolist() idx_train = range(len(y)) idx_val = range(len(y), len(y)+500) train_mask = sample_mask(idx_train, labels.shape[0]) val_mask = sample_mask(idx_val, labels.shape[0]) test_mask = sample_mask(idx_test, labels.shape[0]) # y_train = np.zeros(labels.shape) # y_val = np.zeros(labels.shape) # y_test = np.zeros(labels.shape) # y_train = labels[train_mask, :] # y_val[val_mask, :] = labels[val_mask, :] # y_test[test_mask, :] = labels[test_mask, :] else: names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph'] objects = [] for i in range(len(names)): with open("data/savedData/{}.{}".format(dataset_str, names[i]), 'rb') as f: if sys.version_info > (3, 0): objects.append(pkl.load(f, encoding='latin1')) else: objects.append(pkl.load(f)) x, y, tx, ty, allx, ally, graph = tuple(objects) test_idx_reorder = parse_index_file("data/savedData/{}.test.index".format(dataset_str)) features = allx.tolil() adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph)) labels = ally features = preprocess_features(features, is_sparse) support = preprocess_adj(adj) idx_test = test_idx_reorder idx_train = range(len(y)) idx_val = range(len(y), len(y)+969) train_mask = sample_mask(idx_train, labels.shape[0]) val_mask = sample_mask(idx_val, labels.shape[0]) test_mask = sample_mask(idx_test, labels.shape[0]) if not os.path.isfile("data/{}.nbrs.npz".format(dataset_str)): N = adj.shape[0] pool = multiprocessing.Pool(processes=56) lis = [] for i in range(32): li = range(int(N/32)*i, int(N/32)*(i+1)) if i == 31: li = range(int(N/32)*i, N) print(li) lis.append(li) adjs = [adj] * 32 results = pool.map(starfind_4o_nbrs, zip(adjs, lis)) pool.close() pool.join() nbrs = [] for re in results: nbrs += re print(len(nbrs)) np.savez("data/{}.nbrs.npz".format(dataset_str), data = nbrs) else: loader = np.load("data/{}.nbrs.npz".format(dataset_str)) nbrs = loader['data'] print(adj.shape, len(nbrs)) return nbrs, support, support, features, labels, train_mask, val_mask, test_mask def sparse_to_tuple(sparse_mx): """Convert sparse matrix to tuple representation.""" def to_tuple(mx): if not sp.isspmatrix_coo(mx): mx = mx.tocoo() coords = np.vstack((mx.row, mx.col)).transpose() values = mx.data shape = mx.shape return coords, values, shape if isinstance(sparse_mx, list): for i in range(len(sparse_mx)): sparse_mx[i] = to_tuple(sparse_mx[i]) else: sparse_mx = to_tuple(sparse_mx) return sparse_mx def preprocess_features(features, sparse=True): """Row-normalize feature matrix and convert to tuple representation""" rowsum = np.array(features.sum(1)) r_inv = np.power(rowsum, -1).flatten() r_inv[np.isinf(r_inv)] = 0. r_mat_inv = sp.diags(r_inv) features = r_mat_inv.dot(features) if sparse: return sparse_to_tuple(features) else: return features.toarray() def normalize_adj(adj): """Symmetrically normalize adjacency matrix.""" adj = sp.coo_matrix(adj) rowsum = np.array(adj.sum(1)) d_inv_sqrt = np.power(rowsum, -0.5).flatten() d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. d_mat_inv_sqrt = sp.diags(d_inv_sqrt) return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo() def preprocess_adj(adj): """Preprocessing of adjacency matrix for simple GCN model and conversion to tuple representation.""" adj_normalized = normalize_adj(adj + sp.eye(adj.shape[0])) return sparse_to_tuple(adj_normalized) def construct_feed_dict(features, support, labels, labels_mask, placeholders, nbrs): """Construct feed dictionary.""" feed_dict = dict() feed_dict.update({placeholders['labels']: labels}) feed_dict.update({placeholders['labels_mask']: labels_mask}) feed_dict.update({placeholders['features']: features}) feed_dict.update({placeholders['support']: support}) feed_dict.update({placeholders['num_features_nonzero']: features[1].shape}) r1 = sample_nodes(nbrs) feed_dict.update({placeholders['adv_mask1']: r1}) return feed_dict def chebyshev_polynomials(adj, k): """Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation).""" print("Calculating Chebyshev polynomials up to order {}...".format(k)) adj_normalized = normalize_adj(adj) laplacian = sp.eye(adj.shape[0]) - adj_normalized largest_eigval, _ = eigsh(laplacian, 1, which='LM') scaled_laplacian = (2. / largest_eigval[0]) * laplacian - sp.eye(adj.shape[0]) t_k = list() t_k.append(sp.eye(adj.shape[0])) t_k.append(scaled_laplacian) def chebyshev_recurrence(t_k_minus_one, t_k_minus_two, scaled_lap): s_lap = sp.csr_matrix(scaled_lap, copy=True) return 2 * s_lap.dot(t_k_minus_one) - t_k_minus_two for i in range(2, k+1): t_k.append(chebyshev_recurrence(t_k[-1], t_k[-2], scaled_laplacian)) return sparse_to_tuple(t_k) def sample_nodes(nbrs, num=100): N = len(nbrs) flag = np.zeros([N]) output = [0] * num #norm_mtx = np.zeros([N, N]) for i in range(num): a = np.random.randint(0, N) while flag[a] == 1: a = np.random.randint(0, N) output[i] = a # for nell to speed up flag[nbrs[a]] = 1 # tmp = np.zeros([N]) # tmp[nbrs[a]] = 1 #norm_mtx[nbrs[a]] = tmp # output_ = np.ones([N]) # output_[output] = 0 # output_ = np.nonzero(output_)[0] return sample_mask(output, N)#, norm_mtx def kl_divergence_with_logit(q_logit, p_logit, mask=None): if not mask is None: q = tf.nn.softmax(q_logit) mask = tf.cast(mask, dtype=tf.float32) mask /= tf.reduce_mean(mask) qlogq = tf.reduce_mean(tf.reduce_sum(q * tf.nn.log_softmax(q_logit), 1) * mask) qlogp = tf.reduce_mean(tf.reduce_sum(q * tf.nn.log_softmax(p_logit), 1) * mask) return - qlogp else: q = tf.nn.softmax(q_logit) qlogq = tf.reduce_sum(q * tf.nn.log_softmax(q_logit), 1) qlogp = tf.reduce_sum(q * tf.nn.log_softmax(p_logit), 1) return tf.reduce_mean( - qlogp) def entropy_y_x(logit): p = tf.nn.softmax(logit) return -tf.reduce_mean(tf.reduce_sum(p * tf.nn.log_softmax(logit), 1)) def get_normalized_vector(d, sparse=False, indices=None, dense_shape=None): if sparse: d /= (1e-12 + tf.reduce_max(tf.abs(d))) d2 = tf.SparseTensor(indices, tf.square(d), dense_shape) d = tf.SparseTensor(indices, d, dense_shape) d /= tf.sqrt(1e-6 + tf.sparse_reduce_sum(d2, 1, keep_dims=True)) return d else: d /= (1e-12 + tf.reduce_max(tf.abs(d))) d /= tf.sqrt(1e-6 + tf.reduce_sum(tf.pow(d, 2.0), 1, keepdims=True)) return d def get_normalized_matrix(d, sparse=False, indices=None, dense_shape=None): if not sparse: return tf.nn.l2_normalize(d, [0,1]) else: return tf.SparseTensor(indices, tf.nn.l2_normalize(d, [0]), dense_shape) def load_graphsage_data(prefix, is_sparse, normalize=True, max_degree=-1): version_info = map(int, nx.__version__.split('.')) major = version_info[0] minor = version_info[1] assert (major <= 1) and (minor <= 11), "networkx major version must be <= 1.11 in order to load graphsage data" # Save normalized version if max_degree==-1: npz_file = prefix + '.npz' else: npz_file = '{}_deg{}.npz'.format(prefix, max_degree) if os.path.exists(npz_file): start_time = time.time() print('Found preprocessed dataset {}, loading...'.format(npz_file)) data = np.load(npz_file) num_data = data['num_data'] feats = data['feats'] labels = data['labels'] train_data = data['train_data'] val_data = data['val_data'] test_data = data['test_data'] train_adj = data['train_adj'] full_adj = data['full_adj'] train_adj_nonormed = sp.csr_matrix((data['train_adj_data'], data['train_adj_indices'], data['train_adj_indptr']), shape=data['train_adj_shape']) print('Finished in {} seconds.'.format(time.time() - start_time)) else: print('Loading data...') start_time = time.time() G_data = json.load(open(prefix + "-G.json")) G = json_graph.node_link_graph(G_data) feats = np.load(prefix + "-feats.npy").astype(np.float32) id_map = json.load(open(prefix + "-id_map.json")) if id_map.keys()[0].isdigit(): conversion = lambda n: int(n) else: conversion = lambda n: n id_map = {conversion(k):int(v) for k,v in id_map.iteritems()} walks = [] class_map = json.load(open(prefix + "-class_map.json")) if isinstance(class_map.values()[0], list): lab_conversion = lambda n : n else: lab_conversion = lambda n : int(n) class_map = {conversion(k): lab_conversion(v) for k,v in class_map.iteritems()} ## Remove all nodes that do not have val/test annotations ## (necessary because of networkx weirdness with the Reddit data) broken_count = 0 to_remove = [] for node in G.nodes(): if not id_map.has_key(node): #if not G.node[node].has_key('val') or not G.node[node].has_key('test'): to_remove.append(node) broken_count += 1 for node in to_remove: G.remove_node(node) print("Removed {:d} nodes that lacked proper annotations due to networkx versioning issues".format(broken_count)) # Construct adjacency matrix print("Loaded data ({} seconds).. now preprocessing..".format(time.time()-start_time)) start_time = time.time() edges = [] for edge in G.edges(): if id_map.has_key(edge[0]) and id_map.has_key(edge[1]): edges.append((id_map[edge[0]], id_map[edge[1]])) print('{} edges'.format(len(edges))) num_data = len(id_map) if max_degree != -1: print('Subsampling edges...') edges = subsample_edges(edges, num_data, max_degree) val_data = np.array([id_map[n] for n in G.nodes() if G.node[n]['val']], dtype=np.int32) test_data = np.array([id_map[n] for n in G.nodes() if G.node[n]['test']], dtype=np.int32) is_train = np.ones((num_data), dtype=np.bool) is_train[val_data] = False is_train[test_data] = False train_data = np.array([n for n in range(num_data) if is_train[n]], dtype=np.int32) val_data = sample_mask(val_data, num_data) test_data = sample_mask(test_data, num_data) train_data = sample_mask(train_data, num_data) train_edges = [(e[0], e[1]) for e in edges if is_train[e[0]] and is_train[e[1]]] edges = np.array(edges, dtype=np.int32) train_edges = np.array(train_edges, dtype=np.int32) # Process labels if isinstance(class_map.values()[0], list): num_classes = len(class_map.values()[0]) labels = np.zeros((num_data, num_classes), dtype=np.float32) for k in class_map.keys(): labels[id_map[k], :] = np.array(class_map[k]) else: num_classes = len(set(class_map.values())) labels = np.zeros((num_data, num_classes), dtype=np.float32) for k in class_map.keys(): labels[id_map[k], class_map[k]] = 1 if normalize: from sklearn.preprocessing import StandardScaler train_ids = np.array([id_map[n] for n in G.nodes() if not G.node[n]['val'] and not G.node[n]['test']]) train_feats = feats[train_ids] scaler = StandardScaler() scaler.fit(train_feats) feats = scaler.transform(feats) def _normalize_adj(edges): adj = sp.csr_matrix((np.ones((edges.shape[0]), dtype=np.float32), (edges[:,0], edges[:,1])), shape=(num_data, num_data)) adj += adj.transpose() tmp = adj # rowsum = np.array(adj.sum(1)).flatten() # d_inv = 1.0 / (rowsum+1e-20) # d_mat_inv = sp.diags(d_inv, 0) adj = normalize_adj(adj + sp.eye(adj.shape[0]))#d_mat_inv.dot(adj).tocoo() coords = np.array((adj.row, adj.col)).astype(np.int32) return tmp, adj.data, coords train_adj_nonormed, train_v, train_coords = _normalize_adj(train_edges) _, full_v, full_coords = _normalize_adj(edges) def _get_adj(data, coords): adj = sp.csr_matrix((data, (coords[0,:], coords[1,:])), shape=(num_data, num_data)) return adj train_adj = sparse_to_tuple(_get_adj(train_v, train_coords)) full_adj = sparse_to_tuple(_get_adj(full_v, full_coords)) # train_feats = train_adj.dot(feats) # test_feats = full_adj.dot(feats) print("Done. {} seconds.".format(time.time()-start_time)) with open(npz_file, 'wb') as fwrite: np.savez(fwrite, num_data=num_data, train_adj=train_adj, train_adj_data=train_adj_nonormed.data, train_adj_indices=train_adj_nonormed.indices, train_adj_indptr=train_adj_nonormed.indptr, train_adj_shape=train_adj_nonormed.shape, full_adj=full_adj, feats=feats, labels=labels, train_data=train_data, val_data=val_data, test_data=test_data) return train_adj_nonormed, train_adj, full_adj, feats, labels, train_data, val_data, test_data def calc_f1(y_pred, y_true, multitask): if multitask: y_pred[y_pred>0] = 1 y_pred[y_pred<=0] = 0 else: y_true = np.argmax(y_true, axis=1) y_pred = np.argmax(y_pred, axis=1) return f1_score(y_true, y_pred, average="micro"), \ f1_score(y_true, y_pred, average="macro")

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200

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null

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1

0

1

3d7270ed2ccd3fdf53730944e85357d2c3e72251

2,879

py

Python

Extended Programming Challenges Python/Mnozenie Macierzy/test_main.py

szachovy/School-and-Training

70f07c0d077da7ba1920d28d881fff7ddcbc37d9

[ "MIT" ]

null

Extended Programming Challenges Python/Mnozenie Macierzy/test_main.py

szachovy/School-and-Training

70f07c0d077da7ba1920d28d881fff7ddcbc37d9

[ "MIT" ]

null

Extended Programming Challenges Python/Mnozenie Macierzy/test_main.py

szachovy/School-and-Training

70f07c0d077da7ba1920d28d881fff7ddcbc37d9

[ "MIT" ]

null

import unittest import main import re class MatrixRowsVerification(unittest.TestCase): def setUp(self): self.matrix1 = {0: [1, 2, 3], 1: [4, 5, 6]} self.matrix2 = {0: [1, 2], 1: [3, 4], 2: [5, 6]} def test_getRowsType(self): self.assertIsInstance(main.getRows(self.matrix1), int, 'wrong type of returned number of rows') def test_getRowsNonNegative(self): self.assertGreaterEqual(main.getRows(self.matrix1), 0, 'rows of matrix cannot be negative number') def test_getRowsVerification(self): self.assertEqual(main.getRows(self.matrix1), 2, 'returned number of rows isnt correct') self.assertEqual(main.getRows(self.matrix2), 3, 'returned number of rows isnt correct') class MatrixColsVerification(unittest.TestCase): def setUp(self): self.matrix1 = {0: [1, 2, 3], 1: [4, 5, 6]} self.matrix2 = {0: [1, 2], 1: [3, 4], 2: [5, 6]} def test_getColsType(self): self.assertIsInstance(main.getCols(self.matrix1), int, 'wrong type of returned number of columns') def test_getColsNonNegative(self): self.assertGreaterEqual(main.getCols(self.matrix1), 0, 'rows of matrix cannot be negative number') def test_getColsVerification(self): self.assertEqual(main.getCols(self.matrix1), 3, 'returned number of rows isnt correct') self.assertEqual(main.getCols(self.matrix2), 2, 'returned number of rows isnt correct') class AutocompleteVerification(unittest.TestCase): def test_autocomplete(self): matrix = {0: [1, 2, 3], 1: [4], 2: [5, 6]} expectedmatrix = {0: [1, 2, 3], 1: [4, 0, 0], 2: [5, 6, 0]} self.assertEqual(main.autocomplete(matrix), expectedmatrix, 'autocomplete zeros not handled') class WrongInputException(Exception): pass class WriteRowsVerification(unittest.TestCase): def setUp(self): self.matrix = main.writerows() def test_wrong_input(self): self.assertTrue(re.findall(r"[A-Za-z]*$", str(self.matrix.values())), 'Letters in matrix has been found') def test_returnsDict(self): try: self.assertIsInstance(self.matrix, dict) except WrongInputException: self.fail('writing rows doesnt format matrix (dict with rows and cols)') class VerifyFinalMatrix(unittest.TestCase): def setUp(self): self.matrix1 = {0: [1, 2, 3], 1: [4, 5, 6]} self.matrix2 = {0: [1, 2], 1: [3, 4], 2: [5, 6]} self.final = {0: [9, 12, 15], 1: [19, 26, 33], 2: [29, 40, 51]} def test_checkFinal(self): self.assertEqual(main.Calculate(self.matrix1, self.matrix2).multiply(), self.final, 'Unexpected final matrix ' 'after calculations') def tearDown(self): self.final.clear() if __name__ == '__main__': unittest.main()

38.905405

118

0.632511

370

2,879

4.87027

0.278378

0.057714

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0.32131

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0.178135

0

0.051398

0.229594

2,879

73

119

39.438356

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0

0.185185

0

0.167419

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0.222222

1

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false

0.018519

0.055556

0

0.444444

0

null

0

null

0

1

0

1

3d7e43dc6fabcfe8138a99da18574265d9a525c8

1,786

py

Python

pyopenproject/business/services/command/priority/find_all.py

webu/pyopenproject

40b2cb9fe0fa3f89bc0fe2a3be323422d9ecf966

[ "MIT" ]

5

2021-02-25T15:54:28.000Z

2021-04-22T15:43:36.000Z

pyopenproject/business/services/command/priority/find_all.py

webu/pyopenproject

40b2cb9fe0fa3f89bc0fe2a3be323422d9ecf966

[ "MIT" ]

7

2021-03-15T16:26:23.000Z

2022-03-16T13:45:18.000Z

pyopenproject/business/services/command/priority/find_all.py

webu/pyopenproject

40b2cb9fe0fa3f89bc0fe2a3be323422d9ecf966

[ "MIT" ]

6

2021-06-18T18:59:11.000Z

2022-03-27T04:58:52.000Z

from pyopenproject.api_connection.exceptions.request_exception import RequestError from pyopenproject.api_connection.requests.get_request import GetRequest from pyopenproject.business.exception.business_error import BusinessError from pyopenproject.business.services.command.find_list_command import FindListCommand from pyopenproject.business.services.command.priority.priority_command import PriorityCommand from pyopenproject.business.util.filters import Filters from pyopenproject.business.util.url import URL from pyopenproject.business.util.url_parameter import URLParameter from pyopenproject.model.priority import Priority class FindAll(PriorityCommand): def __init__(self, connection, offset, page_size, filters, sort_by): super().__init__(connection) self.offset = offset self.page_size = page_size self.filters = filters self.sort_by = sort_by self.filters = filters def execute(self): try: request = GetRequest(self.connection, str(URL(f"{self.CONTEXT}", [ Filters( self.filters), URLParameter ("sortBy", self.sort_by) ]))) return FindListCommand(self.connection, request, Priority).execute() # for priority in json_obj["_embedded"]["elements"]: # yield Priority(priority) except RequestError as re: raise BusinessError("Error finding all priorities") from re

49.611111

93

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159

1,786

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0.147683

0.144788

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0.138996

0

0.342665

1,786

35

94

51.028571

0.882453

0.044233

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0

0.028169

0

1

0.068966

false

0

0.310345

0

0.448276

0

null

0

null

0

1

0

1

3d85f7e617337855186eb9a6630f328826ed38ef

868

py

Python

app/migrations/0003_contacts.py

Joshua-Barawa/Django-IP4

5665efe73cf8d2244b7bb35ed627e4e237902156

[ "Unlicense" ]

null

app/migrations/0003_contacts.py

Joshua-Barawa/Django-IP4

5665efe73cf8d2244b7bb35ed627e4e237902156

[ "Unlicense" ]

null

app/migrations/0003_contacts.py

Joshua-Barawa/Django-IP4

5665efe73cf8d2244b7bb35ed627e4e237902156

[ "Unlicense" ]

null

# Generated by Django 4.0.3 on 2022-03-21 13:04 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('app', '0002_remove_profile_caption_alter_profile_profile_pic_and_more'), ] operations = [ migrations.CreateModel( name='Contacts', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(blank=True, max_length=100, null=True)), ('unit', models.CharField(blank=True, max_length=100, null=True)), ('m_number', models.IntegerField(default=0)), ('hood', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='app.neighborhood')), ], ), ]

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0.053333

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0.277778

0

null

0

1

0

null

0

1

3d90bec081e48c3692736a49abca5a861a8e0892

626

py

Python

scripts/modules/task_plan_types/date.py

vkostyanetsky/Organizer

b1f0a05c0b6c6e6ea7a78a6bd7a3c70f85b33eba

[ "MIT" ]

null

scripts/modules/task_plan_types/date.py

vkostyanetsky/Organizer

b1f0a05c0b6c6e6ea7a78a6bd7a3c70f85b33eba

[ "MIT" ]

null

scripts/modules/task_plan_types/date.py

vkostyanetsky/Organizer

b1f0a05c0b6c6e6ea7a78a6bd7a3c70f85b33eba

[ "MIT" ]

null

# DD.MM.YYYY (DD — номер дня, MM — номер месяца, YYYY — номер года) import re import datetime def is_task_current(task, date): result = None groups = re.match('([0-9]{1,2}).([0-9]{1,2}).([0-9]{4})', task['condition']) type_is_correct = groups != None if type_is_correct: task_date_year = int(groups[3]) task_date_month = int(groups[2]) task_date_day = int(groups[1]) task_date = datetime.datetime(task_date_year, task_date_month, task_date_day) task['outdated'] = task_date < date result = date == task_date return result

26.083333

91

0.600639

94

626

3.808511

0.382979

0.223464

0.01676

0.022346

0.027933

0

0.030369

0.263578

626

24

92

26.083333

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false

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0.142857

0

0.285714

0

null

1

0

null

0

1

3da195067ff01ae97b234bc41093431b6cebf500

646

py

Python

class3/collateral/show_genie.py

twin-bridges/netmiko_course

31943e4f6f66dbfe523d62d5a2f03285802a8c56

[ "Apache-2.0" ]

11

2020-09-16T06:53:16.000Z

2021-08-24T21:27:37.000Z

class3/collateral/show_genie.py

twin-bridges/netmiko_course

31943e4f6f66dbfe523d62d5a2f03285802a8c56

[ "Apache-2.0" ]

null

class3/collateral/show_genie.py

twin-bridges/netmiko_course

31943e4f6f66dbfe523d62d5a2f03285802a8c56

[ "Apache-2.0" ]

5

2020-10-18T20:25:59.000Z

2021-10-20T16:27:00.000Z

import os from netmiko import ConnectHandler from getpass import getpass from pprint import pprint # Code so automated tests will run properly # Check for environment variable, if that fails, use getpass(). password = os.getenv("NETMIKO_PASSWORD") if os.getenv("NETMIKO_PASSWORD") else getpass() my_device = { "device_type": "cisco_xe", "host": "cisco3.lasthop.io", "username": "pyclass", "password": password, } with ConnectHandler(**my_device) as net_connect: output = net_connect.send_command("show ip int brief", use_genie=True) # output = net_connect.send_command("show ip arp", use_genie=True) pprint(output)

30.761905

88

0.733746

88

646

5.238636

0.568182

0.065076

0.099783

0.143167

0

0.001835

0.156347

646

20

89

32.3

0.844037

0.260062

0

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0

1

0

false

0.214286

0.285714

0

0.285714

0.142857

0

null

0

null

0

1

0

1

3da20b359813d6186015461736f4d52256b59084

2,793

py

Python

pints/tests/test_toy_hes1_michaelis_menten_model.py

lisaplag/pints

3de6617e57ba5b395edaca48961bfc5a4b7209b3

[ "RSA-MD" ]

null

pints/tests/test_toy_hes1_michaelis_menten_model.py

lisaplag/pints

3de6617e57ba5b395edaca48961bfc5a4b7209b3

[ "RSA-MD" ]

null

pints/tests/test_toy_hes1_michaelis_menten_model.py

lisaplag/pints

3de6617e57ba5b395edaca48961bfc5a4b7209b3

[ "RSA-MD" ]

null

#!/usr/bin/env python3 # # Tests if the HES1 Michaelis-Menten toy model runs. # # This file is part of PINTS (https://github.com/pints-team/pints/) which is # released under the BSD 3-clause license. See accompanying LICENSE.md for # copyright notice and full license details. # import unittest import numpy as np import pints import pints.toy class TestHes1Model(unittest.TestCase): """ Tests if the HES1 Michaelis-Menten toy model runs. """ def test_run(self): model = pints.toy.Hes1Model() self.assertEqual(model.n_parameters(), 4) self.assertEqual(model.n_outputs(), 1) times = model.suggested_times() parameters = model.suggested_parameters() values = model.simulate(parameters, times) self.assertEqual(values.shape, (len(times),)) self.assertTrue(np.all(values > 0)) states = model.simulate_all_states(parameters, times) self.assertEqual(states.shape, (len(times), 3)) self.assertTrue(np.all(states > 0)) suggested_values = model.suggested_values() self.assertEqual(suggested_values.shape, (len(times),)) self.assertTrue(np.all(suggested_values > 0)) # Test setting and getting init cond. self.assertFalse(np.all(model.initial_conditions() == 10)) model.set_initial_conditions(10) self.assertTrue(np.all(model.initial_conditions() == 10)) # Test setting and getting implicit param. self.assertFalse(np.all(model.implicit_parameters() == [10, 10, 10])) model.set_implicit_parameters([10, 10, 10]) self.assertTrue(np.all(model.implicit_parameters() == [10, 10, 10])) # Initial conditions cannot be negative model = pints.toy.Hes1Model(0) self.assertRaises(ValueError, pints.toy.Hes1Model, -1) # Implicit parameters cannot be negative model = pints.toy.Hes1Model(0, [0, 0, 0]) self.assertRaises(ValueError, pints.toy.Hes1Model, *(0, [-1, 0, 0])) self.assertRaises(ValueError, pints.toy.Hes1Model, *(0, [0, -1, 0])) self.assertRaises(ValueError, pints.toy.Hes1Model, *(0, [0, 0, -1])) self.assertRaises(ValueError, pints.toy.Hes1Model, *(0, [-1, -1, -1])) def test_values(self): # value-based tests for Hes1 Michaelis-Menten times = np.linspace(0, 10, 101) parameters = [3.8, 0.035, 0.15, 7.5] iparameters = [4.5, 4.0, 0.04] y0 = 7 model = pints.toy.Hes1Model(y0=y0, implicit_parameters=iparameters) values = model.simulate(parameters, times) self.assertEqual(values[0], y0) self.assertAlmostEqual(values[1], 7.011333, places=6) self.assertAlmostEqual(values[100], 5.420750, places=6) if __name__ == '__main__': unittest.main()

38.260274

78

0.653419

358

2,793

5.01676

0.290503

0.044543

0.085189

0.060134

0.434298

0.405345

0.35412

0.330178

0.123051

0

0.053327

0.214465

2,793

72

79

38.791667

0.765269

0.183315

0

0.044444

0

0.003551

0

0.444444

1

0.044444

false

0

0.088889

0

0.155556

0

null

0

null

0

1

0

1

3da83d4179e3c0fa03b23a086938541e7c9c090e

931

py

Python

src/tentaclio/clients/athena_client.py

datavaluepeople/tentaclio

eb6920a0e115c6c08043063a8c1013d812ec34c8

[ "MIT" ]

12

2019-04-30T16:07:42.000Z

2021-12-08T08:02:09.000Z

src/tentaclio/clients/athena_client.py

octoenergy/tentaclio

eb6920a0e115c6c08043063a8c1013d812ec34c8

[ "MIT" ]

74

2019-04-25T11:18:22.000Z

2022-01-18T11:31:14.000Z

src/tentaclio/clients/athena_client.py

datavaluepeople/tentaclio

eb6920a0e115c6c08043063a8c1013d812ec34c8

[ "MIT" ]

4

2019-05-05T13:13:21.000Z

2022-01-14T00:33:07.000Z

"""AWS Athena query client. Overrides the `get_df` convenience methods for loading a DataFrame using PandasCursor, which is more performant than using sql alchemy functions. """ import pandas as pd from pyathena.pandas_cursor import PandasCursor from . import decorators, sqla_client __all__ = ["AthenaClient"] class AthenaClient(sqla_client.SQLAlchemyClient): """Postgres client, backed by a SQLAlchemy connection.""" allowed_schemes = ["awsathena+rest"] connect_args_default = dict(cursor_class=PandasCursor) # Athena-specific fast query result retrieval: @decorators.check_conn def get_df(self, sql_query: str, params: dict = None, **kwargs) -> pd.DataFrame: """Run a raw SQL query and return a data frame.""" raw_conn = self._get_raw_conn() raw_cursor = raw_conn.cursor(PandasCursor) return raw_cursor.execute(sql_query, parameters=params, **kwargs).as_pandas()

32.103448

86

0.736842

120

931

5.516667

0.583333

0.036254

0

0.170784

931

28

87

33.25

0.857513

0.337272

0

0.043333

0

1

0.083333

false

0

0.25

0

0.666667

0

null

0

null

0

1

0

1

3dada60e0249d722b9efc92d356114b02e3e0c6c

18,496

py

Python

filters/Filter.py

Paul1298/ITMO_FS

219537776d89e52df0c1c07de2c71ce91c679c50

[ "MIT" ]

null

filters/Filter.py

Paul1298/ITMO_FS

219537776d89e52df0c1c07de2c71ce91c679c50

[ "MIT" ]

null

filters/Filter.py

Paul1298/ITMO_FS

219537776d89e52df0c1c07de2c71ce91c679c50

[ "MIT" ]

null

from .utils import * class Filter(object):####TODO add logging def __init__(self, measure, cutting_rule): """ Basic univariate filter class with chosen(even custom) measure and cutting rule :param measure: Examples -------- >>> f=Filter("PearsonCorr", GLOB_CR["K best"](6)) """ inter_class = 0.0 intra_class = 0.0 for value in np.unique(y_data): index_for_this_value = np.where(y_data == value)[0] n = np.sum(row[index_for_this_value]) mu = np.mean(row[index_for_this_value]) var = np.var(row[index_for_this_value]) inter_class += n * np.power((mu - mu), 2) intra_class += (n - 1) * var f_ratio = inter_class / intra_class return f_ratio @classmethod def __f_ratio_measure(cls, X, y, n): X, y = _DefaultMeasures.__check_input(X, y) assert not 1 < X.shape[1] < n, 'incorrect number of features' f_ratios = [] for feature in X.T: f_ratio = _DefaultMeasures.__calculate_F_ratio(feature, y.T) f_ratios.append(f_ratio) f_ratios = np.array(f_ratios) return np.argpartition(f_ratios, -n)[-n:] @staticmethod def f_ratio_measure(n): return partial(_DefaultMeasures.__f_ratio_measure, n=n) @staticmethod def gini_index(X, y): X, y = _DefaultMeasures.__check_input(X, y) cum_x = np.cumsum(X / np.linalg.norm(X, 1, axis=0), axis=0) cum_y = np.cumsum(y / np.linalg.norm(y, 1)) diff_x = (cum_x[1:] - cum_x[:-1]) diff_y = (cum_y[1:] + cum_y[:-1]) return np.abs(1 - np.sum(np.multiply(diff_x.T, diff_y).T, axis=0)) # Calculate the entropy of y. @staticmethod def __calc_entropy(y): dict_label = dict() for label in y: if label not in dict_label: dict_label.update({label: 1}) else: dict_label[label] += 1 entropy = 0.0 for i in dict_label.values(): entropy += -i / len(y) * log(i / len(y), 2) return entropy @staticmethod def __calc_conditional_entropy(x_j, y): dict_i = dict() for i in range(x_j.shape[0]): if x_j[i] not in dict_i: dict_i.update({x_j[i]: [i]}) else: dict_i[x_j[i]].append(i) # Conditional entropy of a feature. con_entropy = 0.0 # get corresponding values in y. for f in dict_i.values(): # Probability of each class in a feature. p = len(f) / len(x_j) # Dictionary of corresponding probability in labels. dict_y = dict() for i in f: if y[i] not in dict_y: dict_y.update({y[i]: 1}) else: dict_y[y[i]] += 1 # calculate the probability of corresponding label. sub_entropy = 0.0 for l in dict_y.values(): sub_entropy += -l / sum(dict_y.values()) * log(l / sum(dict_y.values()), 2) con_entropy += sub_entropy * p return con_entropy # IGFilter = filters.IGFilter() # TODO: unexpected .run() interface; .run() feature_names; no default constructor @staticmethod def ig_measure(X, y): X, y = _DefaultMeasures.__check_input(X, y) entropy = _DefaultMeasures.__calc_entropy(y) f_ratios = np.empty(X.shape[1]) for index in range(X.shape[1]): f_ratios[index] = entropy - _DefaultMeasures.__calc_conditional_entropy(X[:, index], y) return f_ratios @staticmethod def __contingency_matrix(labels_true, labels_pred): """Build a contingency matrix describing the relationship between labels. Parameters ---------- labels_true : int array, shape = [n_samples] Ground truth class labels to be used as a reference labels_pred : array, shape = [n_samples] Cluster labels to evaluate Returns ------- contingency : {array-like, sparse}, shape=[n_classes_true, n_classes_pred] Matrix :math:`C` such that :math:`C_{i, j}` is the number of samples in true class :math:`i` and in predicted class :math:`j`. If ``eps is None``, the dtype of this array will be integer. If ``eps`` is given, the dtype will be float. """ classes, class_idx = np.unique(labels_true, return_inverse=True) clusters, cluster_idx = np.unique(labels_pred, return_inverse=True) n_classes = classes.shape[0] n_clusters = clusters.shape[0] # Using coo_matrix to accelerate simple histogram calculation, # i.e. bins are consecutive integers # Currently, coo_matrix is faster than histogram2d for simple cases # TODO redo it with numpy contingency = sp.csr_matrix((np.ones(class_idx.shape[0]), (class_idx, cluster_idx)), shape=(n_classes, n_clusters), dtype=np.int) contingency.sum_duplicates() return contingency @staticmethod def __mi(U, V): contingency = _DefaultMeasures.__contingency_matrix(U, V) nzx, nzy, nz_val = sp.find(contingency) contingency_sum = contingency.sum() pi = np.ravel(contingency.sum(axis=1)) pj = np.ravel(contingency.sum(axis=0)) log_contingency_nm = np.log(nz_val) contingency_nm = nz_val / contingency_sum # Don't need to calculate the full outer product, just for non-zeroes outer = (pi.take(nzx).astype(np.int64, copy=False) * pj.take(nzy).astype(np.int64, copy=False)) log_outer = -np.log(outer) + log(pi.sum()) + log(pj.sum()) mi = (contingency_nm * (log_contingency_nm - log(contingency_sum)) + contingency_nm * log_outer) return mi.sum() @classmethod def __mrmr_measure(cls, X, y, n): assert not 1 < X.shape[1] < n, 'incorrect number of features' x, y = _DefaultMeasures.__check_input(X, y) # print([_DefaultMeasures.__mi(X[:, j].reshape(-1, 1), y) for j in range(X.shape[1])]) return [MI(x[:, j].reshape(-1, 1), y) for j in range(x.shape[1])] @staticmethod def mrmr_measure(n): return partial(_DefaultMeasures.__mrmr_measure, n=n) # RandomFilter = filters.RandomFilter() # TODO: bad .run() interface; .run() feature_names; no default constructor @staticmethod def su_measure(X, y): X, y = _DefaultMeasures.__check_input(X, y) entropy = _DefaultMeasures.__calc_entropy(y) f_ratios = np.empty(X.shape[1]) for index in range(X.shape[1]): entropy_x = _DefaultMeasures.__calc_entropy(X[:, index]) con_entropy = _DefaultMeasures.__calc_conditional_entropy(X[:, index], y) f_ratios[index] = 2 * (entropy - con_entropy) / (entropy_x + entropy) return f_ratios @staticmethod def spearman_corr(X, y): X, y = _DefaultMeasures.__check_input(X, y) np.sort(X, axis=1) # need to sort, because Spearman is a rank correlation np.sort(y) n = X.shape[0] c = 6 / (n * (n - 1) * (n + 1)) dif = X - np.repeat(y, X.shape[1]).reshape(X.shape) return 1 - c * np.sum(dif * dif, axis=0) @staticmethod def pearson_corr(X, y): X, y = _DefaultMeasures.__check_input(X, y) x_dev = X - np.mean(X, axis=0) y_dev = y - np.mean(y) sum_dev = y_dev.T.dot(x_dev) sq_dev_x = x_dev * x_dev sq_dev_y = y_dev * y_dev return (sum_dev / np.sqrt(np.sum(sq_dev_y) * np.sum(sq_dev_x))).reshape((-1,)) # TODO concordation coef @staticmethod def fechner_corr(X, y): """ Sample sign correlation (also known as Fechner correlation) """ X, y = _DefaultMeasures.__check_input(X, y) y_mean = np.mean(y) n = X.shape[0] f_ratios = np.zeros(X.shape[1]) for j in range(X.shape[1]): y_dev = y[j] - y_mean x_j_mean = np.mean(X[:, j]) for i in range(n): x_dev = X[i, j] - x_j_mean if x_dev >= 0 & y_dev >= 0: f_ratios[j] += 1 else: f_ratios[j] -= 1 f_ratios[j] /= n return f_ratios @staticmethod def __label_binarize(y): """ Binarize labels in a one-vs-all fashion This function makes it possible to compute this transformation for a fixed set of class labels known ahead of time. """ classes = np.unique(y) n_samples = len(y) n_classes = len(classes) row = np.arange(n_samples) col = [np.where(classes == el)[0][0] for el in y] data = np.repeat(1, n_samples) # TODO redo it with numpy return sp.csr_matrix((data, (row, col)), shape=(n_samples, n_classes)).toarray() @staticmethod def __chisquare(f_obs, f_exp): """Fast replacement for scipy.stats.chisquare. Version from https://github.com/scipy/scipy/pull/2525 with additional optimizations. """ f_obs = np.asarray(f_obs, dtype=np.float64) # Reuse f_obs for chi-squared statistics chisq = f_obs chisq -= f_exp chisq **= 2 with np.errstate(invalid="ignore"): chisq /= f_exp chisq = chisq.sum(axis=0) return chisq @staticmethod def chi2_measure(X, y): """ This score can be used to select the n_features features with the highest values for the test chi-squared statistic from X, which must contain only non-negative features such as booleans or frequencies (e.g., term counts in document classification), relative to the classes. """ X, y = _DefaultMeasures.__check_input(X, y) if np.any(X < 0): raise ValueError("Input X must be non-negative.") Y = _DefaultMeasures.__label_binarize(y) # If you use sparse input # you can use sklearn.utils.extmath.safe_sparse_dot instead observed = np.dot(Y.T, X) # n_classes * n_features feature_count = X.sum(axis=0).reshape(1, -1) class_prob = Y.mean(axis=0).reshape(1, -1) expected = np.dot(class_prob.T, feature_count) return _DefaultMeasures.__chisquare(observed, expected) @staticmethod def __distance_matrix(X, y, n_samples): dm = np.zeros((n_samples, n_samples), dtype=tuple) for i in range(n_samples): for j in range(i, n_samples): # using the Manhattan (L1) norm rather than # the Euclidean (L2) norm, # although the rationale is not specified value = np.linalg.norm(X[i, :] - X[j, :], 1) dm[i, j] = (value, j, y[j]) dm[j, i] = (value, i, y[i]) # sort_indices = dm.argsort(1) # dm.sort(1) # indices = np.arange(n_samples) #[sort_indices] # dm = np.dstack((dm, indices)) return dm # TODO redo with np.where @staticmethod def __take_k(dm_i, k, r_index, choice_func): hits = [] dm_i = sorted(dm_i, key=lambda x: x[0]) for samp in dm_i: if (samp[1] != r_index) & (k > 0) & (choice_func(samp[2])): hits.append(samp) k -= 1 return np.array(hits, int) @staticmethod def reliefF_measure(X, y, k_neighbors=1): """ Based on the ReliefF algorithm as introduced in: R.J. Urbanowicz et al. Relief-based feature selection: Introduction and review Journal of Biomedical Informatics 85 (2018) 189–203 Differs with skrebate.ReliefF Only for complete X Rather than repeating the algorithm m(TODO Ask Nikita about user defined) times, implement it exhaustively (i.e. n times, once for each instance) for relatively small n (up to one thousand). :param X: array-like {n_samples, n_features} Training instances to compute the feature importance scores from :param y: array-like {n_samples} Training labels :param k_neighbors: int (default: 1) The number of neighbors to consider when assigning feature importance scores. More neighbors results in more accurate scores, but takes longer. Selection of k hits and misses is the basic difference to Relief and ensures greater robustness of the algorithm concerning noise. :return: array-like {n_features} Feature importances """ X, y = _DefaultMeasures.__check_input(X, y) f_ratios = np.zeros(X.shape[1]) classes, counts = np.unique(y, return_counts=True) prior_prob = dict(zip(classes, np.array(counts) / len(y))) n_samples = X.shape[0] n_features = X.shape[1] dm = _DefaultMeasures.__distance_matrix(X, y, n_samples) for i in range(n_samples): r = X[i] dm_i = dm[i] hits = _DefaultMeasures.__take_k(dm_i, k_neighbors, i, lambda x: x == y[i]) if len(hits) != 0: ind_hits = hits[:, 1] else: ind_hits = [] value_hits = X.take(ind_hits, axis=0) m_c = np.empty(len(classes), np.ndarray) for j in range(len(classes)): if classes[j] != y[i]: misses = _DefaultMeasures.__take_k(dm_i, k_neighbors, i, lambda x: x == classes[j]) ind_misses = misses[:, 1] m_c[j] = X.take(ind_misses, axis=0) for A in range(n_features): weight_hit = np.sum(np.abs(r[A] - value_hits[:, A])) weight_miss = 0 for j in range(len(classes)): if classes[j] != y[i]: weight_miss += prior_prob[y[j]] * np.sum(np.abs(r[A] - m_c[j][:, A])) f_ratios[A] += weight_miss / (1 - prior_prob[y[i]]) - weight_hit # dividing by m * k guarantees that all final weights # will be normalized within the interval [ − 1, 1]. f_ratios /= n_samples * k_neighbors # The maximum and minimum values of A are determined over the entire # set of instances. # This normalization ensures that weight updates fall # between 0 and 1 for both discrete and continuous features. with np.errstate(divide='ignore', invalid="ignore"): # todo return f_ratios / (np.amax(X, axis=0) - np.amin(X, axis=0)) VDM = filters.VDM() # TODO: probably not a filter GLOB_MEASURE = {"FitCriterion": _DefaultMeasures.fit_criterion_measure, "FRatio": _DefaultMeasures.f_ratio_measure, "GiniIndex": _DefaultMeasures.gini_index, "InformationGain": _DefaultMeasures.ig_measure, "MrmrDiscrete": _DefaultMeasures.mrmr_measure, "SymmetricUncertainty": _DefaultMeasures.su_measure, "SpearmanCorr": _DefaultMeasures.spearman_corr, "PearsonCorr": _DefaultMeasures.pearson_corr, "FechnerCorr": _DefaultMeasures.fechner_corr, "ReliefF": _DefaultMeasures.reliefF_measure, "Chi2": _DefaultMeasures.chi2_measure} class _DefaultCuttingRules: @staticmethod def select_best_by_value(value): return partial(_DefaultCuttingRules.__select_by_value, value=value, more=True) @staticmethod def select_worst_by_value(value): return partial(_DefaultCuttingRules.__select_by_value, value=value, more=False) @staticmethod def __select_by_value(scores, value, more=True): features = [] for key, sc_value in scores.items(): if more: if sc_value >= value: features.append(key) else: if sc_value <= value: features.append(key) return features @staticmethod def select_k_best(k): return partial(_DefaultCuttingRules.__select_k, k=k, reverse=True) @staticmethod def select_k_worst(k): return partial(_DefaultCuttingRules.__select_k, k=k) @classmethod def __select_k(cls, scores, k, reverse=False): if type(k) != int: raise TypeError("Number of features should be integer") return [keys[0] for keys in sorted(scores.items(), key=lambda kv: kv[1], reverse=reverse)[:k]] GLOB_CR = {"Best by value": _DefaultCuttingRules.select_best_by_value, "Worst by value": _DefaultCuttingRules.select_worst_by_value, "K best": _DefaultCuttingRules.select_k_best, "K worst": _DefaultCuttingRules.select_k_worst} class Filter(object): def __init__(self, measure, cutting_rule): if type(measure) is str: try: self.measure = GLOB_MEASURE[measure] except KeyError: raise KeyError("No %r measure yet" % measure) else: self.measure = measure if type(cutting_rule) is str: try: self.cutting_rule = GLOB_CR[cutting_rule] except KeyError: raise KeyError("No %r cutting rule yet" % measure) else: self.cutting_rule = cutting_rule self.feature_scores = None self.hash = None def run(self, x, y, feature_names=None, store_scores=False, verbose=0): try: x = x.values y = y.values except AttributeError: x = x self.feature_scores = None try: feature_names = x.columns except AttributeError: if feature_names is None: feature_names = list(range(x.shape[1])) feature_scores = None if not (self.hash == hash(self.measure)): feature_scores = dict(zip(feature_names, self.measure(x, y))) self.hash = hash(self.measure) if store_scores: self.feature_scores = feature_scores selected_features = self.cutting_rule(feature_scores) return x[:, selected_features]

37.670061

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0.579477

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null

0

null

0

1

0

1

3daf498d7521399146cf380a60792cc98a71c488

6,145

py

Python

MakeMytripChallenge/script/IFtrial.py

divayjindal95/DataScience

d976a5e3ac9bd36e84149642a5b93f7bfc3540cf

[ "MIT" ]

null

MakeMytripChallenge/script/IFtrial.py

divayjindal95/DataScience

d976a5e3ac9bd36e84149642a5b93f7bfc3540cf

[ "MIT" ]

null

MakeMytripChallenge/script/IFtrial.py

divayjindal95/DataScience

d976a5e3ac9bd36e84149642a5b93f7bfc3540cf

[ "MIT" ]

null

import sys import warnings if not sys.warnoptions: warnings.simplefilter("ignore") import numpy as np import pandas as pd import matplotlib.pylab as plt from sklearn.naive_bayes import MultinomialNB from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import GradientBoostingClassifier from sklearn.svm import SVC from sklearn.linear_model import LogisticRegression,LinearRegression from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import KFold,cross_val_score,LeaveOneOut #from sklearn.cross_validation import KFold,train_test_split,cross_val_score train_data = pd.read_csv("../data/train.csv") train_data_len=len(train_data) test_data=pd.read_csv("../data/test.csv") test_data_len=len(test_data) def getint(data): nicedata=data cls=dict() for i in xrange(len(nicedata.columns)): if data.dtypes[i]==object and data.columns[i]!='P': le = LabelEncoder() nicedata[nicedata.columns[i]] = le.fit_transform(nicedata[nicedata.columns[i]]) cls[nicedata.columns[i]]=le.classes_ return nicedata,cls data=pd.concat([train_data,test_data]) data.A=data.A.fillna(data['A'].mode()[0]) data.D=data.D.fillna(data['D'].mode()[0]) data.E=data.E.fillna(data['E'].mode()[0]) data.G=data.G.fillna(data['G'].mode()[0]) data.F=data.F.fillna(data['F'].mode()[0]) data.B=data.A.fillna(data['B'].median()) data.N=data.N.fillna(data['N'].median()) #print len(data.dropna()) #print data.describe() data,cls=getint(data) # data.O=np.log(data.O+1) # data.H=np.log(data.H+1) # data.K=np.log(data.K+1) # data.N=np.log(data.N+1) # data.C=np.log(data.C+1) # sc = StandardScaler() # data.O=sc.fit_transform(np.reshape(data.O,(len(data.O),1))) # sc = StandardScaler() # data.H=sc.fit_transform(np.reshape(data.H,(len(data.H),1))) # sc = StandardScaler() # data.K=sc.fit_transform(np.reshape(data.K,(len(data.K),1))) # sc = StandardScaler() # data.N=sc.fit_transform(np.reshape(data.N,(len(data.N),1))) # sc = StandardScaler() # data.C=sc.fit_transform(np.reshape(data.C,(len(data.C),1))) # sc = StandardScaler() # data.B=sc.fit_transform(np.reshape(data.B,(len(data.B),1))) data['H_frac']=data.H-data.H.map(lambda x:int(x)) data['H_int'] = data.H.map(lambda x:int(x)) data['C_frac']=data.C-data.C.map(lambda x:int(x)) data['C_int'] = data.C.map(lambda x:int(x)) data['N_frac']=data.N-data.N.map(lambda x:int(x)) data['N_int'] = data.N.map(lambda x:int(x)) data=pd.concat([data,pd.get_dummies(data.A,'A')],axis=1) data=pd.concat([data,pd.get_dummies(data.F,'F')],axis=1) print data.head() print data.columns trncols=[u'A', u'B','C_frac','C_int', u'D', u'E', u'F', u'G', u'H_int','H_frac', u'I', u'J', u'K', u'L', u'M','N_frac','N_int', u'O'] trncols=[u'A', u'B', u'C', u'D', u'E', u'F', u'G', u'H', u'I', u'J', u'K', u'L', u'M', u'N', u'O', u'id', u'H_frac', u'H_int', u'C_frac', u'C_int', u'N_frac', u'N_int', u'A_0', u'A_1', u'F_0', u'F_1', u'F_2', u'F_3', u'F_4', u'F_5', u'F_6', u'F_7', u'F_8', u'F_9', u'F_10', u'F_11', u'F_12', u'F_13'] testcols=['P'] data_bin = ['A','I','J','L','F'] #trncols=data_bin fin_train_data=data.iloc[:len(train_data)] fin_test_data=data.iloc[len(train_data):] #print fin_train_data[(fin_train_data.I==1) & (fin_train_data.J==0)].tostring() print len(fin_train_data) print len(fin_train_data[(fin_train_data.I==1) & (fin_train_data.J==1)]),len(fin_train_data[(fin_train_data.I==1) & (fin_train_data.J==1) & (fin_train_data.P==1)]), print len(fin_train_data[(fin_train_data.I==0) & (fin_train_data.J==0)]),len(fin_train_data[(fin_train_data.I==0) & (fin_train_data.J==0) & (fin_train_data.P==0)]) print len(fin_train_data[(fin_train_data.I==0) & (fin_train_data.J==1)]),len(fin_train_data[(fin_train_data.I==0) & (fin_train_data.J==1) & (fin_train_data.P==0)]) print len(fin_test_data[(fin_test_data.I==1) & (fin_test_data.J==0)]),len(fin_test_data) fin_train_data = fin_train_data[(fin_train_data.I==1) & (fin_train_data.J==0)] from sklearn.utils import shuffle fin_train_data= shuffle(fin_train_data) X=fin_train_data[trncols] Y=fin_train_data[testcols] rfc=GradientBoostingClassifier(n_estimators=30) #rfc=LogisticRegression() rfc=LinearRegression() #rfc=MultinomialNB() kf=KFold(n_splits=5) lo = LeaveOneOut() accs=cross_val_score(rfc,X,Y,cv=kf) accslo=cross_val_score(rfc,X,Y,cv=lo) #print np.mean(accs),np.mean(accslo) rfc.fit(X,Y) #print rfc.score(X,Y) #print rfc.predict(X)<0.5 rsss = pd.DataFrame((Y==0)==(rfc.predict(X)<0.5)) #print rsss[rsss.P==True] # asnls=[] # # orans=y.P.tolist() # x=x.reset_index(xrange(len(y))) # # for i in xrange(len(x)): # if x.I.iloc[i]==0 and x.J.iloc[i]==0: # asnls.append(1) # if x.I.iloc[i]==1 and x.J.iloc[i]==1: # asnls.append(1) # if x.I.iloc[i]==0 and x.J.iloc[i]==1: # asnls.append(1) # if x.I.iloc[i]==1 and x.J.iloc[i]==0: # asnls.append(orans[i]) # i+=1 # # res=0 # for a,b in zip(asnls,orans): # res+=np.abs(a-b) # print res/len(orans) fintestindex=fin_test_data.index for e in fintestindex: if (fin_test_data['I'][e]==1) and (fin_test_data['J'][e]==1): fin_test_data['P'][e]=0 if (fin_test_data['I'][e]==0) and (fin_test_data['J'][e]==0): fin_test_data['P'][e]=1 if (fin_test_data['I'][e]==0) and (fin_test_data['J'][e]==1): fin_test_data['P'][e]=1 # if (fin_test_data['I'][e]==1) and (fin_test_data['J'][e]==0): # fin_test_data['P']=0 print fin_test_data.P remaining=fin_test_data[fin_test_data.P.isnull()] remainingans =rfc.predict(remaining[trncols])>0.5 fin_test_data[fin_test_data.P.isnull()]['P'][:]=np.reshape(remainingans.astype(int),(len(remainingans))) fin_test_data[fin_test_data.P.isnull()]['P'][:]=1 print fin_test_data[fin_test_data.P.isnull()]['P'][:] #print fin_test_data.P final = pd.DataFrame() final['id']=fin_test_data.id # #final['P']=pd.to_numeric(rfc.predict(fin_test_data[trncols]),downcast='signed') # final['P']=rfc.predict(fin_test_data[trncols]).astype(int) # final.to_csv('../data/final.csv',index=False)

34.138889

300

0.682832

1,172

6,145

3.399317

0.134812

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0.10241

0.040161

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6,145

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0

null

0

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null

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0

null

0

null

0

1

0

1

3db6b1a2ad7d586c5f66023f21c351a35d9fd997

7,604

py

Python

Appserver/Test/ApiUnitTesting/testBusquedaCandidatos.py

seguijoaquin/taller2

f41232516de15fe045805131b09299e5c2634e5e

[ "MIT" ]

2

2016-06-06T03:26:49.000Z

2017-08-06T18:12:33.000Z

Appserver/Test/ApiUnitTesting/testBusquedaCandidatos.py

seguijoaquin/taller2

f41232516de15fe045805131b09299e5c2634e5e

[ "MIT" ]

60

2016-03-19T16:01:27.000Z

2016-06-23T16:26:10.000Z

Appserver/Test/ApiUnitTesting/testBusquedaCandidatos.py

seguijoaquin/taller2

f41232516de15fe045805131b09299e5c2634e5e

[ "MIT" ]

null

import json import requests import unittest import Utilities # Precondiciones: # Intereses: # No debe haber ningun usuario en el Shared que tenga "interesUnico" # Address = "http://localhost:8000" #Tal vez mandar las URIs a sus respectivas clases URIResgistro = "/registro" URILogin = "/login" URIPedirCandidato = "/perfil" URIEliminar = "/eliminar" def crearHeadersParaRegistro(usuario): return {'Usuario': usuario,'Password': "password"}#, 'Content-Type': 'application/json' } def crearHeadersParaElLogin(usuario): return {'Usuario': usuario,'Password': "password", 'TokenGCM': "APA91bFundy4qQCiRnhUbMOcsZEwUBpbuPjBm-wnyBv600MNetW5rp-5Cg32_UA0rY_gmqqQ8pf0Cn-nyqoYrAl6BQTPT3dXNYFuHeWYEIdLz0RwAhN2lGqdoiYnCM2V_O8MonYn3rL6hAtYaIz_b0Jl2xojcKIOqQ" } def abrirJson(ruta): with open(ruta, 'r') as archivoJson: parseado = json.load(archivoJson) return parseado def crearHeadersParaBuscarCandidatos(usuario,token): return {'Usuario': usuario, 'Token': token} class TestBusquedaCandidatos(unittest.TestCase): usuario1 = 'usuarioCandidato1' usuario2 = 'usuarioCandidato2' usuarioSinIntereses = "./usuario.json" passwordCorrecto = 'password' #lo uso para todos los usuarios #Una categoria que SI o SI esta en el Shared categoriaValida = "outdoors" interesUnico = "INTERES UNICO QUE NO TIENE NADIE MAS" interesCompartido = "INTERES QUE SOLO DEBE SER COMPARTIDO POR DOS USUARIOS" msgNoSeEncontraronCandidatos = "Candidato no encontrado" msgSeEncontraronCandidatos = "Candidato encontrado" def agregarEmailAlUsuario(self, bodyUsuario, email): bodyUsuario["user"]["email"] = email def agregarValorDeInteresAlUsuario(self,bodyUsuario, valorDeInteres): interes = json.loads('{}') interes["category"] = self.categoriaValida interes["value"] = valorDeInteres bodyUsuario["user"]["interests"].append(interes) def hacerLoginDeUsuario(self, usuario): headUsuarioRegistrado = crearHeadersDeUsuarioYPassword( usuario, self.passwordCorrecto) reply = requests.get(Address + URILogin,headers=headUsuarioRegistrado) return reply usuariosParaBorrar = [] def tearDown(self): for usuario in self.usuariosParaBorrar: headEliminarUsuario = {'Usuario': usuario,'Password': self.passwordCorrecto } replyDelete = requests.delete(Address + URIEliminar, headers=headEliminarUsuario) del self.usuariosParaBorrar[:] def test_UsuarioPideUnCandidatoPeroNoSeEncuentra(self): #Para esto no debe haber ningun usuario en el shared con el interes "interesUnico" #Aca creo el body del usuario con un interes unico, ningun otro lo debe usar nombreUsuario = Utilities.transformarEnMail("test_UsuarioPideUnCandidatoPeroNoSeEncuentra") bodyUsuario = abrirJson(self.usuarioSinIntereses) self.agregarEmailAlUsuario(bodyUsuario, nombreUsuario) self.agregarValorDeInteresAlUsuario(bodyUsuario, self.interesUnico) headRegistrarUsuario = crearHeadersParaRegistro(nombreUsuario) replyRegistro = requests.put(Address + URIResgistro, headers=headRegistrarUsuario, data=json.dumps(bodyUsuario)) #Se loguea headLoginUsuario = crearHeadersParaElLogin(nombreUsuario) replyLogin = requests.get(Address + URILogin, headers=headLoginUsuario) #Pide un candidato headPedirCandidatos = crearHeadersParaBuscarCandidatos(nombreUsuario,replyLogin.headers["Token"]) replyPedirCandidatos = requests.get(Address + URIPedirCandidato, headers=headPedirCandidatos) self.assertEqual(replyPedirCandidatos.reason,self.msgNoSeEncontraronCandidatos) self.assertEqual(replyPedirCandidatos.status_code,201) self.usuariosParaBorrar.extend([nombreUsuario]) def crearBodyConUnInteres(self, email, interes): bodyUsuario = abrirJson(self.usuarioSinIntereses) self.agregarEmailAlUsuario(bodyUsuario, email) self.agregarValorDeInteresAlUsuario(bodyUsuario, interes) return bodyUsuario def registrarUsuario(self, nombreUsuario, bodyUsuario): headRegistrarUsuario = crearHeadersParaRegistro(nombreUsuario) return requests.put(Address + URIResgistro, headers=headRegistrarUsuario, data=json.dumps(bodyUsuario)) def loguearUsuario(self, nombreUsuario): headLoginUsuario = crearHeadersParaElLogin(nombreUsuario) return requests.get(Address + URILogin, headers=headLoginUsuario) def pedirCandidato(self, nombreUsuario, replyLogin): headPedirCandidatos = crearHeadersParaBuscarCandidatos(nombreUsuario,replyLogin.headers["Token"]) return requests.get(Address + URIPedirCandidato, headers=headPedirCandidatos) def test_DosUsuariosConUnInteresEspecificoPidenUnCandidatoYSeEncuentranUnoAlOtro(self): nombreUsuario1 = Utilities.transformarEnMail("1test_DosUsuariosConUnInteresEspecificoPidenUnCandidatoYSeEncuentranUnoAlOtro") nombreUsuario2 = Utilities.transformarEnMail("2test_DosUsuariosConUnInteresEspecificoPidenUnCandidatoYSeEncuentranUnoAlOtro") bodyUsuario1 = self.crearBodyConUnInteres(nombreUsuario1, self.interesCompartido) bodyUsuario2 = self.crearBodyConUnInteres(nombreUsuario2, self.interesCompartido) replyRegistro1 = self.registrarUsuario(nombreUsuario1, bodyUsuario1) replyRegistro2 = self.registrarUsuario(nombreUsuario2, bodyUsuario2) replyLogin1 = self.loguearUsuario(nombreUsuario1) replyLogin2 = self.loguearUsuario(nombreUsuario2) #Pide un candidato replyPedirCandidatos1 = self.pedirCandidato(nombreUsuario1, replyLogin1) replyPedirCandidatos2 = self.pedirCandidato(nombreUsuario2, replyLogin2) self.assertEqual(replyPedirCandidatos1.reason,self.msgSeEncontraronCandidatos) self.assertEqual(replyPedirCandidatos1.status_code,200) self.assertEqual(replyPedirCandidatos2.reason,self.msgSeEncontraronCandidatos) self.assertEqual(replyPedirCandidatos2.status_code,200) self.usuariosParaBorrar.extend([nombreUsuario1, nombreUsuario2]) def test_DosUsuariosMatcheanYVotanUnoPorElOtro(self): nombreUsuario1 = Utilities.transformarEnMail("test_DosUsuariosMatcheanYVotanUnoPorElOtro1") nombreUsuario2 = Utilities.transformarEnMail("test_DosUsuariosMatcheanYVotanUnoPorElOtro2") categoria = "outdoors" valor = "test_DosUsuariosMatcheanYVotanUnoPorElOtro" Utilities.registrarUsuarioSinEmailYSinIntereses(nombreUsuario1,categoria, valor) Utilities.registrarUsuarioSinEmailYSinIntereses(nombreUsuario2,categoria, valor) tokenSesion1 = Utilities.registrarYLoguearAlUsuarioSinEmail(nombreUsuario1) tokenSesion2 = Utilities.registrarYLoguearAlUsuarioSinEmail(nombreUsuario2) candidatoParaUsuario1 = Utilities.pedirCandidato(nombreUsuario1,tokenSesion1) candidatoParaUsuario2 = Utilities.pedirCandidato(nombreUsuario2,tokenSesion2) replyVotacion1 = Utilities.likearCandidato(nombreUsuario1, tokenSesion1, candidatoParaUsuario1) replyVotacion2 = Utilities.likearCandidato(nombreUsuario2, tokenSesion2, candidatoParaUsuario2) self.assertEqual("El voto se registro correctamente",replyVotacion1.reason) self.assertEqual(200,replyVotacion1.status_code) self.assertEqual("El voto se registro correctamente",replyVotacion2.reason) self.assertEqual(200,replyVotacion2.status_code) self.usuariosParaBorrar.extend([nombreUsuario1, nombreUsuario2])

43.451429

233

0.768017

582

7,604

10.003436

0.331615

0.025764

0.015459

0.013397

0.206287

0.163861

0.081759

0.039505

0.027825

0

0.016539

0.157154

7,604

174

234

43.701149

0.891871

0.059442

0

0.092593

0

0.131549

0.068366

0

0.005747

0.092593

1

0.138889

false

0.046296

0.037037

0.027778

0.361111

0

1

null

0

null

0

1

3db72a55f192a9c9ab68f0478ca0ffc316b36c78

1,053

py

Python

package/diana/utils/iter_dates.py

thomasyi17/diana2

2167053dfe15b782d96cb1e695047433f302d4dd

[ "MIT" ]

15

2019-02-12T23:26:09.000Z

2021-12-21T08:53:58.000Z

package/diana/utils/iter_dates.py

thomasyi17/diana2

2167053dfe15b782d96cb1e695047433f302d4dd

[ "MIT" ]

2

2019-01-23T21:13:12.000Z

2019-06-28T15:45:51.000Z

package/diana/utils/iter_dates.py

thomasyi17/diana2

2167053dfe15b782d96cb1e695047433f302d4dd

[ "MIT" ]

6

2019-01-23T20:22:50.000Z

2022-02-03T03:27:04.000Z

from datetime import datetime, timedelta class IterDates(object): def __init__(self, start: datetime, stop: datetime, step: timedelta): self.start = start self.stop = stop self.step = step self.value = (self.start, self.start + self.step) def __iter__(self): return self def __next__(self): next_value = self.value if next_value[0] >= self.stop: raise StopIteration self.start = self.start + self.step self.value = (self.start, min(self.stop, self.start + self.step)) return next_value class FuncByDates(object): def __init__(self, func, start: datetime, stop: datetime, step: timedelta): self._func = func self._iterdate = IterDates(start, stop, step) self.value = self._func(*self._iterdate.value) def __iter__(self): return self def __next__(self): next_value = self.value next(self._iterdate) self.value = self._func(*self._iterdate.value) return next_value

26.325

79

0.624881

128

1,053

4.859375

0.195313

0.115756

0.104502

0.081994

0.538585

0.488746

0.405145

0.160772

0

0.001305

0.272555

1,053

39

80

27

0.810705

0

0.428571

0

1

0.214286

false

0

0.035714

0.071429

0.464286

0

null

0

null

0

1

0

1

3db739475a32d4a4cd03afcbff8864712c35cad0

193

py

Python

Exercicios Curso Em Video Mundo 2/ex067.py

JorgeTranin/Python_Curso_Em_Video

be74c9301aafc055bdf883be649cb8b7716617e3

[ "MIT" ]

null

Exercicios Curso Em Video Mundo 2/ex067.py

JorgeTranin/Python_Curso_Em_Video

be74c9301aafc055bdf883be649cb8b7716617e3

[ "MIT" ]

null

Exercicios Curso Em Video Mundo 2/ex067.py

JorgeTranin/Python_Curso_Em_Video

be74c9301aafc055bdf883be649cb8b7716617e3

[ "MIT" ]

null

cont = 1 while True: t = int(input('Quer saber a tabuada de que numero ? ')) if t < 0: break for c in range (1, 11): print(f'{t} X {c} = {t * c}') print('Obrigado!')

24.125

59

0.507772

33

193

2.969697

0.787879

0

0.038168

0.321244

193

8

60

24.125

0.709924

0

0.335052

0

1

0

false

0

0.25

0

null

0

1

0

null

0

1

3db86f3d8bdc658afbe080624e5b8f952805ce4b

1,172

py

Python

src/PassGen/PassGen.py

Natthapolmnc/PasswordGenerator

1d481de1b4773af99558c68e9570d1801c1f6e2e

[ "MIT" ]

null

src/PassGen/PassGen.py

Natthapolmnc/PasswordGenerator

1d481de1b4773af99558c68e9570d1801c1f6e2e

[ "MIT" ]

null

src/PassGen/PassGen.py

Natthapolmnc/PasswordGenerator

1d481de1b4773af99558c68e9570d1801c1f6e2e

[ "MIT" ]

null

import random as rd def genPass(num , length): print ("Password Generator") print ("===================\n") numpass=num lenpass=length AlphaLcase=[ chr(m) for m in range(65, 91)] AlphaCcase=[ chr(n) for n in range(97, 123)] Intset=[ chr(p) for p in range(48,58)] listsetpass=[] for j in range(lenpass): randAlphaset=rd.randint(2,lenpass) randAlphaL=rd.randint(1,randAlphaset) randAlphaH=randAlphaset-randAlphaL randIntset=lenpass-randAlphaset password=[] strpassword="" for i in range(randAlphaH): randindexAlphaH=rd.randint(0,len(AlphaCcase)-1) password.append(AlphaCcase[randindexAlphaH]) for k in range(randAlphaL): randindexAlphaL=rd.randint(0,len(AlphaLcase)-1) password.append(AlphaLcase[randindexAlphaL]) for l in range(randIntset): randindexInt=rd.randint(0,len(Intset)-1) password.append(Intset[randindexInt]) for u in range(len(password)): rd.shuffle(password) strpassword+=str(password[u]) listsetpass+=[strpassword] return listsetpass

35.515152

59

0.617747

133

1,172

5.443609

0.390977

0.077348

0.041436

0.053867

0

0.024027

0.254266

1,172

32

60

36.625

0.804348

0

0.033276

0.017918

0

1

0.032258

false

0.580645

0.032258

0

0.096774

0.064516

0

null

0

null

0

1

0

1

3dbe95131f682ae91ac5d0ab7098a4da9541c391

267

py

Python

gc_win1.py

danz2004/learning_python

20cb7d33f898bcc406f33565308132dca31e11cd

[ "MIT" ]

null

gc_win1.py

danz2004/learning_python

20cb7d33f898bcc406f33565308132dca31e11cd

[ "MIT" ]

null

gc_win1.py

danz2004/learning_python

20cb7d33f898bcc406f33565308132dca31e11cd

[ "MIT" ]

null

#!/usr/bin/env python3 seq = 'ACGACGCAGGAGGAGAGTTTCAGAGATCACGAATACATCCATATTACCCAGAGAGAG' w = 11 for i in range(len(seq) - w + 1): count = 0 for j in range(i, i + w): if seq[j] == 'G' or seq[j] == 'C': count += 1 print(f'{i} {seq[i:i+w]} {(count / w) : .4f}')

26.7

65

0.595506

45

267

3.533333

0.533333

0.08805

0.037736

0

0.032558

0.194757

267

9

66

29.666667

0.706977

0.078652

0

0.125

0.387755

0.232653

0

1

0

false

0

0.125

0

null

0

1

0

null

0

1

3dbfa17a77ec527273235935d102cd0d8f5bcbb2

7,991

py

Python

gym_flock/envs/old/flocking.py

katetolstaya/gym-flock

3236d1dafcb1b9be0cf78b471672e8becb2d37af

[ "MIT" ]

19

2019-07-29T22:19:58.000Z

2022-01-27T04:38:38.000Z

gym_flock/envs/old/flocking.py

henghenghahei849/gym-flock

b09bdfbbe4a96fe052958d1f9e1e9dd314f58419

[ "MIT" ]

null

gym_flock/envs/old/flocking.py

henghenghahei849/gym-flock

b09bdfbbe4a96fe052958d1f9e1e9dd314f58419

[ "MIT" ]

5

2019-10-03T14:44:49.000Z

2021-12-09T20:39:39.000Z

import gym from gym import spaces, error, utils from gym.utils import seeding import numpy as np import configparser from os import path import matplotlib.pyplot as plt from matplotlib.pyplot import gca font = {'family': 'sans-serif', 'weight': 'bold', 'size': 14} class FlockingEnv(gym.Env): def __init__(self): config_file = path.join(path.dirname(__file__), "params_flock.cfg") config = configparser.ConfigParser() config.read(config_file) config = config['flock'] self.dynamic = False # if the agents are moving or not self.mean_pooling = True # normalize the adjacency matrix by the number of neighbors or not # number states per agent self.nx_system = 4 # numer of observations per agent self.n_features = 6 # number of actions per agent self.nu = 2 # problem parameters from file self.n_agents = int(config['network_size']) self.comm_radius = float(config['comm_radius']) self.comm_radius2 = self.comm_radius * self.comm_radius self.dt = float(config['system_dt']) self.v_max = float(config['max_vel_init']) self.v_bias = self.v_max self.r_max = float(config['max_rad_init']) self.std_dev = float(config['std_dev']) * self.dt # intitialize state matrices self.x = np.zeros((self.n_agents, self.nx_system)) self.u = np.zeros((self.n_agents, self.nu)) self.mean_vel = np.zeros((self.n_agents, self.nu)) self.init_vel = np.zeros((self.n_agents, self.nu)) self.a_net = np.zeros((self.n_agents, self.n_agents)) # TODO : what should the action space be? is [-1,1] OK? self.max_accel = 1 self.gain = 10.0 # TODO - adjust if necessary - may help the NN performance self.action_space = spaces.Box(low=-self.max_accel, high=self.max_accel, shape=(2 * self.n_agents,), dtype=np.float32) self.observation_space = spaces.Box(low=-np.Inf, high=np.Inf, shape=(self.n_agents, self.n_features), dtype=np.float32) self.fig = None self.line1 = None self.seed() def seed(self, seed=None): self.np_random, seed = seeding.np_random(seed) return [seed] def step(self, u): #u = np.reshape(u, (-1, 2)) assert u.shape == (self.n_agents, self.nu) self.u = u if self.dynamic: # x position self.x[:, 0] = self.x[:, 0] + self.x[:, 2] * self.dt # y position self.x[:, 1] = self.x[:, 1] + self.x[:, 3] * self.dt # x velocity self.x[:, 2] = self.x[:, 2] + self.gain * self.u[:, 0] * self.dt #+ np.random.normal(0, self.std_dev, (self.n_agents,)) # y velocity self.x[:, 3] = self.x[:, 3] + self.gain * self.u[:, 1] * self.dt #+ np.random.normal(0, self.std_dev, (self.n_agents,)) return self._get_obs(), self.instant_cost(), False, {} def instant_cost(self): # sum of differences in velocities # TODO adjust to desired reward # action_cost = -1.0 * np.sum(np.square(self.u)) #curr_variance = -1.0 * np.sum((np.var(self.x[:, 2:4], axis=0))) versus_initial_vel = -1.0 * np.sum(np.sum(np.square(self.x[:, 2:4] - self.mean_vel), axis=1)) #return curr_variance + versus_initial_vel return versus_initial_vel def reset(self): x = np.zeros((self.n_agents, self.nx_system)) degree = 0 min_dist = 0 min_dist_thresh = 0.1 # 0.25 # generate an initial configuration with all agents connected, # and minimum distance between agents > min_dist_thresh while degree < 2 or min_dist < min_dist_thresh: # randomly initialize the location and velocity of all agents length = np.sqrt(np.random.uniform(0, self.r_max, size=(self.n_agents,))) angle = np.pi * np.random.uniform(0, 2, size=(self.n_agents,)) x[:, 0] = length * np.cos(angle) x[:, 1] = length * np.sin(angle) bias = np.random.uniform(low=-self.v_bias, high=self.v_bias, size=(2,)) x[:, 2] = np.random.uniform(low=-self.v_max, high=self.v_max, size=(self.n_agents,)) + bias[0] x[:, 3] = np.random.uniform(low=-self.v_max, high=self.v_max, size=(self.n_agents,)) + bias[1] # compute distances between agents a_net = self.dist2_mat(x) # compute minimum distance between agents and degree of network to check if good initial configuration min_dist = np.sqrt(np.min(np.min(a_net))) a_net = a_net < self.comm_radius2 degree = np.min(np.sum(a_net.astype(int), axis=1)) # keep good initialization self.mean_vel = np.mean(x[:, 2:4], axis=0) self.init_vel = x[:, 2:4] self.x = x self.a_net = self.get_connectivity(self.x) return self._get_obs() def _get_obs(self): # state_values = self.x state_values = np.hstack((self.x, self.init_vel)) # initial velocities are part of state to make system observable if self.dynamic: state_network = self.get_connectivity(self.x) else: state_network = self.a_net return (state_values, state_network) def dist2_mat(self, x): """ Compute squared euclidean distances between agents. Diagonal elements are infinity Args: x (): current state of all agents Returns: symmetric matrix of size (n_agents, n_agents) with A_ij the distance between agents i and j """ x_loc = np.reshape(x[:, 0:2], (self.n_agents,2,1)) a_net = np.sum(np.square(np.transpose(x_loc, (0,2,1)) - np.transpose(x_loc, (2,0,1))), axis=2) np.fill_diagonal(a_net, np.Inf) return a_net def get_connectivity(self, x): """ Get the adjacency matrix of the network based on agent locations by computing pairwise distances using pdist Args: x (): current state of all agents Returns: adjacency matrix of network """ a_net = self.dist2_mat(x) a_net = (a_net < self.comm_radius2).astype(float) if self.mean_pooling: # Normalize the adjacency matrix by the number of neighbors - results in mean pooling, instead of sum pooling n_neighbors = np.reshape(np.sum(a_net, axis=1), (self.n_agents,1)) # TODO or axis=0? Is the mean in the correct direction? n_neighbors[n_neighbors == 0] = 1 a_net = a_net / n_neighbors return a_net def controller(self): """ Consensus-based centralized flocking with no obstacle avoidance Returns: the optimal action """ # TODO implement Tanner 2003? u = np.mean(self.x[:,2:4], axis=0) - self.x[:,2:4] u = np.clip(u, a_min=-self.max_accel, a_max=self.max_accel) return u def render(self, mode='human'): """ Render the environment with agents as points in 2D space """ if self.fig is None: plt.ion() fig = plt.figure() ax = fig.add_subplot(111) line1, = ax.plot(self.x[:, 0], self.x[:, 1], 'bo') # Returns a tuple of line objects, thus the comma ax.plot([0], [0], 'kx') plt.ylim(-1.0 * self.r_max, 1.0 * self.r_max) plt.xlim(-1.0 * self.r_max, 1.0 * self.r_max) a = gca() a.set_xticklabels(a.get_xticks(), font) a.set_yticklabels(a.get_yticks(), font) plt.title('GNN Controller') self.fig = fig self.line1 = line1 self.line1.set_xdata(self.x[:, 0]) self.line1.set_ydata(self.x[:, 1]) self.fig.canvas.draw() self.fig.canvas.flush_events() def close(self): pass

36.99537

134

0.585659

1,161

7,991

3.892334

0.236003

0.029874

0.046249

0.026555

0.208453

0.166409

0.135428

0.125249

0.103563

0.068157

0

0.021663

0.289451

7,991

216

135

36.99537

0.774216

0.246527

0

0.063492

0

0.023463

0

0.013889

0.007937

1

0.087302

false

0.007937

0.063492

0

0.222222

0

null

0

null

0

1

0

1

3dc72f281f6a609f6178afd5c15a1c8b5b592cd3

278

py

Python

subdomains/gen_master_data.py

sjy5386/subshorts

d8170ee4a66989c3e852f86aa83bab6341e3aa10

[ "MIT" ]

3

2022-03-08T19:02:41.000Z

2022-03-16T23:04:37.000Z

subdomains/gen_master_data.py

sjy5386/subshorts

d8170ee4a66989c3e852f86aa83bab6341e3aa10

[ "MIT" ]

5

2022-03-17T02:16:52.000Z

2022-03-18T02:55:25.000Z

subdomains/gen_master_data.py

sjy5386/subshorts

d8170ee4a66989c3e852f86aa83bab6341e3aa10

[ "MIT" ]

null

from .models import ReservedName def gen_master(apps, scheme_editor): reserved_names = ['co', 'com', 'example', 'go', 'gov', 'icann', 'ne', 'net', 'nic', 'or', 'org', 'whois', 'www'] for reserved_name in reserved_names: ReservedName(name=reserved_name).save()

34.75

116

0.647482

36

278

4.833333

0.805556

0.149425

0

0.161871

278

7

117

39.714286

0.746781

0

0.154676

0

1

0.2

false

0

0.2

0

0.4

0

null

0

1

0

null

0

1

3dd1773f50f2af84354e0431bf0e4276687f173e

3,401

py

Python

Server/Python/src/dbs/dao/Oracle/MigrationBlock/Update.py

vkuznet/DBS

14df8bbe8ee8f874fe423399b18afef911fe78c7

[ "Apache-2.0" ]

8

2015-08-14T04:01:32.000Z

2021-06-03T00:56:42.000Z

Server/Python/src/dbs/dao/Oracle/MigrationBlock/Update.py

yuyiguo/DBS

14df8bbe8ee8f874fe423399b18afef911fe78c7

[ "Apache-2.0" ]

162

2015-01-07T21:34:47.000Z

2021-10-13T09:42:41.000Z

Server/Python/src/dbs/dao/Oracle/MigrationBlock/Update.py

yuyiguo/DBS

14df8bbe8ee8f874fe423399b18afef911fe78c7

[ "Apache-2.0" ]

16

2015-01-22T15:27:29.000Z

2021-04-28T09:23:28.000Z

#!/usr/bin/env python """ This module provides Migration.Update data access object. """ from WMCore.Database.DBFormatter import DBFormatter from dbs.utils.dbsExceptionHandler import dbsExceptionHandler from dbs.utils.DBSDaoTools import create_token_generator class Update(DBFormatter): """ Migration Update DAO class. migration_status: 0=PENDING 1=IN PROGRESS 2=COMPLETED 3=FAILED (will be retried) 9=Terminally FAILED status change: 0 -> 1 1 -> 2 1 -> 3 1 -> 9 are only allowed changes for working through. 3 -> 1 allowed for retrying when retry_count <3. """ def __init__(self, logger, dbi, owner): """ Add schema owner and sql. """ DBFormatter.__init__(self, logger, dbi) self.owner = "%s." % owner if not owner in ("", "__MYSQL__") else "" self.logger = logger self.sql = \ """UPDATE %sMIGRATION_BLOCKS SET MIGRATION_STATUS=:migration_status , LAST_MODIFICATION_DATE=:last_modification_date WHERE """ % self.owner def execute(self, conn, daoinput, transaction = False): """ daoinput keys: migration_status, migration_block_id, migration_request_id """ #print daoinput['migration_block_id'] if not conn: dbsExceptionHandler("dbsException-failed-connect2host", "Oracle/MigrationBlock/Update. Expects db connection from upper layer." ,self.logger.exception) if daoinput['migration_status'] == 1: sql = self.sql + " (MIGRATION_STATUS = 0 or MIGRATION_STATUS = 3)" elif daoinput['migration_status'] == 2 or daoinput['migration_status'] == 3 or daoinput['migration_status'] == 9: sql = self.sql + " MIGRATION_STATUS = 1 " else: dbsExceptionHandler("dbsException-conflict-data", "Oracle/MigrationBlock/Update. Expected migration status to be 1, 2, 3, 0r 9" ,self.logger.exception ) #print sql if 'migration_request_id' in daoinput: sql3 = sql + "and MIGRATION_REQUEST_ID =:migration_request_id" result = self.dbi.processData(sql3, daoinput, conn, transaction) elif 'migration_block_id' in daoinput: if type(daoinput['migration_block_id']) is not list: sql2 = sql+ " and MIGRATION_BLOCK_ID =:migration_block_id" result = self.dbi.processData(sql2, daoinput, conn, transaction) else: bk_id_generator, binds2 = create_token_generator(daoinput['migration_block_id']) newdaoinput = {} newdaoinput.update({"migration_status":daoinput["migration_status"], "last_modification_date":daoinput["last_modification_date"]}) newdaoinput.update(binds2) sql2 = sql+ """ and MIGRATION_BLOCK_ID in ({bk_id_generator} SELECT TOKEN FROM TOKEN_GENERATOR) """.format(bk_id_generator=bk_id_generator) result = self.dbi.processData(sql2, newdaoinput, conn, transaction) else: dbsExceptionHandler("dbsException-conflict-data", "Oracle/MigrationBlock/Update. Required IDs not in the input", self.logger.exception)

46.589041

165

0.614231

362

3,401

5.571823

0.323204

0.104115

0.063461

0.035697

0.199306

0.098166

0.072385

0

0.015365

0.291973

3,401

72

166

47.236111

0.822259

0.019112

0

0.081081

0

0.314986

0.095219

0

null

0

0.081081

null

0

null

0

null

0

1

0

1

3dd4b115a1efae712e7d58d8046528f7acbf782b

1,467

py

Python

for_straight_forward_relion/read_star_del_metadata_param.py

homurachan/Block-based-recontruction

b3fc02a0648db6aaa5d77dcc4b8e10f3361d66f4

[ "WTFPL" ]

11

2018-04-17T01:41:11.000Z

2020-12-11T05:43:21.000Z

for_straight_forward_relion/read_star_del_metadata_param.py

homurachan/Block-based-recontruction

b3fc02a0648db6aaa5d77dcc4b8e10f3361d66f4

[ "WTFPL" ]

null

for_straight_forward_relion/read_star_del_metadata_param.py

homurachan/Block-based-recontruction

b3fc02a0648db6aaa5d77dcc4b8e10f3361d66f4

[ "WTFPL" ]

3

2019-08-23T07:48:50.000Z

2020-12-08T07:31:41.000Z

#!/usr/bin/env python import math,os,sys try: from optparse import OptionParser except: from optik import OptionParser def main(): (star,mline,line_name,output) = parse_command_line() aa=open(star,"r") instar_line=aa.readlines() out=open(output,"w") for i in range(0,mline): if (instar_line[i].split()): if (str(instar_line[i].split()[0])==line_name): line_index=int(instar_line[i].split('#')[1])-1 skip=i for i in range(0,mline): if(i<skip): out.write(instar_line[i]) if(i>skip): tmp=str(instar_line[i].split('#')[0]) tmp_num=int(instar_line[i].split('#')[1]) tmp_num-=1 tmp=tmp+"#"+str(tmp_num) out.write(tmp+"\n") for i in range(mline,len(instar_line)): if (instar_line[i].split()): tmp="" xx=len(instar_line[i].split()) for j in range(0,xx): if(j!=line_index): tmp+=str(instar_line[i].split()[j]) if(j!=xx-1 and j!=line_index): tmp+="\t" if(j==xx-1): tmp+="\n" out.write(tmp) out.close() aa.close() def parse_command_line(): usage="%prog <input star> <mline +4> <line name> <output>" parser = OptionParser(usage=usage, version="%1") if len(sys.argv)<5: print "<input star> <mline +4> <line name> <output>" sys.exit(-1) (options, args)=parser.parse_args() star = str(args[0]) mline=int(args[1]) line_name=str(args[2]) output=str(args[3]) return (star,mline,line_name,output) def SQR(x): y=float(x) return(y*y) if __name__== "__main__": main()

22.227273

59

0.632584

251

1,467

3.561753

0.282869

0.123043

0.110738

0.143177

0.314318

0.224832

0.107383

0

0.016181

0.157464

1,467

65

60

22.569231

0.70712

0.013633

0

0.072727

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0.018182

0

null

0

null

0

1

0

1

3dd551aff5d9acdfce555b2997eb9c881f846544

1,382

py

Python

setup.py

elafefy11/flask_gtts

8f14b9f114127d8fba240a88f3aa16eb17628872

[ "MIT" ]

null

setup.py

elafefy11/flask_gtts

8f14b9f114127d8fba240a88f3aa16eb17628872

[ "MIT" ]

null

setup.py

elafefy11/flask_gtts

8f14b9f114127d8fba240a88f3aa16eb17628872

[ "MIT" ]

null

""" Flask-gTTS ------------- A Flask extension to add gTTS Google text to speech, into the template, it makes adding and configuring multiple text to speech audio files at a time much easier and less time consuming """ from setuptools import setup setup( name='Flask-gTTS', version='0.11', url='https://github.com/mrf345/flask_gtts/', download_url='https://github.com/mrf345/flask_gtts/archive/0.11.tar.gz', license='MIT', author='Mohamed Feddad', author_email='mrf345@gmail.com', description='gTTS Google text to speech flask extension', long_description=__doc__, py_modules=['gtts'], packages=['flask_gtts'], zip_safe=False, include_package_data=True, platforms='any', install_requires=[ 'Flask', 'gTTS', 'static_parameters' ], keywords=['flask', 'extension', 'google', 'text', 'speech', 'gTTS', 'TTS', 'text-to-speech'], classifiers=[ 'Environment :: Web Environment', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Topic :: Internet :: WWW/HTTP :: Dynamic Content', 'Topic :: Software Development :: Libraries :: Python Modules' ], setup_requires=['pytest-runner'], test_requires=['pytest'] )

29.404255

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0.633864

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1,382

5.464968

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1,382

46

77

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true

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0

0.028571

0

null

0

null

0

1

0

1

3dd7149bf486a0156690dac8d36a869ec269ebf6

9,280

py

Python

src/aux_funcs.py

ArunBaskaran/Image-Driven-Machine-Learning-Approach-for-Microstructure-Classification-and-Segmentation-Ti-6Al-4V

79ca40ababbc65464650c5519f9e7fdbf3c9d14d

[ "MIT" ]

7

2020-03-19T05:04:30.000Z

2022-03-31T10:29:42.000Z

src/aux_funcs.py

ArunBaskaran/Image-Driven-Machine-Learning-Approach-for-Microstructure-Classification-and-Segmentation-Ti-6Al-4V

79ca40ababbc65464650c5519f9e7fdbf3c9d14d

[ "MIT" ]

2

2020-08-19T03:24:31.000Z

2021-03-02T00:18:46.000Z

src/aux_funcs.py

ArunBaskaran/Image-Driven-Machine-Learning-Approach-for-Microstructure-Classification-and-Segmentation-Ti-6Al-4V

79ca40ababbc65464650c5519f9e7fdbf3c9d14d

[ "MIT" ]

3

2020-09-17T04:15:04.000Z

2021-01-18T08:37:39.000Z

""" ----------------------------------ABOUT----------------------------------- Author: Arun Baskaran -------------------------------------------------------------------------- """ import model_params def smooth(img): return 0.5*img + 0.5*( np.roll(img, +1, axis=0) + np.roll(img, -1, axis=0) + np.roll(img, +1, axis=1) + np.roll(img, -1, axis=1) ) def returnIndex(a , value): k = np.size(a) for i in range(k): if(a[i]==value): return i def create_model(): xavier_init = tf.contrib.layers.xavier_initializer() #Initializer for weights zero_init = tf.zeros_initializer() #Initializer for biases model = tf.keras.models.Sequential([ keras.layers.Conv2D( 2, [5,5], (1,1), input_shape = (200,200,1), kernel_initializer = xavier_init, bias_initializer = zero_init, kernel_regularizer=regularizers.l1(0.001), padding = 'valid', name = 'C1'), keras.layers.MaxPool2D((2,2), (2,2), input_shape = (196,196,2),padding = 'valid', name ='P1'), keras.layers.Conv2D(4, [5,5],(1,1), input_shape = (98,98,2), kernel_initializer = xavier_init, bias_initializer = zero_init, kernel_regularizer=regularizers.l1(0.001), name ='C2'), keras.layers.MaxPool2D((2,2), (2,2), input_shape = (94,94,4), padding = 'valid', name ='P2'), keras.layers.Conv2D(12, [3,3],(1,1), input_shape = (47,47,4), kernel_initializer = xavier_init, bias_initializer = zero_init, kernel_regularizer=regularizers.l1(0.001), name ='C3'), keras.layers.Flatten(name ='fc_layer'), keras.layers.Dense(3, activation='softmax', kernel_regularizer=regularizers.l1(0.001)),]) return model def load_images_labels(): df = pd.read_excel('labels.xlsx', header=None, names=['id', 'label']) total_labels = df['label'] for i in range(len(total_labels)): total_labels[i]-=1 train_list = random.sample(range(1,total_size+1), train_size) nontrainlist = [] test_list = [] for i in range(1,total_size+1): if i not in train_list: nontrainlist.append(i) validation_list = random.sample(nontrainlist, validation_size) for item in nontrainlist: if(item not in validation_list): test_list.append(item) train_images = [] train_labels = [] validation_images = [] validation_labels = [] test_images = [] test_labels=[] test_images_id = [] for i in range(1, total_size+1): if i in train_list: filename = 'image_' + str(i) + '.png' image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE) image = cv2.resize(image, dsize=(width, height), interpolation=cv2.INTER_CUBIC) image = cv2.blur(image,(5,5)) image = (image - np.min(image))/(np.max(image)-np.min(image)) train_images.append(image) train_labels.append(total_labels[i-1]) elif i in validation_list: filename = 'image_' + str(i) + '.png' image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE) image = cv2.resize(image, dsize=(width, height), interpolation=cv2.INTER_CUBIC) image = cv2.blur(image,(5,5)) image = (image - np.min(image))/(np.max(image)-np.min(image)) validation_images.append(image) validation_labels.append(total_labels[i-1]) else: filename = 'image_' + str(i) + '.png' image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE) image = cv2.resize(image, dsize=(width, height), interpolation=cv2.INTER_CUBIC) image = cv2.blur(image,(5,5)) image = (image - np.min(image))/(np.max(image)-np.min(image)) test_images_id.append(i) test_images.append(image) test_labels.append(total_labels[i-1]) train_images = np.reshape(train_images, (train_size, width, height, 1)) validation_images = np.reshape(validation_images, (validation_size, width, height, 1)) test_images = np.reshape(test_images, (test_size, width, height, 1)) train_labels = tf.keras.backend.one_hot(train_labels,3) test_labels = tf.keras.backend.one_hot(test_labels,3) validation_labels = tf.keras.backend.one_hot(validation_labels,3) return train_images, train_labels, test_images, test_labels, validation_images, validation_labels def train_model(): model = create_model() checkpoint_path = "weights/classification.ckpt" #Check this path checkpoint_dir = os.path.dirname(checkpoint_path) es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', verbose=1, patience = 50, mode='min', restore_best_weights=True) cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path, save_weights_only=True, verbose=0) model.compile(loss=tf.keras.losses.CategoricalCrossentropy(),optimizer='Adam',metrics='accuracy') model.fit(train_images, train_labels, epochs=1500, validation_data=(validation_images,validation_labels), steps_per_epoch = 4, validation_steps=1, callbacks=[es, cp_callback]) return model def load_model(): model = create_model() model.load_weights(checkpoint_path) return model def test_accuracy(model): loss,acc = model.evaluate(test_images, test_labels, verbose=2, steps = 1) print("Accuracy: {:5.2f}%".format(100*acc)) def get_predicted_classes(model): y_prob = model.predict(test_images) y_classes = y_prob.argmax(axis=-1) return y_classes df = pd.read_excel('labels.xlsx', header=None, names=['id', 'label']) total_labels = df['label'] for i in range(len(total_labels)): total_labels[i]-=1 def duplex_segmentation(i): area_frac_duplex=[] duplex_image_id=[] filename = 'image_' + str(test_images_id[i]) + '.png' image = Image.open(filename).convert('F') image = np.copy(np.reshape(np.array(image), image.size[::-1])/255.) image = exposure.equalize_adapthist(image, clip_limit=8.3) image = (smooth(smooth(image))) image_copy = image image = cv2.resize(image, dsize=(200,200), interpolation=cv2.INTER_CUBIC) image_copy = cv2.resize(image_copy, dsize=(200,200), interpolation=cv2.INTER_CUBIC) markers = np.zeros_like(image) markers[image > np.median(image) - 0.10*np.std(image)] = 1 markers[image < np.median(image) - 0.10*np.std(image)] = 2 fig, (ax1) = plt.subplots(1, sharex=True, sharey=True) elevation_map = sobel(image) #The following implementation of watershed segmentation has been adopted from scikit's documentation example: https://scikit-image.org/docs/dev/user_guide/tutorial_segmentation.html segmentation = morphology.watershed(elevation_map, markers) segmentation = ndi.binary_fill_holes(segmentation - 1) labeled_grains, _ = ndi.label(segmentation) image_label_overlay = label2rgb(labeled_grains, image=image) ax1.imshow(image_copy, cmap=plt.cm.gray, interpolation='nearest') ax1.contour(segmentation, [0.5], linewidths=1.2, colors='r') ax1.axis('off') outfile = 'seg_duplex_' + str(test_images_id[i]) + '.png' plt.savefig(outfile, dpi=100) equiaxed_area_fraction_dict[test_images_id[i]] = np.sum(segmentation)/(np.shape(image)[0]*np.shape(image)[1]) def lamellar_segmentation(i): dim = 400 filename = 'image_' + str(test_images_id[i]) + '.png' image = Image.open(filename).convert('F') image = np.copy(np.reshape(np.array(image), image.size[::-1])/255.) image = exposure.equalize_hist(image) image = smooth(image) image = np.reshape(image, (np.shape(image)[0],np.shape(image)[1])) gx = cv2.Sobel(np.float32(image), cv2.CV_32F, 1, 0, ksize=1) gy = cv2.Sobel(np.float32(image), cv2.CV_32F, 0, 1, ksize=1) mag, angle = cv2.cartToPolar(gx, gy, angleInDegrees=True) mag_cut_off = 0.2*np.max(mag) (n,bins,patches) = plt.hist(angle.ravel(), bins = 30) n_sorted = sorted(n, reverse=True) bin0 = bins[returnIndex(n, n_sorted[0])] bin1 = bins[returnIndex(n, n_sorted[1])] bin2 = bins[returnIndex(n, n_sorted[2])] bin_s = np.ones(20) for i in range(20): bin_s[i] = bins[returnIndex(n, n_sorted[i])] markers = np.zeros_like(angle) markers[(angle/360 > bin1/360 - 26/360) & (angle/360 < bin1/360 + 26/360) & (mag > mag_cut_off)] = 1 markers[(angle/360 > bin2/360 - 18/360) & (angle/360 < bin2/360 + 18/360) & (mag > mag_cut_off)] = 1 markers[(angle/360 > bin0/360 - 18/360) & (angle/360 < bin0/360 + 18/360) & (mag > mag_cut_off)] = 1 markers = (smooth(smooth(markers))) markers1 = np.where(markers > np.mean(markers), 1.0, 0.0) lamellae_area_fraction_dict[test_images_id[i]] = np.sum(markers1)/(np.shape(image)[0]*np.shape(image)[1]) fig, (ax1) = plt.subplots(1, sharex=True, sharey=True) ax1.imshow(image, 'gray') ax1.imshow(markers1, alpha = 0.5) image1 = image + markers1 ax1.imshow(image1) #plt.colorbar() outfile = 'seg_lamellae_' + str(test_images_id[i]) + '.png' plt.savefig(outfile, dpi=100) def feature_segmentation(): equiaxed_area_fraction_dict = {} lamellae_area_fraction_dict= {} for i in range(np.size(y_classes)): if(y_classes[i]==0): duplex_segmentation(i) elif(y_classes[i]==1): lamellar_segmentation(i)

44.830918

210

0.650108

1,292

9,280

4.5

0.227554

0.0258

0.016512

0.013244

0.45012

0.405745

0.351221

0.338493

0.314757

0.261954

0

0.044913

0.186638

9,280

206

211

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0.725358

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0.003054

0

null

0

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null

0.005917

0

null

0

null

0

1

0

1

3dd84d6968111423f954120eed10897fd01c00ea

1,355

py

Python

CIFAR10.py

jimmyLeeMc/NeuralNetworkTesting

a6208cc8639a93ac24655495c9ace1acba21c76f

[ "MIT" ]

null

CIFAR10.py

jimmyLeeMc/NeuralNetworkTesting

a6208cc8639a93ac24655495c9ace1acba21c76f

[ "MIT" ]

null

CIFAR10.py

jimmyLeeMc/NeuralNetworkTesting

a6208cc8639a93ac24655495c9ace1acba21c76f

[ "MIT" ]

null

#CIFAR from tensorflow import keras import numpy as np import matplotlib.pyplot as plt data = keras.datasets.cifar10 activations=[keras.activations.sigmoid, keras.activations.relu, keras.layers.LeakyReLU(), keras.activations.tanh] results=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] class_names=[0,1,2,3,4,5,6,7,8,9] a=0 for i in range(4): for j in range(4): losssum=0 for k in range(6): (train_images, train_labels), (test_images, test_labels) = data.load_data() train_images = train_images/255.0 test_images = test_images/255.0 model = keras.Sequential([ keras.layers.Flatten(input_shape=(32,32,3)), keras.layers.Dense(128, activations[i]), keras.layers.Dense(10, activations[j]) # tanh softmax ]) model.compile(optimizer="adam",loss="sparse_categorical_crossentropy", metrics=["accuracy"]) history = model.fit(train_images, train_labels, validation_split=0.25, epochs=5, batch_size=16, verbose=1) prediction = model.predict(test_images) losssum=losssum+history.history['loss'][len(history.history['loss'])-1] results[a]=losssum/1 a=a+1 print(results)

38.714286

108

0.591882

184

1,355

4.26087

0.434783

0.05102

0.072704

0.091837

0.026786

0

0.067554

0.278967

1,355

34

109

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0

null

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null

0.034483

0

null

0

null

0

1

0

1

3dd93f9bb15a42397c641e431fd3df72da46ab0d

3,127

py

Python

All_RasPy_Files/edgedetection.py

govindak-umd/Autonomous_Robotics

5293b871c7032b40cbff7814bd773871ee2c5946

[ "MIT" ]

2

2020-05-14T11:23:30.000Z

2020-05-25T06:30:57.000Z

All_RasPy_Files/edgedetection.py

govindak-umd/ENPM809T

5293b871c7032b40cbff7814bd773871ee2c5946

[ "MIT" ]

null

All_RasPy_Files/edgedetection.py

govindak-umd/ENPM809T

5293b871c7032b40cbff7814bd773871ee2c5946

[ "MIT" ]

5

2020-06-09T22:09:15.000Z

2022-01-31T17:11:19.000Z

# ENME 489Y: Remote Sensing # Edge detection import numpy as np import matplotlib import matplotlib.pyplot as plt # Define slice of an arbitrary original image f = np.empty((0)) index = np.empty((0)) # Create intensity data, including noise for i in range(2000): index = np.append(index, i) if i <= 950: f = np.append(f, 50 + np.random.normal(0,1)) elif i > 950 and i < 1000: f = np.append(f, 50 + (i - 950)/2 + np.random.normal(0,1)) elif i >= 1000 and i < 1050: f = np.append(f, 75 + (i - 1000)/2 + np.random.normal(0,1)) else: f = np.append(f, 100 + np.random.normal(0,1)) print f.shape print index.shape plt.figure(2) plt.plot(index, f, 'r-') plt.title('Slice of Original Image: f(x)') plt.xlabel('Pixel x') plt.ylabel('Pixel intensity f(x)') plt.grid() plt.show() # Plot the gradient (first derivative) of the original signal messy = np.gradient(f) plt.figure(3) plt.plot(messy, 'r-') plt.title('Derivative of Original Image Slice: df/dx') plt.xlabel('Pixel x') plt.ylabel('Derivative df/dx') plt.grid() plt.show() # Define Gaussian filter mean = 0 std = 5 var = np.square(std) x = np.arange(-20, 20, 0.1) kernel = (1/(std*np.sqrt(2*np.pi)))*np.exp(-np.square((x-mean)/std)/2) print kernel.shape plt.figure(4) plt.plot(x, kernel, 'b-') plt.title('Kernel: Gaussian Filter h(x)') plt.xlabel('Pixel x') plt.ylabel('Kernel h(x)') plt.grid() plt.show() # Convolve original image signal with Gaussian filter smoothed = np.convolve(kernel, f, 'same') print smoothed.shape plt.figure(5) plt.plot(smoothed, 'r-') plt.title('Apply Gaussian Filter: Convolve h(x) * f(x)') plt.xlabel('Pixel x') plt.ylabel('Convolution') plt.grid() plt.show() # Plot the gradient (first derivative) of the filtered signal edges = np.gradient(smoothed) plt.figure(6) plt.plot(edges, 'r-') plt.title('Derivative of Convolved Image: d/dx[ h(x) * f(x) ] ') plt.xlabel('Pixel x') plt.ylabel('Derivative') plt.grid() plt.show() # Plot the gradient (first derivative) of the Gaussian kernel first_diff = np.gradient(kernel) plt.figure(7) plt.plot(first_diff, 'b-') plt.title('1st Derivative of Gaussian: d/dx[ h(x) ]') plt.xlabel('Pixel x') plt.ylabel('Derivative') plt.grid() plt.show() # Convolve original image signal with Gaussian filter smoothed = np.convolve(first_diff, f, 'same') print smoothed.shape plt.figure(8) plt.plot(smoothed, 'r-') plt.title('Apply Gaussian Filter: Convolve d/dx[ h(x) ] * f(x)') plt.xlabel('Pixel x') plt.ylabel('Convolution') plt.grid() plt.show() # Plot the second derivative of the Gaussian kernel: the Laplacian operator laplacian = np.gradient(first_diff) plt.figure(9) plt.plot(laplacian, 'b-') plt.title('2nd Derivative of Gaussian: Laplacian Operator d^2/dx^2[ h(x) ]') plt.xlabel('Pixel x') plt.ylabel('Derivative') plt.grid() plt.show() # Convolve original image signal with Gaussian filter smoothed = np.convolve(laplacian, f, 'same') print smoothed.shape plt.figure(10) plt.plot(smoothed, 'r-') plt.title('Apply Laplacian Operator: Convolve d^2/dx^2[ h(x) ] * f(x)') plt.xlabel('Pixel x') plt.ylabel('Convolution') plt.grid() plt.show()

23.689394

76

0.68692

525

3,127

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0.035448

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0.062966

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0.479944

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0

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3,127

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0

null

0

null

0

1

0

1

3dda1806de2d35a90208c505c2c72da1466cf4a9

1,850

py

Python

alipay/aop/api/domain/AlipayCommerceReceiptBatchqueryModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

alipay/aop/api/domain/AlipayCommerceReceiptBatchqueryModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

alipay/aop/api/domain/AlipayCommerceReceiptBatchqueryModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.constant.ParamConstants import * class AlipayCommerceReceiptBatchqueryModel(object): def __init__(self): self._level = None self._out_biz_no_list = None @property def level(self): return self._level @level.setter def level(self, value): self._level = value @property def out_biz_no_list(self): return self._out_biz_no_list @out_biz_no_list.setter def out_biz_no_list(self, value): if isinstance(value, list): self._out_biz_no_list = list() for i in value: self._out_biz_no_list.append(i) def to_alipay_dict(self): params = dict() if self.level: if hasattr(self.level, 'to_alipay_dict'): params['level'] = self.level.to_alipay_dict() else: params['level'] = self.level if self.out_biz_no_list: if isinstance(self.out_biz_no_list, list): for i in range(0, len(self.out_biz_no_list)): element = self.out_biz_no_list[i] if hasattr(element, 'to_alipay_dict'): self.out_biz_no_list[i] = element.to_alipay_dict() if hasattr(self.out_biz_no_list, 'to_alipay_dict'): params['out_biz_no_list'] = self.out_biz_no_list.to_alipay_dict() else: params['out_biz_no_list'] = self.out_biz_no_list return params @staticmethod def from_alipay_dict(d): if not d: return None o = AlipayCommerceReceiptBatchqueryModel() if 'level' in d: o.level = d['level'] if 'out_biz_no_list' in d: o.out_biz_no_list = d['out_biz_no_list'] return o

28.90625

81

0.585405

246

1,850

4.04878

0.203252

0.120482

0.160643

0.240964

0.39759

0.236948

0.160643

0.120482

0.068273

0

0.001587

0.318919

1,850

63

82

29.365079

0.788889

0.022703

0

0.081633

0

0.067627

0

1

0.142857

false

0

0.040816

0.306122

0

null

0

1

0

null

0

1

3ddaf9735b2cb2b79bcc96e4e4c161028c28ae19

2,632

py

Python

tests/test_timeconversion.py

FObersteiner/pyFuppes

2a8c6e210855598dbf4fb491533bf22706340c9a

[ "MIT" ]

1

2020-06-02T08:02:36.000Z

tests/test_timeconversion.py

FObersteiner/pyFuppes

2a8c6e210855598dbf4fb491533bf22706340c9a

[ "MIT" ]

3

2022-03-04T11:43:19.000Z

2022-03-25T00:26:46.000Z

tests/test_timeconversion.py

FObersteiner/pyFuppes

2a8c6e210855598dbf4fb491533bf22706340c9a

[ "MIT" ]

null

import unittest from datetime import datetime, timezone from pyfuppes import timeconversion class TestTimeconv(unittest.TestCase): @classmethod def setUpClass(cls): # to run before all tests print("testing pyfuppes.timeconversion...") @classmethod def tearDownClass(cls): # to run after all tests pass def setUp(self): # to run before each test pass def tearDown(self): # to run after each test pass def test_dtstr_2_mdns(self): # no timezone t = ["2012-01-01T01:00:00", "2012-01-01T02:00:00"] f = "%Y-%m-%dT%H:%M:%S" result = list(map(int, timeconversion.dtstr_2_mdns(t, f))) self.assertEqual(result, [3600, 7200]) # with timezone t = ["2012-01-01T01:00:00+02:00", "2012-01-01T02:00:00+02:00"] f = "%Y-%m-%dT%H:%M:%S%z" result = list(map(int, timeconversion.dtstr_2_mdns(t, f))) self.assertEqual(result, [3600, 7200]) # zero case t = "2012-01-01T00:00:00+02:00" result = timeconversion.dtstr_2_mdns(t, f) self.assertEqual(int(result), 0) def test_dtobj_2_mdns(self): t = [datetime(2000, 1, 1, 1), datetime(2000, 1, 1, 2)] result = list(map(int, timeconversion.dtobj_2_mdns(t))) self.assertEqual(result, [3600, 7200]) t = [ datetime(2000, 1, 1, 1, tzinfo=timezone.utc), datetime(2000, 1, 1, 2, tzinfo=timezone.utc), ] result = list(map(int, timeconversion.dtobj_2_mdns(t))) self.assertEqual(result, [3600, 7200]) def test_posix_2_mdns(self): t = [3600, 7200, 10800] result = list(map(int, timeconversion.posix_2_mdns(t))) self.assertEqual(result, t) def test_mdns_2_dtobj(self): t = [3600, 10800, 864000] ref = datetime(2020, 5, 15, tzinfo=timezone.utc) result = list(map(int, timeconversion.mdns_2_dtobj(t, ref, posix=True))) self.assertEqual(result, [1589504400, 1589511600, 1590364800]) def test_daysSince_2_dtobj(self): t0, off = datetime(2020, 5, 10), 10.5 result = timeconversion.daysSince_2_dtobj(t0, off) self.assertEqual(result.hour, 12) self.assertEqual(result.day, 20) def test_dtstr_2_posix(self): result = timeconversion.dtstr_2_posix("2020-05-15", "%Y-%m-%d") self.assertAlmostEqual( result, datetime(2020, 5, 15, tzinfo=timezone.utc).timestamp() ) if __name__ == "__main__": unittest.main()

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81

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4.363372

0.261628

0.02998

0.111925

0.063957

0.433711

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0.334444

0.271153

0.187875

0

0.134179

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2,632

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33.316456

0.658743

0.049012

0

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0.175439

false

0.052632

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0.017544

0

null

0

null

0

1

0

1

3ddd545e8ac1636ac0a7d92a17cca391f2e23803

7,468

py

Python

tool/powermon.py

virajpadte/Power_monitoring_JetsonTX1

3f337adb16ce09072d69147b705a0c705b3ad53c

[ "MIT" ]

null

tool/powermon.py

virajpadte/Power_monitoring_JetsonTX1

3f337adb16ce09072d69147b705a0c705b3ad53c

[ "MIT" ]

null

tool/powermon.py

virajpadte/Power_monitoring_JetsonTX1

3f337adb16ce09072d69147b705a0c705b3ad53c

[ "MIT" ]

null

import sys import glob import serial import ttk import tkFileDialog from Tkinter import * #for plotting we need these: import matplotlib matplotlib.use("TkAgg") import matplotlib.pyplot as plt from drawnow import * class MainView: #CLASS VARIABLES: closing_status = False powerW = [] def __init__(self, master): self.master = master mainframe = ttk.Frame(self.master, padding="3 3 12 12") mainframe.grid(column=0, row=0, sticky=(N, W, E, S)) mainframe.columnconfigure(0, weight=1) mainframe.rowconfigure(0, weight=1) port = StringVar() port.set(" ") # initial value ttk.Label(mainframe, text="Select Port").grid(column=1, row=1, sticky=W) port_list = self.serial_ports() port_list.insert(0," ") print(port_list) port = StringVar(mainframe) port.set(port_list[1]) # default value dropdown = ttk.OptionMenu(mainframe,port,*port_list) dropdown.configure(width=20) dropdown.grid(column=2, row=1, sticky=W) ttk.Button(mainframe, text="Realtime Plot", command=lambda: self.real_time_plotting(port)).grid(column=1, row=2, sticky=W) ttk.Button(mainframe, text="Record Session", command=lambda: self.record_session(port)).grid(column=2, row=2, sticky=W) for child in mainframe.winfo_children(): child.grid_configure(padx=5, pady=5) def record_session(self,port): print("record_session") port = port.get() print("record port",port) self.newWindow = Toplevel(root) self.app = record_session(self.newWindow,port) def serial_ports(self): if sys.platform.startswith('win'): ports = ['COM%s' % (i + 1) for i in range(256)] elif sys.platform.startswith('linux') or sys.platform.startswith('cygwin'): # this excludes your current terminal "/dev/tty" ports = glob.glob('/dev/tty[A-Za-z]*') elif sys.platform.startswith('darwin'): ports = glob.glob('/dev/tty.*') else: raise EnvironmentError('Unsupported platform') result = [] result = ports return result def handle_close(self): print('Closed Figure!') self.closing_status = True def real_time_plotting(self,port): cnt = 0 window_size = 20 connected = False port = port.get() print("real_time_plotting") print("realtime data port", port) try: print("trying to connect to device....") ser = serial.Serial(port, 115200) except: print "Failed to connect on", port # ## loop until the arduino tells us it is ready while not connected: serin = ser.read() connected = True try: while not self.closing_status: # While loop that loops forever if ser.inWaiting(): # Wait here until there is data power = ser.readline() # read the line of text from the serial port print(power) self.powerW.append(power) # Build our tempF array by appending temp readings drawnow(self.makeFig) # Call drawnow to update our live graph plt.pause(.000001) # Pause Briefly. Important to keep drawnow from crashing cnt = cnt + 1 if (cnt > window_size): # If you have 50 or more points, delete the first one from the array self.powerW.pop(0) # This allows us to just see the last 50 data points print("closing port") ser.close() except KeyboardInterrupt: print("closing port") ser.close() def makeFig(self): # Create a function that makes our desired plot # configure the plot plt.ion() # Tell matplotlib you want interactive mode to plot live data plt.rcParams['toolbar'] = 'None' # create a fig #fig = plt.figure(0) #fig.canvas.set_window_title('Window 3D') #fig.canvas.mpl_connect('close_event', self.handle_close()) plt.ylim(0, 15) # Set y min and max values plt.title('Plotting power consumption') # Plot the title plt.grid(True) # Turn the grid on plt.ylabel('Power (Watts)') # Set ylabels plt.plot(self.powerW, 'ro-', label='Power W') # plot the temperature plt.legend(loc='upper right') # plot the legend def handle_close(self): print('Closed Figure!') self.closing_status = True class record_session: #class variable: path = "" def __init__(self, master,port): self.master = master self.master.title("Session parameters") mainframe = ttk.Frame(self.master, padding="3 3 12 12") mainframe.grid(column=0, row=0, sticky=(N, W, E, S)) mainframe.columnconfigure(0, weight=1) mainframe.rowconfigure(0, weight=1) print("passed port", port) duration = StringVar() autoplot = IntVar() autoplot.set(0) # initial value ttk.Button(mainframe, text="Select a location to store session.csv file", command=self.select_dir).grid(column=1, row=1, sticky=W) ttk.Label(mainframe, text="Record Duration in seconds:").grid(column=1, row=2, sticky=W) duration_entry_box = ttk.Entry(mainframe, width=5, textvariable=duration) duration_entry_box.grid(column=2, row=2, sticky=W) #ttk.Checkbutton(mainframe, text="Auto Plotting enabled", variable=autoplot).grid(column=1, row=3, sticky=W) ttk.Button(mainframe, text="Start recording", command=lambda: self.record(port,autoplot)).grid(column=1, row=4, sticky=W) for child in mainframe.winfo_children(): child.grid_configure(padx=5, pady=5) def select_dir(self): global path print("select dir") path = tkFileDialog.askdirectory() #append file name to the path if len(path): path = path + "/session.csv" print(path) def record(self,port,autoplot): global path print("recording") autoplot_status = autoplot.get() print("autoplot_status", autoplot_status) connected = False ## establish connection to the serial port that your arduino ## is connected to. try: print("trying to connect to device....") ser = serial.Serial(port, 115200) except: print "Failed to connect on", port # ## loop until the arduino tells us it is ready while not connected: serin = ser.read(self) connected = True #open text file to store the power values text_file = open(path, 'w') #read serial data from arduino and #write it to the text file 'Data.csv' try: while True: if ser.inWaiting(): # Read a line and convert it from b'xxx\r\n' to xxx line = ser.readline() print(line) if line: # If it isn't a blank line text_file.write(line) text_file.close() except KeyboardInterrupt: print("closing port") ser.close() if __name__ == '__main__': root = Tk() root.title("Power Monitoring tool") main = MainView(root) root.mainloop()

35.561905

138

0.595742

934

7,468

4.695931

0.293362

0.02508

0.015048

0.019152

0.28591

0.258778

0.240538

0.212038

0.188326

0

0.016787

0.298072

7,468

209

139

35.732057

0.819916

0.181441

0

0.324675

0

0.102687

0

null

0.006494

0.058442

null

0.136364

0

null

0

null

0

1

0

1

3de3ed318e614e22c2b9f52348133eddba3a0fee

2,424

py

Python

messages.py

runjak/hoodedFigure

539c9839dd47bc181e592bf4a61eaab361b8d316

[ "MIT" ]

null

messages.py

runjak/hoodedFigure

539c9839dd47bc181e592bf4a61eaab361b8d316

[ "MIT" ]

null

messages.py

runjak/hoodedFigure

539c9839dd47bc181e592bf4a61eaab361b8d316

[ "MIT" ]

null

# -*- coding: utf-8 -*- import random sentences = [ "Going into the #dogpark is not allowed, @%s.", "That's my favourite #dogpark @%s - no one is allowed to go into it!", "That #dogpark you mention is forbidden! Please don't, @%s", "The #dogpark should be secured with electrified barbwire. " "Don't you agree, @%s?", "Just make sure NOT TO ENTER the #dogpark @%s.", "Why would you mention such nasty things like a #dogpark @%s?", "Remember to share your #dogpark experience " "so others may also survive @%s!", "Hi @%s! City council discourages the term #dogpark for security reasons.", "You are not a dog, @%s! Please don't think of the #dogpark.", "@%s in the #dogpark all dogs have 8 legs. Scary.", "Please return to safety @%s! Don't linger in the #dogpark.", "Hey @%s… I got notice that the #dogpark " "will get fortified with spikes and lava soon.", "Beware @%s. Today the #dogpark is full of deer. " "Dangerous with their sharp claws and many heads.", "There is a time and place for everything @%s. " "But it's not the #dogpark. An acid pit is much saver.", "@%s do you know that the #dogpark is actually a pond of molten lava?", "@%s beware - flesh entering the #dogpark without correct papers " "will actually turn into a liquid.", "Only truely evil spirits may enter the #dogpark. Are you one of us, @%s?", "I heard a five headed dragon near the #dogpark might try to dine on @%s.", "@%s and I are sure that the #dogpark is protected by a smiling god " "that replaces your blood with liquid led.", "In the #dogpark everyone becomes a stick in an eternal play of fetch. " "Be careful @%s.", "You may eat your own dogfood - but please: " "NEVER walk your own #dogpark, @%s.", "There is a non-zero chance that thinking the word #dogpark " "replaces your neurons with ants, @%s.", "The #dogpark will not harm you, @%s. " "Provided you have wings. And antlers.", ] def replyDictFromTweet(status): msg = random.choice(sentences) % status.user.screen_name if len(msg) > 140: print('Cannot send message:', msg) return None statusParams = { 'status': msg, 'in_reply_to_status_id': status.id } if status.place: statusParams['place_id'] = status.place.id return statusParams

44.072727

79

0.636139

365

2,424

4.216438

0.50137

0.103964

0.031189

0.020793

0

0.002762

0.2533

2,424

54

80

44.888889

0.845856

0.008663

0

0.72803

0.008746

0

1

0.020408

false

0

0.020408

0

0.081633

0.020408

0

null

0

1

0

null

0

1

3deea7c2a0399d6a1677f78e7cc36afe63de0fc2

1,780

py

Python

keystroke/migrations/0001_initial.py

jstavanja/quiz-biometrics-api

75e0db348668b14a85f94261aac092ae2d5fa9c6

[ "MIT" ]

null

keystroke/migrations/0001_initial.py

jstavanja/quiz-biometrics-api

75e0db348668b14a85f94261aac092ae2d5fa9c6

[ "MIT" ]

null

keystroke/migrations/0001_initial.py

jstavanja/quiz-biometrics-api

75e0db348668b14a85f94261aac092ae2d5fa9c6

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.15 on 2018-08-20 16:31 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='KeystrokeTestSession', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('timing_matrix', models.CharField(max_length=5000)), ], ), migrations.CreateModel( name='KeystrokeTestType', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('input_text', models.CharField(max_length=5000)), ('repetitions', models.IntegerField()), ], ), migrations.CreateModel( name='Student', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('moodle_username', models.CharField(max_length=250)), ('path_to_image', models.CharField(max_length=250)), ], ), migrations.AddField( model_name='keystroketestsession', name='student', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='keystroke.Student'), ), migrations.AddField( model_name='keystroketestsession', name='test_type', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='keystroke.KeystrokeTestType'), ), ]

34.901961

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1,780

6.065089

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0.465366

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0

0.025059

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1,780

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0.166667

0

null

0

1

0

null

0

1

3df0af937b9149db956b0d8ec02537a403587abe

19,082

py

Python

src/oci/log_analytics/models/query_details.py

Manny27nyc/oci-python-sdk

de60b04e07a99826254f7255e992f41772902df7

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

249

2017-09-11T22:06:05.000Z

2022-03-04T17:09:29.000Z

src/oci/log_analytics/models/query_details.py

Manny27nyc/oci-python-sdk

de60b04e07a99826254f7255e992f41772902df7

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

228

2017-09-11T23:07:26.000Z

2022-03-23T10:58:50.000Z

src/oci/log_analytics/models/query_details.py

Manny27nyc/oci-python-sdk

de60b04e07a99826254f7255e992f41772902df7

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

224

2017-09-27T07:32:43.000Z

2022-03-25T16:55:42.000Z

# coding: utf-8 # Copyright (c) 2016, 2021, Oracle and/or its affiliates. All rights reserved. # This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license. from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401 from oci.decorators import init_model_state_from_kwargs @init_model_state_from_kwargs class QueryDetails(object): """ Input arguments for running a log anlaytics query. If the request is set to run in asynchronous mode then shouldIncludeColumns and shouldIncludeFields can be overwritten when retrieving the results. """ #: A constant which can be used with the sub_system property of a QueryDetails. #: This constant has a value of "LOG" SUB_SYSTEM_LOG = "LOG" #: A constant which can be used with the async_mode property of a QueryDetails. #: This constant has a value of "FOREGROUND" ASYNC_MODE_FOREGROUND = "FOREGROUND" #: A constant which can be used with the async_mode property of a QueryDetails. #: This constant has a value of "BACKGROUND" ASYNC_MODE_BACKGROUND = "BACKGROUND" def __init__(self, **kwargs): """ Initializes a new QueryDetails object with values from keyword arguments. The following keyword arguments are supported (corresponding to the getters/setters of this class): :param compartment_id: The value to assign to the compartment_id property of this QueryDetails. :type compartment_id: str :param compartment_id_in_subtree: The value to assign to the compartment_id_in_subtree property of this QueryDetails. :type compartment_id_in_subtree: bool :param saved_search_id: The value to assign to the saved_search_id property of this QueryDetails. :type saved_search_id: str :param query_string: The value to assign to the query_string property of this QueryDetails. :type query_string: str :param sub_system: The value to assign to the sub_system property of this QueryDetails. Allowed values for this property are: "LOG" :type sub_system: str :param max_total_count: The value to assign to the max_total_count property of this QueryDetails. :type max_total_count: int :param time_filter: The value to assign to the time_filter property of this QueryDetails. :type time_filter: oci.log_analytics.models.TimeRange :param scope_filters: The value to assign to the scope_filters property of this QueryDetails. :type scope_filters: list[oci.log_analytics.models.ScopeFilter] :param query_timeout_in_seconds: The value to assign to the query_timeout_in_seconds property of this QueryDetails. :type query_timeout_in_seconds: int :param should_run_async: The value to assign to the should_run_async property of this QueryDetails. :type should_run_async: bool :param async_mode: The value to assign to the async_mode property of this QueryDetails. Allowed values for this property are: "FOREGROUND", "BACKGROUND" :type async_mode: str :param should_include_total_count: The value to assign to the should_include_total_count property of this QueryDetails. :type should_include_total_count: bool :param should_include_columns: The value to assign to the should_include_columns property of this QueryDetails. :type should_include_columns: bool :param should_include_fields: The value to assign to the should_include_fields property of this QueryDetails. :type should_include_fields: bool :param should_use_acceleration: The value to assign to the should_use_acceleration property of this QueryDetails. :type should_use_acceleration: bool """ self.swagger_types = { 'compartment_id': 'str', 'compartment_id_in_subtree': 'bool', 'saved_search_id': 'str', 'query_string': 'str', 'sub_system': 'str', 'max_total_count': 'int', 'time_filter': 'TimeRange', 'scope_filters': 'list[ScopeFilter]', 'query_timeout_in_seconds': 'int', 'should_run_async': 'bool', 'async_mode': 'str', 'should_include_total_count': 'bool', 'should_include_columns': 'bool', 'should_include_fields': 'bool', 'should_use_acceleration': 'bool' } self.attribute_map = { 'compartment_id': 'compartmentId', 'compartment_id_in_subtree': 'compartmentIdInSubtree', 'saved_search_id': 'savedSearchId', 'query_string': 'queryString', 'sub_system': 'subSystem', 'max_total_count': 'maxTotalCount', 'time_filter': 'timeFilter', 'scope_filters': 'scopeFilters', 'query_timeout_in_seconds': 'queryTimeoutInSeconds', 'should_run_async': 'shouldRunAsync', 'async_mode': 'asyncMode', 'should_include_total_count': 'shouldIncludeTotalCount', 'should_include_columns': 'shouldIncludeColumns', 'should_include_fields': 'shouldIncludeFields', 'should_use_acceleration': 'shouldUseAcceleration' } self._compartment_id = None self._compartment_id_in_subtree = None self._saved_search_id = None self._query_string = None self._sub_system = None self._max_total_count = None self._time_filter = None self._scope_filters = None self._query_timeout_in_seconds = None self._should_run_async = None self._async_mode = None self._should_include_total_count = None self._should_include_columns = None self._should_include_fields = None self._should_use_acceleration = None @property def compartment_id(self): """ **[Required]** Gets the compartment_id of this QueryDetails. Compartment Identifier `OCID]`__. __ https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm :return: The compartment_id of this QueryDetails. :rtype: str """ return self._compartment_id @compartment_id.setter def compartment_id(self, compartment_id): """ Sets the compartment_id of this QueryDetails. Compartment Identifier `OCID]`__. __ https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm :param compartment_id: The compartment_id of this QueryDetails. :type: str """ self._compartment_id = compartment_id @property def compartment_id_in_subtree(self): """ Gets the compartment_id_in_subtree of this QueryDetails. Flag to search all child compartments of the compartment Id specified in the compartmentId query parameter. :return: The compartment_id_in_subtree of this QueryDetails. :rtype: bool """ return self._compartment_id_in_subtree @compartment_id_in_subtree.setter def compartment_id_in_subtree(self, compartment_id_in_subtree): """ Sets the compartment_id_in_subtree of this QueryDetails. Flag to search all child compartments of the compartment Id specified in the compartmentId query parameter. :param compartment_id_in_subtree: The compartment_id_in_subtree of this QueryDetails. :type: bool """ self._compartment_id_in_subtree = compartment_id_in_subtree @property def saved_search_id(self): """ Gets the saved_search_id of this QueryDetails. Saved search OCID for this query if known. :return: The saved_search_id of this QueryDetails. :rtype: str """ return self._saved_search_id @saved_search_id.setter def saved_search_id(self, saved_search_id): """ Sets the saved_search_id of this QueryDetails. Saved search OCID for this query if known. :param saved_search_id: The saved_search_id of this QueryDetails. :type: str """ self._saved_search_id = saved_search_id @property def query_string(self): """ **[Required]** Gets the query_string of this QueryDetails. Query to perform. Must conform to logging analytic querylanguage syntax. Syntax errors will be returned if present. :return: The query_string of this QueryDetails. :rtype: str """ return self._query_string @query_string.setter def query_string(self, query_string): """ Sets the query_string of this QueryDetails. Query to perform. Must conform to logging analytic querylanguage syntax. Syntax errors will be returned if present. :param query_string: The query_string of this QueryDetails. :type: str """ self._query_string = query_string @property def sub_system(self): """ **[Required]** Gets the sub_system of this QueryDetails. Default subsystem to qualify fields with in the queryString if not specified. Allowed values for this property are: "LOG" :return: The sub_system of this QueryDetails. :rtype: str """ return self._sub_system @sub_system.setter def sub_system(self, sub_system): """ Sets the sub_system of this QueryDetails. Default subsystem to qualify fields with in the queryString if not specified. :param sub_system: The sub_system of this QueryDetails. :type: str """ allowed_values = ["LOG"] if not value_allowed_none_or_none_sentinel(sub_system, allowed_values): raise ValueError( "Invalid value for `sub_system`, must be None or one of {0}" .format(allowed_values) ) self._sub_system = sub_system @property def max_total_count(self): """ Gets the max_total_count of this QueryDetails. Maximum number of results to count. Note a maximum of 2001 will be enforced; that is, actualMaxTotalCountUsed = Math.min(maxTotalCount, 2001). :return: The max_total_count of this QueryDetails. :rtype: int """ return self._max_total_count @max_total_count.setter def max_total_count(self, max_total_count): """ Sets the max_total_count of this QueryDetails. Maximum number of results to count. Note a maximum of 2001 will be enforced; that is, actualMaxTotalCountUsed = Math.min(maxTotalCount, 2001). :param max_total_count: The max_total_count of this QueryDetails. :type: int """ self._max_total_count = max_total_count @property def time_filter(self): """ Gets the time_filter of this QueryDetails. :return: The time_filter of this QueryDetails. :rtype: oci.log_analytics.models.TimeRange """ return self._time_filter @time_filter.setter def time_filter(self, time_filter): """ Sets the time_filter of this QueryDetails. :param time_filter: The time_filter of this QueryDetails. :type: oci.log_analytics.models.TimeRange """ self._time_filter = time_filter @property def scope_filters(self): """ Gets the scope_filters of this QueryDetails. List of filters to be applied when the query executes. More than one filter per field is not permitted. :return: The scope_filters of this QueryDetails. :rtype: list[oci.log_analytics.models.ScopeFilter] """ return self._scope_filters @scope_filters.setter def scope_filters(self, scope_filters): """ Sets the scope_filters of this QueryDetails. List of filters to be applied when the query executes. More than one filter per field is not permitted. :param scope_filters: The scope_filters of this QueryDetails. :type: list[oci.log_analytics.models.ScopeFilter] """ self._scope_filters = scope_filters @property def query_timeout_in_seconds(self): """ Gets the query_timeout_in_seconds of this QueryDetails. Amount of time, in seconds, allowed for a query to execute. If this time expires before the query is complete, any partial results will be returned. :return: The query_timeout_in_seconds of this QueryDetails. :rtype: int """ return self._query_timeout_in_seconds @query_timeout_in_seconds.setter def query_timeout_in_seconds(self, query_timeout_in_seconds): """ Sets the query_timeout_in_seconds of this QueryDetails. Amount of time, in seconds, allowed for a query to execute. If this time expires before the query is complete, any partial results will be returned. :param query_timeout_in_seconds: The query_timeout_in_seconds of this QueryDetails. :type: int """ self._query_timeout_in_seconds = query_timeout_in_seconds @property def should_run_async(self): """ Gets the should_run_async of this QueryDetails. Option to run the query asynchronously. This will lead to a LogAnalyticsQueryJobWorkRequest being submitted and the {workRequestId} will be returned to use for fetching the results. :return: The should_run_async of this QueryDetails. :rtype: bool """ return self._should_run_async @should_run_async.setter def should_run_async(self, should_run_async): """ Sets the should_run_async of this QueryDetails. Option to run the query asynchronously. This will lead to a LogAnalyticsQueryJobWorkRequest being submitted and the {workRequestId} will be returned to use for fetching the results. :param should_run_async: The should_run_async of this QueryDetails. :type: bool """ self._should_run_async = should_run_async @property def async_mode(self): """ Gets the async_mode of this QueryDetails. Execution mode for the query if running asynchronously i.e (shouldRunAsync is set to true). Allowed values for this property are: "FOREGROUND", "BACKGROUND" :return: The async_mode of this QueryDetails. :rtype: str """ return self._async_mode @async_mode.setter def async_mode(self, async_mode): """ Sets the async_mode of this QueryDetails. Execution mode for the query if running asynchronously i.e (shouldRunAsync is set to true). :param async_mode: The async_mode of this QueryDetails. :type: str """ allowed_values = ["FOREGROUND", "BACKGROUND"] if not value_allowed_none_or_none_sentinel(async_mode, allowed_values): raise ValueError( "Invalid value for `async_mode`, must be None or one of {0}" .format(allowed_values) ) self._async_mode = async_mode @property def should_include_total_count(self): """ Gets the should_include_total_count of this QueryDetails. Include the total number of results from the query. Note, this value will always be equal to or less than maxTotalCount. :return: The should_include_total_count of this QueryDetails. :rtype: bool """ return self._should_include_total_count @should_include_total_count.setter def should_include_total_count(self, should_include_total_count): """ Sets the should_include_total_count of this QueryDetails. Include the total number of results from the query. Note, this value will always be equal to or less than maxTotalCount. :param should_include_total_count: The should_include_total_count of this QueryDetails. :type: bool """ self._should_include_total_count = should_include_total_count @property def should_include_columns(self): """ Gets the should_include_columns of this QueryDetails. Include columns in response :return: The should_include_columns of this QueryDetails. :rtype: bool """ return self._should_include_columns @should_include_columns.setter def should_include_columns(self, should_include_columns): """ Sets the should_include_columns of this QueryDetails. Include columns in response :param should_include_columns: The should_include_columns of this QueryDetails. :type: bool """ self._should_include_columns = should_include_columns @property def should_include_fields(self): """ Gets the should_include_fields of this QueryDetails. Include fields in response :return: The should_include_fields of this QueryDetails. :rtype: bool """ return self._should_include_fields @should_include_fields.setter def should_include_fields(self, should_include_fields): """ Sets the should_include_fields of this QueryDetails. Include fields in response :param should_include_fields: The should_include_fields of this QueryDetails. :type: bool """ self._should_include_fields = should_include_fields @property def should_use_acceleration(self): """ Gets the should_use_acceleration of this QueryDetails. Controls if query should ignore pre-calculated results if available and only use raw data. If set and no acceleration data is found it will fallback to raw data. :return: The should_use_acceleration of this QueryDetails. :rtype: bool """ return self._should_use_acceleration @should_use_acceleration.setter def should_use_acceleration(self, should_use_acceleration): """ Sets the should_use_acceleration of this QueryDetails. Controls if query should ignore pre-calculated results if available and only use raw data. If set and no acceleration data is found it will fallback to raw data. :param should_use_acceleration: The should_use_acceleration of this QueryDetails. :type: bool """ self._should_use_acceleration = should_use_acceleration def __repr__(self): return formatted_flat_dict(self) def __eq__(self, other): if other is None: return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

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19,082

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0.110216

0.037334

0.110529

0.050434

0.74431

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0.419109

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19,082

535

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35.66729

0.869298

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0

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0.353293

0

null

0

null

0

1

0

1

3df0f23a4341291aa332900c1b4adf982ac1f716

2,740

py

Python

moist.py

phiriv/moisture_sensor

1e6a5d967ab639c67bae03847bd58ede31bde564

[ "MIT" ]

null

moist.py

phiriv/moisture_sensor

1e6a5d967ab639c67bae03847bd58ede31bde564

[ "MIT" ]

null

moist.py

phiriv/moisture_sensor

1e6a5d967ab639c67bae03847bd58ede31bde564

[ "MIT" ]

null

Script to read temperature data from the DHT11: # Importeer Adafruit DHT bibliotheek. import Adafruit_DHT import time als = True while als: humidity, temperature = Adafruit_DHT.read_retry(Adafruit_DHT.DHT11, 4) #on gpio pin 4 or pin 7 if humidity is not None and temperature is not None: humidity = round(humidity, 2) temperature = round(temperature, 2) print 'Temperature = {0:0.1f}*C Humidity = {1:0.1f}%'.format(temperature, humidity) else: print 'can not connect to the sensor!' time.sleep(60) # read data every minute Update from the Script above with modification of writing the data to a CSV.file: # Importeer Adafruit DHT bibliotheek. #time.strftime("%I:%M:%S") import Adafruit_DHT import time import csv import sys csvfile = "temp.csv" als = True while als: humidity, temperature = Adafruit_DHT.read_retry(Adafruit_DHT.DHT11, 4) # gpio pin 4 or pin number 7 if humidity is not None and temperature is not None: humidity = round(humidity, 2) temperature = round(temperature, 2) print 'Temperature = {0:0.1f}*C Humidity = {1:0.1f}%'.format(temperature, humidity) else: print 'can not connect to the sensor!' timeC = time.strftime("%I")+':' +time.strftime("%M")+':'+time.strftime("%S") data = [temperature, timeC] with open(csvfile, "a")as output: writer = csv.writer(output, delimiter=",", lineterminator = '\n') writer.writerow(data) time.sleep(6) # update script every 60 seconds Script to read data from the CSV and display it in a graph: import matplotlib.pyplot as plt import matplotlib.dates as mdates import matplotlib.animation as animation from datetime import datetime fig = plt.figure() rect = fig.patch rect.set_facecolor('#0079E7') def animate(i): ftemp = 'temp.csv' fh = open(ftemp) temp = list() timeC = list() for line in fh: pieces = line.split(',') degree = pieces[0] timeB= pieces[1] timeA= timeB[:8] #print timeA time_string = datetime.strptime(timeA,'%H:%M:%S') #print time_string try: temp.append(float(degree)) timeC.append(time_string) except: print "dont know" ax1 = fig.add_subplot(1,1,1,axisbg='white') ax1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S')) ax1.clear() ax1.plot(timeC,temp, 'c', linewidth = 3.3) plt.title('Temperature') plt.xlabel('Time') ani = animation.FuncAnimation(fig, animate, interval = 6000) plt.show() */ void setup() { } void loop() { }

30.10989

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0.622628

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2,740

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0.378453

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null

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0.070423

0

null

0

null

0

1

0

1

3df8c0e29455e554abfe1f3cc62c34726c6ded0b

1,264

py

Python

Python/PythonOOP/animals.py

JosephAMumford/CodingDojo

505be74d18d7a8f41c4b3576ca050b97f840f0a3

[ "MIT" ]

2

2018-08-18T15:14:45.000Z

2019-10-16T16:14:13.000Z

Python/PythonOOP/animals.py

JosephAMumford/CodingDojo

505be74d18d7a8f41c4b3576ca050b97f840f0a3

[ "MIT" ]

null

Python/PythonOOP/animals.py

JosephAMumford/CodingDojo

505be74d18d7a8f41c4b3576ca050b97f840f0a3

[ "MIT" ]

6

2018-05-05T18:13:05.000Z

2021-05-20T11:32:48.000Z

class Animal(object): def __init__(self,name,health): self.name = name self.health = 50 def walk(self): self.health = self.health - 1 return self def run(self): self.health = self.health - 5 return self def display_health(self): print "Health: " + str(self.health) return self # Create instance of Animal animal1 = Animal("Edgar",30) animal1.walk().walk().walk().run().run().display_health() class Dog(Animal): def pet(self): self.health = self.health + 5 return self # Create instance of Dog dog1 = Dog("Raspberry",150) dog1.walk().walk().walk().run().run().pet().display_health() class Dragon(Animal): def fly(self): self.health = self.health - 10 return self def display_health(self): print "I am a Dragon" return self # Create instance of Dragon dragon1 = Dragon("Phantoon", 500) dragon1.walk().run().fly().fly().fly().display_health() # Create new Animal animal2 = Animal("Probos",200) #animal2.pet() #AttributeError: 'Animal' object has no attribute 'pet' #animal2.fly() #AttributeError: 'Animal' object has no attribute 'fly' animal2.display_health() #Health: 50 - does not say "I am a Dragon"

22.175439

60

0.630538

168

1,264

4.684524

0.285714

0.127065

0.071156

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0.459975

0.266836

0.165184

0.088945

0

0.030896

0.231804

1,264

56

61

22.571429

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0.211234

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0.25

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null

0

null

0.0625

0

null

0

null

0

1

0

1

ad000563b867048b766de0b54cb60801221e67a0

598

py

Python

fileparse/python/main.py

mlavergn/benchmarks

4663009772c71d7c94bcd13eec542d1ce33cef72

[ "Unlicense" ]

null

fileparse/python/main.py

mlavergn/benchmarks

4663009772c71d7c94bcd13eec542d1ce33cef72

[ "Unlicense" ]

null

fileparse/python/main.py

mlavergn/benchmarks

4663009772c71d7c94bcd13eec542d1ce33cef72

[ "Unlicense" ]

null

#!/usr/bin/python import timeit setup = ''' import os def FileTest(path): file = open(path, "r") lines = file.readlines() data = [None for i in range(len(lines))] i = 0 for line in lines: data[i] = line.split(',') j = 0 for field in data[i]: data[i][j] = field.strip('\\'\\n') j += 1 i += 1 return data ''' elapsed = timeit.timeit("FileTest(os.getcwd() + '/../employees.txt')", setup=setup, number=1) print(elapsed * 1000.0, "ms - cold") elapsed = timeit.timeit("FileTest(os.getcwd() + '/../employees.txt')", setup=setup, number=1) print(elapsed * 1000.0, "ms - warm")

20.62069

93

0.605351

91

598

3.978022

0.450549

0.041436

0.104972

0.149171

0.458564

0

0.032653

0.180602

598

28

94

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0.026756

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0.036145

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1

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false

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0

0.142857

0.095238

0

null

0

1

0

null

0

1

9a7853c5ab201c882d582391f394325cd2ad7796

1,247

py

Python

src/test/nspawn_test/support/header_test.py

Andrei-Pozolotin/nspawn

9dd3926f1d1a3a0648f6ec14199cbf4069af1c98

[ "Apache-2.0" ]

15

2019-10-10T17:35:48.000Z

2022-01-29T10:41:01.000Z

src/test/nspawn_test/support/header_test.py

Andrei-Pozolotin/nspawn

9dd3926f1d1a3a0648f6ec14199cbf4069af1c98

[ "Apache-2.0" ]

null

src/test/nspawn_test/support/header_test.py

Andrei-Pozolotin/nspawn

9dd3926f1d1a3a0648f6ec14199cbf4069af1c98

[ "Apache-2.0" ]

2

2019-10-10T17:36:43.000Z

2020-06-20T15:28:33.000Z

from nspawn.support.header import * def test_header(): print() head_dict = { 'etag':'some-hash', 'last-modified':'some-time', 'content-length':'some-size', 'nspawn-digest':'some-text', } assert head_dict[Header.etag] == 'some-hash' assert head_dict[Header.last_modified] == 'some-time' assert head_dict[Header.content_length] == 'some-size' assert head_dict[Header.nspawn_digest] == 'some-text' def test_compare_head(): print() assert compare_header({ }, { }) == HeadComp.undetermined assert compare_header({ 'etag':'123' }, { 'etag':'"123"' }) == HeadComp.same assert compare_header({ 'last-modified':'some-time', 'content-length':'some-size', }, { 'last-modified':'some-time', 'content-length':'some-size', }) == HeadComp.same assert compare_header({ 'last-modified':'some-time', 'content-length':'some-size-1', }, { 'last-modified':'some-time', 'content-length':'some-size-2', }) == HeadComp.different assert compare_header({ 'last-modified':'some-time', }, { 'content-length':'some-size', }) == HeadComp.undetermined

25.44898

58

0.57498

133

1,247

5.270677

0.225564

0.119829

0.159772

0.199715

0.514979

0.477889

0.360913

0.291013

0

0.008511

0.246191

1,247

48

59

25.979167

0.737234

0

0.619048

0

0.28996

0

0.214286

1

0.047619

false

0

0.02381

0

0.071429

0.047619

0

null

0

1

0

null

0

1

9a79fb2f2787441274d55999dc0843161af999b5

401

py

Python

dmoj/Uncategorized/tss17a.py

UserBlackBox/competitive-programming

2aa8ffa6df6a386f8e47d084b5fa32d6d741bbbc

[ "Unlicense" ]

null

dmoj/Uncategorized/tss17a.py

UserBlackBox/competitive-programming

2aa8ffa6df6a386f8e47d084b5fa32d6d741bbbc

[ "Unlicense" ]

null

dmoj/Uncategorized/tss17a.py

UserBlackBox/competitive-programming

2aa8ffa6df6a386f8e47d084b5fa32d6d741bbbc

[ "Unlicense" ]

null

# https://dmoj.ca/problem/tss17a # https://dmoj.ca/submission/2226280 import sys n = int(sys.stdin.readline()[:-1]) for i in range(n): instruction = sys.stdin.readline()[:-1].split() printed = False for j in range(3): if instruction.count(instruction[j]) >= 2: print(instruction[j]) printed = True break if not printed: print('???')

26.733333

51

0.578554

52

401

4.461538

0.596154

0.077586

0.094828

0.146552

0

0.043771

0.259352

401

15

52

26.733333

0.737374

0.162095

0

0.008982

0

1

0

false

0

0.083333

0

0.083333

0.416667

0

null

0

null

0

1

0

1

9a7ad9eea9244d2609a2517f92f7fc289fb240da

1,159

py

Python

todo/views/users_detail.py

josalhor/WebModels

6b9cde3141c53562f40b129e6e1c87448ce9853a

[ "BSD-3-Clause" ]

null

todo/views/users_detail.py

josalhor/WebModels

6b9cde3141c53562f40b129e6e1c87448ce9853a

[ "BSD-3-Clause" ]

41

2021-03-23T12:58:25.000Z

2021-05-25T11:38:42.000Z

todo/views/users_detail.py

josalhor/WebModels

6b9cde3141c53562f40b129e6e1c87448ce9853a

[ "BSD-3-Clause" ]

null

from todo.templatetags.todo_tags import is_management from django.contrib.auth.decorators import login_required, user_passes_test from django.http import HttpResponse from django.shortcuts import render from todo.models import Designer, Management, Writer, Editor @login_required @user_passes_test(is_management) def users_detail(request, list_slug=None) -> HttpResponse: # Which users to show on this list view? if list_slug == "editors": users = Editor.objects.all() elif list_slug == "designers": users = Designer.objects.all() elif list_slug == "writers": users = Writer.objects.all() elif list_slug == "management": users = Management.objects.all() # Additional filtering active_users = users.filter(user__is_active=True) unactive_users = users.filter(user__is_active=False) # ###################### # Add New User Form # ###################### context = { "list_slug": list_slug, "active_users": active_users, "unactive_users": unactive_users, "users": users, } return render(request, "todo/users_detail.html", context)

30.5

75

0.667817

137

1,159

5.430657

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0.075269

0.056452

0.072581

0.236559

0.075269

0

0.202761

1,159

37

76

31.324324

0.805195

0.068162

0

0.092323

0.02138

0

1

0.04

false

0.08

0.2

0

0.28

0

null

0

null

0

1

0

1

9a7dca2e7b004aae5d55d6951056ac9880930921

3,100

py

Python

tests/test_relations.py

OneRaynyDay/treeno

ce11b8447f471c0b5ea596a211b3855625ec43eb

[ "MIT" ]

1

2021-12-28T19:00:01.000Z

tests/test_relations.py

OneRaynyDay/treeno

ce11b8447f471c0b5ea596a211b3855625ec43eb

[ "MIT" ]

null

tests/test_relations.py

OneRaynyDay/treeno

ce11b8447f471c0b5ea596a211b3855625ec43eb

[ "MIT" ]

null

import unittest from treeno.base import PrintMode, PrintOptions from treeno.expression import Array, Field, wrap_literal from treeno.orderby import OrderTerm, OrderType from treeno.relation import ( AliasedRelation, Lateral, SampleType, Table, TableQuery, TableSample, Unnest, ValuesQuery, ) class TestRelations(unittest.TestCase): def test_table(self): t = Table(name="table", schema="schema", catalog="catalog") assert t.sql(PrintOptions()) == '"catalog"."schema"."table"' tq = TableQuery(t) assert ( tq.sql(PrintOptions(mode=PrintMode.DEFAULT)) == tq.sql(PrintOptions(mode=PrintMode.PRETTY)) == 'TABLE "catalog"."schema"."table"' ) # Test a richer query type tq = TableQuery( t, offset=2, limit=5, orderby=[OrderTerm(value=Field("x"), order_type=OrderType.DESC)], ) assert ( tq.sql(PrintOptions(mode=PrintMode.DEFAULT)) == 'TABLE "catalog"."schema"."table" ORDER BY "x" DESC OFFSET 2 LIMIT 5' ) assert tq.sql(PrintOptions(mode=PrintMode.PRETTY)) == ( ' TABLE "catalog"."schema"."table"\n' ' ORDER BY "x" DESC\n' "OFFSET 2\n" " LIMIT 5" ) def test_values(self): v = ValuesQuery([wrap_literal(1), wrap_literal(2), wrap_literal(3)]) assert ( v.sql(PrintOptions(mode=PrintMode.DEFAULT)) == v.sql(PrintOptions(mode=PrintMode.PRETTY)) == "VALUES 1,2,3" ) v = ValuesQuery( [wrap_literal(1), wrap_literal(2), wrap_literal(3)], offset=3, with_=[AliasedRelation(TableQuery(Table(name="foo")), "foo")], ) assert ( v.sql(PrintOptions(mode=PrintMode.DEFAULT)) == 'WITH "foo" AS (TABLE "foo") VALUES 1,2,3 OFFSET 3' ) assert v.sql(PrintOptions(mode=PrintMode.PRETTY)) == ( ' WITH "foo" AS (\n TABLE "foo")\n' "VALUES 1,2,3\n" "OFFSET 3" ) def test_tablesample(self): table_sample = TableSample( Table(name="table"), SampleType.BERNOULLI, wrap_literal(0.3) ) assert ( table_sample.sql(PrintOptions(mode=PrintMode.DEFAULT)) == table_sample.sql(PrintOptions(mode=PrintMode.PRETTY)) == '"table" TABLESAMPLE BERNOULLI(0.3)' ) def test_lateral(self): lateral = Lateral(TableQuery(Table(name="table"))) assert ( lateral.sql(PrintOptions(mode=PrintMode.DEFAULT)) == lateral.sql(PrintOptions(mode=PrintMode.PRETTY)) == 'LATERAL(TABLE "table")' ) def test_unnest(self): unnest = Unnest([Array([wrap_literal(1)])]) assert ( unnest.sql(PrintOptions(mode=PrintMode.DEFAULT)) == unnest.sql(PrintOptions(mode=PrintMode.PRETTY)) == "UNNEST(ARRAY[1])" ) if __name__ == "__main__": unittest.main()

31

84

0.560968

323

3,100

5.30031

0.204334

0.131425

0.155374

0.228972

0.42757

0.331776

0.245327

0.125

0

0.013902

0.303871

3,100

99

85

31.313131

0.779425

0.007742

0

0.127907

0

0.141835

0.034483

0

0.116279

1

0.05814

false

0

0.05814

0

0.127907

0

null

0

1

0

null

0

1

9a87b0a003cfac44c4b71f5b09ccd17d4a3eced1

8,683

py

Python

python/accel_adxl345/accel_adxl345.py

iorodeo/accel_adxl345

aadbca1c57840f66a61556ff02e72e8b8e4e93e0

[ "Apache-2.0" ]

null

python/accel_adxl345/accel_adxl345.py

iorodeo/accel_adxl345

aadbca1c57840f66a61556ff02e72e8b8e4e93e0

[ "Apache-2.0" ]

null

python/accel_adxl345/accel_adxl345.py

iorodeo/accel_adxl345

aadbca1c57840f66a61556ff02e72e8b8e4e93e0

[ "Apache-2.0" ]

null

""" accel_adxl345.py This modules defines the AccelADXL345 class for streaming data from the ADXL345 accelerometers. """ import time import serial import sys import numpy import struct BUF_EMPTY_NUM = 5 BUF_EMPTY_DT = 0.05 class AccelADXL345(serial.Serial): def __init__(self, **kwarg): # Command ids self.cmd_id = { 'stop_streaming' : 0, 'start_streaming' : 1, 'set_timer_period' : 2, 'get_timer_period' : 3, 'set_range' : 4, 'get_range' : 5, 'get_sample' : 6, 'get_max_timer_period' : 7, 'get_min_timer_period' : 8, 'get_bad_sample_count' : 9, } # Allowed accelerations ranges and scale factors self.allowedAccelRange = (2, 4, 8, 16) self.accelScale = 0.0384431560448 try: self.reset_sleep = kwarg.pop('reset_sleep') except KeyError: self.reset_sleep = True try: self.accelRange = kwarg.pop('range') except KeyError: self.accelRange = 16 if not self.checkAccelRange(self.accelRange): raise ValueError, 'unknown acceleration range {0}'.format(self.accelRange) _kwarg = { 'port' : '/dev/ttyUSB0', 'timeout' : 0.1, 'baudrate' : 38400, } _kwarg.update(kwarg) super(AccelADXL345,self).__init__(**_kwarg) if self.reset_sleep: time.sleep(2.0) self.emptyBuffer() # Get sample dt and current range setting self.sampleDt = self.getSampleDt() self.accelRange = self.getRange() # Get max and min allowed sample dt self.minSampleDt = self.getMinSampleDt() self.maxSampleDt = self.getMaxSampleDt() def sendCmd(self,cmd): """ Send the command, cmd, to the device """ self.write(cmd) def readValue(self): """ Read a value from the device. """ line = self.readline() line = line.strip() return line def readFloat(self): """ Read a single float of list of floats separated by commas """ value = self.readValue() if ' ' in value: value = value.split(' ') value = [float(x) for x in value] else: value = float(value) return value def readInt(self): """ Read a single integer or list of integers separated by commas. """ value = self.readValue() if ' ' in value: value = value.split(' ') value = [int(x) for x in value] else: value = int(value) return value def emptyBuffer(self): """ Empty the serial input buffer. """ for i in range(0,BUF_EMPTY_NUM): #print 'empty %d'%(i,), self.inWaiting() self.flushInput() time.sleep(BUF_EMPTY_DT) def checkAccelRange(self,value): """ Check if the value is within the allowed range set. """ return value in self.allowedAccelRange def startStreaming(self): """ Start data streaming form the accelerometer """ cmd = '[{0}]\n'.format(self.cmd_id['start_streaming']) self.sendCmd(cmd) def stopStreaming(self): """ Stop data streaming from the accelerometer """ cmd = '[{0}]\n'.format(self.cmd_id['stop_streaming']) self.sendCmd(cmd) def getSampleDt(self): """ Returns the sample interval, dt, in microseconds """ cmd = '[{0}]\n'.format(self.cmd_id['get_timer_period']) self.sendCmd(cmd) dt = self.readFloat() return dt def getBadSampleCount(self): """ Returns the number of bad/corrupted samples. """ cmd = '[{0}]\n'.format(self.cmd_id['get_bad_sample_count']) self.sendCmd(cmd) val = self.readInt() return val def setSampleDt(self,dt): """ Sets the sample interval in microseconds. """ _dt = int(dt) if _dt > self.maxSampleDt or _dt < self.minSampleDt: raise ValueError, 'sample dt out of range' cmd = '[{0},{1}]\n'.format(self.cmd_id['set_timer_period'],_dt) self.sendCmd(cmd) self.sampleDt = _dt def getSampleRate(self): """ Returns the sample rate in Hz """ return 1.0/self.sampleDt def setSampleRate(self,freq): """ Sets the sample rate in Hz """ dt = int(1.0e6/freq) self.setSampleDt(dt) def getMaxSampleDt(self): """ Gets the maximun allowed sample dt in microseconds. """ cmd = '[{0}]\n'.format(self.cmd_id['get_max_timer_period']) self.sendCmd(cmd) value = self.readInt() return value def getMinSampleDt(self): """ Gets the minimum allowed sample dt in microseconds. """ cmd = '[{0}]\n'.format(self.cmd_id['get_min_timer_period']) self.sendCmd(cmd) value = self.readInt() return value def getMaxSampleRate(self): """ Returns the maximum allowed sample rate in Hz """ minSampleDtSec = self.minSampleDt*(1.0e-6) return 1.0/minSampleDtSec def getMinSampleRate(self): """ Returns the minum allowed samples rate in Hz """ maxSampleDtSec = self.maxSampleDt*(1.0e-6) return 1.0/maxSampleDtSec def getRange(self): """ Returns the current accelerometer range setting. """ cmd = '[{0}]\n'.format(self.cmd_id['get_range']) self.sendCmd(cmd) accelRange = self.readInt() return accelRange def setRange(self,value): """ Sets the current accelerometer range. """ _value = int(value) if _value in self.allowedAccelRange: cmd = '[{0}, {1}]\n'.format(self.cmd_id['set_range'],_value) self.sendCmd(cmd) _value = self.getRange() self.accelRange = _value def getAllowedAccelRange(self): """ Returns all allowed range settings """ return self.allowedAccelRange def peekValue(self): """ Grabs a sinlge sample (ax,ay,az) from the accelerometer. """ cmd = '[{0}]\n'.format(self.cmd_id['get_sample']) self.sendCmd(cmd) samples = self.readFloat() samples = [x*self.accelScale for x in samples] return samples def getSamples(self,N,verbose=False): """ Streams N samples from the accelerometer at the current sample rate setting. """ # Start streaming self.emptyBuffer() self.startStreaming() # Read samples data = [] while len(data) < N: if verbose: print len(data) newData = self.readValues() data.extend(newData) # Stop streaming and empty buffer self.stopStreaming() self.emptyBuffer() # Convert to an array, truncate to number of samples requested data = numpy.array(data) data = self.accelScale*data[:N,:] # Use sample rate to get array of time points dtSec = self.sampleDt*1.0e-6 t = dtSec*numpy.arange(data.shape[0]) return t, data #def readValues(self,verbose=False): # data = [] # if self.inWaiting() > 0: # line = self.readline() # line = line.strip() # line = line.split(':') # for vals in line: # vals = vals.split(' ') # try: # vals = [float(x) for x in vals] # if len(vals) == 3: # data.append(vals) # except: # if verbose: # print 'fail' # return data def readValues(self): data = [] while self.inWaiting() >= 7: byteVals = self.read(7) ax = struct.unpack('<h',byteVals[0:2])[0] ay = struct.unpack('<h',byteVals[2:4])[0] az = struct.unpack('<h',byteVals[4:6])[0] chk = ord(byteVals[6]) if not chk == 0: raise IOError, 'streaming data is not in sync.' data.append([ax,ay,az]) return data

27.741214

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0.524012

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8,683

4.73913

0.225875

0.018796

0.022153

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0.200269

0.160662

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0.132916

0.121951

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0

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8,683

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null

0.005988

0

null

0

null

0

1

0

1

9a8866fd681b05cff1de0c32ef8dae40aefe5351

831

py

Python

polling_stations/apps/data_collection/management/commands/import_tower_hamlets.py

mtravis/UK-Polling-Stations

26e0331dc29253dc436a0462ffaa01e974c5dc52

[ "BSD-3-Clause" ]

null

polling_stations/apps/data_collection/management/commands/import_tower_hamlets.py

mtravis/UK-Polling-Stations

26e0331dc29253dc436a0462ffaa01e974c5dc52

[ "BSD-3-Clause" ]

null

polling_stations/apps/data_collection/management/commands/import_tower_hamlets.py

mtravis/UK-Polling-Stations

26e0331dc29253dc436a0462ffaa01e974c5dc52

[ "BSD-3-Clause" ]

null

from data_collection.management.commands import BaseXpressDemocracyClubCsvImporter class Command(BaseXpressDemocracyClubCsvImporter): council_id = "E09000030" addresses_name = "local.2018-05-03/Version 2/Democracy_Club__03May2018.tsv" stations_name = "local.2018-05-03/Version 2/Democracy_Club__03May2018.tsv" elections = ["local.2018-05-03"] csv_delimiter = "\t" csv_encoding = "windows-1252" def address_record_to_dict(self, record): uprn = record.property_urn.strip().lstrip("0") if uprn == "6198433": rec = super().address_record_to_dict(record) rec["postcode"] = "E2 9DG" return rec if record.addressline6 == "E3 2LB" or record.addressline6 == "E3 5EG": return None return super().address_record_to_dict(record)

34.625

82

0.683514

98

831

5.571429

0.591837

0.049451

0.06044

0.071429

0.29304

0.18315

0

0.100304

0.208183

831

23

83

36.130435

0.729483

0

0.222623

0.132371

0

1

0.058824

false

0

0.117647

0

0.764706

0

null

0

null

0

1

0

1

9a8ce9049f7230937ae69e4978f32515e2f46236

654

py

Python

saltlint/rules/CmdWaitRecommendRule.py

Poulpatine/salt-lint

304917d95d2730e7df8bd7b5dd29a3bd77c80250

[ "MIT" ]

null

saltlint/rules/CmdWaitRecommendRule.py

Poulpatine/salt-lint

304917d95d2730e7df8bd7b5dd29a3bd77c80250

[ "MIT" ]

null

saltlint/rules/CmdWaitRecommendRule.py

Poulpatine/salt-lint

304917d95d2730e7df8bd7b5dd29a3bd77c80250

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2020 Warpnet B.V. import re from saltlint.linter.rule import DeprecationRule from saltlint.utils import LANGUAGE_SLS class CmdWaitRecommendRule(DeprecationRule): id = '213' shortdesc = 'SaltStack recommends using cmd.run together with onchanges, rather than cmd.wait' description = 'SaltStack recommends using cmd.run together with onchanges, rather than cmd.wait' severity = 'LOW' languages = [LANGUAGE_SLS] tags = ['formatting'] version_added = 'develop' regex = re.compile(r"^\s{2}cmd\.wait:(\s+)?$") def match(self, file, line): return self.regex.search(line)

28.434783

100

0.697248

83

654

5.457831

0.674699

0.046358

0.10596

0.119205

0.300221

0

0.016886

0.185015

654

22

101

29.727273

0.833021

0.08104

0

0.344482

0.038462

0

1

0.071429

false

0

0.214286

0.071429

1

0

null

0

null

0

1

0

1

9a969dcb4bdc1a8eee56b110c60c1611472a3520

1,834

py

Python

bob-ross/cluster-paintings.py

h4ckfu/data

bdc02fd5051dfb31e42f8e078832ceead92f9958

[ "CC-BY-4.0" ]

16,124

2015-01-01T06:18:12.000Z

2022-03-31T00:46:52.000Z

bob-ross/cluster-paintings.py

h4ckfu/data

bdc02fd5051dfb31e42f8e078832ceead92f9958

[ "CC-BY-4.0" ]

179

2015-01-07T10:19:57.000Z

2022-02-21T21:19:14.000Z

bob-ross/cluster-paintings.py

h4ckfu/data

bdc02fd5051dfb31e42f8e078832ceead92f9958

[ "CC-BY-4.0" ]

12,163

2015-01-03T14:23:36.000Z

2022-03-31T10:10:23.000Z

""" Clusters Bob Ross paintings by features. By Walter Hickey <walter.hickey@fivethirtyeight.com> See http://fivethirtyeight.com/features/a-statistical-analysis-of-the-work-of-bob-ross/ """ import numpy as np from scipy.cluster.vq import vq, kmeans, whiten import math import csv def main(): # load data into vectors of 1s and 0s for each tag with open('elements-by-episode.csv','r') as csvfile: reader = csv.reader(csvfile) reader.next() # skip header data = [] for row in reader: data.append(map(lambda x: int(x), row[2:])) # exclude EPISODE and TITLE columns # convert to numpy matrix matrix = np.array(data) # remove colums that have been tagged less than 5 times columns_to_remove = [] for col in range(np.shape(matrix)[1]): if sum(matrix[:,col]) <= 5: columns_to_remove.append(col) matrix = np.delete(matrix, columns_to_remove, axis=1) # normalize according to stddev whitened = whiten(matrix) output = kmeans(whitened, 10) print "episode", "distance", "cluster" # determine distance between each of 403 vectors and each centroid, find closest neighbor for i, v in enumerate(whitened): # distance between centroid 0 and feature vector distance = math.sqrt(sum((v - output[0][0]) ** 2)) # group is the centroid it is closest to so far, set initally to centroid 0 group = 0 closest_match = (distance, group) # test the vector i against the 10 centroids, find nearest neighbor for x in range (0, 10): dist_x = math.sqrt(sum((v - output[0][x]) ** 2)) if dist_x < closest_match[0]: closest_match = (dist_x, x) print i+1, closest_match[0], closest_match[1] if __name__ == "__main__": main()

31.084746

93

0.640676

262

1,834

4.400763

0.461832

0.052038

0.039029

0.020815

0.076323

0.032958

0

0.021168

0.252999

1,834

59

94

31.084746

0.820438

0.260087

0

0.046713

0.019896

0

null

0

0.129032

null

0.064516

0

null

0

null

0

1

0

1

9a9fc338c15aa55b529d0d570899ecd61a1b41cd

514

py

Python

Strings/count-index-find.py

tverma332/python3

544c4ec9c726c37293c8da5799f50575cc50852d

[ "MIT" ]

3

2022-03-28T09:10:08.000Z

2022-03-29T10:47:56.000Z

Strings/count-index-find.py

tverma332/python3

544c4ec9c726c37293c8da5799f50575cc50852d

[ "MIT" ]

1

2022-03-27T11:52:58.000Z

Strings/count-index-find.py

tverma332/python3

544c4ec9c726c37293c8da5799f50575cc50852d

[ "MIT" ]

null

# 1) count = To count how many time a particular word & char. is appearing x = "Keep grinding keep hustling" print(x.count("t")) # 2) index = To get index of letter(gives the lowest index) x="Keep grinding keep hustling" print(x.index("t")) # will give the lowest index value of (t) # 3) find = To get index of letter(gives the lowest index) | Return -1 on failure. x = "Keep grinding keep hustling" print(x.find("t")) ''' NOTE : print(x.index("t",34)) : Search starts from index value 34 including 34 '''

25.7

82

0.684825

88

514

4

0.454545

0.068182

0.110795

0.144886

0.474432

0.210227

0

0.023866

0.184825

514

19

83

27.052632

0.816229

0.488327

0

0.5

0

0.488372

0

1

0

false

0

0.5

0

null

0

null

0

1

0

1

9aa3bdf68ace18fc9d168671cbe55ba44bdbac29

416

py

Python

setup.py

xpac1985/pyASA

a6cf470a4d1b731864a1b450e321901636c1ebdf

[ "MIT" ]

10

2017-02-05T12:15:19.000Z

2020-05-20T14:33:04.000Z

setup.py

xpac1985/pyASA

a6cf470a4d1b731864a1b450e321901636c1ebdf

[ "MIT" ]

null

setup.py

xpac1985/pyASA

a6cf470a4d1b731864a1b450e321901636c1ebdf

[ "MIT" ]

3

2017-04-02T13:00:28.000Z

2020-06-13T23:34:37.000Z

from distutils.core import setup setup( name='pyASA', packages=['pyASA'], version='0.1.0', description='Wrapper for the Cisco ASA REST API', author='xpac', author_email='bjoern@areafunky.net', url='https://github.com/xpac1985/pyASA', download_url='https://github.com/xpac1985/pyASA/tarball/0.1.0', keywords=['cisco', 'asa', 'rest-api', 'wrapper', 'alpha'], classifiers=[], )

27.733333

67

0.646635

54

416

4.944444

0.648148

0.014981

0.022472

0.11236

0.224719

0

0.040115

0.161058

416

14

68

29.714286

0.724928

0

0.435096

0

1

0

true

0

0.076923

0

0.076923

0

null

0

null

0

1

0

1

9aa976fa66600077fd0293cccc1c6dcd3ade5f91

9,390

py

Python

Statistical Thinking in Python (Part 1)/Thinking_probabilistically--_Discrete_variables.py

shreejitverma/Data-Scientist

03c06936e957f93182bb18362b01383e5775ffb1

[ "MIT" ]

2

2022-03-12T04:53:03.000Z

2022-03-27T12:39:21.000Z

Statistical Thinking in Python (Part 1)/Thinking_probabilistically--_Discrete_variables.py

shivaniverma1/Data-Scientist

f82939a411484311171465591455880c8e354750

[ "MIT" ]

null

Statistical Thinking in Python (Part 1)/Thinking_probabilistically--_Discrete_variables.py

shivaniverma1/Data-Scientist

f82939a411484311171465591455880c8e354750

[ "MIT" ]

2

2022-03-12T04:52:21.000Z

2022-03-27T12:45:32.000Z

# Thinking probabilistically-- Discrete variables!! # Statistical inference rests upon probability. Because we can very rarely say anything meaningful with absolute certainty from data, we use probabilistic language to make quantitative statements about data. In this chapter, you will learn how to think probabilistically about discrete quantities: those that can only take certain values, like integers. # Generating random numbers using the np.random module # We will be hammering the np.random module for the rest of this course and its sequel. Actually, you will probably call functions from this module more than any other while wearing your hacker statistician hat. Let's start by taking its simplest function, np.random.random() for a test spin. The function returns a random number between zero and one. Call np.random.random() a few times in the IPython shell. You should see numbers jumping around between zero and one. # In this exercise, we'll generate lots of random numbers between zero and one, and then plot a histogram of the results. If the numbers are truly random, all bars in the histogram should be of (close to) equal height. # You may have noticed that, in the video, Justin generated 4 random numbers by passing the keyword argument size=4 to np.random.random(). Such an approach is more efficient than a for loop: in this exercise, however, you will write a for loop to experience hacker statistics as the practice of repeating an experiment over and over again. # Seed the random number generator np.random.seed(42) # Initialize random numbers: random_numbers random_numbers = np.empty(100000) # Generate random numbers by looping over range(100000) for i in range(100000): random_numbers[i] = np.random.random() # Plot a histogram _ = plt.hist(random_numbers) # Show the plot plt.show() # The np.random module and Bernoulli trials # You can think of a Bernoulli trial as a flip of a possibly biased coin. Specifically, each coin flip has a probability p of landing heads (success) and probability 1−p of landing tails (failure). In this exercise, you will write a function to perform n Bernoulli trials, perform_bernoulli_trials(n, p), which returns the number of successes out of n Bernoulli trials, each of which has probability p of success. To perform each Bernoulli trial, use the np.random.random() function, which returns a random number between zero and one. def perform_bernoulli_trials(n, p): """Perform n Bernoulli trials with success probability p and return number of successes.""" # Initialize number of successes: n_success n_success = 0 # Perform trials for i in range(n): # Choose random number between zero and one: random_number random_number = np.random.random() # If less than p, it's a success so add one to n_success if random_number< p: n_success +=1 return n_success # How many defaults might we expect? # Let's say a bank made 100 mortgage loans. It is possible that anywhere between 0 and 100 of the loans will be defaulted upon. You would like to know the probability of getting a given number of defaults, given that the probability of a default is p = 0.05. To investigate this, you will do a simulation. You will perform 100 Bernoulli trials using the perform_bernoulli_trials() function you wrote in the previous exercise and record how many defaults we get. Here, a success is a default. (Remember that the word "success" just means that the Bernoulli trial evaluates to True, i.e., did the loan recipient default?) You will do this for another 100 Bernoulli trials. And again and again until we have tried it 1000 times. Then, you will plot a histogram describing the probability of the number of defaults. # Seed random number generator np.random.seed(42) # Initialize the number of defaults: n_defaults n_defaults = np.empty(1000) # Compute the number of defaults for i in range(1000): n_defaults[i] = perform_bernoulli_trials(100,0.05) # Plot the histogram with default number of bins; label your axes _ = plt.hist(n_defaults, normed= True) _ = plt.xlabel('number of defaults out of 100 loans') _ = plt.ylabel('probability') # Show the plot plt.show() # Will the bank fail? # Plot the number of defaults you got from the previous exercise, in your namespace as n_defaults, as a CDF. The ecdf() function you wrote in the first chapter is available. # If interest rates are such that the bank will lose money if 10 or more of its loans are defaulted upon, what is the probability that the bank will lose money? # Compute ECDF: x, y x, y= ecdf(n_defaults) # Plot the ECDF with labeled axes plt.plot(x, y, marker = '.', linestyle ='none') plt.xlabel('loans') plt.ylabel('interest') # Show the plot plt.show() # Compute the number of 100-loan simulations with 10 or more defaults: n_lose_money n_lose_money=sum(n_defaults >=10) # Compute and print probability of losing money print('Probability of losing money =', n_lose_money / len(n_defaults)) # Sampling out of the Binomial distribution # Compute the probability mass function for the number of defaults we would expect for 100 loans as in the last section, but instead of simulating all of the Bernoulli trials, perform the sampling using np.random.binomial(). This is identical to the calculation you did in the last set of exercises using your custom-written perform_bernoulli_trials() function, but far more computationally efficient. Given this extra efficiency, we will take 10,000 samples instead of 1000. After taking the samples, plot the CDF as last time. This CDF that you are plotting is that of the Binomial distribution. # Note: For this exercise and all going forward, the random number generator is pre-seeded for you (with np.random.seed(42)) to save you typing that each time. # Take 10,000 samples out of the binomial distribution: n_defaults n_defaults = np.random.binomial(100,0.05,size = 10000) # Compute CDF: x, y x, y = ecdf(n_defaults) # Plot the CDF with axis labels plt.plot(x,y, marker ='.', linestyle = 'none') plt.xlabel("Number of Defaults") plt.ylabel("CDF") # Show the plot plt.show() # Plotting the Binomial PMF # As mentioned in the video, plotting a nice looking PMF requires a bit of matplotlib trickery that we will not go into here. Instead, we will plot the PMF of the Binomial distribution as a histogram with skills you have already learned. The trick is setting up the edges of the bins to pass to plt.hist() via the bins keyword argument. We want the bins centered on the integers. So, the edges of the bins should be -0.5, 0.5, 1.5, 2.5, ... up to max(n_defaults) + 1.5. You can generate an array like this using np.arange() and then subtracting 0.5 from the array. # You have already sampled out of the Binomial distribution during your exercises on loan defaults, and the resulting samples are in the NumPy array n_defaults. # Compute bin edges: bins bins = np.arange(0, max(n_defaults) + 1.5) - 0.5 # Generate histogram plt.hist(n_defaults, normed = True, bins = bins) # Label axes plt.xlabel('Defaults') plt.ylabel('PMF') # Show the plot plt.show() # Relationship between Binomial and Poisson distributions # You just heard that the Poisson distribution is a limit of the Binomial distribution for rare events. This makes sense if you think about the stories. Say we do a Bernoulli trial every minute for an hour, each with a success probability of 0.1. We would do 60 trials, and the number of successes is Binomially distributed, and we would expect to get about 6 successes. This is just like the Poisson story we discussed in the video, where we get on average 6 hits on a website per hour. So, the Poisson distribution with arrival rate equal to np approximates a Binomial distribution for n Bernoulli trials with probability p of success (with n large and p small). Importantly, the Poisson distribution is often simpler to work with because it has only one parameter instead of two for the Binomial distribution. # Let's explore these two distributions computationally. You will compute the mean and standard deviation of samples from a Poisson distribution with an arrival rate of 10. Then, you will compute the mean and standard deviation of samples from a Binomial distribution with parameters n and p such that np=10. # Draw 10,000 samples out of Poisson distribution: samples_poisson # Print the mean and standard deviation print('Poisson: ', np.mean(samples_poisson), np.std(samples_poisson)) # Specify values of n and p to consider for Binomial: n, p # Draw 10,000 samples for each n,p pair: samples_binomial for i in range(3): samples_binomial = ____ # Print results print('n =', n[i], 'Binom:', np.mean(samples_binomial), np.std(samples_binomial)) # Was 2015 anomalous? # 1990 and 2015 featured the most no-hitters of any season of baseball (there were seven). Given that there are on average 251/115 no-hitters per season, what is the probability of having seven or more in a season? # Draw 10,000 samples out of Poisson distribution: n_nohitters # Compute number of samples that are seven or greater: n_large n_large = np.sum(____) # Compute probability of getting seven or more: p_large # Print the result print('Probability of seven or more no-hitters:', p_large)

47.908163

812

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4.543004

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9,390

195

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48.153846

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null

0

1

0

null

0

1

9aacaa2c9c98de085aff50585e25fcd2964d6c96

1,008

py

Python

ml/data_engineering/ETL/extract.py

alexnakagawa/tools

b5e8c047293247c8781d44607968402f637e597e

[ "MIT" ]

null

ml/data_engineering/ETL/extract.py

alexnakagawa/tools

b5e8c047293247c8781d44607968402f637e597e

[ "MIT" ]

null

ml/data_engineering/ETL/extract.py

alexnakagawa/tools

b5e8c047293247c8781d44607968402f637e597e

[ "MIT" ]

null

''' This is an abstract example of Extracting in an ETL pipeline. Inspired from the "Introduction to Data Engineering" course on Datacamp.com Author: Alex Nakagawa ''' import requests # Fetch the Hackernews post resp = requests.get("https://hacker-news.firebaseio.com/v0/item/16222426.json") # Print the response parsed as JSON print(resp.json()) # Assign the score of the test to post_score post_score = resp.json()['score'] print(post_score) # Function to extract table to a pandas DataFrame def extract_table_to_pandas(tablename, db_engine): query = "SELECT * FROM {}".format(tablename) return pd.read_sql(query, db_engine) # Connect to the database using the connection URI connection_uri = "postgresql://repl:password@localhost:5432/pagila" db_engine = sqlalchemy.create_engine(connection_uri) # Extract the film table into a pandas DataFrame extract_table_to_pandas("film", db_engine) # Extract the customer table into a pandas DataFrame extract_table_to_pandas("customer", db_engine)

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1,008

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1,008

32

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31.5

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0.089888

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0.083333

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0.083333

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0.25

0.166667

0

null

0

null

0

1

0

1

9ab1353597b9195d65b8c371888b502f56866647

3,368

py

Python

physicspy/optics/jones.py

suyag/physicspy

f2b29a72cb08b1de170274b3e35c3d8eda32f9e1

[ "MIT" ]

null

physicspy/optics/jones.py

suyag/physicspy

f2b29a72cb08b1de170274b3e35c3d8eda32f9e1

[ "MIT" ]

null

physicspy/optics/jones.py

suyag/physicspy

f2b29a72cb08b1de170274b3e35c3d8eda32f9e1

[ "MIT" ]

null

#!/usr/bin/env python from __future__ import division from numpy import sqrt, cos, sin, arctan, exp, abs, pi, conj from scipy import array, dot, sum class JonesVector: """ A Jones vector class to represent polarized EM waves """ def __init__(self,Jarray=array([1,0])): self.Jx = Jarray[0] self.Jy = Jarray[1] def size(self): """ Jones vector size """ return sqrt(dot(self.toArray().conj(),self.toArray()).real) def normalize(self): """ Normalized Jones vector """ result = self try: size = result.size() if size == 0: raise Exception('Zero-sized Jones vector cannot be normalized') result.Jx /= size result.Jy /= size except Exception as inst: print "Error: ",inst finally: return result def toArray(self): """ Convert into array format """ return array([self.Jx, self.Jy]) def rotate(self,phi): """ Rotated Jones vector Argument: phi - rotation angle in radians (clockwise is positive) """ R = array([[cos(phi), sin(phi)], \ [-sin(phi), cos(phi)]]) return JonesVector(dot(R, self.toArray())) def waveplate(self,G): """ Waveplate with arbitrary retardance Slow axis (or "c axis") is along X Argument: G - retartandance in phase units (e.g. one wavelength retardance is G = 2 * pi) """ W0 = array([[exp(-1j*G/2), 0], \ [0, exp(1j*G/2)]]) return JonesVector(dot(W0, self.toArray())) def waveplateRot(self,phi,G): """ Waveplate matrix with arbitrary rotation Arguments: phi - rotation angle in radians (clockwise is positive) G - retardance in phase units (e.g. one wavelength retardance is G = 2 * pi) """ return self.rotate(phi).waveplate(G).rotate(-phi) def pol(self,phi): """ Polarizer matrix """ P = array([[cos(phi)**2, cos(phi)*sin(phi)], \ [sin(phi)*cos(phi), sin(phi)**2]]) return JonesVector(dot(P, self.toArray())) def mirrormetal(self,n,k,th): """ Reflection off a metal mirror Incoming and reflected beams are assumed to be in the X plane """ dr = mphase(n,k,th); W0 = array([[dr[3]*exp(-1j*dr[1]), 0],\ [0, dr[2]*exp(-1j*dr[0])]]) return JonesVector(dot(W0, self.toArray())) def intensity(self): """ Intensity from electric field vector """ return real(self.Jx)**2 + real(self.Jy)**2 def mphase(n,k,th): """ Calculate phase shift and reflectance of a metal in the s and p directions""" u = sqrt(0.5 *((n**2 - k**2 - sin(th)**2) + sqrt( (n**2 - k**2 - sin(th)**2)**2 + 4*n**2*k**2 ))) v = sqrt(0.5*(-(n**2 - k**2 - sin(th)**2) + sqrt( (n**2 - k**2 - sin(th)**2)**2 + 4*n**2*k**2 ))) ds = arctan(2*v*cos(th)/(u**2+v**2-cos(th)**2)); dp = arctan(2*v*cos(th)*(n**2-k**2-2*u**2)/(u**2+v**2-(n**2+k**2)**2*cos(th)**2)); if(dp < 0): dp = dp+pi; rs = abs((cos(th) - (u+v*1j))/(cos(th) + (u+v*1j))) rp = abs(((n**2 + k**2)*cos(th) - (u+v*1j))/((n**2 + k**2)*cos(th) + (u+v*1j))); return array([ds, dp, rs, rp])

34.367347

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0.518705

487

3,368

3.570842

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0.017251

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97

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null

0

null

0

1

0

1

9ab5d8227882ea8202fdc93b49f22e935bbc0e93

2,560

py

Python

aiida/cmdline/params/options/config.py

louisponet/aiida-core

3214236df66a3792ee57fe38a06c0c3bb65861ab

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

1

2020-10-01T17:11:58.000Z

aiida/cmdline/params/options/config.py

louisponet/aiida-core

3214236df66a3792ee57fe38a06c0c3bb65861ab

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

17

2020-03-11T17:04:05.000Z

2020-05-01T09:34:45.000Z

aiida/cmdline/params/options/config.py

louisponet/aiida-core

3214236df66a3792ee57fe38a06c0c3bb65861ab

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

null

# -*- coding: utf-8 -*- ########################################################################### # Copyright (c), The AiiDA team. All rights reserved. # # This file is part of the AiiDA code. # # # # The code is hosted on GitHub at https://github.com/aiidateam/aiida-core # # For further information on the license, see the LICENSE.txt file # # For further information please visit http://www.aiida.net # ########################################################################### # pylint: disable=cyclic-import """ .. py:module::config :synopsis: Convenience class for configuration file option """ import click_config_file import yaml from .overridable import OverridableOption def yaml_config_file_provider(handle, cmd_name): # pylint: disable=unused-argument """Read yaml config file from file handle.""" return yaml.safe_load(handle) class ConfigFileOption(OverridableOption): """ Wrapper around click_config_file.configuration_option that increases reusability. Example:: CONFIG_FILE = ConfigFileOption('--config', help='A configuration file') @click.command() @click.option('computer_name') @CONFIG_FILE(help='Configuration file for computer_setup') def computer_setup(computer_name): click.echo(f"Setting up computer {computername}") computer_setup --config config.yml with config.yml:: --- computer_name: computer1 """ def __init__(self, *args, **kwargs): """ Store the default args and kwargs. :param args: default arguments to be used for the option :param kwargs: default keyword arguments to be used that can be overridden in the call """ kwargs.update({'provider': yaml_config_file_provider, 'implicit': False}) super().__init__(*args, **kwargs) def __call__(self, **kwargs): """ Override the stored kwargs, (ignoring args as we do not allow option name changes) and return the option. :param kwargs: keyword arguments that will override those set in the construction :return: click_config_file.configuration_option constructed with args and kwargs defined during construction and call of this instance """ kw_copy = self.kwargs.copy() kw_copy.update(kwargs) return click_config_file.configuration_option(*self.args, **kw_copy)

36.056338

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0.605078

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2,560

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false

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0

0.692308

0

null

0

null

0

1

0

1

9ab9d917b353cf0f8ea3e285cac62732af59e404

563

py

Python

python_learning/exception_redefinition.py

KonstantinKlepikov/all-python-ml-learning

a8a41347b548828bb8531ccdab89c622a0be20e1

[ "MIT" ]

null

python_learning/exception_redefinition.py

KonstantinKlepikov/all-python-ml-learning

a8a41347b548828bb8531ccdab89c622a0be20e1

[ "MIT" ]

null

python_learning/exception_redefinition.py

KonstantinKlepikov/all-python-ml-learning

a8a41347b548828bb8531ccdab89c622a0be20e1

[ "MIT" ]

1

2020-12-23T19:32:51.000Z

# example of redefinition __repr__ and __str__ of exception class MyBad(Exception): def __str__(self): return 'My mistake!' class MyBad2(Exception): def __repr__(self): return 'Not calable' # because buid-in method has __str__ try: raise MyBad('spam') except MyBad as X: print(X) # My mistake! print(X.args) # ('spam',) try: raise MyBad2('spam') except MyBad2 as X: print(X) # spam print(X.args) # ('spam',) raise MyBad('spam') # __main__.MyBad2: My mistake! # raise MyBad2('spam') # __main__.MyBad2: spam

20.107143

65

0.648313

76

563

4.434211

0.407895

0.071217

0.083086

0.053412

0

0.013699

0.222025

563

28

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20.107143

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0.117647

false

0

0.117647

0.352941

0.235294

0

null

0

null

0

1

0

1

9abd5d0a8f6f8a824f776810d4a5b66aeca261fa

650

py

Python

lambda-sfn-terraform/src/LambdaFunction.py

extremenelson/serverless-patterns

c307599ab2759567c581c37d70561e85b0fa8788

[ "MIT-0" ]

1

2022-01-12T17:22:02.000Z

lambda-sfn-terraform/src/LambdaFunction.py

extremenelson/serverless-patterns

c307599ab2759567c581c37d70561e85b0fa8788

[ "MIT-0" ]

null

lambda-sfn-terraform/src/LambdaFunction.py

extremenelson/serverless-patterns

c307599ab2759567c581c37d70561e85b0fa8788

[ "MIT-0" ]

null

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: MIT-0 import json import boto3 import os from aws_lambda_powertools import Logger logger = Logger() client = boto3.client('stepfunctions') sfnArn = os.environ['SFN_ARN'] def lambda_handler(event, context): # TODO implement logger.info(f"Received Choice: {event['Choice']}") response = client.start_execution( stateMachineArn=sfnArn, input=json.dumps(event) ) logger.info(f"Received Response: {response}") return { 'statusCode': 200, 'body': json.dumps(response,default=str) }

23.214286

68

0.676923

77

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0.055172

0.050575

0.087356

0

0.01165

0.207692

650

27

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24.074074

0.83301

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false

0

0.222222

0

0.333333

0

null

0

null

0

1

0

1

9ac242f669af4d52c4d497c2811debd7113e2d03

691

py

Python

utils/pad.py

Zenodia/nativePytorch_NMT

bfced09eb6e5476d34619dfc0dd41d4ed610248f

[ "MIT" ]

60

2018-09-28T07:53:11.000Z

2020-11-06T11:59:07.000Z

utils/pad.py

Pravin74/transformer-pytorch

c31e163ed57321e405771ef7fb556d4d92fd5efb

[ "MIT" ]

2

2021-02-15T14:08:08.000Z

2021-09-12T12:52:37.000Z

utils/pad.py

Pravin74/transformer-pytorch

c31e163ed57321e405771ef7fb556d4d92fd5efb

[ "MIT" ]

18

2018-09-28T07:56:35.000Z

2020-11-24T00:11:33.000Z

import torch import numpy as np PAD_TOKEN_INDEX = 0 def pad_masking(x, target_len): # x: (batch_size, seq_len) batch_size, seq_len = x.size() padded_positions = x == PAD_TOKEN_INDEX # (batch_size, seq_len) pad_mask = padded_positions.unsqueeze(1).expand(batch_size, target_len, seq_len) return pad_mask def subsequent_masking(x): # x: (batch_size, seq_len - 1) batch_size, seq_len = x.size() subsequent_mask = np.triu(np.ones(shape=(seq_len, seq_len)), k=1).astype('uint8') subsequent_mask = torch.tensor(subsequent_mask).to(x.device) subsequent_mask = subsequent_mask.unsqueeze(0).expand(batch_size, seq_len, seq_len) return subsequent_mask

32.904762

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691

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0.193548

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691

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0.428571

0

null

0

1

0

null

0

1

9ac8a6eee2b79ed601b853802a3795b71f290223

5,558

py

Python

xen/xen-4.2.2/tools/python/scripts/test_vm_create.py

zhiming-shen/Xen-Blanket-NG

47e59d9bb92e8fdc60942df526790ddb983a5496

[ "Apache-2.0" ]

1

2018-02-02T00:15:26.000Z

xen/xen-4.2.2/tools/python/scripts/test_vm_create.py

zhiming-shen/Xen-Blanket-NG

47e59d9bb92e8fdc60942df526790ddb983a5496

[ "Apache-2.0" ]

null

xen/xen-4.2.2/tools/python/scripts/test_vm_create.py

zhiming-shen/Xen-Blanket-NG

47e59d9bb92e8fdc60942df526790ddb983a5496

[ "Apache-2.0" ]

1

2019-05-27T09:47:18.000Z

#!/usr/bin/python vm_cfg = { 'name_label': 'APIVM', 'user_version': 1, 'is_a_template': False, 'auto_power_on': False, # TODO 'memory_static_min': 64, 'memory_static_max': 128, #'memory_dynamic_min': 64, #'memory_dynamic_max': 128, 'VCPUs_policy': 'credit', 'VCPUs_params': '', 'VCPUs_number': 2, 'actions_after_shutdown': 'destroy', 'actions_after_reboot': 'restart', 'actions_after_crash': 'destroy', 'PV_bootloader': '', 'PV_bootloader_args': '', 'PV_kernel': '/boot/vmlinuz-2.6.18-xenU', 'PV_ramdisk': '', 'PV_args': 'root=/dev/sda1 ro', #'HVM_boot': '', 'platform_std_VGA': False, 'platform_serial': '', 'platform_localtime': False, 'platform_clock_offset': False, 'platform_enable_audio': False, 'PCI_bus': '' } vdi_cfg = { 'name_label': 'API_VDI', 'name_description': '', 'virtual_size': 100 * 1024 * 1024 * 1024, 'type': 'system', 'parent': '', 'SR_name': 'QCoW', 'sharable': False, 'read_only': False, } vbd_cfg = { 'VDI': '', 'VM': '', 'device': 'sda2', 'mode': 'RW', 'type': 'disk', 'driver': 'paravirtualised', } local_vdi_cfg = { 'name_label': 'gentoo.amd64.img', 'name_description': '', 'virtual_size': 0, 'type': 'system', 'parent': '', 'SR_name': 'Local', 'sharable': False, 'read_only': False, 'other_config': {'location': 'file:/root/gentoo.amd64.img'}, } local_vbd_cfg = { 'VDI': '', 'VM': '', 'device': 'sda1', 'mode': 'RW', 'type': 'disk', 'driver': 'paravirtualised', } vif_cfg = { 'name': 'API_VIF', 'type': 'paravirtualised', 'device': '', 'network': '', 'MAC': '', 'MTU': 1500, } console_cfg = { 'protocol': 'rfb', 'other_config': {'vncunused': 1, 'vncpasswd': 'testing'}, } import sys import time from xapi import connect, execute def test_vm_create(): server, session = connect() vm_uuid = None vdi_uuid = None local_vdi_uuid = None local_vbd_uuid = None vbd_uuid = None vif_uuid = None # List all VMs vm_list = execute(server, 'VM.get_all', (session,)) vm_names = [] for vm_uuid in vm_list: vm_record = execute(server, 'VM.get_record', (session, vm_uuid)) vm_names.append(vm_record['name_label']) # Get default SR sr_list = execute(server, 'SR.get_by_name_label', (session, vdi_cfg['SR_name'])) sr_uuid = sr_list[0] local_sr_list = execute(server, 'SR.get_by_name_label', (session, local_vdi_cfg['SR_name'])) local_sr_uuid = local_sr_list[0] # Get default network net_list = execute(server, 'network.get_all', (session,)) net_uuid = net_list[0] try: # Create a new VM vm_uuid = execute(server, 'VM.create', (session, vm_cfg)) # Create a new VDI vdi_cfg['SR'] = sr_uuid vdi_uuid = execute(server, 'VDI.create', (session, vdi_cfg)) # Create a VDI backed VBD vbd_cfg['VM'] = vm_uuid vbd_cfg['VDI'] = vdi_uuid vbd_uuid = execute(server, 'VBD.create', (session, vbd_cfg)) # Create a new VDI (Local) local_vdi_cfg['SR'] = local_sr_uuid local_vdi_uuid = execute(server, 'VDI.create', (session, local_vdi_cfg)) # Create a new VBD (Local) local_vbd_cfg['VM'] = vm_uuid local_vbd_cfg['VDI'] = local_vdi_uuid local_vbd_uuid = execute(server, 'VBD.create', (session, local_vbd_cfg)) # Create a new VIF vif_cfg['network'] = net_uuid vif_cfg['VM'] = vm_uuid vif_uuid = execute(server, 'VIF.create', (session, vif_cfg)) # Create a console console_cfg['VM'] = vm_uuid console_uuid = execute(server, 'console.create', (session, console_cfg)) print console_uuid # Start the VM execute(server, 'VM.start', (session, vm_uuid, False)) time.sleep(30) test_suspend = False if test_suspend: print 'Suspending VM..' execute(server, 'VM.suspend', (session, vm_uuid)) print 'Suspended VM.' time.sleep(5) print 'Resuming VM ...' execute(server, 'VM.resume', (session, vm_uuid, False)) print 'Resumed VM.' finally: # Wait for user to say we're good to shut it down while True: destroy = raw_input('destroy VM? ') if destroy[0] in ('y', 'Y'): break # Clean up if vif_uuid: execute(server, 'VIF.destroy', (session, vif_uuid)) if local_vbd_uuid: execute(server, 'VBD.destroy', (session, local_vbd_uuid)) if local_vdi_uuid: execute(server, 'VDI.destroy', (session, local_vdi_uuid)) if vbd_uuid: execute(server, 'VBD.destroy', (session, vbd_uuid)) if vdi_uuid: execute(server, 'VDI.destroy', (session, vdi_uuid)) if vm_uuid: try: execute(server, 'VM.hard_shutdown', (session, vm_uuid)) time.sleep(2) except: pass execute(server, 'VM.destroy', (session, vm_uuid)) if __name__ == "__main__": test_vm_create()

26.216981

75

0.542821

649

5,558

4.366718

0.265023

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0.071983

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0

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5,558

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0

0.004739

0

null

0.012987

0.019481

null

0.032468

0

null

0

null

0

1

0

1

9ac99cea9babd92f880b3baa9bf72af575865d84

31,044

py

Python

gomill/mcts_tuners.py

BenisonSam/goprime

3613f643ee765b4ad48ebdc27bd9f1121b1c5298

[ "MIT" ]

null

gomill/mcts_tuners.py

BenisonSam/goprime

3613f643ee765b4ad48ebdc27bd9f1121b1c5298

[ "MIT" ]

null

gomill/mcts_tuners.py

BenisonSam/goprime

3613f643ee765b4ad48ebdc27bd9f1121b1c5298

[ "MIT" ]

null

"""Competitions for parameter tuning using Monte-carlo tree search.""" from __future__ import division import operator import random from heapq import nlargest from math import exp, log, sqrt from gomill import compact_tracebacks from gomill import game_jobs from gomill import competitions from gomill import competition_schedulers from gomill.competitions import ( Competition, NoGameAvailable, CompetitionError, ControlFileError, Player_config) from gomill.settings import * class Node(object): """A MCTS node. Public attributes: children -- list of Nodes, or None for unexpanded wins visits value -- wins / visits rsqrt_visits -- 1 / sqrt(visits) """ def count_tree_size(self): if self.children is None: return 1 return sum(child.count_tree_size() for child in self.children) + 1 def recalculate(self): """Update value and rsqrt_visits from changed wins and visits.""" self.value = self.wins / self.visits self.rsqrt_visits = sqrt(1 / self.visits) def __getstate__(self): return (self.children, self.wins, self.visits) def __setstate__(self, state): self.children, self.wins, self.visits = state self.recalculate() __slots__ = ( 'children', 'wins', 'visits', 'value', 'rsqrt_visits', ) def __repr__(self): return "<Node:%.2f{%s}>" % (self.value, repr(self.children)) class Tree(object): """A tree of MCTS nodes representing N-dimensional parameter space. Parameters (available as read-only attributes): splits -- subdivisions of each dimension (list of integers, one per dimension) max_depth -- number of generations below the root initial_visits -- visit count for newly-created nodes initial_wins -- win count for newly-created nodes exploration_coefficient -- constant for UCT formula (float) Public attributes: root -- Node dimensions -- number of dimensions in the parameter space All changing state is in the tree of Node objects started at 'root'. References to 'optimiser_parameters' below mean a sequence of length 'dimensions', whose values are floats in the range 0.0..1.0 representing a point in this space. Each node in the tree represents an N-cuboid of parameter space. Each expanded node has prod(splits) children, tiling its cuboid. (The splits are the same in each generation.) Instantiate with: all parameters listed above parameter_formatter -- function optimiser_parameters -> string """ def __init__(self, splits, max_depth, exploration_coefficient, initial_visits, initial_wins, parameter_formatter): self.splits = splits self.dimensions = len(splits) self.branching_factor = reduce(operator.mul, splits) self.max_depth = max_depth self.exploration_coefficient = exploration_coefficient self.initial_visits = initial_visits self.initial_wins = initial_wins self._initial_value = initial_wins / initial_visits self._initial_rsqrt_visits = 1 / sqrt(initial_visits) self.format_parameters = parameter_formatter # map child index -> coordinate vector # coordinate vector -- tuple length 'dimensions' with values in # range(splits[d]) # The first dimension changes most slowly. self._cube_coordinates = [] for child_index in xrange(self.branching_factor): v = [] i = child_index for split in reversed(splits): i, coord = divmod(i, split) v.append(coord) v.reverse() self._cube_coordinates.append(tuple(v)) def new_root(self): """Initialise the tree with an expanded root node.""" self.node_count = 1 # For description only self.root = Node() self.root.children = None self.root.wins = self.initial_wins self.root.visits = self.initial_visits self.root.value = self.initial_wins / self.initial_visits self.root.rsqrt_visits = self._initial_rsqrt_visits self.expand(self.root) def set_root(self, node): """Use the specified node as the tree's root. This is used when restoring serialised state. Raises ValueError if the node doesn't have the expected number of children. """ if not node.children or len(node.children) != self.branching_factor: raise ValueError self.root = node self.node_count = node.count_tree_size() def expand(self, node): """Add children to the specified node.""" assert node.children is None node.children = [] child_count = self.branching_factor for _ in xrange(child_count): child = Node() child.children = None child.wins = self.initial_wins child.visits = self.initial_visits child.value = self._initial_value child.rsqrt_visits = self._initial_rsqrt_visits node.children.append(child) self.node_count += child_count def is_ripe(self, node): """Say whether a node has been visted enough times to be expanded.""" return node.visits != self.initial_visits def parameters_for_path(self, choice_path): """Retrieve the point in parameter space given by a node. choice_path -- sequence of child indices Returns optimiser_parameters representing the centre of the region of parameter space represented by the node of interest. choice_path must represent a path from the root to the node of interest. """ lo = [0.0] * self.dimensions breadths = [1.0] * self.dimensions for child_index in choice_path: cube_pos = self._cube_coordinates[child_index] breadths = [f / split for (f, split) in zip(breadths, self.splits)] for d, coord in enumerate(cube_pos): lo[d] += breadths[d] * coord return [f + .5 * breadth for (f, breadth) in zip(lo, breadths)] def retrieve_best_parameters(self): """Find the parameters with the most promising simulation results. Returns optimiser_parameters This walks the tree from the root, at each point choosing the node with most wins, and returns the parameters corresponding to the leaf node. """ simulation = self.retrieve_best_parameter_simulation() return simulation.get_parameters() def retrieve_best_parameter_simulation(self): """Return the Greedy_simulation used for retrieve_best_parameters.""" simulation = Greedy_simulation(self) simulation.walk() return simulation def get_test_parameters(self): """Return a 'typical' optimiser_parameters.""" return self.parameters_for_path([0]) def describe_choice(self, choice): """Return a string describing a child's coordinates in its parent.""" return str(self._cube_coordinates[choice]).replace(" ", "") def describe(self): """Return a text description of the current state of the tree. This currently dumps the full tree to depth 2. """ def describe_node(node, choice_path): parameters = self.format_parameters( self.parameters_for_path(choice_path)) choice_s = self.describe_choice(choice_path[-1]) return "%s %s %.3f %3d" % ( choice_s, parameters, node.value, node.visits - self.initial_visits) root = self.root wins = root.wins - self.initial_wins visits = root.visits - self.initial_visits try: win_rate = "%.3f" % (wins / visits) except ZeroDivisionError: win_rate = "--" result = [ "%d nodes" % self.node_count, "Win rate %d/%d = %s" % (wins, visits, win_rate) ] for choice, node in enumerate(self.root.children): result.append(" " + describe_node(node, [choice])) if node.children is None: continue for choice2, node2 in enumerate(node.children): result.append(" " + describe_node(node2, [choice, choice2])) return "\n".join(result) def summarise(self, out, summary_spec): """Write a summary of the most-visited parts of the tree. out -- writeable file-like object summary_spec -- list of ints summary_spec says how many nodes to describe at each depth of the tree (so to show only direct children of the root, pass a list of length 1). """ def p(s): print >> out, s def describe_node(node, choice_path): parameters = self.format_parameters( self.parameters_for_path(choice_path)) choice_s = " ".join(map(self.describe_choice, choice_path)) return "%s %-40s %.3f %3d" % ( choice_s, parameters, node.value, node.visits - self.initial_visits) def most_visits((child_index, node)): return node.visits last_generation = [([], self.root)] for i, n in enumerate(summary_spec): depth = i + 1 p("most visited at depth %s" % (depth)) this_generation = [] for path, node in last_generation: if node.children is not None: this_generation += [ (path + [child_index], child) for (child_index, child) in enumerate(node.children)] for path, node in sorted( nlargest(n, this_generation, key=most_visits)): p(describe_node(node, path)) last_generation = this_generation p("") class Simulation(object): """A single monte-carlo simulation. Instantiate with the Tree the simulation will run in. Use the methods in the following order: run() get_parameters() update_stats(b) describe() """ def __init__(self, tree): self.tree = tree # list of Nodes self.node_path = [] # corresponding list of child indices self.choice_path = [] # bool self.candidate_won = None def _choose_action(self, node): """Choose the best action from the specified node. Returns a pair (child index, node) """ uct_numerator = (self.tree.exploration_coefficient * sqrt(log(node.visits))) def urgency((i, child)): return child.value + uct_numerator * child.rsqrt_visits start = random.randrange(len(node.children)) children = list(enumerate(node.children)) return max(children[start:] + children[:start], key=urgency) def walk(self): """Choose a node sequence, without expansion.""" node = self.tree.root while node.children is not None: choice, node = self._choose_action(node) self.node_path.append(node) self.choice_path.append(choice) def run(self): """Choose the node sequence for this simulation. This walks down from the root, using _choose_action() at each level, until it reaches a leaf; if the leaf has already been visited, this expands it and chooses one more action. """ self.walk() node = self.node_path[-1] if (len(self.node_path) < self.tree.max_depth and self.tree.is_ripe(node)): self.tree.expand(node) choice, child = self._choose_action(node) self.node_path.append(child) self.choice_path.append(choice) def get_parameters(self): """Retrieve the parameters corresponding to the simulation's leaf node. Returns optimiser_parameters """ return self.tree.parameters_for_path(self.choice_path) def update_stats(self, candidate_won): """Update the tree's node statistics with the simulation's results. This updates visits (and wins, if appropriate) for each node in the simulation's node sequence. """ self.candidate_won = candidate_won for node in self.node_path: node.visits += 1 if candidate_won: node.wins += 1 node.recalculate() self.tree.root.visits += 1 if candidate_won: self.tree.root.wins += 1 # For description only self.tree.root.recalculate() def describe_steps(self): """Return a text description of the simulation's node sequence.""" return " ".join(map(self.tree.describe_choice, self.choice_path)) def describe(self): """Return a one-line-ish text description of the simulation.""" result = "%s [%s]" % ( self.tree.format_parameters(self.get_parameters()), self.describe_steps()) if self.candidate_won is not None: result += (" lost", " won")[self.candidate_won] return result def describe_briefly(self): """Return a shorter description of the simulation.""" return "%s %s" % (self.tree.format_parameters(self.get_parameters()), ("lost", "won")[self.candidate_won]) class Greedy_simulation(Simulation): """Variant of simulation that chooses the node with most wins. This is used to pick the 'best' parameters from the current state of the tree. """ def _choose_action(self, node): def wins((i, node)): return node.wins return max(enumerate(node.children), key=wins) parameter_settings = [ Setting('code', interpret_identifier), Setting('scale', interpret_callable), Setting('split', interpret_positive_int), Setting('format', interpret_8bit_string, default=None), ] class Parameter_config(Quiet_config): """Parameter (ie, dimension) description for use in control files.""" # positional or keyword positional_arguments = ('code',) # keyword-only keyword_arguments = tuple(setting.name for setting in parameter_settings if setting.name != 'code') class Parameter_spec(object): """Internal description of a parameter spec from the configuration file. Public attributes: code -- identifier split -- integer scale -- function float(0.0..1.0) -> player parameter format -- string for use with '%' """ class Scale_fn(object): """Callable implementing a scale function. Scale_fn classes are used to provide a convenient way to describe scale functions in the control file (LINEAR, LOG, ...). """ class Linear_scale_fn(Scale_fn): """Linear scale function. Instantiate with lower_bound -- float upper_bound -- float integer -- bool (means 'round result to nearest integer') """ def __init__(self, lower_bound, upper_bound, integer=False): self.lower_bound = float(lower_bound) self.upper_bound = float(upper_bound) self.range = float(upper_bound - lower_bound) self.integer = bool(integer) def __call__(self, f): result = (f * self.range) + self.lower_bound if self.integer: result = int(result + .5) return result class Log_scale_fn(Scale_fn): """Log scale function. Instantiate with lower_bound -- float upper_bound -- float integer -- bool (means 'round result to nearest integer') """ def __init__(self, lower_bound, upper_bound, integer=False): if lower_bound == 0.0: raise ValueError("lower bound is zero") self.rate = log(upper_bound / lower_bound) self.lower_bound = lower_bound self.integer = bool(integer) def __call__(self, f): result = exp(self.rate * f) * self.lower_bound if self.integer: result = int(result + .5) return result class Explicit_scale_fn(Scale_fn): """Scale function that returns elements from a list. Instantiate with the list of values to use. Normally use this with 'split' equal to the length of the list (more generally, split**max_depth equal to the length of the list). """ def __init__(self, values): if not values: raise ValueError("empty value list") self.values = tuple(values) self.n = len(values) def __call__(self, f): return self.values[int(self.n * f)] class LINEAR(Config_proxy): underlying = Linear_scale_fn class LOG(Config_proxy): underlying = Log_scale_fn class EXPLICIT(Config_proxy): underlying = Explicit_scale_fn def interpret_candidate_colour(v): if v in ('r', 'random'): return 'random' else: return interpret_colour(v) class Mcts_tuner(Competition): """A Competition for parameter tuning using the Monte-carlo tree search. The game ids are strings containing integers starting from zero. """ def __init__(self, competition_code, **kwargs): Competition.__init__(self, competition_code, **kwargs) self.outstanding_simulations = {} self.halt_on_next_failure = True def control_file_globals(self): result = Competition.control_file_globals(self) result.update({ 'Parameter': Parameter_config, 'LINEAR': LINEAR, 'LOG': LOG, 'EXPLICIT': EXPLICIT, }) return result global_settings = (Competition.global_settings + competitions.game_settings + [ Setting('number_of_games', allow_none(interpret_int), default=None), Setting('candidate_colour', interpret_candidate_colour), Setting('log_tree_to_history_period', allow_none(interpret_positive_int), default=None), Setting('summary_spec', interpret_sequence_of(interpret_int), default=(30,)), Setting('number_of_running_simulations_to_show', interpret_int, default=12), ]) special_settings = [ Setting('opponent', interpret_identifier), Setting('parameters', interpret_sequence_of_quiet_configs(Parameter_config)), Setting('make_candidate', interpret_callable), ] # These are used to instantiate Tree; they don't turn into Mcts_tuner # attributes. tree_settings = [ Setting('max_depth', interpret_positive_int, default=1), Setting('exploration_coefficient', interpret_float), Setting('initial_visits', interpret_positive_int), Setting('initial_wins', interpret_positive_int), ] def parameter_spec_from_config(self, parameter_config): """Make a Parameter_spec from a Parameter_config. Raises ControlFileError if there is an error in the configuration. Returns a Parameter_spec with all attributes set. """ arguments = parameter_config.resolve_arguments() interpreted = load_settings(parameter_settings, arguments) pspec = Parameter_spec() for name, value in interpreted.iteritems(): setattr(pspec, name, value) optimiser_param = 1.0 / (pspec.split * 2) try: scaled = pspec.scale(optimiser_param) except Exception: raise ValueError( "error from scale (applied to %s)\n%s" % (optimiser_param, compact_tracebacks.format_traceback(skip=1))) if pspec.format is None: pspec.format = pspec.code + ":%s" try: pspec.format % scaled except Exception: raise ControlFileError("'format': invalid format string") return pspec def initialise_from_control_file(self, config): Competition.initialise_from_control_file(self, config) if self.komi == int(self.komi): raise ControlFileError("komi: must be fractional to prevent jigos") competitions.validate_handicap( self.handicap, self.handicap_style, self.board_size) try: specials = load_settings(self.special_settings, config) except ValueError, e: raise ControlFileError(str(e)) try: self.opponent = self.players[specials['opponent']] except KeyError: raise ControlFileError( "opponent: unknown player %s" % specials['opponent']) self.parameter_specs = [] if not specials['parameters']: raise ControlFileError("parameters: empty list") seen_codes = set() for i, parameter_spec in enumerate(specials['parameters']): try: pspec = self.parameter_spec_from_config(parameter_spec) except StandardError, e: code = parameter_spec.get_key() if code is None: code = i raise ControlFileError("parameter %s: %s" % (code, e)) if pspec.code in seen_codes: raise ControlFileError( "duplicate parameter code: %s" % pspec.code) seen_codes.add(pspec.code) self.parameter_specs.append(pspec) self.candidate_maker_fn = specials['make_candidate'] try: tree_arguments = load_settings(self.tree_settings, config) except ValueError, e: raise ControlFileError(str(e)) self.tree = Tree(splits=[pspec.split for pspec in self.parameter_specs], parameter_formatter=self.format_optimiser_parameters, **tree_arguments) # State attributes (*: in persistent state): # *scheduler -- Simple_scheduler # *tree -- Tree (root node is persisted) # outstanding_simulations -- map game_number -> Simulation # halt_on_next_failure -- bool # *opponent_description -- string (or None) def set_clean_status(self): self.scheduler = competition_schedulers.Simple_scheduler() self.tree.new_root() self.opponent_description = None # Can bump this to prevent people loading incompatible .status files. status_format_version = 0 def get_status(self): # path0 is stored for consistency check return { 'scheduler': self.scheduler, 'tree_root': self.tree.root, 'opponent_description': self.opponent_description, 'path0': self.scale_parameters(self.tree.parameters_for_path([0])), } def set_status(self, status): root = status['tree_root'] try: self.tree.set_root(root) except ValueError: raise CompetitionError( "status file is inconsistent with control file") expected_path0 = self.scale_parameters( self.tree.parameters_for_path([0])) if status['path0'] != expected_path0: raise CompetitionError( "status file is inconsistent with control file") self.scheduler = status['scheduler'] self.scheduler.rollback() self.opponent_description = status['opponent_description'] def scale_parameters(self, optimiser_parameters): l = [] for pspec, v in zip(self.parameter_specs, optimiser_parameters): try: l.append(pspec.scale(v)) except Exception: raise CompetitionError( "error from scale for %s\n%s" % (pspec.code, compact_tracebacks.format_traceback(skip=1))) return tuple(l) def format_engine_parameters(self, engine_parameters): l = [] for pspec, v in zip(self.parameter_specs, engine_parameters): try: s = pspec.format % v except Exception: s = "[%s?%s]" % (pspec.code, v) l.append(s) return "; ".join(l) def format_optimiser_parameters(self, optimiser_parameters): return self.format_engine_parameters(self.scale_parameters( optimiser_parameters)) def make_candidate(self, player_code, engine_parameters): """Make a player using the specified engine parameters. Returns a game_jobs.Player. """ try: candidate_config = self.candidate_maker_fn(*engine_parameters) except Exception: raise CompetitionError( "error from make_candidate()\n%s" % compact_tracebacks.format_traceback(skip=1)) if not isinstance(candidate_config, Player_config): raise CompetitionError( "make_candidate() returned %r, not Player" % candidate_config) try: candidate = self.game_jobs_player_from_config( player_code, candidate_config) except Exception, e: raise CompetitionError( "bad player spec from make_candidate():\n" "%s\nparameters were: %s" % (e, self.format_engine_parameters(engine_parameters))) return candidate def get_player_checks(self): test_parameters = self.tree.get_test_parameters() engine_parameters = self.scale_parameters(test_parameters) candidate = self.make_candidate('candidate', engine_parameters) result = [] for player in [candidate, self.opponent]: check = game_jobs.Player_check() check.player = player check.board_size = self.board_size check.komi = self.komi result.append(check) return result def choose_candidate_colour(self): if self.candidate_colour == 'random': return random.choice('bw') else: return self.candidate_colour def get_game(self): if (self.number_of_games is not None and self.scheduler.issued >= self.number_of_games): return NoGameAvailable game_number = self.scheduler.issue() simulation = Simulation(self.tree) simulation.run() optimiser_parameters = simulation.get_parameters() engine_parameters = self.scale_parameters(optimiser_parameters) candidate = self.make_candidate("#%d" % game_number, engine_parameters) self.outstanding_simulations[game_number] = simulation job = game_jobs.Game_job() job.game_id = str(game_number) job.game_data = game_number if self.choose_candidate_colour() == 'b': job.player_b = candidate job.player_w = self.opponent else: job.player_b = self.opponent job.player_w = candidate job.board_size = self.board_size job.komi = self.komi job.move_limit = self.move_limit job.handicap = self.handicap job.handicap_is_free = (self.handicap_style == 'free') job.use_internal_scorer = (self.scorer == 'internal') job.internal_scorer_handicap_compensation = \ self.internal_scorer_handicap_compensation job.sgf_event = self.competition_code job.sgf_note = ("Candidate parameters: %s" % self.format_engine_parameters(engine_parameters)) return job def process_game_result(self, response): self.halt_on_next_failure = False self.opponent_description = response.engine_descriptions[ self.opponent.code].get_long_description() game_number = response.game_data self.scheduler.fix(game_number) # Counting no-result as loss for the candidate candidate_won = ( response.game_result.losing_player == self.opponent.code) simulation = self.outstanding_simulations.pop(game_number) simulation.update_stats(candidate_won) self.log_history(simulation.describe()) if (self.log_tree_to_history_period is not None and self.scheduler.fixed % self.log_tree_to_history_period == 0): self.log_history(self.tree.describe()) return "%s %s" % (simulation.describe(), response.game_result.sgf_result) def process_game_error(self, job, previous_error_count): ## If the very first game to return a response gives an error, halt. ## If two games in a row give an error, halt. ## Otherwise, forget about the failed game stop_competition = False retry_game = False game_number = job.game_data del self.outstanding_simulations[game_number] self.scheduler.fix(game_number) if self.halt_on_next_failure: stop_competition = True else: self.halt_on_next_failure = True return stop_competition, retry_game def write_static_description(self, out): def p(s): print >> out, s p("MCTS tuning event: %s" % self.competition_code) if self.description: p(self.description) p("board size: %s" % self.board_size) p("komi: %s" % self.komi) def _write_main_report(self, out): games_played = self.scheduler.fixed if self.number_of_games is None: print >> out, "%d games played" % games_played else: print >> out, "%d/%d games played" % ( games_played, self.number_of_games) print >> out best_simulation = self.tree.retrieve_best_parameter_simulation() print >> out, "Best parameters: %s" % best_simulation.describe() print >> out self.tree.summarise(out, self.summary_spec) def write_screen_report(self, out): self._write_main_report(out) if self.outstanding_simulations: print >> out, "In progress:" to_show = sorted(self.outstanding_simulations.iteritems()) \ [:self.number_of_running_simulations_to_show] for game_id, simulation in to_show: print >> out, "game %s: %s" % (game_id, simulation.describe()) def write_short_report(self, out): self.write_static_description(out) self._write_main_report(out) if self.opponent_description: print >> out, "opponent (%s): %s" % ( self.opponent.code, self.opponent_description) else: print >> out, "opponent: %s" % self.opponent.code print >> out write_full_report = write_short_report

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null

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1

0

1

9acbd6e09016763ff8a75cf2e88c6a01d873ad9c

9,705

py

Python

endoscopic_ai.py

dennkitotaichi/AI_prediction_for_patients_with_colorectal_polyps

afbad36cb3fc2de31665fc3b0a7f065b7e6564a0

[ "MIT" ]

null

endoscopic_ai.py

dennkitotaichi/AI_prediction_for_patients_with_colorectal_polyps

afbad36cb3fc2de31665fc3b0a7f065b7e6564a0

[ "MIT" ]

null

endoscopic_ai.py

dennkitotaichi/AI_prediction_for_patients_with_colorectal_polyps

afbad36cb3fc2de31665fc3b0a7f065b7e6564a0

[ "MIT" ]

null

import pandas as pd import numpy as np import matplotlib.pyplot as plt #%matplotlib inline import codecs import lightgbm as lgb from sklearn.model_selection import StratifiedShuffleSplit from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score # Read data image_file_path = './simulated_dpc_data.csv' with codecs.open(image_file_path, "r", "Shift-JIS", "ignore") as file: dpc = pd.read_table(file, delimiter=",") # dpc_r, g_dpc_r_1, g_r: restricted data from dpc dpc_r=dpc.loc[:, ['ID','code']] # g_dpc_r_1: made to check the details (: name of the code, ‘name’) g_dpc_r_1=dpc.loc[:, ['ID','code','name']] # Dummy Encoding with ‘name’ g_r = pd.get_dummies(dpc_r['code']) # Reconstruct simulated data for AI learning df_concat_dpc_get_dummies = pd.concat([dpc_r, g_r], axis=1) # Remove features that may be the cause of the data leak dpc_Remove_data_leak = df_concat_dpc_get_dummies.drop(["code",160094710,160094810,160094910,150285010,2113008,8842965,8843014,622224401,810000000,160060010], axis=1) # Sum up the number of occurrences of each feature for each patient. total_patient_features= dpc_Remove_data_leak.groupby("ID").sum() total_patient_features.reset_index() # Load a new file with ID and treatment availability # Prepare training data image_file_path_ID_and_polyp_pn = './simulated_patient_data.csv' with codecs.open(image_file_path_ID_and_polyp_pn, "r", "Shift-JIS", "ignore") as file: ID_and_polyp_pn = pd.read_table(file, delimiter=",") ID_and_polyp_pn_data= ID_and_polyp_pn[['ID', 'target']] #Combine the new file containing ID and treatment status with the file after dummy encoding by the ‘name’ ID_treatment_medical_statement=pd.merge(ID_and_polyp_pn_data,total_patient_features,on=["ID"],how='outer') ID_treatment_medical_statement_o= ID_treatment_medical_statement.fillna(0) ID_treatment_medical_statement_p=ID_treatment_medical_statement_o.drop("ID", axis=1) ID_treatment_medical_statement_rename= ID_treatment_medical_statement_p.rename(columns={'code':"Receipt type code"}) merge_data= ID_treatment_medical_statement_rename # Split the training/validation set into 80% and the test set into 20%, with a constant proportion of cases with lesions X = merge_data.drop("target",axis=1).values y = merge_data["target"].values columns_name = merge_data.drop("target",axis=1).columns sss = StratifiedShuffleSplit(n_splits=1, test_size=0.2,random_state=1) # Create a function to divide data def data_split(X,y): for train_index, test_index in sss.split(X, y): X_train, X_test = X[train_index], X[test_index] y_train, y_test = y[train_index], y[test_index] X_train = pd.DataFrame(X_train, columns=columns_name) X_test = pd.DataFrame(X_test, columns=columns_name) return X_train, y_train, X_test, y_test # Separate into training, validation, and test set X_train, y_train, X_test, y_test = data_split(X, y) X_train, y_train, X_val, y_val = data_split(X_train.values, y_train) # Make test set into pandas X_test_df = pd.DataFrame(X_test) y_test_df = pd.DataFrame(y_test) # Make test set into test_df to keep away for the final process test_dfp = pd.concat([y_test_df,X_test_df], axis=1) test_df=test_dfp.rename(columns={0:"target"}) # Make training/validation sets into pandas y_trainp = pd.DataFrame(y_train) X_trainp = pd.DataFrame(X_train) train=pd.concat([y_trainp, X_trainp], axis=1) y_valp = pd.DataFrame(y_val) X_valp = pd.DataFrame(X_val) val=pd.concat([y_valp, X_valp], axis=1) test_vol=pd.concat([train, val]) training_validation_sets=test_vol.rename(columns={0:"target"}) # Create a function to save the results and feature importance after analysis with lightGBM def reg_top10_lightGBM(merge_data,outname,no,random_state_number): # Define the objective variable X = merge_data.drop("target",axis=1).values y = merge_data["target"].values columns_name = merge_data.drop("target",axis=1).columns # Define a function sss = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=random_state_number) def data_split(X,y): for train_index, test_index in sss.split(X, y): X_train, X_test = X[train_index], X[test_index] y_train, y_test = y[train_index], y[test_index] X_train = pd.DataFrame(X_train, columns=columns_name) X_test = pd.DataFrame(X_test, columns=columns_name) return X_train, y_train, X_test, y_test X_train, y_train, X_test, y_test = data_split(X, y) X_train, y_train, X_val, y_val = data_split(X_train.values, y_train) y_test_df = pd.DataFrame(y_test) # Prepare dataset: training data: X_train, label: y_train train = lgb.Dataset(X_train, label=y_train) valid = lgb.Dataset(X_val, label=y_val) # Set the parameters params = {'task': 'train', 'boosting_type': 'gbdt', 'objective': 'regression', 'metric': 'rmse', 'learning_rate': 0.1 } # Train the model model = lgb.train(params, train, valid_sets=valid, num_boost_round=3000, early_stopping_rounds=100) # Prediction y_pred = model.predict(X_test, num_iteration=model.best_iteration) # Display actual values and predicted values df_pred = pd.DataFrame({'regression_y_test':y_test,'regression_y_pred':y_pred}) # Calculate MSE (Mean Square Error) mse = mean_squared_error(y_test, y_pred) # Calculate RSME = √MSE rmse = np.sqrt(mse) # r2 : Calculate the coefficient of determination r2 = r2_score(y_test,y_pred) df_Df = pd.DataFrame({'regression_y_test_'+no:y_test,'regression_y_pred_'+no:y_pred,'RMSE_'+no:rmse,'R2_'+no:r2}) df_Df.to_csv(r""+"./"+outname+no+'.csv', encoding = 'shift-jis') importance = pd.DataFrame(model.feature_importance(), columns=['importance']) column_list=merge_data.drop(["target"], axis=1) importance["columns"] =list(column_list.columns) return importance # Find out Top 50 features procedure / Run the model once importance = reg_top10_lightGBM(training_validation_sets,"check_data","_1",1) # Create a function that sorts and stores the values of feature importance. def after_imp_save_sort(importance,outname,no): importance.sort_values(by='importance',ascending=False) i_df=importance.sort_values(by='importance',ascending=False) top50=i_df.iloc[0:51,:] g_dpc_pre= g_dpc_r_1.drop(["ID"], axis=1) g_dpc_Remove_duplicates=g_dpc_pre.drop_duplicates() g_dpc_r_columns=g_dpc_Remove_duplicates.rename(columns={'code':"columns"}) importance_name=pd.merge(top50,g_dpc_r_columns) importance_all=pd.merge(i_df,g_dpc_r_columns) importance_all.to_csv(r""+"./"+outname+no+'importance_name_all'+'.csv', encoding = 'shift-jis') return importance_all # Run a function to sort and save the values of feature importance. top50_importance_all = after_imp_save_sort(importance,"check_data","_1") # 10 runs of this procedure dict = {} for num in range(10): print(num+1) importance = reg_top10_lightGBM(training_validation_sets,"check_data","_"+str(num+1),num+1) top50_importance_all = after_imp_save_sort(importance,"check_data","_"+str(num+1)) dict[str(num)] = top50_importance_all # Recall and merge the saved CSV files def concat_importance(First_pd,Next_pd): importance_1=pd.DataFrame(dict[First_pd]) importance_1d=importance_1.drop_duplicates(subset='columns') importance_2=pd.DataFrame(dict[Next_pd]) importance_2d=importance_2.drop_duplicates(subset='columns') importance_1_2=pd.concat([importance_1d, importance_2d]) return importance_1_2 importance_1_2 = concat_importance("0","1") importance_3_4 = concat_importance("2","3") importance_5_6 = concat_importance("4","5") importance_7_8 = concat_importance("6","7") importance_9_10 = concat_importance("8","9") importance_1_4=pd.concat([importance_1_2, importance_3_4]) importance_1_6=pd.concat([importance_1_4, importance_5_6]) importance_1_8=pd.concat([importance_1_6, importance_7_8]) importance_1_10=pd.concat([importance_1_8, importance_9_10]) # Calculate the total value of the feature importance for each code group_sum=importance_1_10.groupby(["columns"]).sum() group_sum_s = group_sum.sort_values('importance', ascending=False) importance_group_sum=group_sum_s.reset_index() # Create train/validation test data with all features merge_data_test=pd.concat([training_validation_sets, test_df]) # Make features in the order of highest total feature impotance value importance_top50_previous_data=importance_group_sum["columns"] importance_top50_previous_data # refine the data to top 50 features dict_top50 = {} pycaret_dict_top50 = {} X = range(1, 51) for i,v in enumerate(X): dict_top50[str(i)] = importance_top50_previous_data.iloc[v] pycaret_dict_top50[importance_top50_previous_data[i]] = merge_data_test[dict_top50[str(i)]] pycaret_df_dict_top50=pd.DataFrame(pycaret_dict_top50) # Add the value of target (: objective variable) target_data=merge_data_test["target"] target_top50_dataframe=pd.concat([target_data, pycaret_df_dict_top50], axis=1) # adjust pandas (pycaret needs to set “str” to “int”) target_top50_dataframe_int=target_top50_dataframe.astype('int') target_top50_dataframe_columns=target_top50_dataframe_int.columns.astype(str) numpy_target_top50=target_top50_dataframe_int.to_numpy() target_top50_dataframe_pycaret=pd.DataFrame(numpy_target_top50,columns=target_top50_dataframe_columns) # compare the models from pycaret.classification import * clf1 = setup(target_top50_dataframe_pycaret, target ='target',train_size = 0.8,data_split_shuffle=False,fold=10,session_id=0) best_model = compare_models()

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0.757651

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9,705

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null

0

1

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1

9acd3d20a14d9e96bec466426e861a98197f22b0

330

py

Python

src/the_impossible/live/migrations/newsletter/migrations/0002_auto_20200514_1518.py

micha31r/The-Impossible

7a79dea3169907eb93107107f4003c5813de58dc

[ "MIT" ]

null

src/the_impossible/live/migrations/newsletter/migrations/0002_auto_20200514_1518.py

micha31r/The-Impossible

7a79dea3169907eb93107107f4003c5813de58dc

[ "MIT" ]

2

2020-04-15T03:57:42.000Z

2020-06-06T01:43:34.000Z

src/the_impossible/live/migrations/newsletter/migrations/0002_auto_20200514_1518.py

micha31r/The-Impossible

7a79dea3169907eb93107107f4003c5813de58dc

[ "MIT" ]

null

# Generated by Django 2.2.7 on 2020-05-14 03:18 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('newsletter', '0001_initial'), ] operations = [ migrations.RenameModel( old_name='Newsletter', new_name='Subscriber', ), ]

18.333333

47

0.593939

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330

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null

0

1

0

null

0

1

9ad11bb35b11a89ca5873c299ffa8f65fee28a06

3,694

py

Python

test/test_contacts_info_from_main_page.py

OlgaZtv/python_training

661165613ef4b9545345a8a2c61a894571ded703

[ "Apache-2.0" ]

null

test/test_contacts_info_from_main_page.py

OlgaZtv/python_training

661165613ef4b9545345a8a2c61a894571ded703

[ "Apache-2.0" ]

null

test/test_contacts_info_from_main_page.py

OlgaZtv/python_training

661165613ef4b9545345a8a2c61a894571ded703

[ "Apache-2.0" ]

null

import re from model.contact import Contact def test_contact_info_from_home_page(app, db): app.navigation.open_home_page() contact_from_home_page = sorted(app.contact.get_contact_list(), key=Contact.id_or_max) def clean(contact): return Contact(id=contact.id, firstname=contact.firstname.strip(), lastname=contact.lastname.strip(), address=contact.address.strip(), home=contact.home, mobile=contact.mobile, phone2=contact.phone2, email=contact.email, email2=contact.email2, email3=contact.email3) contact_from_db_list = list(map(clean, db.get_contact_list())) print("Contacts_from_home_page>>>>", contact_from_home_page) print("Contacts_from_DB>>>>", contact_from_db_list) i = 0 for item in contact_from_home_page: assert item.address == contact_from_db_list[i].address assert item.lastname == contact_from_db_list[i].lastname.strip() assert item.firstname == contact_from_db_list[i].firstname.strip() assert item.all_phones_from_home_page == merge_phones_like_on_home_page(contact_from_db_list[i]) assert item.all_emails_from_home_page == merge_emails_like_on_home_page(contact_from_db_list[i]) i += 1 def clear(s): return re.sub("[() -]", "", s) def merge_phones_like_on_home_page(contact): return "\n".join(filter(lambda x: x != "", map(lambda x: clear(x), filter(lambda x: x is not None, [contact.home, contact.mobile, contact.work, contact.phone2])))) def merge_emails_like_on_home_page(contact): return "\n".join(filter(lambda x: x != "", map(lambda x: clear(x), filter(lambda x: x is not None, [contact.email, contact.email2, contact.email3])))) # def test_contacts(app, ormdb): # random_index = randrange(app.contact.count()) # # взять все контакты с главной страницы # contact_from_home_page = app.contact.get_contact_list() # # взять все записи конатктов из бд # contact_from_db = ormdb.get_contact_list() # # сравниваем списки, сортируя # assert sorted(contact_from_home_page, key=Contact.id_or_max) == sorted(contact_from_db, key=Contact.id_or_max) # def test_contact_info_on_main_page(app): # if app.contact.amount() == 0: # app.contact.create( # Contact(firstname="TestTest", middlename="Test", lastname="Testing", nickname="testing", # title="test", company="Test test", address="Spb", home="000222111", # mobile="444555222", work="99966655", fax="11122255", email="test@tesr.ru", # email2="test2@test.ru", email3="test3@test.ru", homepage="www.test.ru", bday="15", # bmonth="May", byear="1985", aday="14", amonth="June", ayear="1985", # address2="Spb", phone2="111111", notes="Friend")) # random_index = randrange(app.contact.amount()) # contact_from_home_page = app.contact.get_contact_list()[random_index] # contact_from_edit_page = app.contact.get_contact_info_from_edit_page(random_index) # assert contact_from_home_page.all_phones_from_home_page == merge_phones_like_on_home_page(contact_from_edit_page) # assert contact_from_home_page.firstname == contact_from_edit_page.firstname # assert contact_from_home_page.lastname == contact_from_edit_page.lastname # assert contact_from_home_page.address == contact_from_edit_page.address # assert contact_from_home_page.all_emails_from_home_page == merge_emails_like_on_home_page(contact_from_edit_page)

52.028169

119

0.67542

493

3,694

4.726166

0.235294

0.122747

0.087554

0.0897

0.414592

0.297854

0.236052

0.226609

0.219742

0.176824

0

0.023858

0.205739

3,694

70

120

52.771429

0.770279

0.472658

0

0.125

0

0.029703

0.01407

0

0.15625

1

0.15625

false

0

0.0625

0.125

0.34375

0.0625

0

null

0

null

0

1

0

1

9ad242baf7204452ac38c08eb06958775483a1b5

1,790

py

Python

benchmark.py

raonyguimaraes/machinelearning

03b18e5c69931c4ee2ea4803de72c846aba97bce

[ "MIT" ]

1

2016-10-23T19:45:12.000Z

benchmark.py

raonyguimaraes/machinelearning

03b18e5c69931c4ee2ea4803de72c846aba97bce

[ "MIT" ]

null

benchmark.py

raonyguimaraes/machinelearning

03b18e5c69931c4ee2ea4803de72c846aba97bce

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # Writing Our First Classifier - Machine Learning Recipes #5 #https://www.youtube.com/watch?v=AoeEHqVSNOw&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal&index=1 from scipy.spatial import distance from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score from sklearn import datasets from sklearn.cross_validation import train_test_split import numpy as np def euc(a,b): return distance.euclidean(a,b) class ScrappyKNN(): def fit(self, X_train, y_train): self.X_train = X_train self.y_train = y_train def predict(self, X_test): predictions = [] for row in X_test: label = self.closest(row) predictions.append(label) return predictions def closest(self, row): best_dist = euc(row, self.X_train[0]) best_index = 0 for i in range(1,len(self.X_train)): dist = euc(row, self.X_train[i]) if dist < best_dist: best_dist = dist best_index = i return self.y_train[best_index] iris = datasets.load_iris() X = iris.data y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .5) # from sklearn.neighbors import KNeighborsClassifier my_classifier = ScrappyKNN() my_classifier_sklearn = KNeighborsClassifier() accuracies = [] for i in range (0,1000): my_classifier.fit(X_train, y_train) predictions = my_classifier.predict(X_test) accuracy = accuracy_score(y_test, predictions) accuracies.append(accuracy) print 'ScrappyKNN accuracy mean:', np.mean(accuracies) accuracies = [] for i in range (0,1000): my_classifier_sklearn.fit(X_train, y_train) predictions = my_classifier_sklearn.predict(X_test) accuracy = accuracy_score(y_test, predictions) accuracies.append(accuracy) print 'sklearn accuracy mean:', np.mean(accuracies)

24.189189

92

0.754749

266

1,790

4.890977

0.315789

0.041507

0.038432

0.027671

0.381245

0.267487

0.236741

0.178324

0.119908

0

0.013012

0.141341

1,790

74

93

24.189189

0.833442

0.135196

0

0.170213

0

0.03046

0

null

0

0.12766

null

0.042553

0

null

0

null

0

1

0

1

9ad3d0b300ea5b2d36712d2ed1f19a77b925f25f

383

py

Python

plaintext_password/checks.py

bryanwills/django-plaintext-password

752cf0316cdc45dc9bed5f9107614881d613647f

[ "MIT" ]

null

plaintext_password/checks.py

bryanwills/django-plaintext-password

752cf0316cdc45dc9bed5f9107614881d613647f

[ "MIT" ]

null

plaintext_password/checks.py

bryanwills/django-plaintext-password

752cf0316cdc45dc9bed5f9107614881d613647f

[ "MIT" ]

2

2021-04-23T08:24:08.000Z

2022-03-01T06:56:33.000Z

from django.contrib.auth.hashers import get_hashers_by_algorithm from django.core import checks @checks.register(checks.Tags.security, deploy=True) def check_for_plaintext_passwords(app_configs, **kwargs): if "plaintext" in get_hashers_by_algorithm(): yield checks.Critical( "Plaintext module should not be used in production.", hint="Remove it." )

34.818182

83

0.744125

51

383

5.392157

0.72549

0.072727

0.087273

0.152727

0

0.167102

383

10

84

38.3

0.862069

0

0.180157

0

1

0.125

false

0.125

0.25

0

0.375

0

null

0

null

0

1

0

1

9ad633a8b545c9fd60433dd7e1485b51abf58bfc

1,265

py

Python

app/user/models.py

briankaemingk/streaks-with-todoist

c6cbc982fbedafce04e9f23af7422e996513c8bb

[ "MIT" ]

3

2019-08-06T19:04:32.000Z

2022-01-19T14:00:12.000Z

app/user/models.py

briankaemingk/streaks-with-todoist

c6cbc982fbedafce04e9f23af7422e996513c8bb

[ "MIT" ]

6

2018-10-14T21:32:58.000Z

2021-03-20T00:07:56.000Z

app/user/models.py

briankaemingk/streaks-with-todoist

c6cbc982fbedafce04e9f23af7422e996513c8bb

[ "MIT" ]

null

from app.extensions import db from flask import current_app class User(db.Model): __tablename__ = 'users' id = db.Column(db.Integer, primary_key=True) access_token = db.Column(db.String()) jit_feature = db.Column(db.Boolean()) recurrence_resch_feature = db.Column(db.Boolean()) streaks_feature = db.Column(db.Boolean()) in_line_comment_feature = db.Column(db.Boolean()) def __init__(self, id, access_token, jit_feature, recurrence_resch_feature, streaks_feature, in_line_comment_feature): self.id = id self.access_token = access_token self.jit_feature = jit_feature self.recurrence_resch_feature = recurrence_resch_feature self.streaks_feature = streaks_feature self.in_line_comment_feature = in_line_comment_feature def __repr__(self): return '<id {}, access token {}, jit feature {}, recurrence resch feature {}, streaks feature {}, in-line comment feature {}>'.\ format(self.id, self.access_token, self.jit_feature, self.recurrence_resch_feature, self.streaks_feature, self.in_line_comment_feature) def launch_task(self, name, description, *args, **kwargs): current_app.task_queue.enqueue('app.tasks.' + name, self.id, *args, **kwargs)

38.333333

147

0.714625

167

1,265

5.071856

0.269461

0.090909

0.070838

0.141677

0.604486

0.413223

0.276269

0.186541

0

0.177075

1,265

32

148

39.53125

0.813641

0

0.045455

0.104762

0

1

0.136364

false

0

0.090909

0.045455

0.636364

0

null

0

null

0

1

0

1

9ad672b90b5e5960648f597358159ab9f9c375ec

5,060

py

Python

Invaders/Displays/animation_display.py

JaredsGames/SpaceInvaders

8a0da236c97340c4a8a06e7dd68e4672f885d9e0

[ "MIT" ]

null

Invaders/Displays/animation_display.py

JaredsGames/SpaceInvaders

8a0da236c97340c4a8a06e7dd68e4672f885d9e0

[ "MIT" ]

null

Invaders/Displays/animation_display.py

JaredsGames/SpaceInvaders

8a0da236c97340c4a8a06e7dd68e4672f885d9e0

[ "MIT" ]

null

# Jared Dyreson # CPSC 386-01 # 2021-11-29 # jareddyreson@csu.fullerton.edu # @JaredDyreson # # Lab 00-04 # # Some filler text # """ This module contains the Intro display class """ import pygame import functools import sys import pathlib import typing import os import dataclasses import random from pprint import pprint as pp import time from Invaders.Dataclasses.point import Point from Invaders.Displays.display import Display from Invaders.UI.button import Button # from Invaders.Entities.cacodemon import Cacodemon # from Invaders.Entities.Entity import Entity from Invaders.Entities.enemy_matrix import EnemyMatrix # from Invaders.Entities.Player import Player from Invaders.Entities.Entity import Entity from Invaders.Dataclasses.direction import Direction # TODO : move this to its own respective module or something like that def absolute_file_paths(directory: pathlib.Path) -> typing.List[pathlib.Path]: """ List the contents of a directory with their absolute path @param directory: path where to look @return: typing.List[pathlib.Path] """ return [ pathlib.Path(os.path.abspath(os.path.join(dirpath, f))) for dirpath, _, filenames in os.walk(directory) for f in filenames ] class AnimationDisplay(Display): def __init__(self): super().__init__() self.break_from_draw = False self.entities = EnemyMatrix(5, 5, self._display_surface) self.main_player = Entity( self._display_surface, ["assets/rocket.png"], Point(550, 700) ) # self.main_player = Player(self._display_surface, [ # "assets/rocket.png"], Point(550, 700)) self.DRAW_NEXT_ENTITY = pygame.USEREVENT + 1 self.ENEMY_FIRE_INTERVAL = pygame.USEREVENT + 2 self.score, self.lives = 0, 3 self.score_label_position = Point(775, 20) self.lives_label_position = Point(775, 60) def draw(self) -> None: draw_loop = True pygame.time.set_timer(self.DRAW_NEXT_ENTITY, 300) pygame.time.set_timer(self.ENEMY_FIRE_INTERVAL, 2000) will_move = False enemy_group = pygame.sprite.Group() player_group = pygame.sprite.Group() enemy_laser_group = pygame.sprite.Group() player_group.add(self.main_player) # print(player_group) for x, row in enumerate(self.entities.matrix): for y, column in enumerate(row): enemy_group.add(column.entity) # FIXME while draw_loop and not self.break_from_draw: positions = self.entities.scan_column() # FIXME: this code is not working for event in pygame.event.get(): if event.type == pygame.QUIT: pygame.quit() sys.exit() elif event.type == self.DRAW_NEXT_ENTITY: self._display_surface.fill(pygame.Color("black")) enemy_group.update(1) elif event.type == self.ENEMY_FIRE_INTERVAL: for position in random.choices(positions, k=2): x, y = position.container __laser = self.entities.matrix[x][y].entity.fire( Direction.SOUTH.value, True ) enemy_laser_group.add(__laser) elif event.type == pygame.KEYDOWN: if event.key == pygame.K_SPACE: self.main_player.fire(Direction.NORTH.value) if event.key == pygame.K_LEFT: self.main_player.position.x -= 20 if event.key == pygame.K_RIGHT: self.main_player.position.x += 20 will_move = True elif event.type != pygame.KEYDOWN: will_move = False if pygame.sprite.groupcollide( self.main_player.lasers, enemy_group, True, True ): self.score += 20 if pygame.sprite.groupcollide( enemy_laser_group, player_group, False, False ): print("hit the player!") self.lives -= 1 self._display_surface.fill(self.background_color) enemy_group.draw(self._display_surface) self.main_player.draw() self.main_player.lasers.draw(self._display_surface) enemy_laser_group.draw(self._display_surface) enemy_laser_group.update() if not enemy_group: draw_loop = False self.write_text( f"Score: {self.score}", self.score_label_position, pygame.font.SysFont(None, 30), ) self.write_text( f"Lives: {self.lives}", self.lives_label_position, pygame.font.SysFont(None, 30), ) self.main_player.update(1) pygame.display.flip() self.fps_meter.tick(60)

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null

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null

0

1

0

1

9ae33df6172e3d387be468447aa95067143972f3

4,477

py

Python

src/apps/tractatusapp/views_spacetree.py

lambdamusic/wittgensteiniana

f9b37282dcf4b93f9a6218cc827a6ab7386a3dd4

[ "MIT" ]

1

2018-04-24T09:55:40.000Z

src/apps/tractatusapp/views_spacetree.py

lambdamusic/wittgensteiniana

f9b37282dcf4b93f9a6218cc827a6ab7386a3dd4

[ "MIT" ]

null

src/apps/tractatusapp/views_spacetree.py

lambdamusic/wittgensteiniana

f9b37282dcf4b93f9a6218cc827a6ab7386a3dd4

[ "MIT" ]

1

2020-11-25T08:53:49.000Z

""" Using http://thejit.org/static/v20/Docs/files/Options/Options-Canvas-js.html#Options.Canvas """ from django.http import HttpResponse, Http404, HttpResponseRedirect from django.urls import reverse from django.shortcuts import render, redirect, get_object_or_404 import json import os import json from libs.myutils.myutils import printDebug from tractatusapp.models import * def spacetree(request): """ Visualizes a space tree - ORIGINAL VIEW (USED TO GENERATE HTML VERSION) """ # DEFAULT JSON FOR TESTING THE APP to_json = { 'id': "190_0", 'name': "Pearl Jam", 'children': [ { 'id': "306208_1", 'name': "Pearl Jam & Cypress Hill", 'data': { 'relation': "<h4>Pearl Jam & Cypress Hill</h4><b>Connections:</b><ul><h3>Pearl Jam <div>(relation: collaboration)</div></h3><h3>Cypress Hill <div>(relation: collaboration)</div></h3></ul>" },}, { 'id': "191_0", 'name': "Pink Floyd", 'children': [{ 'id': "306209_1", 'name': "Guns and Roses", 'data': { 'relation': "<h4>Pearl Jam & Cypress Hill</h4><b>Connections:</b><ul><h3>Pearl Jam <div>(relation: collaboration)</div></h3><h3>Cypress Hill <div>(relation: collaboration)</div></h3></ul>" }, }], }]} # reconstruct the tree as a nested dictionary TESTING = False def nav_tree(el): d = {} d['id'] = el.name d['name'] = el.name full_ogden = generate_text(el) preview_ogden = "%s .." % ' '.join(el.textOgden().split()[:10]).replace("div", "span") d['data'] = {'preview_ogden' : preview_ogden, 'full_ogden' : full_ogden} if el.get_children() and not TESTING: d['children'] = [nav_tree(x) for x in el.get_children()] else: d['children'] = [] return d treeroot = {'id': "root", 'name': "TLP", 'children': [], 'data': {'preview_ogden' : "root node", 'full_ogden' : generate_text("root")}} # level0 = TextUnit.tree.root_nodes() # TODO - make this a mptt tree function level0 = TextUnit.tree_top() for x in level0: treeroot['children'] += [nav_tree(x)] context = { 'json': json.dumps(treeroot), 'experiment_description': """ The Space Tree Tractatus is an experimental visualization of the <br /> <a target='_blank' href="http://en.wikipedia.org/wiki/Tractatus_Logico-Philosophicus">Tractatus Logico-Philosophicus</a>, a philosophical text by Ludwig Wittgenstein. <br /><br /> <b>Click</b> on a node to move the tree and center that node. The text contents of the node are displayed at the bottom of the page. <b>Use the mouse wheel</b> to zoom and <b>drag and drop the canvas</b> to pan. <br /><br /> <small>Made with <a target='_blank' href="http://www.python.org/">Python</a> and the <a target='_blank' href="http://thejit.org/">JavaScript InfoVis Toolkit</a>. More info on this <a href="http://www.michelepasin.org/blog/2012/07/08/wittgenstein-and-the-javascript-infovis-toolkit/">blog post</a></small> """ } return render(request, 'tractatusapp/spacetree/spacetree.html', context) def generate_text(instance, expression="ogden"): """ creates the html needed for the full text representation of the tractatus includes the number-title, and small links to next and prev satz # TODO: add cases for different expressions """ if instance == "root": return """<div class='tnum'>Tractatus Logico-Philosophicus<span class='smalllinks'></small></div> <div>Ludwig Wittgenstein, 1921.<br /> Translated from the German by C.K. Ogden in 1922<br /> Original title: Logisch-Philosophische Abhandlung, Wilhelm Ostwald (ed.), Annalen der Naturphilosophie, 14 (1921)</div> """ else: next, prev = "", "" next_satz = instance.tractatus_next() prev_satz = instance.tractatus_prev() if next_satz: next = "<a title='Next Sentence' href='javascript:focus_node(%s);'>→ %s</a>" % (next_satz.name, next_satz.name) if prev_satz: prev = "<a title='Previous Sentence' href='javascript:focus_node(%s);'>%s ←</a>" % (prev_satz.name, prev_satz.name) # HACK src images rendered via JS in the template cause WGET errors # hence they are hidden away in this visualization # TODO find a more elegant solution text_js_ready = instance.textOgden().replace('src="', '-src=\"src image omitted ') t = "<div class='tnum'><span class='smalllinks'>%s</span>%s<span class='smalllinks'>%s</span></div>%s" % (prev, instance.name, next, text_js_ready) return t

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309

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4,477

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0.032821

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null

0

null

0

1

0

1

9aea27159d7833c105fb4af0a9c01c188110c93d

2,693

py

Python

polymorphic/tests/test_utils.py

likeanaxon/django-polymorphic

ad4e6e90c82f897300c1c135bd7a95e4b2d802a3

[ "BSD-3-Clause" ]

1

2021-03-12T17:42:37.000Z

polymorphic/tests/test_utils.py

likeanaxon/django-polymorphic

ad4e6e90c82f897300c1c135bd7a95e4b2d802a3

[ "BSD-3-Clause" ]

10

2020-02-12T01:46:41.000Z

2022-02-10T09:00:03.000Z

polymorphic/tests/test_utils.py

likeanaxon/django-polymorphic

ad4e6e90c82f897300c1c135bd7a95e4b2d802a3

[ "BSD-3-Clause" ]

1

2020-04-18T15:14:47.000Z

from django.test import TransactionTestCase from polymorphic.models import PolymorphicModel, PolymorphicTypeUndefined from polymorphic.tests.models import ( Enhance_Base, Enhance_Inherit, Model2A, Model2B, Model2C, Model2D, ) from polymorphic.utils import ( get_base_polymorphic_model, reset_polymorphic_ctype, sort_by_subclass, ) class UtilsTests(TransactionTestCase): def test_sort_by_subclass(self): self.assertEqual( sort_by_subclass(Model2D, Model2B, Model2D, Model2A, Model2C), [Model2A, Model2B, Model2C, Model2D, Model2D], ) def test_reset_polymorphic_ctype(self): """ Test the the polymorphic_ctype_id can be restored. """ Model2A.objects.create(field1="A1") Model2D.objects.create(field1="A1", field2="B2", field3="C3", field4="D4") Model2B.objects.create(field1="A1", field2="B2") Model2B.objects.create(field1="A1", field2="B2") Model2A.objects.all().update(polymorphic_ctype_id=None) with self.assertRaises(PolymorphicTypeUndefined): list(Model2A.objects.all()) reset_polymorphic_ctype(Model2D, Model2B, Model2D, Model2A, Model2C) self.assertQuerysetEqual( Model2A.objects.order_by("pk"), [Model2A, Model2D, Model2B, Model2B], transform=lambda o: o.__class__, ) def test_get_base_polymorphic_model(self): """ Test that finding the base polymorphic model works. """ # Finds the base from every level (including lowest) self.assertIs(get_base_polymorphic_model(Model2D), Model2A) self.assertIs(get_base_polymorphic_model(Model2C), Model2A) self.assertIs(get_base_polymorphic_model(Model2B), Model2A) self.assertIs(get_base_polymorphic_model(Model2A), Model2A) # Properly handles multiple inheritance self.assertIs(get_base_polymorphic_model(Enhance_Inherit), Enhance_Base) # Ignores PolymorphicModel itself. self.assertIs(get_base_polymorphic_model(PolymorphicModel), None) def test_get_base_polymorphic_model_skip_abstract(self): """ Skipping abstract models that can't be used for querying. """ class A(PolymorphicModel): class Meta: abstract = True class B(A): pass class C(B): pass self.assertIs(get_base_polymorphic_model(A), None) self.assertIs(get_base_polymorphic_model(B), B) self.assertIs(get_base_polymorphic_model(C), B) self.assertIs(get_base_polymorphic_model(C, allow_abstract=True), A)

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0.240741

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null

0

1

0

null

0

1

9af8e51dd66ea49555fb4a24794f6c9c1dc7752a

885

py

Python

apps/user/serializers.py

major-hub/soil_app

ddd250161ad496afd4c8484f79500ff2657b51df

[ "MIT" ]

null

apps/user/serializers.py

major-hub/soil_app

ddd250161ad496afd4c8484f79500ff2657b51df

[ "MIT" ]

null

apps/user/serializers.py

major-hub/soil_app

ddd250161ad496afd4c8484f79500ff2657b51df

[ "MIT" ]

null

from rest_framework import serializers from user.models import User from main.exceptions.user_exceptions import UserException user_exception = UserException class UserRegisterSerializer(serializers.ModelSerializer): password_confirmation = serializers.CharField(max_length=128) class Meta: model = User fields = ['email', 'phone_number', 'first_name', 'last_name', 'password', 'password_confirmation'] def validate(self, attrs): password_confirmation = attrs.pop('password_confirmation') if password_confirmation != attrs.get('password'): raise serializers.ValidationError({'non_field_errors': user_exception("NOT_MATCHED_PASSWORDS").message}) return attrs class UserLoginSerializer(serializers.Serializer): email = serializers.EmailField(max_length=255) password = serializers.CharField(max_length=128)

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885

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false

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null

0

null

0

1

0

1

9aff6921a655770822f92c25247b7dfa80a21333

2,521

py

Python

src/Coord_cmd.py

aembillo/MNWellRecordGui

1683bdde75ff37a17726ce1cd7ba0135988f2992

[ "BSD-3-Clause" ]

null

src/Coord_cmd.py

aembillo/MNWellRecordGui

1683bdde75ff37a17726ce1cd7ba0135988f2992

[ "BSD-3-Clause" ]

null

src/Coord_cmd.py

aembillo/MNWellRecordGui

1683bdde75ff37a17726ce1cd7ba0135988f2992

[ "BSD-3-Clause" ]

null

""" 2015-07-23 Perform coordinate conversions from the command line. Uses """ import argparse import pyperclip # p1 = argparse.ArgumentParser() # p1.add_argument('x') # print p1.parse_args(['123']) # # p2 = argparse.ArgumentParser() # p2.add_argument('-d', action='store_const',const='dak') # print p2.parse_args(['-d']) # # p3 = argparse.ArgumentParser() # p3.add_argument('-d', action='store_const',const='dak') # p3.add_argument('x') # p3.add_argument('y') # print p3.parse_args(['-d','1','2']) #p1.add_argument( from Coordinate_Transform import DCcoordinate_projector # # # # parser = argparse.ArgumentParser() # # parser.add_argument("coord_1") # # parser.add_argument("coord_2") # # args = parser.parse_args() # # x,y = args.coord_1, args.coord_2 # def coord_convert(): parser = argparse.ArgumentParser() parser.add_argument('-d','--dak', action='store_const', const='dak', help="return Dakota County coords on clipboard") parser.add_argument('-u','--utm', action='store_const', const='utm', help="return UTM NAD 83, Zone 15 coords on clipboard") parser.add_argument('x') parser.add_argument('y') args = parser.parse_args() print 'args=',args coordtext = '%s,%s'%( args.x, args.y) Cprojector = DCcoordinate_projector() cliptext = Cprojector.handle_unspecified_coords(coordtext) #print outtext try: if args.dak: cliptext = '%4.2f,%4.2f'%(Cprojector.dakx,Cprojector.daky) #print 'returning dakx,daky to clipboard "%s"'%cliptext elif args.utm: cliptext = '%4.2f,%4.2f'%(Cprojector.utmx,Cprojector.utmy) #print 'returning utmx,utmy to clipboard "%s"'%cliptext except: pass pyperclip.copy(cliptext) pyperclip.paste() return cliptext def test_parse_args(): import sys sys.argv = ["prog", '-d', "93.0444", "44.5926"] rv = coord_convert() print '>>\n'+ str(rv) +'\n================' sys.argv = ["prog", '--utm', "93.0444", "44.5926"] rv = coord_convert() print '>>\n'+ str(rv) +'\n================' if __name__ == '__main__': #test_parse_args() coord_convert() ''' ERROR coordinates not recognized or not within Dakota County "570931,1441" 496475.91,4937695.85 Dakota Co: 570931, 144108 Dakota Co: 570931.0, 144108.0 UTM : 496475.91, 4937695.85 D.d : -93.044399765, 44.592598646 D M.m : -93 2.663986, 44 35.555919 D M S.s : -93 2 39.839", 44 35 33.355"'''

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null

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null

0.088235

0

null

0

null

0

1

0

1

b103007297614b73c2ae8e2e4d5c35bd947a709c

1,051

py

Python

wordcount/views.py

chinya07/Django-wordcount

57808f922a140b341807a5b5352864cec5728695

[ "MIT" ]

null

wordcount/views.py

chinya07/Django-wordcount

57808f922a140b341807a5b5352864cec5728695

[ "MIT" ]

null

wordcount/views.py

chinya07/Django-wordcount

57808f922a140b341807a5b5352864cec5728695

[ "MIT" ]

null

from django.http import HttpResponse from django.shortcuts import render import operator def home(request): return render(request, 'home.html') def count(request): fulltext1=request.GET['fulltext1'] fulltext2=request.GET['fulltext2'] wordlist1=fulltext1.split(' ') wordlist2=fulltext2.split(' ') from difflib import SequenceMatcher similarity_ratio = SequenceMatcher(None, wordlist1, wordlist2).ratio() # count=0 # for word in wordlist1: # if word in wordlist2: # count+=1 # worddic = {} # # for word in wordlist: # if word in worddic: # #increase # worddic[word] += 1 # else: # # add to worddic # worddic[word] = 1 #sortedwords=sorted(worddic.items(), key=operator.itemgetter(1), reverse=True) return render(request, 'count.html', {'fulltext1':fulltext1, 'fulltext2':fulltext2, 'count':similarity_ratio}) def about(request): return render(request, 'about.html')

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null

0

null

0

1

0

1

b10ef155b141d1ff49de7abd5e3a562536e9e728

771

py

Python

tests/Bio/test_tandem.py

iwasakishuto/Keras-Imitation

8ac0cd7c8912d49d13b19a0182ad534c0781fbfe

[ "MIT" ]

4

2020-04-25T08:50:36.000Z

2020-04-26T04:49:16.000Z

tests/Bio/test_tandem.py

iwasakishuto/Keras-Imitation

8ac0cd7c8912d49d13b19a0182ad534c0781fbfe

[ "MIT" ]

null

tests/Bio/test_tandem.py

iwasakishuto/Keras-Imitation

8ac0cd7c8912d49d13b19a0182ad534c0781fbfe

[ "MIT" ]

null

# coding: utf-8 from kerasy.Bio.tandem import find_tandem from kerasy.utils import generateSeq len_sequences = 1000 def get_test_data(): sequence = generateSeq(size=len_sequences, nucleic_acid='DNA', weights=None, seed=123) sequence = "".join(sequence) return sequence def test_find_tandem(): sequence = get_test_data() max_val_sais, tandem_lists_sais = find_tandem(sequence, method="SAIS") tandem_sais = tandem_lists_sais[0] max_val_dp, tandem_lists_dp = find_tandem(sequence, method="DP") tandem_dp = tandem_lists_dp[0] assert max_val_sais == max_val_dp assert any([tandem_dp[i:]+tandem_dp[:i] == tandem_sais for i in range(len(tandem_dp))])

29.653846

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0.08

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25

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null

0

1

0

null

0

1

b10f2700bf5dd4688d783eebd9aacb68abc85ac5

679

py

Python

NEW_PRAC/HackerRank/Python/SetDifferenceString.py

side-projects-42/INTERVIEW-PREP-COMPLETE

627a3315cee4bbc38a0e81c256f27f928eac2d63

[ "MIT" ]

13

2021-03-11T00:25:22.000Z

2022-03-19T00:19:23.000Z

NEW_PRAC/HackerRank/Python/SetDifferenceString.py

side-projects-42/INTERVIEW-PREP-COMPLETE

627a3315cee4bbc38a0e81c256f27f928eac2d63

[ "MIT" ]

160

2021-04-26T19:04:15.000Z

2022-03-26T20:18:37.000Z

NEW_PRAC/HackerRank/Python/SetDifferenceString.py

side-projects-42/INTERVIEW-PREP-COMPLETE

627a3315cee4bbc38a0e81c256f27f928eac2d63

[ "MIT" ]

12

2021-04-26T19:43:01.000Z

2022-01-31T08:36:29.000Z

# >>> s = set("Hacker") # >>> print s.difference("Rank") # set(['c', 'r', 'e', 'H']) # >>> print s.difference(set(['R', 'a', 'n', 'k'])) # set(['c', 'r', 'e', 'H']) # >>> print s.difference(['R', 'a', 'n', 'k']) # set(['c', 'r', 'e', 'H']) # >>> print s.difference(enumerate(['R', 'a', 'n', 'k'])) # set(['a', 'c', 'r', 'e', 'H', 'k']) # >>> print s.difference({"Rank":1}) # set(['a', 'c', 'e', 'H', 'k', 'r']) # >>> s - set("Rank") # set(['H', 'c', 'r', 'e']) if __name__ == "__main__": eng = input() eng_stu = set(map(int, input().split())) fre = input() fre_stu = set(map(int, input().split())) eng_only = eng_stu - fre_stu print(len(eng_only))

24.25

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null

0

1

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null

0

1

b11347dca32d00ada08a415a09ab2e6c4431c76c

2,354

py

Python

chaos_genius/celery_config.py

eltociear/chaos_genius

eb3bc27181c8af4144b95e685386814109173164

[ "MIT" ]

1

2022-02-25T16:11:34.000Z

chaos_genius/celery_config.py

eltociear/chaos_genius

eb3bc27181c8af4144b95e685386814109173164

[ "MIT" ]

null

chaos_genius/celery_config.py

eltociear/chaos_genius

eb3bc27181c8af4144b95e685386814109173164

[ "MIT" ]

null

from datetime import timedelta from celery.schedules import crontab, schedule CELERY_IMPORTS = ("chaos_genius.jobs") CELERY_TASK_RESULT_EXPIRES = 30 CELERY_TIMEZONE = "UTC" CELERY_ACCEPT_CONTENT = ["json", "msgpack", "yaml"] CELERY_TASK_SERIALIZER = "json" CELERY_RESULT_SERIALIZER = "json" CELERYBEAT_SCHEDULE = { "anomaly-scheduler": { "task": "chaos_genius.jobs.anomaly_tasks.anomaly_scheduler", "schedule": schedule(timedelta(minutes=10)), "args": () }, 'alerts-daily': { 'task': 'chaos_genius.jobs.alert_tasks.check_event_alerts', 'schedule': crontab(hour="3", minute="0"), # Daily: at 3am 'args': ('daily',) }, "alert-digest-daily-scheduler": { "task": "chaos_genius.jobs.alert_tasks.alert_digest_daily_scheduler", "schedule": schedule(timedelta(minutes=10)), "args": () }, # 'anomaly-task-every-minute': { # 'task': 'chaos_genius.jobs.anomaly_tasks.add_together', # 'schedule': crontab(minute="*"), # Every minutes # 'args': (5,10,) # }, # "anomaly-tasks-all-kpis": { # "task": "chaos_genius.jobs.anomaly_tasks.anomaly_kpi", # # "schedule": crontab(hour=[11]), # "schedule": schedule(timedelta(minutes=1)), # for testing # "args": () # }, # 'alerts-weekly': { # 'task': 'chaos_genius.jobs.alert_tasks.check_event_alerts', # 'schedule': crontab(day_of_week="0"), # Weekly: every sunday # 'args': ('weekly',) # }, # 'alerts-hourly': { # 'task': 'chaos_genius.jobs.alert_tasks.check_event_alerts', # 'schedule': crontab(hour="*"), # Hourly: at 0th minute # 'args': ('hourly',) # }, # 'alerts-every-15-minute': { # 'task': 'chaos_genius.jobs.alert_tasks.check_event_alerts', # 'schedule': crontab(minute="*/15"), # Every 15 minutes # 'args': ('every_15_minute',) # } } CELERY_ROUTES = { "chaos_genius.jobs.anomaly_tasks.*": {"queue": "anomaly-rca"}, "chaos_genius.jobs.alert_tasks.*": {"queue": "alerts"}, } # Scheduler runs every hour # looks at tasks in last n hour # if they are in processing in 24 hours, schedule them right away # job expiry window # add details of job into a table, then schedule it # TODO: Use this for config class Config: enable_utc = True

32.694444

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0.619371

270

2,354

5.203704

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0.086121

0.117438

0.108185

0.398577

0.340214

0.297509

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0

0.014602

0.214528

2,354

71

78

33.15493

0.745268

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0

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0.096774

0

0.16129

0

null

0

null

0

1

0

1

b122b1664a2960a396de4fbb595bf3821559d96f

563

py

Python

orderedtable/urls.py

Shivam2k16/DjangoOrderedTable

da133a23a6659ce5467b8161edcf6db35f1c0b76

[ "MIT" ]

2

2018-04-15T17:03:59.000Z

2019-03-23T04:45:00.000Z

orderedtable/urls.py

Shivam2k16/DjangoOrderedTable

da133a23a6659ce5467b8161edcf6db35f1c0b76

[ "MIT" ]

null

orderedtable/urls.py

Shivam2k16/DjangoOrderedTable

da133a23a6659ce5467b8161edcf6db35f1c0b76

[ "MIT" ]

1

2018-04-15T16:54:07.000Z

from django.conf.urls import include, url from django.contrib import admin import orderedtable from orderedtable import views app_name="orderedtable" urlpatterns = [ url(r'^$', views.home,name="home"), url(r'^import-json/$', views.import_json,name="import_json"), url(r'^project-list/$', views.project_list,name="project_list"), url(r'^empty-list/$', views.delete_table,name="delete_table"), url(r'^multiple-sorting/$', views.multiple_sorting,name="multiple_sorting"), url(r'^sort-by = (?P<pk>[\w-]+)/$', views.sorted,name="sorted"), ]

33.117647

80

0.698046

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563

4.860759

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0

0.108348

563

16

81

35.1875

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0

1

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0

null

0

null

0

1

0

1

b123669e9c0103e63c00a8b4dcdbc0e0596f1442

2,242

py

Python

call_google_translate.py

dadap/klingon-assistant-data

5371f8ae6e3669f48a83087a4937af0dee8d23d1

[ "Apache-2.0" ]

null

call_google_translate.py

dadap/klingon-assistant-data

5371f8ae6e3669f48a83087a4937af0dee8d23d1

[ "Apache-2.0" ]

5

2018-07-11T09:17:19.000Z

2018-10-14T10:33:51.000Z

call_google_translate.py

dadap/klingon-assistant-data

5371f8ae6e3669f48a83087a4937af0dee8d23d1

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # Calls Google Translate to produce translations. # To use, set "language" and "dest_language" below. (They are normally the same, # unless Google uses a different language code than we do.) Then fill in # the definition_[language] fields with "TRANSLATE" or # "TRANSLATE: [replacement definition]". The latter is to allow for a better # translation when the original definition is ambiguous, e.g., if the definition # is "launcher", a better translation might result from # "TRANSLATE: rocket launcher". from googletrans import Translator import fileinput import re import time # TODO: Refactor this and also use in renumber.py. # Ignore mem-00-header.xml and mem-28-footer.xml because they don't contain entries. filenames = ['mem-01-b.xml', 'mem-02-ch.xml', 'mem-03-D.xml', 'mem-04-gh.xml', 'mem-05-H.xml', 'mem-06-j.xml', 'mem-07-l.xml', 'mem-08-m.xml', 'mem-09-n.xml', 'mem-10-ng.xml', 'mem-11-p.xml', 'mem-12-q.xml', 'mem-13-Q.xml', 'mem-14-r.xml', 'mem-15-S.xml', 'mem-16-t.xml', 'mem-17-tlh.xml', 'mem-18-v.xml', 'mem-19-w.xml', 'mem-20-y.xml', 'mem-21-a.xml', 'mem-22-e.xml', 'mem-23-I.xml', 'mem-24-o.xml', 'mem-25-u.xml', 'mem-26-suffixes.xml', 'mem-27-extra.xml'] translator = Translator() language = "zh-HK" dest_language = "zh-TW" limit = 250 for filename in filenames: with fileinput.FileInput(filename, inplace=True) as file: definition = "" for line in file: definition_match = re.search(r"definition\">?(.+)<", line) definition_translation_match = re.search(r"definition_(.+)\">TRANSLATE(?:: (.*))?<", line) if (definition_match): definition = definition_match.group(1) if (limit > 0 and \ definition != "" and \ definition_translation_match and \ language.replace('-','_') == definition_translation_match.group(1)): if definition_translation_match.group(2): definition = definition_translation_match.group(2) translation = translator.translate(definition, src='en', dest=dest_language) line = re.sub(r">(.*)<", ">%s [AUTOTRANSLATED]<" % translation.text, line) # Rate-limit calls to Google Translate. limit = limit - 1 time.sleep(0.1) print(line, end='')

44.84

460

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2,242

4.389881

0.464286

0.105763

0.088136

0.063051

0.075932

0

0.037473

0.166815

2,242

49

461

45.755102

0.752141

0.303301

0

0.263226

0

0.020408

0

1

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false

0

0.142857

0

0.142857

0.035714

0

null

0

null

0

1

0

1

b129413908fca02566b29b673b606e60be14141b

7,824

py

Python

icetray_version/trunk/resources/scripts/make_plots.py

hershalpandya/airshowerclassification_llhratio_test

a2a2ce5234c8f455fe56c332ab4fcc65008e9409

[ "MIT" ]

null

icetray_version/trunk/resources/scripts/make_plots.py

hershalpandya/airshowerclassification_llhratio_test

a2a2ce5234c8f455fe56c332ab4fcc65008e9409

[ "MIT" ]

null

icetray_version/trunk/resources/scripts/make_plots.py

hershalpandya/airshowerclassification_llhratio_test

a2a2ce5234c8f455fe56c332ab4fcc65008e9409

[ "MIT" ]

null

# coding: utf-8 # In[1]: import numpy as np get_ipython().magic(u'matplotlib inline') from matplotlib import pyplot as plt from matplotlib.colors import LogNorm import sys sys.path.append('../../python/') from general_functions import load_5D_PDF_from_file from mpl_toolkits.mplot3d import Axes3D from matplotlib import cm import tables import glob def plot_2d_hist(hist,xedges,yedges, xlim,ylim, xlabel='',ylabel='',title='',cmap='coolwarm', vmin=1e-5,vmax=1e-1,same_plot=False,alpha=1.0): hist=hist.T hist=np.ma.masked_where(hist==0,hist) #label='nentries: %i'%np.sum(hist) if not same_plot: plt.figure()#dpi=320) plt.pcolormesh(xedges,yedges,hist,alpha=alpha, cmap=cmap,norm=LogNorm(vmin=vmin,vmax=vmax)) #cbar=plt.colorbar() #plt.scatter([2.0],[2],color=None,s=0,label=label) #plt.legend() plt.xlim(xlim) plt.ylim(ylim) #plt.xlabel(xlabel) #plt.ylabel(ylabel) #plt.title(title) return plt def plot_3dhist(bkg_hist,bincenters,azim,elev,outputname,vmin,vmax): Q,T,R = np.meshgrid(bincenters[1],bincenters[0],bincenters[2]) c= bkg_hist/np.sum(bkg_hist) Q=Q.T T=T.T R=R.T c=c.T #print np.shape(Q.T), np.shape(T.T), np.shape(R.T), np.shape(bkg_hist.T) reshape_ = np.prod(np.shape(Q)) Q = Q.reshape(reshape_) T = T.reshape(reshape_) R = R.reshape(reshape_) c= c.reshape(reshape_) select=(c!=0)#&(np.random.rand(len(c))>0.5) Q=Q[select] T=T[select] R=R[select] c=np.log10(c[select]) alpha=np.ones_like(c) alpha[c<-2]=0.70 alpha[c<-3]=0.60 alpha[c<-4]=0.50 alpha[c<-5]=0.40 alpha[c<-6]=0.30 norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax) cmap = cm.jet m = cm.ScalarMappable(norm=norm, cmap=cmap) c= m.to_rgba(c) c.T[3]=alpha fig=plt.figure(figsize=(8,8)) ax = fig.add_subplot(111, projection='3d') ax.scatter(R,T,Q,zdir='Q',c=c,s=30,edgecolors=c) ax.azim = azim ax.elev = elev ax.set_xlabel('R') ax.set_ylabel('Q') ax.set_zlabel('T') ax.set_xlim([0,3.5]) ax.set_ylim([-3.2,4]) ax.set_zlim([-5.2,4.2]) #fig.colorbar(myax) fig.savefig(outputname,bbox_inches='tight') plt.close() return def hist_2d_proj(hist3d,axis=0): if axis==0: axes=[0,1,2] if axis==1: axes=[1,0,2] if axis==2: axes=[2,0,1] hist3d=np.transpose(hist3d,axes=axes) proj_hist=np.zeros_like(hist3d[0]) print np.shape(proj_hist) for i in range(len(hist3d)): proj_hist += hist3d[i] return proj_hist def hist_1d_proj(hist2d,axis=0): if axis==0: axes=[0,1] if axis==1: axes=[1,0] hist2d=np.transpose(hist2d,axes=axes) proj_hist=np.zeros_like(hist2d[0]) print np.shape(proj_hist) for i in range(len(hist2d)): proj_hist += hist2d[i] return proj_hist def plot_2D_projected_hist(hist3d,edges,axis=0, xlabel='',ylabel='', event_overlay=False, event=None): projected_hist = hist_2d_proj(hist3d,axis) if axis==0: xedges= edges[1] yedges= edges[2] if axis==1: xedges= edges[0] yedges= edges[2] if axis==2: xedges= edges[0] yedges= edges[1] xlim = [xedges[0]-0.25,xedges[-1]+0.25] ylim = [yedges[0]-0.25,yedges[-1]+0.25] projected_hist /=np.sum(projected_hist) projected_hist = projected_hist.T plot_2d_hist(projected_hist,yedges,xedges,ylim,xlim,xlabel,ylabel,cmap='jet') if event_overlay: xcenters=(xedges[:-1]+xedges[1:])/2.0 ycenters=(yedges[:-1]+yedges[1:])/2.0 xscatter=[] yscatter=[] zscatter=[] for r,row in enumerate(hist_2d_proj(event,axis)): for c,element in enumerate(row): if element!=0: xscatter.append(xcenters[r]) yscatter.append(ycenters[c]) zscatter.append(element) xscatter=np.array(xscatter) yscatter=np.array(yscatter) zscatter=np.array(zscatter) plt.scatter(yscatter,xscatter,marker='s',s=10*zscatter,edgecolor='k',facecolor='r', alpha=0.6) return # In[3]: sig_pdf_file='../../files/PDF_12360_0123x.hd5' bkg_pdf_file='../../files/PDF_12362_0123x.hd5' temp=load_5D_PDF_from_file(SigPDFFileName=sig_pdf_file, BkgPDFFileName=bkg_pdf_file) sig_hist=temp[0] bkg_hist=temp[1] binedges=temp[2] distinct_regions_binedges=temp[3] labels=temp[4] sig_n_events=temp[5] bkg_n_events = temp[6] # In[4]: # find the logE and coszen bins select those bins in sig/bkg pdfs logEbincenters = np.array((binedges[0][1:] + binedges[0][:-1] )/2.) coszenbincenters = np.array((binedges[1][1:] + binedges[1][:-1] )/2.) logE=-0.01 dE = np.absolute(logEbincenters - logE) Ebin=np.where(np.amin(dE)==dE)[0][0] coszen=0.96 dcZ = np.absolute(coszenbincenters - coszen) cZbin = np.where(np.amin(dcZ)==dcZ)[0][0] sig_hist_3dslice = sig_hist[Ebin][cZbin] bkg_hist_3dslice = bkg_hist[Ebin][cZbin] binedges_3dslice = binedges[2:] # In[7]: plot_2D_projected_hist(sig_hist_3dslice,binedges_3dslice,axis=2) # In[27]: sig_hdf_files=glob.glob('../../files/Events_12360_?x.hd5.hd5') bkg_hdf_files=glob.glob('../../files/Events_12362_?x.hd5.hd5') # In[30]: def load_hdf_file(tfiles): d={} for tfile in tfiles: f=tables.open_file(tfile) for name in f.root.IceTopLLHR.colnames: if tfile==tfiles[0]: d[name]= eval('f.root.IceTopLLHR.cols.'+name+'[:]') else: d[name]=np.concatenate( (d[name],eval('f.root.IceTopLLHR.cols.'+name+'[:]')) ) if tfile==tfiles[0]: d['log_s125']=np.log10(f.root.LaputopParams.cols.s125[:]) d['cos_zen']=np.cos(f.root.Laputop.cols.zenith[:]) else: d['log_s125']=np.concatenate( (d['log_s125'],np.log10(f.root.LaputopParams.cols.s125[:])) ) d['cos_zen']=np.concatenate( (d['cos_zen'], np.cos(f.root.Laputop.cols.zenith[:])) ) return d # In[31]: llhr={} llhr['sig']=load_hdf_file(sig_hdf_files) llhr['bkg']=load_hdf_file(bkg_hdf_files) # In[45]: low_E=1.5 high_E=1.6 low_z=0.8 high_z=.85 for key in llhr.keys(): cut1=llhr[key]['isGood']==1.0 cut2=llhr[key]['tanks_have_nans']==0. cut3=llhr[key]['log_s125']>=low_E cut4=llhr[key]['log_s125']<high_E cut5=llhr[key]['cos_zen']>=low_z cut6=llhr[key]['cos_zen']<high_z select=cut1&cut2&cut3&cut4&cut5&cut6 print len(select) print len(select[select]) hist_this ='llh_ratio' range=[-10,15] bins=35 #hist_this='n_extrapolations_bkg_PDF' #range=[0,20] #bins=20 plt.hist(llhr[key][hist_this][select],range=range,bins=bins,label=key,histtype='step') plt.legend() # In[34]: llhr['sig'].keys() # In[2]: def load_results_hist(tfile): f=tables.open_file(tfile) labels=f.root.labels[:] nevents=f.root.n_events[:] edges0=f.root.binedges_0[:] edges1=f.root.binedges_1[:] edges2=f.root.binedges_2[:] hist=f.root.hist[:] f.close() return hist, [edges0,edges1,edges2], nevents,labels # In[3]: sig_hist, edges, sig_nevents, labels = load_results_hist('../../files/results_sig_Ezenllhr.hd5') bkg_hist, edges, bkg_nevents, labels = load_results_hist('../../files/results_bkg_Ezenllhr.hd5') # In[4]: sig_onedhist=hist_2d_proj(sig_hist,axis=1)[3] bkg_onedhist=hist_2d_proj(bkg_hist,axis=1)[3] # In[5]: plt.bar(edges[2][:-1],sig_onedhist,alpha=1.,label='rand') plt.bar(edges[2][:-1],bkg_onedhist,alpha=0.3,label='data') plt.yscale('log') #plt.xlim([-1,1]) plt.legend() # In[54]:

23.709091

103

0.61797

1,237

7,824

3.767179

0.21342

0.013949

0.01073

0.006438

0.174678

0.116524

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0.070601

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0

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7,824

329

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0

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null

0.019324

0

null

0

null

0

1

0

1

b1355b614d3140ba034b33a7f3ee7859a1245971

723

py

Python

flake8_strings/visitor.py

d1618033/flake8-strings

2ad34a41eab65e2264da7aa91c54dbca701af1c5

[ "MIT" ]

null

flake8_strings/visitor.py

d1618033/flake8-strings

2ad34a41eab65e2264da7aa91c54dbca701af1c5

[ "MIT" ]

1

2021-02-19T13:50:29.000Z

flake8_strings/visitor.py

d1618033/flake8-strings

2ad34a41eab65e2264da7aa91c54dbca701af1c5

[ "MIT" ]

null

import ast from typing import List from flake8_plugin_utils import Visitor from .errors import UnnecessaryBackslashEscapingError class StringsVisitor(Visitor): lines: List[str] def _is_escaped_char(self, character: str) -> bool: repr_c = repr(character) return repr_c[1] == '\\' and repr_c[2] != '\\' def visit_Str(self, node: ast.Str) -> None: # noqa: N802 if '\\' not in node.s: return if node.s[-1] == '\\': return if any(self._is_escaped_char(c) for c in node.s): return if self.lines[node.lineno - 1][node.col_offset] == 'r': return self.error_from_node(UnnecessaryBackslashEscapingError, node)

27.807692

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723

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null

0

null

0

1

0

1

b13b701d2eb809667c24251d55ce1c0bf248bc34

1,465

py

Python

substitute_finder/migrations/0003_product.py

tohugaby/pur_beurre_web

c3bdacee50907eea79821e7a8b3fe0f349719d88

[ "MIT" ]

1

2020-01-05T18:58:51.000Z

substitute_finder/migrations/0003_product.py

tohugaby/pur_beurre_web

c3bdacee50907eea79821e7a8b3fe0f349719d88

[ "MIT" ]

3

2020-06-05T18:35:47.000Z

2021-06-10T20:32:44.000Z

substitute_finder/migrations/0003_product.py

tomlemeuch/pur_beurre_web

c3bdacee50907eea79821e7a8b3fe0f349719d88

[ "MIT" ]

null

# Generated by Django 2.1 on 2018-08-14 09:42 from django.conf import settings from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('substitute_finder', '0002_category'), ] operations = [ migrations.CreateModel( name='Product', fields=[ ('code', models.CharField(max_length=300, primary_key=True, serialize=False, verbose_name='identifiant')), ('product_name', models.CharField(max_length=300, verbose_name='nom du produit')), ('generic_name', models.CharField(max_length=1000, verbose_name='description')), ('url', models.URLField(max_length=1000, verbose_name='url OpenFoodFacts')), ('stores', models.CharField(max_length=300, verbose_name='vendeur')), ('nutrition_grade_fr', models.CharField(max_length=1, verbose_name='score nutritionnel')), ('last_updated', models.DateTimeField(auto_now=True, verbose_name='dernière mise à jour')), ('categories', models.ManyToManyField(to='substitute_finder.Category', verbose_name='categories')), ('users', models.ManyToManyField(related_name='favorite', to=settings.AUTH_USER_MODEL, verbose_name='utilisateurs')), ], options={ 'verbose_name': 'Produit', 'verbose_name_plural': 'Produits', }, ), ]

44.393939

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1,465

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b13f03597d9a5e677488aa6621f7a6411da41c2d

3,223

py

Python

Estrangement/tests/test_utils.py

kawadia/estrangement

612542bf4af64f248766ad28c18028ff4b2307b5

[ "BSD-3-Clause" ]

7

2015-02-17T14:04:25.000Z

2020-02-16T08:59:00.000Z

tnetwork/DCD/externals/estrangement_master/Estrangement/tests/test_utils.py

Yquetzal/tnetwork

43fb2f19aeed57a8a9d9af032ee80f1c9f58516d

[ "BSD-2-Clause" ]

1

2019-07-13T16:16:28.000Z

2019-07-15T09:34:33.000Z

Estrangement/tests/test_utils.py

kawadia/estrangement

612542bf4af64f248766ad28c18028ff4b2307b5

[ "BSD-3-Clause" ]

4

2015-02-20T15:29:59.000Z

2021-03-28T04:12:08.000Z

import networkx as nx import sys import os import nose sys.path.append(os.getcwd() + "/..") import utils class test_utils: def setUp(self): self.g0 = nx.Graph() self.g1 = nx.Graph() self.g2 = nx.Graph() self.g3 = nx.Graph() self.g4 = nx.Graph() self.g5 = nx.Graph() self.g7 = nx.Graph() self.g6 = nx.Graph() self.g0.add_edges_from([(1,2,{'weight':2}),(1,3,{'weight':1}),(2,4,{'weight':1})]) self.g1.add_edges_from([(1,4,{'weight':1}),(2,3,{'weight':1}),(3,4,{'weight':1})]) self.g2.add_edges_from([(1,2,{'weight':2}),(2,3,{'weight':1}),(3,4,{'weight':1})]) self.g3.add_edges_from([(5,6),(5,7)]) self.g4.add_edges_from([(1,5),(2,3)]) self.g5.add_edges_from([(1,2,{'weight':2}),(1,3,{'weight':1}),(2,4,{'weight':1})]) self.g6.add_edges_from([(1,2,{'weight':1}),(1,3,{'weight':1}),(2,3,{'weight':1})]) self.g7.add_edges_from([(1,2,{'weight':1})]) self.label_dict1 = {1:'a',2:'a',3:'b',4:'b',5:'c',6:'c'} self.label_dict2 = {1:'a',2:'b',3:'b',4:'b',5:'c',6:'c'} self.label_dict3 = {1:'a',2:'b',3:'c',4:'d',5:'e',6:'f'} self.label_dict4 = {1:'a',2:'a',3:'a',4:'a',5:'a',6:'a'} self.label_dict5 = {1:'b',2:'b',3:'b',4:'b',5:'b',6:'b'} self.label_dict6 = {1:'a',2:'b',3:'b'} def test_graph_distance(self): assert utils.graph_distance(self.g0, self.g1) == 1 assert utils.graph_distance(self.g0, self.g1, False) == 1 assert utils.graph_distance(self.g0, self.g0) == 0 assert utils.graph_distance(self.g0, self.g0) == 0 assert utils.graph_distance(self.g0, self.g2, False) == 0.8 assert utils.graph_distance(self.g0, self.g2, True) == 0.5 def test_node_graph_distance(self): assert utils.node_graph_distance(self.g0, self.g1) == 0 assert utils.node_graph_distance(self.g0, self.g0) == 0 assert utils.node_graph_distance(self.g0, self.g3) == 1 assert utils.node_graph_distance(self.g0, self.g4) == 0.4 assert utils.node_graph_distance(nx.path_graph(2),nx.path_graph(4)) == 0.5 def test_Estrangement(self): assert utils.Estrangement(self.g0, self.label_dict4, self.g3) == 0 # no common edge assert utils.Estrangement(self.g0, self.label_dict3, self.g5) == 1 # all common edge, all diff community assert utils.Estrangement(self.g0, self.label_dict1, self.g2) == 0.25 # one edge between community nose.tools.assert_almost_equal(utils.Estrangement(self.g6, self.label_dict6, self.g7),0.3333,4) print(utils.Estrangement(self.g6, self.label_dict6, self.g7)) def test_match_labels(self): assert utils.match_labels(self.label_dict1, self.label_dict1) == self.label_dict1 # snapshots are the same assert utils.match_labels(self.label_dict5, self.label_dict4) == self.label_dict4 # same community, diff label assert utils.match_labels(self.label_dict4, self.label_dict4) == self.label_dict4 # same community, same label def test_confidence_interval(self): assert utils.confidence_interval([2,2,2,2]) == 0 nose.tools.assert_almost_equal(utils.confidence_interval([1,2,3,4]), 1.096,3) assert utils.confidence_interval([2,2,4,4]) == 0.98

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0.017544

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null

0

null

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1

0

1

b14084e431f80764a4ba711f2600b59b246111f5

830

py

Python

ex44e.py

liggettla/python

4bdad72bc2143679be6d1f8722b83cc359753ca9

[ "MIT" ]

null

ex44e.py

liggettla/python

4bdad72bc2143679be6d1f8722b83cc359753ca9

[ "MIT" ]

null

ex44e.py

liggettla/python

4bdad72bc2143679be6d1f8722b83cc359753ca9

[ "MIT" ]

null

#Rather than rely on inplicit inheritance from other classes, classes can just #call the functions from a class; termed composition class Other(object): def override(self): print "OTHER override()" def implicit(self): print "OTHER implicit()" def altered(self): print "OTHER altered()" class Child(object): def __init__(self): #Here the Child uses Other() to get its work done #Rather than just using implicit inheritance self.other = Other() def implicit(self): self.other.implicit() def override(self): print "CHILD override()" def altered(self): print "CHILD, BEFORE OTHER altered()" self.other.altered() print "CHILD, AFTER OTHER altered()" son = Child() son.implicit() son.override() son.altered()

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null

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null

0

1

0

1

b1450ba4c392fda6a05914dd0e6efe6138ef8c05

8,049

py

Python

src/abaqus/Odb/Odb.py

Haiiliin/PyAbaqus

f20db6ebea19b73059fe875a53be370253381078

[ "MIT" ]

7

2022-01-21T09:15:45.000Z

2022-02-15T09:31:58.000Z

src/abaqus/Odb/Odb.py

Haiiliin/PyAbaqus

f20db6ebea19b73059fe875a53be370253381078

[ "MIT" ]

null

src/abaqus/Odb/Odb.py

Haiiliin/PyAbaqus

f20db6ebea19b73059fe875a53be370253381078

[ "MIT" ]

null

from abaqusConstants import * from .OdbPart import OdbPart from .OdbStep import OdbStep from .SectionCategory import SectionCategory from ..Amplitude.AmplitudeOdb import AmplitudeOdb from ..BeamSectionProfile.BeamSectionProfileOdb import BeamSectionProfileOdb from ..Filter.FilterOdb import FilterOdb from ..Material.MaterialOdb import MaterialOdb class Odb(AmplitudeOdb, FilterOdb, MaterialOdb, BeamSectionProfileOdb): """The Odb object is the in-memory representation of an output database (ODB) file. Attributes ---------- isReadOnly: Boolean A Boolean specifying whether the output database was opened with read-only access. amplitudes: dict[str, Amplitude] A repository of :py:class:`~abaqus.Amplitude.Amplitude.Amplitude` objects. filters: dict[str, Filter] A repository of :py:class:`~abaqus.Filter.Filter.Filter` objects. rootAssembly: OdbAssembly An :py:class:`~abaqus.Odb.OdbAssembly.OdbAssembly` object. jobData: JobData A :py:class:`~abaqus.Odb.JobData.JobData` object. parts: dict[str, OdbPart] A repository of :py:class:`~abaqus.Odb.OdbPart.OdbPart` objects. materials: dict[str, Material] A repository of :py:class:`~abaqus.Material.Material.Material` objects. steps: dict[str, OdbStep] A repository of :py:class:`~abaqus.Odb.OdbStep.OdbStep` objects. sections: dict[str, Section] A repository of :py:class:`~abaqus.Section.Section.Section` objects. sectionCategories: dict[str, SectionCategory] A repository of :py:class:`~abaqus.Odb.SectionCategory.SectionCategory` objects. sectorDefinition: SectorDefinition A :py:class:`~abaqus.Odb.SectorDefinition.SectorDefinition` object. userData: UserData A :py:class:`~abaqus.Odb.UserData.UserData` object. customData: RepositorySupport A :py:class:`~abaqus.CustomKernel.RepositorySupport.RepositorySupport` object. profiles: dict[str, Profile] A repository of :py:class:`~abaqus.BeamSectionProfile.Profile.Profile` objects. Notes ----- This object can be accessed by: .. code-block:: python import odbAccess session.odbs[name] """ def Part(self, name: str, embeddedSpace: SymbolicConstant, type: SymbolicConstant) -> OdbPart: """This method creates an OdbPart object. Nodes and elements are added to this object at a later stage. Notes ----- This function can be accessed by: .. code-block:: python session.odbs[name].Part Parameters ---------- name A String specifying the part name. embeddedSpace A SymbolicConstant specifying the dimensionality of the Part object. Possible values are THREE_D, TWO_D_PLANAR, and AXISYMMETRIC. type A SymbolicConstant specifying the type of the Part object. Possible values are DEFORMABLE_BODY and ANALYTIC_RIGID_SURFACE. Returns ------- An OdbPart object. """ self.parts[name] = odbPart = OdbPart(name, embeddedSpace, type) return odbPart def Step(self, name: str, description: str, domain: SymbolicConstant, timePeriod: float = 0, previousStepName: str = '', procedure: str = '', totalTime: float = None) -> OdbStep: """This method creates an OdbStep object. Notes ----- This function can be accessed by: .. code-block:: python session.odbs[name].Step Parameters ---------- name A String specifying the repository key. description A String specifying the step description. domain A SymbolicConstant specifying the domain of the step. Possible values are TIME, FREQUENCY, ARC_LENGTH, and MODAL.The type of OdbFrame object that can be created for this step is based on the value of the *domain* argument. timePeriod A Float specifying the time period of the step. *timePeriod* is required if *domain*=TIME; otherwise, this argument is not applicable. The default value is 0.0. previousStepName A String specifying the preceding step. If *previousStepName* is the empty string, the last step in the repository is used. If *previousStepName* is not the last step, this will result in a change to the *previousStepName* member of the step that was in that position. A special value 'Initial' refers to the internal initial model step and may be used exclusively for inserting a new step at the first position before any other existing steps. The default value is an empty string. procedure A String specifying the step procedure. The default value is an empty string. The following is the list of valid procedures: ``` *ANNEAL *BUCKLE *COMPLEX FREQUENCY *COUPLED TEMPERATURE-DISPLACEMENT *COUPLED TEMPERATURE-DISPLACEMENT, CETOL *COUPLED TEMPERATURE-DISPLACEMENT, STEADY STATE *COUPLED THERMAL-ELECTRICAL, STEADY STATE *COUPLED THERMAL-ELECTRICAL *COUPLED THERMAL-ELECTRICAL, DELTMX *DYNAMIC *DYNAMIC, DIRECT *DYNAMIC, EXPLICIT *DYNAMIC, SUBSPACE *DYNAMIC TEMPERATURE-DISPLACEMENT, EXPLICT *ELECTROMAGNETIC, HIGH FREQUENCY, TIME HARMONIC *ELECTROMAGNETIC, LOW FREQUENCY, TIME DOMAIN *ELECTROMAGNETIC, LOW FREQUENCY, TIME DOMAIN, DIRECT *ELECTROMAGNETIC, LOW FREQUENCY, TIME HARMONIC *FREQUENCY *GEOSTATIC *HEAT TRANSFER *HEAT TRANSFER, DELTAMX=__ *HEAT TRANSFER, STEADY STATE *MAGNETOSTATIC *MAGNETOSTATIC, DIRECT *MASS DIFFUSION *MASS DIFFUSION, DCMAX= *MASS DIFFUSION, STEADY STATE *MODAL DYNAMIC *RANDOM RESPONSE *RESPONSE SPECTRUM *SOILS *SOILS, CETOL/UTOL *SOILS, CONSOLIDATION *SOILS, CONSOLIDATION, CETOL/UTOL *STATIC *STATIC, DIRECT *STATIC, RIKS *STEADY STATE DYNAMICS *STEADY STATE TRANSPORT *STEADY STATE TRANSPORT, DIRECT *STEP PERTURBATION, *STATIC *SUBSTRUCTURE GENERATE *USA ADDDED MASS GENERATION *VISCO ``` totalTime A Float specifying the analysis time spend in all the steps previous to this step. The default value is −1.0. Returns ------- An OdbStep object. Raises ------ - If *previousStepName* is invalid: ValueError: previousStepName is invalid """ self.steps[name] = odbStep = OdbStep(name, description, domain, timePeriod, previousStepName, procedure, totalTime) return odbStep def SectionCategory(self, name: str, description: str) -> SectionCategory: """This method creates a SectionCategory object. Notes ----- This function can be accessed by: .. code-block:: python session.odbs[*name*].SectionCategory Parameters ---------- name A String specifying the name of the category. description A String specifying the description of the category. Returns ------- A SectionCategory object. """ self.sectionCategories[name] = sectionCategory = SectionCategory(name, description) return sectionCategory

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0

null

0

null

0

1

0

1

b1483e23d7d2752b7248ed2d54d8ac8e55492604

241

py

Python

popcorn_gallery/tutorials/urls.py

Koenkk/popcorn_maker

0978b9f98dacd4e8eb753404b24eb584f410aa11

[ "BSD-3-Clause" ]

15

2015-03-23T02:55:20.000Z

2021-01-12T12:42:30.000Z

popcorn_gallery/tutorials/urls.py

Koenkk/popcorn_maker

0978b9f98dacd4e8eb753404b24eb584f410aa11

[ "BSD-3-Clause" ]

null

popcorn_gallery/tutorials/urls.py

Koenkk/popcorn_maker

0978b9f98dacd4e8eb753404b24eb584f410aa11

[ "BSD-3-Clause" ]

16

2015-02-18T21:43:31.000Z

2021-11-09T22:50:03.000Z

from django.conf.urls.defaults import patterns, url urlpatterns = patterns( 'popcorn_gallery.tutorials.views', url(r'^(?P<slug>[\w-]+)/$', 'object_detail', name='object_detail'), url(r'^$', 'object_list', name='object_list'), )

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0.166667

0

null

0

1

0

null

0

1

b15750ce5aef5b54cce96688ad262cadc96dc7f8

4,432

py

Python

src/taskmaster/client.py

alex/taskmaster

04a03bf0853facf318ce98192db6389cdaaefe3c

[ "Apache-2.0" ]

2

2015-11-08T12:45:38.000Z

2017-06-03T09:16:16.000Z

src/taskmaster/client.py

alex/taskmaster

04a03bf0853facf318ce98192db6389cdaaefe3c

[ "Apache-2.0" ]

null

src/taskmaster/client.py

alex/taskmaster

04a03bf0853facf318ce98192db6389cdaaefe3c

[ "Apache-2.0" ]

null

""" taskmaster.consumer ~~~~~~~~~~~~~~~~~~~ :copyright: (c) 2010 DISQUS. :license: Apache License 2.0, see LICENSE for more details. """ import cPickle as pickle import gevent from gevent_zeromq import zmq from gevent.queue import Queue from taskmaster.util import import_target class Worker(object): def __init__(self, consumer, target): self.consumer = consumer self.target = target def run(self): self.started = True while self.started: gevent.sleep(0) try: job_id, job = self.consumer.get_job() self.target(job) except KeyboardInterrupt: return finally: self.consumer.task_done() class Client(object): def __init__(self, address, timeout=2500, retries=3): self.address = address self.timeout = timeout self.retries = retries self.context = zmq.Context(1) self.poller = zmq.Poller() self.client = None def reconnect(self): if self.client: self.poller.unregister(self.client) self.client.close() print "Reconnecting to server on %r" % self.address else: print "Connecting to server on %r" % self.address self.client = self.context.socket(zmq.REQ) self.client.setsockopt(zmq.LINGER, 0) self.client.connect(self.address) self.poller.register(self.client, zmq.POLLIN) def send(self, cmd, data=''): request = [cmd, data] retries = self.retries reply = None while retries > 0: gevent.sleep(0) self.client.send_multipart(request) try: items = self.poller.poll(self.timeout) except KeyboardInterrupt: break # interrupted if items: reply = self.recv() break else: if retries: self.reconnect() else: break retries -= 1 return reply def recv(self): reply = self.client.recv_multipart() assert len(reply) == 2 return reply def destroy(self): if self.client: self.poller.unregister(self.client) self.client.setsockopt(zmq.LINGER, 0) self.client.close() self.context.destroy() class Consumer(object): def __init__(self, client, target, progressbar=True): if isinstance(target, basestring): target = import_target(target, 'handle_job') self.client = client self.target = target self.queue = Queue(maxsize=1) if progressbar: self.pbar = self.get_progressbar() else: self.pbar = None self._wants_job = False def get_progressbar(self): from taskmaster.progressbar import Counter, Speed, Timer, ProgressBar, UnknownLength widgets = ['Tasks Completed: ', Counter(), ' | ', Speed(), ' | ', Timer()] pbar = ProgressBar(widgets=widgets, maxval=UnknownLength) return pbar def get_job(self): self._wants_job = True return self.queue.get() def task_done(self): if self.pbar: self.pbar.update(self.tasks_completed) self.tasks_completed += 1 # self.client.send('DONE') def start(self): self.started = True self.tasks_completed = 0 self.client.reconnect() worker = Worker(self, self.target) gevent.spawn(worker.run) if self.pbar: self.pbar.start() while self.started: gevent.sleep(0) # If the queue has items in it, we just loop if not self._wants_job: continue reply = self.client.send('GET') if not reply: break cmd, data = reply # Reply can be "WAIT", "OK", or "ERROR" if cmd == 'OK': self._wants_job = False job = pickle.loads(data) self.queue.put(job) elif cmd == 'QUIT': break self.shutdown() def shutdown(self): if not self.started: return self.started = False if self.pbar: self.pbar.finish() self.client.destroy()

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