Reset23/the-stack-v2-python · Datasets at Hugging Face

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/data/plot_minTimeClimb.py

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naylor-b/om_bench

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""" Create plots for the Beam problem benchmarks. """ from __future__ import print_function from om_bench.post import post_process, BenchPost filename = 'minTimeClimb_state_nl_ln_drv.dat' bp = BenchPost('Min Time Climb') bp.flagtxt="Simultaneous Derivatives" bp.title_driver = "Compute Coloring" bp.special_plot_driver_on_linear = True bp.equal_axis = True bp.post_process(filename)

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kenneth.t.moore-1@nasa.gov

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/Que.Common.Test/Distributions/ConstantDistributionTests.py

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colgategao/Que

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import unittest class ConstantDistributionTests(unittest.TestCase): def MinimumParameter(self): self.fail("Not implemented") def MinusParameter(self): self.fail("Not implemented") if __name__ == '__main__': unittest.main()

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esemeniuc/CycleGAN-Music-Style-Transfer

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import argparse import os import tensorflow as tf from model import cyclegan from style_classifier import Classifer tf.set_random_seed(19) os.environ["CUDA_VISIBLE_DEVICES"] = os.environ['SGE_GPU'] parser = argparse.ArgumentParser(description='') parser.add_argument('--dataset_dir', dest='dataset_dir', default='JAZZ2ROCK', help='path of the dataset') parser.add_argument('--dataset_A_dir', dest='dataset_A_dir', default='JC_J', help='path of the dataset of domain A') parser.add_argument('--dataset_B_dir', dest='dataset_B_dir', default='JC_C', help='path of the dataset of domain B') parser.add_argument('--epoch', dest='epoch', type=int, default=100, help='# of epoch') parser.add_argument('--epoch_step', dest='epoch_step', type=int, default=10, help='# of epoch to decay lr') parser.add_argument('--batch_size', dest='batch_size', type=int, default=16, help='# images in batch') parser.add_argument('--train_size', dest='train_size', type=int, default=1e8, help='# images used to train') parser.add_argument('--load_size', dest='load_size', type=int, default=286, help='scale images to this size') parser.add_argument('--fine_size', dest='fine_size', type=int, default=128, help='then crop to this size') parser.add_argument('--time_step', dest='time_step', type=int, default=64, help='time step of pianoroll') parser.add_argument('--pitch_range', dest='pitch_range', type=int, default=84, help='pitch range of pianoroll') parser.add_argument('--ngf', dest='ngf', type=int, default=64, help='# of gen filters in first conv layer') parser.add_argument('--ndf', dest='ndf', type=int, default=64, help='# of discri filters in first conv layer') parser.add_argument('--input_nc', dest='input_nc', type=int, default=1, help='# of input image channels') parser.add_argument('--output_nc', dest='output_nc', type=int, default=1, help='# of output image channels') parser.add_argument('--lr', dest='lr', type=float, default=0.0002, help='initial learning rate for adam') parser.add_argument('--beta1', dest='beta1', type=float, default=0.5, help='momentum term of adam') parser.add_argument('--which_direction', dest='which_direction', default='AtoB', help='AtoB or BtoA') parser.add_argument('--phase', dest='phase', default='train', help='train, test') parser.add_argument('--save_freq', dest='save_freq', type=int, default=1000, help='save a model every save_freq iterations') parser.add_argument('--print_freq', dest='print_freq', type=int, default=100, help='print the debug information every print_freq iterations') parser.add_argument('--continue_train', dest='continue_train', type=bool, default=False, help='if continue training, load the latest model: 1: true, 0: false') parser.add_argument('--checkpoint_dir', dest='checkpoint_dir', default='./checkpoint', help='models are saved here') parser.add_argument('--sample_dir', dest='sample_dir', default='./samples', help='sample are saved here') parser.add_argument('--test_dir', dest='test_dir', default='./test', help='test sample are saved here') parser.add_argument('--log_dir', dest='log_dir', default='./log', help='logs are saved here') parser.add_argument('--L1_lambda', dest='L1_lambda', type=float, default=10.0, help='weight on L1 term in objective') parser.add_argument('--gamma', dest='gamma', type=float, default=1.0, help='weight of extra discriminators') parser.add_argument('--use_midi_G', dest='use_midi_G', type=bool, default=False, help='select generator for midinet') parser.add_argument('--use_midi_D', dest='use_midi_D', type=bool, default=False, help='select disciminator for midinet') parser.add_argument('--use_lsgan', dest='use_lsgan', type=bool, default=False, help='gan loss defined in lsgan') parser.add_argument('--max_size', dest='max_size', type=int, default=50, help='max size of image pool, 0 means do not use image pool') parser.add_argument('--sigma_c', dest='sigma_c', type=float, default=1.0, help='sigma of gaussian noise of classifiers') parser.add_argument('--sigma_d', dest='sigma_d', type=float, default=1.0, help='sigma of gaussian noise of discriminators') parser.add_argument('--model', dest='model', default='base', help='three different models, base, partial, full') parser.add_argument('--type', dest='type', default='cyclegan', help='cyclegan or classifier') args = parser.parse_args() def main(_): if not os.path.exists(args.checkpoint_dir): os.makedirs(args.checkpoint_dir) if not os.path.exists(args.sample_dir): os.makedirs(args.sample_dir) if not os.path.exists(args.test_dir): os.makedirs(args.test_dir) tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth = True with tf.Session(config=tfconfig) as sess: if args.type == 'cyclegan': model = cyclegan(sess, args) model.train(args) if args.phase == 'train' else model.test(args) if args.type == 'classifier': classifier = Classifer(sess, args) classifier.train(args) if args.phase == 'train' else classifier.test(args) if __name__ == '__main__': tf.app.run()

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/ion/util/testing_utils.py

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unwin/coi-services

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#!/usr/bin/env python """Utilities for unit and integration testing""" from pyon.core import bootstrap from pyon.ion.resource import get_restype_lcsm from pyon.public import BadRequest, PRED, RT def create_dummy_resources(res_list, assoc_list=None, container=None): """ Creates all resources of res_list. The names of the resources will be the keys in the returned dict The elements of the list are tuples with resource object as first element and optional actor name as second element. Can also create associations linking resources by name. Assoc_list is a list of 3-tuples subject name, predicate, object name. """ container = container or bootstrap.container_instance rr = container.resource_registry res_by_name = {} for res_entry in res_list: if isinstance(res_entry, dict): res_obj = res_entry["res"] actor_id = res_by_name[res_entry["act"]] if "act" in res_entry else None elif type(res_entry) in (list, tuple): res_obj = res_entry[0] actor_id = res_by_name[res_entry[1]] if len(res_entry) > 1 else None else: raise BadRequest("Unknown resource entry format") res_name = res_obj.name res_obj.alt_ids.append("TEST:%s" % res_name) res_lcstate = res_obj.lcstate rid, _ = rr.create(res_obj, actor_id=actor_id) res_by_name[res_name] = rid lcsm = get_restype_lcsm(res_obj.type_) if lcsm and res_lcstate != lcsm.initial_state: rr.set_lifecycle_state(rid, res_lcstate) if isinstance(res_entry, dict): if "org" in res_entry: rr.create_association(res_by_name[res_entry["org"]], PRED.hasResource, rid) if assoc_list: for assoc in assoc_list: sname, p, oname = assoc if type(sname) in (list, tuple) and type(oname) in (list, tuple): for sname1 in sname: for oname1 in oname: s, o = res_by_name[sname1], res_by_name[oname1] rr.create_association(s, p, o) elif type(oname) in (list, tuple): for oname1 in oname: s, o = res_by_name[sname], res_by_name[oname1] rr.create_association(s, p, o) elif type(sname) in (list, tuple): for sname1 in sname: s, o = res_by_name[sname1], res_by_name[oname] rr.create_association(s, p, o) else: s, o = res_by_name[sname], res_by_name[oname] rr.create_association(s, p, o) return res_by_name def create_dummy_events(event_list, container=None): container = container or bootstrap.container_instance ev_by_alias = {} for (alias, event) in event_list: evid, _ = container.event_repository.put_event(event) ev_by_alias[alias] = evid return ev_by_alias

[ "mmeisinger@ucsd.edu" ]

mmeisinger@ucsd.edu

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/rami/bin/rami/bin/easy_install

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whosawme/Loglist

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#!/Users/brocks/rami/rami/bin/rami/bin/python # -*- coding: utf-8 -*- import re import sys from setuptools.command.easy_install import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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/api/view_basic.py

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linyup/CrazyTester

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from django.http import JsonResponse, FileResponse from django.views.decorators.http import require_http_methods from django.forms.models import model_to_dict from django.contrib.auth.decorators import login_required from django.contrib.auth.models import User from django.conf import settings from concurrent.futures import ThreadPoolExecutor, as_completed from django.db import connection, close_old_connections from api.extensions.custom_response import * from queue import Queue from api.models import * import json import time import datetime import os import base64 # @require_http_methods(["GET"]) def test(req): if req.user.is_superuser: print("是") else: print("不是") def task_count(): tasks = TestTask.m.filter() all_count = 0 for task in tasks: count = len(str_to_list(task.case_ids)) print(task.id, count) all_count += count print(all_count) return response_200() # 发送邮件调试 def test2(req): wwx = WorkWXApply(2) # response_400(data={"a":1}, raise_flag=True) if not wwx.msg: textcard = { "description": "接口测试平台主页：\n<a href=\"http://10.112.16.6/home\">http://10.112.16.6/home</a>", "title": "测试任务-测试报告", "btntxt": "查看详情", "url": "http://10.112.16.6/home" } wwx.send_msg("guojing02", textcard) if wwx.msg: return response_400(wwx.msg) else: return response_200() else: return response_400(wwx.msg) # item = { # "body": str(req.body), # "GET": dict(req.GET), # "POST": dict(req.POST), # "user": req.POST.get("user", None) # } return response_200(item) def get_now_time(): return datetime.datetime.now() def json_dumps(data): return json.dumps(data, ensure_ascii=False) def json_dumps_indent4(data): return json.dumps(data, ensure_ascii=False, indent=4) def page_handel(req, raw_data, model_to_dict_handle=True): item = { "page": None, "page_size": None, "count": len(raw_data), "data": [], } page = req.GET.get("page", "1") page_size = req.GET.get("page_size", "10") # page_size为-1表示要所有数据不分页 if page_size == "-1": data = raw_data else: # page 如果只包含正整数并且不为0 则 int(), 否则为1 page = int(page) if page.isdigit() and page != "0" else 1 page_size = int(page_size) if page_size.isdigit() and page_size != "0" else 10 data = raw_data[((page - 1) * page_size):((page - 1) * page_size) + page_size] item["page"] = page item["page_size"] = page_size if model_to_dict_handle: for i in data: item["data"].append(model_to_dict(i)) else: item["data"] = data return item def get_user_info_for_session(req, item=None, create=False): """ 从用户信息 :param req: :param create: 是否为创建 :param item: 如果传了item，会把用户信息user_info update到item中， :return: user_info """ try: user = str(req.session.get("user")) user_id = req.session.get('_auth_user_id') # print(req.session) except Exception as e: response_400_raise_exception("获取用户名，用户id时出错：{}".format(e)) else: c_time = str(get_now_time()) user_info = { "latest_update_user": user, "latest_update_user_id": user_id, "u_date": c_time, } if create: user_info["create_user"] = user user_info["create_user_id"] = user_id user_info["c_date"] = c_time if item: item.update(user_info) return user_info @login_required @require_http_methods(["POST"]) def excel_json_auto_switch(req): sample_data_raw = req.POST.get("sample_data", "") sample_data = None if not sample_data_raw: return response_400("样例数据为空！") try: sample_data_json = json.loads(sample_data_raw) except: sample_data = sample_data_wsitch_json(sample_data_raw) else: if type(sample_data_json) == str: sample_data = sample_data_wsitch_json(sample_data_raw) elif type(sample_data_json) == list: sample_data = sample_data_wsitch_excel(sample_data_json) else: return response_400("样例数据格式有误，请检查修改！") return response_200(sample_data=sample_data) def sample_data_wsitch_excel(sample_data_json): lines = [] if not sample_data_json: return "" try: headers = sample_data_json[0].keys() lines.append("\t".join(headers)) for i in sample_data_json: item = [] for h in headers: item.append(i[h]) lines.append(" ".join(item)) sample_data_str = "\n".join(lines) return sample_data_str except Exception as e: return response_400("样例数据格式有误，请检查修改！") def sample_data_wsitch_json(sample_data_raw): sample_data_json = [] try: sample_data_list = [i.split("\t") for i in sample_data_raw.strip(" ").split("\n") if i] row = len(sample_data_list[0]) cos = len(sample_data_list) if cos == 1: response_400_raise_exception("测试样例数据有误1,请检查!（至少两行数据）") for c in range(1, cos): sample_data_item = {} for r in range(row): sample_data_item[sample_data_list[0][r]] = sample_data_list[c][r] sample_data_json.append(sample_data_item) sample_data_json = json_dumps_indent4(sample_data_json) return sample_data_json except Exception as e: response_400_raise_exception("测试样例数据有误2,请检查!\r\n{}".format(e)) # 分割逗号 def str_to_list(strs, flag=","): """ :param strs: 字符串 :param flag: 分隔符 :return: 字符串以分隔符分割，并且每个元素strip并且不要空的 "1,2 ,2 ,3, ," ==> ['1', '2', '2', '3'] """ strs = strs or "" return [i.strip() for i in strs.strip().split(flag) if i.strip()] @require_http_methods(["POST"]) # 只限params, kv格式转成json格式 def switch_json(req): param_keys = req.POST.getlist("param_key", []) param_values = req.POST.getlist("param_value", []) try: warning = [] params = {} # 键值对格式转换成dict格式 for i in range(len(param_keys)): if param_keys[i].strip(): params[param_keys[i]] = param_values[i] else: warning.append("params名称为空的参数已被忽略！") # print(params) warning = ",".join(warning) return response_200(data=params, warning=warning) except Exception as e: return response_400("出错:{}".format(e)) @require_http_methods(["POST"]) def switch_kv(req): jp = req.POST.get("json_params", "") try: data = json.loads(jp) return response_200(data=data) except: return response_400("json格式数据有错误或者为空！") @require_http_methods(["POST"]) # f12 粘贴的数据格式转换成json格式 def F12_p_to_json(req): params = req.POST.get("params", "") try: dict_params = {i.split(": ")[0]: i.split(": ", 1)[-1] for i in params.split("\n") if i} data = json_dumps_indent4(dict_params) return response_200(data=data) except Exception as e: return response_400("出错:{}".format(e)) # 将从表单获取的kv形式的param和header转换成字典 def kv_switch_dict(k, v): """ k,v 皆为长度一样的列表,返回{k[i]:v[i]...} """ item = {} warning = [] for i in range(len(k)): if k[i].strip(): item[k[i]] = v[i] else: warning.append("名称为空的参数已被忽略！") return { "temp": item, "warning": warning, } # 验证非空 def verify_not_is_None(item, fileds): """ :param item: 字典格式数据 :param fileds: 要验证的字段 :return: 如果是空的字段，返回字段名，所有字段都不为空，返回None """ for i in fileds: if not item[i]: return i else: return None # 验证是否为json格式 def verify_is_json_and_switch(item, fileds, switch=True): """ :param item: 字典格式数据 :param fileds: 字典里面要验证的字段 :return: 是josn格式，返回None，否则返回错误信息 """ for i in fileds: # 如果值不为None if item[i]: try: j = json.loads(item[i]) if type(j) == str or type(j) == int: return "{}必须为json格式,当前为字符串或数字格式!".format(i) if switch: item[i] = j except: return "{}必须为json格式".format(i) else: if type(j) == str: return "{}必须为json格式".format(i) # 字典转换json def dict_to_json(item, fileds): for i in fileds: if item[i]: item[i] = json_dumps(item[i]) else: item[i] = "" @require_http_methods(["POST"]) # 添加时间戳 def add_sign(req): """ 获取cookies值,转换成字典格式,将字典中sign的值赋为当前时间戳 """ try: cookies = req.POST["cookies"] if cookies: dict_cookies = json.loads(cookies) else: dict_cookies = {} timestamp = str(int(time.time() * 1000)) dict_cookies['sign'] = timestamp data = json_dumps_indent4(dict_cookies) return response_200(data=data) except Exception as e: return response_400("错误:{}".format(e)) # 多线程 class Futures: def __init__(self, max_workers): self.executor = ThreadPoolExecutor(max_workers=max_workers) # 线程池，执行器 self.tasks = [] # 线程集合 def submit(self, func, arg, *args, **kwargs): task = self.executor.submit(func, arg, *args, **kwargs) self.tasks.append(task) return task def as_completed(self): """ :return: 阻塞主进程,直到所有线程完成任务 """ for future in as_completed(self.tasks): # print("等待...{}".format(len(self.tasks))) future.result() @require_http_methods(["GET"]) def download(req, file_path): file_path_real = os.path.join(settings.MEDIA_ROOT_VIRTUAL, file_path) if not os.path.exists(file_path_real): return response_404("文件不存在：{}！".format(file_path)) file = open(file_path_real, 'rb') response = FileResponse(file) response['Content-Type'] = 'application/octet-stream' # response['Content-Type'] = 'application/vnd.ms-excel' # 注意格式 response['Content-Disposition'] = 'attachment;filename="{}"'.format(file_path_real.rsplit("/")[-1]) return response def customer_get_list(model, filter_item=None, order_by=None): """ :param model: 模型类对象 :param filter_item: 字典 :param order_by: 元祖，传入 () 时不排序 :return: """ filter_item = filter_item or {} order_by = order_by or ("title", ) if order_by != -1 else () datas = [] try: infos = model.m.filter(**filter_item).order_by(*order_by) for i in infos: datas.append(model_to_dict_custom(i)) except Exception as e: return response_400("错误信息：{}".format(e)) return response_200(data=datas) def model_to_dict_custom(model): data = model_to_dict(model, exclude=["isDelete", "c_date", "u_date"]) data["c_date"] = model.c_date.strftime('%Y-%m-%d %H:%M:%S') data["u_date"] = model.u_date.strftime('%Y-%m-%d %H:%M:%S') return data def api_ids_handle(item): """ :param item: 根据item中的project_id, group_ids,api_ids获取对应所有api_id 会在item中补充group_title_list,api_title_list字段 :return: 返回 msg(错误信息)和api_id_list """ api_id_list = None def set_msg_and_return(msg=""): if msg: item["msg"] = msg item["isValid"] = False # item["group_title_list"] = json_dumps(['测试内容失效!:{}'.format(msg)]) # item["api_title_list"] = json_dumps(['测试内容失效!:{}'.format(msg)]) item["group_title_list"] = "" item["api_title_list"] = "" return msg, api_id_list if not item["group_ids"] and not item["api_ids"]: return set_msg_and_return("未填写分组id或接口id！") # 验证project_id if item["project_id"]: try: pro_data = ApiProject.m.get(id=item["project_id"]) except: return set_msg_and_return("不存在的项目id:{}".format(item["project_id"])) else: item["project_title"] = pro_data.title else: return set_msg_and_return("未填写项目id！") # 获取group_id_list， group_title_list group_id_list = [] group_title_list = [] if item["group_ids"] == "all": group_list = ApiGroup.m.filter(project=item["project_id"]) for i in group_list: group_id_list.append(i.id) group_title_list.append(i.title) else: # 判断所有group_id是否存在 group_id_list = str_to_list(item["group_ids"]) for group_id in group_id_list: try: group_data = ApiGroup.m.get(id=int(group_id)) except: return set_msg_and_return("不存在的分组id:{}".format(group_id)) else: group_title_list.append("分组:{} -- {}".format(group_data.id, group_data.title)) # 获取api_id_list， api_title_list api_id_list = str_to_list(item["api_ids"]) api_title_list = [] # 判断所有api_id是否存在 for api_id in api_id_list: try: api_data = ApiApi.m.get(id=int(api_id)) except: return set_msg_and_return("不存在的接口id:{}".format(api_id)) else: api_title_list.append("接口:{} -- {}".format(api_data.id, api_data.title)) # 解析group下的所有api,放到api_id_list中 for group_id in group_id_list: apis = ApiApi.m.filter(group=int(group_id)) for api in apis: api_id_list.append(str(api.id)) api_id_list = list(set(api_id_list)) # 去重,防止group下的api和api重复 if not api_id_list: return set_msg_and_return("分组id与接口id 清洗后没有符合的数据！") item["group_title_list"] = json_dumps(group_title_list) if group_title_list else "" item["api_title_list"] = json_dumps(api_title_list) if api_title_list else "" # return group_id_list, api_id_list return set_msg_and_return() def case_ids_handle(item): def set_msg_and_return(msg=""): if msg: item["msg"] = msg item["isValid"] = False # item["case_title_list"] = json_dumps(['测试内容失效!:{}'.format(msg)]) item["case_title_list"] = "" return msg, case_id_list case_title_list = [] case_id_list = str_to_list(item["case_ids"]) for case_id in case_id_list: try: case_data = ApiCase.m.get(id=int(case_id)) case_title_list.append("用例:{} -- {}".format(case_data.id, case_data.title)) except: return set_msg_and_return("不存在的用例id:{}".format(case_id)) if not case_title_list: return set_msg_and_return("没有填写用例！") item["case_title_list"] = json_dumps(case_title_list) if case_title_list else "" return set_msg_and_return() def get_all_projects(filter_item=None, **kwargs): if filter_item is None: filter_item = {} filter_item.update(kwargs) return ApiProject.m.filter(**filter_item).order_by("title") # 每个项目分组接口用例统计柱形图 def staticitem_project(req): # data = [] project_title_list = [] group_count_list = [] api_count_list = [] case_count_list = [] cursor = connection.cursor() case_sql = "SELECT api_id, count(*) FROM api_case WHERE isDelete=false group by api_id;" cursor.execute(case_sql) cases = cursor.fetchall() projects = get_all_projects() for project in projects: project_title_list.append(project.title) groups = ApiGroup.m.filter(project_id=project.id) group_count = len(groups) api_count = 0 case_count = 0 for gourp in groups: apis = ApiApi.m.filter(group_id=gourp.id) api_count += len(apis) for api in apis: for case in cases: if case[0] == api.id: case_count += case[1] group_count_list.append(group_count) api_count_list.append(api_count) case_count_list.append(case_count) # data.append({project.title: [group_count, api_count, case_count]}) # print("{}：分组：{}个，接口{}个，用例{}个\n".format( # project.title, group_count, api_count, case_count)) # print("总计{}个用例".format(len(ApiCase.m.filter()))) return response_200( project_title_list=project_title_list, group_count_list=group_count_list, api_count_list=api_count_list, case_count_list=case_count_list, all_count=len(ApiCase.m.filter())) # 项目下任务数量统计、任务中包含总用例数量统计 def staticitem_task(req): data = [] projects = get_all_projects() for project in projects: task_count = TestTask.m.filter(project_id=project.id).count() data.append({"name": project.title, "value": task_count}) tasks = TestTask.m.filter() case_count_for_task = 0 for task in tasks: count = len(str_to_list(task.case_ids)) case_count_for_task += count return response_200(data=data, task_count=TestTask.m.filter().count(), case_count_for_task=case_count_for_task) # 每个项目最近100次、10次成功情况/最近七天成功失败次数 def staticitem_recent(req): # 最近七天成功失败次数 project_id = req.GET.get("project_id", "") filter_item = {} if project_id: filter_item["id"] = project_id days = [] # 最近七天 datetime时间格式，需要str才能得到 2021-07-02 today = datetime.date.today() # 获得今天的日期 for i in range(7): day = today - datetime.timedelta(days=i) days.insert(0, day) colors = ["#5470c6", "#91cc75", "#fac858", "#ee6666", "#73c0de", "#3ba272", "#fc8452", "#9a60b4", "#ea7ccc", "#f173ac", "#f05b72", "#fdb933", "#f26522", "#ef5b9c"] colors_q = Queue() for color in colors: colors_q.put(color) titles = [] series = [] cursor = connection.cursor() projects = get_all_projects(filter_item) username = str(req.session.get("user")) for project in projects: if username not in str_to_list(project.users or ""): continue if colors_q.empty(): for color in colors: colors_q.put(color) title_succeed = "{} - 成功数量".format(project.title) titles.append(title_succeed) title_fail = "{} - 失败数量".format(project.title) titles.append(title_fail) temp_succeed = { "name": title_succeed, "type": 'bar', "stack": project.title, "emphasis": { "focus": 'series' }, "data": [], "itemStyle": { "normal": { "label": { "show": True, "position": 'middle', "textStyle": { "color": 'black', "fontSize": 24 if project_id else 12 } }, "color": "#73c0de" if project_id else colors_q.get() } } } temp_fail = { "name": title_fail, "type": 'bar', "stack": project.title, "emphasis": { "focus": 'series' }, "data": [], "itemStyle": { "normal": { "label": { "show": True, "position": 'top', "textStyle": { "color": 'red', "fontSize": 24 if project_id else 14 } }, "color": "#ee6666" if project_id else colors_q.get() } } } for d in days: cursor.execute( "select count(*) from api_test_report where (c_date between '{}' and '{}') and project_title = '{}' and test_ret = 1".format( str(d), str(d + datetime.timedelta(days=1)), project.title )) succed_count = str(cursor.fetchone()[0]) temp_succeed["data"].append(succed_count) cursor.execute( "select count(*) from api_test_report where (c_date between '{}' and '{}') and project_title = '{}' and test_ret = 0".format( str(d), str(d + datetime.timedelta(days=1)), project.title )) fail_count = cursor.fetchone()[0] temp_fail["data"].append(str(fail_count)) series.append(temp_succeed) series.append(temp_fail) return response_200(series=series, titles=titles, days=days) # 按人员统计用例总数，近7周增加 def staticitem_user(req): days_raw = [] # ["2021-07-01", "2021-07-07", ...] days = [] # ["2021-07-01 -- 2021-07-07", "2021-07-01 -- 2021-07-07", ...] # 测试组全部成员，有限使用自定义的，没有自定义的，用全部的django用户 try: users = str_to_list(ApiUser.m.get(type_id="1").users or "") except: users = [] users_raw = User.objects.filter() for user in users_raw: users.append(user.username) series = [] today = datetime.date.today() # 获得今天的日期 # 本周第一天和最后一天 this_week_start = today - datetime.timedelta(days=today.weekday()) # this_week_end = today + datetime.timedelta(days=6 - today.weekday()) days_raw.append([this_week_start, today]) for i in range(1, 7): # 上周第一天和最后一天 last_week_start = today - datetime.timedelta(days=today.weekday() + 7 * i) last_week_end = today - datetime.timedelta(days=today.weekday() + 1 + 7 * (i-1)) days_raw.append([last_week_start, last_week_end]) # print(days) for user in users: temp = { "name": user, "type": 'line', "data": [] } series.append(temp) cursor = connection.cursor() for day in days_raw: days.append("{} - {}".format(str(day[0]).replace("-", "/"), str(day[1]).replace("-", "/"))) sql = "SELECT create_user, count(*) FROM api_case WHERE (c_date between '{}' and '{}') and isDelete=false GROUP BY create_user;".format( str(day[0]), str(day[1] + datetime.timedelta(days=1))) # print(sql) cursor.execute(sql) rets = cursor.fetchall() for serie in series: for ret in rets: if ret[0] == serie["name"]: serie["data"].append(ret[1]) break else: serie["data"].append(0) return response_200(series=series, users=users, days=days) # 统计每个人创建的用例数量 def staticitem_user2(req): series = [] cursor = connection.cursor() sql = "SELECT create_user_id, count(*) FROM api_case WHERE isDelete=false group by create_user_id;" cursor.execute(sql) for line in cursor.fetchall(): series.append({"name": User.objects.get(id=line[0]).username or "无", "value": line[1]}) return response_200(series=series) # def get_host_ip(): # try: # s = socket.socket() # s.connect(("www.baidu.com", 80)) # ip = s.getsockname()[0] # finally: # s.close() # return ip def strToBase64(s): ''' 将字符串转换为base64字符串 :param s: :return: ''' strEncode = base64.b64encode(s.encode('utf8')) return str(strEncode, encoding='utf8') def base64ToStr(s): ''' 将base64字符串转换为字符串 :param s: :return: ''' strDecode = base64.b64decode(bytes(s, encoding="utf8")) return str(strDecode, encoding='utf8')

[ "547684776@qq.com" ]

547684776@qq.com

7b45039e57dfbcd87822b96699a38579a3b9b079

c4e5cb5d744c5c0fdf031121073c34016f6a9721

/insureAnts/comView/views.py

a4d6f73dfad534fd6f811707f01de5689e13e361

[]

no_license

nfgrawker/Django_project

0d7e819db025e732596a2034d146e2f668931948

fee58cc419e084b7c4769184b03663b9852db1b4

refs/heads/master

2021-06-15T23:55:39.064692

2021-02-21T16:35:52

155,213,541

0

null

UTF-8

Python

false

595

py

from django.shortcuts import render from django.http import HttpResponse from .models import Post def home(request): return render(request, "comView/login.html") def about(request): return render(request, "comView/about.html") def index(request): return render(request, "comView/index.html") def tables(request): context = { 'posts' : Post.objects.all()} return render(request, "comView/tables.html", context) def charts(request): return render(request, "comView/charts.html") def forgot(request): return render(request, "comView/forgot-password.html")

[ "nfgrawker@gmail.com" ]

nfgrawker@gmail.com

ef8f564e90f6902bd7e910905b8b070b870a167f

9a6bbda7b44ef12895058eac593fd13964209acd

/main.py

f9c9d13dfba51be58128bbbed5164838bd4a6f2e

[]

no_license

m358807551/game_tree

b30369ee3c72e7571b79da4aad7a124eca7822ad

fb45614263e8ce1bd05f9a0ef744e90eac38b5f4

refs/heads/master

2021-01-20T08:07:45.590414

2017-05-03T03:17:14

90,103,182

0

null

UTF-8

Python

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368

py

# coding=utf-8 from core import GameTree from game import HorseRace from game import ChampionSolo def main1(): game_tree = GameTree(6, HorseRace) print game_tree.best_strategy(), game_tree.root.weight def main2(): game_tree = GameTree(10, ChampionSolo) print game_tree.best_strategy(), game_tree.root.weight if __name__ == "__main__": main2()

[ "mayupeng@wanwujieshu.net" ]

mayupeng@wanwujieshu.net

bcee10fcaec210f62e55fa54bd72e2abe27cc9b7

754188242cd651e6398391f0917e0f3212176874

/icheckgames/templatetags/isUserGame.py

8cab96d4ec0fe269bfe5af8a47aa75ec81628da4

[]

no_license

abhidevmumbai/checkit

7d5fee6f96b891f13dc4bd9b4266ef0328a351d5

93677d6556f47285537bbc3fcc93ac4af30cd211

refs/heads/master

2021-01-13T14:38:03.483501

2013-07-10T12:10:16

9,027,338

0

null

UTF-8

Python

false

302

py

from django import template from icheckgames.models import GameMap register = template.Library() ''' Template tag function to check if the game exists in the user's list ''' @register.simple_tag def isUserGame(user, obj): try: user.gamemap_set.get(game=obj) return 'checked' except: return

[ "abhishekd@snapstick.com" ]

abhishekd@snapstick.com

8b33329ab9ca98f83c03387c11ff53907a43e3bd

6fa6ad60ef3150176b840de908088ccc34221c16

/venv/Scripts/pip3.6-script.py

f72b1df01b56efdf6e110f807e3b049900fcde9d

[]

no_license

ZeddZoo/Jingle

2212c78364b9931830047ae949d8ea8a31a6fdf1

40aff13614e558cbe5e8ff9405fb30c53a94e6ca

refs/heads/master

2020-04-07T14:19:04.654954

2018-12-05T19:52:14

158,442,298

0

null

UTF-8

Python

false

412

py

#!C:\Users\Ziyi\Desktop\TermProject\venv\Scripts\python.exe # EASY-INSTALL-ENTRY-SCRIPT: 'pip==10.0.1','console_scripts','pip3.6' __requires__ = 'pip==10.0.1' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('pip==10.0.1', 'console_scripts', 'pip3.6')() )

[ "44912877+ZeddZoo@users.noreply.github.com" ]

44912877+ZeddZoo@users.noreply.github.com

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/backup/user_077/ch4_2020_03_20_12_34_54_305180.py

a7402ae72f0e4cfe2f3c9cb1eb90a66f7458df8c

[]

no_license

gabriellaec/desoft-analise-exercicios

b77c6999424c5ce7e44086a12589a0ad43d6adca

01940ab0897aa6005764fc220b900e4d6161d36b

refs/heads/main

2023-01-31T17:19:42.050628

2020-12-16T05:21:31

306,735,108

0

null

UTF-8

Python

false

154

py

def classifica_idade(x): if x<=11: return (crianca) else: if x<=17 x=>12: return (adolescente) else: if x=>18: return(adulto)

[ "you@example.com" ]

you@example.com

7e1b3ac3647139d0f383dedf5b9d049c36240904

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/apps/order/models.py

e05fe1220b66320d4b25adfb6ee81215187135d5

[]

no_license

zl6892191/daydayflash

f7db20d0d5153e425e03f5c1f3e44c27e4ce170a

a921a0dd1d12e1586ff4ffdc5b485fb830b8980a

refs/heads/master

2020-03-13T13:56:14.660412

2018-04-26T11:53:03

131,148,328

0

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UTF-8

Python

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py

from django.db import models from db.base_model import BaseModel # Create your models here. class OrderInfo(BaseModel): '''订单模型类''' PAY_METHODS = { '1': "货到付款", '2': "微信支付", '3': "支付宝", '4': '银联支付' } PAY_METHOD_CHOICES = ( (1, '货到付款'), (2, '微信支付'), (3, '支付宝'), (4, '银联支付') ) ORDER_STATUS = { 1:'待支付', 2:'待发货', 3:'待收货', 4:'待评价', 5:'已完成' } ORDER_STATUS_CHOICES = ( (1, '待支付'), (2, '待发货'), (3, '待收货'), (4, '待评价'), (5, '已完成') ) order_id = models.CharField(max_length=128, primary_key=True, verbose_name='订单id') user = models.ForeignKey('user.User', verbose_name='用户') addr = models.ForeignKey('user.Address', verbose_name='地址') pay_method = models.SmallIntegerField(choices=PAY_METHOD_CHOICES, default=3, verbose_name='支付方式') total_count = models.IntegerField(default=1, verbose_name='商品数量') total_price = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='商品总价') transit_price = models.DecimalField(max_digits=10, decimal_places=2,verbose_name='订单运费') order_status = models.SmallIntegerField(choices=ORDER_STATUS_CHOICES, default=1, verbose_name='订单状态') trade_no = models.CharField(max_length=128, default='', verbose_name='支付编号') class Meta: db_table = 'df_order_info' verbose_name = '订单' verbose_name_plural = verbose_name class OrderGoods(BaseModel): '''订单商品模型类''' order = models.ForeignKey('OrderInfo', verbose_name='订单') sku = models.ForeignKey('goods.GoodsSKU', verbose_name='商品SKU') count = models.IntegerField(default=1, verbose_name='商品数目') price = models.DecimalField(max_digits=10, decimal_places=2, verbose_name='商品价格') comment = models.CharField(max_length=256, default='', verbose_name='评论') class Meta: db_table = 'df_order_goods' verbose_name = '订单商品' verbose_name_plural = verbose_name # Create your models here.

[ "258306908@qq.com" ]

258306908@qq.com

96b1b935b4050b4196ba8415dbd5dc3187a6da93

01c0c44238b060aabbb92c51d77d4f8b9db78b20

/core_application/urls.py

564eb03222670908f32ae30ee665f9b5eedf7688

[]

no_license

Manimaran11/Video_Upload

5312b9cc04d81c07c39355202a5328a8ac1586d4

313a3064b25aca296adfa46718b1cb21109c6121

refs/heads/master

2020-07-31T12:45:50.405805

2019-09-30T13:15:58

210,607,929

0

null

UTF-8

Python

false

745

py

from django.contrib import admin from django.urls import path from django.conf import settings from django.conf.urls import include, url # For django versions before 2.0 from django.urls import include, path # For django versions from 2.0 and up from video_application.views import HomeView, NewVideo, CommentView, LoginView, RegisterView, VideoView, VideoFileView,LogoutView urlpatterns = [ path('admin/', admin.site.urls,name='admin'), path('', include('video_application.urls')), ] if settings.DEBUG: import debug_toolbar urlpatterns = [ path('__debug__/', include(debug_toolbar.urls)), # For django versions before 2.0: # url(r'^__debug__/', include(debug_toolbar.urls)), ] + urlpatterns

[ "pmanimaran097@gmail.com" ]

pmanimaran097@gmail.com

bf296306ff4e50b59c299c2b4abdedafb41ee1ac

df46cf831cba3e2b91e794be6e03f0cbd1f2f8af

/p53.py

19085be8d40163444a5cba5fc64cd3578d6ab333

[]

no_license

manjupinky/Phyton

7653022f8a9b64f7213902c34998e7900f529b5e

711bb5f491a904c1e35b4204223a90d8f3c16a6e

refs/heads/master

2020-03-27T10:10:07.963601

2019-01-22T07:58:54

146,399,913

0

null

UTF-8

Python

false

132

py

m=int(input("Enter a number:")) tot=0 while(m>0): dig=m%10 tot=tot+dig m=m//10 print("The total sum of digits is:",tot)

[ "noreply@github.com" ]

manjupinky.noreply@github.com

b78d1274026ad79ba65fd9251177d41ebcc34b16

a17b602c8c2ce0405622c57dd47218b17e57718d

/test_class/language_survey.py

4216813046b47700e3f50d81111a63b623419d6a

[]

no_license

rowlingz/Deepen-learning-Python

a5f2fe75b6633c6786912d23d414d303f1d4ade8

9b55b49b468ff412c5d78773a03279bdc77a3874

refs/heads/master

2020-04-29T15:22:30.453074

2019-06-06T06:52:49

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# -*- coding:utf-8 -*- from survey import AnonymousSurvey question = "What language did you first learn to speak?" my_survey = AnonymousSurvey(question) my_survey.show_question() print("Enter 'q' at any time to quit.") while True: response = input("Language: ") if response == 'q': break my_survey.store_response(response) print("\nThank you to everyone who participated in the survey!") my_survey.show_results()

[ "ning_ML@126.com" ]

ning_ML@126.com

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/indices/safer.py

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[]

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psdh/WhatsintheVector

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refs/heads/master

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ii = [('GodwWSL2.py', 1), ('ChanWS.py', 1), ('WilbRLW4.py', 2), ('WilbRLW5.py', 1), ('LeakWTI3.py', 3), ('PeckJNG.py', 1), ('AdamWEP.py', 1), ('WilbRLW2.py', 1), ('ClarGE2.py', 2), ('CarlTFR.py', 3), ('LyttELD.py', 1), ('CookGHP2.py', 1), ('BailJD1.py', 1), ('MarrFDI2.py', 1), ('ClarGE.py', 1), ('LandWPA.py', 1), ('GilmCRS.py', 1), ('NewmJLP.py', 1), ('GodwWLN.py', 1), ('CoopJBT.py', 2), ('SoutRD2.py', 1), ('SoutRD.py', 2), ('DickCSG.py', 1), ('WheeJPT.py', 1), ('MartHRW.py', 1), ('WestJIT.py', 1), ('CoolWHM3.py', 1), ('FitzRNS.py', 1), ('StorJCC.py', 1), ('WilbRLW3.py', 1), ('AinsWRR2.py', 1), ('MartHRW2.py', 1), ('FitzRNS2.py', 5), ('MartHSI.py', 1), ('EvarJSP.py', 3), ('WordWYR.py', 2), ('KeigTSS.py', 1)]

[ "prabhjyotsingh95@gmail.com" ]

prabhjyotsingh95@gmail.com

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2023-04-10T22:22:35.035542

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# type: ignore r''' **This feature is under a Beta release and its API may change.** FX is a toolkit for developers to use to transform ``nn.Module`` instances. FX consists of three main components: a **symbolic tracer,** an **intermediate representation**, and **Python code generation**. A demonstration of these components in action: :: import torch # Simple module for demonstration class MyModule(torch.nn.Module): def __init__(self): super().__init__() self.param = torch.nn.Parameter(torch.rand(3, 4)) self.linear = torch.nn.Linear(4, 5) def forward(self, x): return self.linear(x + self.param).clamp(min=0.0, max=1.0) module = MyModule() from torch.fx import symbolic_trace # Symbolic tracing frontend - captures the semantics of the module symbolic_traced : torch.fx.GraphModule = symbolic_trace(module) # High-level intermediate representation (IR) - Graph representation print(symbolic_traced.graph) """ graph(x): %param : [#users=1] = self.param %add_1 : [#users=1] = call_function[target=<built-in function add>](args = (%x, %param), kwargs = {}) %linear_1 : [#users=1] = call_module[target=linear](args = (%add_1,), kwargs = {}) %clamp_1 : [#users=1] = call_method[target=clamp](args = (%linear_1,), kwargs = {min: 0.0, max: 1.0}) return clamp_1 """ # Code generation - valid Python code print(symbolic_traced.code) """ def forward(self, x): param = self.param add_1 = x + param; x = param = None linear_1 = self.linear(add_1); add_1 = None clamp_1 = linear_1.clamp(min = 0.0, max = 1.0); linear_1 = None return clamp_1 """ The **symbolic tracer** performs “symbolic execution” of the Python code. It feeds fake values, called Proxies, through the code. Operations on theses Proxies are recorded. More information about symbolic tracing can be found in the :func:`symbolic_trace` and :class:`Tracer` documentation. The **intermediate representation** is the container for the operations that were recorded during symbolic tracing. It consists of a list of Nodes that represent function inputs, callsites (to functions, methods, or :class:`torch.nn.Module` instances), and return values. More information about the IR can be found in the documentation for :class:`Graph`. The IR is the format on which transformations are applied. **Python code generation** is what makes FX a Python-to-Python (or Module-to-Module) transformation toolkit. For each Graph IR, we can create valid Python code matching the Graph’s semantics. This functionality is wrapped up in :class:`GraphModule`, which is a :class:`torch.nn.Module` instance that holds a :class:`Graph` as well as a ``forward`` method generated from the Graph. Taken together, this pipeline of components (symbolic tracing → intermediate representation → transforms → Python code generation) constitutes the Python-to-Python transformation pipeline of FX. In addition, these components can be used separately. For example, symbolic tracing can be used in isolation to capture a form of the code for analysis (and not transformation) purposes. Code generation can be used for programmatically generating models, for example from a config file. There are many uses for FX! Several example transformations can be found at the `examples <https://github.com/pytorch/examples/tree/master/fx>`__ repository. ''' from .graph_module import GraphModule from .symbolic_trace import symbolic_trace, Tracer, wrap from .graph import Graph from .node import Node, map_arg from .proxy import Proxy from .interpreter import Interpreter as Interpreter, Transformer as Transformer from .subgraph_rewriter import replace_pattern

[ "952361195@qq.com" ]

952361195@qq.com

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/part03/numpy/code/example_4.py

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[]

no_license

askmr03/nd-AIPython

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refs/heads/master

2021-10-09T08:44:02.525056

2018-12-24T15:42:35

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# We create a 3 x 4 ndarray full of zeros. import numpy as np X = np.zeros((3,4)) # We print X print() print('X = \n', X) print() # We print information about X print('X has dimensions:', X.shape) print('X is an object of type:', type(X)) print('The elements in X are of type:', X.dtype) ''' X = [[0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.]] X has dimensions: (3, 4) X is an object of type: <class 'numpy.ndarray'> The elements in X are of type: float64 '''

[ "1193094618@qq.com" ]

1193094618@qq.com

a79302a9ae686eca28c7323d67f92a5c18171bb8

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/mot_imgnet/mot_util.py

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[]

no_license

UniLauX/mot_scripts

877fb22c532016821d6d68d3c2ca7058e6a22ba8

4afcdfee4c0630e6d903ae43cdd8345e0ed6d524

refs/heads/master

2021-06-17T21:05:37.903723

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import _init_paths from common_lib import create_dir from common_lib import load_txt_to_strarr from common_lib import save_strarr_to_txt from trans_util import rename_copy_images from trans_util import rename_create_ppmimgs import numpy as np import os debug_flag=True def load_seq_names(seqmap_path): str_arr=load_txt_to_strarr(seqmap_path) seq_names=str_arr[1:] return seq_names def generate_jpgimgs(seq_data_dir,seq_names,dest_jpgimgs_dir): for seq_name in seq_names: seq_folder_dir=os.path.join(seq_data_dir,seq_name) seq_img1_dir=os.path.join(seq_folder_dir,'img1') rename_copy_images(seq_img1_dir,dest_jpgimgs_dir,seq_name) def generate_ppmimgs(seq_data_dir,seq_names,dest_ppmimgs_dir): if not os.path.exists(dest_ppmimgs_dir): create_dir(dest_ppmimgs_dir) for seq_name in seq_names: seq_folder_dir=os.path.join(seq_data_dir,seq_name) seq_img1_dir=os.path.join(seq_folder_dir,'img1') rename_create_ppmimgs(seq_img1_dir,dest_ppmimgs_dir,seq_name) def generate_imgsets(seqs_data_dir,seq_names,dest_imgsets_path): ##im_names(.jpg) str_arr=[] for seq_name in seq_names: seq_data_dir=os.path.join(seqs_data_dir,seq_name) seq_img1_dir=os.path.join(seq_data_dir,'img1') im_names=os.listdir(seq_img1_dir) im_names=np.sort(im_names) for im_name in im_names: base_im_name=os.path.splitext(im_name)[0] dest_im_name=seq_name+'_'+base_im_name str_arr.append(dest_im_name) dest_im_names=np.array(str_arr) ##save save_strarr_to_txt(dest_imgsets_path,dest_im_names) ##get annotations when necessary def generate_annots(): print 'generate_annots....' # if year==2015: # opticn one # else: # option two def generate_bmfsets(seq_data_dir,seq_names,dest_imgsets_dir): dest_ppmsets_dir=os.path.join(dest_imgsets_dir,'Bmf') if debug_flag: print dest_ppmsets_dir ##im_names(.ppm) for seq_name in seq_names: seq_folder_dir=os.path.join(seq_data_dir,seq_name) seq_img1_dir=os.path.join(seq_folder_dir,'img1') im_names=os.listdir(seq_img1_dir) im_names=np.sort(im_names) ## file_path dest_bmffile_path=os.path.join(dest_ppmsets_dir,seq_name+'.bmf') ## im_names str_arr=[] first_line=str(len(im_names))+" "+'1' str_arr.append(first_line) for im_name in im_names: tmp_im_name=seq_name+'_'+im_name dest_im_name=tmp_im_name.replace('.jpg','.ppm') str_arr.append(dest_im_name) dest_im_names=np.array(str_arr) ##save ## add total_frame_num and view_count(1) at the very begining save_strarr_to_txt(dest_bmffile_path,dest_im_names) ## need to concise the data format ## write det_arr in MOT-like format( with split_sign) def write_detarr_as_mot(file_path,det_float_arr,split_sign=','): print '============================write detarr as mot====================================================' det_str_arr=det_float_arr.astype(str) col_num=det_str_arr.shape[1] with open(file_path, 'w') as f: for str_row in det_str_arr: tmp_row=str_row[0] for col_id in xrange(1,col_num): tmp_row=tmp_row+split_sign+str_row[col_id] f.write('{:s}\n'. format(tmp_row)) f.close()

[ "UniLau13@gmail.com" ]

UniLau13@gmail.com

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/course/migrations/0001_initial.py

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no_license

andaljulistiawan/pi

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refs/heads/master

2020-05-30T07:15:37.044228

2016-09-26T12:22:25

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# -*- coding: utf-8 -*- # Generated by Django 1.9.4 on 2016-03-15 11:38 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Course', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=30)), ], ), ]

[ "andalwebsite@gmail.com" ]

andalwebsite@gmail.com

d8daf90c06bca790d512d11543a0cd73c5952f96

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/python_code/chuanzhi/python_advance/19/python进程之间通讯.py

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[]

no_license

googleliyang/gitbook_cz_python

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refs/heads/master

2020-04-02T17:47:58.400424

2018-12-22T09:48:59

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- # @File : python进程之间通讯.py # @Author: ly # @Date : 2018/12/8 import multiprocessing import time def get_data(q): """子进程入口""" while True: if q.empty(): print('队列是空的，一会再来看看') time.sleep(5) else: print('获取到的数据是%s' % q.get()) time.sleep(2) if __name__ == '__main__': q = multiprocessing.Queue(1000) c_p = multiprocessing.Process(target=get_data, args=(q,)) c_p.start() while True: val = input("请输入") if q.full(): print('客官已经满了，请稍后再来') time.sleep(1) else: q.put(val)

[ "yang.li.gogle@gmail.com" ]

yang.li.gogle@gmail.com

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/Hack_A_Roo/Main.py

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[]

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Tvkqd/Hackathon-F19-IBM2-ConfigPrediction

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refs/heads/master

2020-09-06T11:09:47.121512

2019-11-11T01:15:39

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import dash import dash_core_components as dcc import dash_html_components as html import os from dash.dependencies import Input, Output import Read_input as grph import plotly.graph_objs as go import pandas as pd import csv stylesheets = ['style.css'] py_read_input = __import__('Read_input') mainP = dash.Dash(__name__, external_stylesheets=stylesheets) mainP.layout = html.Div(children=[ html.H1(children="I DONT KNOW WHAT IS THIS CALL"), html.Div(children=''' Group Meh... '''), dcc.Input(id = 'input', value = '', type = 'text'), ##html.Div(grph.res(grph.read_input_file_Expansion(), grph.read_input_file_Update())), html.Div(dcc.Input(id='input-box', type='text')), html.Button('Submit', id='button'), html.Div(id='container-button-basic', children='Enter a value and press submit'), html.Div([html.H1("Customer Requests for Provisioning", style={'textAlign': 'center'}), dcc.Dropdown(id='my-dropdown',options=[{'label': 'Product 1', 'value': 'Product 1'},{'label': 'Product 2', 'value': 'Product 2'}, {'label': 'Product 3', 'value': 'Product 3'}], multi=True,value=['Product 1'],style={"display": "block","margin-left": "auto","margin-right": "auto","width": "60%"}), dcc.Graph(id='my-graph') ], className="container") ]) @mainP.callback(Output('my-graph', 'figure'), [Input('my-dropdown', 'value')]) def update_graph(selected_dropdown_value): dropdown = {"Product 1": "Product 1","Product 2": "Product 2","Product 3": "Product 3",} trace1 = [] trace2 = [] trace3 = [] df = grph.read_input_file_Update() #df['Job Creation Time'] = pd.to_datetime(df['Job Creation Time'], infer_datetime_format=True).dt.date df['Job Creation Time'].groupby(df['Job Creation Time'].dt.date).count() print(len(df)) for product in selected_dropdown_value: #print(df[df["Product Name"] == 'SUCCESS']["Job Result"]) #print(df[df["Product Name"] == product]) trace1 = [dict( x=df[df["Product Name"] == product]["Job Creation Time"], y=df[df["Job Result"] == 'SUCCESS']["Job Result"], autobinx=True, autobiny=True, marker=dict(color='rgb(255, 192, 203)'), name='Success', type='histogram', xbins=dict( # end =df[df["Product Name"] == product]["Job Completion Time"] , size='M1', # start = df[df["Product Name"] == 'SUCCESS']["Job Creation Time"], ) )] trace2 = [dict( x=df[df["Product Name"] == product]["Job Creation Time"], y=df[df["Job Result"] == 'FAILURE']["Job Result"], autobinx=True, autobiny=True, marker=dict(color='rgb(0, 0, 0)'), name='Fail', type='histogram', xbins=dict( # end = '', size='M1', # start = '' ) )] trace3 = [dict( x=df[df["Product Name"] == product]["Job Creation Time"], y=df[df["Job Result"] == 'CANCELLED']["Job Result"], autobinx=True, autobiny=True, marker=dict(color='rgb(152, 251, 152)'), name='Cancel', type='histogram', xbins=dict( # end = '', size='M1', # start = '' ) )] traces = [trace1, trace2, trace3] data = [val for sublist in traces for val in sublist] figure = {'data': data, 'layout': go.Layout(colorway=["#5E0DAC", '#FF4F00', '#375CB1', '#FF7400', '#FFF400', '#FF0056'], height=600,title=f"{', '.join(str(dropdown[i]) for i in selected_dropdown_value)} Over Time", xaxis={"title":"Date", 'rangeselector': {'buttons': list([{'count': 1, 'label': '1M', 'step': 'month', 'stepmode': 'backward'}, {'count': 6, 'label': '6M', 'step': 'month', 'stepmode': 'backward'}, {'step': 'all'}])}, 'rangeslider': {'visible': True}, 'type': 'date'})} return figure if __name__ == '__main__': mainP.run_server(debug=True)

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refs/heads/master

2023-04-11T11:38:21.157249

2021-08-16T21:50:44

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# -*- python -*- # ex: set syntax=python: # This is a sample buildmaster config file. It must be installed as # 'master.cfg' in your buildmaster's base directory (although the filename # can be changed with the --basedir option to 'mktap buildbot master'). # It has one job: define a dictionary named BuildmasterConfig. This # dictionary has a variety of keys to control different aspects of the # buildmaster. They are documented in docs/config.xhtml . # This is the dictionary that the buildmaster pays attention to. We also use # a shorter alias to save typing. c = BuildmasterConfig = {} ####### BUILDSLAVES # the 'slaves' list defines the set of allowable buildslaves. Each element is # a tuple of bot-name and bot-password. These correspond to values given to # the buildslave's mktap invocation. from buildbot.buildslave import BuildSlave c['slaves'] = [BuildSlave("Lenny_64", "password"), BuildSlave("Squeeze_64", "password",max_builds=1), BuildSlave("Squeeze_32", "password",max_builds=1), BuildSlave("Inferno", "password",max_builds=1), BuildSlave("bigmac", "password",max_builds=1), BuildSlave("GPU-Linux-64bit", "password",max_builds=1), BuildSlave("Testing", "password",max_builds=1)] # to limit to two concurrent builds on a slave, use # c['slaves'] = [BuildSlave("bot1name", "bot1passwd", max_builds=2)] # 'slavePortnum' defines the TCP port to listen on. This must match the value # configured into the buildslaves (with their --master option) c['slavePortnum'] = 9989 ####### CHANGESOURCES # the 'change_source' setting tells the buildmaster how it should find out # about source code changes. Any class which implements IChangeSource can be # put here: there are several in buildbot/changes/*.py to choose from. #svn_poller = SVNPoller(svnurl=source_code_svn_url, # pollinterval=1*60, # seconds -- every 1 minute # split_file=split_file_branches) from buildbot.changes.svnpoller import SVNPoller, split_file_branches #from buildbot.changes.gitpoller import GitPoller #source_code_svn_url='https://gforge.sci.utah.edu/svn/uintah/trunk/src' source_code_svn_url='https://gforge.sci.utah.edu/svn/uintah/' gpu_svn_url='https://gforge.sci.utah.edu/svn/uintah/branches/gpu_dev' doc_svn_url='https://gforge.sci.utah.edu/svn/uintah/trunk/doc' exprlib_git_url='git://software.crsim.utah.edu/ExprLib' spatialops_git_url='git://software.crsim.utah.edu/SpatialOps' svn_poller = SVNPoller(svnurl=source_code_svn_url, pollinterval=1*60, # seconds -- every 1 minute split_file=split_file_branches) #svn_poller = SVNPoller(svnurl=source_code_svn_url, # pollinterval=1*60, # seconds -- every 1 minute # ) gpu_svn_poller = SVNPoller(svnurl=gpu_svn_url, pollinterval=1*60, # seconds -- every 1 minute ) doc_svn_poller = SVNPoller(svnurl=doc_svn_url, pollinterval=10*60, # seconds -- every 10 minutes ) #exprlib_git_poller = GitPoller(giturl=exprlib_git_url, # pollinterval=1*60, # seconds -- every 1 minute # ) #spatialops_git_poller = GitPoller(giturl=spatialops_git_url, # pollinterval=1*60, # seconds -- every 1 minute # ) c['change_source'] = [ svn_poller] #c['change_source'] = [ svn_poller, doc_svn_poller , gpu_svn_poller] #c['change_source'] = [ svn_poller, doc_svn_poller,exprlib_git_poller, # spatialops_git_poller] ####### SCHEDULERS ## configure the Schedulers from buildbot import scheduler #testing = scheduler.Scheduler(name="testing-build", branch=None, # treeStableTimer=1*60, # wait 1 minutes # builderNames=["Testing-64bit"]) #gpu = scheduler.AnyBranchScheduler(name="gpu-build", # branches=["branches/gpu_dev"], # treeStableTimer=1*60, # wait 1 minutes # builderNames=["GPU-64bit"]) gpu = scheduler.Scheduler(name="gpu-build",branch=None, treeStableTimer=1*60, # wait 1 minutes builderNames=["GPU-64bit"]) #gpu = scheduler.Scheduler(name="gpu-build", branch="branches/gpu_dev", # treeStableTimer=1*60, # wait 1 minutes # builderNames=["GPU-64bit"]) bigmac = scheduler.Scheduler(name="bigmac-build", branch=None, treeStableTimer=1*60, # wait 1 minutes builderNames=["BigMac-OSX"]) quick = scheduler.Scheduler(name="quick-build", branch=None, treeStableTimer=1*60, # wait 1 minutes builderNames=["Linux-Debug-64bit"]) #inferno = scheduler.Scheduler(name="inferno-build", branch=None, # treeStableTimer=1*60, # wait 1 minutes # builderNames=["inferno-linux"]) quick_32 = scheduler.Dependent(name="quick-build-32", upstream=quick, builderNames=["Linux-Debug-32bit-Static-Libraries"]) #quickWasatch = scheduler.Scheduler(name="quick-wasatch", branch=None, # treeStableTimer=1*60, # wait 1 minutes # builderNames=["Linux-dbg-static-Wasatch"]) #quickWasatch = scheduler.Periodic(name="quick-wasatch", branch=None, # periodicBuildTimer=6*60*60, # build every 1 hour # builderNames=["Linux-dbg-static-Wasatch"]) full = scheduler.Dependent(name="full-build & test", upstream=quick_32, builderNames=["Linux-Optimize-Test-64bit"]) #gpu_night = scheduler.Nightly(name='nightly', # builderNames=['GPU-64bit-nightly'], # branch="branches/gpu_dev", # hour=1, minute=1) gpu_night = scheduler.Nightly(name='nightly', branch=None, builderNames=['GPU-64bit-nightly'], hour=1, minute=1) doc = scheduler.Scheduler(name="doc-build",branch=None, treeStableTimer=1*60, builderNames=["Documentation-Builder"]) c['schedulers'] = [] #c['schedulers'] = [ doc, quick, quick_32, full, night ] c['schedulers'] = [ quick, quick_32, bigmac, full, doc ] #c['schedulers'] = [ gpu, gpu_night,quick, quick_32, bigmac, full, doc,testing ] ##c['schedulers'] = [ doc, quick, quick_32, quickWasatch, full,testing,gpu,bigmac] #c['schedulers'] = [ doc, quick, quick_32, full, night ] ####### BUILDERS # the 'builders' list defines the Builders. Each one is configured with a # dictionary, using the following keys: # name (required): the name used to describe this bilder # slavename (required): which slave to use, must appear in c['bots'] # builddir (required): which subdirectory to run the builder in # factory (required): a BuildFactory to define how the build is run # periodicBuildTime (optional): if set, force a build every N seconds # buildbot/process/factory.py provides several BuildFactory classes you can # start with, which implement build processes for common targets (GNU # autoconf projects, CPAN perl modules, etc). The factory.BuildFactory is the # base class, and is configured with a series of BuildSteps. When the build # is run, the appropriate buildslave is told to execute each Step in turn. # the first BuildStep is typically responsible for obtaining a copy of the # sources. There are source-obtaining Steps in buildbot/steps/source.py for # CVS, SVN, and others. from buildbot.process import factory from buildbot.steps import source, shell, transfer compiler_env = {'CC' : '/usr/lib/ccache/gcc', 'CXX' : '/usr/lib/ccache/g++', 'F77' : 'gfortran', 'SCI_MAKE_BE_QUIET' : 'true', 'LANG' : '' } compiler_env_old = {'CC' : 'gcc', 'CXX' : 'g++', 'F77' : 'gfortran', 'SCI_MAKE_BE_QUIET' : 'true', 'LANG' : '' } gpu_compiler_env = {'CC' : 'gcc', 'CXX' : 'g++', 'F77' : 'gfortran', 'SCI_MAKE_BE_QUIET' : 'true', 'LANG' : '' } bigmac_compiler_env = {'CC' : 'gcc', 'CXX' : 'g++', 'F77' : '/usr/local/bin/gfortran', 'SCI_MAKE_BE_QUIET' : 'true', 'LANG' : '' } bigmac_compiler_env_ = {'CC' : 'gcc', 'CXX' : 'g++', 'SCI_MAKE_BE_QUIET' : 'true', 'LANG' : '' } compiler_env_32 = {'CC' : '/usr/lib/ccache/gcc', 'CXX' : '/usr/lib/ccache/g++', 'F77' : 'gfortran-4.4', 'SCI_MAKE_BE_QUIET' : 'true', 'LANG' : '' } inferno_env = {'SCI_MAKE_BE_QUIET' : 'true', 'LANG' : '' } rt_env = {'GOLD_STANDARD': '/usr/local/TestData'} dbg_configure = ["../src/configure", "--enable-debug", "--enable-sci-malloc" ] dbg_configure_command = dbg_configure + ["--enable-64bit"] dbg_configure_command_32 = dbg_configure + ["--enable-static"] gpu_configure_command = ["../src/configure", "--build=x86_64-linux-gnu", "--host=x86_64-linux-gnu", "--target=x86_64-linux-gnu", "--with-hypre=/usr/local/hypre", "--with-boost=/usr/local/boost", "--with-petsc=/usr/local/petsc", "--with-mpi=/usr/local/openmpi", "--with-cuda=/usr/local/cuda", # "--with-cuda-sdk=/usr/local/NVIDIA_GPU_Computing_SDK", "--enable-threads=pthreads", "--enable-optimize=-O3 -mfpmath=sse", "--enable-64bit", "USE_WASATCH=yes" , "--enable-wasatch_3p"] test_gpu_configure_command = ["../src/configure", "--with-hypre=/usr/local/hypre", "--with-petsc=/usr/local/petsc", "--with-mpi=/usr/local/openmpi", "--with-cuda=/usr/local/cuda", # "--with-cuda-sdk=/usr/local/NVIDIA_GPU_Computing_SDK", "--enable-optimize"] bigmac_configure_command = ["../src/configure", "--with-mpi-include=/opt/local/include/openmpi", "--with-mpi-lib=/opt/local/lib" , "--with-boost=/opt/local" , "USE_MPM=yes USE_ICE=yes USE_MPMICE=yes USE_ARCHES=yes" , "USE_WASATCH=yes" , "--enable-wasatch_3p" , "--with-petsc=/usr/local/petsc-2.3.3" , "PETSC_ARCH=darwin10.8.0-c-opt" , "--with-hypre=/usr/local/hypre-2.7.0b" , "--enable-optimize=-O3"] wasatch_configure_command = ["../src/configure", "--enable-debug", "--enable-64bit", "--with-boost=/usr", "--enable-wasatch_3p", "--enable-static", "--without-fortran", "USE_MPM=no USE_ICE=no USE_MPMICE=no USE_ARCHES=no USE_MODELS_RADIATION=no", "USE_WASATCH=yes" ] opt_configure_command = ["../src/configure", "--enable-optimize=-O3 -mfpmath=sse", "--enable-64bit", "--enable-assertion-level=0", # "--disable-sci-malloc", "--enable-static" "--disable-sci-malloc" ] wasatch_configure = ["--enable-wasatch_3p", "--with-boost=/usr", "USE_WASATCH=yes" ] opt_configure_command = opt_configure_command + wasatch_configure malloc_trace = ["--with-MallocTrace=/usr/local/MallocTrace"] opt_nightly_configure_command = opt_configure_command + malloc_trace dbg_nightly_configure_command = dbg_configure_command + wasatch_configure rt_command = ["../../src/scripts/regression_tester", "-exact", "-restart", "-nice"] def AddTestStep(fac,test_case,build_type): fac.addStep(shell.ShellCommand, description=["Running " + test_case.upper() + " " + build_type + " tests"], command=rt_command + ["-" + test_case, "-" + build_type], env=rt_env, workdir= 'build/'+build_type+'/StandAlone', warnOnWarnings=True, timeout=60*60, # timeout after 1 hour name=test_case + "_test") tests=["wasatch","arches","examples","models","ice","ice_amr","mpm","mpmice","mpmice_amr","impm","mpmarches"] #tests=["mpmarches"] #RT_tests = ["ARCHES", "Examples","ICE", "IMPM", "Models", "MPMARCHES", "MPMF", "MPMICE", "MPM", "UCF", "Wasatch"] RT_tests = ["ARCHES", "Examples","ICE", "IMPM", "Models", "MPMICE", "MPM", "UCF", "Wasatch"] def AddTestStepRT(fac,test_case,build_type): RT_env = {'TEST_COMPONENTS': test_case , 'WHICH_TESTS': 'NIGHTLYTESTS'} fac.addStep(shell.ShellCommand, description=["Running " + test_case.upper() + " " + build_type + " tests"], command=["make", "runLocalRT"], env=RT_env, workdir= 'build/' + build_type, warnOnWarnings=True, timeout=60*60, # timeout after 1 hour name=test_case + "_test") doc_f = factory.BuildFactory() #doc_f.addStep(source.SVN, svnurl=doc_svn_url,directory='build/doc', # mode="update",retry=(10,2)) doc_f.addStep(source.SVN, baseURL=source_code_svn_url, defaultBranch="trunk/doc", workdir='build/doc', mode="update",retry=(10,2)) doc_f.addStep(shell.ShellCommand, description=["Building documentation"], command=["./runLatex"], workdir='build/doc', warnOnWarnings=False, warnOnFailure=False, name="build documentation") debug_f = factory.BuildFactory() #debug_f.addStep(source.SVN, svnurl=source_code_svn_url,directory='build/src', # mode="update",retry=(10,2)) debug_f.addStep(source.SVN, baseURL=source_code_svn_url, defaultBranch="trunk/src", workdir='build/src', mode="update",retry=(10,2)) debug_f.addStep(shell.Configure, command=dbg_configure_command, env=compiler_env, workdir='build/dbg') debug_f.addStep(shell.Compile, command=["python", "../src/scripts/make.py", " 4"], env=compiler_env, workdir='build/dbg') gpu_f = factory.BuildFactory() #gpu_f.addStep(source.SVN, svnurl=gpu_svn_url,directory='build', # mode="update",retry=(10,2)) #gpu_f.addStep(source.SVN, baseURL=source_code_svn_url, # defaultBranch="branches/gpu_dev",workdir='build', # mode="update",retry=(10,2)) gpu_f.addStep(source.SVN, baseURL=source_code_svn_url, defaultBranch="trunk/src",workdir='build/src', mode="update",retry=(10,2)) gpu_f.addStep(shell.Configure, command=gpu_configure_command, env=gpu_compiler_env, workdir='build/opt') gpu_f.addStep(shell.Compile, command=["python", "../src/scripts/make.py", " 2"], env=gpu_compiler_env, workdir='build/opt') gpu_f.addStep(shell.ShellCommand, command=["../src/scripts/make_test_data_link.sh", "/home/csafe-tester/Linux/TestData/opt"], env=compiler_env, workdir='build/opt') AddTestStepRT(gpu_f,"GPU","opt") # gpu_f.addStep(shell.ShellCommand, # command=["../src/scripts/make_test_data_link.sh", "/home/csafe-tester/Linux/TestData/opt"], # env=compiler_env, # workdir='build/opt') # for test in RT_tests: # AddTestStepRT(gpu_f,test,"opt") gpu_nightly_f = factory.BuildFactory() #gpu_f.addStep(source.SVN, svnurl=gpu_svn_url,directory='build', # mode="update",retry=(10,2)) #gpu_nightly_f.addStep(source.SVN, baseURL=source_code_svn_url, # defaultBranch="branches/gpu_dev",workdir='build', # defaultBranch="trunk/src",workdir='build', # mode="update",retry=(10,2)) gpu_nightly_f.addStep(source.SVN, baseURL=source_code_svn_url, defaultBranch="trunk/src",workdir='build/src', mode="update",retry=(10,2)) gpu_nightly_f.addStep(shell.Configure, command=gpu_configure_command, env=gpu_compiler_env, workdir='build/opt') gpu_nightly_f.addStep(shell.Compile, command=["python", "../src/scripts/make.py", " 4"], env=gpu_compiler_env, workdir='build/opt') gpu_nightly_f.addStep(shell.ShellCommand, command=["../src/scripts/make_test_data_link.sh", "/home/csafe-tester/Linux/TestData/opt"], env=compiler_env, workdir='build/opt') GPU_tests = RT_tests + ["GPU"] for test in GPU_tests: AddTestStepRT(gpu_nightly_f,test,"opt") # AddTestStepRT(gpu_nightly_f,"Examples","opt") # AddTestStepRT(gpu_nightly_f,"ICE","opt") bigmac_f = factory.BuildFactory() #bigmac_f.addStep(source.SVN, svnurl=source_code_svn_url,directory='build/src', # mode="update",retry=(10,2)) bigmac_f.addStep(source.SVN, baseURL=source_code_svn_url, defaultBranch="trunk/src", workdir='build/src', mode="update",retry=(10,2)) bigmac_f.addStep(shell.ShellCommand, command=["rm","-rf","CCA",";","rm","-rf","Core"], env=bigmac_compiler_env, workdir='build/dbg') bigmac_f.addStep(shell.Configure, command=bigmac_configure_command, env=bigmac_compiler_env, workdir='build/dbg') bigmac_f.addStep(shell.ShellCommand, command=["../src/scripts/mac_zlib_fix"], env=bigmac_compiler_env, workdir='build/dbg') bigmac_f.addStep(shell.Compile, command=["python", "../src/scripts/make.py", " 10"], env=bigmac_compiler_env, workdir='build/dbg') inferno_f = factory.BuildFactory() #inferno_f.addStep(source.SVN, svnurl=source_code_svn_url, # directory='build/src', mode="update",retry=(10,2)) inferno_f.addStep(source.SVN, baseURL=source_code_svn_url, defaultBranch="trunk/src", workdir='build/src', mode="update",retry=(10,2)) inferno_f.addStep(shell.Configure, command=dbg_configure, env=inferno_env, workdir='build/dbg') inferno_f.addStep(shell.Compile, command=["../src/scripts/pump_make.sh"], env=inferno_env, workdir='build/dbg') debug_f_32 = factory.BuildFactory() #debug_f_32.addStep(source.SVN, svnurl=source_code_svn_url, # directory='build/src', mode="update",retry=(10,2)) debug_f_32.addStep(source.SVN, baseURL=source_code_svn_url, defaultBranch="trunk/src", workdir='build/src', mode="update",retry=(10,2)) debug_f_32.addStep(shell.Configure, command=dbg_configure_command_32, env=compiler_env_32, workdir='build/dbg') debug_f_32.addStep(shell.Compile, command=["python", "../src/scripts/make.py", " 4"], env=compiler_env_32, workdir='build/dbg') #wasatchOnly_f = factory.BuildFactory() #wasatchOnly_f.addStep(source.SVN, svnurl=source_code_svn_url, # directory='build/src', mode="update",retry=(10,2)) #wasatchOnly_f.addStep(source.SVN, baseURL=source_code_svn_url, # defaultBranch="trunk/src", # workdir='build/src', mode="update",retry=(10,2)) #wasatchOnly_f.addStep(shell.Configure, # command=wasatch_configure_command, # env=compiler_env, # workdir='build/dbg') #wasatchOnly_f.addStep(shell.Compile, # command=["python", "../src/scripts/make.py", " 4"], # env=compiler_env, # workdir='build/dbg') optimize_f = factory.BuildFactory() #optimize_f.addStep(source.SVN, svnurl=source_code_svn_url, # directory='build/src', mode="update",retry=(10,2)) optimize_f.addStep(source.SVN, baseURL=source_code_svn_url, defaultBranch="trunk/src", workdir='build/src', mode="update",retry=(10,2)) optimize_f.addStep(shell.Configure, env=compiler_env, workdir='build/opt', command=opt_configure_command) optimize_f.addStep(shell.Compile, command=["python", "../src/scripts/make.py", " 4"], env=compiler_env, workdir='build/opt') optimize_f.addStep(shell.ShellCommand, command=["../src/scripts/make_test_data_link.sh", " /usr/local/TestData/opt"], env=compiler_env, workdir='build/opt') #RT_env = {'TEST_COMPONENTS': 'ARCHES Examples ICE IMPM Models MPMARCHES MPMF MPMICE MPM UCF Wasatch', # 'WHICH_TESTS': 'nightly'} for test in RT_tests: AddTestStepRT(optimize_f,test,"opt") # optimize_f.addStep(shell.ShellCommand, # command=["python", "../../src/scripts/buildbot_testresults.py", # test + "-results"], # env=compiler_env, # workdir='build/opt/local_RT') #optimize_f.addStep(shell.ShellCommand, # command=["make", "runLocalRT"], # env=RT_env, # workdir='build/opt') #optimize_f.addStep(shell.ShellCommand, # command=["cat", "local_RT/log"], # env=RT_env, # workdir='build/opt') import os, shutil, glob from buildbot.process.properties import WithProperties class TransferTestResults(transfer.DirectoryUpload): def __init__(self,extradir=None,**kwargs): self.extradir=extradir transfer.DirectoryUpload.__init__(self,**kwargs) def finished(self, result): bname = self.getProperty("buildername") bnum = self.getProperty("buildnumber") url = "http://www.uintah.utah.edu:8010/TestResults/"+ bname+"/"+ str(bnum) +"/" print self.extradir if self.extradir != None: url = url + self.extradir self.addURL("TestResults", url) result = transfer.DirectoryUpload.finished(self, result) self.step_status.setText(["uploaded results"]) return result #optimize_f.addStep(TransferTestResults(workdir='build/opt/StandAlone', # slavesrc="TestResults", # masterdest=WithProperties("~/master/public_html/TestResults/%(buildername)s/%(buildnumber)s/")) # ) night_f = factory.BuildFactory() #night_f.addStep(source.SVN, svnurl=source_code_svn_url, directory='build/src', # mode="update",retry=(10,2)) night_f.addStep(source.SVN, baseURL=source_code_svn_url, defaultBranch="trunk/src", workdir='build/src', mode="update",retry=(10,2)) night_f.addStep(shell.Configure, command=dbg_nightly_configure_command, env=compiler_env, workdir='build/dbg') night_f.addStep(shell.Compile, command=["python", "../src/scripts/make.py", " 4"], env=compiler_env, workdir='build/dbg') for test in tests: AddTestStep(night_f,test,"dbg") night_f.addStep(TransferTestResults(workdir='build/dbg/StandAlone', slavesrc="TestResults", masterdest=WithProperties("~/master/public_html/TestResults/%(buildername)s/%(buildnumber)s/dbg/"), extradir="dbg") ) night_f.addStep(shell.Configure, command=opt_configure_command, env=compiler_env, workdir='build/opt') night_f.addStep(shell.Compile, command=["python", "../src/scripts/make.py", " 4"], env=compiler_env, workdir='build/opt') for test in tests: AddTestStep(night_f,test,"opt") night_f.addStep(TransferTestResults(workdir='build/opt/StandAlone', slavesrc="TestResults", masterdest=WithProperties("~/master/public_html/TestResults/%(buildername)s/%(buildnumber)s/opt/"), extradir="opt") ) #### BUILDERS ##### testing = {'name': "Testing-64bit", 'slavename': "Testing", 'builddir': "testing_quick", 'factory': debug_f, } gpu = {'name': "GPU-64bit", 'slavename': "GPU-Linux-64bit", 'builddir': "gpu_quick", 'factory': gpu_f, } gpu_nightly = {'name': "GPU-64bit-nightly", 'slavename': "GPU-Linux-64bit", 'builddir': "gpu_nightly", 'factory': gpu_nightly_f, } bigmac = {'name': "BigMac-OSX", 'slavename': "bigmac", 'builddir': "bigmac_quick", 'factory': bigmac_f, } doc_squeeze = {'name': "Documentation-Builder", 'slavenames':[ "Squeeze_64","Inferno"], # 'slavename': "Squeeze_64", 'builddir': "uintah_doc", 'factory': doc_f, } #ql_squeeze_wasatch = {'name': "Linux-dbg-static-Wasatch", # 'slavename': "Squeeze_64", # 'builddir': "wasatch_quick", # 'factory': wasatchOnly_f, # } ql_squeeze = {'name': "Linux-Debug-64bit", 'slavename': "Squeeze_64", 'builddir': "uintah_quick", 'factory': debug_f, } inferno_builder = {'name': "inferno-linux", 'slavename': "Inferno", 'builddir': "uintah_inferno", 'factory': inferno_f, } ql_squeeze_32 = {'name': "Linux-Debug-32bit-Static-Libraries", # 'slavename': "Squeeze_32", 'slavenames': ["Squeeze_32","Inferno"], 'builddir': "uintah_quick_32", 'factory': debug_f_32, } fl_squeeze = {'name': "Linux-Optimize-Test-64bit", 'slavename': "Squeeze_64", 'builddir': "uintah_full", 'factory': optimize_f, } nl_squeeze = {'name': "Linux-Debug-Optimize-Test-64bit", 'slavename': "Squeeze_64", 'builddir': "uintah_nightly", 'factory': night_f, } c['builders'] = [doc_squeeze, ql_squeeze, ql_squeeze_32, fl_squeeze,bigmac] #c['builders'] = [doc_squeeze, ql_squeeze, ql_squeeze_wasatch, ql_squeeze_32, inferno_builder, fl_squeeze, nl_squeeze] #c['builders'] = [doc_squeeze, ql_squeeze, ql_squeeze_32, fl_squeeze, nl_squeeze] ####### STATUS TARGETS # 'status' is a list of Status Targets. The results of each build will be # pushed to these targets. buildbot/status/*.py has a variety to choose from, # including web pages, email senders, and IRC bots. c['status'] = [] from buildbot.status import html c['status'].append(html.WebStatus(http_port="8010",allowForce=True)) from buildbot.status import mail c['status'].append(mail.MailNotifier(fromaddr="uintah-developer@gforge.sci.utah.edu", extraRecipients=["uintah-developer@gforge.sci.utah.edu"], sendToInterestedUsers=True, lookup="sci.utah.edu", mode="failing")) ####### DEBUGGING OPTIONS # if you set 'debugPassword', then you can connect to the buildmaster with # the diagnostic tool in contrib/debugclient.py . From this tool, you can # manually force builds and inject changes, which may be useful for testing # your buildmaster without actually commiting changes to your repository (or # before you have a functioning 'sources' set up). The debug tool uses the # same port number as the slaves do: 'slavePortnum'. c['debugPassword'] = "debugpassword" # if you set 'manhole', you can ssh into the buildmaster and get an # interactive python shell, which may be useful for debugging buildbot # internals. It is probably only useful for buildbot developers. You can also # use an authorized_keys file, or plain telnet. #from buildbot import manhole #c['manhole'] = manhole.PasswordManhole("tcp:9999:interface=127.0.0.1", # "admin", "password") ####### PROJECT IDENTITY # the 'projectName' string will be used to describe the project that this # buildbot is working on. For example, it is used as the title of the # waterfall HTML page. The 'projectURL' string will be used to provide a link # from buildbot HTML pages to your project's home page. c['projectName'] = "Uintah" c['projectURL'] = "http://www.uintah.utah.edu/" # the 'buildbotURL' string should point to the location where the buildbot's # internal web server (usually the html.Waterfall page) is visible. This # typically uses the port number set in the Waterfall 'status' entry, but # with an externally-visible host name which the buildbot cannot figure out # without some help. c['buildbotURL'] = "http://www.uintah.utah.edu:8010/"

[ "damodars@sci.utah.edu" ]

damodars@sci.utah.edu

0424f15c39322d5a2be3e56ef253864e1b0daebc

ba7feebada17c625167e6c76486abfee6485a4e5

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MetalChief/PySqlite

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import sqlite3 conn = sqlite3.connect('test.db') print("Opened database successfully") conn.execute("UPDATE COMPANY set SALARY = 25000.00 where ID=1") conn.commit print("Total number of rows updated :", conn.total_changes) cursor = conn.execute("SELECT id, name, address, salary from COMPANY") for row in cursor: print("ID = ", row[0]) print("NAME = ", row[1]) print("ADDRESS = ", row[2]) print("SALARY = ", row[3], "\n") print("Operation done successfully") conn.close()

[ "robert.parrish@emc.com" ]

robert.parrish@emc.com

c1d6a62788febfe17ebcb9c3c93d8704bf333be0

89b7eb25e742af4457e73fc9d3e04a111d701032

/wals_ml_engine/trainer/wals.py

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[]

no_license

jkaartoluoma/recommender

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refs/heads/master

2020-04-21T18:13:41.362431

2018-12-11T05:45:39

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# Copyright 2017 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """WALS model core functions.""" import math import numpy as np import tensorflow as tf from tensorflow.contrib.factorization.python.ops import factorization_ops def get_rmse(output_row, output_col, actual): """Compute rmse between predicted and actual ratings. Args: output_row: evaluated numpy array of row_factor output_col: evaluated numpy array of col_factor actual: coo_matrix of actual (test) values Returns: rmse """ mse = 0 for i in xrange(actual.data.shape[0]): row_pred = output_row[actual.row[i]] col_pred = output_col[actual.col[i]] err = actual.data[i] - np.dot(row_pred, col_pred) mse += err * err mse /= actual.data.shape[0] rmse = math.sqrt(mse) return rmse def simple_train(model, input_tensor, num_iterations): """Helper function to train model on input for num_iterations. Args: model: WALSModel instance input_tensor: SparseTensor for input ratings matrix num_iterations: number of row/column updates to run Returns: tensorflow session, for evaluating results """ sess = tf.Session(graph=input_tensor.graph) with input_tensor.graph.as_default(): row_update_op = model.update_row_factors(sp_input=input_tensor)[1] col_update_op = model.update_col_factors(sp_input=input_tensor)[1] sess.run(model.initialize_op) sess.run(model.worker_init) for _ in xrange(num_iterations): sess.run(model.row_update_prep_gramian_op) sess.run(model.initialize_row_update_op) sess.run(row_update_op) sess.run(model.col_update_prep_gramian_op) sess.run(model.initialize_col_update_op) sess.run(col_update_op) return sess LOG_RATINGS = 0 LINEAR_RATINGS = 1 LINEAR_OBS_W = 100.0 def make_wts(data, wt_type, obs_wt, feature_wt_exp, axis): """Generate observed item weights. Args: data: coo_matrix of ratings data wt_type: weight type, LOG_RATINGS or LINEAR_RATINGS obs_wt: linear weight factor feature_wt_exp: logarithmic weight factor axis: axis to make weights for, 1=rows/users, 0=cols/items Returns: vector of weights for cols (items) or rows (users) """ # recipricol of sum of number of items across rows (if axis is 0) frac = np.array(1.0/(data > 0.0).sum(axis)) # filter any invalid entries frac[np.ma.masked_invalid(frac).mask] = 0.0 # normalize weights according to assumed distribution of ratings if wt_type == LOG_RATINGS: wts = np.array(np.power(frac, feature_wt_exp)).flatten() else: wts = np.array(obs_wt * frac).flatten() # check again for any numerically unstable entries assert np.isfinite(wts).sum() == wts.shape[0] return wts def wals_model(data, dim, reg, unobs, weights=False, wt_type=LINEAR_RATINGS, feature_wt_exp=None, obs_wt=LINEAR_OBS_W): """Create the WALSModel and input, row and col factor tensors. Args: data: scipy coo_matrix of item ratings dim: number of latent factors reg: regularization constant unobs: unobserved item weight weights: True: set obs weights, False: obs weights = unobs weights wt_type: feature weight type: linear (0) or log (1) feature_wt_exp: feature weight exponent constant obs_wt: feature weight linear factor constant Returns: input_tensor: tensor holding the input ratings matrix row_factor: tensor for row_factor col_factor: tensor for col_factor model: WALSModel instance """ row_wts = None col_wts = None num_rows = data.shape[0] num_cols = data.shape[1] if weights: assert feature_wt_exp is not None row_wts = np.ones(num_rows) col_wts = make_wts(data, wt_type, obs_wt, feature_wt_exp, 0) row_factor = None col_factor = None with tf.Graph().as_default(): input_tensor = tf.SparseTensor(indices=zip(data.row, data.col), values=(data.data).astype(np.float32), dense_shape=data.shape) model = factorization_ops.WALSModel(num_rows, num_cols, dim, unobserved_weight=unobs, regularization=reg, row_weights=row_wts, col_weights=col_wts) # retrieve the row and column factors row_factor = model.row_factors[0] col_factor = model.col_factors[0] return input_tensor, row_factor, col_factor, model

[ "toni.linnusmaki@power.fi" ]

toni.linnusmaki@power.fi

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/check_new_version.py

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rpmfusion/lpf-spotify-client

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#!/usr/bin/python3 """ Warning not complete """ import requests import re import os import subprocess def runme(cmd, env, cwd='.'): """Simple function to run a command and return 0 for success, 1 for failure. cmd is a list of the command and arguments, action is a name for the action (for logging), pkg is the name of the package being operated on, env is the environment dict, and cwd is where the script should be executed from.""" try: subprocess.check_call(cmd, env=env, cwd=cwd, stderr=subprocess.PIPE) except subprocess.CalledProcessError as e: sys.stderr.write('%s failed: %s\n' % (cmd, e)) return 1 return 0 text = "cat spotify-client.spec.in | grep ^Version" texts = text.split('|') text0 = texts[0].strip().split(' ') #print(text0) text1 = texts[1].strip().split(' ') #print(text1) ps1 = subprocess.run(text0, check=True, capture_output=True) ps2 = subprocess.run(text1, input=ps1.stdout, capture_output=True) print("Current %s" % ps2.stdout.decode()) html = requests.get('http://repository.spotify.com/pool/non-free/s/spotify-client/') #print (html.text) str_mx = re.compile('href="(spotify-client.*?i386.deb)"') str_mx2 = re.compile('href="(spotify-client.*?amd64.deb)"') res = str_mx.findall(html.text) res2 = str_mx2.findall(html.text) deb32 = res[-1] deb64 = res2[-1] regexp = re.compile('spotify-client_(\d{1,2}[.]\d{1,2}[.]\d{1,3}[.]\d{1,3})([.].*)') (version64, minor64) = regexp.findall(deb64)[0] print ("deb64 = %s\nVersions: %s %s" % (deb64, version64, minor64)) print ("Latest Version: %s" % version64) (version32, minor32) = regexp.findall(deb32)[0] #print ("deb32 = %s\nVersions: %s %s\n" % (deb32, version32, minor32)) print ("Latest deb32 Version: %s \n" % version32) spec = open('spotify-client.spec.in').read() #print (spec) #str_mx3 = re.compile('(Version:\s*) .*') #spec2 = re.sub(str_mx3, r'\1 %s' % version64, spec) str_mx4 = re.compile('(Source1:.*?)[.].*') spec3 = re.sub(str_mx4, r'\1%s' % minor64, spec) str_mx5 = re.compile('(Source2:.*?/).*') spec4 = re.sub(str_mx5, r'\1%s' % deb32, spec3) if spec != spec3: open('spotify-client.spec.in', 'w').write(spec4) enviro = os.environ pkgcmd = ['rpmdev-bumpspec', '-n', version64, '-c', 'Update to %s%s' % (version64, minor64[:10]), 'spotify-client.spec.in'] #pkgcmd = ['rpmdev-bumpspec -n %s -c "Update to %s%s" spotify-client.spec.in' % (version64, version64, minor64[:4])] if runme(pkgcmd, enviro): print('error running runme') pkgcmd = ['rpmdev-bumpspec', '-n', version64, '-c', 'Update to %s%s' % (version64, minor64[:10]), 'lpf-spotify-client.spec'] # 2>/dev/null if runme(pkgcmd, enviro): print('error running runme') print("New version available! ACTION REQUIRED !!!") print('rfpkg mockbuild -N --default-mock-resultdir --root fedora-38-x86_64-rpmfusion_nonfree') else: print("Already updated !") print('rfpkg ci -c && git show && echo Press enter to push and build; read dummy; rfpkg push && rfpkg build --nowait') print('git checkout f38 && git merge master && git push && rfpkg build --nowait; git checkout master') print('git checkout f37 && git merge master && git push && rfpkg build --nowait; git checkout master') print('git checkout el9 && git merge master && git push && rfpkg build --nowait; git checkout master') print('git checkout el8 && git merge master && git push && rfpkg build --nowait; git checkout master')

[ "sergio@serjux.com" ]

sergio@serjux.com

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/打卡/water_catch/Largest_Rectangle_in_Histogram.py

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pppppwj/leetcode

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from typing import List class Solution: def largestRectangleArea(self, heights: List[int]) -> int: # build an non-decreasing stack # store index stack = [-1] res = 0 # return value for i in range(len(heights)): while stack[-1] != -1 and heights[i] < heights[stack[-1]]: index = stack.pop() res = ( res if res >= heights[index] * (i - stack[-1] - 1) else heights[index] * (i - stack[-1] - 1) ) stack.append(i) while stack[-1] != -1: index = stack.pop() res = ( res if res >= heights[index] * (len(heights) - stack[-1] - 1) else heights[index] * (len(heights) - stack[-1] - 1) ) return res

[ "pppppwj424@gmail.com" ]

pppppwj424@gmail.com

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"""The tests for Z-Wave JS automation triggers.""" from unittest.mock import AsyncMock, patch import pytest import voluptuous as vol from zwave_js_server.const import CommandClass from zwave_js_server.event import Event from zwave_js_server.model.node import Node from homeassistant.components import automation from homeassistant.components.zwave_js import DOMAIN from homeassistant.components.zwave_js.helpers import get_device_id from homeassistant.components.zwave_js.trigger import async_validate_trigger_config from homeassistant.components.zwave_js.triggers.trigger_helpers import ( async_bypass_dynamic_config_validation, ) from homeassistant.const import SERVICE_RELOAD from homeassistant.core import HomeAssistant from homeassistant.helpers.device_registry import async_get as async_get_dev_reg from homeassistant.setup import async_setup_component from .common import SCHLAGE_BE469_LOCK_ENTITY from tests.common import async_capture_events async def test_zwave_js_value_updated( hass: HomeAssistant, client, lock_schlage_be469, integration ) -> None: """Test for zwave_js.value_updated automation trigger.""" trigger_type = f"{DOMAIN}.value_updated" node: Node = lock_schlage_be469 dev_reg = async_get_dev_reg(hass) device = dev_reg.async_get_device( {get_device_id(client.driver, lock_schlage_be469)} ) assert device no_value_filter = async_capture_events(hass, "no_value_filter") single_from_value_filter = async_capture_events(hass, "single_from_value_filter") multiple_from_value_filters = async_capture_events( hass, "multiple_from_value_filters" ) from_and_to_value_filters = async_capture_events(hass, "from_and_to_value_filters") different_value = async_capture_events(hass, "different_value") def clear_events(): """Clear all events in the event list.""" no_value_filter.clear() single_from_value_filter.clear() multiple_from_value_filters.clear() from_and_to_value_filters.clear() different_value.clear() assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: [ # no value filter { "trigger": { "platform": trigger_type, "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "command_class": CommandClass.DOOR_LOCK.value, "property": "latchStatus", }, "action": { "event": "no_value_filter", }, }, # single from value filter { "trigger": { "platform": trigger_type, "device_id": device.id, "command_class": CommandClass.DOOR_LOCK.value, "property": "latchStatus", "from": "ajar", }, "action": { "event": "single_from_value_filter", }, }, # multiple from value filters { "trigger": { "platform": trigger_type, "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "command_class": CommandClass.DOOR_LOCK.value, "property": "latchStatus", "from": ["closed", "opened"], }, "action": { "event": "multiple_from_value_filters", }, }, # from and to value filters { "trigger": { "platform": trigger_type, "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "command_class": CommandClass.DOOR_LOCK.value, "property": "latchStatus", "from": ["closed", "opened"], "to": ["opened"], }, "action": { "event": "from_and_to_value_filters", }, }, # different value { "trigger": { "platform": trigger_type, "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "command_class": CommandClass.DOOR_LOCK.value, "property": "boltStatus", }, "action": { "event": "different_value", }, }, ] }, ) # Test that no value filter is triggered event = Event( type="value updated", data={ "source": "node", "event": "value updated", "nodeId": node.node_id, "args": { "commandClassName": "Door Lock", "commandClass": 98, "endpoint": 0, "property": "latchStatus", "newValue": "boo", "prevValue": "hiss", "propertyName": "latchStatus", }, }, ) node.receive_event(event) await hass.async_block_till_done() assert len(no_value_filter) == 1 assert len(single_from_value_filter) == 0 assert len(multiple_from_value_filters) == 0 assert len(from_and_to_value_filters) == 0 assert len(different_value) == 0 clear_events() # Test that a single_from_value_filter is triggered event = Event( type="value updated", data={ "source": "node", "event": "value updated", "nodeId": node.node_id, "args": { "commandClassName": "Door Lock", "commandClass": 98, "endpoint": 0, "property": "latchStatus", "newValue": "boo", "prevValue": "ajar", "propertyName": "latchStatus", }, }, ) node.receive_event(event) await hass.async_block_till_done() assert len(no_value_filter) == 1 assert len(single_from_value_filter) == 1 assert len(multiple_from_value_filters) == 0 assert len(from_and_to_value_filters) == 0 assert len(different_value) == 0 clear_events() # Test that multiple_from_value_filters are triggered event = Event( type="value updated", data={ "source": "node", "event": "value updated", "nodeId": node.node_id, "args": { "commandClassName": "Door Lock", "commandClass": 98, "endpoint": 0, "property": "latchStatus", "newValue": "boo", "prevValue": "closed", "propertyName": "latchStatus", }, }, ) node.receive_event(event) await hass.async_block_till_done() assert len(no_value_filter) == 1 assert len(single_from_value_filter) == 0 assert len(multiple_from_value_filters) == 1 assert len(from_and_to_value_filters) == 0 assert len(different_value) == 0 clear_events() # Test that from_and_to_value_filters is triggered event = Event( type="value updated", data={ "source": "node", "event": "value updated", "nodeId": node.node_id, "args": { "commandClassName": "Door Lock", "commandClass": 98, "endpoint": 0, "property": "latchStatus", "newValue": "opened", "prevValue": "closed", "propertyName": "latchStatus", }, }, ) node.receive_event(event) await hass.async_block_till_done() assert len(no_value_filter) == 1 assert len(single_from_value_filter) == 0 assert len(multiple_from_value_filters) == 1 assert len(from_and_to_value_filters) == 1 assert len(different_value) == 0 clear_events() event = Event( type="value updated", data={ "source": "node", "event": "value updated", "nodeId": node.node_id, "args": { "commandClassName": "Door Lock", "commandClass": 98, "endpoint": 0, "property": "boltStatus", "newValue": "boo", "prevValue": "hiss", "propertyName": "boltStatus", }, }, ) node.receive_event(event) await hass.async_block_till_done() assert len(no_value_filter) == 0 assert len(single_from_value_filter) == 0 assert len(multiple_from_value_filters) == 0 assert len(from_and_to_value_filters) == 0 assert len(different_value) == 1 clear_events() with patch("homeassistant.config.load_yaml", return_value={}): await hass.services.async_call(automation.DOMAIN, SERVICE_RELOAD, blocking=True) async def test_zwave_js_value_updated_bypass_dynamic_validation( hass: HomeAssistant, client, lock_schlage_be469, integration ) -> None: """Test zwave_js.value_updated trigger when bypassing dynamic validation.""" trigger_type = f"{DOMAIN}.value_updated" node: Node = lock_schlage_be469 no_value_filter = async_capture_events(hass, "no_value_filter") with patch( "homeassistant.components.zwave_js.triggers.value_updated.async_bypass_dynamic_config_validation", return_value=True, ): assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: [ # no value filter { "trigger": { "platform": trigger_type, "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "command_class": CommandClass.DOOR_LOCK.value, "property": "latchStatus", }, "action": { "event": "no_value_filter", }, }, ] }, ) # Test that no value filter is triggered event = Event( type="value updated", data={ "source": "node", "event": "value updated", "nodeId": node.node_id, "args": { "commandClassName": "Door Lock", "commandClass": 98, "endpoint": 0, "property": "latchStatus", "newValue": "boo", "prevValue": "hiss", "propertyName": "latchStatus", }, }, ) node.receive_event(event) await hass.async_block_till_done() assert len(no_value_filter) == 1 async def test_zwave_js_value_updated_bypass_dynamic_validation_no_nodes( hass: HomeAssistant, client, lock_schlage_be469, integration ) -> None: """Test value_updated trigger when bypassing dynamic validation with no nodes.""" trigger_type = f"{DOMAIN}.value_updated" node: Node = lock_schlage_be469 no_value_filter = async_capture_events(hass, "no_value_filter") with patch( "homeassistant.components.zwave_js.triggers.value_updated.async_bypass_dynamic_config_validation", return_value=True, ): assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: [ # no value filter { "trigger": { "platform": trigger_type, "entity_id": "sensor.test", "command_class": CommandClass.DOOR_LOCK.value, "property": "latchStatus", }, "action": { "event": "no_value_filter", }, }, ] }, ) # Test that no value filter is NOT triggered because automation failed setup event = Event( type="value updated", data={ "source": "node", "event": "value updated", "nodeId": node.node_id, "args": { "commandClassName": "Door Lock", "commandClass": 98, "endpoint": 0, "property": "latchStatus", "newValue": "boo", "prevValue": "hiss", "propertyName": "latchStatus", }, }, ) node.receive_event(event) await hass.async_block_till_done() assert len(no_value_filter) == 0 async def test_zwave_js_value_updated_bypass_dynamic_validation_no_driver( hass: HomeAssistant, client, lock_schlage_be469, integration ) -> None: """Test zwave_js.value_updated trigger without driver.""" trigger_type = f"{DOMAIN}.value_updated" node: Node = lock_schlage_be469 driver = client.driver client.driver = None no_value_filter = async_capture_events(hass, "no_value_filter") assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: [ # no value filter { "trigger": { "platform": trigger_type, "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "command_class": CommandClass.DOOR_LOCK.value, "property": "latchStatus", }, "action": { "event": "no_value_filter", }, }, ] }, ) await hass.async_block_till_done() client.driver = driver # Test that no value filter is NOT triggered because automation failed setup event = Event( type="value updated", data={ "source": "node", "event": "value updated", "nodeId": node.node_id, "args": { "commandClassName": "Door Lock", "commandClass": 98, "endpoint": 0, "property": "latchStatus", "newValue": "boo", "prevValue": "hiss", "propertyName": "latchStatus", }, }, ) node.receive_event(event) await hass.async_block_till_done() assert len(no_value_filter) == 0 async def test_zwave_js_event( hass: HomeAssistant, client, lock_schlage_be469, integration ) -> None: """Test for zwave_js.event automation trigger.""" trigger_type = f"{DOMAIN}.event" node: Node = lock_schlage_be469 dev_reg = async_get_dev_reg(hass) device = dev_reg.async_get_device( {get_device_id(client.driver, lock_schlage_be469)} ) assert device node_no_event_data_filter = async_capture_events(hass, "node_no_event_data_filter") node_event_data_filter = async_capture_events(hass, "node_event_data_filter") controller_no_event_data_filter = async_capture_events( hass, "controller_no_event_data_filter" ) controller_event_data_filter = async_capture_events( hass, "controller_event_data_filter" ) driver_no_event_data_filter = async_capture_events( hass, "driver_no_event_data_filter" ) driver_event_data_filter = async_capture_events(hass, "driver_event_data_filter") node_event_data_no_partial_dict_match_filter = async_capture_events( hass, "node_event_data_no_partial_dict_match_filter" ) node_event_data_partial_dict_match_filter = async_capture_events( hass, "node_event_data_partial_dict_match_filter" ) def clear_events(): """Clear all events in the event list.""" node_no_event_data_filter.clear() node_event_data_filter.clear() controller_no_event_data_filter.clear() controller_event_data_filter.clear() driver_no_event_data_filter.clear() driver_event_data_filter.clear() node_event_data_no_partial_dict_match_filter.clear() node_event_data_partial_dict_match_filter.clear() assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: [ # node filter: no event data { "trigger": { "platform": trigger_type, "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "event_source": "node", "event": "interview stage completed", }, "action": { "event": "node_no_event_data_filter", }, }, # node filter: event data { "trigger": { "platform": trigger_type, "device_id": device.id, "event_source": "node", "event": "interview stage completed", "event_data": {"stageName": "ProtocolInfo"}, }, "action": { "event": "node_event_data_filter", }, }, # controller filter: no event data { "trigger": { "platform": trigger_type, "config_entry_id": integration.entry_id, "event_source": "controller", "event": "inclusion started", }, "action": { "event": "controller_no_event_data_filter", }, }, # controller filter: event data { "trigger": { "platform": trigger_type, "config_entry_id": integration.entry_id, "event_source": "controller", "event": "inclusion started", "event_data": {"secure": True}, }, "action": { "event": "controller_event_data_filter", }, }, # driver filter: no event data { "trigger": { "platform": trigger_type, "config_entry_id": integration.entry_id, "event_source": "driver", "event": "logging", }, "action": { "event": "driver_no_event_data_filter", }, }, # driver filter: event data { "trigger": { "platform": trigger_type, "config_entry_id": integration.entry_id, "event_source": "driver", "event": "logging", "event_data": {"message": "test"}, }, "action": { "event": "driver_event_data_filter", }, }, # node filter: event data, no partial dict match { "trigger": { "platform": trigger_type, "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "event_source": "node", "event": "value updated", "event_data": {"args": {"commandClassName": "Door Lock"}}, }, "action": { "event": "node_event_data_no_partial_dict_match_filter", }, }, # node filter: event data, partial dict match { "trigger": { "platform": trigger_type, "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "event_source": "node", "event": "value updated", "event_data": {"args": {"commandClassName": "Door Lock"}}, "partial_dict_match": True, }, "action": { "event": "node_event_data_partial_dict_match_filter", }, }, ] }, ) # Test that `node no event data filter` is triggered and `node event data filter` is not event = Event( type="interview stage completed", data={ "source": "node", "event": "interview stage completed", "stageName": "NodeInfo", "nodeId": node.node_id, }, ) node.receive_event(event) await hass.async_block_till_done() assert len(node_no_event_data_filter) == 1 assert len(node_event_data_filter) == 0 assert len(controller_no_event_data_filter) == 0 assert len(controller_event_data_filter) == 0 assert len(driver_no_event_data_filter) == 0 assert len(driver_event_data_filter) == 0 assert len(node_event_data_no_partial_dict_match_filter) == 0 assert len(node_event_data_partial_dict_match_filter) == 0 clear_events() # Test that `node no event data filter` and `node event data filter` are triggered event = Event( type="interview stage completed", data={ "source": "node", "event": "interview stage completed", "stageName": "ProtocolInfo", "nodeId": node.node_id, }, ) node.receive_event(event) await hass.async_block_till_done() assert len(node_no_event_data_filter) == 1 assert len(node_event_data_filter) == 1 assert len(controller_no_event_data_filter) == 0 assert len(controller_event_data_filter) == 0 assert len(driver_no_event_data_filter) == 0 assert len(driver_event_data_filter) == 0 assert len(node_event_data_no_partial_dict_match_filter) == 0 assert len(node_event_data_partial_dict_match_filter) == 0 clear_events() # Test that `controller no event data filter` is triggered and `controller event data filter` is not event = Event( type="inclusion started", data={ "source": "controller", "event": "inclusion started", "secure": False, }, ) client.driver.controller.receive_event(event) await hass.async_block_till_done() assert len(node_no_event_data_filter) == 0 assert len(node_event_data_filter) == 0 assert len(controller_no_event_data_filter) == 1 assert len(controller_event_data_filter) == 0 assert len(driver_no_event_data_filter) == 0 assert len(driver_event_data_filter) == 0 assert len(node_event_data_no_partial_dict_match_filter) == 0 assert len(node_event_data_partial_dict_match_filter) == 0 clear_events() # Test that both `controller no event data filter` and `controller event data filter` are triggered event = Event( type="inclusion started", data={ "source": "controller", "event": "inclusion started", "secure": True, }, ) client.driver.controller.receive_event(event) await hass.async_block_till_done() assert len(node_no_event_data_filter) == 0 assert len(node_event_data_filter) == 0 assert len(controller_no_event_data_filter) == 1 assert len(controller_event_data_filter) == 1 assert len(driver_no_event_data_filter) == 0 assert len(driver_event_data_filter) == 0 assert len(node_event_data_no_partial_dict_match_filter) == 0 assert len(node_event_data_partial_dict_match_filter) == 0 clear_events() # Test that `driver no event data filter` is triggered and `driver event data filter` is not event = Event( type="logging", data={ "source": "driver", "event": "logging", "message": "no test", "formattedMessage": "test", "direction": ">", "level": "debug", "primaryTags": "tag", "secondaryTags": "tag2", "secondaryTagPadding": 0, "multiline": False, "timestamp": "time", "label": "label", }, ) client.driver.receive_event(event) await hass.async_block_till_done() assert len(node_no_event_data_filter) == 0 assert len(node_event_data_filter) == 0 assert len(controller_no_event_data_filter) == 0 assert len(controller_event_data_filter) == 0 assert len(driver_no_event_data_filter) == 1 assert len(driver_event_data_filter) == 0 assert len(node_event_data_no_partial_dict_match_filter) == 0 assert len(node_event_data_partial_dict_match_filter) == 0 clear_events() # Test that both `driver no event data filter` and `driver event data filter` are triggered event = Event( type="logging", data={ "source": "driver", "event": "logging", "message": "test", "formattedMessage": "test", "direction": ">", "level": "debug", "primaryTags": "tag", "secondaryTags": "tag2", "secondaryTagPadding": 0, "multiline": False, "timestamp": "time", "label": "label", }, ) client.driver.receive_event(event) await hass.async_block_till_done() assert len(node_no_event_data_filter) == 0 assert len(node_event_data_filter) == 0 assert len(controller_no_event_data_filter) == 0 assert len(controller_event_data_filter) == 0 assert len(driver_no_event_data_filter) == 1 assert len(driver_event_data_filter) == 1 assert len(node_event_data_no_partial_dict_match_filter) == 0 assert len(node_event_data_partial_dict_match_filter) == 0 clear_events() # Test that only `node with event data and partial match dict filter` is triggered event = Event( type="value updated", data={ "source": "node", "event": "value updated", "nodeId": node.node_id, "args": { "commandClassName": "Door Lock", "commandClass": 49, "endpoint": 0, "property": "latchStatus", "newValue": "closed", "prevValue": "open", "propertyName": "latchStatus", }, }, ) node.receive_event(event) await hass.async_block_till_done() assert len(node_no_event_data_filter) == 0 assert len(node_event_data_filter) == 0 assert len(controller_no_event_data_filter) == 0 assert len(controller_event_data_filter) == 0 assert len(driver_no_event_data_filter) == 0 assert len(driver_event_data_filter) == 0 assert len(node_event_data_no_partial_dict_match_filter) == 0 assert len(node_event_data_partial_dict_match_filter) == 1 clear_events() # Test that `node with event data and partial match dict filter` is not triggered # when partial dict doesn't match event = Event( type="value updated", data={ "source": "node", "event": "value updated", "nodeId": node.node_id, "args": { "commandClassName": "fake command class name", "commandClass": 49, "endpoint": 0, "property": "latchStatus", "newValue": "closed", "prevValue": "open", "propertyName": "latchStatus", }, }, ) node.receive_event(event) await hass.async_block_till_done() assert len(node_no_event_data_filter) == 0 assert len(node_event_data_filter) == 0 assert len(controller_no_event_data_filter) == 0 assert len(controller_event_data_filter) == 0 assert len(driver_no_event_data_filter) == 0 assert len(driver_event_data_filter) == 0 assert len(node_event_data_no_partial_dict_match_filter) == 0 assert len(node_event_data_partial_dict_match_filter) == 0 clear_events() with patch("homeassistant.config.load_yaml", return_value={}): await hass.services.async_call(automation.DOMAIN, SERVICE_RELOAD, blocking=True) async def test_zwave_js_event_bypass_dynamic_validation( hass: HomeAssistant, client, lock_schlage_be469, integration ) -> None: """Test zwave_js.event trigger when bypassing dynamic config validation.""" trigger_type = f"{DOMAIN}.event" node: Node = lock_schlage_be469 node_no_event_data_filter = async_capture_events(hass, "node_no_event_data_filter") with patch( "homeassistant.components.zwave_js.triggers.event.async_bypass_dynamic_config_validation", return_value=True, ): assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: [ # node filter: no event data { "trigger": { "platform": trigger_type, "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "event_source": "node", "event": "interview stage completed", }, "action": { "event": "node_no_event_data_filter", }, }, ] }, ) # Test that `node no event data filter` is triggered and `node event data filter` is not event = Event( type="interview stage completed", data={ "source": "node", "event": "interview stage completed", "stageName": "NodeInfo", "nodeId": node.node_id, }, ) node.receive_event(event) await hass.async_block_till_done() assert len(node_no_event_data_filter) == 1 async def test_zwave_js_event_bypass_dynamic_validation_no_nodes( hass: HomeAssistant, client, lock_schlage_be469, integration ) -> None: """Test event trigger when bypassing dynamic validation with no nodes.""" trigger_type = f"{DOMAIN}.event" node: Node = lock_schlage_be469 node_no_event_data_filter = async_capture_events(hass, "node_no_event_data_filter") with patch( "homeassistant.components.zwave_js.triggers.event.async_bypass_dynamic_config_validation", return_value=True, ): assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: [ # node filter: no event data { "trigger": { "platform": trigger_type, "entity_id": "sensor.fake", "event_source": "node", "event": "interview stage completed", }, "action": { "event": "node_no_event_data_filter", }, }, ] }, ) # Test that `node no event data filter` is NOT triggered because automation failed # setup event = Event( type="interview stage completed", data={ "source": "node", "event": "interview stage completed", "stageName": "NodeInfo", "nodeId": node.node_id, }, ) node.receive_event(event) await hass.async_block_till_done() assert len(node_no_event_data_filter) == 0 async def test_zwave_js_event_invalid_config_entry_id( hass: HomeAssistant, client, integration, caplog: pytest.LogCaptureFixture ) -> None: """Test zwave_js.event automation trigger fails when config entry ID is invalid.""" trigger_type = f"{DOMAIN}.event" assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: [ { "trigger": { "platform": trigger_type, "config_entry_id": "not_real_entry_id", "event_source": "controller", "event": "inclusion started", }, "action": { "event": "node_no_event_data_filter", }, } ] }, ) assert "Config entry 'not_real_entry_id' not found" in caplog.text caplog.clear() async def test_async_validate_trigger_config(hass: HomeAssistant) -> None: """Test async_validate_trigger_config.""" mock_platform = AsyncMock() with patch( "homeassistant.components.zwave_js.trigger._get_trigger_platform", return_value=mock_platform, ): mock_platform.async_validate_trigger_config.return_value = {} await async_validate_trigger_config(hass, {}) mock_platform.async_validate_trigger_config.assert_awaited() async def test_invalid_trigger_configs(hass: HomeAssistant) -> None: """Test invalid trigger configs.""" with pytest.raises(vol.Invalid): await async_validate_trigger_config( hass, { "platform": f"{DOMAIN}.event", "entity_id": "fake.entity", "event_source": "node", "event": "value updated", }, ) with pytest.raises(vol.Invalid): await async_validate_trigger_config( hass, { "platform": f"{DOMAIN}.value_updated", "entity_id": "fake.entity", "command_class": CommandClass.DOOR_LOCK.value, "property": "latchStatus", }, ) async def test_zwave_js_trigger_config_entry_unloaded( hass: HomeAssistant, client, lock_schlage_be469, integration ) -> None: """Test zwave_js triggers bypass dynamic validation when needed.""" dev_reg = async_get_dev_reg(hass) device = dev_reg.async_get_device( {get_device_id(client.driver, lock_schlage_be469)} ) assert device # Test bypass check is False assert not async_bypass_dynamic_config_validation( hass, { "platform": f"{DOMAIN}.value_updated", "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "command_class": CommandClass.DOOR_LOCK.value, "property": "latchStatus", }, ) await hass.config_entries.async_unload(integration.entry_id) # Test full validation for both events assert await async_validate_trigger_config( hass, { "platform": f"{DOMAIN}.value_updated", "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "command_class": CommandClass.DOOR_LOCK.value, "property": "latchStatus", }, ) assert await async_validate_trigger_config( hass, { "platform": f"{DOMAIN}.event", "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "event_source": "node", "event": "interview stage completed", }, ) # Test bypass check assert async_bypass_dynamic_config_validation( hass, { "platform": f"{DOMAIN}.value_updated", "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "command_class": CommandClass.DOOR_LOCK.value, "property": "latchStatus", }, ) assert async_bypass_dynamic_config_validation( hass, { "platform": f"{DOMAIN}.value_updated", "device_id": device.id, "command_class": CommandClass.DOOR_LOCK.value, "property": "latchStatus", "from": "ajar", }, ) assert async_bypass_dynamic_config_validation( hass, { "platform": f"{DOMAIN}.event", "entity_id": SCHLAGE_BE469_LOCK_ENTITY, "event_source": "node", "event": "interview stage completed", }, ) assert async_bypass_dynamic_config_validation( hass, { "platform": f"{DOMAIN}.event", "device_id": device.id, "event_source": "node", "event": "interview stage completed", "event_data": {"stageName": "ProtocolInfo"}, }, ) assert async_bypass_dynamic_config_validation( hass, { "platform": f"{DOMAIN}.event", "config_entry_id": integration.entry_id, "event_source": "controller", "event": "nvm convert progress", }, )

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"""textutils URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path from . import views urlpatterns = [ path('admin/', admin.site.urls), path('', views.index, name='index'), path('analyze', views.analyze, name='analyze'), # path('removepunc/', views.removePunc, name='remP'), # path('capF/', views.capFirst, name='capF'), # path('newLR/', views.newLineRemove, name='newLR'), # path('spcR/', views.spaceRemove, name='spcR'), # path('chrC/', views.charCount, name='chrC'), # path('about', views.about, name='about'), # path('read', views.readText, name='read'), # path('navigator',views.personalNavigator, name='navigator'), ]

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ttcakan/creator-studio-cli

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# Based on https://github.com/timgrossmann/InstaPy/blob/c3ad0c869bcaa402a738804b9a20f55a82c12edb/instapy/browser.py import os import zipfile from os.path import sep from selenium import webdriver from selenium.webdriver.support import expected_conditions from selenium.webdriver.common.by import By from selenium.webdriver.support.ui import WebDriverWait from selenium.common.exceptions import TimeoutException def create_firefox_extension(): ext_path = os.path.abspath(os.path.dirname(__file__) + sep + "firefox_extension") # safe into assets folder zip_file = os.path.abspath(os.path.dirname(__file__) + sep) +"extension.xpi" files = ["manifest.json", "content.js", "arrive.js"] with zipfile.ZipFile(zip_file, "w", zipfile.ZIP_DEFLATED, False) as zipf: for file in files: zipf.write(ext_path + sep + file, file) return zip_file def get_browser(browser_profile_path=None): """Start the driver and retur it.""" if browser_profile_path is not None: firefox_profile = webdriver.FirefoxProfile(browser_profile_path) else: firefox_profile = webdriver.FirefoxProfile() browser = webdriver.Firefox(firefox_profile) browser.implicitly_wait(10) # seconds # add extenions to hide selenium browser.install_addon(create_firefox_extension(), temporary=True) return browser def explicit_wait_visibility_of_element_located(browser, xpath, timeout=35): """Explicitly wait until visibility on element.""" locator = (By.XPATH, xpath) condition = expected_conditions.visibility_of_element_located(locator) try: wait = WebDriverWait(browser, timeout) result = wait.until(condition) except TimeoutException: print("Timeout Exception in explicit wait") return False return result

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arielbmx@gmail.com

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AdamZhouSE/pythonHomework

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n=int(input()) num_array=[] for h in range(n): num_array.append(int(input())) def create_array(n): temp=n res=[] res.append(n) while n>0: n=n-5 res.append(n) while not n==temp: n=n+5 res.append(n) strresult=str(res[0]) for n in range(1,len(res)): strresult=strresult+" "+str(res[n]) strresult+=" " return strresult+" \n" for _ in range(0,len(num_array)): print(create_array(num_array[_]))

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1069583789@qq.com

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grayondream/meachinelearning-note

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from utils.dataset import mnist_txt

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grayondream@outlook.com

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NOVIAS/python_game

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# -*- coding: utf-8 -*- import pygame from pygame.sprite import Sprite class Bullet(Sprite): """一个对飞船发射的子弹进行管理的类""" def __init__(self, ai_settings, screen, ship): """在飞船所处的位置创建一个子弹对象""" # 在Python2.7中 super(Bullet, self).__init__() super().__init__() self.screen = screen # 在（0，0）处使用 pygame 创建一个表示子弹的矩形，再设置正确的位置 self.rect = pygame.Rect(0, 0, ai_settings.bullet_width, ai_settings.bullet_height) # 用飞船的位置定位子弹的位置 self.rect.centerx = ship.rect.centerx self.rect.top = ship.rect.top # 用小数表示子弹的位置 self.y = float(self.rect.y) self.color = ai_settings.bullet_color self.speed_factor = ai_settings.bullet_speed_factor def update(self): """向上移动子弹""" # 让子弹按照一定的速度向上发射 self.y -= self.speed_factor # 绘制子弹在每次运动时的位置信息 self.rect.y = self.y def draw_bullet(self): """在屏幕上绘制子弹""" pygame.draw.rect(self.screen, self.color, self.rect)

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275411032@qq.com

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/client.py

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import http.client, subprocess c = http.client.HTTPConnection('localhost', 8080) c.request('POST', '/return', '{}') doc = c.getresponse().read() print (doc)

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/python_scripts/inputs.py

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chaitnyap/LearnPython

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refs/heads/master

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name = raw_input("What's your name? ") print("Nice to meet you " + name + "!") age = input("Your age? ") print("So, you are are already " + str(age) + " years old, " + name + "!")

[ "chaitanya.padmanabha@gmail.com" ]

chaitanya.padmanabha@gmail.com

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/post_rec/retrievers/utils.py

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2020-08-16T09:16:54.510718

2019-10-16T07:47:39

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#!/usr/bin/env python3 # Copyright 2017-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. """Various retriever utilities.""" import regex import unicodedata import numpy as np import scipy.sparse as sp from sklearn.utils import murmurhash3_32 def getTF_IDF_Data(data_source,ngram=2,hash_size=16777216,tokenizer_name='bert'): dataName='tf_idf_hash/{}-docs-tfidf-ngram={}-hash={}-tokenizer={}.npz'.format(data_source,ngram,hash_size,tokenizer_name) return dataName # ------------------------------------------------------------------------------ # Sparse matrix saving/loading helpers. # ------------------------------------------------------------------------------ def save_sparse_csr(filename, matrix, metadata=None): #print("saving shape",np.shape(matrix)) data = { 'data': matrix.data, 'indices': matrix.indices, 'indptr': matrix.indptr, 'shape': matrix.shape, 'metadata': metadata, } np.savez(filename, **data) def load_sparse_csr(filename): loader = np.load(filename) matrix = sp.csr_matrix((loader['data'], loader['indices'], loader['indptr']), shape=loader['shape']) return matrix, loader['metadata'].item(0) if 'metadata' in loader else None # ------------------------------------------------------------------------------ # Token hashing. # ------------------------------------------------------------------------------ def hash(token, num_buckets): """Unsigned 32 bit murmurhash for feature hashing.""" return murmurhash3_32(token, positive=True) % num_buckets # ------------------------------------------------------------------------------ # Text cleaning. # ------------------------------------------------------------------------------ STOPWORDS = { 'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', 'should', 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', 'couldn', 'didn', 'doesn', 'hadn', 'hasn', 'haven', 'isn', 'ma', 'mightn', 'mustn', 'needn', 'shan', 'shouldn', 'wasn', 'weren', 'won', 'wouldn', "'ll", "'re", "'ve", "n't", "'s", "'d", "'m", "''", "``" } def normalize(text): """Resolve different type of unicode encodings.""" return unicodedata.normalize('NFD', text) def filter_word(text): """Take out english stopwords, punctuation, and compound endings.""" text = normalize(text) if regex.match(r'^\p{P}+$', text): return True if text.lower() in STOPWORDS: return True return False def filter_ngram(gram, mode='any'): """Decide whether to keep or discard an n-gram. Args: gram: list of tokens (length N) mode: Option to throw out ngram if 'any': any single token passes filter_word 'all': all tokens pass filter_word 'ends': book-ended by filterable tokens """ filtered = [filter_word(w) for w in gram] if mode == 'any': return any(filtered) elif mode == 'all': return all(filtered) elif mode == 'ends': return filtered[0] or filtered[-1] else: raise ValueError('Invalid mode: %s' % mode)

[ "bird@local.com" ]

bird@local.com

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/shop_app/migrations/0014_auto_20210205_0928.py

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[]

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vincenttpham/dekage

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refs/heads/main

2023-04-21T12:19:03.898801

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# Generated by Django 2.2 on 2021-02-05 09:28 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('shop_app', '0013_auto_20210202_0808'), ] operations = [ migrations.RemoveField( model_name='order', name='address1', ), migrations.RemoveField( model_name='order', name='address2', ), migrations.RemoveField( model_name='order', name='card_number', ), migrations.RemoveField( model_name='order', name='city', ), migrations.RemoveField( model_name='order', name='country', ), migrations.RemoveField( model_name='order', name='cvv', ), migrations.RemoveField( model_name='order', name='expiration', ), migrations.RemoveField( model_name='order', name='name_on_card', ), migrations.RemoveField( model_name='order', name='state', ), migrations.RemoveField( model_name='order', name='zipcode', ), migrations.RemoveField( model_name='product', name='image', ), migrations.CreateModel( name='Image', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255)), ('img', models.ImageField(upload_to='products/')), ('default', models.BooleanField(default=False)), ('product', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='image', to='shop_app.Product')), ], ), ]

[ "crystalpham@Crystals-MacBook-Pro.local" ]

crystalpham@Crystals-MacBook-Pro.local

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/django_bot/wsgi.py

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[]

no_license

JbFiras/text_to_speech_django

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5f9bc481600f0524571fad47247c530e96605157

refs/heads/master

2023-06-03T06:26:27.405605

2021-06-23T17:45:59

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""" WSGI config for django_bot project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.1/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'django_bot.settings') application = get_wsgi_application()

[ "contact@firas-jebari.tn" ]

contact@firas-jebari.tn

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/src/audio_all_methods.py

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[]

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pcatrou/microtonal_analysis

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refs/heads/master

2023-04-05T23:59:36.787254

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import numpy as np import Consts def getEnvelope(ampl,timeCoeff): """ Get the amplitude envelope of audio data. the time indent is changed to timeCoeff Parameters: ampl,timeCoeff Returns: filteredEnvelope """ absAmpl = abs(ampl) envelope = np.array([]) #tempEnvelope = np.array([]) for i in range (timeCoeff,len(absAmpl),timeCoeff): maximum = 0 for j in range(timeCoeff): maximum = max(absAmpl[i-j],maximum) envelope = np.append(envelope,[maximum]) envelope = np.append(envelope,[0]) # last mesure added to fit with other data (unless lenght of envelope is missing one element) return envelope def getfilteredEnvelope (envelope,intensityThreshold): """ filter the amplitude envelope to the specified threshold Parameters: envelope,intensityThreshold Returns: filteredEnvelope """ filteredEnvelope = np.array([]) for i in range(len(envelope)): if envelope[i]>intensityThreshold: filteredEnvelope = np.append(filteredEnvelope, envelope[i]) else: filteredEnvelope = np.append(filteredEnvelope, None) return filteredEnvelope # note : faster with np.append than x[i] def getMaxIndex (dbData,filteredEnvelope,timeCoef,lowFilter,highFilter): """ generate the table with the index of the max amplitude for each time indent of the db data of stft transformed sound signal use of a filtered envelop to avoid noise data timeCoeff is the time indent of the envelope The dbData is also filtered, if the maximum of the dbData at one moment t is inferior to a threshold value Parameters: dbData,filteredEnvelope,timeCoef,lowFilter Returns: maxIndex """ maxIndex = np.array([]) #dbDataToFilter = dbData # to not alter dbData rev_data = filterHighLowFreq(dbData,lowFilter,highFilter).transpose() #rev_data = dbData.transpose() for i in range(len(dbData[1])-timeCoef-1): if (filteredEnvelope[i*timeCoef] == None or np.max(rev_data[i])<Consts.THRESHOLD_VALUE_FOR_FILTERING): #TODO mettre la valeur en variable ? maxIndex =np.append(maxIndex,None) else: maxIndex =np.append(maxIndex, int(np.argmax(rev_data[i]))) maxIndex = np.append(maxIndex,[None,None]) return maxIndex def getPitch(dbData,filteredEnvelope,timeCoef,frequencies,lowFilter, highFilter): """ transform the maxIndex to frequencies values. Uses getMaxIndex Parameters: dbData,filteredEnvelope,timeCoef,frequencies,lowFilter Returns: pitchValues """ maxIndex = getMaxIndex (dbData,filteredEnvelope,timeCoef,lowFilter,highFilter) pitchValues = [] for i in range(len(maxIndex)): if (maxIndex[i] != None and maxIndex[i]>10): pitchValues.append(frequencies[int(maxIndex[i])]) else: pitchValues.append(None) return pitchValues def getAllNotesVariations(pitchLines): noteValues = [] for i in range(len(pitchLines)): noteValuesForOneNote = [] for j in range(len(pitchLines[i])-1): if pitchLines[i][j] != None : noteValuesForOneNote.append(pitchLines[i][j]) noteValues.append(noteValuesForOneNote) return noteValues def filterHighLowFreq (dbData,lowFilter,highFilter): """ abrupt filter of high and low frequencies by setting their value to 0 lowFilter sets the index of minimum frequencies High freq max are divided by 2 freqData could be sftf signal or its db amplitude Parameters: freqData,lowFilter Returns: freqData """ for i in range(lowFilter): dbData[i] = 0 for i in range(len(dbData)-highFilter): dbData[highFilter + i] = 0 """for i in range(len(dbData)//2+len(dbData)//3): dbData[len(dbData)-1-i] = 0""" return dbData def freqToIndex (IntegerFrequencies,desiredFrequency): """ converts the input given in Hz desiredFrequency to the index of this frequency in the freq table. """ return np.where(IntegerFrequencies >= desiredFrequency)[0][0] def divideFreq(db,factor): """ divides the frequencies of db amplitude of stft or db data by the integer factor FFT(x)=FFT_O(2x) heart of harmonic produc spectrum method transposed matrix are used to put freq data on rows, to easily access to them Parameters: db,factor Returns: dividedData """ dividedDataTransposed = np.ones((len(db),len(db[0]))).transpose() dbTransp = db.transpose() for f in range(len(db)//factor): for t in range (len(db[0])): dividedDataTransposed [t][f] = dbTransp[t][factor*f] dividedData = dividedDataTransposed.transpose() return dividedData def harmonicProductSpectrum (db,iterations, coeff): """ Makes the product of the db data with frequency divided db data (uses divideFreq) with interations number of iterations. A corrector factor coeff could be used to ponderate the intensity of the HPS data with initial data Parameters: db,iterations, coeff Returns: dbHPS+coeff*db """ dbHPS = db for i in range(iterations-1): dbHPS = (divideFreq(db,i+2))*dbHPS return dbHPS+coeff*db

[ "pierre.catrou@soprasteria.com" ]

pierre.catrou@soprasteria.com

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/models/Pooling.py

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[]

no_license

yiique/NNSample

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2021-01-17T20:02:58.457376

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__author__ = 'liushuman' import theano import theano.tensor as T class Pooling(object): def __init__(self): self.funcs = {'max': self._max, 'mean': self._mean} def apply(self, func_input, pooling_type='mean', axis=0): return self.funcs[pooling_type](func_input, axis) def _max(self, func_input, axis): func_output = T.max(func_input, axis) return func_output def _mean(self, func_input, axis): func_output = T.mean(func_input, axis) return func_output

[ "liushuman@liushumandeMBP.lan" ]

liushuman@liushumandeMBP.lan

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/Objects and Classes/03. Catalogue.py

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[]

no_license

dvasilev7/PYTHON-FUNDAMENTALS

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refs/heads/main

2023-05-30T07:26:47.516972

2021-06-06T23:26:04

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class Catalogue: def __init__(self, name): self.name = name self.products = [] def add_product(self, product): self.products.append(product) return self.products def get_by_letter(self, first_letter): letter_product = [product for product in self.products if first_letter == product[0]] return letter_product def __repr__(self): self.products.sort() result = f"Items in the {self.name} catalogue:\n" result += "\n".join(self.products) return result catalogue = Catalogue("Furniture") catalogue.add_product("Sofa") catalogue.add_product("Mirror") catalogue.add_product("Desk") catalogue.add_product("Chair") catalogue.add_product("Carpet") print(catalogue.get_by_letter("C")) print(catalogue)

[ "noreply@github.com" ]

dvasilev7.noreply@github.com

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/test.py

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[ "MIT" ]

permissive

rbentl/codeastro_workshop

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refs/heads/master

2022-11-11T05:13:02.706886

2020-06-26T13:18:21

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import numpy as np import matplotlib.pyplot as plt def test_func(x,y): '''plot x versus y arguments: x (int, float, or array) : x value to be plotted y (int, flot, or array) : y value to be plotted returns: plot ''' plt.plot(x,y) plt.show()

[ "kkosmo@hera.astro.ucla.edu" ]

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/pyJoules/energy_device/__init__.py

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[ "MIT" ]

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danglotb/pyJoules

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refs/heads/master

2022-12-17T05:17:01.196809

2020-07-15T14:26:53

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2020-09-10T10:28:29

2020-09-10T10:28:28

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# MIT License # Copyright (c) 2019, INRIA # Copyright (c) 2019, University of Lille # All rights reserved. # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. from .energy_domain import EnergyDomain from .energy_device import EnergyDevice, NotConfiguredDeviceException from .energy_device_factory import EnergyDeviceFactory

[ "arthur.d-azemar@inria.fr" ]

arthur.d-azemar@inria.fr

d0d33c23e9d9c46de7523b766d1dbb15a15e8608

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/views.py

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[]

no_license

phzhou/fbpixels

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76e6b38b0936523da72f8b602627aca9ce8ef3e9

refs/heads/master

2020-04-24T07:36:12.401420

2015-08-21T10:15:23

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import os import json from django.shortcuts import render def _load_messages(language): messages = {} message_path = os.path.join( os.path.dirname(os.path.realpath(__file__)), 'static', 'messages', ) with open(os.path.join(message_path, "{}.json".format(language))) as f: messages = json.load(f) return messages def render_pixel_docs(request, template): return render(request, template, {'messages': _load_messages('en')}) def overview(request): return render_pixel_docs(request, 'pixels/overview.html') def up(request): return render_pixel_docs(request, 'pixels/up.html') def wca(request): return render_pixel_docs(request, 'pixels/wca.html') def conversion(request): return render_pixel_docs(request, 'pixels/conversion.html') def manager(request): return render_pixel_docs(request, 'pixels/manager.html') def dpa(request): return render_pixel_docs(request, 'pixels/dpa.html')

[ "phzhou@fb.com" ]

phzhou@fb.com

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/tictactoe.py

0431854e8d6e53f2c6745e1a0ea48256a76dcad5

[]

no_license

mohaned2014/tic-tac-toc-AI

d87f6c71cb9269eeb4f85a54354972318577088e

0b52c92010a2353c1f1cb297369c78dad76dafc1

refs/heads/main

2023-01-21T02:50:39.878136

2020-11-27T16:02:58

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""" Tic Tac Toe Player """ import math import copy import itertools X = "X" O = "O" EMPTY = None OO = 2 def initial_state(): """ Returns starting state of the board. """ return [[EMPTY, EMPTY, EMPTY], [EMPTY, EMPTY, EMPTY], [EMPTY, EMPTY, EMPTY]] def player(board): """ Returns player who has the next turn on a board. """ turn = 0 for i in range(len(board)): for j in range(len(board[0])): turn += (board[i][j] != EMPTY) return X if (turn % 2 == 0) else O def actions(board): """ Returns set of all possible actions (i, j) available on the board. """ possible_moves = [] for i in range(len(board)): for j in range(len(board[0])): if board[i][j] == EMPTY: possible_moves.append((i, j)) return possible_moves def result(board, action): """ Returns the board that results from making move (i, j) on the board. """ new_board = copy.deepcopy(board) if new_board[action[0]][action[1]] == EMPTY: new_board[action[0]][action[1]] = player(board) else: raise NameError("Invalid move") return new_board def winner(board): """ Returns the winner of the game, if there is one. """ global X rows = [0] * 3 cols = [0] * 3 main_diagonal = [0] * 5 rev_diagonal = [0] * 5 def update_cell(_i, _j, _val): rows[_i] += _val cols[_j] += _val main_diagonal[_i - _j + 2] += _val rev_diagonal[_i + j] += _val for i in range(3): for j in range(3): value = 1 if board[i][j] == X else -1 if board[i][j] == O else 0 update_cell(i, j, value) for val in itertools.chain(rows, cols, main_diagonal, rev_diagonal): if val == 3: return X if val == -3: return O return None def terminal(board): """ Returns True if game is over, False otherwise. """ if winner(board) is not None: return True for i in range(3): for j in range(3): if board[i][j] == EMPTY: return False return True def utility(board): """ Returns 1 if X has won the game, -1 if O has won, 0 otherwise. """ winner_player = winner(board) return 0 if winner_player is None else 1 if winner_player == X else -1 def minimax(board): """ Returns the optimal action for the current player on the board. """ if terminal(board): return None return maximize(board)[1] if player(board) == X else minimize(board)[1] def maximize(_board, beta=OO): """ Return tuple(max_score_for_x,action=> best_move_for_x) return score,None in case of terminal board """ if terminal(_board): return utility(_board), None score = -OO best_action = None for action in actions(_board): # Alpha-beta pruning if beta <= score: return score, best_action new_score, act = minimize(result(_board, action), score) if new_score > score: score = new_score best_action = action return score, best_action def minimize(_board, alpha=-OO): """ Return tuple(min_score_for_O,action=> best_move_for_O) return score,None in case of terminal board """ if terminal(_board): return utility(_board), None score = OO best_action = None for action in actions(_board): # Alpha-beta pruning if alpha >= score: return score, best_action new_score, act = maximize(result(_board, action), score) if new_score < score: score = new_score best_action = action return score, best_action

[ "noreply@github.com" ]

mohaned2014.noreply@github.com

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/challenges/bitwise_and.py

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[]

no_license

ritakalach/30-days-of-code

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refs/heads/master

2022-11-08T07:19:34.799772

2020-06-22T23:44:45

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""" Given set S={1,2,3,...,N}. Find two integers, A and B (where A<B), from set S such that the value of A&B is the maximum possible and also less than a given integer, K. In this case, & represents the bitwise AND operator. """ if __name__ == '__main__': t = int(input()) for t_itr in range(t): nk = input().split() n = int(nk[0]) k = int(nk[1]) print(k-1 if ((k-1) | k) <= n else k-2)

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######## Video Object Detection Using Tensorflow-trained Classifier ######### #importing necessary packages for video classification import os import cv2 import numpy as np import tensorflow as tf import sys sys.path.append("..") #imported utilities also visualization_utils_db for fruits database from utils import label_map_util from utils import visualization_utils_DB as vis_util # Name of the directory that contains the object detection models MODEL_NAME = 'inference_graph' VIDEO_NAME = 'test_mango.mp4' CWD_PATH = os.getcwd() #path to the frozen_inference.pb file PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph_mango.pb') #Path to label map file PATH_TO_LABELS = os.path.join(CWD_PATH,'training','mango_labelmap.pbtxt') #path to video file PATH_TO_VIDEO = os.path.join(CWD_PATH,VIDEO_NAME) NUM_CLASSES = 1 label_map = label_map_util.load_labelmap(PATH_TO_LABELS) categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True) category_index = label_map_util.create_category_index(categories) detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0') detection_scores = detection_graph.get_tensor_by_name('detection_scores:0') detection_classes = detection_graph.get_tensor_by_name('detection_classes:0') num_detections = detection_graph.get_tensor_by_name('num_detections:0') #this opens the video video = cv2.VideoCapture(PATH_TO_VIDEO) while(video.isOpened()): ret, frame = video.read() frame_expanded = np.expand_dims(frame, axis=0) (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: frame_expanded}) #this function imported from visualization utils is used to draw the box around the images and detect vis_util.visualize_boxes_and_labels_on_image_array( frame, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=8, min_score_thresh=0.80) cv2.imshow('Object detector', frame) if cv2.waitKey(1) == ord('q'): break video.release() cv2.destroyAllWindows()

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import downDB import get_estudiantes import get_calificaciones import calcVar import final as finalpy #import predRF import pandas as pd import predict import predictEnsemble import stats_pred from sqlalchemy import create_engine #import psycopg2, psycopg2.extras from numpy import inf import numpy as np import features import os import warnings warnings.filterwarnings("ignore") from sklearn.metrics import accuracy_score from datetime import date, time, datetime #Cambiar para marcar el semestre de graduados SEMESTRE_GRADUADOS = 7 #file = 'estudiantes.csv' #estudiantes = pd.read_csv(file) #file = 'calificaciones.csv' #calificaciones = pd.read_csv(file, encoding = "ISO-8859-1") print("Prediciendo Dropout...\n") #descargar datos para estudiantes y calificaciones de la BD estudiantes,calificaciones = downDB.download() estudiantes = estudiantes.astype({'id': 'int','codigo_carrera': 'int', 'codigo_malla': 'int', 'total_horas_carrera': 'int', 'total_asignaturas': 'int', 'minnivel': 'int', 'edad': 'int', 'quintil': 'int'}) estudiantes['fecha_nacimiento'] = pd.to_datetime(estudiantes["fecha_nacimiento"], format = '%Y-%m-%d') estudiantes["estudia_casa"] = estudiantes.apply(lambda x: "SI" if str(x["ciudaddomicilio"]) == str(x["sede"]) else "NO", axis=1) estudiantes.info() calificaciones['id'] = calificaciones['id'].astype(int) calificaciones['codigo_curriculum'] = calificaciones['codigo_curriculum'].astype(int) calificaciones['codigo_asignatura'] = calificaciones['codigo_asignatura'].astype(int) calificaciones['nota_final'] = calificaciones['nota_final'].astype(int) calificaciones['numero_matricula'] = calificaciones['numero_matricula'].astype(int) calificaciones['codigo_carrera'] = calificaciones['codigo_carrera'].astype(int) calificaciones['total_horas_asignatura'] = calificaciones['total_horas_asignatura'].astype(int) calificaciones['anio_curso_asignatura'] = calificaciones['anio_curso_asignatura'].astype(int) calificaciones['semestre_en_carrera'] = calificaciones['semestre_en_carrera'].astype(int) calificaciones['semestre_cursa'] = calificaciones['semestre_cursa'].astype(int) calificaciones['numero_creditos'] = calificaciones['numero_creditos'].astype(int) calificaciones.info() calificaciones.loc[calificaciones['semestre_en_carrera']>calificaciones['semestre_cursa'],'semestre_cursa'] = calificaciones['semestre_en_carrera'] #Etiquetamos a los graduados estudiantes.loc[estudiantes["minnivel"] >= SEMESTRE_GRADUADOS, "graduado"] = "S" estudiantes.drop("minnivel",1,inplace = True) print("Prediciendo Dropout...\n") #estudiantes,calificaciones = downDB.download() print("Archivos descargados de BD\n") print("Número total de estudiantes: " + str(estudiantes.id.count())) print("Número total de estudiantes en el historial: "+ str(calificaciones.drop_duplicates(subset = 'id').id.count())) cambioEstudiante = get_estudiantes.cambioEst(estudiantes) cambioHistorial = get_calificaciones.cambCal(calificaciones) cambioHistorial = cambioHistorial.sort_values(["id","codigo_carrera","semestre_cursa"]) #Asignaturas reprobadas con 0 no se toman en cuenta cambioHistorial = cambioHistorial[cambioHistorial['nota_final']!=0] #NÃºmero de semestres que lleva cursando cada alumno asigxSem = cambioHistorial.groupby(["id","codigo_carrera","semestre_cursa"],as_index = False).codigo_asignatura.count() alumSem = asigxSem.groupby(["id","codigo_carrera"],as_index = False).count().drop("codigo_asignatura",1) alumSem.columns = ["id","codigo_carrera","Semestres"] #Cogemos los estudiantes del primer semestre primerSem = cambioHistorial.groupby(["id","codigo_carrera"], as_index = False).semestre_cursa.min().astype(object) cambioHistorial1Sem = cambioHistorial.merge(primerSem, on=['id','codigo_carrera','semestre_cursa'], how='inner') alum1Sem = alumSem[alumSem['Semestres'] == 1] al1Sem = alumSem[alumSem['Semestres'] >= 1] #Cogemos el primer y segundo semestre cambioHistorial2 = pd.concat([cambioHistorial, cambioHistorial1Sem]).drop_duplicates(keep=False) segSem = cambioHistorial2.groupby(["id","codigo_carrera"], as_index = False).semestre_cursa.min().astype(object) cambioHistorial2Sem = cambioHistorial.merge(segSem, on=['id','codigo_carrera','semestre_cursa'], how='inner') cambioHistorial12Sem = pd.concat([cambioHistorial1Sem, cambioHistorial2Sem]) alum2Sem = alumSem[alumSem['Semestres'] == 2] al2Sem = alumSem[alumSem['Semestres'] >= 2] #Primer, segundo y tercer semestre cambioHistorial3 = pd.concat([cambioHistorial, cambioHistorial12Sem]).drop_duplicates(keep=False) terSem = cambioHistorial3.groupby(["id","codigo_carrera"], as_index = False).semestre_cursa.min().astype(object) cambioHistorial3Sem = cambioHistorial.merge(terSem, on=['id','codigo_carrera','semestre_cursa'], how='inner') cambioHistorial123Sem = pd.concat([cambioHistorial12Sem, cambioHistorial3Sem]) alum3Sem = alumSem[alumSem['Semestres'] == 3] al3Sem = alumSem[alumSem['Semestres'] >= 3] #Primer, segundo, tercer y cuarto semestre cambioHistorial4 = pd.concat([cambioHistorial, cambioHistorial123Sem]).drop_duplicates(keep=False) cuatSem = cambioHistorial4.groupby(["id","codigo_carrera"], as_index = False).semestre_cursa.min().astype(object) cambioHistorial4Sem = cambioHistorial.merge(cuatSem, on=['id','codigo_carrera','semestre_cursa'], how='inner') cambioHistorial1234Sem = pd.concat([cambioHistorial123Sem, cambioHistorial4Sem]) alum4Sem = alumSem[alumSem['Semestres'] == 4] al4Sem = alumSem[alumSem['Semestres'] >= 4] #Primer, segundo, tercer, cuarto y quinto semestre cambioHistorial5 = pd.concat([cambioHistorial, cambioHistorial1234Sem]).drop_duplicates(keep=False) quinSem = cambioHistorial5.groupby(["id","codigo_carrera"], as_index = False).semestre_cursa.min().astype(object) cambioHistorial5Sem = cambioHistorial.merge(quinSem, on=['id','codigo_carrera','semestre_cursa'], how='inner') cambioHistorial12345Sem = pd.concat([cambioHistorial1234Sem, cambioHistorial5Sem]) alum5Sem = alumSem[alumSem['Semestres'] == 5] al5Sem = alumSem[alumSem['Semestres'] >= 5] #Alumnos a partir de 5 semestre alumMas5Sem = alumSem[alumSem['Semestres'] > 5] resultados = pd.DataFrame() datosBD = pd.DataFrame() carrerasSinActivos = "" carrerasSinActivosAUC = "" abandonoXSem = pd.DataFrame() abandonoXSem['carrera'] = cambioEstudiante['nombre_carrera'].drop_duplicates().dropna() abandonoXSem.set_index('carrera',inplace = True) gradXSem = pd.DataFrame() gradXSem['carrera'] = cambioEstudiante['nombre_carrera'].drop_duplicates().dropna() gradXSem.set_index('carrera',inplace = True) correlation = {} featureSelectionRF = {} featureSelection = {} stats = pd.DataFrame(index='Accuracy AUC Log_loss'.split()) carreras = pd.DataFrame(estudiantes['nombre_carrera'].drop_duplicates().dropna()) try: os.mkdir('Archivos') except Exception as e: print("Error 1 ", e) pass for carrera in carreras['nombre_carrera']: try: os.mkdir('Archivos/' + carrera) except Exception as e: print("Error 2 ", e) pass for i in range(6): if i == 0: print("Semestre 1") cambioHistorial1Sem = cambioHistorial1Sem.copy() cambioHistorialSemActual = cambioHistorial1Sem.copy() alum1Sem = alum1Sem.copy() al1Sem = al1Sem.copy() elif i == 1: print("Semestre 2") cambioHistorial1Sem = cambioHistorial12Sem.copy() cambioHistorialSemActual = cambioHistorial2Sem.copy() alum1Sem = alum2Sem.copy() al1Sem = al2Sem.copy() elif i == 2: print("Semestre 3") cambioHistorial1Sem = cambioHistorial123Sem.copy() cambioHistorialSemActual = cambioHistorial3Sem.copy() alum1Sem = alum3Sem.copy() al1Sem = al3Sem.copy() elif i == 3: print("Semestre 4") cambioHistorial1Sem = cambioHistorial1234Sem.copy() cambioHistorialSemActual = cambioHistorial4Sem.copy() alum1Sem = alum4Sem.copy() al1Sem = al4Sem.copy() elif i == 4: print("Semestre 5") cambioHistorial1Sem = cambioHistorial12345Sem.copy() cambioHistorialSemActual = cambioHistorial5Sem.copy() alum1Sem = alum5Sem.copy() al1Sem = al5Sem.copy() elif i == 5: print("Semestre 6") cambioHistorial1Sem = cambioHistorial.copy() cambioHistorialSemActual = cambioHistorial5Sem.copy() alum1Sem = alumMas5Sem.copy() al1Sem = al5Sem.copy() dropout, dataMedia, credPass = calcVar.calcular(cambioHistorial,cambioEstudiante,cambioHistorial1Sem,cambioHistorialSemActual) dropout = dropout.reset_index(level=['id', 'codigo_carrera']) print("Variables de entrada calculadas\n") final = finalpy.finalData(dataMedia, cambioEstudiante, credPass,dropout) final = final.drop_duplicates(subset = "id") print("Datos finales listos\n") carreras = pd.DataFrame(final['nombre_carrera']) carreras = carreras.drop_duplicates().dropna() abandonoXSem['dropoutSem'+str(i+1)] = 0 gradXSem['gradSem'+str(i+1)] = 0 final.dropout = final.dropout.astype(str) #Se han observado muchos alumnos que han abandonado pero no por temas academicos dropoutNoAcad = final[((final['rateAprobadas']>=0.95) & (final['dropout'] == "0"))] dropoutNoAcad = dropoutNoAcad.merge(alum1Sem, on=['id','codigo_carrera'], how='inner') datosBD = pd.concat([datosBD, dropoutNoAcad],axis = 0) final = final[~((final['rateAprobadas']>=0.95) & (final['dropout'] == "0"))] #Quitamos los alumnos que hayan abandonado ya (mejor para la predicción final puesto que predice valores más bajos #de abandono pero peor para las estadísticas) """ final1 = final[final['dropout']== "0"] final1 = final1.merge(al1Sem, on=['id','codigo_carrera'], how='inner') final2 = final[final['dropout']!= "0"] final = pd.concat([final1,final2],ignore_index = True).drop("Semestres",1) """ #final.loc[final['porcentaje_carrera']>=np.mean(final['porcentaje_carrera']*2),'dropout'] = "0" for carrera in carreras['nombre_carrera']: print("\n#####################################################################\n") print("\n"+carrera + str(i+1) +"\n") try: os.mkdir('Archivos/' + carrera + '/' + carrera + str(i+1)) except Exception as e: print("Error 3 ", e) pass #carrera = "INGENIERÍA CIVIL" try: final = final.fillna(0) final = final.replace([np.inf, -np.inf], 0) featureSelection[carrera + "" +str(i+1)] = features.selection(final, carrera, i) accuracy, auc_stat, log_loss, description = stats_pred.statistics(final, carrera) description.to_excel('Archivos/' + carrera + '/' + carrera + str(i+1) + '/' + "description.xlsx") stats[carrera + "" +str(i+1)] = 0.1 stats[carrera + "" +str(i+1)]["Accuracy"] = accuracy stats[carrera + "" +str(i+1)]["AUC"] = auc_stat stats[carrera + "" +str(i+1)]["Log_loss"] = log_loss except Exception as e: print("Error 4 ", e) stats[carrera + "" +str(i+1)] = 0.0 print("Error") try: #featureSelectionRF[carrera + "" +str(i+1)] = features.selection(finalTot, carrera) #finalData1 = predict.predict(final, carrera) finalData1 = predictEnsemble.predict(final, carrera, i) finalData1 = finalData1.merge(alum1Sem, on=['id','codigo_carrera'], how='inner') abandSemX = finalData1[finalData1['dropout'] == 0] gradSemX = finalData1[finalData1['dropout'] == 1] abandonoXSem['dropoutSem'+str(i+1)][carrera] = abandSemX['id'].count() gradXSem['gradSem'+str(i+1)][carrera] = gradSemX['id'].count() finalData1['Accuracy'] = accuracy datosBD = pd.concat([datosBD, finalData1],axis = 0) except Exception as e: print("Error 5 ", e) print("Error predict") carrerasSinActivos = carrerasSinActivos + carrera+ str(i+1)+ "," final1 = final[final["nombre_carrera"] == carrera] final1["codigo_carrera"] = final1["codigo_carrera"].astype(int) final1 = final1.merge(alum1Sem, on=['id','codigo_carrera'], how='inner') final1['Accuracy'] = 0.1 datosBD = pd.concat([datosBD, final1],axis = 0, sort = True) datosBD["dropout"] = datosBD["dropout"].replace("3","0").astype(str) datosBD.rename(columns={'rate':'dropoutCarrera'}, inplace=True) datosBD.rename(columns={'dropout':'dropout_RF'}, inplace=True) datosBD.rename(columns={'dropout_avrg':'dropout'}, inplace=True) datosBD.drop_duplicates(subset='id', inplace =True) datosBD["dropout_RF"] = datosBD["dropout_RF"].fillna(-999).astype(str) ##Subimos resultados a la base de datos print("Subiendo probabilidad de abandono") abandonoXSem['carreras'] = abandonoXSem.index engine = create_engine('postgresql://lala:PB2Cx3fDEgfFTpPn@172.16.101.55:5432/lala') datosBD.to_sql('dropoutporsemestres', engine,if_exists = 'replace', index=False) abandonoXSem.to_sql('abandono', engine,if_exists = 'replace', index=False) print("Predicción acabada.") try: os.mkdir('Resultados/') except Exception as e: print("Error 5 ", e) print("Error predict") pass stats.to_excel("Resultados/metricas.xlsx") datosBD.to_excel("Resultados/predicciones.xlsx") abandonoXSem.reset_index(level=0, inplace=True) abandonoXSem.to_excel("Resultados/dropoutNumberPerSemesterAndDegree.xlsx") gradXSem.reset_index(level=0, inplace=True) gradXSem.to_excel("Resultados/graduateNumberPerSemesterAndDegree.xlsx")

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from flask import Flask,request from flask_restful import Resource, Api app = Flask(__name__) api = Api(app) items = [] class Item(Resource): def get(self, name): item = next(filter(lambda x:x[item] == name,items),None) return {"item":item} ,200 if item else 404 def post(self, name): if next(filter(lambda x: x[item] == name, items), None) not None : return {"message": "item with {} already exists".format(name)},400 data = request.get_jsion() item = {"name": data["name"], "price": data["price"]} items.append(item) return {"student": name} class Itemlist(Resource): def get(self): return {"items": items} api.add_resource(Item, "/item/<string:name>") api.add_resource(Itemlist,"/item") app.run(debug=True)

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import abc from typing import Optional, Dict, Tuple import numpy as np import torch from models.base_model import BaseModel class EndTaskBaseModel(BaseModel, abc.ABC): def loss(self, network_outputs: Optional[Dict]) -> Dict[str, Optional[Tuple[float, torch.Tensor]]]: raise NotImplementedError def get_metrics(self, network_outputs: Optional[Dict]) -> Dict[str, Optional[float]]: raise NotImplementedError def get_image_output(self, network_outputs) -> Dict[str, np.ndarray]: raise NotImplementedError

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# 修饰器学习 import time def spendTime(func): def warrer(): start = time.time() func() end = time.time() return end - start return warrer @spendTime def ctForLoop(): ls = []; for i in range(1000000): ls.append(i) @spendTime def newForLoop(): [i for i in range(1000000)] c = ctForLoop(); n = newForLoop(); print('old: ', c, 'new: ', n)

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""" Copyright (c) 2015 SONATA-NFV ALL RIGHTS RESERVED. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. Neither the name of the SONATA-NFV [, ANY ADDITIONAL AFFILIATION] nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. This work has been performed in the framework of the SONATA project, funded by the European Commission under Grant number 671517 through the Horizon 2020 and 5G-PPP programmes. The authors would like to acknowledge the contributions of their colleagues of the SONATA partner consortium (www.sonata-nfv.eu). """ import logging import json import time import os from sonmanobase import messaging logging.basicConfig(level=logging.INFO) LOG = logging.getLogger("son-mano-fakeslm") LOG.setLevel(logging.DEBUG) logging.getLogger("son-mano-base:messaging").setLevel(logging.INFO) class fakealert(object): def __init__(self): self.name = 'fake-alert' self.version = '0.1-dev' self.description = 'description' LOG.info("Starting alert:...") # create and initialize broker connection self.manoconn = messaging.ManoBrokerRequestResponseConnection(self.name) self.path_descriptors = 'test/test_descriptors/' self.end = False self.publish_nsd() self.run() def run(self): # go into infinity loop while self.end == False: time.sleep(1) def publish_nsd(self): LOG.info("Sending alert request") message = {"exported_instance": "fw-vnf","core": "cpu","group": "development","exported_job": "vnf","value": "1", "instance": "pushgateway:9091","job": "sonata","serviceID": "263fd6b7-8cfb-4149-b6c1-fb082553ca71", "alertname": "mon_rule_vm_cpu_usage_85_perc","time": "2016-09-13T17:29:22.807Z","inf": "None","alertstate": "firing", "id": "01ccc69f-c925-42f5-9418-e1adb075527e","monitor": "sonata-monitor"} self.manoconn.publish('son.monitoring',json.dumps(message)) def main(): fakealert() if __name__ == '__main__': main()

[ "hadi_ksr_2004@yahoo.com" ]

hadi_ksr_2004@yahoo.com

5ad6e2afea33ec765d6f2204acc87b47403a5697

804d501d0e12981dd9531e21f98c4362c88a820f

/SevenWAYS/SevenWAYS/settings.py

92bc20c0729b716e55251af86957713b1a5af8b4

[]

no_license

Farad2020/SevenWAYS

1157c1f3c91803c1fefc4430aa236409e935232d

849681b8a55b71089140bd8cf044c2a2d14a3a40

refs/heads/master

2022-11-21T10:54:42.807414

2020-07-17T23:57:47

280,533,429

0

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Python

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3,276

py

""" Django settings for SevenWAYS project. Generated by 'django-admin startproject' using Django 2.2.6. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 's1c7b#)^(u6h+b*ms_wkxa5$evvg)q%r2-9_%*mim2b#7zd8_n' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'MainApp', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'SevenWAYS.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'SevenWAYS.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS = ( os.path.join(BASE_DIR, "static"), ) MEDIA_ROOT = os.path.join(BASE_DIR, 'Media') MEDIA_URL = '/media/'

[ "fnirc@mail.ru" ]

fnirc@mail.ru

847a494f7e3525d879724351003eb1bde72ac82d

45e49fe63320b8dc0b1e7f9f82896b2ac4163a95

/test_helpers/tf_test_hit.py

dd8a28ca113a99ed87f4c5bec8853dafd7931b76

[ "LicenseRef-scancode-warranty-disclaimer" ]

no_license

MyOwnClone/tweak_af

67b6a4a0d0171e1ffa5278fdc853a4410a298f21

d18ced0522a980e52edf65792f897ecc58e7b33d

refs/heads/master

2022-12-15T10:53:54.658058

2020-09-14T19:16:50

292,057,648

4

0

null

UTF-8

Python

false

192

py

from tweak_af import tf def dummy_function(): print("I quit!") return 42 while True: returned_value = tf(lambda: dummy_function()) if returned_value == 42: exit(0)

[ "tomas.vymazal@gmail.com" ]

tomas.vymazal@gmail.com

060350e3a8da78b116c73280d804e9458e73c258

4ca44b7bdb470fcbbd60c2868706dbd42b1984c9

/20.03.29/2016_calendar_5515.py

cad16966dac33cc4e9e100aaa95a513481fde389

[]

no_license

titiman1013/Algorithm

3b3d14b3e2f0cbc4859029eb73ad959ec8778629

8a67e36931c42422779a4c90859b665ee468255b

refs/heads/master

2023-06-29T17:04:40.015311

2021-07-06T01:37:29

242,510,483

2

0

null

UTF-8

Python

false

615

py

import sys; sys.stdin = open('2016_calendar.txt', 'r') for tc in range(1, int(input())+1): m, d = map(int, input().split()) # 1월 1일이 금요일 / 2월을 29일까지 # 월화수목 ... => 0 1 2 3 ... list_31 = [1, 3, 5, 7, 8, 10, 12] list_30 = [4, 6, 9, 11] list_29 = [2] start = 4 # 월 지날때 일수 더해주는 계산 for i in range(1, m): if i in list_31: start += 31 elif i in list_30: start += 30 elif i in list_29: start += 29 start += d print(f'#{tc} {(start-1)%7}') # print((366-1)%7)

[ "hyunsukr1013@gmail.com" ]

hyunsukr1013@gmail.com

45d19ee45e16fcf2ba6377c39776d4d1745ff31a

171ea5737fa9cb0ef763e0ba20847e1ba61c0199

/assignment2/ie590/layer_utils.py

88538bb217dc260dced7a903168bc2b84b4f8e54

[]

no_license

ShuzhanSun/IE59000_Deep_Learning_In_Machine_Vision

0be6d2eefe3ed203c385b0995cca426b89acb566

ef9c823277c69d4f25487a4db3c48fddd1ec1107

refs/heads/master

2022-12-08T10:31:50.314933

2019-12-16T03:01:35

209,534,434

0

null

2022-11-22T02:41:19

2019-09-19T11:17:18

Jupyter Notebook

UTF-8

Python

false

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py

from ie590.layers import * from ie590.fast_layers import * def affine_leaky_relu_forward(x, w, b): """ Convenience layer that perorms an affine transform followed by a Leaky ReLU Inputs: - x: Input to the affine layer - w, b: Weights for the affine layer Returns a tuple of: - out: Output from the Leaky ReLU - cache: Object to give to the backward pass """ a, fc_cache = affine_forward(x, w, b) out, leaky_relu_cache = leaky_relu_forward(a) cache = (fc_cache, leaky_relu_cache) return out, cache def affine_leaky_relu_backward(dout, cache): """ Backward pass for the affine-leaky_relu convenience layer """ fc_cache, leaky_relu_cache = cache da = leaky_relu_backward(dout, leaky_relu_cache) dx, dw, db = affine_backward(da, fc_cache) return dx, dw, db def affine_bn_leaky_relu_forward(x, w, b, gamma, beta, bn_param): """ Convenience layer that performs an affine transform - a batch normalization - a Leaky ReLU Inputs: - x: Input to the affine layer - w, b: Weights for the affine layer - gamma, beta: paramters used in the batch normalization - bn_param: Dictionary with the following keys: - mode: 'train' or 'test'; required - eps: Constant for numeric stability - momentum: Constant for running mean / variance. - running_mean: Array of shape (D,) giving running mean of features - running_var Array of shape (D,) giving running variance of features """ a, fc_cache = affine_forward(x, w, b) a, bn_cache = batchnorm_forward(a, gamma, beta, bn_param) out, leaky_relu_cache = leaky_relu_forward(a) cache = (fc_cache, bn_cache, leaky_relu_cache) return out, cache def affine_bn_leaky_relu_backward(dout, cache): """ Backward pass for the affine_bn_leaky_relu convenience layer """ fc_cache, bn_cache, leaky_relu_cache = cache da = leaky_relu_backward(dout, leaky_relu_cache) da, dgamma, dbeta = batchnorm_backward_alt(da, bn_cache) dx, dw, db = affine_backward(da, fc_cache) return dx, dw, db, dgamma, dbeta def affine_ln_leaky_relu_forward(x, w, b, gamma, beta, bn_param): """ Convenience layer that performs an affine transform - a layer normalization - a Leaky ReLU Inputs: - x: Input to the affine layer - w, b: Weights for the affine layer - gamma, beta: paramters used in the batch normalization - bn_param: Dictionary with the following keys: - mode: 'train' or 'test'; required - eps: Constant for numeric stability - momentum: Constant for running mean / variance. - running_mean: Array of shape (D,) giving running mean of features - running_var Array of shape (D,) giving running variance of features """ a, fc_cache = affine_forward(x, w, b) a, bn_cache = layernorm_forward(a, gamma, beta, bn_param) out, leaky_relu_cache = leaky_relu_forward(a) cache = (fc_cache, bn_cache, leaky_relu_cache) return out, cache def affine_ln_leaky_relu_backward(dout, cache): """ Backward pass for the affine_ln_leaky_relu convenience layer """ fc_cache, bn_cache, leaky_relu_cache = cache da = leaky_relu_backward(dout, leaky_relu_cache) da, dgamma, dbeta = layernorm_backward(da, bn_cache) dx, dw, db = affine_backward(da, fc_cache) return dx, dw, db, dgamma, dbeta def affine_relu_forward(x, w, b): """ Convenience layer that perorms an affine transform followed by a ReLU Inputs: - x: Input to the affine layer - w, b: Weights for the affine layer Returns a tuple of: - out: Output from the ReLU - cache: Object to give to the backward pass """ a, fc_cache = affine_forward(x, w, b) out, relu_cache = relu_forward(a) cache = (fc_cache, relu_cache) return out, cache def affine_relu_backward(dout, cache): """ Backward pass for the affine-relu convenience layer """ fc_cache, relu_cache = cache da = relu_backward(dout, relu_cache) dx, dw, db = affine_backward(da, fc_cache) return dx, dw, db def conv_relu_forward(x, w, b, conv_param): """ A convenience layer that performs a convolution followed by a ReLU. Inputs: - x: Input to the convolutional layer - w, b, conv_param: Weights and parameters for the convolutional layer Returns a tuple of: - out: Output from the ReLU - cache: Object to give to the backward pass """ a, conv_cache = conv_forward_fast(x, w, b, conv_param) out, relu_cache = relu_forward(a) cache = (conv_cache, relu_cache) return out, cache def conv_relu_backward(dout, cache): """ Backward pass for the conv-relu convenience layer. """ conv_cache, relu_cache = cache da = relu_backward(dout, relu_cache) dx, dw, db = conv_backward_fast(da, conv_cache) return dx, dw, db def conv_bn_relu_forward(x, w, b, gamma, beta, conv_param, bn_param): a, conv_cache = conv_forward_fast(x, w, b, conv_param) an, bn_cache = spatial_batchnorm_forward(a, gamma, beta, bn_param) out, relu_cache = relu_forward(an) cache = (conv_cache, bn_cache, relu_cache) return out, cache def conv_bn_relu_backward(dout, cache): conv_cache, bn_cache, relu_cache = cache dan = relu_backward(dout, relu_cache) da, dgamma, dbeta = spatial_batchnorm_backward(dan, bn_cache) dx, dw, db = conv_backward_fast(da, conv_cache) return dx, dw, db, dgamma, dbeta def conv_relu_pool_forward(x, w, b, conv_param, pool_param): """ Convenience layer that performs a convolution, a ReLU, and a pool. Inputs: - x: Input to the convolutional layer - w, b, conv_param: Weights and parameters for the convolutional layer - pool_param: Parameters for the pooling layer Returns a tuple of: - out: Output from the pooling layer - cache: Object to give to the backward pass """ a, conv_cache = conv_forward_fast(x, w, b, conv_param) s, relu_cache = relu_forward(a) out, pool_cache = max_pool_forward_fast(s, pool_param) cache = (conv_cache, relu_cache, pool_cache) return out, cache def conv_relu_pool_backward(dout, cache): """ Backward pass for the conv-relu-pool convenience layer """ conv_cache, relu_cache, pool_cache = cache ds = max_pool_backward_fast(dout, pool_cache) da = relu_backward(ds, relu_cache) dx, dw, db = conv_backward_fast(da, conv_cache) return dx, dw, db

[ "sun630@purdue.edu" ]

sun630@purdue.edu

d04e54f5130361e3fe585a2ba187137808d4ab98

9292ccf53497f168e9e7db404c8ca9bc906f5e95

/Geometry/223_rectangleArea.py

a88e3cd50da3f75aeffa36bb8823589e963a9a41

[]

no_license

ZheyuWalker/Leetcode-in-python

510a25eded77fe2e6d8192b15da9769b445e62f1

d99019d07b2178bb17f0d1f7a6eafc69c2e5cdd9

refs/heads/master

2023-01-23T17:02:09.412318

2023-01-21T14:48:46

150,246,538

0

null

UTF-8

Python

false

1,344

py

''' Given the coordinates of two rectilinear rectangles in a 2D plane, return the total area covered by the two rectangles. The first rectangle is defined by its bottom-left corner (ax1, ay1) and its top-right corner (ax2, ay2). The second rectangle is defined by its bottom-left corner (bx1, by1) and its top-right corner (bx2, by2). ''' class Solution: def computeArea(self, ax1: int, ay1: int, ax2: int, ay2: int, bx1: int, by1: int, bx2: int, by2: int) -> int: if ax1 < bx1: left_box_x = (ax1, ax2) right_box_x = (bx1, bx2) else: left_box_x = (bx1, bx2) right_box_x = (ax1, ax2) if ay1 < by1: down_box_y = (ay1, ay2) up_box_y = (by1, by2) else: down_box_y = (by1, by2) up_box_y = (ay1, ay2) area1 = (ay2-ay1) * (ax2-ax1) area2 = (by2-by1) * (bx2-bx1) # 左框的右边界 < 右框的左边界或下框的上边界 < 上框的下边界 if left_box_x[1] <= right_box_x[0] or down_box_y[1] <= up_box_y[0]: common_area = 0 else: x_len = min(left_box_x[1], right_box_x[1]) - right_box_x[0] y_len = min(down_box_y[1], up_box_y[1]) - up_box_y[0] common_area = x_len * y_len return area1 + area2 - common_area

[ "zheyu_chaser@foxmail.com" ]

zheyu_chaser@foxmail.com

143612b36b65d1518138ee99aaacbdfe96a36ab9

c1c9d9e52727a3480a4f3255f53920d9c78b68ba

/cis498/mongodb/customers.py

1e7d0ff8af630be1cff20bdc591b6af12eca7347

[]

no_license

carlosmoreno2020/cis498Final

f6ee4afe699adcf3a4fe87fbfbb94b93d5ebcdf9

757802e8e7f8a1b060d7608a65b59fe0ca079202

refs/heads/master

2022-07-14T18:30:15.777912

2020-05-12T00:21:16

263,181,120

0

null

2020-05-12T00:12:11

2020-05-11T23:22:31

null

UTF-8

Python

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1,957

py

from cis498.mongodb.mongoclient import MongoClientHelper class Customers: # Established mongo connection and customers collection def __init__(self): self.mc = MongoClientHelper() self.customers_db = self.mc.db['customers'] def createCustomer(self, form): name = form.cleaned_data.get('name') email = form.cleaned_data.get('email').lower() address = form.cleaned_data.get('address') phone_number = form.cleaned_data.get('phonenumber') customer = {"name": name, "email": email, "address": address, "phonenumber": phone_number} self.customers_db.insert_one(customer) # def generateCustomerReport(dateRange) # def getCustomerHistory() # This method will update customer the customer order list for maintaining order history def updateCustomerOrders(self, email, uuid): customer_query = {"email": email.lower()} customer = self.customers_db.find_one(customer_query) # Use some try/catch logic to check for existing orders, if it breaks its the customers first order try: customer['orders'].append(uuid) self.customers_db.update_one(customer_query, {"$set": {"orders": customer['orders']}}) except KeyError: uuidList = [uuid] self.customers_db.update_one(customer_query, {"$set": {"orders": uuidList}}) def findCustomerByEmail(self, email): customer_query = {"email": email.lower()} result = self.customers_db.find_one(customer_query) return Customer(result['name'], result['email'], result['address'], result['phonenumber'], result['orders']) class Customer: def __init__(self, name, email, address, phone_number, orders): self.name = name self.email = email self.address = address self.phone_number = phone_number self.orders = orders

[ "kridler240@gmail.com" ]

kridler240@gmail.com

303a75cf73d6d544d98ad4d47583f62fd528836d

8fef7e2eced3ac03018d56cb2c2026ef17b98fda

/lab_14_1.py

f23690d52fae5c505e7ccc893fadf23d6a5e3468

[]

no_license

Igor-Polatajko/python-labs

089536edf79a35e29fd613733c052314fb159b09

091b5c5d798414889c1919d6507373d848d57614

refs/heads/master

2020-08-31T22:44:34.529776

2020-01-04T00:25:49

218,804,180

0

null

UTF-8

Python

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8,249

py

#!/usr/bin/env python import sqlite3 ITEM_PRIORITY = {'1': 'HIGH (1)', '2': 'NORMAL (2)', '3': 'LOW (3)'} class ToDoItem: def __init__(self, item_id, title, content, priority, completed): self.item_id = item_id self.title = title self.content = content self.priority = priority self.completed = completed @staticmethod def db_store(method): def wrapper(*args, **kwargs): method(*args, **kwargs) DbConnector().db.commit() return wrapper @staticmethod def db_read(method): def wrapper(*args, **kwargs): results = method(*args, **kwargs).fetchall() return [ToDoItem(result[0], result[1], result[2], result[3], result[4]) for result in results] return wrapper class DbConnector: _instance = None def __new__(cls): if not cls._instance: cls._instance = object.__new__(cls) return cls._instance def __init__(self): if hasattr(self, 'db'): return self.db = sqlite3.connect("todo_items") self.db.cursor().execute( 'CREATE TABLE IF NOT EXISTS todo_items (' 'id integer PRIMARY KEY,' 'title text NOT NULL,' 'content text NOT NULL,' 'priority text NOT NULL,' 'completed integer NOT NULL)') self.db.commit() class ToDoItemDao: @ToDoItem.db_store def __init__(self): self.db = DbConnector().db @ToDoItem.db_read def find_by_field(self, field_name, field_value): return self.db.cursor().execute(f"SELECT * FROM todo_items WHERE {field_name} LIKE ?", (f'%{field_value}%',)) @ToDoItem.db_read def find_all(self): return self.db.cursor().execute("SELECT * FROM todo_items") @ToDoItem.db_store def create(self, item): self.db.cursor().execute("INSERT INTO todo_items (title, content, priority, completed)" " VALUES (?, ?, ?, ?)", (item.title, item.content, item.priority, item.completed)) @ToDoItem.db_store def update(self, item): self.db.cursor().execute("UPDATE todo_items SET title = ?, content = ?, priority = ?, " "completed = ? WHERE id = ?", (item.title, item.content, item.priority, item.completed, item.item_id)) @ToDoItem.db_store def delete(self, item_id): self.db.cursor().execute("DELETE FROM todo_items WHERE id = ?", (item_id,)) class View: def show_hint(self): print('\n###############################################\n' 'type [help] - to see all the commands available\n' 'type [exit] - to exit\n' '###############################################\n') def show_help(self): print('\n################ Commands ####################\n' 'list - list all the items\n' 'add - add new todo\n' 'rm item_id - remove todo\n' 'find field value - find by field value\n' 'update item_id - find by field value\n' 'complete item_id - mark as finished\n' 'uncomplete item_id - unmark finished\n' 'help - see all the commands available\n' 'exit - exit the program\n' '###############################################\n') def item_view(self, item=None): old_title = '' old_content = '' old_priority = '' completed = 'No' if item: old_title = f' ({item.title})' old_content = f' ({item.content})' old_priority = f' ({item.priority})' completed = item.completed view_type = "Update" if item else "Add" print(f"### {view_type} item: ###") title = input(f"Enter title{old_title}: ") content = input(f"Enter content{old_content}: ") while True: priority = input(f"Enter priority{old_priority}: ") if priority in ('1', '2', '3'): break print("Error! Valid priority values: 1, 2, 3") return ToDoItem(None, title, content, ITEM_PRIORITY[priority], completed) def show_list(self, data): if len(data) < 1: print("Collection is empty!") return headers = {d: self._get_max_length(data, d, len(d)) for d in data[0].__dict__.keys()} print('##### List #####') for header in headers.keys(): print(f"{header.capitalize():<{headers[header]}} | ", end='') print() for d in data: for header in headers.keys(): d_dict = d.__dict__ if header in d_dict.keys(): col = d_dict[header] else: col = '' print(f"{col:<{headers[header]}} | ", end='') print() print() def _get_max_length(self, items, field, header_length): items_dicts = [item.__dict__ for item in items] length_list = [len(str(item[field])) for item in items_dicts if field in item.keys()] length_list.append(header_length) return max(length_list) class ViewHandler: DB_COLUMNS_ADAPTER = {'item_id': 'id', 'title': 'title', 'content': 'content', 'priority': 'priority', 'completed': 'completed'} def __init__(self, view, todo_dao): self._view = view self._todo_dao = todo_dao def handle(self, user_input): i = user_input.strip().split() # user_input_parts if len(i) == 0: return if i[0] == 'exit': exit(0) elif i[0] == 'help': self._view.show_help() elif i[0] == 'add': item = self._view.item_view() self._todo_dao.create(item) elif i[0] == 'list': self._view.show_list(self._todo_dao.find_all()) elif i[0] == 'rm': if len(i) > 1: self._todo_dao.delete(i[1]) else: print("Incorrect command syntax!") self._view.show_hint() elif i[0] == 'update': def _update(item): updated_item = self._view.item_view(item) updated_item.item_id = item.item_id self._todo_dao.update(updated_item) self.__update(i, _update) elif i[0] == 'complete': def _check(item): item.completed = 'YES' self._todo_dao.update(item) self.__update(i, _check) elif i[0] == 'uncomplete': def _uncheck(item): item.completed = 'NO' self._todo_dao.update(item) self.__update(i, _uncheck) elif i[0] == 'find': if len(i) > 2: field = i[1].lower() if field in self.DB_COLUMNS_ADAPTER.keys(): items = self._todo_dao.find_by_field(self.DB_COLUMNS_ADAPTER[field], i[2]) self._view.show_list(items) else: print("Not valid field name") print(f"Valid field names: {self.DB_COLUMNS_ADAPTER.keys()}") else: print("Incorrect command syntax!") self._view.show_hint() else: print("Unknown command!") self._view.show_hint() def __update(self, req, update_handler): if len(req) > 1: item = self._todo_dao.find_by_field('id', req[1]) if item: item = item[0] update_handler(item) else: print('Item does not exist') else: print("Incorrect command syntax!") self._view.show_hint() class Controller: def run(self): view = View() todo_dao = ToDoItemDao() view_handler = ViewHandler(view, todo_dao) view.show_hint() while True: user_input = input("> ") view_handler.handle(user_input) def main(): controller = Controller() controller.run() if __name__ == '__main__': main()

[ "igor.bogdanovich39@gmail.com" ]

igor.bogdanovich39@gmail.com

0f525147539707c4ee461b2a614a896a4ab24427

26d6c34df00a229dc85ad7326de6cb5672be7acc

/msgraph-cli-extensions/beta/schemaextensions_beta/azext_schemaextensions_beta/generated/custom.py

9ed2ef7a2cad12e4916e3c911ac6eb7d1a783fc8

[ "MIT" ]

permissive

BrianTJackett/msgraph-cli

87f92471f68f85e44872939d876b9ff5f0ae6b2c

78a4b1c73a23b85c070fed2fbca93758733f620e

refs/heads/main

2023-06-23T21:31:53.306655

2021-07-09T07:58:56

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# -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- # pylint: disable=too-many-lines def schemaextensions_schema_extension_schema_extension_create_schema_extension(client, id_=None, description=None, owner=None, properties=None, status=None, target_types=None): body = {} body['id'] = id_ body['description'] = description body['owner'] = owner body['properties'] = properties body['status'] = status body['target_types'] = target_types return client.create_schema_extension(body=body) def schemaextensions_schema_extension_schema_extension_delete_schema_extension(client, schema_extension_id, if_match=None): return client.delete_schema_extension(schema_extension_id=schema_extension_id, if_match=if_match) def schemaextensions_schema_extension_schema_extension_list_schema_extension(client, orderby=None, select=None, expand=None): return client.list_schema_extension(orderby=orderby, select=select, expand=expand) def schemaextensions_schema_extension_schema_extension_show_schema_extension(client, schema_extension_id, select=None, expand=None): return client.get_schema_extension(schema_extension_id=schema_extension_id, select=select, expand=expand) def schemaextensions_schema_extension_schema_extension_update_schema_extension(client, schema_extension_id, id_=None, description=None, owner=None, properties=None, status=None, target_types=None): body = {} body['id'] = id_ body['description'] = description body['owner'] = owner body['properties'] = properties body['status'] = status body['target_types'] = target_types return client.update_schema_extension(schema_extension_id=schema_extension_id, body=body)

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#! /usr/bin/env python # Cerealizer # Copyright (C) 2005-2012 Jean-Baptiste LAMY # # This program is free software. # It is available under the Python licence. import os.path import setuptools import sys setuptools.setup( name = "Cerealizer", version = "0.9.0", license = "Python licence", description = "A secure pickle-like module", long_description = """A secure pickle-like module. It support basic types (int, string, unicode, tuple, list, dict, set,...), old and new-style classes (you need to register the class for security), object cycles, and it can be extended to support C-defined type.""", author = "Lamy Jean-Baptiste (Jiba)", author_email = "jibalamy@free.fr", url = "http://home.gna.org/oomadness/en/cerealizer/index.html", classifiers = [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "License :: OSI Approved :: Python Software Foundation License", "Programming Language :: Python :: 2", "Topic :: Security", "Topic :: Software Development :: Libraries :: Python Modules", ], package_dir = {"cerealizer" : "."}, packages = ["cerealizer"], include_package_data = True, )

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#!/usr/bin/env python from __future__ import division ''' *********************************************************** File: gaussianMixtures.py Classes: GM,Gaussian Allows for the creation, use, and compression of mixtures of multivariate normals, or Gaussian Mixture Models (GMM). Version 1.3.5: added normalized ISD Version 1.3.6: removed warning filtering Version 1.3.7: added pointEval function for gaussian Version 1.3.8: added random mixture generation and fixed scalerMulitply to scalarMultiply *********************************************************** ''' __author__ = "Luke Burks" __copyright__ = "Copyright 2016, Cohrint" __credits__ = ["Luke Burks", "Nisar Ahmed"] __license__ = "GPL" __version__ = "1.3.8" __maintainer__ = "Luke Burks" __email__ = "luke.burks@colorado.edu" __status__ = "Development" from matplotlib.colors import LogNorm import numpy as np; import random; from random import random; import matplotlib.pyplot as plt from scipy.stats import multivariate_normal as mvn import warnings import math import copy import time from numpy.linalg import inv,det class Gaussian: def __init__(self,u = None,sig = None,w=1): #warnings.filterwarnings("ignore") if(u is None): self.mean = [0,0]; else: self.mean = u; if(sig is None): self.sig = [[1,0],[0,1]]; else: self.var = sig; self.weight = w; def display(self): print("Mean: "); print(self.mean); print("Variance: "); print(self.var); print("Weight"); print(self.weight); def fullComp(self,b): if(not np.array_equal(self.mean,b.mean)): return False; if(not np.array_equal(self.var,b.var)): return False; if(self.weight != b.weight): return False; return True; def pointEval(self,x): ''' Evaluates the Gaussian at a point x ''' self.clean(); return g.weight*mvn.pdf(x,g.mean,g.var); class GM: def __init__(self,u=None,s=None,w=None): ''' Initialize with either: 1. Nothing, empty mixture 2. Single values, mixture of size 1 3. Lists of values, mixture of size n ''' self.Gs = []; if(w == None): self.size = 0; elif(isinstance(w,float) or isinstance(w,int)): self.size = 0; self.addG(Gaussian(u,s,w)); elif(len(w) > 1): for i in range(0,len(w)): self.Gs += [Gaussian(u[i],s[i],w[i])]; self.size = len(self.Gs); self.action = -1; #Index Overrides def __getitem__(self,key): return self.Gs[key]; def __setitem__(self,key,value): self.Gs[key] = value; def getMeans(self): ''' Returns a list containing the mean of each mixand ''' ans = []; for g in self.Gs: ans.append(g.mean); return ans; def getVars(self): ''' Returns a list containing the variance of each mixand ''' ans = []; for g in self.Gs: ans.append(g.var); return ans; def getWeights(self): ''' Returns a list containing the weights of each mixand ''' ans = []; for g in self.Gs: ans.append(g.weight); return ans; def makeRandomMixture(self,size=10,dims=2,perMax = 10,lowBound = 0, highBound = 10): #In case you forget, perMax refers to percision for i in range(0,size): self.addG(self.sampleWishart(dims,perMax,lowBound,highBound)); self.normalizeWeights(); def sampleWishart(self,dims,sigMax,lowBound,highBound): sigPrior = np.diag(np.ones(dims))*sigMax; df = dims; cholesky = np.linalg.cholesky(sigPrior); X = np.dot(cholesky,np.random.normal(size=(dims,df))); sigma = np.linalg.inv(np.dot(X,X.T)); weight = np.random.random(); lowInit = [lowBound]*dims; highInit = [highBound]*dims; mu = []; for i in range(0,dims): mu.append(np.random.random()*(highInit[i]-lowInit[i]) + lowInit[i]); sigma=sigma.tolist(); return Gaussian(mu,sigma,weight); def clean(self): for g in self.Gs: if(not isinstance(g.mean,list) and not isinstance(g.mean,int) and not isinstance(g.mean,float)): g.mean = g.mean.tolist(); if(not isinstance(g.var,list) and not isinstance(g.var,int) and not isinstance(g.var,float)): g.var = g.var.tolist(); if(not isinstance(g.mean,int) and not isinstance(g.mean,float)): while(len(g.mean) != len(g.var)): g.mean = g.mean[0]; if(not isinstance(g.var,int) and not isinstance(g.var,float)): for i in range(0,len(g.var)): g.var[i][i] = abs(g.var[i][i]); def findMAP2D(self): ''' Retreives a 2D grid and returns the maximum point ''' [a,b,res] = self.plot2D(vis=False); MAP= [0,0]; meanVal = [-10000]; for i in range(0,len(res)): for j in range(0,len(res[i])): if(res[i][j] > meanVal): meanVal = res[i][j]; MAP = [i/20,j/20]; return MAP; def findMAPN(self): ''' Bad approximation for the MAP point of an N-dimensional GMM. Returns the mixand mean with the highest contribution from all mixands. ''' cands = [0]*self.size; for i in range(0,self.size): for j in range(0,self.size): cands[i] += mvn.pdf(self.Gs[i].mean,self.Gs[j].mean,self.Gs[j].var)*self.Gs[j].weight; best = cands.index(max(cands)); return(self.Gs[best].mean); def plot(self,low = -20,high = 20,num = 1000,vis = True): ''' Plots a 1D GMM from low to high, with resolution=num. If vis argument is false it returns the values at each point. ''' a = np.linspace(low,high,num= num); b = [0.0]*num; for g in self.Gs: b += mvn.pdf(a,g.mean,g.var)*g.weight; if(vis): plt.plot(a,b); plt.show(); else: return [a,b]; def plot2D(self,low = [0,0], high = [5,5],vis = True,res = 100,xlabel = 'Cop Belief',ylabel = 'Robber Belief',title = 'Belief'): ''' Plots a contour plot of a 2D GMM from low to high in each dimension, with resolution=res. If vis argument is false it returns the arguments required to plot in order of the x values, the y values, and the calculated mixture values. Note: This may not be very efficient depending on the resolution ''' c = [[0 for i in range(0,res)] for j in range(0,res)]; x, y = np.mgrid[low[0]:high[0]:(float(high[0]-low[0])/res), low[1]:high[1]:(float(high[1]-low[1])/res)] pos = np.dstack((x, y)) #self.clean(); for g in self.Gs: try: c += mvn.pdf(pos,g.mean,g.var)*g.weight; except: g.display(); raise; if(vis): fig,ax = plt.subplots(); ax.contourf(x,y,c,cmap = 'viridis'); #fig.colorbar(); ax.set_xlabel(xlabel); ax.set_ylabel(ylabel); ax.set_title(title); plt.show(); else: return x,y,c; def slice2DFrom4D(self,low = [0,0],high = [5,5],res = 100, dims = [2,3],vis = True,retGS = False): ''' Plots a 2D GMM from a 4D GMM by ignoring entries in the mean or variance not associated with those dimensions Argument retGS = True will return the 2D GMM Argument vis = True will plot the 2D GMM using the plot2D function Otherwise the results are returned through the plot2D(vis=False) function. ''' newGM = GM(); for g in self.Gs: mean = [g.mean[dims[0]],g.mean[dims[1]]]; var = [[g.var[dims[0]][dims[0]],g.var[dims[0]][dims[1]]],[g.var[dims[1]][dims[0]],g.var[dims[1]][dims[1]]]] weight = g.weight; newGM.addG(Gaussian(mean,var,weight)); if(vis): newGM.plot2D(low = low,high = high,res=res,vis = vis,xlabel = 'RobberX',ylabel = 'RobberY',title = 'Cops Belief of Robber'); elif(retGS): return newGM; else: return newGM.plot2D(low = low,high = high,res=res,vis = vis,xlabel = 'RobberX',ylabel = 'RobberY',title = 'Cops Belief of Robber'); def normalizeWeights(self): ''' Normalizes the weights of the mixture such that they all add up to 1. ''' suma = 0; for g in self.Gs: suma += g.weight; for g in self.Gs: g.weight = g.weight/suma; self.size = len(self.Gs); def addGM(self,b): ''' Combines a new mixture with this one. ''' for i in range(0,len(b.Gs)): self.addG(b.Gs[i]); self.size = len(self.Gs); def addNewG(self,mean,var,weight): ''' Adds another mixand to this mixture by specifying the parameters of the Gaussian. ''' self.addG(Gaussian(mean,var,weight)); def addG(self,b): ''' Adds another mixand to this mixture by specifying the Gaussian directly ''' self.Gs += [b]; self.size+=1; self.size = len(self.Gs); def display(self): print("Means"); print([self.Gs[i].mean for i in range(0,self.size)]); print("Variances"); print([self.Gs[i].var for i in range(0,self.size)]); print("Weights"); print([self.Gs[i].weight for i in range(0,self.size)]); if(self.action is not None): print("Action"); print(self.action); def fullComp(self,b): ''' Compares two GMMs. If they are identical, return true, else return false. Works for the general case ''' if(self.size != b.size): return False; for i in range(0,self.size): if(not np.array_equal(self.Gs[i].mean,b.Gs[i].mean)): return False; if(not np.array_equal(self.Gs[i].var,b.Gs[i].var)): return False; if(self.Gs[i].weight != b.Gs[i].weight): return False; return True; def pointEval(self,x): ''' Evaluates the GMM at a point x by summing together each mixands contribution ''' suma = 0; self.clean(); for g in self.Gs: suma += g.weight*mvn.pdf(x,g.mean,g.var); return suma; def distance(self,a,b): #General N-dimensional euclidean distance dist = 0; for i in range(0,len(a)): dist += (a[i]-b[i])**2; dist = math.sqrt(dist); return dist; def ISD(self,g2,normed=True): #Integrated Squared Difference #From "Cost-Function-Based Gaussian Mixture Reduction for Target Tracking" #by Jason Williams, Peter Maybeck #Normalized Itegrated Squared Difference #From "Gaussian Mixture reduction based on fuzzy adaptive resonance theory for extended target tracking", 2013 #NISD(g1,g2) = sqrt((int(g1^2) - 2int(g1*g2) + int(g2^2)) / (int(g1^2) + int(g2^2))) #NISD(g1,g2) = sqrt((int(Jhh) - 2int(Jhr) + int(Jrr)) / (int(Jhh) + int(Jrr))) #Js = Jhh - 2Jhr + Jrr #Jhh = self-likeness for g1 #Jhr = cross-likeness #Jrr = self-likeness for g2 Jhh = 0; for g in self.Gs: for h in self.Gs: Jhh += g.weight*h.weight*mvn.pdf(g.mean,h.mean,np.matrix(g.var) + np.matrix(h.var)); Jrr = 0; for g in g2.Gs: for h in g2.Gs: Jrr += g.weight*h.weight*mvn.pdf(g.mean,h.mean,np.matrix(g.var) + np.matrix(h.var)); Jhr = 0; for g in self.Gs: for h in g2.Gs: Jhr += g.weight*h.weight*mvn.pdf(g.mean,h.mean,np.matrix(g.var) + np.matrix(h.var)); if(normed): Js = np.sqrt((Jhh-2*Jhr+Jrr)/(Jhh+Jrr)); else: Js = Jhh-2*Jhr+Jrr; return Js; #General N-dimensional def kmeansCondensationN(self,k=10,lowInit=None,highInit = None,maxIter = 100): ''' Condenses mixands by first clustering them into k groups, using k-means. Then each group is condensed to a single Gaussian using Runnalls Method. Each Gaussian is then added to a new GMM. Has a tendency to overcondense Inputs: k: number of mixands in the returned GMM lowInit: lower bound on the placement of initial grouping means highInit: upper bound on placement of initial grouping means ''' if(self.size <= k): return self; if(lowInit == None): lowInit = [0]*len(self.Gs[0].mean); if(highInit == None): highInit = [5]*len(self.Gs[0].mean) #Initialize the means. Spread randomly through the bounded space means = [0]*k; for i in range(0,k): tmp = []; if(isinstance(self.Gs[0].mean,list)): for j in range(0,len(self.Gs[0].mean)): tmp.append(random()*(highInit[j]-lowInit[j]) + lowInit[j]); else: tmp.append(random()*(highInit-lowInit) + lowInit); means[i] = tmp; converge = False; count = 0; newMeans = [0]*k; while(converge == False and count < maxIter): clusters = [GM() for i in range(0,k)]; for g in self.Gs: #put the gaussian in the cluster which minimizes the distance between the distribution mean and the cluster mean if(isinstance(g.mean,list)): clusters[np.argmin([self.distance(g.mean,means[j]) for j in range(0,k)])].addG(g); else: clusters[np.argmin([self.distance([g.mean],means[j]) for j in range(0,k)])].addG(g); #find the mean of each cluster newMeans = [0]*k; for i in range(0,k): if(isinstance(self.Gs[0].mean,list)): newMeans[i] = np.array([0]*len(self.Gs[0].mean)); for g in clusters[i].Gs: newMeans[i] = np.add(newMeans[i],np.divide(g.mean,clusters[i].size)); if(np.array_equal(means,newMeans)): converge = True; count = count+1; for i in range(0,len(newMeans)): for j in range(0,len(newMeans[i])): means[i][j] = newMeans[i][j]; #condense each cluster for c in clusters: c.condense(1); #add each cluster back together ans = GM(); for c in clusters: ans.addGM(c); ans.action = self.action; #Make sure everything is positive semidefinite #TODO: dont just remove them, fix them? dels = []; for g in ans.Gs: if(det(np.matrix(g.var)) <= 0): dels.append(g); for rem in dels: if(rem in ans.Gs): ans.Gs.remove(rem); ans.size -= 1 #return the resulting GMM return ans; def printClean(self,slices): ''' Cleans lists in preparation for printing to plain text files ''' slices = str(slices); slices = slices.replace(']',''); slices = slices.replace(',',''); slices = slices.replace('[',''); return slices; def printGMArrayToFile(self,GMArr,fileName): ''' Prints an Array of GMs to a text file, in a way that can be read by the readGMArry4D function or similar functions. Note: The only reason this exists is due to a phantom error using numpy load and save on one of our lab computers. Highly recommend just pickleing these things. ''' f = open(fileName,"w"); for i in range(0,len(GMArr)): GMArr[i].printToFile(f); f.close(); def printToFile(self,file): ''' Prints a single Gaussian Mixture to a plain text file Note: The only reason this exists is due to a phantom error using numpy load and save on one of our lab computers. Highly recommend just pickleing these things. ''' #first line is N, number of gaussians #next N lines are, mean, variance, weight file.write(str(self.size) + " " + str(self.action) + "\n"); for g in self.Gs: m = self.printClean(g.mean); var = self.printClean(g.var); w = self.printClean(g.weight); file.write(m + " " + var + " " + w + "\n"); def readGMArray4D(self,fileName): ''' Extracts a 4 dimensional Gaussian Mixture from a text file created by printGMArrayToFile function. Note: The only reason this exists is due to a phantom error using numpy load and save on one of our lab computers. Highly recommend just pickleing these things. ''' file = open(fileName,"r"); lines = np.fromfile(fileName,sep = " "); ans = [] count = 0; countL = len(lines); while(count < countL): tmp = lines[count:]; num = int(tmp[0]); act = int(tmp[1]); count = count + 2; cur = GM(); cur.action = act; for i in range(0,num): tmp = lines[count:] count = count + 21; mean = [float(tmp[0]),float(tmp[1]),float(tmp[2]),float(tmp[3])]; var = [[float(tmp[4]),float(tmp[5]),float(tmp[6]),float(tmp[7])],[float(tmp[8]),float(tmp[9]),float(tmp[10]),float(tmp[11])],[float(tmp[12]),float(tmp[13]),float(tmp[14]),float(tmp[15])],[float(tmp[16]),float(tmp[17]),float(tmp[18]),float(tmp[19])]]; weight = float(tmp[20]); cur.addG(Gaussian(mean,var,weight)); ans += [cur]; return ans; def scalarMultiply(self,s): ''' Multiplies the weight of each mixand by scalar s ''' for g in self.Gs: g.weight = s*g.weight; def GMProduct(self,b,cond = -1): ''' Returns the product of two Gaussian Mixtures, which is also a Gaussian Mixture If cond != -1, condenses the mixture to cond mixands before returning ''' result = GM(); for g1 in self.Gs: u1 = copy.deepcopy(np.matrix(g1.mean)); var1 = np.matrix(g1.var); w1 = g1.weight; for g2 in b.Gs: u2 = copy.deepcopy(np.matrix(g2.mean)); var2 = np.matrix(g2.var); w2 = g2.weight; weight = w1*w2*mvn.pdf(u1.tolist()[0],u2.tolist()[0],var1+var2); var = (var1.I + var2.I).I; mean = var*(var1.I*np.transpose(u1) + var2.I*np.transpose(u2)); mean = np.transpose(mean).tolist()[0]; var = var.tolist(); result.addNewG(mean,var,weight); if(cond != -1): result.condense(cond); return result; def sample(self,num): w = copy.deepcopy(self.getWeights()); suma = 0; for i in range(0,len(w)): suma+=w[i]; for i in range(0,len(w)): w[i] = w[i]/suma; means = self.getMeans(); var = self.getVars(); allSamps = []; for count in range(0,num): cut = np.random.choice(range(0,len(w)),p=w); if(isinstance(means[0],int) or isinstance(means[0],float)): samp = np.random.normal(means[cut],var[cut],1).tolist()[0]; else: samp = np.random.multivariate_normal(means[cut],var[cut],1).tolist()[0]; allSamps.append(samp); return allSamps; def discretize2D(self,low = [0,0],high=[10,10], delta = 0.1): #Added in version 1.3.1 #Inputs: # low, lower bounds on x and y axes # high, upper bounds on x and y axes # delta, discretization constant, grid-cell length #Output: # A 2D numpy array with grid cells from low to high by delta x, y = np.mgrid[low[0]:high[0]:delta, low[1]:high[1]:delta] pos = np.dstack((x, y)) #self.clean(); # c = None; c = np.zeros(shape=(pos.shape[0],pos.shape[1])) for g in self.Gs: try: # print('THIS IS THE VALUE OF c is {}'.format(c)) # if(c == None): # c = mvn.pdf(pos,g.mean,g.var)*g.weight; # else: c += mvn.pdf(pos,g.mean,g.var)*g.weight; except: g.display(); raise; return c; def condense(self, max_num_mixands=None): ''' Runnalls Method for Gaussian Mixture Condensation. Adapted from Nick Sweets gaussian_mixture.py https://github.com/COHRINT/cops_and_robots/blob/dev/src/cops_and_robots/fusion/gaussian_mixture.py Now valid for negative weights If mixture contains all identical mixands at any point, it returns the mixture as is. ''' if max_num_mixands is None: max_num_mixands = self.max_num_mixands #Check if any mixands are small enough to not matter #specifically if they're weighted really really low dels = []; for g in self.Gs: if(abs(g.weight) < 0.000001): dels.append(g); for rem in dels: if(rem in self.Gs): self.Gs.remove(rem); self.size = self.size-1; #Check if any mixands are identical dels = []; for i in range(0,self.size): for j in range(0,self.size): if(i==j): continue; g1 = self.Gs[i]; g2 = self.Gs[j]; if(g1.fullComp(g2) and g1 not in dels): dels.append(g2); g1.weight = g1.weight*2; for rem in dels: if(rem in self.Gs): self.Gs.remove(rem); self.size = self.size-1; #Check if merging is useful if self.size <= max_num_mixands: return 0; # Create lower-triangle of dissimilarity matrix B #<>TODO: this is O(n ** 2) and very slow. Speed it up! parallelize? B = np.zeros((self.size, self.size)) for i in range(self.size): mix_i = (self.Gs[i].weight, self.Gs[i].mean, self.Gs[i].var) for j in range(i): if i == j: continue mix_j = (self.Gs[j].weight, self.Gs[j].mean, self.Gs[j].var) B[i,j] = self.mixand_dissimilarity(mix_i, mix_j) # Keep merging until we get the right number of mixands deleted_mixands = [] toRemove = []; while self.size > max_num_mixands: # Find most similar mixands try: min_B = B[abs(B)>0].min() except: #self.display(); #raise; return; ind = np.where(B==min_B) i, j = ind[0][0], ind[1][0] # Get merged mixand mix_i = (self.Gs[i].weight, self.Gs[i].mean, self.Gs[i].var) mix_j = (self.Gs[j].weight, self.Gs[j].mean, self.Gs[j].var) w_ij, mu_ij, P_ij = self.merge_mixands(mix_i, mix_j) # Replace mixand i with merged mixand ij = i self.Gs[ij].weight = w_ij self.Gs[ij].mean = mu_ij.tolist(); self.Gs[ij].var = P_ij.tolist(); # Fill mixand i's B values with new mixand's B values mix_ij = (w_ij, mu_ij, P_ij) deleted_mixands.append(j) toRemove.append(self.Gs[j]); #print(B.shape[0]); for k in range(0,B.shape[0]): if k == ij or k in deleted_mixands: continue # Only fill lower triangle # print(self.size,k) mix_k = (self.Gs[k].weight, self.Gs[k].mean, self.Gs[k].var) if k < i: B[ij,k] = self.mixand_dissimilarity(mix_k, mix_ij) else: B[k,ij] = self.mixand_dissimilarity(mix_k, mix_ij) # Remove mixand j from B B[j,:] = np.inf B[:,j] = np.inf self.size -= 1 # Delete removed mixands from parameter arrays for rem in toRemove: if(rem in self.Gs): self.Gs.remove(rem); #Make sure everything is positive semidefinite #TODO: dont just remove them, fix them? dels = []; for g in self.Gs: if(det(np.matrix(g.var)) <= 0): dels.append(g); for rem in dels: if(rem in self.Gs): self.Gs.remove(rem); self.size -= 1 def mixand_dissimilarity(self,mix_i, mix_j): """Calculate KL descriminiation-based dissimilarity between mixands. """ # Get covariance of moment-preserving merge w_i, mu_i, P_i = mix_i w_j, mu_j, P_j = mix_j _, _, P_ij = self.merge_mixands(mix_i, mix_j) ''' #TODO: This is different if(w_i < 0 and w_j< 0): w_i = abs(w_i); w_j = abs(w_j); ''' if(P_ij.ndim == 1 or len(P_ij.tolist()[0]) == 1): if(not isinstance(P_ij,(int,list,float))): P_ij = P_ij.tolist()[0]; while(isinstance(P_ij,list)): P_ij = P_ij[0]; if(not isinstance(P_i,(int,list,float))): P_i = P_i.tolist()[0]; while(isinstance(P_i,list)): P_i = P_i[0]; if(not isinstance(P_j,(int,list,float))): P_j = P_j.tolist()[0]; while(isinstance(P_j,list)): P_j = P_j[0]; logdet_P_ij = P_ij; logdet_P_i = P_i; logdet_P_j = P_j; else: # Use slogdet to prevent over/underflow _, logdet_P_ij = np.linalg.slogdet(P_ij) _, logdet_P_i = np.linalg.slogdet(P_i) _, logdet_P_j = np.linalg.slogdet(P_j) # <>TODO: check to see if anything's happening upstream if np.isinf(logdet_P_ij): logdet_P_ij = 0 if np.isinf(logdet_P_i): logdet_P_i = 0 if np.isinf(logdet_P_j): logdet_P_j = 0 #print(logdet_P_ij,logdet_P_j,logdet_P_i) b = 0.5 * ((w_i + w_j) * logdet_P_ij - w_i * logdet_P_i - w_j * logdet_P_j) return b def merge_mixands(self,mix_i, mix_j): """Use moment-preserving merge (0th, 1st, 2nd moments) to combine mixands. """ # Unpack mixands w_i, mu_i, P_i = mix_i w_j, mu_j, P_j = mix_j mu_i = np.array(mu_i); mu_j = np.array(mu_j); P_j = np.matrix(P_j); P_i = np.matrix(P_i); # Merge weights w_ij = w_i + w_j w_i_ij = w_i / (w_i + w_j) w_j_ij = w_j / (w_i + w_j) # Merge means mu_ij = w_i_ij * mu_i + w_j_ij * mu_j P_j = np.matrix(P_j); P_i = np.matrix(P_i); # Merge covariances P_ij = w_i_ij * P_i + w_j_ij * P_j + \ w_i_ij * w_j_ij * np.outer(self.subMu(mu_i,mu_j), self.subMu(mu_i,mu_j)) return w_ij, mu_ij, P_ij def subMu(self,a,b): if(isinstance(a,np.ndarray)): return a-b; if(isinstance(a,(float,int))): return a-b; else: c = [0]*len(a); for i in range(0,len(a)): c[i] = a[i]-b[i]; return c; def TestGMProduct(): a = GM([1,8,3],[1,1,1],[1,1,1]); b = GM([4,2,6],[1,1,1],[1,1,1]); c = a.GMProduct(b); low = 0; high = 10; num = 1000; x = np.linspace(low,high,num); aPlot = a.plot(low=low,high = high,num=num,vis = False); bPlot = b.plot(low=low,high = high,num=num,vis=False); cPlot = c.plot(low=low,high = high,num=num,vis=False); plt.plot(x,aPlot); plt.plot(x,bPlot); plt.plot(x,cPlot); plt.title("Gaussian Mixture Product Test"); plt.legend(['First Mixture','Second Mixture','Product']); plt.show(); def Test2DGMProduct(): g1 = GM([2,1],[[1,0],[0,2]],1); g2 = GM([1,5],[[4,0],[0,1]],1); mix = g2.GMProduct(g1,cond=-1); [x1,y1,c1] = g1.plot2D(vis = False); [x2,y2,c2] = g2.plot2D(vis = False); [x3,y3,c3] = mix.plot2D(vis = False); fig,axarr = plt.subplots(3,sharex = True); axarr[0].contourf(x1,y1,c1,cmap = 'viridis'); axarr[0].set_title('First Mixture'); axarr[1].contourf(x2,y2,c2,cmap = 'viridis'); axarr[0].set_title('Second Mixture'); axarr[2].contourf(x3,y3,c3,cmap = 'viridis'); axarr[0].set_title('Product Mixture'); plt.suptitle('Testing the product of 2D Gaussians'); plt.show(); def Test4DGMProduct(): #Courtesy of Mike Ouimet m1 = [[0, 0, 0, 0], [1,1,1,1]] #means s1 = [np.eye(4), 2*np.eye(4)] #variances m2 = [[0, 1, -1, 0], [1,0,-1,1]] s2 = [4*np.eye(4), 1*np.eye(4)] g1 = GM(u=m1, s=s1, w=[1,1]) g2 = GM(u=m2, s=s2, w=[1,1]) mix = g2.GMProduct(g1,cond = -1) print("The resulting mixture:"); mix.display(); fig,ax = plt.subplots(2,2); [x1,y1,c1] = mix.slice2DFrom4D(vis=False,dims=[0,2]); ax[0,0].contourf(x1,y1,c1,cmap = 'viridis'); ax[0,0].set_title('X1 by X3'); [x2,y2,c2] = mix.slice2DFrom4D(vis=False,dims=[0,3]); ax[0,1].contourf(x2,y2,c2,cmap = 'viridis'); ax[0,1].set_title('X1 by X4'); [x3,y3,c3] = mix.slice2DFrom4D(vis=False,dims=[1,2]); ax[1,0].contourf(x3,y3,c3,cmap = 'viridis'); ax[1,0].set_title('X2 by X3'); [x4,y4,c4] = mix.slice2DFrom4D(vis=False,dims=[1,3]); ax[1,1].contourf(x4,y4,c4,cmap = 'viridis'); ax[1,1].set_title('X2 by X4'); fig.suptitle("Slices along Various Axis in 2D from 4D"); plt.show(); def TestTextFilePrinting(): prior = GM([0,-2,1,2],[[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]],1); prior.addG(Gaussian([0,-2,1,2],[[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]],1)) pri = GM([0,-2,1,2],[[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]],1); pri.addG(Gaussian([0,-2,1,2],[[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]],1)) file = './loadTest.txt'; prior.printGMArrayToFile([prior,pri],file); tmp = GM(); post = tmp.readGMArray4D(file); post[0].display(); def TestCondense(): test = GM(); for i in range(0,100): test.addNewG(random()*10,random()*2,random()*5); testKmeans = copy.deepcopy(test); low = 0; high = 10; num = 1000; x = np.linspace(low,high,num); [x0,testPlot] = test.plot(low=low,high = high,num=num,vis = False); test.condense(10); [x1,testCondensePlot] = test.plot(low=low,high = high,num=num,vis = False); testKmeans = testKmeans.kmeansCondensationN(k=10,lowInit = low, highInit = high); [x2,testKmeansPlot] = testKmeans.plot(low=low,high = high,num=num,vis = False); plt.plot(x0,testPlot); plt.plot(x1,testCondensePlot); plt.plot(x2,testKmeansPlot); plt.legend(['Original Mixture','Condensed Mixture (Runnalls)','Condensed Mixture (K-means Runnalls)']); plt.title('Condensation Test: 100 to 10 mixands') plt.show(); def TestCondense2D(): test = GM(); for i in range(0,100): test.addG(Gaussian([random()*10,random()*10],[[random()*2,0],[0,random()*2]],random()*5)); testKmeans = copy.deepcopy(test); low = [0,0]; high = [10,10]; [x1,y1,c1] = test.plot2D(vis=False); test.condense(10); [x2,y2,c2] = test.plot2D(vis = False); testKmeans = testKmeans.kmeansCondensationN(k = 10, lowInit = low, highInit = high); [x3,y3,c3] = testKmeans.plot2D(vis=False); fig,axarr = plt.subplots(3,sharex = True); axarr[0].contourf(x1,y1,c1,cmap = 'viridis'); axarr[0].set_title('Original Mixture'); axarr[1].contourf(x2,y2,c2,cmap = 'viridis'); axarr[1].set_title('Runnalls Method Condensed Mixture'); axarr[2].contourf(x3,y3,c3,cmap = 'viridis'); axarr[2].set_title('K-means + Runnalls Method Condensed Mixture'); plt.suptitle('2D Condensation Test: 100 to 10 mixands'); plt.show(); def TestComparison(): test1 = GM(); test1.addG(Gaussian([0,1],[[1,0],[0,1]],1)); test1.addG(Gaussian([1,2],[[1,0],[0,1]],1)); test2 = GM(); test2.addG(Gaussian([0,1],[[1,0],[0,1]],1)); test2.addG(Gaussian([1,2],[[1,0],[0,1]],1)); test3 = GM(); test3.addG(Gaussian([0,5],[[1,0],[0,1]],1)); test3.addG(Gaussian([1,2],[[1,0],[0,1]],1)); print('Test1 and Test2: ' + str(test1.fullComp(test2))); print('Test1 and Test3: ' + str(test1.fullComp(test3))); def TestSample(): test1 = GM(); test1.addG(Gaussian(0,1,.33)); test1.addG(Gaussian(10,1,.33)); test1.addG(Gaussian(-5,1,.33)); samps = test1.sample(10000); plt.hist(samps,normed=1,bins = 100); plt.show(); def TestSample2D(): test1 = GM(); test1.addG(Gaussian([0,0],[[1,0],[0,1]],.33)); test1.addG(Gaussian([3,3],[[1,0],[0,1]],.33)); test1.addG(Gaussian([-2,-2],[[1,0],[0,1]],.33)); samps = test1.sample(10000); sampsx = [samps[i][0] for i in range(0,len(samps))]; sampsy = [samps[i][1] for i in range(0,len(samps))]; plt.hist2d(sampsx,sampsy,normed = 1,bins=100); plt.show(); def TestDiscretization(): test1 = GM(); test1.addG(Gaussian([0,0],[[1,0],[0,1]],.33)); test1.addG(Gaussian([3,3],[[1,0],[0,1]],.33)); test1.addG(Gaussian([-2,-2],[[1,0],[0,1]],.33)); grid = test1.discretize2D(low=[-15,-15],high=[15,15],delta=0.01); print(grid.shape); plt.contourf(grid); plt.show(); def TestRandomMixtureCreation(): dims=2; size = 10; low = 1; high = 2; per = 3; gm = GM(); gm.makeRandomMixture(size,dims,per,low,high); gm.plot2D(); if __name__ == "__main__": #TestGMProduct(); #Test2DGMProduct(); #Test4DGMProduct(); #TestTextFilePrinting(); #TestCondense(); #TestCondense2D(); #TestComparison(); #TestSample(); #TestSample2D(); #TestDiscretization(); TestRandomMixtureCreation();

[ "matthew.luebbers@colorado.edu" ]

matthew.luebbers@colorado.edu

2b077ef9b1f4711fd171d4573c7c3bee70cb0122

96e03ae90e906e8dea62dd495b2d10679c4a4489

/YelpCamp/manage.py

86cb701f6f0ba88b2c1b743b591b7b64dc237bc9

[]

no_license

ryanMiranda98/YelpCamp

0cbb9776aa6dd28849e1be776b278adcef364d98

f369f41f67a0fbb00fa2d831e1df8ffd7c0530c8

refs/heads/master

2021-02-07T16:31:55.982852

2020-03-03T11:25:17

244,051,691

0

null

UTF-8

Python

false

628

py

#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'YelpCamp.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

[ "ryanjude.miranda@gmail.com" ]

ryanjude.miranda@gmail.com

6b4cdd9254180d22591588ada9e0d4b49941a84e

acff427a36d6340486ff747ae9e52f05a4b027f2

/main/programming/language/perl/perl-JSON/actions.py

406126c3b23c3ef8a36f701469832a2f10bc1367

[]

no_license

jeremie1112/pisilinux

8f5a03212de0c1b2453132dd879d8c1556bb4ff7

d0643b537d78208174a4eeb5effeb9cb63c2ef4f

refs/heads/master

2020-03-31T10:12:21.253540

2018-10-08T18:53:50

152,126,584

2

1

null

2018-10-08T18:24:17

null

UTF-8

Python

false

531

py

#!/usr/bin/python # -*- coding: utf-8 -*- # # Licensed under the GNU General Public License, version 3. # See the file http://www.gnu.org/licenses/gpl.txt from pisi.actionsapi import perlmodules from pisi.actionsapi import pisitools from pisi.actionsapi import get WorkDir = "%s-%s" % (get.srcNAME()[5:], get.srcVERSION()) def setup(): perlmodules.configure() def build(): perlmodules.make() def check(): perlmodules.make("test") def install(): perlmodules.install() pisitools.dodoc("Changes", "README")

[ "erkanisik@yahoo.com" ]

erkanisik@yahoo.com

b9c2f137115eb5978937bc2aed8add1ebb519386

e10ff4ab332f88418c8d74c1b9d15130af7662ec

/loginadmin.py

c3f4c8801ccf08543af8fed3c4f2b5eb76e5b972

[]

no_license

Skull1991/Nitesh-Employee

eecb2f41b1888208fd3c48f2541f2cb4f8f6865d

1572be5551146abbe441ed77f5bdc45a3d479330

refs/heads/master

2023-07-14T16:20:46.025271

2021-08-22T11:20:21

398,762,663

0

null

UTF-8

Python

false

1,042

py

from tkinter import * from tkinter import messagebox from PIL import Image,ImageTk import os root=Tk() root.geometry("1366x768+60+10") root.resizable(0, 0) global e1 global e2 def ok(): uname=e1.get() password=e2.get() if(uname==""and password==""): messagebox.showinfo("","Blank Not Allowed") elif(uname=="admin"and password=="admin"): messagebox.showinfo("","Login Success") root.withdraw() os.system("admin.py") else: messagebox.showinfo("","Incorrect") root.title("Login") myimage=ImageTk.PhotoImage(Image.open('./images/adminlogin.png')) Label(image=myimage).pack() #e1 entry for username entry e1=Entry(root,width=40,border=0,font=('Consolas',13)) e1.place(x=510,y=210) #e2 entry for password entry e2=Entry(root,width=40,border=0,show='*',font=('Consolas',13)) e2.place(x=510,y=300) Button(root,text='LOGIN',font=('Consolas',20), padx=20,pady=10,cursor='hand2',border=0,bg="#6dcff6", activebackground="#6dcff6",command=ok).place(x=625,y=515) root.mainloop()

[ "skull.shanto10@gmail.com" ]

skull.shanto10@gmail.com

6d4853e5820ffc6d3d4ed4902424ee265288cdae

ca3d5eeb740b372095d9169591a572d32efd672e

/script/utils.py

e47222fe96eab2ec2399f73a75f12cfe4ae477f5

[]

no_license

JianqiaoAirport/SARN_CTR

514a23b49414cbe049c503adaa3cc7ca143cff38

b3467be05eca4c2374c3f5341a61ed9273babd0a

refs/heads/master

2022-05-30T14:45:33.382155

2020-05-01T06:10:58

260,198,437

0

null

UTF-8

Python

false

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py

# -*- coding: UTF-8 -*- import tensorflow as tf from tensorflow.python.ops.rnn_cell import * from tensorflow.python.ops.rnn_cell_impl import _Linear from tensorflow import keras from tensorflow.python.ops import math_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import variable_scope as vs from keras import backend as K class QAAttGRUCell(RNNCell): """Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078). Args: num_units: int, The number of units in the GRU cell. activation: Nonlinearity to use. Default: `tanh`. reuse: (optional) Python boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. kernel_initializer: (optional) The initializer to use for the weight and projection matrices. bias_initializer: (optional) The initializer to use for the bias. """ def __init__(self, num_units, activation=None, reuse=None, kernel_initializer=None, bias_initializer=None): super(QAAttGRUCell, self).__init__(_reuse=reuse) self._num_units = num_units self._activation = activation or math_ops.tanh self._kernel_initializer = kernel_initializer self._bias_initializer = bias_initializer self._gate_linear = None self._candidate_linear = None @property def state_size(self): return self._num_units @property def output_size(self): return self._num_units def __call__(self, inputs, state, att_score): return self.call(inputs, state, att_score) def call(self, inputs, state, att_score=None): """Gated recurrent unit (GRU) with nunits cells.""" if self._gate_linear is None: bias_ones = self._bias_initializer if self._bias_initializer is None: bias_ones = init_ops.constant_initializer(1.0, dtype=inputs.dtype) with vs.variable_scope("gates"): # Reset gate and update gate. self._gate_linear = _Linear( [inputs, state], 2 * self._num_units, True, bias_initializer=bias_ones, kernel_initializer=self._kernel_initializer) value = math_ops.sigmoid(self._gate_linear([inputs, state])) r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1) r_state = r * state if self._candidate_linear is None: with vs.variable_scope("candidate"): self._candidate_linear = _Linear( [inputs, r_state], self._num_units, True, bias_initializer=self._bias_initializer, kernel_initializer=self._kernel_initializer) c = self._activation(self._candidate_linear([inputs, r_state])) new_h = (1. - att_score) * state + att_score * c return new_h, new_h class VecAttGRUCell(RNNCell): """Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078). Args: num_units: int, The number of units in the GRU cell. activation: Nonlinearity to use. Default: `tanh`. reuse: (optional) Python boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. kernel_initializer: (optional) The initializer to use for the weight and projection matrices. bias_initializer: (optional) The initializer to use for the bias. """ def __init__(self, num_units, activation=None, reuse=None, kernel_initializer=None, bias_initializer=None): super(VecAttGRUCell, self).__init__(_reuse=reuse) self._num_units = num_units self._activation = activation or math_ops.tanh self._kernel_initializer = kernel_initializer self._bias_initializer = bias_initializer self._gate_linear = None self._candidate_linear = None @property def state_size(self): return self._num_units @property def output_size(self): return self._num_units def __call__(self, inputs, state, att_score): return self.call(inputs, state, att_score) def call(self, inputs, state, att_score=None): """Gated recurrent unit (GRU) with nunits cells.""" if self._gate_linear is None: bias_ones = self._bias_initializer if self._bias_initializer is None: bias_ones = init_ops.constant_initializer(1.0, dtype=inputs.dtype) with vs.variable_scope("gates"): # Reset gate and update gate. self._gate_linear = _Linear( [inputs, state], 2 * self._num_units, True, bias_initializer=bias_ones, kernel_initializer=self._kernel_initializer) value = math_ops.sigmoid(self._gate_linear([inputs, state])) r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1) r_state = r * state if self._candidate_linear is None: with vs.variable_scope("candidate"): self._candidate_linear = _Linear( [inputs, r_state], self._num_units, True, bias_initializer=self._bias_initializer, kernel_initializer=self._kernel_initializer) c = self._activation(self._candidate_linear([inputs, r_state])) u = (1.0 - att_score) * u new_h = u * state + (1 - u) * c return new_h, new_h def prelu(_x, scope=''): """parametric ReLU activation""" with tf.variable_scope(name_or_scope=scope, default_name="prelu",reuse=tf.AUTO_REUSE): _alpha = tf.get_variable("prelu_"+scope, shape=_x.get_shape()[-1], dtype=_x.dtype, initializer=tf.constant_initializer(0.1)) return tf.maximum(0.0, _x) + _alpha * tf.minimum(0.0, _x) def calc_auc(raw_arr): """Summary Args: raw_arr (TYPE): Description Returns: TYPE: Description """ # 根据预测值进行降序 arr = sorted(raw_arr, key=lambda d:d[0], reverse=True) pos, neg = 0., 0. # 记录正样本和负样本的数量 for record in arr: if record[1] == 1.: pos += 1 else: neg += 1 fp, tp = 0., 0. xy_arr = [] # 相当于将阈值从大变到小，记录那时的tp和fp for record in arr: if record[1] == 1.: tp += 1 else: fp += 1 xy_arr.append([fp/neg, tp/pos]) # 计算面积。矩形面积的一半。 auc = 0. prev_x = 0. prev_y = 0. for x, y in xy_arr: if x != prev_x: auc += ((x - prev_x) * (y + prev_y) / 2.) prev_x = x prev_y = y return auc def attention(query, facts, attention_size, mask, stag='null', mode='LIST', softmax_stag=1, time_major=False, return_alphas=False): if isinstance(facts, tuple): # In case of Bi-RNN, concatenate the forward and the backward RNN outputs. facts = tf.concat(facts, 2) if time_major: # (T,B,D) => (B,T,D) facts = tf.array_ops.transpose(facts, [1, 0, 2]) mask = tf.equal(mask, tf.ones_like(mask)) hidden_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer input_size = query.get_shape().as_list()[-1] # Trainable parameters w1 = tf.Variable(tf.random_normal([hidden_size, attention_size], stddev=0.1)) w2 = tf.Variable(tf.random_normal([input_size, attention_size], stddev=0.1)) b = tf.Variable(tf.random_normal([attention_size], stddev=0.1)) v = tf.Variable(tf.random_normal([attention_size], stddev=0.1)) with tf.name_scope('v'): # Applying fully connected layer with non-linear activation to each of the B*T timestamps; # the shape of `tmp` is (B,T,D)*(D,A)=(B,T,A), where A=attention_size tmp1 = tf.tensordot(facts, w1, axes=1) tmp2 = tf.tensordot(query, w2, axes=1) tmp2 = tf.reshape(tmp2, [-1, 1, tf.shape(tmp2)[-1]]) tmp = tf.tanh((tmp1 + tmp2) + b) # For each of the timestamps its vector of size A from `tmp` is reduced with `v` vector v_dot_tmp = tf.tensordot(tmp, v, axes=1, name='v_dot_tmp') # (B,T) shape key_masks = mask # [B, 1, T] # key_masks = tf.expand_dims(mask, 1) # [B, 1, T] paddings = tf.ones_like(v_dot_tmp) * (-2 ** 32 + 1) v_dot_tmp = tf.where(key_masks, v_dot_tmp, paddings) # [B, 1, T] alphas = tf.nn.softmax(v_dot_tmp, name='alphas') # (B,T) shape # Output of (Bi-)RNN is reduced with attention vector; the result has (B,D) shape #output = tf.reduce_sum(facts * tf.expand_dims(alphas, -1), 1) output = facts * tf.expand_dims(alphas, -1) output = tf.reshape(output, tf.shape(facts)) # output = output / (facts.get_shape().as_list()[-1] ** 0.5) if not return_alphas: return output else: return output, alphas def din_attention(query, facts, attention_size, mask, stag='null', mode='SUM', softmax_stag=1, time_major=False, return_alphas=False): if isinstance(facts, tuple): # In case of Bi-RNN, concatenate the forward and the backward RNN outputs. facts = tf.concat(facts, 2) print ("querry_size mismatch") query = tf.concat(values = [ query, query, ], axis=1) if time_major: # (T,B,D) => (B,T,D) facts = tf.array_ops.transpose(facts, [1, 0, 2]) mask = tf.equal(mask, tf.ones_like(mask)) facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer querry_size = query.get_shape().as_list()[-1] queries = tf.tile(query, [1, tf.shape(facts)[1]]) queries = tf.reshape(queries, tf.shape(facts)) din_all = tf.concat([queries, facts, queries-facts, queries*facts], axis=-1) d_layer_1_all = tf.layers.dense(din_all, 80, activation=tf.nn.sigmoid, name='f1_att' + stag) d_layer_2_all = tf.layers.dense(d_layer_1_all, 40, activation=tf.nn.sigmoid, name='f2_att' + stag) d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att' + stag) d_layer_3_all = tf.reshape(d_layer_3_all, [-1, 1, tf.shape(facts)[1]]) scores = d_layer_3_all # Mask # key_masks = tf.sequence_mask(facts_length, tf.shape(facts)[1]) # [B, T] key_masks = tf.expand_dims(mask, 1) # [B, 1, T] paddings = tf.ones_like(scores) * (-2 ** 32 + 1) scores = tf.where(key_masks, scores, paddings) # [B, 1, T] # Scale # scores = scores / (facts.get_shape().as_list()[-1] ** 0.5) # Activation if softmax_stag: scores = tf.nn.softmax(scores) # [B, 1, T] # Weighted sum if mode == 'SUM': output = tf.matmul(scores, facts) # [B, 1, H] # output = tf.reshape(output, [-1, tf.shape(facts)[-1]]) else: scores = tf.reshape(scores, [-1, tf.shape(facts)[1]]) output = facts * tf.expand_dims(scores, -1) output = tf.reshape(output, tf.shape(facts)) return output def din_fcn_attention(query, facts, attention_size, mask, stag='null', mode='SUM', softmax_stag=1, time_major=False, return_alphas=False, forCnn=False): if isinstance(facts, tuple): # In case of Bi-RNN, concatenate the forward and the backward RNN outputs. facts = tf.concat(facts, 2) if len(facts.get_shape().as_list()) == 2: facts = tf.expand_dims(facts, 1) if time_major: # (T,B,D) => (B,T,D) facts = tf.array_ops.transpose(facts, [1, 0, 2]) # Trainable parameters mask = tf.equal(mask, tf.ones_like(mask)) facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer querry_size = query.get_shape().as_list()[-1] query = tf.layers.dense(query, facts_size, activation=None, name='f1' + stag, reuse=tf.AUTO_REUSE) query = prelu(query) queries = tf.tile(query, [1, tf.shape(facts)[1]]) queries = tf.reshape(queries, tf.shape(facts)) din_all = tf.concat([queries, facts, queries-facts, queries*facts], axis=-1) d_layer_1_all = tf.layers.dense(din_all, 80, activation=tf.nn.sigmoid, name='f1_att' + stag, reuse=tf.AUTO_REUSE) d_layer_2_all = tf.layers.dense(d_layer_1_all, 40, activation=tf.nn.sigmoid, name='f2_att' + stag, reuse=tf.AUTO_REUSE) d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att' + stag, reuse=tf.AUTO_REUSE) d_layer_3_all = tf.reshape(d_layer_3_all, [-1, 1, tf.shape(facts)[1]]) scores = d_layer_3_all # Mask # key_masks = tf.sequence_mask(facts_length, tf.shape(facts)[1]) # [B, T] key_masks = tf.expand_dims(mask, 1) # [B, 1, T] paddings = tf.ones_like(scores) * (-2 ** 32 + 1) if not forCnn: scores = tf.where(key_masks, scores, paddings) # [B, 1, T] # Scale # scores = scores / (facts.get_shape().as_list()[-1] ** 0.5) # Activation if softmax_stag: scores = tf.nn.softmax(scores) # [B, 1, T] # Weighted sum if mode == 'SUM': output = tf.matmul(scores, facts) # [B, 1, H] # output = tf.reshape(output, [-1, tf.shape(facts)[-1]]) else: scores = tf.reshape(scores, [-1, tf.shape(facts)[1]]) output = facts * tf.expand_dims(scores, -1) output = tf.reshape(output, tf.shape(facts)) if return_alphas: return output, scores return output def clu_tar_din_fcn_attention(query, facts, attention_size, mask, stag='clu_tar', mode='SUM', softmax_stag=1, time_major=False, return_alphas=False, forCnn=False): if isinstance(facts, tuple): # In case of Bi-RNN, concatenate the forward and the backward RNN outputs. facts = tf.concat(facts, 2) print("facts is tuple") if len(facts.get_shape().as_list()) == 2: facts = tf.expand_dims(facts, 1) print("len(facts) == 2") if time_major: # (T,B,D) => (B,T,D) facts = tf.array_ops.transpose(facts, [1, 0, 2]) # Trainable parameterstf.layers.dense(bn1, 50, activation=None, name='f1') mask = tf.equal(mask, tf.ones_like(mask)) facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer querry_size = query.get_shape().as_list()[-1] query = tf.layers.dense(query, facts_size, activation=None, name='f1' + stag) query = prelu(query, scope=stag+"prelu") queries = tf.tile(query, [1, tf.shape(facts)[1]]) queries = tf.reshape(queries, tf.shape(facts)) din_all = tf.concat([queries, facts, queries-facts, queries*facts], axis=-1) d_layer_1_all = tf.layers.dense(din_all, 80, activation=tf.nn.sigmoid, name='f1_att' + stag) d_layer_2_all = tf.layers.dense(d_layer_1_all, 40, activation=tf.nn.sigmoid, name='f2_att' + stag) d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att' + stag) d_layer_3_all = tf.reshape(d_layer_3_all, [-1, 1, tf.shape(facts)[1]]) scores = d_layer_3_all # Mask # key_masks = tf.sequence_mask(facts_length, tf.shape(facts)[1]) # [B, T] # key_masks = tf.expand_dims(mask, 1) # [B, 1, T] # paddings = tf.ones_like(scores) * (-2 ** 32 + 1) # if not forCnn: # scores = tf.where(key_masks, scores, paddings) # [B, 1, T] # Scale # scores = scores / (facts.get_shape().as_list()[-1] ** 0.5) # Activation if softmax_stag: scores = tf.nn.softmax(scores) # [B, 1, T] tf.summary.histogram('clu_tar scores', scores) # Weighted sum if mode == 'SUM': output = tf.matmul(scores, facts) # [B, 1, H] # output = tf.reshape(output, [-1, tf.shape(facts)[-1]]) else: scores = tf.reshape(scores, [-1, tf.shape(facts)[1]]) output = facts * tf.expand_dims(scores, -1) output = tf.reshape(output, tf.shape(facts)) if return_alphas: return output, scores return output def din_multi_fcn_attention(query, facts, attention_size, mask, stag='clu_tar', mode='SUM', softmax_stag=1, time_major=False, return_alphas=False, forCnn=False): if isinstance(facts, tuple): # In case of Bi-RNN, concatenate the forward and the backward RNN outputs. facts = tf.concat(facts, 2) print("facts is tuple") if len(facts.get_shape().as_list()) == 2: facts = tf.expand_dims(facts, 1) # print("len(facts) == 2") if time_major: # (T,B,D) => (B,T,D) facts = tf.array_ops.transpose(facts, [1, 0, 2]) # Trainable parameters # 如果query为0，则mask全部转换成0 query_sum = tf.reduce_sum(query, 1) query_sum = tf.expand_dims(query_sum, 1) query_sums = tf.tile(query_sum, [1, tf.shape(mask)[1]]) temp_mask = tf.multiply(tf.cast(mask, tf.float32), query_sums) one = tf.ones_like(temp_mask) zero = tf.zeros_like(temp_mask) mask = tf.where(tf.abs(temp_mask) > 0, x=one, y=zero) mask = tf.equal(mask, tf.ones_like(mask)) facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer query = tf.layers.dense(query, facts_size, activation=None, use_bias=False, name='f1' + stag, reuse=tf.AUTO_REUSE) queries = tf.tile(query, [1, tf.shape(facts)[1]]) queries = tf.reshape(queries, tf.shape(facts)) din_all = tf.concat([queries, facts, queries-facts, queries*facts], axis=-1) d_layer_1_all = tf.layers.dense(din_all, 10, activation=tf.nn.sigmoid, use_bias=False, name='f1_att' + stag, reuse=tf.AUTO_REUSE) d_layer_2_all = tf.layers.dense(d_layer_1_all, 5, activation=tf.nn.sigmoid, use_bias=False, name='f2_att' + stag, reuse=tf.AUTO_REUSE) d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, use_bias=False, name='f3_att' + stag, reuse=tf.AUTO_REUSE) d_layer_3_all = tf.reshape(d_layer_3_all, [-1, 1, tf.shape(facts)[1]]) scores = d_layer_3_all # scores = tf.reduce_sum(din_all, 2) # Mask # key_masks = tf.sequence_mask(facts_length, tf.shape(facts)[1]) # [B, T] key_masks = tf.expand_dims(mask, 1) # [B, 1, T] paddings = tf.zeros_like(scores) + 1e-8 if not forCnn: # scores = tf.where(key_masks, scores, paddings) # [B, 1, T] scores = tf.where(key_masks, scores, paddings) # [B, 1, T] # scores = tf.where(mask, scores, paddings) # Scale # scores = scores / (facts.get_shape().as_list()[-1] ** 0.5) # Activation # if softmax_stag: # scores = tf.nn.softmax(scores) # [B, 1, T] # tf.summary.histogram('clu_tar scores', scores) # Weighted sum if mode == 'SUM': output = tf.matmul(scores, facts) # [B, 1, H] # output = tf.reshape(output, [-1, tf.shape(facts)[-1]]) else: scores = tf.reshape(scores, [-1, tf.shape(facts)[1]]) output = facts * tf.expand_dims(scores, -1) output = tf.reshape(output, tf.shape(facts)) if return_alphas: return output, scores return output def self_attention(facts, ATTENTION_SIZE, mask, stag='null'): if len(facts.get_shape().as_list()) == 2: facts = tf.expand_dims(facts, 1) def cond(batch, output, i): return tf.less(i, tf.shape(batch)[1]) def body(batch, output, i): self_attention_tmp = din_fcn_attention(batch[:, i, :], batch[:, 0:i+1, :], ATTENTION_SIZE, mask[:, 0:i+1], softmax_stag=1, stag=stag, mode='LIST') self_attention_tmp = tf.reduce_sum(self_attention_tmp, 1) output = output.write(i, self_attention_tmp) return batch, output, i + 1 output_ta = tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True, element_shape=(facts[:, 0, :].get_shape())) _, output_op, _ = tf.while_loop(cond, body, [facts, output_ta, 0]) self_attention = output_op.stack() self_attention = tf.transpose(self_attention, perm = [1, 0, 2]) return self_attention def self_all_attention(facts, ATTENTION_SIZE, mask, stag='null'): if len(facts.get_shape().as_list()) == 2: facts = tf.expand_dims(facts, 1) def cond(batch, output, i): return tf.less(i, tf.shape(batch)[1]) def body(batch, output, i): self_attention_tmp = din_fcn_attention(batch[:, i, :], batch, ATTENTION_SIZE, mask, softmax_stag=1, stag=stag, mode='LIST') self_attention_tmp = tf.reduce_sum(self_attention_tmp, 1) output = output.write(i, self_attention_tmp) return batch, output, i + 1 output_ta = tf.TensorArray(dtype=tf.float32, size=0, dynamic_size=True, element_shape=(facts[:, 0, :].get_shape())) _, output_op, _ = tf.while_loop(cond, body, [facts, output_ta, 0]) self_attention = output_op.stack() self_attention = tf.transpose(self_attention, perm = [1, 0, 2]) return self_attention def din_fcn_shine(query, facts, attention_size, mask, stag='null', mode='SUM', softmax_stag=1, time_major=False, return_alphas=False): if isinstance(facts, tuple): # In case of Bi-RNN, concatenate the forward and the backward RNN outputs. facts = tf.concat(facts, 2) if time_major: # (T,B,D) => (B,T,D) facts = tf.array_ops.transpose(facts, [1, 0, 2]) # Trainable parameters mask = tf.equal(mask, tf.ones_like(mask)) facts_size = facts.get_shape().as_list()[-1] # D value - hidden size of the RNN layer querry_size = query.get_shape().as_list()[-1] query = tf.layers.dense(query, facts_size, activation=None, name='f1_trans_shine' + stag) query = prelu(query) queries = tf.tile(query, [1, tf.shape(facts)[1]]) queries = tf.reshape(queries, tf.shape(facts)) din_all = tf.concat([queries, facts, queries-facts, queries*facts], axis=-1) d_layer_1_all = tf.layers.dense(din_all, facts_size, activation=tf.nn.sigmoid, name='f1_shine_att' + stag) d_layer_2_all = tf.layers.dense(d_layer_1_all, facts_size, activation=tf.nn.sigmoid, name='f2_shine_att' + stag) d_layer_2_all = tf.reshape(d_layer_2_all, tf.shape(facts)) output = d_layer_2_all return output

[ "rommeldhy@163.com" ]

rommeldhy@163.com

b6974055275f4d4c538d2f027d95efb2b3997546

0de9850635f48fd7851340c19e78f5bcebfd917b

/lampara.py

6f954c3f98d74f47fb88ff4a303e06d8bc6a24e9

[]

no_license

tzalejo/pyhton-base

133f94072f8ad9da7f9311e7434a3d5fa08269bf

a463e05b60b79e4c0e2d1c38932a86696fce826d

refs/heads/main

2023-01-14T14:30:08.420197

2020-11-30T04:58:22

317,115,088

0

null

UTF-8

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false

55

py

class Lampara: def __init__(self): pass

[ "tzalejo@gmail.com" ]

tzalejo@gmail.com

a73e13191382599751df1a91276c5ea0b6aaffb2

0bf59691e4511ebada2efcdb8d0dc8a506c947e5

/binary_inverse.py

6e2c67697704d8bc4cdbab96eb56cf5b02ef547a

[]

no_license

slayer96/binary_inverse

c48130682356ad74ec009bf434645c87eea76ad6

2ad4e54e00dd4b95f3aa06be4903a86adaad86ad

refs/heads/master

2021-01-10T04:13:18.133081

2016-02-01T23:34:27

50,882,278

0

null

UTF-8

Python

false

518

py

#binary inverse #python3.4 def O(n): if len(n) == 1 and n[0] == 0: return [0] #n.reverse() dec = 0 index = 0 for i in n: tmp = i * ((-2) ** index) index += 1 dec += tmp #print(dec) dec *= -1 result = [] while dec: tmp = dec % (-2) dec //= (-2) if tmp < 0: tmp += 2 dec += 1 result.append(tmp) #result.reverse() return result if __name__ == '__main__': print(O([1, 0, 1, 1, 0, 1]))

[ "myhailo.mykytyn.96@mail.ru" ]

myhailo.mykytyn.96@mail.ru

33fc505b3949ed8cfde4c4eb3218e19ba7f5cbfd

81277d47f0928be7656500d85f4201f963bb4746

/swapping-nodes-in-a-linked-list/swapping-nodes-in-a-linked-list.py

6b60f459b5b50d8bf0dd2c3cd2224b4f6c7b3f7c

[]

no_license

siva4646/LeetCode_Python

db1d535a174f38b616cddfeeac412c0d02afa372

52bf12095996a9137b1ea213ac43e1fe07806956

refs/heads/main

2023-04-13T22:39:23.474034

2021-04-23T02:12:19

null

0

null

UTF-8

Python

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544

py

# Definition for singly-linked list. # class ListNode(object): # def __init__(self, val=0, next=None): # self.val = val # self.next = next class Solution(object): def swapNodes(self, head, k): slow = fast = head for _ in range(k - 1): fast = fast.next first_node_swap = fast fast = fast.next while fast != None: slow = slow.next fast = fast.next slow.val, first_node_swap.val = first_node_swap.val, slow.val return head

[ "sachindevatarhs777@gmail.com" ]

sachindevatarhs777@gmail.com

2e324c7fe29b14a6933e5d4c9b493d4981b2b948

68753b38ec8e4eaaf95eae4202a9a22391080fa3

/learning_templates/basic_app/templatetags/__init__.py

134284b9dc631b704885ca912289581dc1e05bd7

[]

no_license

siddharth288/learning-templates-django

9a35e0b8d102bdb71acfd23cbacd94130b558784

84e082c9ea56fe4a5c81cbd464aa155c40433acd

refs/heads/main

2023-06-20T08:51:33.555131

2021-07-21T12:33:11

388,111,631

0

null

UTF-8

Python

false

72

py

# tells python to treat this file as a module so that we can load it in

[ "siddharth@siddharth.com" ]

siddharth@siddharth.com

f89bf721f31caee152bc2e8884cf341032766206

92aad61825bea12ccdfd8d52487b43430d4e5cf3

/CS61002 Labs/lab08.py

453dbc1351867b16f027eea44f4d91dc26a8ae92

[]

no_license

radam0/Python

ec9324a18579a2d4be1bde7011182842dba79abf

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refs/heads/master

2022-04-21T23:54:02.173575

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#Nikhil Vemula #March 5,2016 #CS61002 ## Matching DNA Program # function for adjusting hypens for given strings def getcurrentstring(firstString,secString): firstlen = len(firstString) seclen = len(secString) if firstlen>seclen: secString = secString.ljust(firstlen,'-') if seclen>firstlen: firstString = firstString.ljust(seclen,'-') return firstString,secString # function for getting hyphen indexes for strings def getindexces(string): indexes = [] for i in range(len(string)): if string[i] == '-': indexes.append(i+1) return indexes # Mainfunction for entire show def mainfunction(): firstString = raw_input('String 1: ').lower() secString = raw_input('String 2: ').lower() if firstString=='' or secString=='': print 'Given inputs are not valid, Please try again.' return quit_sel = 0 while not quit_sel: selection = raw_input('\nWhat do you want to do:\n\ta (Add an Indel)\n\td (Delete an Indel)\n\ts (Score)\n\tq (Quit) : ') if selection=='s': firstString,secString = getcurrentstring(firstString,secString) match = 0 misMatch = 0 Str1 = '' Str2 = '' for i in range(len(firstString)): if firstString[i]=='-' or secString[i]=='-': misMatch = misMatch+1 Str1 = Str1+firstString[i].upper() Str2 = Str2+secString[i].upper() else: if firstString[i]==secString[i]: match = match+1 Str1 = Str1+firstString[i] Str2 = Str2+secString[i] else: misMatch = misMatch+1 Str1 = Str1+firstString[i].upper() Str2 = Str2+secString[i].upper() print '\nMatches: %s\tMismatches: %s'%(match,misMatch) print 'Str1: %s'%(Str1) print 'Str2: %s'%(Str2) elif selection=='a': stringsel = raw_input('Which string to change (1 or 2): ') if stringsel!='1' and stringsel!='2': print 'Given input is wrong, Please try again.' else: toaddString = [firstString,secString][int(stringsel)-1] indexpoint = raw_input('At what index do you wish to place Indel (1 to %s): '%(len(toaddString))) try: indexpoint = int(indexpoint) except: indexpoint= 0 if indexpoint<1 or indexpoint>(len(toaddString)): print 'Given input is wrong, Please try again.' else: hashlist = list(toaddString) hashlist.insert(indexpoint-1,'-') toaddString = ''.join(hashlist) if stringsel=='1': firstString,secString = getcurrentstring(toaddString,secString) elif stringsel=='2': firstString,secString = getcurrentstring(firstString,toaddString) elif selection == 'd': stringsel = raw_input('Which string to change (1 or 2): ') if stringsel!='1' and stringsel!='2': print 'Given input is wrong, Please try again.' else: toaddString = [firstString,secString][int(stringsel)-1] indexes = getindexces(toaddString) if indexes==[]: print 'No Indels present in the selected string. Please try another option.' else: indexpoint = raw_input('At what index do you wish to delete Indel (%s): '%(', '.join(map(str, indexes)))) try: indexpoint = int(indexpoint) except: indexpoint= 0 if indexpoint<1 or indexpoint not in indexes: print 'Given input is wrong, Please try again.' else: hashlist = list(toaddString) del hashlist[indexpoint-1] toaddString = ''.join(hashlist) if stringsel=='1': firstString,secString = getcurrentstring(toaddString,secString) elif stringsel=='2': firstString,secString = getcurrentstring(firstString,toaddString) elif selection == 'q': quit_sel = 1 else: print 'Given input is wrong, Please try again.' mainfunction()

[ "vemulanikhil23@gmail.com" ]

vemulanikhil23@gmail.com

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/beyond Python V26/Modules/beyond/Reaper/Settings.py

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no_license

kadirahiska/Reaper

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# Step 1 - RemoteControl.py Action and its Cmd ID # ======================================================================= Reaper_RemoteControl_CmdID = 55310 # Will this change? # From Reaper's "Actions/Show Action List…" press "ReaScript: New/load…" # Find and select "...\Modules\beyond\Reaper\RemoteControl.py" # Back on the Actions list, you will see "Custom: RemoteControl.py" # Right click on the Actions list and check "Show action IDs" # Find the "Cmd ID" column for "Custom: RemoteControl.py" # Enter that Cmd ID number above. # # NOTE that this number will shift and change when a previously defined # Action is deleted or a Reaper extension like SWS is un/installed. # # In that case, look up the changed Cmd ID from Actions and update it above. # # In the future, when Reaper's OSC supports the stable "Custom ID", # this will no longer be an issue. In the mean time, try to setup # RemoteControl.py early on, so that any Actions you may add or delete # afterwards will not change its Cmd ID. # Step 2 - Reaper's OSC and Addresses # ======================================================================= Reaper_OSC_Address = ("localhost", 8000) External_Program_Address = ("localhost", 8001) # From Reaper's "Options/Preferences" select "Control Surfaces" page. # # If the "Control Surfaces" has an item beginning with "OSC:", you have # a preexisting OSC setup, see below. # # Press "Add" and select "OSC (Open Sound Control)". # # Make sure the "Pattern config:" is set to Default. # # Check and Activate "Receive on port:" # # The default Port of 8000 and the "localhost" addresses here are fine # for connecting to Reaper locally on the same computer only. If you need # to connect to Reaper across a network, change these addresses to match # Reaper and where External Programs will be running. # # # Preexisting OSC Setup: # ====================== # # If your "Pattern config:" is not Default, make sure you have the following # line in your Pattern config file: # # ACTION i/action t/action/@ # # Or, you may setup another Default OSC configuration as above on # another Port and enter that Port above. # Step 3 - Python Executable # ======================================================================= Python = "/Library/Frameworks/Python.framework/Versions/3.4/Resources/Python.app/Contents/MacOS/Python" # Enter the path of your preferred Python executable # # On Windows, example: # Python = r"C:\Python34\pythonw.exe" # Keep the r" to preserve the \'s # # On OSX, example: # Python = "/Library/Frameworks/Python.framework/Versions/3.4/Resources/Python.app/Contents/MacOS/Python" # # This will allow you to launch External beyond.Reaper programs as # Reaper Actions bound to keyboard shortcuts, menus and toolbars.

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sizeoverload@gmail.com

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/marketing/mixins.py

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austin89213/Ecommerce_Website

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from django.utils.decorators import method_decorator from django.views.decorators.csrf import csrf_exempt class CsrfExemptMixin(object): @method_decorator(csrf_exempt) def dispatch(self, request, *args, **kwargs): return super(CsrfExemptMixin, self).dispatch(request, *args, **kwargs)

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/sorting_animate.py

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ankit-kumar-dwivedi/SortingwithTurtle

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refs/heads/master

2020-03-25T07:20:21.671603

2018-08-04T19:02:20

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"""Just sorting algorithms""" from turtle import * import random class Block(Turtle): def __init__(self, size): self.size = size Turtle.__init__(self, shape="square", visible=False) self.pu() self.shapesize(size * 1.5, 1.5, 2) # square-->rectangle self.fillcolor("black") self.st() def glow(self): self.fillcolor("red") def unglow(self): self.fillcolor("black") def __repr__(self): return "Block size: {0}".format(self.size) class Shelf(list): def __init__(self, y): "create a shelf. y is y-position of first block" self.y = y self.x = -150 def push(self, d): width, _, _ = d.shapesize() # align blocks by the bottom edge y_offset = width / 2 * 20 d.sety(self.y + y_offset) d.setx(self.x + 34 * len(self)) self.append(d) def _close_gap_from_i(self, i): for b in self[i:]: xpos, _ = b.pos() b.setx(xpos - 34) def _open_gap_from_i(self, i): for b in self[i:]: xpos, _ = b.pos() b.setx(xpos + 34) def pop(self, key): b = list.pop(self, key) b.glow() b.sety(200) self._close_gap_from_i(key) return b def insert(self, key, b): self._open_gap_from_i(key) list.insert(self, key, b) b.setx(self.x + 34 * key) width, _, _ = b.shapesize() # align blocks by the bottom edge y_offset = width / 2 * 20 b.sety(self.y + y_offset) b.unglow() def isort(shelf): length = len(shelf) for i in range(1, length): hole = i while hole > 0 and shelf[i].size < shelf[hole - 1].size: hole = hole - 1 shelf.insert(hole, shelf.pop(i)) return def ssort(shelf): length = len(shelf) for j in range(0, length - 1): imin = j for i in range(j + 1, length): if shelf[i].size < shelf[imin].size: imin = i if imin != j: shelf.insert(j, shelf.pop(imin)) def partition(shelf, left, right, pivot_index): pivot = shelf[pivot_index] shelf.insert(right, shelf.pop(pivot_index)) store_index = left for i in range(left, right): # range is non-inclusive of ending value if shelf[i].size < pivot.size: shelf.insert(store_index, shelf.pop(i)) store_index = store_index + 1 shelf.insert(store_index, shelf.pop(right)) # move pivot to correct position return store_index def qsort(shelf, left, right): if left < right: pivot_index = left pivot_new_index = partition(shelf, left, right, pivot_index) qsort(shelf, left, pivot_new_index - 1) qsort(shelf, pivot_new_index + 1, right) def randomize(): disable_keys() clear() target = list(range(10)) random.shuffle(target) for i, t in enumerate(target): for j in range(i, len(s)): if s[j].size == t + 1: s.insert(i, s.pop(j)) show_text(instructions1) show_text(instructions2, line=1) enable_keys() def show_text(text, line=0): line = 20 * line goto(0,-250 - line) write(text, align="center", font=("Courier", 16, "bold")) def start_ssort(): disable_keys() clear() show_text("Selection Sort") ssort(s) clear() show_text(instructions1) show_text(instructions2, line=1) enable_keys() def start_isort(): disable_keys() clear() show_text("Insertion Sort") isort(s) clear() show_text(instructions1) show_text(instructions2, line=1) enable_keys() def start_qsort(): disable_keys() clear() show_text("Quicksort") qsort(s, 0, len(s) - 1) clear() show_text(instructions1) show_text(instructions2, line=1) enable_keys() def init_shelf(): global s s = Shelf(-200) vals = (4, 2, 8, 9, 1, 5, 10, 3, 7, 6) for i in vals: s.push(Block(i)) def disable_keys(): onkey(None, "s") onkey(None, "i") onkey(None, "q") onkey(None, "r") def enable_keys(): onkey(start_isort, "i") onkey(start_ssort, "s") onkey(start_qsort, "q") onkey(randomize, "r") onkey(bye, "space") def main(): getscreen().clearscreen() ht(); penup() init_shelf() show_text(instructions1) show_text(instructions2, line=1) enable_keys() listen() return "EVENTLOOP" instructions1 = "press i(insertion sort), s(selection sort), q (quicksort)" instructions2 = "spacebar to quit, r randomize" if __name__=="__main__": msg = main() mainloop()

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2021-01-17T15:04:09.950521

2020-05-11T10:18:48

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import numpy as np #numpy.linalg for SVD etc. #import numpy.linalg as alg import scipy.spatial.distance as spd import sklearn.decomposition as skd #sklearn.decomposition import tables as tb import time corpus_mtrx_fname = '/home/dimitrios/Synergy-Crawler/Santinis_7-web_genre/Kfolds_Vocs_Inds_4Chars_7Genres/kfold_CorpusMatrix_9100000.h5' #Loading a table from any corpus available at a time. h5f = tb.open_file(corpus_mtrx_fname, 'r') corpus_mtrx = h5f.get_node('/', 'corpus_earray') #Testing Mahalanobis distance using randomly selected dimentions and timeing it. #Using projections of a random vector on new PC/Coordinates system. def RandomMahal(): print "COVARIANCE Manualy Made:" ts = time.time() #Calculate Variables Means mean_vect = np.mean(corpus_mtrx[0:100, 0:50000], axis=0) #Convert Raw data vectors to Zero-mean Vectors data_zero_mean = corpus_mtrx[0:100, 0:50000] - mean_vect #Display Manualy Crreated Covariance Matrix: print np.matrix(data_zero_mean).T.shape cov1 = np.ascontiguousarray( np.matrix(data_zero_mean).T ) * np.matrix(data_zero_mean) / (np.matrix(data_zero_mean).shape[0] - 1) print cov1 print np.linalg.eig(cov1) ts -= time.time() print ts print print "COVARIANCE" #ts = time.time() #print np.cov(corpus_mtrx[0:100, 0:50000].T) #ts -= time.time() #print ts print print "COVARIANCE from SVD" ts = time.time() U, S, V = np.linalg.svd(data_zero_mean, full_matrices = False) #print np.diag(S) print U.shape, np.matrix(np.diag(S)).shape, V.shape #print np.matrix(U).T.shape print len(S) #print np.ascontiguousarray( np.matrix(V).T ) * ( np.ascontiguousarray( np.matrix(np.diag(S)).T ) * np.matrix(np.diag(S)) ) * np.ascontiguousarray( np.matrix(V) ) / (np.matrix(data_zero_mean).shape[0] - 1) #print np.matrix(U).shape, (np.matrix(np.diag(S)) * np.matrix(np.diag(S)).T).shape, np.matrix(U).shape # np.matrix(U) * (np.matrix(np.diag(S)) * np.matrix(np.diag(S)).T) * np.matrix(U).T #SAME AS# print np.matrix(data_zero_mean) * np.matrix(data_zero_mean).T ts -= time.time() print ts RandomMahal() #Testing Mahalanobis distnace using PCA/SVD for selecting the Principal Components. #Using projections of a random vector on new PC/Coordinates system. def PCAMahal(): pca = skd.PCA() print pca.fit(corpus_mtrx) #PCAMahal() #mean = [.1,.1,.1,.1,.1,.5,.1,.1,.1,.1,.1,.1,.6,.1,.1,.1,.1,.1,.1] #cov = np.diag([.5,.7,.9,.2,.10,.5,.9,.8,.9,.1,.4,.1,.1,.6,.7,.3,.5,.8,.4]) #train_set = np.random.multivariate_normal(mean,cov,10) #estim = np.mean(train_set, axis=0) #print np.cov(train_set.T) #print estim #test_vetc = [.0,.0,.0,.0,.0,.0,.0,.0,.0,.0,.0,.0,.0,.0,.0,.0,.0,.0,.0] #pca = PCA() #pca.fit(train_set) #incov = alg.inv( alg.pinv(train_set.T) ) #print cov.shape #print np.cov(train_set).shape #print train_set.shape #print incov #test_vetc = pca.transform(test_vetc) #mean = pca.transform(mean) #print spd.mahalanobis(test_vetc,mean,incov) #print incov #print alg.pinv( np.cov(d) ) #print alg.svd(d) #print l h5f.close()

[ "dpritsos@extremepro.gr" ]

dpritsos@extremepro.gr

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/team_9/cocos/samples/tetrico/gamectrl.py

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[ "LGPL-2.1-only", "CC-BY-NC-4.0", "LicenseRef-scancode-other-copyleft", "LicenseRef-scancode-proprietary-license", "CC-BY-NC-SA-2.0", "BSD-3-Clause", "CC-BY-NC-ND-3.0" ]

permissive

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from __future__ import division, print_function, unicode_literals # This code is so you can run the samples without installing the package import sys import os sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../..')) # stdlib import copy import random # pyglet related import pyglet from pyglet.window import key # cocos2d related from cocos.layer import Layer from cocos.scene import Scene from cocos.euclid import Point2 # tetrico related from constants import * from status import status __all__ = ['GameCtrl'] # # Controller ( MVC ) # class GameCtrl( Layer ): is_event_handler = True #: enable pyglet's events def __init__(self, model): super(GameCtrl,self).__init__() self.used_key = False self.paused = True self.model = model self.elapsed = 0 def on_key_press(self, k, m ): if self.paused: return False if self.used_key: return False if k in (key.LEFT, key.RIGHT, key.DOWN, key.UP, key.SPACE): if k == key.LEFT: self.model.block_left() elif k == key.RIGHT: self.model.block_right() elif k == key.DOWN: self.model.block_down() elif k == key.UP: self.model.block_rotate() elif k == key.SPACE: # let the player move the block after it was dropped self.elapsed = 0 self.model.block_drop() self.used_key = True return True return False def on_text_motion(self, motion): if self.paused: return False if self.used_key: return False if motion in (key.MOTION_DOWN, key.MOTION_RIGHT, key.MOTION_LEFT): if motion == key.MOTION_DOWN: self.model.block_down() elif motion == key.MOTION_LEFT: self.model.block_left() elif motion == key.MOTION_RIGHT: self.model.block_right() self.used_key = True return True return False def pause_controller( self ): '''removes the schedule timer and doesn't handler the keys''' self.paused = True self.unschedule( self.step ) def resume_controller( self ): '''schedules the timer and handles the keys''' self.paused = False self.schedule( self.step ) def step( self, dt ): '''updates the engine''' self.elapsed += dt if self.elapsed > status.level.speed: self.elapsed = 0 self.model.block_down( sound=False) def draw( self ): '''draw the map and the block''' self.used_key = False

[ "a.campello@wellcome.ac.uk" ]

a.campello@wellcome.ac.uk

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langqy/AutoTestFramework

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# -*- coding: utf-8 -*- from selenium.common.exceptions import WebDriverException from ConfigParser import NoSectionError, NoOptionError class Error(Exception): """Base package Exception.""" pass class FileException(Error): """Base file exception.Thrown when a file is not available. For example: file not exists. """ pass class ConfigFileException(FileException): """Thrown when config file not exists.""" pass class ConfigError(Error): """Thrown when basic config error, such as no [path] section or no 'base' option.""" pass class DataFileNotAvailableException(FileException): """Thrown when data file not available.""" pass class SheetTypeError(Error): """Thrown when sheet type passed in not int or str.""" pass class SheetError(Error): """Thrown when specified sheet not exists.""" pass class DataError(Error): """Thrown when something wrong with the data.""" pass class LogFileNotAvailableException(FileException): """Thrown when log file not available.""" pass class LogError(Error): """Thrown when something wrong when logging.""" pass class ReportFileNotAvailableException(FileException): """Thrown when report file not available.""" pass class ReportError(Error): """Thrown when something wrong when generate the report file.""" pass class DriverNotExistsException(WebDriverException): """Thrown when driver not exists.""" pass class UnSupportBrowserTypeException(WebDriverException): """Thrown when the browser type not support.""" pass class ParameterError(Error): """Thrown when pass wrong parameter to a method.""" pass class UploadFileError(Error): """Thrown when upload files not available.""" pass class UploadWindowNotOpenError(Error): """Thrown when upload window not open.""" pass class UploadWindowOpenError(Error): """Thrown when open upload window error.""" pass class UnSupportMethod(Error): """Thrown when http method not allowed.""" pass

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"""Cache Management """ # The following comment should be removed at some point in the future. # mypy: strict-optional=False import errno import hashlib import logging import os from pip._vendor.packaging.utils import canonicalize_name from pip._internal.models.link import Link from pip._internal.utils.compat import expanduser from pip._internal.utils.temp_dir import TempDirectory from pip._internal.utils.typing import MYPY_CHECK_RUNNING from pip._internal.utils.urls import path_to_url from pip._internal.wheel import InvalidWheelFilename, Wheel if MYPY_CHECK_RUNNING: from typing import Optional, Set, List, Any from pip._internal.index import FormatControl from pip._internal.pep425tags import Pep425Tag logger = logging.getLogger(__name__) class Cache(object): """An abstract class - provides cache directories for data from links :param cache_dir: The root of the cache. :param format_control: An object of FormatControl class to limit binaries being read from the cache. :param allowed_formats: which formats of files the cache should store. ('binary' and 'source' are the only allowed values) """ def __init__(self, cache_dir, format_control, allowed_formats): # type: (str, FormatControl, Set[str]) -> None super(Cache, self).__init__() self.cache_dir = expanduser(cache_dir) if cache_dir else None self.format_control = format_control self.allowed_formats = allowed_formats _valid_formats = {"source", "binary"} assert self.allowed_formats.union(_valid_formats) == _valid_formats def _get_cache_path_parts(self, link): # type: (Link) -> List[str] """Get parts of part that must be os.path.joined with cache_dir """ # We want to generate an url to use as our cache key, we don't want to # just re-use the URL because it might have other items in the fragment # and we don't care about those. key_parts = [link.url_without_fragment] if link.hash_name is not None and link.hash is not None: key_parts.append("=".join([link.hash_name, link.hash])) key_url = "#".join(key_parts) # Encode our key url with sha224, we'll use this because it has similar # security properties to sha256, but with a shorter total output (and # thus less secure). However the differences don't make a lot of # difference for our use case here. hashed = hashlib.sha224(key_url.encode()).hexdigest() # We want to nest the directories some to prevent having a ton of top # level directories where we might run out of sub directories on some # FS. parts = [hashed[:2], hashed[2:4], hashed[4:6], hashed[6:]] return parts def _get_candidates(self, link, package_name): # type: (Link, Optional[str]) -> List[Any] can_not_cache = ( not self.cache_dir or not package_name or not link ) if can_not_cache: return [] canonical_name = canonicalize_name(package_name) formats = self.format_control.get_allowed_formats( canonical_name ) if not self.allowed_formats.intersection(formats): return [] root = self.get_path_for_link(link) try: return os.listdir(root) except OSError as err: if err.errno in {errno.ENOENT, errno.ENOTDIR}: return [] raise def get_path_for_link(self, link): # type: (Link) -> str """Return a directory to store cached items in for link. """ raise NotImplementedError() def get( self, link, # type: Link package_name, # type: Optional[str] supported_tags, # type: List[Pep425Tag] ): # type: (...) -> Link """Returns a link to a cached item if it exists, otherwise returns the passed link. """ raise NotImplementedError() def _link_for_candidate(self, link, candidate): # type: (Link, str) -> Link root = self.get_path_for_link(link) path = os.path.join(root, candidate) return Link(path_to_url(path)) def cleanup(self): # type: () -> None pass class SimpleWheelCache(Cache): """A cache of wheels for future installs. """ def __init__(self, cache_dir, format_control): # type: (str, FormatControl) -> None super(SimpleWheelCache, self).__init__( cache_dir, format_control, {"binary"} ) def get_path_for_link(self, link): # type: (Link) -> str """Return a directory to store cached wheels for link Because there are M wheels for any one sdist, we provide a directory to cache them in, and then consult that directory when looking up cache hits. We only insert things into the cache if they have plausible version numbers, so that we don't contaminate the cache with things that were not unique. E.g. ./package might have dozens of installs done for it and build a version of 0.0...and if we built and cached a wheel, we'd end up using the same wheel even if the source has been edited. :param link: The link of the sdist for which this will cache wheels. """ parts = self._get_cache_path_parts(link) # Store wheels within the root cache_dir return os.path.join(self.cache_dir, "wheels", *parts) def get( self, link, # type: Link package_name, # type: Optional[str] supported_tags, # type: List[Pep425Tag] ): # type: (...) -> Link candidates = [] for wheel_name in self._get_candidates(link, package_name): try: wheel = Wheel(wheel_name) except InvalidWheelFilename: continue if not wheel.supported(supported_tags): # Built for a different python/arch/etc continue candidates.append( (wheel.support_index_min(supported_tags), wheel_name) ) if not candidates: return link return self._link_for_candidate(link, min(candidates)[1]) class EphemWheelCache(SimpleWheelCache): """A SimpleWheelCache that creates it's own temporary cache directory """ def __init__(self, format_control): # type: (FormatControl) -> None self._temp_dir = TempDirectory(kind="ephem-wheel-cache") super(EphemWheelCache, self).__init__( self._temp_dir.path, format_control ) def cleanup(self): # type: () -> None self._temp_dir.cleanup() class WheelCache(Cache): """Wraps EphemWheelCache and SimpleWheelCache into a single Cache This Cache allows for gracefully degradation, using the ephem wheel cache when a certain link is not found in the simple wheel cache first. """ def __init__(self, cache_dir, format_control): # type: (str, FormatControl) -> None super(WheelCache, self).__init__( cache_dir, format_control, {'binary'} ) self._wheel_cache = SimpleWheelCache(cache_dir, format_control) self._ephem_cache = EphemWheelCache(format_control) def get_path_for_link(self, link): # type: (Link) -> str return self._wheel_cache.get_path_for_link(link) def get_ephem_path_for_link(self, link): # type: (Link) -> str return self._ephem_cache.get_path_for_link(link) def get( self, link, # type: Link package_name, # type: Optional[str] supported_tags, # type: List[Pep425Tag] ): # type: (...) -> Link retval = self._wheel_cache.get( link=link, package_name=package_name, supported_tags=supported_tags, ) if retval is not link: return retval return self._ephem_cache.get( link=link, package_name=package_name, supported_tags=supported_tags, ) def cleanup(self): # type: () -> None self._wheel_cache.cleanup() self._ephem_cache.cleanup()

[ "alejandro.santorum@gmail.com" ]

alejandro.santorum@gmail.com

5e78c2d4c4a562ebf196ffaa7d086e43931cd05b

467a320ff8e80e92d9ad42d514d45858ab4bc8f5

/Archived_Versions/TankDuel.py

397e58d8080e3610f6b839db3d1d44df464f9ba9

[]

no_license

theTrueEnder/TankDuel-for-World-of-Tanks-Blitz

c81cd8015e97095f6c873355726b6371269c8dfb

c9fe1f49949495a592bd09dce8dcd589430e7372

refs/heads/master

2022-12-26T13:46:46.080283

2020-09-19T20:33:15

296,938,145

1

0

null

UTF-8

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false

2,183

py

#Copyright 2020, Kai Delsing, all rights reserved ##TO-DO## #classes for each tank (no [2] pointers, etc, tank2.hp) #av vs max/min rolls #shell velocity and distancwe #Name, HP, Damage, Reload, Adrenaline tank1 = ['Obj. 704', 1500, 640, 11.79, False] tank2 = ['T-55A', 1650, 310, 6.32, False] ################################################################ time = [0.000, tank1[3], tank2[3]] active = True adr = [0, 0, 75, 75] def tank1_fire(): tank2[1] -= tank1[2] print(tank1[0], 'fires, dealing', tank1[2], 'damage.') state() time[1] = tank1[3] def tank2_fire(): tank1[1] -= tank2[2] print(tank2[0], 'fires, dealing', tank2[2], 'damage.') state() time[2] = tank2[3] def state(): print('Time: {:0.2f}'.format(time[0]), 'seconds') print(tank1[0], 'has', tank1[1], 'HP remaining.') print(tank2[0], 'has', tank2[1], 'HP remaining.\n') def check_sim(): if tank1[1] <= 0 or tank2[1] <=0: return False else: return True def run_sim(): if tank1[4]: adr[0] = 1.2 else: adr[0] = 1 if tank2[4]: adr[1] = 1.2 else: adr[1] = 1 print('Begin Simulation:', tank1[0], 'vs', tank2[0]) tank1_fire() time[0] += .001 tank2_fire() while(time[0] < 1000): time[0] += .001 adr[2] -= 0.001 adr[3] -= 0.001 time[1], time[2] = time[1] - (.001 * adr[0]), time[2] - (.001 * adr[1]) if time[1] <= 0: i = time[1] tank1_fire() time[1] -= i #eliminate error from excess speed if not check_sim(): break; if time[2] <= 0: i = time[2] tank2_fire() time[2] -= i #eliminate error from excess speed if not check_sim(): break; print('Simulation Complete.') state() if time[1] <= .001 or time[2] <= .001: print('Result: Tie') print('Tanks destroyed each other within margin of error.') elif tank1[1] > tank2[1]: print('Result:', tank1[0], 'wins.') elif tank1[1] < tank2[1]: print('Result:', tank2[0], 'wins.') else: print('Error') run_sim()

[ "Kai" ]

Kai

80b74e908fcaf01359e389423255e9543b69d931

328db27bcee118ea3d841120de56c203fd6ab05f

/coc-stats-gen.py

da54a4aa79188130a3aff0abf818388ac616e776

[ "MIT" ]

permissive

mikeshoe/coc-stats-gen

ec80a0fcdef27d0f8ddc57fc69b626d21b529217

d1f9322a74b73ec0917fb1d9202b84f58c3cfeab

refs/heads/master

2021-01-11T04:16:24.058262

2016-10-21T18:42:25

71,191,071

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null

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py

#! /usr/local/bin/python ''' Created on Oct 17, 2016 @author: mike ''' import xlrd import time import datetime if __name__ == '__main__': MY_DIR = "/Users/mike/Documents/coc/" XL_FILE = "CerebralChaosStats.xlsx" XL_FILE_PATH = MY_DIR + XL_FILE XL_PLAYER_WS = "Player Metrics" XL_WAR_WS = "War Metrics" workbook = xlrd.open_workbook(XL_FILE_PATH) player_worksheet = workbook.sheet_by_name(XL_PLAYER_WS) war_worksheet = workbook.sheet_by_name(XL_WAR_WS) #print "Rank:", worksheet.cell(2, 0).value #print "Level:", worksheet.cell(2, 1).value #print "Player:", worksheet.cell(2, 2).value #print "Defensibility:", worksheet.cell(2, 3).value #print "Included:", worksheet.cell(2, 4).value #print "Attack Utilization:", worksheet.cell(2, 5).value #print "Star Differential:", worksheet.cell(2, 6).value #print "Star Differential Average:", worksheet.cell(2, 7).value #print "Total Destruction:", worksheet.cell(2, 8).value html_str = "" star_diff_color = "" star_diff_avg_color = "" for row_index in range (2,28): html_str = html_str + "<tr>\n" rank = player_worksheet.cell(row_index, 0).value html_str = html_str + " <td style=\"text-align: center;\">" + str(rank) + "</td>\n" level = player_worksheet.cell(row_index, 1).value html_str = html_str + " <td style=\"text-align: center;\">" + str(int(level)) + "</td>\n" player = player_worksheet.cell(row_index, 2).value html_str = html_str + " <td style=\"text-align: center;\">" + str(player) + "</td>\n" defense = player_worksheet.cell(row_index, 3).value html_str = html_str + " <td style=\"text-align: center;\">" + str(round(defense,1)) + "</td>\n" included = player_worksheet.cell(row_index, 4).value html_str = html_str + " <td style=\"text-align: center;\">" + str(int(included)) + "</td>\n" attack_util = player_worksheet.cell(row_index, 5).value html_str = html_str + " <td style=\"text-align: center;\">" + str(round(attack_util,2)*100) + "%</td>\n" star_diff = player_worksheet.cell(row_index, 6).value if star_diff < 0: star_diff_color = "style=\"text-align: center; color: red;\"" else: star_diff_color = "style=\"text-align: center;\"" html_str = html_str + " <td " + star_diff_color +" >" + str(int(star_diff)) + "</td>\n" star_diff_avg = player_worksheet.cell(row_index, 7).value html_str = html_str + " <td " + star_diff_color +" >" + str(round(star_diff_avg, 1)) + "</td>\n" tot_dest = player_worksheet.cell(row_index, 8).value html_str = html_str + " <td style=\"text-align: center;\">" + str(int(tot_dest)) + "</td>\n" html_str = html_str + "</tr>\n" html_str = html_str + "\n\n\n\n" #print html_str utc_ts = datetime.datetime.utcnow() utc_string = utc_ts.strftime('%Y-%m-%d-%H%M%SZ') # Open a file filename = MY_DIR + "coc-stats-" + utc_string + ".txt" print filename fo = open(filename, "wb") fo.write(html_str); # Close opend file fo.close()

[ "mike@shoesobjects.com" ]

mike@shoesobjects.com

848c3cc97d6fef8cf42b5ffbd94a9cfaf4a08159

4ac8fc7f26ddfeb71078f7fd4cc2a35c601d2d4a

/core/models.py

01e19f5a51ee71626b1d50faec929ff7c141e442

[]

no_license

gillie1022/question-box

7ea1d43f045e7abeb82ad83139d68efaa66fdc9f

12c737f3335a2ed090a1007b91c39c0c529a9b87

refs/heads/main

2022-12-02T05:55:59.598476

2020-06-25T18:14:32

286,825,766

0

null

UTF-8

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false

1,455

py

from django.db import models from users.models import User from django.db.models import Q from django.contrib.postgres.search import SearchVector class Question(models.Model): user = models.ForeignKey( to=User, on_delete=models.CASCADE, related_name="questions" ) title = models.CharField(max_length=255) body = models.CharField(max_length=500) asked_on = models.DateTimeField(auto_now_add=True, null=True, blank=True) starred_by = models.ManyToManyField(to=User, related_name="starred_questions") def __str__(self): return self.title class Answer(models.Model): author = models.ForeignKey( to=User, on_delete=models.CASCADE, null=True, related_name="answers" ) question = models.ForeignKey( to=Question, on_delete=models.CASCADE, related_name="answers" ) body = models.CharField(verbose_name="Response", max_length=500) answered_on = models.DateTimeField(auto_now_add=True, null=True, blank=True) marked_correct = models.BooleanField(default=False) def is_marked_correct(self, answer): return self.marked_correct.filter(pk=answer.pk).count() == 1 def __str__(self): return self.body def search_questions_for_user(user, search_term): questions = Question.objects.all() return questions \ .annotate(search=SearchVector('title', 'body', 'answers__body')) \ .filter(search=search_term) \ .distinct('pk')

[ "dave.gillie@icloud.com" ]

dave.gillie@icloud.com

5230f72826ff9866fd73f5f90d37d9be2cf58bc2

d4c82eb9ae3037cf8742c3fc8c31cf4a80f5d21c

/examples/Python/examples/Demo/scripts/update.py

5e44d3ab0f01709540f9165030f0f97f90ca127d

[]

no_license

gzhu-team-509/programming-knowledge-base

68132b1a669f208dab94dcf2401ce39d89ebe53b

3f3d026927157b7fdf210da195cb912366975e75

refs/heads/master

2021-05-05T12:17:12.686569

2017-11-04T07:30:28

104,754,220

0

null

UTF-8

Python

false

2,749

py

#! /usr/bin/python2.6 # Update a bunch of files according to a script. # The input file contains lines of the form <filename>:<lineno>:<text>, # meaning that the given line of the given file is to be replaced # by the given text. This is useful for performing global substitutions # on grep output: import os import sys import re pat = '^([^: \t\n]+):([1-9][0-9]*):' prog = re.compile(pat) class FileObj: def __init__(self, filename): self.filename = filename self.changed = 0 try: self.lines = open(filename, 'r').readlines() except IOError, msg: print '*** Can\'t open "%s":' % filename, msg self.lines = None return print 'diffing', self.filename def finish(self): if not self.changed: print 'no changes to', self.filename return try: os.rename(self.filename, self.filename + '~') fp = open(self.filename, 'w') except (os.error, IOError), msg: print '*** Can\'t rewrite "%s":' % self.filename, msg return print 'writing', self.filename for line in self.lines: fp.write(line) fp.close() self.changed = 0 def process(self, lineno, rest): if self.lines is None: print '(not processed): %s:%s:%s' % ( self.filename, lineno, rest), return i = eval(lineno) - 1 if not 0 <= i < len(self.lines): print '*** Line number out of range: %s:%s:%s' % ( self.filename, lineno, rest), return if self.lines[i] == rest: print '(no change): %s:%s:%s' % ( self.filename, lineno, rest), return if not self.changed: self.changed = 1 print '%sc%s' % (lineno, lineno) print '<', self.lines[i], print '---' self.lines[i] = rest print '>', self.lines[i], def main(): if sys.argv[1:]: try: fp = open(sys.argv[1], 'r') except IOError, msg: print 'Can\'t open "%s":' % sys.argv[1], msg sys.exit(1) else: fp = sys.stdin curfile = None while 1: line = fp.readline() if not line: if curfile: curfile.finish() break n = prog.match(line) if n < 0: print 'Funny line:', line, continue filename, lineno = prog.group(1, 2) if not curfile or filename <> curfile.filename: if curfile: curfile.finish() curfile = FileObj(filename) curfile.process(lineno, line[n:]) if __name__ == "__main__": main()

[ "lightyears1998@hotmail.com" ]

lightyears1998@hotmail.com

4b9dad88c993897ba4aadd6fd538b20926a43b97

a70325c82e2486d67624dea1e053924fb9a5af06

/PiBotXBeeExperiments/ZmqFastSubscriber.py

954f1fd788cf73bef4c27d4ac1c503ace09cd2fc

[ "MIT" ]

permissive

RocketRedNeck/PythonPlayground

88ce1b46c0134ea0d4fac6e5840e1325c44d6d92

a6f002625c9326de6995bc52960f25f78e9b2843

refs/heads/master

2023-05-26T09:42:22.960096

2023-05-15T00:22:47

63,455,232

1

null

UTF-8

Python

false

1,369

py

# Standard import time # 3rd Party import zmq # Local import DataModel context = zmq.Context() socket = context.socket(zmq.SUB) socket.connect('tcp://localhost:12345') # In the guide, the subscribers connect to well know addresses (in this we stay local) socket.setsockopt_string(zmq.SUBSCRIBE, '') # If you don't actually subscribe, you don't get anything! In this case any topic. socket.RCVTIMEO = 1000 # timeout in milliseconds so we can tell if there is anything happening while waiting for data socket.set_hwm(1) # High Water Marks help define the backlog we can handle, in this case if we are behind, just let things drop header = DataModel.Header() wait_count = 0 last_header_count = 0 while True: try: pyobj = socket.recv_pyobj() type_pyobj = type(pyobj) if isinstance(pyobj,DataModel.Header): print(f'@ {pyobj.time} = {pyobj.count}') if last_header_count != 0 and pyobj.count > last_header_count + 1: print(f'Lost {pyobj.count - last_header_count - 1} Messages !!!!!') last_header_count = pyobj.count else: print(f'Unknown Type Received: {type_pyobj}') wait_count = 0 except KeyboardInterrupt: break except zmq.error.Again: wait_count += 1 print(f'Waiting {wait_count}...')

[ "the.rocketredneck@gmail.com" ]

the.rocketredneck@gmail.com

c3977d5efccd68fcd03236ed68cf1a4f3e3d36e6

2fc9c51dbf57f72d7499359795c3f6ab5aada522

/graph/boj2251.py

f9cb04da48dfa586ca3b0c7855631f2c6015c72f

[]

no_license

chankoo/problem-solving

9e45bed20310e5847392c2e9e88c0b7d41b73de6

aae3b469b0644d39f774946798130566a310a20b

refs/heads/master

2020-04-27T11:51:11.214826

2019-10-12T10:26:22

174,311,189

2

0

null

UTF-8

Python

false

1,507

py

# https://www.acmicpc.net/problem/2251 # sol # 물통의 수가 3개 뿐이고 가능한 경우의 수가 많지 않으므로 dfs로 풀었다 # 가능한 물통들의 상태를 status 배열로 만들고 이 배열 자체로 방문체크를 하였다(멤버쉽 연산 이용) # 이때 mutable 타입은 멤버쉽 연산이 가능한 key로 hash될 수 없기에 visited에 넣을땐 tuple로 변환하였다 def pour(_from, _to, status): global V if status[_from] > 0: n_status = [*status] if V[_to] - status[_to] == 0: return status else: if V[_to] - status[_to] >= status[_from]: n_status[_to] = status[_to] + status[_from] n_status[_from] = 0 else: n_status[_to] = V[_to] n_status[_from] = n_status[_from] - (V[_to] - status[_to]) return n_status return status def dfs(status): global visited, output if status[0] == 0: output.append(status[2]) for _from,_to in [(0,1),(0,2),(1,2),(1,0),(2,0),(2,1)]: n_status = tuple(pour(_from, _to, status)) if n_status not in visited: visited.add(n_status) dfs(n_status) if __name__ == "__main__": V = tuple(map(int, input().split())) visited = set() output = [] status = [0,0,V[2]] visited.add(tuple(status)) dfs(status) for vol in sorted(output)[:-1]: print(vol, end=' ') print(sorted(output)[-1])

[ "ckbaek1125@gmail.com" ]

ckbaek1125@gmail.com

67a462dc96b70a7f72c50eb90bed09125f1e8d8f

274bc4ef4ef5f18d296c94a562b491d2f85f7905

/day58.py

3dbfd1cff6ec5099f15546728fcda7efabb4778c

[]

no_license

BanoutAlketbi/100DaysofCode

e7a20f4981af34be6b0690db75ec418e7ae3aedc

5aaf9f52e5f306030e623a8f3b5e60798d79f60c

refs/heads/master

2020-07-10T05:19:43.602770

2019-11-23T19:35:50

204,176,402

0

null

UTF-8

Python

false

359

py

import re str = "The weather is rainy today" x = re.findall("in", str) print(x) y = re.findall("windy", str) print(y) if (y): print("Yes, there is a match") else: print("no match") x = re.search("\s", str) print("the first white-space character is located in postion:", x.start()) y = re.findall("windy", str) print(y) x = re.split("\s", str) print(x)

[ "banoot@Banoots-MBP-5.home" ]

banoot@Banoots-MBP-5.home

4c6f9178b232902d6851301a1aefc4b240271b0b

b5c3c06245c91b732b6a1d58220c5946bd4a86a7

/test/char_ngrams_test.py

93ef739f96796eb2524525584797e381ef825e59

[ "MIT" ]

permissive

ritlinguine/linguine-python

aaff5198e7d0820fa513c2bd70bd240eb6d77d5b

5dee96dea64523d673ae1efc88d50e9309f4dd5e

refs/heads/master

2021-08-29T08:49:18.041601

2021-08-16T01:55:05

90,911,583

1

null

2017-05-10T21:53:50

null

UTF-8

Python

false

2,054

py

import unittest from linguine.corpus import Corpus from linguine.ops.char_ngrams import CharNgrams class CharNgramsOpTest(unittest.TestCase): def setUp(self): self.op = CharNgrams() def test_run(self): self.test_data = [Corpus("0", "Test", "The quick brown fox jumped over the lazy dog.\n")] results = self.op.run(self.test_data) desired_results = [{"corpus_id": "0", "unigrams": {"_": 8, "a": 1, "b": 1, "c": 1, "d": 2, "e": 4, "f": 1, "g": 1, "h": 2, "i": 1, "j": 1, "k": 1, "l": 1, "m": 1, "n": 1, "o": 4, "p": 1, "q": 1, "r": 2, "t": 2, "u": 2, "v": 1, "w": 1, "x": 1, "y": 1, "z": 1}, "bigrams": {"_b": 1, "_d": 1, "_f": 1, "_j": 1, "_l": 1, "_o": 1, "_q": 1, "_t": 1, "az": 1, "br": 1, "ck": 1, "d_": 1, "do": 1, "e_": 2, "ed": 1, "er": 1, "fo": 1, "he": 2, "ic": 1, "ju": 1, "k_": 1, "la": 1, "mp": 1, "n_": 1, "og": 1, "ov": 1, "ow": 1, "ox": 1, "pe": 1, "qu": 1, "r_": 1, "ro": 1, "th": 2, "ui": 1, "um": 1, "ve": 1, "wn": 1, "x_": 1, "y_": 1, "zy": 1}, "trigrams": {"_br": 1, "_do": 1, "_fo": 1, "_ju": 1, "_la": 1, "_ov": 1, "_qu": 1, "_th": 1, "azy": 1, "bro": 1, "ck_": 1, "d_o": 1, "dog": 1, "e_l": 1, "e_q": 1, "ed_": 1, "er_": 1, "fox": 1, "he_": 2, "ick": 1, "jum": 1, "k_b": 1, "laz": 1, "mpe": 1, "n_f": 1, "ove": 1, "own": 1, "ox_": 1, "ped": 1, "qui": 1, "r_t": 1, "row": 1, "the": 2, "uic": 1, "ump": 1, "ver": 1, "wn_": 1, "x_j": 1, "y_d": 1, "zy_": 1}}] self.assertEqual(results, desired_results) if __name__ == '__main__': unittest.main()

[ "aphedges1@gmail.com" ]

aphedges1@gmail.com

1daf7407cdf724ed73ebdce48a67a949af8b1698

c969779819894ad624a53273115a1a482181cb67

/src/models/api/plan_entry.py

68be97173886460c815f14471d7fe2ea11b5f130

[]

no_license

nareshbandi123/sqldm104

c328edb38d42c6f3ed72e056f1f91ac3c5b0edeb

3705fdc43f177ebf5d7a0db687d1cd1d5d45985f

refs/heads/master

2022-01-18T00:16:10.451744

2019-06-19T13:06:39

192,322,400

0

null

UTF-8

Python

false

476

py

from src.models import api # Fields required for creating an entity have a value of True # Fields that are optional or present only on a newly created # entity are False. PlanEntry = api.create_model_class( "PlanEntry", { "case_ids": False, "suite_id": False, "runs": False, "description": False, "name": False, "include_all": False, "config_ids": False, "id": False, "assignedto_id": False } )

[ "E004415@cigniti.com" ]

E004415@cigniti.com

bbfea187f1197f779a6f46a13d3160c00942ada2

ac45b55915e634815922329195c203b1e810458c

/minionOC169_6.py

659b01807dece629aec12ea54c7963aecb6ffaca

[]

no_license

mj1e16lsst/iridisPeriodicNew

96a8bfef0d09f13e18adb81b89e25ae885e30bd9

dc0214b1e702b454e0cca67d4208b2113e1fbcea

refs/heads/master

2020-03-23T15:01:23.583944

2018-07-23T18:58:59

141,715,292

0

null

UTF-8

Python

false

12,177

py

from operator import add #from astropy import units as u #from astropy.coordinates import SkyCoord #from astropy.stats import LombScargle #from gatspy.periodic import LombScargleFast from functools import partial #from gatspy import periodic #import matplotlib.pyplot as plt #from matplotlib.font_manager import FontProperties import lomb_scargle_multiband as periodic from multiprocessing import Pool import numpy as np import os #from sqlite3 import * import random from random import shuffle from random import randint import Observations import Magnitudes # In[13]: #conn = connect('minion_1016_sqlite.db') #conn = connect('astro_lsst_01_1004_sqlite.db') #conn = connect('minion_1020_sqlite.db') # In[14]: # LSST zero points u,g,r,i,z,y zeroPoints = [0,26.5,28.3,28.13,27.79,27.4,26.58] FWHMeff = [0.8,0.92,0.87,0.83,0.80,0.78,0.76] # arcmins? pixelScale = 0.2 readOut = 12.7 sigSys = 0.005 flareperiod = 4096 flarecycles = 10 dayinsec=86400 background = 40 # sat mag u,g,r,i,z,y=14.7,15.7,15.8,15.8,15.3 and 13.9 # start date 59580.033829 end date + 10 years #maglist=[20]*7 lim = [0, 23.5, 24.8, 24.4, 23.9, 23.3, 22.1] # limiting magnitude ugry sat = [0, 14.7, 15.7, 15.8, 15.8, 15.3, 13.9] # sat mag as above # In[15]: looooops = 10000 maglength = 20 freqlength = 20 processors = 20 startnumber = 0 + 6 endnumber = startnumber + 1 #observingStrategy = 'minion' observingStrategy = 'astroD' #observingStrategy = 'panstars' inFile = '/home/mj1e16/periodic/in'+str(startnumber)+'.txt' outFile = '/home/mj1e16/periodic/outminionOC169'+str(startnumber)+'.txt' #inFile = '/home/ubuntu/vagrant/'+observingStrategy+'/in'+observingStrategy+'KtypefullresultsFile'+str(startnumber)+'.txt' #outFile = '/home/ubuntu/vagrant/'+observingStrategy+'/out'+observingStrategy+'KtypefullresultsFile'+str(startnumber)+'.txt' obs = Observations.obsminionOC169 for y in range(len(obs)): for x in range(len(obs[y])): obs[y][x] = obs[y][x] + ((random.random()*2.)-1.) # In[19]: def magUncertainy(Filter, objectmag, exposuretime,background, FWHM): # b is background counts per pixel countsPS = 10**((Filter-objectmag)/2.5) counts = countsPS * exposuretime uncertainty = 1/(counts/((counts/2.3)+(((background/2.3)+(12.7**2))*2.266*((FWHM/0.2)**2)))**0.5) # gain assumed to be 1 return uncertainty #from lsst should have got the website! https://smtn-002.lsst.io/ # In[20]: def averageFlux(observations, Frequency, exptime): b = [0]*len(observations) for seconds in range(0, exptime): a = [np.sin((2*np.pi*(Frequency))*(x+(seconds/(3600*24)))) for x in observations] # optical modulation b = map(add, a, b) c = [z/exptime for z in b] return c def Flux(observations,Frequency,exptime): a = [np.sin((2*np.pi*(Frequency)*x)) for x in observations] return a # In[21]: def ellipsoidalFlux(observations, Frequency,exptime): period = 1/(Frequency) phase = [(x % (2*period)) for x in observations] b = [0]*len(observations) for seconds in range(0, exptime): a = [np.sin((2*np.pi*(Frequency))*(x+(seconds/(3600*24)))) for x in observations] # optical modulation b = map(add, a, b) c = [z/exptime for z in b] for x in range(0,len(phase)): if (phase[x]+(1.5*period)) < (3*period): c[x] = c[x]*(1./3.) else: c[x] = c[x]*(2./3.) return c ## this is doing something but not the right something, come back to it # In[22]: def flaring(B, length, dayinsec=86400,amplitude=1): global flareMag, minutes fouriers = np.linspace(0.00001,0.05,(dayinsec/30)) logF = [np.log(x) for x in fouriers] # start at 30 go to a day in 30 sec increments real = [random.gauss(0,1)*((1/x)**(B/2)) for x in fouriers] #random.gauss(mu,sigma) to change for values from zurita # imaginary = [random.gauss(0,1)*((1/x)**(B/2)) for x in fouriers] IFT = np.fft.ifft(real) seconds = np.linspace(0,dayinsec, (dayinsec/30)) # the day in 30 sec increments minutes = [x for x in seconds] minimum = (np.max(-IFT)) positive = [x + minimum for x in IFT] # what did this even achieve? it helped with normalisation! normalised = [x/(np.mean(positive)) for x in positive] # find normalisation normalisedmin = minimum/(np.mean(positive)) normalised = [x - normalisedmin for x in normalised] flareMag = [amplitude * x for x in normalised] # normalise to amplitude logmins = [np.log(d) for d in minutes] # for plotting? # plt.plot(minutes,flareMag) # plt.title('lightcurve') # plt.show() return flareMag # In[55]: def lombScargle(frequencyRange,objectmag=20,loopNo=looooops,df=0.001,fmin=0.001,numsteps=100000,modulationAmplitude=0.1,Nquist=200): # frequency range and object mag in list #global totperiod, totmperiod, totpower, date, amplitude, frequency, periods, LSperiod, power, mag, error, SigLevel results = {} totperiod = [] totmperiod = [] totpower = [] # reset SigLevel = [] filterletter = ['o','u','g','r','i','z','y'] period = 1/(frequencyRange) if period > 0.5: numsteps = 10000 elif period > 0.01: numsteps = 100000 else: numsteps = 200000 freqs = fmin + df * np.arange(numsteps) # for manuel allobsy, uobsy, gobsy, robsy, iobsy, zobsy, yobsy = [], [], [], [], [], [], [] #reset measuredpower = [] # reset y = [allobsy, uobsy, gobsy, robsy, iobsy, zobsy, yobsy] # for looping only for z in range(1, len(y)): #y[z] = averageFlux(obs[z], frequencyRange[frange], 30) # amplitde calculation for observations, anf frequency range y[z] = ellipsoidalFlux(obs[z], frequencyRange,30) y[z] = [modulationAmplitude * t for t in y[z]] # scaling for G in range(0, len(y[z])): flareMinute = int(round((obs[z][G]*24*60*2)%((dayinsec/(30*2))*flarecycles))) y[z][G] = y[z][G] + longflare[flareMinute] # add flares swapped to second but not changing the name intrtoduces fewer bugs date = [] amplitude = [] mag = [] error = [] filts = [] for z in range(1, len(y)): if objectmag[z] > sat[z] and objectmag[z] < lim[z]: #date.extend([x for x in obs[z]]) date.extend(obs[z]) amplitude = [t + random.gauss(0,magUncertainy(zeroPoints[z],objectmag[z],30,background,FWHMeff[z])) for t in y[z]] # scale amplitude and add poisson noise mag.extend([objectmag[z] - t for t in amplitude]) # add actual mag error.extend([sigSys + magUncertainy(zeroPoints[z],objectmag[z],30,background,FWHMeff[z])+0.2]*len(amplitude)) filts.extend([filterletter[z]]*len(amplitude)) phase = [(day % (period*2))/(period*2) for day in obs[z]] pmag = [objectmag[z] - t for t in amplitude] # plt.plot(phase, pmag, 'o', markersize=4) # plt.xlabel('Phase') # plt.ylabel('Magnitude') # plt.gca().invert_yaxis() # plt.title('filter'+str(z)+', Period = '+str(period))#+', MeasuredPeriod = '+str(LSperiod)+', Periodx20 = '+(str(period*20))) # plt.show() # plt.plot(date, mag, 'o') # plt.xlim(lower,higher) # plt.xlabel('time (days)') # plt.ylabel('mag') # plt.gca().invert_yaxis() # plt.show() model = periodic.LombScargleMultibandFast(fit_period=False) model.fit(date, mag, error, filts) power = model.score_frequency_grid(fmin, df, numsteps) if period > 10.: model.optimizer.period_range=(10, 110) elif period > 0.51: model.optimizer.period_range=(0.5, 10) elif period > 0.011: model.optimizer.period_range=(0.01, 0.52) else: model.optimizer.period_range=(0.0029, 0.012) LSperiod = model.best_period if period < 10: higher = 10 else: higher = 100 # fig, ax = plt.subplots() # ax.plot(1./freqs, power) # ax.set(xlim=(0, higher), ylim=(0, 1.2), # xlabel='period (days)', # ylabel='Lomb-Scargle Power', # title='Period = '+str(period)+', MeasuredPeriod = '+str(LSperiod)+', Periodx20 = '+(str(period*20))); # plt.show() phase = [(day % (period*2))/(period*2) for day in date] #idealphase = [(day % (period*2))/(period*2) for day in dayZ] #print(len(phase),len(idealphase)) #plt.plot(idealphase,Zmag,'ko',) # plt.plot(phase, mag, 'o', markersize=4) # plt.xlabel('Phase') # plt.ylabel('Magnitude') # plt.gca().invert_yaxis() # plt.title('Period = '+str(period)+', MeasuredPeriod = '+str(LSperiod)+', Periodx20 = '+(str(period*20))) # plt.show() #print(period, LSperiod, period*20) # print('actualperiod', period, 'measured period', np.mean(LSperiod),power.max())# 'power',np.mean(power[maxpos])) # print(frequencyRange[frange], 'z', z) # totperiod.append(period) # totmperiod.append(np.mean(LSperiod)) # totpower.append(power.max()) mpower = power.max() measuredpower.append(power.max()) # should this correspond to period power and not max power? maxpower = [] counter = 0. for loop in range(0,loopNo): random.shuffle(date) model = periodic.LombScargleMultibandFast(fit_period=False) model.fit(date, mag, error, filts) power = model.score_frequency_grid(fmin, df, numsteps) maxpower.append(power.max()) for X in range(0, len(maxpower)): if maxpower[X] > measuredpower[-1]: counter = counter + 1. Significance = (1.-(counter/len(maxpower))) #print('sig', Significance, 'counter', counter) SigLevel.append(Significance) #freqnumber = FrangeLoop.index(frequencyRange) #magnumber = MagRange.index(objectmag) #print(fullmaglist) #listnumber = (magnumber*maglength)+freqnumber # print(listnumber) # measuredperiodlist[listnumber] = LSperiod # periodlist[listnumber] = period # powerlist[listnumber] = mpower # siglist[listnumber] = Significance # fullmaglist[listnumber] = objectmag # results order, 0=mag,1=period,2=measuredperiod,3=siglevel,4=power,5=listnumber results[0] = objectmag[3] results[1] = period results[2] = LSperiod results[3] = Significance results[4] = mpower results[5] = 0#listnumber return results # In[24]: #findObservations([(630,)]) #remove25(obs) #averageFlux(obs[0], 1, 30) longflare = [] for floop in range(0,flarecycles): flareone = flaring(-1, flareperiod, amplitude=0.3) flareone = flareone[0:1440] positiveflare = [abs(x) for x in flareone] longflare.extend(positiveflare) # In[25]: PrangeLoop = np.logspace(-2.5,2,freqlength) FrangeLoop = [(1/x) for x in PrangeLoop] # In[26]: # reset results file with open(inFile,'w') as f: f.write('fullmaglist \n\n periodlist \n\n measuredperiodlist \n\n siglist \n\n powerlist \n\n listnumberlist \n\n end of file') # In[57]: results = [] fullmeasuredPeriod = [] fullPeriod = [] fullPower = [] fullSigLevel = [] fullMag = [] MagRangearray = np.linspace(17,24,maglength) MagRange = [x for x in MagRangearray] maglist = [] for x in range(len(MagRange)): maglist.append([MagRange[x]]*7) newlist = Magnitudes.mag169 pool = Pool(processors) for h in range(startnumber,endnumber): print(newlist[h]) results.append(pool.map(partial(lombScargle, objectmag=newlist[h]),FrangeLoop)) twoDlist = [[],[],[],[],[],[]] for X in range(len(results)): for Y in range(len(results[X])): twoDlist[0].append(results[X][Y][0]) twoDlist[1].append(results[X][Y][1]) twoDlist[2].append(results[X][Y][2]) twoDlist[3].append(results[X][Y][3]) twoDlist[4].append(results[X][Y][4]) twoDlist[5].append(results[X][Y][5]) with open(inFile, 'r') as istr: with open(outFile,'w') as ostr: for i, line in enumerate(istr): # Get rid of the trailing newline (if any). line = line.rstrip('\n') if i % 2 != 0: line += str(twoDlist[int((i-1)/2)])+',' ostr.write(line+'\n')

[ "mj1e16@soton.ac.uk" ]

mj1e16@soton.ac.uk

75e97332a66f40ccf2aeb33930b33748a7aab86e

23ce866f30ac93e1e47f31a50f94caf6b4cefd5b

/roles_exporter.py

0fedb68bc8e0199c6c75d64fd4368ef12f03ef8a

[]

no_license

nsv777/ranger_automate

ca148ad566541c18144d3411d382e5fda288c209

cd25888fbf5b146b88eb6f82b28ae1aab8382dde

refs/heads/main

2023-03-18T19:21:31.839480

2021-01-14T19:34:23

null

0

null

UTF-8

Python

false

2,504

py

# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # This script will import Ranger roles from one cluster to another # # It requires python 2.7 for now, # # Created by Ajinkya Patil # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # import json from requests import get, post import requests import time from getpass import getpass from requests.packages.urllib3.exceptions import InsecureRequestWarning requests.packages.urllib3.disable_warnings(InsecureRequestWarning) IMPORT_RANGER_URL = raw_input("SOURCE_RANGER URL:- ") ROLES_API = "/service/roles/roles/" IMPORT_RANGER_ADMIN_USER = raw_input("SOURCE_RANGER ADMIN USER:- ") IMPORT_RANGER_ADMIN_PASSWORD = getpass(prompt='SOURCE_RANGER ADMIN PASSWORD:- ', stream=None) headers = {'Accept' : 'application/json'} # Importing roles with all the configured users and groups response = get(IMPORT_RANGER_URL + ROLES_API, headers=headers, verify=False, auth=(IMPORT_RANGER_ADMIN_USER, IMPORT_RANGER_ADMIN_PASSWORD)) roles_convert = json.loads(response.content) ROLES = roles_convert['roles'] TOTAL_ROLES = len(ROLES) print "Total number of roles " + str(TOTAL_ROLES) + " will be exported." EXPORT_RANGER_URL = raw_input("DEST_RANGER URL:- ") EXPORT_RANGER_ADMIN_USER = raw_input("DEST_RANGER ADMIN USER:- ") EXPORT_RANGER_ADMIN_PASSWORD = getpass(prompt='DEST_RANGER ADMIN PASSWORD:- ', stream=None) headers = {'Accept' : 'application/json'} # Exporting roles with all the configured users and groups FAILED_ROLES = [] IMPORTED_ROLES = [] for ROLE in ROLES : time.sleep(5) del ROLE['id'] response = post(EXPORT_RANGER_URL + ROLES_API, headers=headers, json=ROLE, verify=False, auth=(EXPORT_RANGER_ADMIN_USER, EXPORT_RANGER_ADMIN_PASSWORD)) STATUS = response.status_code ROLENAME = ROLE['name'] if STATUS is 200: print "Successfully Imported Role" + ROLENAME + " with status " + str(STATUS) SUCCESS_ROLES.append(ROLENAME) IMPORTED_ROLES.append(ROLENAME) else: print "Import for role " + ROLENAME + " failed with status " + str(STATUS) FAILED_ROLES.append(ROLENAME) print "\n" + str(len(FAILED_ROLES)) + " Roles have failed to export" + \ "\n" + str(len(IMPORTED_ROLES)) + " Roles have been imported successfully" print "\nCould not import Following roles:- " for FAILED_ROLE in FAILED_ROLES: print FAILED_ROLE

[ "noreply@github.com" ]

nsv777.noreply@github.com

d4df30377b84c2a7d24afe5d182fc910ec733e56

4677a6e500c9dba3172492f58b0efac715b7f28e

/ironman_py_scripts15.py

54b84ff8a69c59b5c5de0593e091022f5005ce14

[]

no_license

ironmanscripts/py_scripts

e54e8b0f809ba27d490e5c041423263fdedcb41a

cf6d9fc43545c9d6d7748297d06178850411d5b4

refs/heads/master

2021-07-01T16:27:18.499229

2017-09-18T03:04:36

103,217,898

0

null

UTF-8

Python

false

182

py

#!/bin/bash/python #python escape sequence print "BackSlash \\\"" print "SingleQuote \'" print 'DoubleQuote \"' print "\a" print "\a" print "\\\b" print "\f" print "\n new line"

[ "jnaguboina@gmail.com" ]

jnaguboina@gmail.com

c7a08290ba0875283258f8e558b06287f0e4869e

2ecbe098ce01b889c1373523a5f07e117370206a

/backend/home/management/commands/load_initial_data.py

332d9ce61f3ee997f28e5036a456e35c25a431b7

[]

no_license

crowdbotics-apps/jooo-4480

25ac69547a3d40bafccfcb1fdc6c4c7603671273

d51a7e6764e7f582da65954ce4297cab547e8f35

refs/heads/master

2022-12-13T18:01:32.627608

2019-06-10T11:43:37

191,157,420

0

null

2022-12-09T05:45:38

2019-06-10T11:43:18

Python

UTF-8

Python

false

711

py

from django.core.management import BaseCommand from home.models import CustomText, HomePage def load_initial_data(): homepage_body = """ <h1 class="display-4 text-center">jooo</h1> <p class="lead"> This is the sample application created and deployed from the crowdbotics slack app. You can view list of packages selected for this application below </p>""" customtext_title = 'jooo' CustomText.objects.create(title=customtext_title) HomePage.objects.create(body=homepage_body) class Command(BaseCommand): can_import_settings = True help = 'Load initial data to db' def handle(self, *args, **options): load_initial_data()

[ "team@crowdbotics.com" ]

team@crowdbotics.com

75db8b2e2ae8a679cfb3ef10c07e051f0c6c30c9

3440109c1b29c780a0a6092dace02f91b358382b

/list.py

2900fdb6b7539712a3bfe34f3d0aba203ee7f5ad

[]

no_license

victor-py/victor

bacb3baf3178fc5f51cbe4cbb538e10970c39627

a9d09624e1845b5ab64d99e24d20f4b7a643725d

refs/heads/master

2022-04-25T04:29:47.610032

2020-04-19T10:10:56

256,967,691

0

null

UTF-8

Python

false

124

py

lists = [] print(lists) lists.append(10) lists.append(20) print(lists) tuples = tuple("hello. world!") print(tuples)

[ "noreply@github.com" ]

victor-py.noreply@github.com

450c22b5982c4514c0b654e41f040d0381883252

3bbc83ccd311b690c2fff6b1a1dbd38f4570ee4f

/setup.py

2f7884315b4044deae8123710eb2eddb34e38b14

[]

no_license

hsharma35/bitfusion

25efbb4d52fa216429daaf2a8044c7e9f65ed61a

04ec1c87c91ef4a681858f84f5a7832d5b65e83b

refs/heads/master

2021-07-09T00:43:09.413463

2020-08-06T02:49:34

153,977,735

74

18

null

UTF-8

Python

false

99

py

from distutils.core import setup setup(name='bitfusion', version='1.0.0', packages=['bitfusion'])

[ "hsharma@gatech.edu" ]

hsharma@gatech.edu

d63b2bd9d11950fc74f7c66616a0174c6b2b8c7b

979358a415b173657552890750c867fb75b69cb0

/papeletas.py

f026147bb80547d9c23ded9eafeafda29182527e

[]

no_license

artus-lla/papeletas

1041ab6a9ad855c1fa196d5561bfecbf98f57d92

c69426d10e527e06127a6b6fe777c413aae1d9d3

refs/heads/master

2020-06-06T04:15:48.070179

2013-08-12T03:40:39

null

0

null

UTF-8

Python

false

10,718

py

#! /usr/bin/env python3 # -*- coding: utf-8 -*- from gi.repository import Gtk import sqlite3 import src.misutils as misutils import src.bd as bd #global autorizado_para #global motivo #autorizado_para = 'Salir' class VentanaPrincipal: """ Renderiza la ventana principal de la aplicación """ def __init__ (self): """ Inicialización de la interface """ b = Gtk.Builder() b.add_from_file("./ui/main.ui") # Obtener ventana self.window1 = b.get_object("window1") self.notebook = b.get_object("notebook") #self.window1.set_activate_default(True) self.aboutdialog1 = b.get_object("aboutdialog1") # Obtener botones de barra de tareas self.btn_papeleta = b.get_object("btn_papeleta") self.btn_reporte = b.get_object("btn_reporte") self.btn_personal = b.get_object("btn_personal") self.tbtn_salir = b.get_object("tbtn_salir") # Obtener objetos ingreso personal self.ent_dni = b.get_object("ent_dni") self.ent_ape_nom = b.get_object("ent_ape_nom") self.ent_profesion = b.get_object("ent_profesion") self.ent_oficina = b.get_object("ent_oficina") self.btn_guardar_personal = b.get_object("btn_guardar_personal") self.vista_personal = b.get_object("vista_personal") # Obtener objetos ingreso papeleta self.ent_ing_num_papel = b.get_object("ent_ing_num_papel") self.ent_ing_fecha = b.get_object("ent_ing_fecha") self.ent_ing_nombres = b.get_object("ent_ing_nombres") self.radio_contituar = b.get_object("radio_contituar") self.radio_ingresar = b.get_object("radio_ingresar") self.radio_salir = b.get_object("radio_salir") self.ent_ing_hsalida = b.get_object("ent_ing_hsalida") self.ent_ing_hretorno = b.get_object("ent_ing_hretorno") self.radio2_particulares = b.get_object("radio2_particulares") self.radio2_enfermedad = b.get_object("radio2_enfermedad") self.radio2_personales = b.get_object("radio2_personales") self.radio2_comision = b.get_object("radio2_comision") self.text_fundamento = b.get_object("text_fundamento") self.btn_ing_guardar = b.get_object("btn_ing_guardar") self.btn_ing_nuevo = b.get_object("btn_ing_nuevo") self.acerca_de = b.get_object("acerca_de") # Reportes self.combo_anio = b.get_object("combo_anio") self.combo_mes = b.get_object("combo_mes") self.btn_report = b.get_object("btn_report") # Llenar combos misutils.llenar_combo_anio(self.combo_anio) misutils.llenar_combo_mes(self.combo_mes) # Auto conectar con las señales b.connect_signals(self) self.window1.show() self.notebook.hide() # Destruir ventana self.window1.connect('destroy', lambda w: Gtk.main_quit()) def on_btn_papeleta_toggled(self, widget, data=None): if widget.get_active(): self.notebook.set_current_page(0) #self.btn_papeleta.set_active(True) self.notebook.show() self.btn_reporte.set_active(False) self.btn_personal.set_active(False) def on_btn_reporte_toggled(self, widget, data=None): if widget.get_active(): self.notebook.set_current_page(1) #self.btn_reporte.set_active(True) self.notebook.show() self.btn_papeleta.set_active(False) self.btn_personal.set_active(False) def on_btn_personal_toggled(self, widget, data=None): if widget.get_active(): self.notebook.set_current_page(2) self.notebook.show() self.btn_papeleta.set_active(False) self.btn_reporte.set_active(False) self.ent_dni.grab_focus() def on_tbtn_salir_clicked(self, widget, data=None): print("Good bye") Gtk.main_quit() # ========= Ingresar papeleta =================== def on_acerca_de_clicked(self, widget, data=None): self.aboutdialog1.run() self.aboutdialog1.hide() def on_ent_ing_num_papel_insert_text(self, widget, Text, position, data=None): misutils.solo_numeros(self.ent_ing_num_papel, Text) def on_ent_ing_fecha_insert_text(self, widget, Text, position, data=None): misutils.fecha(self.ent_ing_fecha, Text) def on_ent_ing_hsalida_insert_text(self, widget, Text, position, data=None): misutils.hora(self.ent_ing_hsalida, Text) def on_ent_ing_hretorno_insert_text(self, widget, Text, position, data=None): misutils.hora(self.ent_ing_hretorno, Text) def on_btn_ing_guardar_clicked(self, widget, data=None): """Grabar datos de papeleta""" autorizado_para = "" motivo = "" # * Obtener datos num_papeleta = self.ent_ing_num_papel.get_text() fecha = self.ent_ing_fecha.get_text() nombres = self.ent_ing_nombres.get_text() # ** Determinar el valor de la variable autorizado para if self.radio_contituar.get_active() is True: autorizado_para = "Continuar" elif self.radio_ingresar.get_active() is True: autorizado_para = "Ingresar" else: autorizado_para = "Salir" #print(autorizado_para) hora_salida = self.ent_ing_hsalida.get_text() hora_retorno = self.ent_ing_hretorno.get_text() # ** Determinar el valor de la variable motivo if self.radio2_enfermedad.get_active() is True: motivo = "Enfermedad" elif self.radio2_personales.get_active() is True: motivo = "Personales" elif self.radio2_comision.get_active() is True: motivo = "Comisión de servicios" else: motivo = "Particulares" #print(motivo) fundamento = self.text_fundamento.get_text() campos = ( num_papeleta, fecha, nombres, autorizado_para, hora_salida, hora_retorno, motivo, fundamento ) try: fecha = misutils.arreglar_fecha(fecha) bd_papeletas = sqlite3.connect("./data/papeletas.db") cursor = bd_papeletas.cursor() cursor.execute(bd.insertar_papeleta, campos) bd_papeletas.commit() bd_papeletas.close() # Limpiar campos self.ent_ing_num_papel.set_text("") self.ent_ing_fecha.set_text("") self.ent_ing_nombres.set_text("") self.ent_ing_hsalida.set_text("") self.ent_ing_hretorno.set_text("") self.text_fundamento.set_text("") self.ent_ing_num_papel.grab_focus() except sqlite3.IntegrityError: misutils.mensaje_dialogo("Debe llenar todos los campos") except IndexError: misutils.mensaje_dialogo("La fecha proporsionada no es correcta") self.ent_ing_fecha.grab_focus() def on_btn_report_clicked(self, widget, data=None): """Generación del reporte""" anio = misutils.valor_combobox(self.combo_anio) mes = misutils.valor_combobox(self.combo_mes) if anio == None: misutils.mensaje_dialogo("Debe seleccionar un año") #try: bd_papeletas = sqlite3.connect("./data/papeletas.db") cursor = bd_papeletas.cursor() cursor.execute(bd.reporte) reporte = cursor.fetchall() print(reporte) bd_papeletas.commit() bd_papeletas.close() #except: # ========== Ingreso de Personal =================== def on_ent_dni_insert_text(self, widget, Text, position, data=None): """Permitir sólo números dni personal""" misutils.solo_numeros(self.ent_dni, Text) def on_btn_guardar_personal_clicked(self, widget, data=None): """Graba datos de personal en la tabla personal""" # Obtener datos dni = self.ent_dni.get_text() nombre = self.ent_ape_nom.get_text() profesion = self.ent_profesion.get_text() oficina = self.ent_oficina.get_text() # Tupla con los campos campos = (dni, nombre, profesion, oficina) falta_campo = False # si falta algún campo for campo in campos: if campo is None or campo == "": # comprobar que ningún campo esté vacío falta_campo = True if falta_campo is True: misutils.mensaje_dialogo("Debe llenar todos los campos") else: try: bd_papeletas = sqlite3.connect("./data/papeletas.db") cursor = bd_papeletas.cursor() cursor.execute(bd.insertar_datos, campos) cursor.execute(bd.select_personal) personal = cursor.fetchall() bd_papeletas.commit() bd_papeletas.close() # Limpar campos self.ent_dni.set_text("") self.ent_ape_nom.set_text("") self.ent_profesion.set_text("") self.ent_oficina.set_text("") self.ent_dni.grab_focus() # Poblar tree view vista_personal lista = Gtk.ListStore(str, str, str, str) for tupla in personal: lista.append([tupla[0], tupla[1], tupla[2], tupla[3]]) #print(tupla) #lista.append(["Negro", 12]) render = Gtk.CellRendererText() columna1 = Gtk.TreeViewColumn("DNI", render, text=0) columna2 = Gtk.TreeViewColumn("Apellidos y Nombres",render,text=1) columna3 = Gtk.TreeViewColumn("Profesión",render,text=2) columna4 = Gtk.TreeViewColumn("Oficina o servicio",render,text=3) self.vista_personal.set_model(lista) self.vista_personal.append_column(columna1) self.vista_personal.append_column(columna2) self.vista_personal.append_column(columna3) self.vista_personal.append_column(columna4) self.vista_personal.show() except sqlite3.IntegrityError: misutils.mensaje_dialogo("El campo DNI debe tener 8 dígitos o el DNI ya existe") self.ent_dni.grab_focus() if __name__ == "__main__": gui = VentanaPrincipal() Gtk.main()

[ "artus.lla@autistici.org" ]

artus.lla@autistici.org

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stacy867/instagram

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refs/heads/master

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#!/home/wecode/insta_app/virtual/bin/python # -*- coding: utf-8 -*- import re import sys from wheel.cli import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "stacymurenzi@gmail.com" ]

stacymurenzi@gmail.com

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/hw2_template_dev.py

d61d06bdfb5262ce9eac9dddf00239fe0852079c

[]

no_license

roseteague/Computational_Methods_HW2

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e6bf765597a8e2342a29b1242bd8586f5b9117c9

refs/heads/master

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""" Rosemary Teague 00828351 Assumes cost.f90 has been compiled with f2py to generate the module, hw2mod.so (filename may also be of form hw2mod.xxx.so where xxx is system-dependent text) using """ import numpy as np import matplotlib.pyplot as plt from matplotlib import colors, ticker, cm import scipy.optimize from hw2mod import cost from hw2mod import hw2 from time import time from time import clock import timeit def visualize(Nx,Ny,xrange=[-10,10],yrange=[-10,10], Noise=1.0): """ =============================================================== Visualization of a 2D cost function, j, of the form: j = j + (1 - x)^2 + 20(y - x^2)^2 =============================================================== Parameters ------------ Nx : Integer Number of points along the x-direction to be plotted Ny : Integer Number of points along the y-direction to be plotted xrange : list, optional Range of x-points to be considered. Default from -10<x<10 yrange : list, optional Range of y-points to be considered. Default from -10<y<10 Noise : float Amplitude of Noise to be considered in second plot. Returns ---------- N/A Calling this function will save two figures to the users directory. A plot titled hw211.png will display a contour plot of the cost function, on a logarithmic scale in j, between the values specified in xrange and yrange. A second plot titled hw212.png will display the same function over the same range but with a random noise added, the amplitude of which can be set as a parameter. """ #Create 2D array of points [X,Y]=np.linspace(xrange[0],xrange[1],Nx),np.linspace(yrange[0],yrange[1],Ny) #calculate noiseless cost function at each point on 2D grid cost.c_noise=False j=[[cost.costj([xi,yi]) for xi in X] for yi in Y] #calculate noisey cost function at each point in 2D grid. cost.c_noise = True cost.c_noise_amp = Noise jn=[[cost.costj([xi,yi]) for xi in X] for yi in Y] #create contour plots of cost functions with and without noise plt.figure() fig, ax = plt.subplots() cp = ax.contourf(X, Y, j, locator=ticker.LogLocator(), cmap=cm.GnBu) cbar = fig.colorbar(cp) plt.title('Rosemary Teague, Visualize \n 2D cost function, no noise.') plt.savefig('hw211', dpi = 700) plt.figure() fig, ax = plt.subplots() cpn = ax.contourf(X, Y, jn, locator=ticker.LogLocator(), cmap=cm.GnBu) cbar = fig.colorbar(cpn) plt.title('Rosemary Teague, Visualize \n 2D cost function, Noise amplitude='+str(cost.c_noise_amp)) plt.savefig('hw212', dpi = 700) def newton_test(xg,display=False,i=1,timing=False): """ ============================================================================ Use Newton's method to minimize a cost function, j, defined in cost module. ============================================================================ Parameters ---------- xg : list Initial guess display : Boolean, Optional If set to True, figures will be created to illustrate the optimization path taken and the distance from convergence at each step. i=1 : Integer, Optional Sets the name of the figures as hw22i.png timing : Boolean, Optional If set to true, an average time will be calculated for the completion of finding a minimum and will be appended to the tuple output. Returns --------- xf : ndarray Computed location of minimum jf : float Computed minimum output : Tuple containing the time taken for the minimia to be found. An average over 10 tests, only set if timining parameter set to True, otherwise empty. Calling this function will produce a figure containing two subplots. The first will illustrate the location of each step in the minimization path, overlayed over the initial cost function. The second will illustrate the distance from the final, computed minimum at each iteration. """ cost.c_noise=False hw2.tol=10**(-6) hw2.itermax=1000 t21=0 output=() if timing: N=10 else: N=1 for j in range(1,N): t1=time() hw2.newton(xg) t2=time() t21=t21+(t2-t1) X,Y=hw2.xpath xf=[X[-1],Y[-1]] jpathn=[j for j in hw2.jpath] jf=hw2.jpath[-1] output=(t21/N, X, Y, jpathn) if display: Minx=min(X)-1 Maxx=max(X)+1 Miny=min(Y)-1 Maxy=max(Y)+1 [Xj,Yj]=np.linspace(Minx,Maxx,200),np.linspace(Miny,Maxy,200) #calculate noiseless cost function at each point on 2D grid j=[[cost.costj([xi,yi]) for xi in Xj] for yi in Yj] f, (p1,p2) = plt.subplots(1,2) p1.contourf(Xj, Yj, j, locator=ticker.LogLocator(), cmap=cm.GnBu) p1.plot(X,Y,'g',marker='d') p1.set_xlim(min(X)-1,max(X)+1) p1.set_xlabel('X1-location') p1.set_ylabel('X2-location') p1.set_title('Convergence Path') p2.plot(np.linspace(0,len(X)-1,len(X)),hw2.jpath-jf) p2.set_xlabel('Iteration number') p2.set_ylabel('distance from converged minimum') p2.set_title('Rate') plt.suptitle('Rosemary Teague, Newton_test, initial guess ='+str(xg)+' \n Convergence of a cost function') plt.tight_layout(pad=4) plt.savefig('hw22'+str(i), dpi=700) return xf,jf,output def bracket_descent_test(xg,display=False,compare=False,i=1): """ ====================================================================================== Use the Bracket Descent method to minimize a cost function, j, defined in cost module. ====================================================================================== Parameters ---------- xg : list Initial guess display : Boolean, Optional If set to True, figures will be created to illustrate the optimization path taken and the distance from convergence at each step. compare : Boolean, optional If set to True, a figure will be created to directly compare Newton and Bracket Descent methods. i=1 : Integer, Optional Sets the name of the figures as hw231(/2)_i.png. Returns --------- xf : ndarray Computed location of minimum jf : float Computed minimum output : Tuple containing the time taken for the minimia to be found for each of newton and bracket descent methods. An average over 10 tests is taken, only set if compare parameter set to True, otherwise empty. Calling this function will produce two figures. The first will containing two subplots illustrating the location of each step in the minimization path, overlayed over the initial cost function, and the distance of j from the final, computed minimum at each iteration. The second plot (which is only produced when 'compare' is set to True) demonstrates the distance of each step from the final, converged minimum at each iteration. This shows that the newton method requires significantly fewer steps and is hence faster. Trends Observed ---------------- Figures hw321_i show the path taken during a bracket descent conversion is much longer than in a newton conversion (shown in figures hw22i). This is because the B-D method limits the size of a step to 2*L where L is definied by the size of an equilateral triangle whose centroid moved with each step. The method is furthermore designed such that this triangle will only decrease in size per iteration, and hence the maximum length a step can take can only be decreased (not increased) throughout the convergence. The figures further show that steps appear to be taken initially perpendicular to the curvature, finding the minimum along that strip, and then converging in down the parallel Path until they reach a level of tolerance. In contrast, the Newton approach is not limited in the size of the steps it is able to take and can hence converge in a much smaller number of iterations. This is a result of the use of gradients in this method. Figures hw22i illustrate how each step travels through many bands on the contour plot (representing differences of 1 order of magnitude each) as the method searches for the direction of minimisation. """ cost.c_noise=False hw2.tol=10**(-6) hw2.itermax=1000 t34=0 output = () if compare: N=10 else: N=1 for j in range(1,N): t3=time() hw2.bracket_descent(xg) t4=time() t34=t34+(t4-t3) X,Y=hw2.xpath xf=[X[-1],Y[-1]] jf=hw2.jpath[-1] d1=np.sqrt((X-xf[0])**2+(Y-xf[1])**2) if display: Minx=min(X)-1 Maxx=max(X)+1 Miny=min(Y)-1 Maxy=max(Y)+1 [Xj,Yj]=np.linspace(Minx,Maxx,200),np.linspace(Miny,Maxy,200) #calculate noiseless cost function at each point on 2D grid j=[[cost.costj([xi,yi]) for xi in Xj] for yi in Yj] f, (p1,p2) = plt.subplots(1,2) p1.contourf(Xj, Yj, j, locator=ticker.LogLocator(), cmap=cm.GnBu) p1.plot(X,Y,'g',marker='d') p1.set_xlabel('X1-location') p1.set_ylabel('X2-location') p1.set_title('Convergence Path') p2.semilogy(np.linspace(1,len(X),len(X)),hw2.jpath) p2.set_xlabel('Iteration number') p2.set_ylabel('distance from converged minimum') p2.set_title('Rate') plt.suptitle('Rosemary Teague, bracket_descent_test, initial guess ='+str(xg)+' \n Rate of convergence of a cost function') plt.tight_layout(pad=4) plt.savefig('hw231_'+str(i), dpi=700) if compare: plt.close('all') One,=plt.loglog(np.linspace(1,len(X),len(X)),hw2.jpath) xf2,jf2,outputn=newton_test(xg,timing=True) X2,Y2=outputn[1],outputn[2] d2=np.sqrt((X2-xf2[0])**2+(Y2-xf2[1])**2) print(np.linspace(1,len(X2),len(X2)),outputn[3]) Two,=plt.loglog(np.linspace(1,len(X2),len(X2)),outputn[3]) One.set_label('Bracket Descent') Two.set_label('Newton') plt.xlabel('Iteration number') plt.ylabel('Distance from converged minimum') plt.legend() plt.title('Rosemary Teague, bracket_descent_test, initial guess ='+str(xg)+' \n Comparison of Newton and Bracket Descent Methods') plt.savefig('hw232_'+str(i), dpi=700) output=(outputn[0],t34/N) return xf,jf,output def performance(tol): """ ============================================================================ Assesses the performance of Bracket Descent and Scipy L-BFGS-B Methods ============================================================================ Parameters ------------ tol : float Determines the tolerance for minimization Returns ------------ This function will produce 4 figures. The first 3 will represent a comparison of the precison of each method while the 4th will represent a comparison of the timing. The first three show the location of the computed minima for initial guesses of [-100,-3], [-50,-3], [-10,-3] and [-1,-3]. These are overlayed onto the original cost function; the Scipy L-BFGS-B results are represented by red diamonds while the Bracket Descent results are represented by blue diamonds. The three figures represent the cases when the noise amplitude is set to 0, 1, and 10. The final figure consists of four subplots, the upper row represents the computational time taken for convergence, given an initial x starting point, while the lower represents the number of iterations requried. In each case the Scipy L-BFGS-B method is shown on the left and the Bracket descent is shown on the right. A legend on each plot differentiates the cases when the Noise Ampplitude is set to 0, 1, and 10. Trends Observed ---------------- For all cases, the Scipy minimization function appears to be more consistent (to rely less on the initial guess) than the fortran Bracket Descent method. This is seen in figures hw241-hw243, where the B-D results are seen to cover a broader spead of final coordinated. These figures also illustrate that as the level of noise of the cost function is increased, the Scipy L-BFGS-B method becomes increasingly favourable over the Bracket descent approach, producing more precise results each time. This is a result of the lack of consideration for noise within the Bracket Descent method; that is to say that any random fluctations which result in two neighbouring points (along the convergence path) lying within the tolerance limit will be assumed to be the true minimum of the function as defined by the B-D method. However, it is likely that the Scipy L-BFGS-B method is adapted to smooth out noisy functions and hence find the true minimum more reliably. A consideration of figure hw244, however, demonstrates an advantage of the B-D method over the Scipy L-BGFS-B minimization in the form of timing. It can be seen that despite requiring more iterations before converging to within a set tolerance, the total computational time is less to within a factor of 10. """ plt.close('all') count=0 hw2.tol=tol nintb=[];nintl=[]; tlbfgsb=[]; txfbd=[]; lbfgsx=[]; lbfgsy=[]; xfbdx=[]; xfbdy=[]; cost.c_noise=True for cost.c_noise_amp in [0., 1., 10.]: count=count+1 for [x,y] in [[-100.,-3.],[-50.,-3.],[-10.,-3.],[-1.,-3.]]: t12=0;t34=0 for i in range(0,1000): t1=time() scipy.optimize.minimize(cost.costj, [x,y], method='L-BFGS-B' ,tol=tol) t2=time() t12=t12+(t2-t1) t3=time() hw2.bracket_descent([x,y]) t4=time() t34=t34+(t4-t3) tlbfgsb.append(t12/1000); txfbd.append(t34/1000) info=scipy.optimize.minimize(cost.costj, [x,y], method='L-BFGS-B' ,tol=tol) xfbd,jfbd,i2=hw2.bracket_descent([x,y]) # print('method: ', 'Fortran Bracket Descent') # print('Value: ', jfbd) # print('number of iterations:', i2) # print('x: ', xfbd) # print('c_noise: ', cost.c_noise) # print(' ') #print(info) x=info.x lbfgsx.append(x[0]) lbfgsy.append(x[1]) xfbdx.append(xfbd[0]) xfbdy.append(xfbd[1]) nint=info.nit nintl.append(nint) nintb.append(i2) Minx=1+(min([min(xfbdx[(count-1)*4:count*4]),min(lbfgsx[(count-1)*4:count*4])])-1)*1.1 Maxx=1+(max([max(xfbdx[(count-1)*4:count*4]),max(lbfgsx[(count-1)*4:count*4])])-1)*1.1 Miny=1+(min([min(xfbdy[(count-1)*4:count*4]),min(lbfgsy[(count-1)*4:count*4])])-1)*1.1 Maxy=1+(max([max(xfbdy[(count-1)*4:count*4]),max(lbfgsy[(count-1)*4:count*4])])-1)*1.1 [X,Y]=np.linspace(Minx,Maxx,200),np.linspace(Miny,Maxy,200) #calculate noiseless cost function at each point on 2D grid j=[[cost.costj([xi,yi]) for xi in X] for yi in Y] #create contour plots of cost functions with and without noise fig, p4 = plt.subplots() cp = p4.contourf(X, Y, j, locator=ticker.LogLocator(), cmap=cm.GnBu) cbar = fig.colorbar(cp) BD,=p4.plot(xfbdx[(count-1)*4:count*4],xfbdy[(count-1)*4:count*4],'b',linestyle='None',marker='d',markersize=6) Scipy,=p4.plot(lbfgsx[(count-1)*4:count*4],lbfgsy[(count-1)*4:count*4],'r',linestyle='None',marker='d',markersize=6) BD.set_label('Fortran Bracket Descent') Scipy.set_label('Scipy optimize L-BFGS-B') plt.legend(loc='upper left', fontsize='small') plt.suptitle('Rosemary Teague, performance \n Comparison of converged values, Noise='+str(int(cost.c_noise_amp))) #plt.tight_layout(pad=5) plt.savefig('hw24'+str(count), dpi=700) print(tlbfgsb) plt.close('all') f4, (p414,p424) = plt.subplots(2,2,sharey=True) one,=p414[0].plot(tlbfgsb[:4],[np.abs(-100.),np.abs(-50.),np.abs(-10.),np.abs(-1.)],'r',marker='x',markersize=12) two,=p414[0].plot(tlbfgsb[4:8],[np.abs(-100.),np.abs(-50.),np.abs(-10.),np.abs(-1.)],'m',marker='x',markersize=12) three,=p414[0].plot(tlbfgsb[8:],[np.abs(-100.),np.abs(-50.),np.abs(-10.),np.abs(-1.)],'#c79fef',marker='x',markersize=12) one.set_label('No Noise') two.set_label('Noise = 1.0') three.set_label('Noise = 10.0') p414[0].set_title('Scipy Optimise L-BFGS-B') p414[0].set_xlabel('Time Taken') p414[0].legend( loc = 'upper right', fontsize = 'x-small') p414[0].xaxis.set_ticks(np.linspace(min(tlbfgsb),max(tlbfgsb),3)) p414[0].ticklabel_format(useOffset=False) uno,=p414[1].plot(txfbd[:4],[np.abs(-100.-xfbdx[0]),np.abs(-50.-xfbdx[1]),np.abs(-10.-xfbdx[2]),np.abs(-1.-xfbdx[3])],'b',marker='x',markersize=12) dos,=p414[1].plot(txfbd[4:8],[np.abs(-100.-xfbdx[4]),np.abs(-50.-xfbdx[5]),np.abs(-10.-xfbdx[6]),np.abs(-1.-xfbdx[7])],'g',marker='x',markersize=12) tres,=p414[1].plot(txfbd[8:],[np.abs(-100.-xfbdx[8]),np.abs(-50.-xfbdx[9]),np.abs(-10.-xfbdx[10]),np.abs(-1.-xfbdx[11])],'c',marker='x',markersize=12) uno.set_label('No Noise') dos.set_label('Noise = 1.0') tres.set_label('Noise = 10.0') p414[1].set_title('Fortran Bracket Descent') p414[1].set_xlabel('Time Taken') p414[1].legend(loc = 'upper left', fontsize = 'x-small') p414[1].xaxis.set_ticks(np.linspace(min(txfbd),max(txfbd),3)) one1,=p424[0].plot(nintl[:4],[np.abs(-100.-lbfgsx[0]),np.abs(-50.-lbfgsx[1]),np.abs(-10.-lbfgsx[2]),np.abs(-1.-lbfgsx[3])],'r',marker='x',markersize=12) two2,=p424[0].plot(nintl[4:8],[np.abs(-100.-lbfgsx[4]),np.abs(-50.-lbfgsx[5]),np.abs(-10.-lbfgsx[6]),np.abs(-1.-lbfgsx[7])],'m',marker='x',markersize=12) three3,=p424[0].plot(nintl[8:],[np.abs(-100.-lbfgsx[8]),np.abs(-50.-lbfgsx[9]),np.abs(-10.-lbfgsx[10]),np.abs(-1.-lbfgsx[11])],'#c79fef',marker='x',markersize=12) one1.set_label('No Noise') two2.set_label('Noise = 1.0') three3.set_label('Noise = 10.0') p424[0].set_xlabel('Number of Iterations') p424[0].legend( loc = 'upper left', fontsize = 'x-small') p424[0].ticklabel_format(useOffset=False) uno1,=p424[1].plot(nintb[:4],[np.abs(-100.-xfbdx[0]),np.abs(-50.-xfbdx[1]),np.abs(-10.-xfbdx[2]),np.abs(-1.-xfbdx[3])],'b',marker='x',markersize=12) dos2,=p424[1].plot(nintb[4:8],[np.abs(-100.-xfbdx[4]),np.abs(-50.-xfbdx[5]),np.abs(-10.-xfbdx[6]),np.abs(-1.-xfbdx[7])],'g',marker='x',markersize=12) tres3,=p424[1].plot(nintb[8:],[np.abs(-100.-xfbdx[8]),np.abs(-50.-xfbdx[9]),np.abs(-10.-xfbdx[10]),np.abs(-1.-xfbdx[11])],'c',marker='x',markersize=12) uno1.set_label('No Noise') dos2.set_label('Noise = 1.0') tres3.set_label('Noise = 10.0') p424[1].set_xlabel('Number of Iterations') p424[1].legend(loc = 'upper left', fontsize = 'x-small') f4.text(0.04, 0.5, 'Initial x-distance from Converged minimum', va='center', rotation='vertical') plt.suptitle('Rosemary Teague, performance \n Time taken for values to converge',fontsize='large') plt.tight_layout(pad=3.5, h_pad=1,w_pad=1) plt.savefig('hw244', dpi=700) if __name__ == '__main__': # # visualize(200,200) # # newton_test([10.,10.],display=True,i=1) # newton_test([5.,5.],display=True,i=2) # newton_test([2.,2.],display=True,i=3) # # bracket_descent_test([10.,10.],display=True,compare=True,i=1) # bracket_descent_test([5.,5.],display=True,compare=True,i=2) # bracket_descent_test([2.,2.],display=True,compare=True,i=3) performance(10**(-6))

[ "rdt13@ic.ac.uk" ]

rdt13@ic.ac.uk

45371882e8a70294d4262634c9fd50c2d4c24f59

5da77c20e1f3cbec5f5f3320549d3982a4722fb9

/baselines/netmf.py

e25cb4344e800f645cf38e1d4d3fe6718fb48e1b

[ "MIT" ]

permissive

samihaija/tf-fsvd

02f6f0237a0e0cfe08f07943909d3ee14f3ea350

677cad8cfa21668369ce39c515874dabfbc021d5

refs/heads/main

2023-03-09T22:22:33.805652

2021-02-23T11:01:41

339,504,112

16

2

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UTF-8

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6,247

py

# THIS IS MODIFIED FROM ORIGINAL BY SAMI TO RUN ASYM-PROJ DATASETS # Original: https://github.com/xptree/NetMF/ # encoding: utf-8 # File Name: eigen.py # Author: Jiezhong Qiu # Create Time: 2017/07/13 16:05 # TODO: import time import scipy.io import scipy.sparse as sparse from scipy.sparse import csgraph import numpy as np import argparse import logging import theano from theano import tensor as T logger = logging.getLogger(__name__) theano.config.exception_verbosity='high' def load_adjacency_matrix(file, variable_name="network"): if file.endswith('.mat'): data = scipy.io.loadmat(file) logger.info("loading mat file %s", file) return data[variable_name] elif file.endswith('.txt.npy'): train_edges = np.load(file) train_edges = np.concatenate([train_edges, train_edges[:, ::-1]], axis=0) csr = scipy.sparse.csr_matrix((np.ones([len(train_edges)]), (train_edges[:, 0], train_edges[:, 1]) )) return csr def deepwalk_filter(evals, window): for i in range(len(evals)): x = evals[i] evals[i] = 1. if x >= 1 else x*(1-x**window) / (1-x) / window evals = np.maximum(evals, 0) logger.info("After filtering, max eigenvalue=%f, min eigenvalue=%f", np.max(evals), np.min(evals)) return evals def approximate_normalized_graph_laplacian(A, rank, which="LA"): n = A.shape[0] L, d_rt = csgraph.laplacian(A, normed=True, return_diag=True) # X = D^{-1/2} W D^{-1/2} X = sparse.identity(n) - L logger.info("Eigen decomposition...") #evals, evecs = sparse.linalg.eigsh(X, rank, # which=which, tol=1e-3, maxiter=300) evals, evecs = sparse.linalg.eigsh(X, rank, which=which) logger.info("Maximum eigenvalue %f, minimum eigenvalue %f", np.max(evals), np.min(evals)) logger.info("Computing D^{-1/2}U..") D_rt_inv = sparse.diags(d_rt ** -1) D_rt_invU = D_rt_inv.dot(evecs) return evals, D_rt_invU def approximate_deepwalk_matrix(evals, D_rt_invU, window, vol, b): evals = deepwalk_filter(evals, window=window) X = sparse.diags(np.sqrt(evals)).dot(D_rt_invU.T).T m = T.matrix() mmT = T.dot(m, m.T) * (vol/b) f = theano.function([m], T.log(T.maximum(mmT, 1))) Y = f(X.astype(theano.config.floatX)) logger.info("Computed DeepWalk matrix with %d non-zero elements", np.count_nonzero(Y)) return sparse.csr_matrix(Y) def svd_deepwalk_matrix(X, dim): u, s, v = sparse.linalg.svds(X, dim, return_singular_vectors="u") # return U \Sigma^{1/2} return sparse.diags(np.sqrt(s)).dot(u.T).T def netmf_large(args): logger.info("Running NetMF for a large window size...") logger.info("Window size is set to be %d", args.window) # load adjacency matrix A = load_adjacency_matrix(args.input, variable_name=args.matfile_variable_name) vol = float(A.sum()) # perform eigen-decomposition of D^{-1/2} A D^{-1/2} # keep top #rank eigenpairs evals, D_rt_invU = approximate_normalized_graph_laplacian(A, rank=args.rank, which="LA") # approximate deepwalk matrix deepwalk_matrix = approximate_deepwalk_matrix(evals, D_rt_invU, window=args.window, vol=vol, b=args.negative) # factorize deepwalk matrix with SVD deepwalk_embedding = svd_deepwalk_matrix(deepwalk_matrix, dim=args.dim) logger.info("Save embedding to %s", args.output) np.save(args.output, deepwalk_embedding, allow_pickle=False) def direct_compute_deepwalk_matrix(A, window, b): n = A.shape[0] vol = float(A.sum()) L, d_rt = csgraph.laplacian(A, normed=True, return_diag=True) # X = D^{-1/2} A D^{-1/2} X = sparse.identity(n) - L S = np.zeros_like(X) X_power = sparse.identity(n) for i in range(window): logger.info("Compute matrix %d-th power", i+1) X_power = X_power.dot(X) S += X_power S *= vol / window / b D_rt_inv = sparse.diags(d_rt ** -1) M = D_rt_inv.dot(D_rt_inv.dot(S).T) m = T.matrix() f = theano.function([m], T.log(T.maximum(m, 1))) Y = f(M.todense().astype(theano.config.floatX)) return sparse.csr_matrix(Y) def netmf_small(args): logger.info("Running NetMF for a small window size...") logger.info("Window size is set to be %d", args.window) # load adjacency matrix A = load_adjacency_matrix(args.input, variable_name=args.matfile_variable_name) # directly compute deepwalk matrix deepwalk_matrix = direct_compute_deepwalk_matrix(A, window=args.window, b=args.negative) # factorize deepwalk matrix with SVD deepwalk_embedding = svd_deepwalk_matrix(deepwalk_matrix, dim=args.dim) logger.info("Save embedding to %s", args.output) np.save(args.output, deepwalk_embedding, allow_pickle=False) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--input", type=str, required=True, help=".mat input file path") parser.add_argument('--matfile-variable-name', default='network', help='variable name of adjacency matrix inside a .mat file.') parser.add_argument("--output", type=str, required=True, help="embedding output file path") parser.add_argument("--rank", default=256, type=int, help="#eigenpairs used to approximate normalized graph laplacian.") parser.add_argument("--dim", default=128, type=int, help="dimension of embedding") parser.add_argument("--window", default=10, type=int, help="context window size") parser.add_argument("--negative", default=1.0, type=float, help="negative sampling") parser.add_argument('--large', dest="large", action="store_true", help="using netmf for large window size") parser.add_argument('--small', dest="large", action="store_false", help="using netmf for small window size") parser.set_defaults(large=True) args = parser.parse_args() logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s') # include timestamp now = time.time() if args.large: netmf_large(args) else: netmf_small(args) train_time = time.time() - now print('train time: %g' % train_time)

[ "samihaija@yahoo.com" ]

samihaija@yahoo.com

ded166bd201ae177beb2bcf2cf992a42f026e48f

4f4ec663b3acfc22ac8ae7336dec18d28a691b9f

/image_Scrap.py

f9e56fe16761c469d84b9ad2fee9b458e5e60dec

[]

no_license

lakshsharma07/Influencer-predictor

ff347f9db87acd1603555a5aba1b88202119397b

e320481ccdebdf0cc31d8eacba7f7069c6eac076

refs/heads/master

2020-03-25T20:30:10.603398

2019-06-26T17:54:29

144,133,865

0

null

UTF-8

Python

false

417

py

from bs4 import BeautifulSoup import requests handle = raw_input('Input your account name on Twitter: ') temp = requests.get('https://twitter.com/'+handle) bs = BeautifulSoup(temp.text,'lxml') pic=bs.find_all("img",{"class": "ProfileAvatar-image "}) url=pic[0]['src'] from PIL import Image import requests from io import BytesIO response = requests.get(url) img = Image.open(BytesIO(response.content)) img.show()

[ "sharma1997lak@gmail.com" ]

sharma1997lak@gmail.com

26a85bf9be92af4ff2847a5f6ffb6fc857174409

4abbdd9cbe83d28e900e22666a077337e89b741a

/smallspider/spider_28_asyncio.py

e88a8b7960e4a684dae254a69d7c104b4fd5cb85

[]

no_license

Pysuper/small_spider

4f0394a3853d9803d27f9dd86f570398ad9a5af5

33e38424b93bb76b58862d7a9bd82741a680f599

refs/heads/master

2022-12-11T17:14:52.509160

2020-09-24T13:37:34

184,028,706

2

1

null

2022-07-29T22:36:03

2019-04-29T08:13:46

Python

UTF-8

Python

false

2,682

py

# !/usr/bin/env python # -*- coding: utf-8 -*- # Author : Zheng Xingtao # File : spider_28_asyncio # Datetime : 2020/8/21 上午9:19 import asyncio import aiohttp from lxml import etree from pprint import pprint loop = asyncio.get_event_loop() async def get_category(): async with aiohttp.ClientSession() as session: while True: try: async with session.get("http://www.nipic.com/photo") as response: html = await response.text() x_html = etree.HTML(html) category_href_list = x_html.xpath("//div[@class='menu-box-bd']/dl/dd/a/@href") category_title_list = x_html.xpath("//div[@class='menu-box-bd']/dl/dd/a/text()") for category_href, category_title in zip(category_href_list, category_title_list): category_task = { 'category_title': category_title, 'category_href': "http://www.nipic.com" + category_href, } loop.create_task(parse_detail(session, category_task)) except Exception as e: print(e) await asyncio.sleep(5) async def parse_detail(session, task): try: async with session.get(task["category_href"]) as response: html = await response.text() x_html = etree.HTML(html) detail_href_list = x_html.xpath("//div[@class='mainV2']//li/a/@href") detail_title_list = x_html.xpath("//div[@class='mainV2']//li/a/@title") for detail_href, detail_title in zip(detail_href_list, detail_title_list): detail_task = { 'category_href': task["category_href"], 'category_title': task["category_title"], 'detail_href': detail_href, 'detail_title': detail_title } loop.create_task(download_img(session, detail_task)) except Exception as e: print(e) await asyncio.sleep(5) async def download_img(session, detail_task): try: async with session.get(detail_task["detail_href"]) as response: html = await response.text() x_html = etree.HTML(html) img_href = x_html.xpath("//img[@class='works-img']/@src")[0] # for i in img_href: # print(i) pprint(detail_task) print(img_href) except Exception as e: print(e) await asyncio.sleep(5) # python 3.7 # asyncio.run(get_category()) # python 3.5 asyncio.get_event_loop().run_until_complete(get_category())

[ "zxt@yj0407" ]

zxt@yj0407

bec248fcae9a6598aed74b05d28f8eb2b0b7c95e

6ec4b51f7298b2daf4211518a6146ee4b0746a1c

/products/migrations/0002_auto_20210718_1356.py

fd0dd0b60e25eb0b99901f2e644ee64c426a43f2

[]

no_license

Code-Institute-Submissions/proper_clobber

d2f88a495527ffb28eb999c9380ff2e24567891d

574297bfb669c71f4d2bd16c122ee547f0261c9b

refs/heads/master

2023-08-19T07:32:52.652181

2021-09-18T20:33:20

null

0

null

UTF-8

Python

false

484

py

# Generated by Django 3.2.4 on 2021-07-18 13:56 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('products', '0001_initial'), ] operations = [ migrations.RenameField( model_name='product', old_name='has_clothing_size', new_name='has_sizing', ), migrations.RemoveField( model_name='product', name='has_footwear_size', ), ]

[ "franky.p@hotmail.co.uk" ]

franky.p@hotmail.co.uk