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

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#! /usr/bin/env python # encoding: utf-8 """ Create a waf file able to read wscript files ending in ".py" execute a small test to show that it works The waf file includes "extpy.py" which performs the required modifications """ import os, subprocess up = os.path.dirname join = os.path.join cwd = os.getcwd() extpy = join(cwd, 'extpy.py') args = 'python waf-light --tools=compat15,%s --prelude=$"\tfrom waflib.extras import extpy\n" ' % extpy root = up(up(cwd)) subprocess.Popen(args, cwd=root, shell=True).wait() os.rename(join(root, 'waf'), join(cwd, 'waf.py')) env = dict(os.environ) if 'WAFDIR' in env: del env['WAFDIR'] subprocess.Popen('python waf.py configure', cwd=cwd, shell=True, env=env).wait()

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from homework_06.models import Session, Author, Post from homework_06.models.texts import text_dict # заполнение базы данных def create_users_posts(): session = Session() author_1 = Author(author='А.И. Тургенев') session.add(author_1) author_2 = Author(author='Л.Н. Андреев') session.add(author_2) author_3 = Author(author='А.М. Горький') session.add(author_3) session.flush() post_1 = Post( author_id=author_1.id, title='Отцы и дети', text=text_dict['Отцы и дети'], year='1862' ) post_2 = Post( author_id=author_2.id, title='Красный смех', text=text_dict['Красный смех'], year='1904' ) post_3 = Post( author_id=author_2.id, title='Елеазар', text=text_dict['Елеазар'], year='1906' ) post_4 = Post( author_id=author_3.id, title='Хозяева жизни', text=text_dict['Хозяева жизни'], year='1906' ) post_5 = Post( author_id=author_3.id, title='Городок Окуров', text=text_dict['Городок Окуров'], year='1909' ) session.add(post_1) session.add(post_2) session.add(post_3) session.add(post_4) session.add(post_5) session.commit() session.close() if __name__ == "__main__": create_users_posts()

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/SW_academy/Intermediate/01.List1_summary.py

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miniii222/Algorithm_Study

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# -*- coding: utf-8 -*- # Exhaustive Search - 모든 경우를 탐색. 속도는 느리지만 틀리지 않음 ## 1,2,3을 포함하는 모든 순열을 생성하는 코드 for i1 in range(1,4) : for i2 in range(1,4) : if i2 != i1 : for i3 in range(1,4) : if i3 != i1 and i3 != i2 : print(i1, i2, i3) # Greedy Algorithm - 그 순간에 최적이라고 생각되는 것을 선택해 나가는 방식. 전체 최적이라는 보장 없음 # 해 선택 -> 실행 가능성 검사 -> 해 검사 ## Baby-gin 문제 num = 456789 c = [0] * 12 for i in range(6) : c[num % 10] += 1 num //= 10 i = 0 tri = run = 0 while i < 10 : if c[i] >= 3 : #triplet 확인 c[i] -=3 tri +=1 continue; if c[i] >= 1 and c[i+1] >= 1 and c[i+2] >= 1 : c[i] -=1 c[i+1] -=1 c[i+2] -=1 run +=1 continue i +=1 if run + tri ==2 : print('Baby Gin') # 답을 찾지 못하는 경우도 있음 # Bubble Sort -> 시간 복잡도 O(n^2) # Counting Sort -> 식나 복잡도 O(n + k) n : 리스트의 개수 / k : 정수의 최댓값 ## 시간 복잡도가 1차이지만, n이 너무 커지면 메모리 에러 가능성이 있음 def CountingSort(A, B, k) : #A(n) : 입력 리스트 사용된 숫자(1 ~ k) #B(n) : 정렬된 리스트 #C(k) : 카운트 리스트 C = [0] * k for i in range(len(B)) : C[A[i]] += 1 #각 숫자들이 몇 개인지 센다 for i in range(1, len(C)) : C[i] += C[i-1] #누적합을 구함으로써 각 숫자들의 정렬 후 마지막 위치를 저장 for i in range(len(B) -1, -1, -1) : B[C[A[i]]-1] = A[i] C[A[i]]-=1

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OrionApplePie/tec_analysis

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import argparse import errno import os from pathlib import Path import pandas as pd # Параметры которые нужны NEEDED_PARAMETERS = ( "hmax", "FiltWindow", "Time Range", "Minimum duration of series", "time step" ) def get_params(filename=""): """Функция для сбора параметров из файла .dat из строк в начале файла начинающихся с символа #. Возвращает словарь. """ if not filename: raise ValueError("Filename is needed.") if not os.path.isfile(filename): raise FileNotFoundError( errno.ENOENT, os.strerror(errno.ENOENT), filename ) params = [] with open(filename, 'r') as tec_file: for line in tec_file.readlines(): if line.strip().startswith("#"): # это параметр param = line.strip()[1:] param = param.split("=") if len(param) != 2: # заголовки пропускаем continue param = tuple(el.strip() for el in param) params.append(param) params = dict(params) # оставляем только нужные параметры return { key: params[key] for key in NEEDED_PARAMETERS } def main(): parser = argparse.ArgumentParser( description="""Утилита для работы с файлами TEC. Загрузка данных их файла в структуру данных.""" ) parser.add_argument( '-tec_file', type=Path, action='store', dest='tec_file', default=Path(''), help='Файл с данными TEC (.dat).' ) args = parser.parse_args() params = get_params(args.tec_file) print(params) headers = ( "tsn", "time", "el", "az", "latp", "lonp", "tec", "tec_filtered", "validity" ) dataframe = pd.read_csv( filepath_or_buffer=args.tec_file, delim_whitespace=True, # разделитель - пробел(ы) comment="#", # пропускаем комменты names=headers, # задание заголовков из списка ) print(dataframe) if __name__ == '__main__': main()

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caoxin1988/leetcode

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class Solution: def twoSum(self, nums, target): """ :type nums: List[int] :type target: int :rtype: List[int] """ d = {} for i in range(len(nums)): d[nums[i]] = i for i in range(len(nums)): data1 = nums[i] data2 = target - data1 if data2 in d.keys() and d[data2] != i: return [i, d[data2]] return None

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#!/usr/bin/env python # -*- coding: utf-8 -*- import requests import json __version__ = '0.1.3' __author__ = "Tasdik Rahman" class Pyzipcode(object): BASE_URL_ZIPTASTIC_API = 'http://zip.getziptastic.com/v2/{code}/{pin}' BASE_URL_GOOGLE_API = 'http://maps.googleapis.com/maps/api/geocode/json?address={pin}' @staticmethod def query_ziptastic_api(pincode, country_code="IN", return_json=False): """ appends the pincode to the base API url and queries the API. country_code defaults to 'IN' :param pincode: The pincode :param country_code: The country for the pincode. Defaults to "IN" :returns: returns the JSON data returned by the API """ url = Pyzipcode.BASE_URL_ZIPTASTIC_API.format(code=country_code, pin=pincode) response = requests.get(url) if response.status_code == 200: json_obj = response.json() if return_json == True: return json.dumps(json_obj) else: return json_obj else: return False @staticmethod def query_google_api(pincode, return_json=False): """ queries the Google maps API for getting the longitude and latitude for a given pincode :param pincode: The pincode :returns: returns the latitude and longitude """ url = Pyzipcode.BASE_URL_GOOGLE_API.format(pin=pincode) response = requests.get(url) if response.status_code == 200: json_obj = response.json() if json_obj["status"] == "OK": results = json_obj["results"][0]["geometry"] '''Storing the JSON data''' data = { "location": results["location"], "location_type": results["location_type"], "bounds": results["bounds"] } if return_json == True: return json.dumps(data) else: return data else: return False else: return False @staticmethod def get(pincode, country_code="IN",return_json=False): """ Unifies the JSON data from different API's into a single one :param pincode: pincode for the place :param country_code: country code for the pincode. You can find the list of country codes in "https://github.com/prodicus/pyzipcode-cli/blob/master/pyzipcode_cli/countries.json" :returns: A unified result of both ziptastic and google maps API """ data_API_1 = Pyzipcode.query_ziptastic_api(pincode, country_code) data_API_2 = Pyzipcode.query_google_api(pincode) if data_API_2 is not False and data_API_1 is not False: # final_dictionary = { # "ziptastic": data_API_1, # "google_maps": data_API_2 # } data_API_1.update(data_API_2) ## merges the two dictionaries if return_json == True: return json.dumps(data_API_1) else: return data_API_1 else: return False

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import sys import gym.spaces import itertools import numpy as np import random import tensorflow as tf import tensorflow.contrib.layers as layers from collections import namedtuple from dqn_utils import * import pickle OptimizerSpec = namedtuple("OptimizerSpec", ["constructor", "kwargs", "lr_schedule"]) def learn(env, q_func, optimizer_spec, session, exploration=LinearSchedule(1000000, 0.1), stopping_criterion=None, replay_buffer_size=1000000, batch_size=32, gamma=0.99, learning_starts=50000, learning_freq=4, frame_history_len=4, target_update_freq=10000, grad_norm_clipping=10, lr_multiplier=1.0): """Run Deep Q-learning algorithm. You can specify your own convnet using q_func. All schedules are w.r.t. total number of steps taken in the environment. Parameters ---------- env: gym.Env gym environment to train on. q_func: function Model to use for computing the q function. It should accept the following named arguments: img_in: tf.Tensor tensorflow tensor representing the input image num_actions: int number of actions scope: str scope in which all the model related variables should be created reuse: bool whether previously created variables should be reused. optimizer_spec: OptimizerSpec Specifying the constructor and kwargs, as well as learning rate schedule for the optimizer session: tf.Session tensorflow session to use. exploration: rl_algs.deepq.utils.schedules.Schedule schedule for probability of chosing random action. stopping_criterion: (env, t) -> bool should return true when it's ok for the RL algorithm to stop. takes in env and the number of steps executed so far. replay_buffer_size: int How many memories to store in the replay buffer. batch_size: int How many transitions to sample each time experience is replayed. gamma: float Discount Factor learning_starts: int After how many environment steps to start replaying experiences learning_freq: int How many steps of environment to take between every experience replay frame_history_len: int How many past frames to include as input to the model. target_update_freq: int How many experience replay rounds (not steps!) to perform between each update to the target Q network grad_norm_clipping: float or None If not None gradients' norms are clipped to this value. """ assert type(env.observation_space) == gym.spaces.Box assert type(env.action_space) == gym.spaces.Discrete ############### # BUILD MODEL # ############### if len(env.observation_space.shape) == 1: # This means we are running on low-dimensional observations (e.g. RAM) input_shape = env.observation_space.shape else: img_h, img_w, img_c = env.observation_space.shape input_shape = (img_h, img_w, frame_history_len * img_c) num_actions = env.action_space.n # set up placeholders # placeholder for current observation (or state) obs_t_ph = tf.placeholder(tf.uint8, [None] + list(input_shape)) # placeholder for current action act_t_ph = tf.placeholder(tf.int32, [None]) # placeholder for current reward rew_t_ph = tf.placeholder(tf.float32, [None]) # placeholder for next observation (or state) obs_tp1_ph = tf.placeholder(tf.uint8, [None] + list(input_shape)) # placeholder for end of episode mask # this value is 1 if the next state corresponds to the end of an episode, # in which case there is no Q-value at the next state; at the end of an # episode, only the current state reward contributes to the target, not the # next state Q-value (i.e. target is just rew_t_ph, not rew_t_ph + gamma * q_tp1) done_mask_ph = tf.placeholder(tf.float32, [None]) # casting to float on GPU ensures lower data transfer times. obs_t_float = tf.cast(obs_t_ph, tf.float32) / 255.0 obs_tp1_float = tf.cast(obs_tp1_ph, tf.float32) / 255.0 # Here, you should fill in your own code to compute the Bellman error. This requires # evaluating the current and next Q-values and constructing the corresponding error. # TensorFlow will differentiate this error for you, you just need to pass it to the # optimizer. See assignment text for details. # Your code should produce one scalar-valued tensor: total_error # This will be passed to the optimizer in the provided code below. # Your code should also produce two collections of variables: # q_func_vars # target_q_func_vars # These should hold all of the variables of the Q-function network and target network, # respectively. A convenient way to get these is to make use of TF's "scope" feature. # For example, you can create your Q-function network with the scope "q_func" like this: # <something> = q_func(obs_t_float, num_actions, scope="q_func", reuse=False) # And then you can obtain the variables like this: # q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_func') # Older versions of TensorFlow may require using "VARIABLES" instead of "GLOBAL_VARIABLES" ###### # YOUR CODE HERE ###### # Declare variables for logging t_log = [] mean_reward_log = [] best_mean_log = [] episodes_log = [] exploration_log = [] learning_rate_log = [] current_q_func = q_func(obs_t_float, num_actions, scope="q_func", reuse=False) # Current Q-Value Function q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='q_func') target_q_func = q_func(obs_tp1_float, num_actions, scope="target_q_func", reuse=False) # Target Q-Value Function target_q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='target_q_func') act_t = tf.one_hot(act_t_ph, depth=num_actions, dtype=tf.float32, name="action_one_hot") q_act_t = tf.reduce_sum(act_t*current_q_func, axis=1) y = rew_t_ph + gamma * tf.reduce_max(target_q_func, reduction_indices=[1]) #which axis for max? total_error = tf.square(tf.subtract(y, q_act_t)) #(reward + gamma*V(s') - Q(s, a))**2 # construct optimization op (with gradient clipping) learning_rate = tf.placeholder(tf.float32, (), name="learning_rate") optimizer = optimizer_spec.constructor(learning_rate=learning_rate, **optimizer_spec.kwargs) train_fn = minimize_and_clip(optimizer, total_error, var_list=q_func_vars, clip_val=grad_norm_clipping) # update_target_fn will be called periodically to copy Q network to target Q network update_target_fn = [] for var, var_target in zip(sorted(q_func_vars, key=lambda v: v.name), sorted(target_q_func_vars, key=lambda v: v.name)): update_target_fn.append(var_target.assign(var)) update_target_fn = tf.group(*update_target_fn) # construct the replay buffer replay_buffer = ReplayBuffer(replay_buffer_size, frame_history_len) ############### # RUN ENV # ############### model_initialized = False num_param_updates = 0 mean_episode_reward = -float('nan') best_mean_episode_reward = -float('inf') last_obs = env.reset() LOG_EVERY_N_STEPS = 10000 SAVE_EVERY_N_STEPS = 200000 for t in itertools.count(): ### 1. Check stopping criterion if stopping_criterion is not None and stopping_criterion(env, t): break ### 2. Step the env and store the transition # At this point, "last_obs" contains the latest observation that was # recorded from the simulator. Here, your code needs to store this # observation and its outcome (reward, next observation, etc.) into # the replay buffer while stepping the simulator forward one step. # At the end of this block of code, the simulator should have been # advanced one step, and the replay buffer should contain one more # transition. # Specifically, last_obs must point to the new latest observation. # Useful functions you'll need to call: # obs, reward, done, info = env.step(action) # this steps the environment forward one step # obs = env.reset() # this resets the environment if you reached an episode boundary. # Don't forget to call env.reset() to get a new observation if done # is true!! # Note that you cannot use "last_obs" directly as input # into your network, since it needs to be processed to include context # from previous frames. You should check out the replay buffer # implementation in dqn_utils.py to see what functionality the replay # buffer exposes. The replay buffer has a function called # encode_recent_observation that will take the latest observation # that you pushed into the buffer and compute the corresponding # input that should be given to a Q network by appending some # previous frames. # Don't forget to include epsilon greedy exploration! # And remember that the first time you enter this loop, the model # may not yet have been initialized (but of course, the first step # might as well be random, since you haven't trained your net...) ##### # YOUR CODE HERE # Store last_obs into replay buffer idx = replay_buffer.store_frame(last_obs) if t == 0: act, reward, done = env.action_space.sample(), 0, False # Choose action epsilon = exploration.value(t) if not model_initialized or random.random() < epsilon: # With probability epsilon OR if model hasn't been initialized, choose a random action act = env.action_space.sample() else: # With probability 1 - epsilon, choose the best action from Q input_batch = replay_buffer.encode_recent_observation() q_vals = session.run(current_q_func, {obs_t_ph: input_batch[None, :]}) act = np.argmax(q_vals) # Step simulator forward one step last_obs, reward, done, info = env.step(act) replay_buffer.store_effect(idx, act, reward, done) # Store action taken after last_obs and corresponding reward if done == True: # done was True in latest transition; we have already stored that last_obs = env.reset() # Reset observation done = False ##### # at this point, the environment should have been advanced one step (and # reset if done was true), and last_obs should point to the new latest # observation ### 3. Perform experience replay and train the network. # note that this is only done if the replay buffer contains enough samples # for us to learn something useful -- until then, the model will not be # initialized and random actions should be taken if (t > learning_starts and t % learning_freq == 0 and replay_buffer.can_sample(batch_size)): # Here, you should perform training. Training consists of four steps: # 3.a: use the replay buffer to sample a batch of transitions (see the # replay buffer code for function definition, each batch that you sample # should consist of current observations, current actions, rewards, # next observations, and done indicator). # 3.b: initialize the model if it has not been initialized yet; to do # that, call # initialize_interdependent_variables(session, tf.global_variables(), { # obs_t_ph: obs_t_batch, # obs_tp1_ph: obs_tp1_batch, # }) # where obs_t_batch and obs_tp1_batch are the batches of observations at # the current and next time step. The boolean variable model_initialized # indicates whether or not the model has been initialized. # Remember that you have to update the target network too (see 3.d)! # 3.c: train the model. To do this, you'll need to use the train_fn and # total_error ops that were created earlier: total_error is what you # created to compute the total Bellman error in a batch, and train_fn # will actually perform a gradient step and update the network parameters # to reduce total_error. When calling session.run on these you'll need to # populate the following placeholders: # obs_t_ph # act_t_ph # rew_t_ph # obs_tp1_ph # done_mask_ph # (this is needed for computing total_error) # learning_rate -- you can get this from optimizer_spec.lr_schedule.value(t) # (this is needed by the optimizer to choose the learning rate) # 3.d: periodically update the target network by calling # session.run(update_target_fn) # you should update every target_update_freq steps, and you may find the # variable num_param_updates useful for this (it was initialized to 0) ##### # YOUR CODE HERE # 3.a Sample a batch of transitions obs_t_batch, act_batch, rew_batch, obs_tp1_batch, done_mask = replay_buffer.sample(batch_size) # 3.b Initialize model if not initialized yet if not model_initialized: initialize_interdependent_variables(session, tf.global_variables(), { obs_t_ph: obs_t_batch, obs_tp1_ph: obs_tp1_batch, }) session.run(update_target_fn) model_initialized = True # 3.c Train the model using train_fn and total_error session.run(train_fn, {obs_t_ph: obs_t_batch, act_t_ph: act_batch, rew_t_ph: rew_batch, obs_tp1_ph: obs_tp1_batch, done_mask_ph: done_mask, learning_rate: optimizer_spec.lr_schedule.value(t)}) # 3.d Update target network every taret_update_freq steps if t % target_update_freq == 0: session.run(update_target_fn) num_param_updates += 1 ##### ### 4. Log progress episode_rewards = get_wrapper_by_name(env, "Monitor").get_episode_rewards() if len(episode_rewards) > 0: mean_episode_reward = np.mean(episode_rewards[-100:]) if len(episode_rewards) > 100: best_mean_episode_reward = max(best_mean_episode_reward, mean_episode_reward) if t % LOG_EVERY_N_STEPS == 0 and model_initialized: print("Timestep %d" % (t,)) t_log.append(t) print("mean reward (100 episodes) %f" % mean_episode_reward) mean_reward_log.append(mean_episode_reward) print("best mean reward %f" % best_mean_episode_reward) best_mean_log.append(best_mean_episode_reward) print("episodes %d" % len(episode_rewards)) episodes_log.append(len(episode_rewards)) print("exploration %f" % exploration.value(t)) exploration_log.append(exploration.value(t)) print("learning_rate %f" % optimizer_spec.lr_schedule.value(t)) learning_rate_log.append(optimizer_spec.lr_schedule.value(t)) sys.stdout.flush() if t % SAVE_EVERY_N_STEPS == 0 and model_initialized: training_log = ({'t_log': t_log, 'mean_reward_log': mean_reward_log, 'best_mean_log': best_mean_log, 'episodes_log': episodes_log, 'exploration_log': exploration_log, 'learning_rate_log': learning_rate_log}) output_file_name = 'ram_lr'+str(lr_multiplier)+'_' + str(t) + '_data.pkl' with open(output_file_name, 'wb') as f: pickle.dump(training_log, f)

[ "prasannakumar2012@gmail.com" ]

prasannakumar2012@gmail.com

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/day04/test5.py

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susan025/myproj01

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# 对一字符串进行翻转操作 def myStr(): str = "nameisgod" mlist = [] for i in str: mlist.append(i) print(mlist) mlist.reverse() print(mlist) mstr = "" for j in mlist: mstr += j print(mstr) # 创建一个列表，存储公司10个名单，对这些名单进行排序，要求按姓名的字符多少来排 def compName(): clist = ["john","susan","backham","roy","乔","William","karry"] llist = [] mdict = dict() for i in clist: v = len(i) llist.append(v) print(llist) for j in clist: mdict[j] = llist[clist.index(j)] print(mdict) mdict.values() for n in mdict.keys(): pass #输入用户名密码进行注册，要求用户名允许数字字母6-16位，密码6-16位，不允许出现*#! def regist(): username = str(input("请输入用户名：")) password = str(input("请输入密码：")) namebool1 = len(username) >= 6 and len(username)<=16 namebool2 = "*" not in username and "#" not in username and "!" not in username pwdbool1 = len(password) >= 6 and len(password)<=16 pwdbool2 = "*" not in password and "#" not in password and "!" not in password if namebool1: if namebool2: if pwdbool1: if pwdbool2: print("注册成功！") else: print("密码中不能含有*#！") else: print("密码必须6-16位") else: print("用户名中不能含有*#！") else: print("用户名必须6-16位") def sstr(): #输入一个字符串为社会主义核心价值观的全拼，每个词用空格进行分隔，将这个字符串，转成列表，遍历输出 str = "she hui zhu yi he xin jia zhi guan" mlist = list(str.split(" ")) print(mlist) if __name__ == '__main__': myStr() compName() regist() sstr()

[ "542711570@qq.com" ]

542711570@qq.com

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/solution/141/answer.py

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youjiajia/learn-leetcode

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refs/heads/master

2021-09-14T23:39:26.459338

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class ListNode(object): def __init__(self, x): self.val = x self.next = None def printval(self): print self.val class Solution(object): def hasCycle(self, head): fastindex = head slowindex = head while fastindex and slowindex: fastindex = fastindex.next slowindex = slowindex.next if not fastindex: return False fastindex = fastindex.next if fastindex is slowindex: return True return False if __name__ == "__main__": a = ListNode(1) b = ListNode(2) c = ListNode(3) d = ListNode(4) a.next = b b.next = c c.next = d d.next = b s = Solution() result = s.hasCycle(a) print result

[ "jasonyou.info@gmail.com" ]

jasonyou.info@gmail.com

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/week5_7.py

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Sokiryanskaya/learn_python

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for i in range(10, 100): if i == 2 * (i // 10) * (i % 10): print(i)

[ "sokiryanskaya@gmail.com" ]

sokiryanskaya@gmail.com

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/master/app_url.py

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Chunshan-Theta/worker-sample

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2023-08-11T09:30:23.077020

2021-10-04T09:03:28

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import os import tornado.web from tornado_swagger.setup import setup_swagger from handler.api.query.testHandler import testHandler # jaeger # from jaeger_client import Config # import tornado_opentracing # def init_jaeger_tracer(service_name): # config = Config( # config={ # usually read from some yaml config # 'sampler': { # 'type': 'probabilistic', # 'param': 1, # }, # 'local_agent': { # 'reporting_host': '10.205.48.66', # }, # }, # service_name=service_name, # validate=True, # ) # # Create your opentracing tracer using TornadoScopeManager for active Span handling. # return config.initialize_tracer() # urls initial urls = [ ] class Application(tornado.web.Application): _routes = [ tornado.web.url(r"/status/Test", testHandler) ] def __init__(self, **settings): setup_swagger( self._routes, swagger_url="/docs", api_base_url="/", description="", api_version="1.0.0", title="Journal API", contact="name@domain", schemes=["http"], security_definitions={ "ApiKeyAuth": {"type": "apiKey", "in": "header", "name": "X-API-Key"} }, ) super(Application, self).__init__(self._routes, **settings) # tornado_opentracing.init_tracing() app = Application()

[ "gavin19950511@gmail.com" ]

gavin19950511@gmail.com

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/backend/medicar/medicos/serializers.py

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[]

no_license

FabriciaDiniz/desafio-drf

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refs/heads/master

2023-08-11T00:49:52.209838

2021-07-10T14:46:14

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from rest_framework import serializers from medicar.medicos.models import Medico from medicar.especialidades.serializers import EspecialidadeSerializer class MedicoSerializer(serializers.ModelSerializer): especialidade = EspecialidadeSerializer() class Meta: model = Medico fields = ['id', 'crm', 'nome', 'especialidade']

[ "afabriciadiniz@gmail.com" ]

afabriciadiniz@gmail.com

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/thonnycontrib/m5stack/esp32_api_stubs/flashbdev.py

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thonny/thonny-m5stack

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2020-04-20T14:57:15.605699

2019-11-18T22:28:36

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class FlashBdev: '' SEC_SIZE = 4096 START_SEC = 512 def ioctl(): pass def readblocks(): pass def writeblocks(): pass bdev = None esp = None size = 4194304

[ "aivar.annamaa@gmail.com" ]

aivar.annamaa@gmail.com

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/run_all_case.py

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[]

no_license

wing1king/test_app_api

fac36ff9e72b74d3ad182f35cacea9920c77e79b

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refs/heads/master

2020-04-25T22:25:01.423592

2019-03-19T06:33:30

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import sys import time import unittest from imp import reload from HTMLTestRunner import HTMLTestRunner reload(sys) # 定义测试用例的目录为当前目录 test_dir = 'E:\\test_api\\test_case' discover = unittest.defaultTestLoader.discover(test_dir, pattern="test*.py") if __name__ == "__main__": # 按照一定的格式获取当前的时间 now = time.strftime("%Y-%m-%d %H-%M-%S") # 定义报告存放路径 filename = r'D:' + now + 'test_teacher_result.html' fp = open(filename, "wb") # 定义测试报告 runner = HTMLTestRunner(stream=fp, title="接口测试报告", description="测试用例执行情况：") # 运行测试 runner.run(discover) # 关闭报告文件 fp.close()

[ "zhang4475153@126.com" ]

zhang4475153@126.com

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/Ejercicios/Tupla.py

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no_license

rramosaveros/CursoPythonCisco

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refs/heads/master

2023-06-27T12:07:52.652780

2021-08-01T14:19:19

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miTupla = (1, 10, 100, 1000) print(miTupla[0]) print(miTupla[-1]) print(miTupla[1:]) print(miTupla[:-2]) for elem in miTupla: print(elem)

[ "ramoslenin32@gmail.com" ]

ramoslenin32@gmail.com

6bedb5a9d61f5d22a8e0acab5e5d79d7a4cc82ee

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/posts/migrations/0007_post_category.py

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no_license

mabutotalent/tah_cms

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refs/heads/master

2022-12-28T18:58:08.224622

2020-10-10T19:08:43

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2020-10-10T19:08:44

2020-10-10T18:28:46

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# Generated by Django 3.1 on 2020-09-07 23:26 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('categories', '0001_initial'), ('posts', '0006_auto_20200907_1041'), ] operations = [ migrations.AddField( model_name='post', name='category', field=models.ForeignKey(default=True, on_delete=django.db.models.deletion.CASCADE, to='categories.category'), ), ]

[ "noreply@github.com" ]

mabutotalent.noreply@github.com

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/Assignment4/view.py

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[]

no_license

Kskulski/Software-Engineering

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refs/heads/master

2020-09-09T05:52:42.756023

2019-12-08T22:47:10

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import tkinter as Tk class View(): '''View in the MVC pattern assumes role of rendering user interface to the user, and maintaining an up to date view as it handles user interaction it receives from Controller. ''' def _add_numbers_keypad(self, frame): # calculator display self.display = Tk.Label(frame, text=0, width=12, height=1) self.display.grid(row=0, column=0, columnspan=10, pady=5) #calculator numbers pad self.one = Tk.Button(frame, text="1") self.one.grid(row=1, column=0) self.two = Tk.Button(frame, text="2") self.two.grid(row=1, column=1) self.three = Tk.Button(frame, text="3") self.three.grid(row=1, column=2) self.four = Tk.Button(frame, text="4") self.four.grid(row=2, column=0) self.five = Tk.Button(frame, text="5") self.five.grid(row=2, column=1) self.six = Tk.Button(frame, text="6") self.six.grid(row=2, column=2) self.seven = Tk.Button(frame, text="7") self.seven.grid(row=3, column=0) self.eight = Tk.Button(frame, text="8") self.eight.grid(row=3, column=1) self.nine = Tk.Button(frame, text="9") self.nine.grid(row=3, column=2) self.zero = Tk.Button(frame, text="0") self.zero.grid(row=4, column=1) def _add_operations_keypad(self, frame): #operations pad self.clear = Tk.Button(frame, text="C") self.clear.grid(row=4, column=0) self.equal = Tk.Button(frame, text="=") self.equal.grid(row=4, column=2) self.add = Tk.Button(frame, text="+") self.add.grid(row=2, column=5) self.sub = Tk.Button(frame, text="-") self.sub.grid(row=3, column=5) self.mul = Tk.Button(frame, text="*") self.mul.grid(row=2, column=6) self.div = Tk.Button(frame, text="/") self.div.grid(row=3, column=6) def __init__(self): self.root = Tk.Tk() self.root.title("MVC example: Calculator") self.root.geometry() self._frame = Tk.Frame(self.root) self._frame.pack() self._add_numbers_keypad(self._frame) self._add_operations_keypad(self._frame) def refresh(self, value): self.display.config(text= value) def attach_keyboard(self, callback): self.root.bind("<Key>", callback) def start(self): self.root.mainloop()

[ "kevinpskulski@lewisu.edu" ]

kevinpskulski@lewisu.edu

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/scripts/PrefixScanSVG/steps-to-svg.py

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[ "Apache-2.0" ]

permissive

s-ueno/Insights

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refs/heads/main

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import json from graphviz import Digraph def remove_prefix(prefix, value): if len(prefix) == 0 or len(prefix) == len(value): return value if not value.startswith(prefix): raise "{value} does not start with {prefix}".format(value, prefix) suffix = value[len(prefix):] if len(suffix) == 0: print(repr(prefix)) print(repr(value)) raise 'oops!' return "..." + suffix def get_package_id(value): return value.split('$')[-1] def get_package_url(value, version=None): id = get_package_id(value) url = "https://www.nuget.org/packages/{id}".format(id=id) if version: url += "/" + version return url bfs_data = json.load(open('steps-example1-BFS.json', 'rb')) dfs_data = json.load(open('steps-example1-DFS.json', 'rb')) is_nuget = False dfs = Digraph(comment='DFS', filename='dfs', format='svg') dfs.attr('graph', rankdir='LR') id_to_node = {node['Id']: node for node in dfs_data} id_to_children = {} for node in dfs_data: if node['ParentId']: if node['ParentId'] not in id_to_children: id_to_children[node['ParentId']] = [] id_to_children[node['ParentId']].append(node) query_count = 0 for step in dfs_data: if step['ParentId']: prefix_parent = id_to_node[step['ParentId']] while 'PartitionKeyPrefix' not in prefix_parent['Data']: prefix_parent = id_to_node[prefix_parent['ParentId']] prefix = prefix_parent['Data']['PartitionKeyPrefix'] else: prefix = '' if step['Data']['Type'] == 'Start': if not is_nuget: continue dfs.attr('node', shape='box', style='rounded') label = '<<b>Start</b>>' elif step['Data']['Type'] == 'EntitySegment': dfs.attr('node', shape='box', style='filled', fillcolor='darkseagreen1') if not is_nuget: if step['Data']['Count'] == 1: label = "<<b>1 result</b>: {0}, {1}>".format( step['Data']['First']['PartitionKey'], step['Data']['First']['RowKey'], step['Data']['Count'] ) else: label = "<<b>{0} results</b>: {1}, {2} - {3}, {4}>".format( step['Data']['Count'], step['Data']['First']['PartitionKey'], step['Data']['First']['RowKey'], step['Data']['Last']['PartitionKey'], step['Data']['Last']['RowKey'] ) elif (step['Data']['First']['PartitionKey'] == step['Data']['Last']['PartitionKey']): label = """< <table border="0" cellspacing="0" cellpadding="5"> <tr> <td href="{url}" title="{id}"><u><font color="blue">{pk}</font></u></td> <td>({count} total)</td> </tr> </table> >""".format( id=get_package_id(step['Data']['First']['PartitionKey']), url=get_package_url(step['Data']['First']['PartitionKey']), pk=remove_prefix(prefix, step['Data']['First']['PartitionKey']), count=step['Data']['Count']) else: label = """< <table border="0" cellspacing="0" cellpadding="5"> <tr> <td title="{first_id}" href="{first_url}"><u><font color="blue">{first_pk}</font></u></td> <td>-</td> <td title="{last_id}" href="{last_url}"><u><font color="blue">{last_pk}</font></u></td> <td> ({count} total)</td> </tr> </table> >""".format( first_id=get_package_id(step['Data']['First']['PartitionKey']), first_url=get_package_url(step['Data']['First']['PartitionKey'], step['Data']['First']['RowKey']), first_pk=remove_prefix(prefix, step['Data']['First']['PartitionKey']), last_id=get_package_id(step['Data']['Last']['PartitionKey']), last_url=get_package_url(step['Data']['Last']['PartitionKey'], step['Data']['Last']['RowKey']), last_pk=remove_prefix(prefix, step['Data']['Last']['PartitionKey']), count=step['Data']['Count']) elif step['Data']['Type'] == 'PartitionKeyQuery': query_count += 1 dfs.attr('node', shape='box', style='filled', fillcolor='beige') if not is_nuget: label = "<<b>Query {0}</b>: PK = '{1}' and RK > '{2}'>".format( query_count, step['Data']['PartitionKey'], step['Data']['RowKeySkip'], ) elif step['Id'] in id_to_children: label = """< <table border="0" cellspacing="0" cellpadding="5"> <tr> <td title="{id}" href="{url}"><u><font color="blue">{pk}</font></u></td> </tr> </table> >""".format( id=get_package_id(step['Data']['PartitionKey']), url=get_package_url(step['Data']['PartitionKey']), pk=remove_prefix(prefix, step['Data']['PartitionKey'])) else: label = "pk: " + remove_prefix(prefix, step['Data']['PartitionKey']) elif step['Data']['Type'] == 'PrefixQuery': query_count += 1 dfs.attr('node', shape='box', style='filled', fillcolor='darkslategray1') if not is_nuget: if len(step['Data']['PartitionKeyLowerBound']) == 0: label = "<<b>Query {0}</b>: PK = '{1}*'>".format( query_count, step['Data']['PartitionKeyPrefix']) else: label = "<<b>Query {0}</b>: PK = '{1}*' and PK > '{2}'>".format( query_count, step['Data']['PartitionKeyPrefix'], step['Data']['PartitionKeyLowerBound']) else: label = remove_prefix(prefix, step['Data']['PartitionKeyPrefix']) + "*" else: raise 'Unknown node type' # dfs.attr('node', rank=str(step['Data']['Depth'])) id = str(step['Id']) dfs.node(id, label=label) if step['ParentId'] and step['ParentId'] > 1: dfs.edge(str(step['ParentId']), id) dfs.save(filename="dfs.dot") dfs.view()

[ "joel.verhagen@gmail.com" ]

joel.verhagen@gmail.com

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/alipay/aop/api/response/ZhimaMerchantContractOnofferQueryResponse.py

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permissive

alipay/alipay-sdk-python-all

8bd20882852ffeb70a6e929038bf88ff1d1eff1c

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refs/heads/master

2023-08-27T21:35:01.778771

2023-08-23T07:12:26

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2023-04-25T04:54:02

2018-05-14T09:40:54

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#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.response.AlipayResponse import AlipayResponse class ZhimaMerchantContractOnofferQueryResponse(AlipayResponse): def __init__(self): super(ZhimaMerchantContractOnofferQueryResponse, self).__init__() self._cancel_operator = None self._cancel_supported = None self._contract_content = None self._contract_no = None self._contract_principal_desc = None self._contract_principal_logo = None self._contract_status = None self._ext_info = None self._fufilment_callback_url = None self._fufilment_cnt = None self._fufilment_desc = None self._fufilment_end_time = None self._fufilment_period_type = None self._fufilment_start_time = None self._gmt_accept = None self._gmt_cancel = None self._gmt_due = None self._gmt_end = None self._item_end_time = None self._item_no = None self._item_start_time = None self._offer_creater_id = None self._offer_creater_name = None self._offer_creater_type = None self._out_biz_no = None self._out_content_no = None self._sign_principal_id = None self._sign_principal_type = None self._subjects = None @property def cancel_operator(self): return self._cancel_operator @cancel_operator.setter def cancel_operator(self, value): self._cancel_operator = value @property def cancel_supported(self): return self._cancel_supported @cancel_supported.setter def cancel_supported(self, value): self._cancel_supported = value @property def contract_content(self): return self._contract_content @contract_content.setter def contract_content(self, value): self._contract_content = value @property def contract_no(self): return self._contract_no @contract_no.setter def contract_no(self, value): self._contract_no = value @property def contract_principal_desc(self): return self._contract_principal_desc @contract_principal_desc.setter def contract_principal_desc(self, value): self._contract_principal_desc = value @property def contract_principal_logo(self): return self._contract_principal_logo @contract_principal_logo.setter def contract_principal_logo(self, value): self._contract_principal_logo = value @property def contract_status(self): return self._contract_status @contract_status.setter def contract_status(self, value): self._contract_status = value @property def ext_info(self): return self._ext_info @ext_info.setter def ext_info(self, value): self._ext_info = value @property def fufilment_callback_url(self): return self._fufilment_callback_url @fufilment_callback_url.setter def fufilment_callback_url(self, value): self._fufilment_callback_url = value @property def fufilment_cnt(self): return self._fufilment_cnt @fufilment_cnt.setter def fufilment_cnt(self, value): self._fufilment_cnt = value @property def fufilment_desc(self): return self._fufilment_desc @fufilment_desc.setter def fufilment_desc(self, value): self._fufilment_desc = value @property def fufilment_end_time(self): return self._fufilment_end_time @fufilment_end_time.setter def fufilment_end_time(self, value): self._fufilment_end_time = value @property def fufilment_period_type(self): return self._fufilment_period_type @fufilment_period_type.setter def fufilment_period_type(self, value): self._fufilment_period_type = value @property def fufilment_start_time(self): return self._fufilment_start_time @fufilment_start_time.setter def fufilment_start_time(self, value): self._fufilment_start_time = value @property def gmt_accept(self): return self._gmt_accept @gmt_accept.setter def gmt_accept(self, value): self._gmt_accept = value @property def gmt_cancel(self): return self._gmt_cancel @gmt_cancel.setter def gmt_cancel(self, value): self._gmt_cancel = value @property def gmt_due(self): return self._gmt_due @gmt_due.setter def gmt_due(self, value): self._gmt_due = value @property def gmt_end(self): return self._gmt_end @gmt_end.setter def gmt_end(self, value): self._gmt_end = value @property def item_end_time(self): return self._item_end_time @item_end_time.setter def item_end_time(self, value): self._item_end_time = value @property def item_no(self): return self._item_no @item_no.setter def item_no(self, value): self._item_no = value @property def item_start_time(self): return self._item_start_time @item_start_time.setter def item_start_time(self, value): self._item_start_time = value @property def offer_creater_id(self): return self._offer_creater_id @offer_creater_id.setter def offer_creater_id(self, value): self._offer_creater_id = value @property def offer_creater_name(self): return self._offer_creater_name @offer_creater_name.setter def offer_creater_name(self, value): self._offer_creater_name = value @property def offer_creater_type(self): return self._offer_creater_type @offer_creater_type.setter def offer_creater_type(self, value): self._offer_creater_type = value @property def out_biz_no(self): return self._out_biz_no @out_biz_no.setter def out_biz_no(self, value): self._out_biz_no = value @property def out_content_no(self): return self._out_content_no @out_content_no.setter def out_content_no(self, value): self._out_content_no = value @property def sign_principal_id(self): return self._sign_principal_id @sign_principal_id.setter def sign_principal_id(self, value): self._sign_principal_id = value @property def sign_principal_type(self): return self._sign_principal_type @sign_principal_type.setter def sign_principal_type(self, value): self._sign_principal_type = value @property def subjects(self): return self._subjects @subjects.setter def subjects(self, value): self._subjects = value def parse_response_content(self, response_content): response = super(ZhimaMerchantContractOnofferQueryResponse, self).parse_response_content(response_content) if 'cancel_operator' in response: self.cancel_operator = response['cancel_operator'] if 'cancel_supported' in response: self.cancel_supported = response['cancel_supported'] if 'contract_content' in response: self.contract_content = response['contract_content'] if 'contract_no' in response: self.contract_no = response['contract_no'] if 'contract_principal_desc' in response: self.contract_principal_desc = response['contract_principal_desc'] if 'contract_principal_logo' in response: self.contract_principal_logo = response['contract_principal_logo'] if 'contract_status' in response: self.contract_status = response['contract_status'] if 'ext_info' in response: self.ext_info = response['ext_info'] if 'fufilment_callback_url' in response: self.fufilment_callback_url = response['fufilment_callback_url'] if 'fufilment_cnt' in response: self.fufilment_cnt = response['fufilment_cnt'] if 'fufilment_desc' in response: self.fufilment_desc = response['fufilment_desc'] if 'fufilment_end_time' in response: self.fufilment_end_time = response['fufilment_end_time'] if 'fufilment_period_type' in response: self.fufilment_period_type = response['fufilment_period_type'] if 'fufilment_start_time' in response: self.fufilment_start_time = response['fufilment_start_time'] if 'gmt_accept' in response: self.gmt_accept = response['gmt_accept'] if 'gmt_cancel' in response: self.gmt_cancel = response['gmt_cancel'] if 'gmt_due' in response: self.gmt_due = response['gmt_due'] if 'gmt_end' in response: self.gmt_end = response['gmt_end'] if 'item_end_time' in response: self.item_end_time = response['item_end_time'] if 'item_no' in response: self.item_no = response['item_no'] if 'item_start_time' in response: self.item_start_time = response['item_start_time'] if 'offer_creater_id' in response: self.offer_creater_id = response['offer_creater_id'] if 'offer_creater_name' in response: self.offer_creater_name = response['offer_creater_name'] if 'offer_creater_type' in response: self.offer_creater_type = response['offer_creater_type'] if 'out_biz_no' in response: self.out_biz_no = response['out_biz_no'] if 'out_content_no' in response: self.out_content_no = response['out_content_no'] if 'sign_principal_id' in response: self.sign_principal_id = response['sign_principal_id'] if 'sign_principal_type' in response: self.sign_principal_type = response['sign_principal_type'] if 'subjects' in response: self.subjects = response['subjects']

[ "jiandong.jd@antfin.com" ]

jiandong.jd@antfin.com

40dbde0013cb65e7a43ae769c10ad23ad832acc2

9897526c0a108f1d0d423ca7290286de3ed85727

/parser_django/breuninger_shop/models.py

742d1d8ba624d092e122c2bf747548eed1556d94

[]

no_license

NedelkoA/shop_parser

faf7064ede5612748664beea6fb8bd75b1f3682a

0d0c7cb7e924d6cb5c999cdc82a392f6171010a8

refs/heads/master

2020-03-25T08:34:12.928353

2018-08-10T15:43:22

143,620,412

0

null

UTF-8

Python

false

394

py

from django.db import models class ItemModel(models.Model): name = models.CharField(max_length=60) brand = models.CharField(max_length=60) price = models.FloatField() description = models.TextField() size = models.TextField(null=True) currency = models.CharField(max_length=4, null=True) image = models.TextField() def __str__(self): return self.name

[ "nedartem@mail.ru" ]

nedartem@mail.ru

f3536713520f35a420c9733cf76fdefc9270f359

d9d44028fe7fdc5c5d8a9cf8af423b1bb46ebff0

/data_api/api/tests.py

cb85397057f2a4945b2bdda92c1b8b01d10556f7

[]

no_license

xkmato/data_api

b19f7d19706bd354740d960be66038d36106676d

732c1c147a8346e22b744699d3ce449e3c5a70f2

refs/heads/master

2023-01-03T03:51:18.764841

2016-09-22T09:54:25

40,356,292

0

1

null

2022-12-26T19:50:57

2015-08-07T11:01:15

Python

UTF-8

Python

false

6,237

py

from datetime import datetime from django.utils import unittest from data_api.api.models import Org, Urn, Group, Contact, Broadcast, Campaign, Flow, Event, Label, Message, Run, \ Boundary, Result __author__ = 'kenneth' class FakeTemba(object): __dict__ = {} def __init__(self, **kwargs): for k, v in kwargs.items(): self.__dict__ = kwargs setattr(self, k, v) class TestModels(unittest.TestCase): def setUp(self): org = Org.objects.first() self.org = org self.urns = ['tel:1234', 'twitter:5678', '876565'] self.temba_group = FakeTemba(uuid='090IOU98', name='test_group', size=1) self.temba_contact = FakeTemba(uuid='97976768', name='test_contact', urns=self.urns, groups=[self.temba_group.uuid], fields={'name': 'test_field'}, language='en', modified_on=datetime.now()) self.temba_broadcast = FakeTemba(id=1, urns=self.urns, contacts=[self.temba_contact.uuid], groups=[self.temba_group.uuid], text='test test message', status='S', created_on=datetime.now()) self.temba_campaign = FakeTemba(uuid='IOUIU8908', name='test_campaign', group=self.temba_group.uuid, created_on=datetime.now()) self.rule_set = FakeTemba(uuid='iteueiot', label='some label', response_type='I') self.temba_flow = FakeTemba(uuid='89077897897', name='test_flow', archived='T', labels=[], participants=3, runs=3, completed_runs=2, rulesets=[self.rule_set], created_on=datetime.now()) self.temba_event = FakeTemba(uuid='79079079078', campaign=self.temba_campaign.uuid, relative_to='yuyyer', offset=5, unit='something', delivery_hour=4, message='Some message', flow=self.temba_flow.uuid, created_on=datetime.now()) self.temba_label = FakeTemba(uuid='0789089789', name='test_label', count=5) self.temba_message = FakeTemba(id=242, broadcast=self.temba_broadcast.id, contact=self.temba_contact.uuid, urn=self.urns[0], status='S', type='F', labels=[self.temba_label.name], direction='I', archived='F', text='Hi There', created_on=datetime.now(), delivered_on=datetime.now(), sent_on=datetime.now()) self.temba_run_value_set = FakeTemba(node='90890', category='SC', text='Some Text', rule_value='Y', value='yes', label='some', time=datetime.now()) self.temba_flow_step = FakeTemba(node='Some Node', text='Yo yo', value='youngh', type='I', arrived_on=datetime.now(), left_on=datetime.now()) self.temba_run = FakeTemba(id=43, flow=self.temba_flow.uuid, contact=self.temba_contact.uuid, steps=[self.temba_flow_step], values=[self.temba_run_value_set], create_on=datetime.now(), completed='y') self.temba_geometry = FakeTemba(type='some geo type', coordinates='gulu lango') self.temba_boundary = FakeTemba(boundary='some boundary', name='test_boundary', level='U', parent='b', geometry=[self.temba_geometry]) self.temba_category_stats = FakeTemba(count=10, label='stats') self.temba_result = FakeTemba(boundary=None, set=4, unset=5, open_ended='open ended?', label='result1', categories=[self.temba_category_stats]) def test_create_from_temba(self): urn = Urn.create_from_temba(self.urns[0]) self.assertEqual((urn.type, urn.identity), tuple(self.urns[0].split(':'))) group_count = Group.objects.count() Group.create_from_temba(self.org, self.temba_group) self.assertEqual(group_count+1, Group.objects.count()) contact_count = Contact.objects.count() Contact.create_from_temba(self.org, self.temba_contact) self.assertEqual(contact_count+1, Contact.objects.count()) broadcast_count = Broadcast.objects.count() Broadcast.create_from_temba(self.org, self.temba_broadcast) self.assertEqual(broadcast_count+1, Broadcast.objects.count()) campaign_count = Campaign.objects.count() Campaign.create_from_temba(self.org, self.temba_campaign) self.assertEqual(campaign_count+1, Campaign.objects.count()) flow_count = Flow.objects.count() flow = Flow.create_from_temba(self.org, self.temba_flow) self.assertEqual(flow_count+1, Flow.objects.count()) self.assertEqual(flow.rulesets[0].uuid, self.rule_set.uuid) event_count = Event.objects.count() Event.create_from_temba(self.org, self.temba_event) self.assertEqual(event_count+1, Event.objects.count()) label_count = Label.objects.count() Label.create_from_temba(self.org, self.temba_label) self.assertEqual(label_count+1, Label.objects.count()) message_count = Message.objects.count() Message.create_from_temba(self.org, self.temba_message) self.assertEqual(message_count+1, Message.objects.count()) run_count = Run.objects.count() run = Run.create_from_temba(self.org, self.temba_run) self.assertEqual(run_count+1, Run.objects.count()) self.assertEqual(run.values[0].text, self.temba_run_value_set.text) self.assertEqual(run.steps[0].text, self.temba_flow_step.text) boundary_count = Boundary.objects.count() boundary = Boundary.create_from_temba(self.org, self.temba_boundary) self.assertEqual(boundary_count+1, Boundary.objects.count()) self.assertEqual(boundary.geometry[0].coordinates, self.temba_geometry.coordinates) result_count = Result.objects.count() result = Result.create_from_temba(self.org, self.temba_result) self.assertEqual(result_count+1, Result.objects.count()) self.assertEqual(result.categories[0].label, self.temba_category_stats.label)

[ "kbonky@gmail.com" ]

kbonky@gmail.com

29f41c2d015defff1c1e2476495e93db48b05ffd

53d030c44cff0a7da956f406c434fe1b2a00589a

/token_preds_evaluate.py

d3880daa0b39fd139eaaf195521d5a13ed39ef9f

[]

no_license

bujol12/bert-seq-interpretability

ef68716f69482cac334679867849618f1eb53bc2

eed39dceff3560ea3311bc2540671783414e3cce

refs/heads/master

2023-05-11T07:15:23.339979

2021-06-06T12:42:07

2021-06-06T12:42:34

294,078,771

10

1

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py

import sys import os from utils.tsv_dataset import ( TSVClassificationDataset, Split, get_labels, compute_seq_classification_metrics, ) from utils.arguments import datasets, DataTrainingArguments, ModelArguments from sklearn.metrics import average_precision_score import logging from math import sqrt from utils.arguments import ( datasets, DataTrainingArguments, ModelArguments, parse_config, ) from utils.model import SeqClassModel logging.basicConfig(level=logging.INFO) logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger = logging.getLogger(__name__) def choose_top_and_threshold( examples, importance_threshold, top_count, pos_label, default_label ): y_pred = [] # print(importance_threshold) for example in examples: predictions = [] scores = [ (idx, float(example.labels[idx])) for idx in range(0, len(example.labels)) ] scores.sort(key=lambda x: x[1]) labels = list(map(lambda x: x[0], scores[-top_count:])) count = 0 for idx in range(0, len(example.labels)): label = example.labels[idx] # label = 1.0 - float(label) # print(label, importance_threshold, idx) # print(float(label) >= importance_threshold, idx in labels) if float(label) >= importance_threshold and idx in labels: predictions.append(pos_label) count += 1 else: predictions.append(default_label) example.predictions = predictions y_pred.append(predictions) return y_pred def pred_stats(y_true, y_pred, label): predicted_cnt = 0 correct_cnt = 0 total_cnt = 0 for i in range(0, len(y_true)): # print(i, len(y_true[i]), len(y_pred[i])) for j in range(0, len(y_true[i])): if y_pred[i][j] == label: predicted_cnt += 1 if y_pred[i][j] == label and y_pred[i][j] == y_true[i][j]: correct_cnt += 1 if y_true[i][j] == label: total_cnt += 1 return { "predicted_cnt": predicted_cnt, "correct_cnt": correct_cnt, "total_cnt": total_cnt, } def get_pred_scores(y_true, y_pred, label): pred_stats_res = pred_stats(y_true, y_pred, label) print(pred_stats_res) res = {} res["precision"] = ( pred_stats_res["correct_cnt"] / pred_stats_res["predicted_cnt"] if pred_stats_res["predicted_cnt"] > 0 else 0.0 ) res["recall"] = ( pred_stats_res["correct_cnt"] / pred_stats_res["total_cnt"] if pred_stats_res["total_cnt"] > 0 else 0.0 ) res["f1"] = ( (2.0 * res["precision"] * res["recall"] / (res["precision"] + res["recall"])) if (res["precision"] + res["recall"]) > 0 else 0.0 ) res["f0.5"] = ( ((1 + 0.5 * 0.5) * res["precision"] * res["recall"]) / (0.5 * 0.5 * res["precision"] + res["recall"]) if (0.5 * 0.5 * res["precision"] + res["recall"]) > 0 else 0.0 ) return res def get_corr(y_target, y_pred): sum_pred = 0.0 sum_target = 0.0 count = 0.0 assert len(y_pred) == len(y_target) for i in range(0, len(y_target)): assert len(y_pred[i]) == len(y_target[i]) for j in range(0, len(y_target[i])): if y_target[i][j] >= 0: # print(y_target[i][j], y_pred[i][j]) count += 1.0 sum_pred += y_pred[i][j] sum_target += y_target[i][j] sq_diff_pred = 0.0 sq_diff_target = 0.0 diff_sum = 0.0 mean_pred = sum_pred / count mean_target = sum_target / count for i in range(0, len(y_target)): assert len(y_pred[i]) == len(y_target[i]) for j in range(0, len(y_target[i])): if y_target[i][j] >= 0: sq_diff_pred += (y_pred[i][j] - mean_pred) ** 2 sq_diff_target += (y_target[i][j] - mean_target) ** 2 diff_sum += (y_pred[i][j] - mean_pred) * (y_target[i][j] - mean_target) # print (diff_sum, sq_diff_pred, sq_diff_target, mean_pred, mean_target) corr = ( diff_sum / (sqrt(sq_diff_pred) * sqrt(sq_diff_target)) if sq_diff_pred != 0 and sq_diff_target != 0 else 0.0 ) return corr def get_map(y_true, y_pred, label): sum_val = 0.0 assert len(y_true) == len(y_pred) cnt = 0 for i in range(len(y_true)): if ( max(y_true[i]) > 0.0 ): # only calculate MAP over sentences with positive tokens # logger.info("Results:") # logger.info(y_true[i]) # logger.info(y_pred[i]) ap = average_precision_score(y_true[i], y_pred[i]) # logger.info(ap) sum_val += ap cnt += 1 return sum_val / cnt # mean AP if __name__ == "__main__": if len(sys.argv) < 2: logger.error("Required args: [config_path]") exit() logger.info("Parsing Config.") config_dict = parse_config(sys.argv[1]) dataset = datasets[config_dict["dataset"]] labels = get_labels(dataset.labels) positive_label = dataset.positive_label attn_head_id = None attn_layer_id = None if config_dict["method"] == "model_attention": if len(sys.argv) != 4: logger.error("Required args: [config_path] [layer_id] [head_id]") exit() attn_head_id = int(sys.argv[3]) attn_layer_id = int(sys.argv[2]) input_dir = config_dict["results_input_dir"].format( method=config_dict["method"], experiment_name=config_dict["experiment_name"].format( attn_layer_id, attn_head_id ), model_name=config_dict["model_name"], dataset_name=config_dict["dataset"], datetime=config_dict.get("datetime", ""), ) str2mode = {"dev": Split.dev, "train": Split.train, "test": Split.test} mode = str2mode[config_dict["dataset_split"]] data_config = dict( labels=labels, max_seq_length=config_dict["max_seq_length"], overwrite_cache=dataset.overwrite_cache, make_all_labels_equal_max=False, default_label=config_dict["test_label_dummy"], is_seq_class=False, lowercase=config_dict["lowercase"], mode=mode, model_type="token_eval", ) logger.info("Reading Token Results.") results_dataset = TSVClassificationDataset( input_dir, tokenizer=None, file_name=config_dict["results_input_filename"], normalise_labels=config_dict.get("normalise_preds", False), **data_config, ) logger.info("Reading gold labels.") eval_dataset = TSVClassificationDataset( dataset.data_dir, tokenizer=None, file_name=dataset.file_name_token, **data_config, ) print(len(eval_dataset.examples)) print(len(results_dataset.examples)) logger.info("Apply threshold and top count") y_pred = choose_top_and_threshold( results_dataset.examples, config_dict["importance_threshold"], int(config_dict["top_count"]), default_label=labels[0] if labels[0] != positive_label else labels[1], pos_label=positive_label, ) y_true = [] for example in eval_dataset.examples: y_true.append(example.labels) logger.info("Get pred scores") res = get_pred_scores(y_true, y_pred, label=positive_label) y_pred_values = list( map( lambda ex: list(map(lambda x: max(float(x), 0.0), ex.labels)), results_dataset.examples, ) # make labels be within 0 and 1 ) y_true_values = list( map( lambda ex: list( map(lambda l: (1.0 if l == positive_label else 0.0), ex.labels) ), eval_dataset.examples, ) ) for i in range(0, len(y_true)): if max(y_true_values[i]) > 0: logger.info(y_true[i]) logger.info(y_true_values[i]) break logger.info("Get MAP and Correlation metrics") res["MAP"] = get_map(y_true_values, y_pred_values, positive_label) res["corr"] = get_corr(y_true_values, y_pred_values) logger.info("RESULTS:") logger.info(str(res)) if config_dict.get("eval_results_filename", None) is not None: logger.info("saving eval results") filename = os.path.join(input_dir, config_dict["eval_results_filename"]) with open(filename, "w") as fhand: fhand.write(str(res))

[ "kamil.bujel19@imperial.ac.uk" ]

kamil.bujel19@imperial.ac.uk

320808f49265a4c45a70b217ea9fb74e72f5643f

3465b5932ef610742ea75e4b7935ef8b2bd0c84a

/Programmers_Spicier.py

041e713c299f068de4f84d8c15de7b793c57e3a3

[]

no_license

taehopark0/algorithm_study

25f4c4b9b5b3622ef38314d883b19e0c930f0855

8485234db6b0f93e99abe29124a4f9c5bcbfd36f

refs/heads/main

2023-07-08T18:23:01.759851

2021-08-03T14:46:40

363,688,854

1

0

null

UTF-8

Python

false

1,644

py

#매운 것을 좋아하는 Leo는 모든 음식의 스코빌 지수를 K 이상으로 만들고 싶습니다. #모든 음식의 스코빌 지수를 K 이상으로 만들기 위해 Leo는 스코빌 지수가 가장 낮은 두 개의 음식을 아래와 같이 특별한 방법으로 섞어 새로운 음식을 만듭니다. #섞은 음식의 스코빌 지수 = 가장 맵지 않은 음식의 스코빌 지수 + (두 번째로 맵지 않은 음식의 스코빌 지수 * 2) #Leo는 모든 음식의 스코빌 지수가 K 이상이 될 때까지 반복하여 섞습니다. #Leo가 가진 음식의 스코빌 지수를 담은 배열 scoville과 원하는 스코빌 지수 K가 주어질 때, #모든 음식의 스코빌 지수를 K 이상으로 만들기 위해 섞어야 하는 최소 횟수를 return 하도록 solution 함수를 작성해주세요. # - scoville의 길이는 2 이상 1,000,000 이하입니다. # - K는 0 이상 1,000,000,000 이하입니다. # - scoville의 원소는 각각 0 이상 1,000,000 이하입니다. # - 모든 음식의 스코빌 지수를 K 이상으로 만들 수 없는 경우에는 -1을 return 합니다. scoville = [1, 2, 3, 9, 10, 12] K = 7 def solution(scoville, K): answer = 0 while scoville[0]< K: a = scoville[0]+scoville[1]*2 scoville.remove(scoville[0]) scoville.remove(scoville[0]) scoville.append(a) scoville = sorted(scoville) if scoville[0] < K: answer+=1 elif scoville[0] < K and len(scoville) == 0: return -1 if scoville[0] >= K: break return answer+1 print(solution(scoville,K))

[ "taehojj@gmail.com" ]

taehojj@gmail.com

395f94d1dc84e406686d748d046cf456b3ed5e25

70d5cfec4410f4d90d87988aa5c935387c7df32a

/depth4.py

a3793fbd1a753f46fbe5ee086907b595a2038292

[]

no_license

csd-robocon-nitk/Robocon-2020

6c190961a8502d4d7f626b7e2657b9145cae8512

09a10f655137cfbf2d89883098561d96441c16ec

refs/heads/master

2022-04-09T08:08:46.674327

2020-02-18T18:51:02

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py

import pygame import numpy as np import sys import cv2 as cv from freenect import sync_get_depth as get_depth def make_gamma(): """ Create a gamma table """ num_pix = 2048 # there's 2048 different possible depth values npf = float(num_pix) _gamma = np.empty((num_pix, 3), dtype=np.uint16) for i in xrange(num_pix): v = i / npf v = pow(v, 3) * 6 pval = int(v * 6 * 256) lb = pval & 0xff pval >>= 8 if pval == 0: a = np.array([255, 255 - lb, 255 - lb], dtype=np.uint8) elif pval == 1: a = np.array([255, lb, 0], dtype=np.uint8) elif pval == 2: a = np.array([255 - lb, lb, 0], dtype=np.uint8) elif pval == 3: a = np.array([255 - lb, 255, 0], dtype=np.uint8) elif pval == 4: a = np.array([0, 255 - lb, 255], dtype=np.uint8) elif pval == 5: a = np.array([0, 0, 255 - lb], dtype=np.uint8) else: a = np.array([0, 0, 0], dtype=np.uint8) _gamma[i] = a return _gamma gamma = make_gamma() if __name__ == "__main__": fpsClock = pygame.time.Clock() FPS = 30 # kinect only outputs 30 fps disp_size = (640, 480) pygame.init() screen = pygame.display.set_mode(disp_size) font = pygame.font.SysFont('comicsans', 32) # provide your own font #np.set_printoptions(threshold=sys.maxsize) while True: events = pygame.event.get() for e in events: if e.type == pygame.QUIT: sys.exit() fps_text = "FPS: {0:.2f}".format(fpsClock.get_fps()) # draw the pixels depth = np.rot90(get_depth()[0]) # get the depth readinngs from the camera pixels = gamma[depth] # the colour pixels are the depth readings overlayed onto the gamma table depth = 100.0 / (depth*-0.0030711016 + 3.3309495161); depth = depth-(0.0003*(depth)*(depth)-0.09633*(depth)-0.6032)- 0.04*(depth) +0.511; # sum = 0; # im2, contours, hierarchy = cv.findContours(depth, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE) # cv.drawContours(img, contours, -1, (0,255,0), 3) #for i in range(640): # if(depth[i][0] < 0): # sum = sum + depth[i-100][j] # # # print(sum/240) for i in range(640): if(depth[i][240]-depth[i+1]>50): b1=i elif(depth[i][240]-depth[i+1]<50): b2=i b3=(b1+b2)/2 print(depth[b3][240]) #print(depth.shape) #print(pixels) temp_surface = pygame.Surface(disp_size) pygame.surfarray.blit_array(temp_surface, pixels) pygame.transform.scale(temp_surface, disp_size, screen) screen.blit(font.render(fps_text, 1, (255, 255, 255)), (30, 30)) pygame.display.flip() fpsClock.tick(FPS)

[ "noreply@github.com" ]

csd-robocon-nitk.noreply@github.com

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/component/io/mspa_io.py

d1211d6efb9465ac30813611b5328e094bf4a26e

[ "MIT" ]

permissive

dfguerrerom/gwb

39c2c0163aeceea14d55421b2c9958c210fa839e

162f7e1a885945814c584a188e78d571a32a9d80

refs/heads/master

2023-04-19T21:06:54.497659

2021-05-19T19:30:24

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py

import json from .gwb_io import GWBIo from component import parameter as cp class MspaIo(GWBIo): def __init__(self): # the init file self.file = None # all the bytes values self.background = [] self.foreground = [] # the process self.connectivity = cp.connectivity[0]['value'] self.edge_width = 1 self.transition = 1 self.int_ext = 1 super().__init__(process = 'mspa') def update_byte_list(self): """manually update the byte_list""" return super().update_byte_list([ self.background, self.foreground ]) def update_params_list(self): """manually update the params list""" return super().update_params_list([ self.connectivity, self.edge_width, self.transition, self.int_ext, ]) def get_params_list(self): """get the params list for naming purposes (_ and no spaces)""" self.update_params_list() return super().get_params_list(self.params_list)

[ "pierrick.rambaud49@gmail.com" ]

pierrick.rambaud49@gmail.com

769d8d41af18f2039f539e85f24c3803bc0ea6d4

1e74c6b35a4cf9d9b56a7172433c532abd3e5955

/LFs/__init__.py

d506dde046d84ed649de970d5fef5de11507bdb2

[]

no_license

dsteam2021/snorkel_app

fbc9ddce7562953fdcc090cda4e76ef5f351b717

2dec23a9bb3064720216752edc35f222fcb50ffb

refs/heads/master

2023-07-15T10:55:42.821224

2021-08-27T04:34:08

393,338,167

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UTF-8

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614

py

import os import regex as re import numpy as np import pandas as pd from unidecode import unidecode from snorkel.labeling.apply.dask import PandasParallelLFApplier from snorkel.labeling import labeling_function, PandasLFApplier, LFAnalysis from snorkel.analysis import get_label_buckets lfs = [] # n0_bat_dong_san.py -> n0_bat_dong_san # lọc các file lfs để auto import list_lfs = [i.split('.')[0] for i in os.listdir('LFs') if (i != 'util.py' and i[0] != '_' and i.split('.')[-1] == 'py')] for i in list_lfs: file_lfs = __import__('LFs.' + i, fromlist=['get_lfs']) lfs.extend(file_lfs.get_lfs())

[ "tranhuuhuy297@gmail.com" ]

tranhuuhuy297@gmail.com

a3f5430553d660e901a3ca13a353ab5b2be852d6

422dd5d3c48a608b093cbfa92085e95a105a5752

/students/stefanjp1/lesson06/calculator/divider.py

98c8637ce4e5f4c2efa63587991b974a17b30611

[]

no_license

UWPCE-PythonCert-ClassRepos/SP_Online_Course2_2018

a2052fdecd187d7dd6dbe6f1387b4f7341623e93

b1fea0309b3495b3e1dc167d7029bc9e4b6f00f1

refs/heads/master

2021-06-07T09:06:21.100330

2019-11-08T23:42:42

130,731,872

4

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null

2021-06-01T22:29:19

2018-04-23T17:24:22

Python

UTF-8

Python

false

229

py

''' Module to provide division functionality ''' class Divider(object): ''' Division Operator Class ''' @staticmethod def calc(operand_1, operand_2): ''' return a / b ''' return operand_1/operand_2

[ "stefanjp@gmail.com" ]

stefanjp@gmail.com

20c499deff02d5afe014d4e8b4070e7cd1c9e86f

cac359ca1bc562ea58cfcc9550b9c5317567b028

/src/posts/forms.py

4bfa8f7ebb5aa4c8b729c7e68abbaff64f00794b

[]

no_license

Ekluv/Social-Media-WebApp-Django

1b8f17ade1c55b191cf249470451326904fb9124

1194a8fd013d1e832ef02be7a24a6074fbbde210

refs/heads/master

2021-01-10T17:07:40.569535

2015-12-12T07:53:54

2015-12-12T07:54:09

47,636,962

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UTF-8

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py

from django import forms from django.contrib.auth.models import User from .models import Post,Like class PostForm(forms.ModelForm): class Meta: model = Post fields = ['title'] def clean_title(self): title = self.cleaned_data.get('title') return title class LikeForm(forms.ModelForm): class Meta: model = Like fields = ['voter','post'] exclude = ['voter','post']

[ "ekluv@live.com" ]

ekluv@live.com

e7b7e896cef558deb4016b44df60bcae04522288

f66faf8003b73157b45e96cceffa9036bfb9bfc3

/chapter1/transposingVector.py

8f07fb43d041f30efccc128bc8e0b4fb1e33e4bc

[]

no_license

anupkumarsahu/Machine_Learning_with_python_cookbook

eb5db923c005bf991143cfb8071077ebc676be90

1c67325280aa324b599941fefdaf82203caf8538

refs/heads/master

2020-03-22T10:07:14.635555

2018-08-02T17:16:21

139,879,409

0

null

UTF-8

Python

false

235

py

# Load library import numpy as np # Create matrix matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # Transpose matrix print(matrix.T) # Transpose vector print(np.array([1, 2, 3, 4, 5, 6]).T)

[ "anup-kumar.sahu@hpe.com" ]

anup-kumar.sahu@hpe.com

0c3524ccd8f2582c4b9f9d5dac08f4c41406858a

cfee610219adff6e997893a760252b7a9b1a3b00

/fac_staff.py

6f33763e8674badd86424544ab815210d5f5d982

[]

no_license

elylib/patron_uploads

8c2a98e23633920dc534208b23b4a7a2e9292a07

2f0c4bcd5213731b1bcc2f8739abee4274709148

refs/heads/master

2020-03-28T14:48:21.605055

2018-09-14T18:17:12

148,524,399

0

null

UTF-8

Python

false

4,285

py

""" We get Faculty/Staff data from payroll to load into Worldshare Management Services. Currently this file comes from Melissa Cody. This code reads that file and transforms the data into the format WMS expects. It also constructs university IDs and let's the user know if any conflicts exist, i.e. John Smith and Jane Smith both would have university IDs of jsmith, so the user can check the university directory to clear up these situations. This script allows for a less error-prone, more efficient, and more consistent method of loading Fac/Staff accounts in WMS each year. """ import csv from pprint import pprint import string import openpyxl from constants import OCLC_FIELDS def make_wsu_username(first, last): """ Combine first and last names to make a WSU campus ID WSU uses the first letter of your first name and your last name to construct IDs. This gives us most of the names correctly, but you will have to manually check some against the directory because sometimes there is overlap (i.e. Steve and Suzanne Adams) and sometimes there is a space or punctuation in the last name that makes it reflect differently. Also, some emails will be wrong because some groups, like IT, have special email addresses. This has not traditionally been a problem, can revisit if it becomes one. """ return first[0] + sanitize_last_name(last) translator = str.maketrans('', '', string.punctuation) def sanitize_last_name(last): """Get rid of spaces and punctuation to fit our typical user id rules""" out = last.translate(translator) return out.replace(' ', '') def make_email(user): return '{0}@westfield.ma.edu'.format(user) def main(file_location, today): year, month, day = today.year, today.month, today.day sheet = openpyxl.load_workbook(file_location).active with open(f'WEX_{year}_{month}_{day}_StaffPatrons_1_Tab_1.1.txt', 'w', newline="") as w: """ This is not necessarily the ideal way to go about this (lining up two lists for field names and data rows), but this iteration is working. Some information that is constant among users is hardcoded. The WEX_2018_09_07_StaffPatrons_1_Tab_1.1.txt that is generated will need to be gone over to double check the rows with user ids in double_check that are printed out at the end. These are accounts that are likely to have errors because either a duplicate user id or a space in the last name. """ writer = csv.writer(w, delimiter='\t') writer.writerow(OCLC_FIELDS) usernames = set() barcodes = set() double_check = [] for row in sheet.iter_rows(min_row=2, max_row=sheet.max_row): row = [cell.value for cell in row] if row[6] in barcodes: # The file from payroll includes some people multiple times, i.e. if they teach in the # day and CGCE units, they are listed as employees in two areas. This will skip them # if we've already seen them. continue username = make_wsu_username(row[3].lower(), row[2].lower()) # All these empty strings are to make sure the format of the tsv is just so # Should probably make the facstaff upload more like the student upload expiration_date = today.replace(year=today.year + 3).strftime('%Y-%m-%d') new_row = ['', row[3], '', row[2], '', '', '', '', '', '1410', row[6], username, 'urn:mace:oclc:idm:westfieldstatecollege:ldap', 'Faculty and Staff', '', expiration_date, '134347', row[4], '577 Western Avenue', 'Westfield', 'MA', '01086', '', '', '', '', '', '', '', '', '', make_email(username), '', '', '', '', '', '', '', '', '', '', '', '', '', ''] writer.writerow(new_row) # This makes it so we can double check people who may be given an incorrect or duplicate # idAtSource value, so we can double check them in the directory if username in usernames or ' ' in row[2]: double_check.append(username) usernames.add(username) barcodes.add(row[6]) pprint(double_check)

[ "hill.charles2@gmail.com" ]

hill.charles2@gmail.com

9bffa2243c34caecd45580e6277415e8e49a06d4

a57a7604bf34f1d6bda29f797c275a441d2192f9

/request.py

0aa60f48cf20f557db5814b3fd52d477757a5538

[]

no_license

ibaguio/magpy

289ad063278124b3ccef3ed5af238fa59d28e258

f34f6ead7f1415e001ea3a902d7e969f06f9ecbb

refs/heads/master

2020-03-11T07:48:59.924728

2018-04-17T07:52:19

129,867,451

0

null

UTF-8

Python

false

1,642

py

import os import json import logging import requests SANDBOX_URL = 'https://sandbox.api.magpie.im/v1' PRODUCTION_URL = 'https://api.magpie.im/v1' class MagpieRequest(object): def __init__(self, is_sandbox=False, **kwargs): self.is_sandbox = is_sandbox self.pk = os.environ.get('MAGPIE_PUBLIC_KEY', kwargs.get('pk')) self.sk = os.environ.get('MAGPIE_SECRET_KEY', kwargs.get('sk')) if not self.pk or not self.sk: raise Exception('Public or Secret Key not set in Environment') elif 'test' in self.pk: self.is_sandbox = True self.url = SANDBOX_URL if is_sandbox else PRODUCTION_URL self.session = requests.Session() self.session.auth = (self.sk, '') self.session.headers = { "Content-Type": "application/json", "Accept": "application/json", } self.logger = logging.getLogger('magpy.request') def _process_response(self, response): if response.status_code in [200, 201]: return json.loads(response.content) elif response.status_code == 401: self.logger.error('Authorization error. Check your token') print 'Authorization error. Check your token' elif response.status_code == 402: self.logger.error('Invalid card!') print 'Invalid card!' elif response.status_code == 404: self.logger.error('Authorization error. Token not found') print 'Authorization error. Token not found' else: self.logger.error('Invalid request') print 'Invalid request'

[ "baguio.ivan@gmail.com" ]

baguio.ivan@gmail.com

d358b439bce6a1153439fe7b5d3a260db780a5ed

e7ff78f98fb4fc8a61df797a1904ba800d8b812b

/day7/gameProcessor.py

6f18a1545f7fe89bf4117a4f099430700b306163

[]

no_license

EvanFCMoses/adventofcode2020

13e6f0d6a552f12d9c177713db23bc327eff2e0b

25f9dcd0011d49320b2d3f06b1ab9a8506ce0ab4

refs/heads/master

2023-01-22T12:00:18.505814

2020-12-10T01:30:29

318,029,930

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null

UTF-8

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py

class GameProcessor: def __init__(self): self.instructions = self.readInstructions('input.txt') self.accumulator = 0 def readInstructions(self, fileName): file = open(fileName, 'r') array = [] for row in file: array.append(Instruction(row)) return array def executeInstruction(self, position): print(position) inst = self.instructions[position] if inst.executed != 0: return -1 if inst.instruction == "acc": self.accumulator = self.accumulator + inst.number inst.executed = inst.executed + 1 return position + 1 elif inst.instruction == "nop": inst.executed = inst.executed + 1 return position + 1 elif inst.instruction == "jmp": inst.executed = inst.executed + 1 return position + inst.number else: print("halt and catch fire") def runInstructionsOnce(self, startingLocation): execResult = self.executeInstruction(startingLocation) while execResult != -1: execResult = self.executeInstruction(execResult) return execResult def changeAnInstructionAndRun(self): for x in range(len(0, self.instructions)): if self.instructions[x].instruction == "jmp": self.instructions[x] = self.instructions[x].replace("jmp","nop") return "" class Instruction: def __init__(self, inputString): self.instruction = inputString[0:3] self.number = self.processRowNumber(inputString[4:]) self.executed = 0 def processRowNumber(self, rowNumber): if rowNumber[0] == "+": return int(rowNumber[1:]) else: return int(rowNumber) gameProcessor = GameProcessor() gameProcessor.runInstructionsOnce(0) # for inst in gameProcessor.instructions: # print(inst.instruction) # print(inst.number) # print(inst.executed) print(gameProcessor.accumulator)

[ "evanfcmoses@gmail.com" ]

evanfcmoses@gmail.com

0e6bcee8fa2cbf16435bd7c5031d1e777fb2bd60

b8849355e1b4adabc6054477ec087745fd979c6d

/tools/DokuWatch/setup.py

764eedc80caf78e4e8e0f7a1913b777858c21dc5

[]

no_license

LittleBuster/DokuMail

85a4a558f1a4b4fa2b6eedba48d6e466f709b8df

574d4ebf3b2ea77b9b874c640bf463ab20533974

refs/heads/master

2021-01-24T06:07:23.082725

2015-05-08T13:52:12

21,331,417

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1

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py

import sys from cx_Freeze import setup, Executable base = None if sys.platform == 'linux': base = 'Console' setup(name = 'DokuMail', version = '0.0.1', executables = [Executable('main.py', base=base)], options = {'build_exe': {'includes': ['sip']}})

[ "denisov172@gmail.com" ]

denisov172@gmail.com

f6be638237f9d4aeddf2bd844e81bbe67ff1f719

5f89f95fabb7a9d41d023ac1e7451fbf74c57c15

/Fonctionnels/Générateurs et Itérateurs/generateur_close() (5).py

54c2a50edabfe3c2aca3e939f6f211f465ca4e9c

[]

no_license

MINCARELLI13/Fonctionnels

4ed5f439dae3032d5941eb0a61706ca472dd6a3c

d074c56bac0beebd118c525777e53be7035fd09a

refs/heads/master

2023-03-08T21:43:02.290575

2021-02-28T21:49:38

343,183,452

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UTF-8

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py

#!/usr/bin/env python print() def generateur(debut, fin): count = debut while count < fin: yield count count += 1 intervalle = generateur(5, 20) # on crée le générateur "intervalle" for element in intervalle: print(element, end=" ") if element >= 17: intervalle.close() # interruption de la boucle # pour appeler les méthodes du générateur, on doit le stocker dans une variable "intervalle" avant la boucle. # Si on avait écrit directement "for nombre in generateur(5, 20)", on n'aurait pas pu appeler la méthode "close()" du générateur.

[ "sancho.poncho@laposte.net" ]

sancho.poncho@laposte.net

f2cb6896b36c104fa99aebec1b5b6f318daaeca3

9df2fb0bc59ab44f026b0a2f5ef50c72b2fb2ceb

/sdk/network/azure-mgmt-dns/generated_samples/delete_ptr_recordset.py

8e4ce27f33bed7dfe65428aac9d804dfa85506a6

[ "MIT", "LGPL-2.1-or-later", "LicenseRef-scancode-generic-cla" ]

permissive

openapi-env-test/azure-sdk-for-python

b334a2b65eeabcf9b7673879a621abb9be43b0f6

f61090e96094cfd4f43650be1a53425736bd8985

refs/heads/main

2023-08-30T14:22:14.300080

2023-06-08T02:53:04

222,384,897

1

0

MIT

2023-09-08T08:38:48

2019-11-18T07:09:24

Python

UTF-8

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false

1,566

py

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from azure.identity import DefaultAzureCredential from azure.mgmt.dns import DnsManagementClient """ # PREREQUISITES pip install azure-identity pip install azure-mgmt-dns # USAGE python delete_ptr_recordset.py Before run the sample, please set the values of the client ID, tenant ID and client secret of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET. For more info about how to get the value, please see: https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal """ def main(): client = DnsManagementClient( credential=DefaultAzureCredential(), subscription_id="subid", ) response = client.record_sets.delete( resource_group_name="rg1", zone_name="0.0.127.in-addr.arpa", relative_record_set_name="1", record_type="PTR", ) print(response) # x-ms-original-file: specification/dns/resource-manager/Microsoft.Network/stable/2018-05-01/examples/DeletePTRRecordset.json if __name__ == "__main__": main()

[ "noreply@github.com" ]

openapi-env-test.noreply@github.com

994d32bf85b9f805c0e419fe5ccaa4d5e1bfce35

439d2d30b61514bd6a9072c175b124c63f124834

/data_statistics/base/index.py

e42fe210e0b49d548bb2b143113f01362df60b73

[]

no_license

meteor-gogogo/python

6f92e3a680c653f92a8d9b04a2d09be6d6ea126a

7d03e2f57d4dfb7c7aeb15bf836a6e0d4af9b89d

refs/heads/master

2022-12-10T07:45:31.085951

2019-09-04T06:01:50

197,333,904

0

1

null

2022-12-08T05:19:07

2019-07-17T07:01:55

Python

UTF-8

Python

false

190

py

#!/usr/bin/env python # coding=utf-8 from .baselist import BaseList class IndexList(BaseList): @classmethod def is_registrar_for(cls, listtype): return listtype == 'index'

[ "liuhang@aplum.com.cn" ]

liuhang@aplum.com.cn

af55d24daf0a5c4aebe8237d7030069a16e8052d

01548099ec20976d31cca7a720102c11c56fc9be

/scripts/handle_request.py

d1e6caf1c8ee8b338b8fd6985490f517befa6a56

[]

no_license

OMEN001/Lemon_Api_Test

6a27a9a9ccf28623006b465a107d53b17ad30404

373c9f1a1f1f3160bbe8edcc4b5740f9779947ae

refs/heads/master

2023-02-25T22:31:39.824908

2021-01-24T14:22:52

329,324,658

1

0

null

UTF-8

Python

false

3,664

py

# -*- coding: utf-8 -*- # @Time : BaLiang # @Author : 86187 import requests import json class HandleRequest: """ 如果是session会话认证requests请求不能模仿浏览器维护cookie,创建session会话对象可以解决这一问题 """ def __init__(self): # 创建回话对象，通过回话对象向服务器发起请求 self.one_session = requests.Session() # 添加请求头 def add_headers(self, header): # 构造请求头 return self.one_session.headers.update(header) def send(self, url, method="post", data=None, is_json=True, **kwargs): """ 发起请求 :param url: url地址 :param method: 请求方法, 通常为get、post、put、delete、patch :param data: 传递的参数, 可以传字典、json格式的字符串、字典类型的字符串, 默认为None :param is_json: 是否以json的形式来传递参数, 如果为True, 则以json形式来传, 如果为False则以www-form形式来传, 默认为True :param kwargs: 可变参数, 可以接收关键字参数, 如headers、params、files等 :return: None 或者 Response对象 """ # data可以为如下三种类型： # data = {"name": '可优', 'gender': True} # 字典类型 # data = '{"name": "可优", "gender": true}' # json格式的字符串 # data = "{'name': '优优', 'gender': True}" # 字典类型的字符串 if isinstance(data, str): #判断data是否为str字符串类型, 如果为str类型, 会返回True, 否则返回False try: # 假设为json字符串, 先使用json.loads转化为字典 data = json.loads(data) except Exception as e: # 如果不为json字符串会抛出异常, 然后使用eval函数来转化 print("使用日志记录") data = eval(data) # 转换字符串中所有大写字符为小写 method = method.lower() if method == "get": # 如果为get请求, 那么传递的data, 默认传查询字符串参数 # res = self.one_session.get(url, params=data, **kwargs) res = self.one_session.request(method,url, params=data, **kwargs) elif method in ("post", "delete", "patch","put"): if is_json: # 如果is_json为True, 那么以json格式的形式来传参 # res = self.one_session.post(url, json=data, **kwargs) res = self.one_session.request(method,url, json=data, **kwargs) else: # 如果is_json为False, 那么以www-form的形式来传参 # res = self.one_session.post(url, data=data, **kwargs) res = self.one_session.request(method,url, data=data, **kwargs) else: res = None print(f"不支持【{method}】的请求方法") return res def close(self): # 关闭回话对象 self.one_session.close() # do_request = HandleRequest() # if __name__ == '__main__': # # 1. 构造请求的url # login_url = "http://api.lemonban.com/futureloan/member/login" # # # 2. 创建请求参数 # headers = { # "User-Agent": "Mozilla/5.0 BaLiang", # "X-Lemonban-Media-Type": "lemonban.v2" # } # # login_params = { # "mobile_phone": "18244446667", # "pwd": "12345678", # } # # # 3. 执行登录 # do_request = HandleRequest() # 创建HandleRequest对象 # do_request.add_headers(headers) # 添加公共请求头 # login_res = do_request.send(login_url, data=login_params)

[ "1668317403@qq.com" ]

1668317403@qq.com

a6c44e285c3f0befd0a72c4a0e406c390f91eb8b

27e4de1b3c2693c9dc6de4dca2db5d9f8f569ec2

/Interfaces/AI/Inventory.py

ee072e20a3508a6f4a70f27ac98541befdde4edb

[]

no_license

Ipukema/PokeStats

2d2becf48af285cbae1ed5e054c2ff5bc3ad08a5

727ca7b72dcadc6c15f2afd51459312181a6fd2a

refs/heads/master

2021-01-22T15:01:12.892842

2016-08-05T10:35:51

null

0

null

UTF-8

Python

false

10,183

py

# -*- coding: utf-8 -*- import logging from enum import Enum log = logging.getLogger(__name__) class InventoryItem(Enum): ITEM_UNKNOWN = 0 ITEM_POKE_BALL = 1 ITEM_GREAT_BALL = 2 ITEM_ULTRA_BALL = 3 ITEM_MASTER_BALL = 4 ITEM_POTION = 101 ITEM_SUPER_POTION = 102 ITEM_HYPER_POTION = 103 ITEM_MAX_POTION = 104 ITEM_REVIVE = 201 ITEM_MAX_REVIVE = 202 ITEM_LUCKY_EGG = 301 ITEM_INCENSE_ORDINARY = 401 ITEM_INCENSE_SPICY = 402 ITEM_INCENSE_COOL = 403 ITEM_INCENSE_FLORAL = 404 ITEM_TROY_DISK = 501 ITEM_X_ATTACK = 602 ITEM_X_DEFENSE = 603 ITEM_X_MIRACLE = 604 ITEM_RAZZ_BERRY = 701 ITEM_BLUK_BERRY = 702 ITEM_NANAB_BERRY = 703 ITEM_WEPAR_BERRY = 704 ITEM_PINAP_BERRY = 705 ITEM_SPECIAL_CAMERA = 801 ITEM_INCUBATOR_BASIC_UNLIMITED = 901 ITEM_INCUBATOR_BASIC = 902 ITEM_POKEMON_STORAGE_UPGRADE = 1001 ITEM_ITEM_STORAGE_UPGRADE = 1002 class Inventory: def __init__(self, thread): self.scanner = thread.scanner self.session = thread.session self.api = thread.api self.inventory = list() def update(self): log.info("Обновляем данные сундука") self.api.get_player().get_inventory() response_dict = self.api.call() if response_dict and 'status_code' in response_dict: if response_dict['status_code'] is 1: if 'responses' in response_dict: if 'GET_INVENTORY' in response_dict['responses']: if 'inventory_delta' in response_dict['responses']['GET_INVENTORY']: inventory_res = response_dict['responses']['GET_INVENTORY']['inventory_delta']['inventory_items'] pokecount = 0 itemcount = 1 for item in inventory_res: try: if 'inventory_item_data' in item: if 'pokemon_data' in item['inventory_item_data']: pokecount = pokecount + 1 if 'item' in item['inventory_item_data']: if 'count' in item['inventory_item_data']['item']: itemcount = itemcount + item['inventory_item_data']['item']['count'] except Exception as e: log.error("Ошибка:{0}".format(e)) try: if 'inventory_item_data' in item: if 'player_stats' in item['inventory_item_data']: playerdata = item['inventory_item_data']['player_stats'] if 'level' in playerdata: self.scanner.account.statistic.level = playerdata['level'] if 'experience' in playerdata: self.scanner.account.statistic.experience = playerdata['experience'] if 'next_level_xp' in playerdata and 'experience' in playerdata: self.scanner.account.statistic.experience_to_level = (int(playerdata.get('next_level_xp', 0)) -int(playerdata.get('experience', 0))) if 'pokemons_captured' in playerdata: self.scanner.account.statistic.catched_pokemons = playerdata['pokemons_captured'] if 'poke_stop_visits' in playerdata: self.scanner.account.statistic.visited_pokestops = playerdata['poke_stop_visits'] #if 'km_walked' in playerdata: self.scanner.account.statistic.walked = playerdata['km_walked'] except Exception as e: log.error("Ошибка:{0}".format(e)) try: if 'inventory_item_data' in item: if 'item' in item['inventory_item_data']: try: self.inventory.append(item['inventory_item_data']['item']) except Exception as e: log.error("Ошибка:{0}".format(e)) if 'item_id' in item['inventory_item_data']['item'] and 'count' in item['inventory_item_data']['item']: item_id = item['inventory_item_data']['item']['item_id'] item_count = item['inventory_item_data']['item']['count'] if item_id == InventoryItem.ITEM_POKE_BALL: self.scanner.account.statistic.item_ball_poke = item_count if item_id == InventoryItem.ITEM_GREAT_BALL: self.scanner.account.statistic.item_ball_great = item_count if item_id == InventoryItem.ITEM_ULTRA_BALL: self.scanner.account.statistic.item_ball_ultra = item_count if item_id == InventoryItem.ITEM_MASTER_BALL: self.scanner.account.statistic.item_ball_master = item_count if item_id == InventoryItem.ITEM_POTION: self.scanner.account.statistic.item_potion = item_count if item_id == InventoryItem.ITEM_SUPER_POTION: self.scanner.account.statistic.item_potion_super = item_count if item_id == InventoryItem.ITEM_HYPER_POTION: self.scanner.account.statistic.item_potion_hyper = item_count if item_id == InventoryItem.ITEM_MAX_POTION: self.scanner.account.statistic.item_potion_master = item_count if item_id == InventoryItem.ITEM_REVIVE: self.scanner.account.statistic.item_revive = item_count if item_id == InventoryItem.ITEM_MAX_REVIVE: self.scanner.account.statistic.item_revive_master = item_count if item_id == InventoryItem.ITEM_RAZZ_BERRY: self.scanner.account.statistic.item_berry_razz = item_count if item_id == InventoryItem.ITEM_BLUK_BERRY: self.scanner.account.statistic.item_berry_bluk = item_count if item_id == InventoryItem.ITEM_NANAB_BERRY: self.scanner.account.statistic.item_berry_nanab = item_count if item_id == InventoryItem.ITEM_WEPAR_BERRY: self.scanner.account.statistic.item_berry_wepar = item_count if item_id == InventoryItem.ITEM_PINAP_BERRY: self.scanner.account.statistic.item_berry_pinap = item_count except Exception as e: log.error("Ошибка:{0}".format(e)) self.scanner.account.statistic.bag_pokemons = pokecount self.scanner.account.statistic.bag_items = itemcount self.session.commit() else: log.warning("Получен неверный статус: {0}".format(response_dict['status_code'])) def pokeball(self): self.update() balls_stock = {1: 0, 2: 0, 3: 0, 4: 0} for item in self.inventory: # print(item['inventory_item_data']['item']) item_id = int(item['item_id']) item_count = int(item['count']) if item_id == 1: # print('Poke Ball count: ' + str(item_count)) balls_stock[1] = item_count if item_id == 2: # print('Great Ball count: ' + str(item_count)) balls_stock[2] = item_count if item_id == 3: # print('Ultra Ball count: ' + str(item_count)) balls_stock[3] = item_count if item_id == 4: # print('Ultra Ball count: ' + str(item_count)) balls_stock[4] = item_count return balls_stock def drop_item(self, item_id, count): self.api.recycle_inventory_item(item_id=item_id, count=count) response_dict = self.api.call() if response_dict and 'status_code' in response_dict: if response_dict['status_code'] is 1: if 'responses' in response_dict: if 'RECYCLE_INVENTORY_ITEM' in response_dict['responses']: if 'status' in response_dict['responses']['RECYCLE_INVENTORY_ITEM']: if response_dict['responses']['RECYCLE_INVENTORY_ITEM']['status'] is 1: return True else: log.warning("Получен неверный статус: {0}".format(response_dict['responses']['RECYCLE_INVENTORY_ITEM']['status'])) else: log.warning("Получен неверный статус: {0}".format(response_dict['status_code'])) return False def recycle(self): for item in self.inventory: if "item_id" in item: item_db = self.scanner.account.statistic.get_by_item_id(int(item["item_id"])) if 'count' in item: if item['count'] > item_db[1]: log.info("Membership {0} is overdraft, drop {1} items".format(item["item_id"], (item['count']-item_db[1]))) if not self.drop_item(item["item_id"], (item['count']-item_db[1])): log.warning("Неудалось удалить обьекты из инвентаря") self.update()

[ "viktor@tatarnikov.org" ]

viktor@tatarnikov.org

38737226b6765f186c820e52fbc945e19418cc57

06f14e666306a99daa7fa0b3fb5ea1487521c2ad

/Project UAS/PyGame/CodeMentah.py

362c423043cd3f92ff6564b5e240edcd3bbec819

[]

no_license

iluljr/CatatanPBO

58d193d13da89b9aa46eb6b8a0e0dbba261628da

d9933a49369ff56ac3067631a6ef027e7036ce23

refs/heads/master

2020-04-02T04:54:00.881756

2018-12-22T13:35:17

154,041,727

0

null

UTF-8

Python

false

8,579

py

import pygame # Global constants # Colors BLACK = (0, 0, 0) WHITE = (255, 255, 255) GREEN = (0, 255, 0) RED = (255, 0, 0) BLUE = (0, 0, 255) # Screen dimensions SCREEN_WIDTH = 800 SCREEN_HEIGHT = 600 class Player(pygame.sprite.Sprite): """ This class represents the bar at the bottom that the player controls. """ # -- Methods def __init__(self): """ Constructor function """ # Call the parent's constructor super().__init__() # Create an image of the block, and fill it with a color. # This could also be an image loaded from the disk. width = 40 height = 60 self.image = pygame.Surface([width, height]) self.image.fill(RED) # Set a referance to the image rect. self.rect = self.image.get_rect() # Set speed vector of player self.change_x = 0 self.change_y = 0 # List of sprites we can bump against self.level = None def update(self): """ Move the player. """ # Gravity self.calc_grav() # Move left/right self.rect.x += self.change_x # See if we hit anything block_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False) for block in block_hit_list: # If we are moving right, # set our right side to the left side of the item we hit if self.change_x > 0: self.rect.right = block.rect.left elif self.change_x < 0: # Otherwise if we are moving left, do the opposite. self.rect.left = block.rect.right # Move up/down self.rect.y += self.change_y # Check and see if we hit anything block_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False) for block in block_hit_list: # Reset our position based on the top/bottom of the object. if self.change_y > 0: self.rect.bottom = block.rect.top elif self.change_y < 0: self.rect.top = block.rect.bottom # Stop our vertical movement self.change_y = 0 def calc_grav(self): """ Calculate effect of gravity. """ if self.change_y == 0: self.change_y = 1 else: self.change_y += .35 # See if we are on the ground. if self.rect.y >= SCREEN_HEIGHT - self.rect.height and self.change_y >= 0: self.change_y = 0 self.rect.y = SCREEN_HEIGHT - self.rect.height def jump(self): """ Called when user hits 'jump' button. """ # move down a bit and see if there is a platform below us. # Move down 2 pixels because it doesn't work well if we only move down # 1 when working with a platform moving down. self.rect.y += 2 platform_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False) self.rect.y -= 2 # If it is ok to jump, set our speed upwards if len(platform_hit_list) > 0 or self.rect.bottom >= SCREEN_HEIGHT: self.change_y = -10 # Player-controlled movement: def go_left(self): """ Called when the user hits the left arrow. """ self.change_x = -6 def go_right(self): """ Called when the user hits the right arrow. """ self.change_x = 6 def stop(self): """ Called when the user lets off the keyboard. """ self.change_x = 0 class Platform(pygame.sprite.Sprite): """ Platform the user can jump on """ def __init__(self, width, height): """ Platform constructor. Assumes constructed with user passing in an array of 5 numbers like what's defined at the top of this code. """ super().__init__() self.image = pygame.Surface([width, height]) self.image.fill(GREEN) self.rect = self.image.get_rect() class Level(object): """ This is a generic super-class used to define a level. Create a child class for each level with level-specific info. """ def __init__(self, player): """ Constructor. Pass in a handle to player. Needed for when moving platforms collide with the player. """ self.platform_list = pygame.sprite.Group() self.enemy_list = pygame.sprite.Group() self.player = player # Background image self.background = None # Update everythign on this level def update(self): """ Update everything in this level.""" self.platform_list.update() self.enemy_list.update() def draw(self, screen): """ Draw everything on this level. """ # Draw the background screen.fill(BLUE) # Draw all the sprite lists that we have self.platform_list.draw(screen) self.enemy_list.draw(screen) # Create platforms for the level class Level_01(Level): """ Definition for level 1. """ def __init__(self, player): """ Create level 1. """ # Call the parent constructor Level.__init__(self, player) # Array with width, height, x, and y of platform level = [[210, 70, 500, 500], [210, 70, 200, 400], [210, 70, 600, 300], [210, 70, 200, 50], [70, 20, 400, 150], ] # Go through the array above and add platforms for platform in level: block = Platform(platform[0], platform[1]) block.rect.x = platform[2] block.rect.y = platform[3] block.player = self.player self.platform_list.add(block) def main(): """ Main Program """ pygame.init() # Set the height and width of the screen size = [SCREEN_WIDTH, SCREEN_HEIGHT] screen = pygame.display.set_mode(size) pygame.display.set_caption("Platformer Jumper") # Create the player player = Player() # Create all the levels level_list = [] level_list.append( Level_01(player) ) # Set the current level current_level_no = 0 current_level = level_list[current_level_no] active_sprite_list = pygame.sprite.Group() player.level = current_level player.rect.x = 340 player.rect.y = SCREEN_HEIGHT - player.rect.height active_sprite_list.add(player) # Loop until the user clicks the close button. done = False # Used to manage how fast the screen updates clock = pygame.time.Clock() # -------- Main Program Loop ----------- while not done: for event in pygame.event.get(): if event.type == pygame.QUIT: done = True if event.type == pygame.KEYDOWN: if event.key == pygame.K_LEFT: player.go_left() if event.key == pygame.K_RIGHT: player.go_right() if event.key == pygame.K_UP: player.jump() if event.type == pygame.KEYUP: if event.key == pygame.K_LEFT and player.change_x < 0: player.stop() if event.key == pygame.K_RIGHT and player.change_x > 0: player.stop() # Update the player. active_sprite_list.update() # Update items in the level current_level.update() # If the player gets near the right side, shift the world left (-x) if player.rect.right > SCREEN_WIDTH: player.rect.right = SCREEN_WIDTH # If the player gets near the left side, shift the world right (+x) if player.rect.left < 0: player.rect.left = 0 # ALL CODE TO DRAW SHOULD GO BELOW THIS COMMENT current_level.draw(screen) active_sprite_list.draw(screen) # ALL CODE TO DRAW SHOULD GO ABOVE THIS COMMENT # Limit to 60 frames per second clock.tick(60) # Go ahead and update the screen with what we've drawn. pygame.display.flip() # Be IDLE friendly. If you forget this line, the program will 'hang' # on exit. pygame.quit() if __name__ == "__main__": main()

[ "noreply@github.com" ]

iluljr.noreply@github.com

ea2054eb337966fb0493a467059ba157e75ea7e1

cc2ddb1220f185658b58e7ac705b794576e1ecc1

/avantTrainee/src/test3/scripts/listener.py

ff613dde8a2e0d5bfd22ca431a723963f05cd4a5

[]

no_license

BielLopes/myROSTeaching

cfc23c2986bf4fe162720ac5899a48b8aef37e6b

bd89f573461397d3d8ebd6f76aaa3e419a7cfd54

refs/heads/master

2023-02-28T20:07:23.435810

2021-02-07T17:40:56

336,846,820

0

null

UTF-8

Python

false

2,411

py

#!/usr/bin/env python # Software License Agreement (BSD License) # # Copyright (c) 2008, Willow Garage, Inc. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # * Neither the name of Willow Garage, Inc. nor the names of its # contributors may be used to endorse or promote products derived # from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS # FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE # COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT # LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN # ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # # Revision $Id$ ## Simple talker demo that listens to std_msgs/Strings published ## to the 'chatter' topic import rospy from test3.msg import Num def callback(data): rospy.loginfo(rospy.get_caller_id() + ' I heard the number: %s', data.num) def listener(): # In ROS, nodes are uniquely named. If two nodes with the same # name are launched, the previous one is kicked off. The # anonymous=True flag means that rospy will choose a unique # name for our 'listener' node so that multiple listeners can # run simultaneously. rospy.init_node('listener', anonymous=False) rospy.Subscriber('chatter', Num, callback) # spin() simply keeps python from exiting until this node is stopped rospy.spin() if __name__ == '__main__': listener()

[ "gabriel.galoma@gmail.com" ]

gabriel.galoma@gmail.com

c6089ccaa1b6c92062efbfe48de53c909a29e87f

b9f97b3457100ffa978e5b9c659ff5b4a0189a7c

/django_summernote/migrations/0001_initial.py

bb4366437ddb498ed0c11524070afbc9688d6316

[ "MIT" ]

permissive

yuong1979/django-summernote

e1cf974daa2b4cdffd251e9f82bd50049a864ab9

cf9b2d838310211738162c2acbc7b9145d3970b2

refs/heads/master

2020-03-19T09:02:22.831819

2018-06-06T02:15:09

136,255,223

0

MIT

2018-06-06T02:15:10

2018-06-06T01:34:28

JavaScript

UTF-8

Python

false

821

py

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations import django_summernote.settings class Migration(migrations.Migration): dependencies = [ ] operations = [ migrations.CreateModel( name='Attachment', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('name', models.CharField(max_length=255, null=True, blank=True)), ('file', models.FileField(upload_to=django_summernote.settings.uploaded_filepath)), ('uploaded', models.DateTimeField(auto_now_add=True)), ], options={ 'abstract': False, }, bases=(models.Model,), ), ]

[ "ez@smartstudy.co.kr" ]

ez@smartstudy.co.kr

730329994488dcd29e2316dd9aa2c78c5f1252ac

386b7c61cd8a8c981fd32e73b64352b0fba7d53e

/Problem-2-search-in-a-rotated-sorted-array.py

003770bd11cc341b039ed1787db705e4edcdf3e9

[]

no_license

negacy/problems-vs-aglorithms

9c13ba51a514617ce21b3493f625f78a93df2340

b7af3b499e4261db67dcf6adfd34920cd6ff6503

refs/heads/master

2020-07-10T15:30:36.875058

2019-09-13T22:17:45

204,299,421

0

null

UTF-8

Python

false

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import sys idx = -1 def rotated_array_search(input_list, number): """ Find the index by searching in a rotated sorted array Args: input_list(array), number(int): Input array to search and the target Returns: int: Index or -1 """ start_index = 0 end_index = len(input_list) - 1 while start_index <= end_index: mid_idx = (start_index + end_index)//2 left = input_list[:mid_idx] right = input_list[mid_idx:] try: if input_list[mid_idx] == number: return mid_idx found = False if number >= left[0] and number <= left[-1]: start_index = 0 end_index = mid_idx - 1 found = True else:# number >= right[0] and number <= right[-1]: start_index = mid_idx + 1 if not found: if number >= right[0] and number <= right[-1]: start_index = mid_idx + 1 else: end_index = mid_idx + 1 except IndexError: #if element not found, return -1 return -1 return -1 def linear_search(input_list, number): for index, element in enumerate(input_list): if element == number: return index return -1 def test_function(test_case): input_list = test_case[0] number = test_case[1] if linear_search(input_list, number) == rotated_array_search(input_list, number): print("Pass") else: print("Fail") test_function([[6, 7, 8, 9, 10, 1, 2, 3, 4], 6]) test_function([[6, 7, 8, 9, 10, 1, 2, 3, 4], 1]) test_function([[6, 7, 8, 1, 2, 3, 4], 8]) test_function([[6, 7, 8, 1, 2, 3, 4], 1]) test_function([[6, 7, 8, 1, 2, 3, 4], 10]) #test empty cases test_function([[], 10])

[ "negacy.hailu@gmail.com" ]

negacy.hailu@gmail.com

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/src/main/python/json2pandasjs.py

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no_license

hcy7910/json2pandas

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# -*- coding:utf-8 -*- import pandas as pd import json import re def json2pandasjs(file): df = pd.DataFrame() with open(file) as f: line = f.readline() while line: strinfo = re.compile('null') datare = strinfo.sub('None', line) data = eval(datare) tmp = pd.DataFrame(data=data, index=range(1)) df = df.append(tmp) line = f.readline() df = df.set_index(df['first_visit_time']) df = df.sort_index(ascending=False) return df

[ "1289630384@qq.com" ]

1289630384@qq.com

29fd856261e0af5f9622f6056ef4e5c961b85144

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/record_wav.py

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CHANShu0508/Record-wav-and-FFT

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# -*- coding: utf-8 -*- ''' Record the audio file as .wav format ''' import pyaudio import wave class Record: chunk = 1024 # Record in chunks of 1024 samples sample_format = pyaudio.paInt16 # 16 bits per second channel = 2 sample_rate = 44100 # Record 44100 samples per second duration = 3 filename = "output.wav" def __init__(self, time, file_name): self.duration = time self.filename = file_name def start_record(self): p = pyaudio.PyAudio() #Create an interface to PortAudio print('* Recording') stream = p.open(format=self.sample_format, channels=self.channel, rate=self.sample_rate, frames_per_buffer=self.chunk, input=True ) frames = [] # Init an array to store frames for index in range(0, int(self.sample_rate / self.chunk * self.duration)): data = stream.read(self.chunk) frames.append(data) # Stop and close the stream stream.stop_stream() stream.close() # Stop the PortAudio interface p.terminate() print('* Stop recording') wf = wave.open(self.filename, 'wb') wf.setnchannels(self.channel) wf.setsampwidth(p.get_sample_size(self.sample_format)) wf.setframerate(self.sample_rate) wf.writeframes(b''.join(frames)) wf.close()

[ "chanshuwork@gmail.com" ]

chanshuwork@gmail.com

b176b9733e8a18e756e59c6e0349ac7bb3faf6f8

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/src/alg/qca.py

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zyymax/online_community_detection

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refs/heads/master

2021-07-14T05:12:27.542175

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#!/usr/bin/env python # -*-coding:utf8-*- """ Coder: max.zhang Date: 2015-01-23 Desc: implementation of QCA in INFOCOM'11 Adaptive Algorithms for Detecting Community Structure in Dynamic Social Networks by Nam P. Nguyen et.al. """ from pprint import pprint from collections import defaultdict import random import itertools from copy import deepcopy as dcp import sys from time import clock from network import Network, timing from community import WeightCommunity as Community from node import DisjointNode as Node import cProfile gene_pair = lambda l: list( itertools.chain.from_iterable( [zip(l[:step+1], l[-step-1:]) for step in xrange(len(l)-1)])) def randList(ori_data): random.seed(None) ran_data = dcp(ori_data) size = len(ran_data) for idx1 in xrange(size): idx2 = random.randint(idx1, size-1) ran_data[idx1], ran_data[idx2] = ran_data[idx2], ran_data[idx1] return ran_data class QCAImp(Network): def __init__(self): Network.__init__(self) self._m = 0. def _overlap_modul(self): m = 0. for node1 in self._node_list: for node2_idx, weight in node1._nb.items(): m += weight m /= 2. modul = 0. for com_idx, com in enumerate(self._com_list): if com is None: continue tmp_modul = 0 for node1_idx in com._node.keys(): node1 = self._node_list[node1_idx] ov = 1. for node2_idx in com._node.keys(): node2 = self._node_list[node2_idx] ow = 1. weight = node1._nb.get(node2_idx, 0) tmp_modul += (weight-node1._du*node2._du/(2*m*ov*ow)) modul += tmp_modul return modul/(2*m) def _modularity(self, comno_set=None): if self._m < 1e-6: return 0. if comno_set is None: comno_set = filter( lambda idx: self._com_list[idx] is not None, range(self._com_num)) else: comno_set = set(comno_set) modul = 0. for com_no in comno_set: com = self._com_list[com_no] modul += com._win/self._m - pow(com._wtot/(2*self._m), 2) return modul def _getNodeIdx(self, node_str): if node_str in self._nodestr_dict: node_idx = self._nodestr_dict[node_str] if self._node_list[node_idx] is None: self._node_list[node_idx] = Node(node_idx, node_str) else: node_idx = self._node_num self._nodestr_dict[node_str] = node_idx node = Node(node_idx, node_str) self._node_list.append(node) return node_idx, self._node_list[node_idx] def _nr_comno_set(self, node_idx): com_set = set() node = self._node_list[node_idx] for nb_idx in node._nb.keys(): com_idx = self._node_list[nb_idx]._com if com_idx != node._com: com_set.add(com_idx) return com_set def _buildN2C(self, node_idx): tot_weight = 0. node = self._node_list[node_idx] n2c_dict = defaultdict(float) for nb_idx, weight in self._node_list[node_idx]._nb.items(): com_idx = self._node_list[nb_idx]._com n2c_dict[com_idx] += weight return n2c_dict def _in_force(self, node_du, com_wtot, n2c_weight): return n2c_weight - node_du*(com_wtot-node_du)/(2*self._m) def _out_force(self, node_du, com_wtot, n2c_weight): return n2c_weight - node_du*com_wtot/(2*self._m) def _nrtCom(self, node_idx, comno_set=None): if comno_set is None: comno_set = set(filter(lambda idx: self._com_list is not None, range(self._com_num))) else: comno_set = set(comno_set) node = self._node_list[node_idx] n2c_dict = self._buildN2C(node_idx) max_force = self._in_force(node._du, self._com_list[node._com]._wtot, n2c_dict[node._com]) nrtcom_idx = node._com for com_idx in comno_set: tmp_force = self._out_force(node._du, self._com_list[com_idx]._wtot, n2c_dict[com_idx]) if tmp_force > max_force: max_force = tmp_force nrtcom_idx = com_idx return max_force, nrtcom_idx """ Set of nodes in one community """ def _nrtComofNodeSet(self, nodeidx_set, ori_comidx, comno_set=None): if comno_set is None: comno_set = set(filter(lambda idx: self._com_list is not None, range(self._com_num))) else: comno_set = set(comno_set) n2c_dict = defaultdict(float) node_du = 0. for node_idx in nodeidx_set: node = self._node_list[node_idx] node_du += node._du for nnode_idx, weight in node._nb.items(): com_idx = self._node_list[nnode_idx]._com n2c_dict[com_idx] += weight max_force = self._in_force(node_du, self._com_list[ori_comidx]._wtot, n2c_dict[ori_comidx]) nrtcom_idx = ori_comidx for com_idx in comno_set: tmp_force = self._out_force(node_du, self._com_list[com_idx]._wtot, n2c_dict[com_idx]) if tmp_force > max_force: max_force = tmp_force nrtcom_idx = com_idx # print '_nrtComofNodeSet', nodeidx_set, ori_comidx, comno_set, max_force, nrtcom_idx return max_force, nrtcom_idx def _detBestCom(self, node): node_idx = node._id comno_set = self._nr_comno_set(node_idx) if len(comno_set) == 0: return None, None max_force, nrt_comidx = self._nrtCom(node_idx, comno_set) ori_comidx = node._com if nrt_comidx != ori_comidx: ori_com = self._com_list[node._com] best_com = self._com_list[nrt_comidx] best_com.addNode(node) node.setCom(nrt_comidx) ori_com.delNode(node_idx) if ori_com._empty: self._com_list[ori_com._id] = None return ori_comidx, nrt_comidx """ Set of nodes in one community """ def _detBestComofNodeSet(self, nodeidx_set, ori_comidx): comno_set = set() for node_idx in nodeidx_set: comno_set.update(self._nr_comno_set(node_idx)) if len(comno_set) == 0: return max_force, nrt_comidx = self._nrtComofNodeSet(nodeidx_set, ori_comidx, comno_set) if nrt_comidx != ori_comidx: ori_com = self._com_list[ori_comidx] for node_idx in nodeidx_set: node = self._node_list[node_idx] best_com = self._com_list[nrt_comidx] best_com.addNode(node) node.setCom(nrt_comidx) ori_com.delNode(node_idx) if ori_com._empty: self._com_list[ori_com._id] = None return nrt_comidx # print '_detBestComofNodeSet', nodeidx_set, max_force, nrt_comidx """ Delta modularity of putting src_node into dst_com self._m, src_node._du, dst_com._tot not updated param node_idx: node param dst_comidx: dest community param weight: delta weight """ def _delta_q(self, node_idx, dst_comidx, weight): node = self._node_list[node_idx] src_com = self._com_list[node._com] dst_com = self._com_list[dst_comidx] n2c_dict = self._buildN2C(node_idx) src_n2c = n2c_dict[src_com._id] dst_n2c = n2c_dict[dst_comidx] src_tot = src_com._wtot dst_tot = dst_com._wtot return 4*(self._m+weight)*(dst_n2c+weight-src_n2c)+src_n2c*( 2*dst_tot-2*node._du-src_n2c)-2*(node._du+weight)*( node._du+weight+dst_tot-src_tot) """ Delta modularity of putting src_node into dst_com self._m, src_node._du already updated param node_idx: node param dst_comidx: dest community """ def _extract_delta_q(self, srcnode_idx, dst_comidx): return self._delta_q(srcnode_idx, dst_comidx, 0.) def _findCliq(self, node, node_set): for nnode1_idx in node._nb.keys(): if nnode1_idx not in node_set: continue nnode1 = self._node_list[nnode1_idx] for nnode2_idx in nnode1._nb.keys(): if nnode2_idx not in node_set: continue return nnode1_idx, nnode2_idx return None def _cliqPerco(self, cliq, node_set): tmp_set = dcp(node_set) for node3_idx in tmp_set: if node3_idx in cliq: continue node3 = self._node_list[node3_idx] for node1_idx, node2_idx in gene_pair(list(cliq)): node2 = self._node_list[node2_idx] if node1_idx in node2._nb and node1_idx in node3._nb and node2_idx in node3._nb: cliq.add(node3_idx) node_set.remove(node3_idx) self._cliqPerco(cliq, node_set) break @timing def addNode(self, new_node_str, edge_dict): new_nodeidx, new_node = self._getNodeIdx(new_node_str) com = Community(self._com_num) self._com_list.append(com) com.addNode(new_node) new_node.setCom(com._id) nnodeidx_list = [] for nnode_str, weight in edge_dict.items(): nnode_idx, nnode = self._getNodeIdx(nnode_str) new_node.addNB(nnode_idx, weight) nnode.addNB(new_node._id, weight) nnodeidx_list.append(nnode_idx) self._m += weight if abs(self._m) < 1e-6: return for nnode_idx in nnodeidx_list: self._detBestCom(self._node_list[nnode_idx]) self._detBestCom(self._node_list[new_nodeidx]) @timing def addEdge(self, node1_str, node2_str, weight): node1_idx, node1 = self._getNodeIdx(node1_str) node2_idx, node2 = self._getNodeIdx(node2_str) ori_weight = node1._nb.get(node2_idx, 0.) node1.addNB(node2_idx, weight) node2.addNB(node1_idx, weight) weight -= ori_weight self._m += weight if weight < 0: return if node1._com != node2._com: # dq_1to2 = self._delta_q(node1_idx, node2._com, weight) # dq_2to1 = self._delta_q(node2_idx, node1._com, weight) dq_1to2 = self._extract_delta_q(node1_idx, node2._com) dq_2to1 = self._extract_delta_q(node2_idx, node1._com) if dq_1to2 < 0 and dq_2to1 < 0: return if dq_1to2 > dq_2to1: # move node1 to node2._com dst_com = self._com_list[node2._com] src_node = node1 else: # move node2 to node1._com dst_com = self._com_list[node1._com] src_node = node2 ori_com = self._com_list[src_node._com] ori_com.delNode(src_node._id) if ori_com._empty: self._com_list[ori_com._id] = None dst_com.addNode(src_node) src_node.setCom(dst_com._id) for nnode_idx in src_node._nb.keys(): self._detBestCom(self._node_list[nnode_idx]) @timing def rmNode(self, node_str): assert self._hasNodeName(node_str), 'node %s do not exist' % node_str node_idx, node = self._getNodeIdx(node_str) nnode_set = set() for nnode_idx, weight in node._nb.items(): self._m -= weight nnode = self._node_list[nnode_idx] nnode.delNB(node_idx) if nnode._com == node._com: nnode_set.add(nnode_idx) ori_com = self._com_list[node._com] ori_com.delNode(node_idx) if ori_com._empty: self._com_list[ori_com._id] = None self._node_list[node_idx] = None del self._nodestr_dict[node_str] subcom_list = [] while len(nnode_set) > 0: cur_nodeidx = nnode_set.pop() subcom = self._findCliq(self._node_list[cur_nodeidx], nnode_set) if subcom is None: subcom = {cur_nodeidx} else: nnode_set.remove(subcom[0]) nnode_set.remove(subcom[1]) subcom = {subcom[0], subcom[1], cur_nodeidx} self._cliqPerco(subcom, nnode_set) subcom_list.append(subcom) # print cur_nodeidx, subcom, self._node_list[cur_nodeidx] for subcom in subcom_list: if len(subcom) == 1: self._detBestCom(self._node_list[subcom.pop()]) else: self._detBestComofNodeSet(subcom, node._com) """ Seperate single node from its original community """ def _sepSingNode(self, node): ori_com = self._com_list[node._com] if len(ori_com._node) == 1: return new_com = Community(self._com_num) self._com_list.append(new_com) new_com.addNode(node) node.setCom(new_com._id) ori_com.delNode(node._id) if ori_com._empty: self._com_list[ori_com._id] = None """ Seprate single community by edge removal Split as 3 parts, detBestCom of 2 node set and other singleton nodes """ def _sepSingComByEdge(self, node1_str, node2_str): node1_idx, node1 = self._getNodeIdx(node1_str) node2_idx, node2 = self._getNodeIdx(node2_str) assert node1._com == node2._com, 'Seperate community by inter-edge' com = self._com_list[node1._com] # find 'quasi-clique's and singleton nodes in original community node1_set = set() node2_set = set() onode_set = set() for node_idx, node in com._node.items(): if node_idx == node1_idx or node_idx in node1._nb: if node_idx in node2._nb: onode_set.add(node_idx) else: node1_set.add(node_idx) elif node_idx == node2_idx or node_idx in node2._nb: node2_set.add(node_idx) else: onode_set.add(node_idx) # detect best community of 'quasi-clique' in node1_set self._detBestComofNodeSet(node1_set, com._id) # detect best community of 'quasi-clique' in node2_set self._detBestComofNodeSet(node2_set, com._id) # detect best community of singleton nodes in onode_set for node_idx in onode_set: ocd, nrd = self._detBestCom(self._node_list[node_idx]) """ Seprate single community by edge removal Split as 3 communities, detBestCom of 3 node set """ def _sepSingComByEdgev2(self, node1_str, node2_str): node1_idx, node1 = self._getNodeIdx(node1_str) node2_idx, node2 = self._getNodeIdx(node2_str) assert node1._com == node2._com, 'Seperate community by inter-edge' com = self._com_list[node1._com] # find 'quasi-clique's and singleton nodes in original community node1_set = set() node2_set = set() onode_set = set() for node_idx, node in com._node.items(): if node_idx == node1_idx or node_idx in node1._nb: if node_idx in node2._nb: onode_set.add(node_idx) else: node1_set.add(node_idx) elif node_idx == node2_idx or node_idx in node2._nb: node2_set.add(node_idx) else: onode_set.add(node_idx) new_com1 = Community(self._com_num) self._com_list.append(new_com1) for node_idx in node2_set: node = self._node_list[node_idx] node.setCom(new_com1._id) new_com1.addNode(node) com.delNode(node._id) for node_idx in onode_set: new_com = Community(self._com_num) self._com_list.append(new_com) node = self._node_list[node_idx] node.setCom(new_com._id) new_com.addNode(node) com.delNode(node._id) # detect best community of singleton nodes in onode_set for node_idx in onode_set: self._detBestCom(self._node_list[node_idx]) # detect best community of 'quasi-clique' in node1_set self._detBestComofNodeSet(node1_set, com._id) # detect best community of 'quasi-clique' in node2_set self._detBestComofNodeSet(node2_set, new_com1._id) """ Seprate single community by edge removal Split as singleton nodes, detBestCom of all nodes """ def _sepSingComByEdgev3(self, node1_str, node2_str): node1_idx, node1 = self._getNodeIdx(node1_str) node2_idx, node2 = self._getNodeIdx(node2_str) assert node1._com == node2._com, 'Seperate community by inter-edge' com = self._com_list[node1._com] node_list = com._node.keys() for node_idx in node_list: new_com = Community(self._com_num) self._com_list.append(new_com) node = self._node_list[node_idx] node.setCom(new_com._id) new_com.addNode(node) com.delNode(node._id) if com._empty: self._com_list[com._id] = None for node_idx in node_list: self._detBestCom(self._node_list[node_idx]) @timing def rmEdge(self, node1_str, node2_str): node1_idx, node1 = self._getNodeIdx(node1_str) node2_idx, node2 = self._getNodeIdx(node2_str) assert node2_idx in node1._nb, 'edge %s-%s do not exist' % (node1_str, node2_str) weight = node1._nb[node2_idx] self._m -= weight node1.delNB(node2_idx) node2.delNB(node1_idx) if node1._com != node2._com: return ori_com = self._com_list[node1._com] if len(node1._nb) == 0 and len(node2._nb) == 0: self._sepSingNode(node1) elif len(node1._nb) == 0: self._sepSingNode(node1) # self._detBestComofNodeSet(set(ori_com._node.keys()), ori_com._id) elif len(node2._nb) == 0: self._sepSingNode(node2) # self._detBestComofNodeSet(set(ori_com._node.keys()), ori_com._id) else: # find maximal quasi-cliques # detBestCom of singletons and quasi-cliques self._sepSingComByEdgev3(node1_str, node2_str) def clear(self): Network.clear(self) self._m = 0. def checkCom(self): for com in self._com_list: if com is None: continue s = 0. for node in com._node.values(): s += node._du assert abs(s-com._wtot) < 1e-6, 'com:%s, s:%.3f' % (com, s) for node in self._node_list: if node is None: continue du = 0. for nb_idx, weight in node._nb.items(): du += weight assert abs(du-node._du) < 1e-6, 'node:%s, du:%.3f' % (node, du) def show(self): Network.show(self) print('Totally: %d nodes, %d communities, %d outlier nodes' % ( len(filter(lambda idx: self._node_list[idx] is not None, range(self._node_num))), len(filter(lambda idx: self._com_list[idx] is not None, range(self._com_num))), len(filter(lambda idx: self._node_list[idx] is not None and len(self._com_list[self._node_list[idx]._com]._node) == 1, range(self._node_num))))) print('M:%.3f' % self._m) print('Modularity:%.6f' % self._modularity()) def toComStruc(self): s_list = [] for node in self._node_list: if node is None: continue s_list.append('%s\t%d' % (node._name, node._com)) return '\n'.join(s_list) """ Load community structure from output of toComStruc """ def fromComStruc(self, fname): com_node_dict = {} nodeidx_set = set() with open(fname) as ins: for line in ins: node_str, com_idx = line.strip().split() com_idx = int(com_idx) if com_idx not in com_node_dict: com_node_dict[com_idx] = [node_str] else: com_node_dict[com_idx].append(node_str) if len(com_node_dict) == 0: return self._com_list = [None] * (max(com_node_dict.keys())+1) for com_idx, nodestr_list in com_node_dict.items(): com = Community(com_idx) self._com_list[com_idx] = com for node_str in nodestr_list: assert self._hasNodeName(node_str) node_idx, node = self._getNodeIdx(node_str) nodeidx_set.add(node_idx) node.setCom(com_idx) com.addNode(node) for node in self._node_list: if node._id not in nodeidx_set: com = Community(self._com_num) self._com_list.append(com) node.setCom(com._id) com.addNode(node) def main(): dy_fname = sys.argv[1] com = QCAImp() with open(dy_fname) as ins: for line_no, line in enumerate(ins): if line.startswith('addEdge'): nodes_pair, weight = line.strip().split('\t')[1:] weight = float(weight) weight = 1. node1_str, node2_str = nodes_pair.split('-') com.addEdge(node1_str, node2_str, weight) elif line.startswith('addNode'): node_str, node_w, edges_str = line.strip('\n').split('\t')[1:] edge_dict = {} if len(edges_str.strip()) != 0: for edge_str in edges_str.split(','): nnode_str, weight = edge_str.split('-') weight = 1. edge_dict[nnode_str] = float(weight) com.addNode(node_str, edge_dict) elif line.startswith('rmEdge'): nodes_pair = line.strip().split('\t', 1)[-1] node1_str, node2_str = nodes_pair.split('-') com.rmEdge(node1_str, node2_str) elif line.startswith('rmNode'): com.rmNode(line.strip().split('\t')[-1]) elif line.startswith('TIME:'): time = int(line.strip().split()[-1]) print(time) com.checkCom() com.show() com.show() if __name__ == "__main__": assert len(sys.argv) == 2, 'Usage:\t%s <dy_fname>' % sys.argv[0] main() # cProfile.run('main()', '%s.stat' % sys.argv[0])

[ "zhangly@dtdream.com" ]

zhangly@dtdream.com

2270363e1629e3404120fa029bd191181db861e2

c934598867a2c91676c1f0876ff38c3231e176e4

/HomeWork_1.py

4aba2ea08322d436053b4d0d89390ddd701b8515

[]

no_license

MustafaSarimesinli/GlobalAIHubPythonHomework

54cdcb434927ddfef37be74a362650eb82fd53b2

23cea4d510fd6b08c5cbf62cf7418c97c883783d

refs/heads/master

2023-02-08T18:37:00.591984

2020-12-27T13:17:51

323,607,272

0

null

UTF-8

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899

py

import time print(""" ------------------------------------------- SCHOOL DATABASE SYSTEM --------------------------------------------- """) values_1 = input("Please enter your name: ") values_2 = input("Please enter your surname: ") values_3 = int(input("Please enter your age: ")) values_4 = input("Please enter your school name: ") values_5 = input("Please enter school number: ") print("Your information is saved in the database, please wait...") time.sleep(1) print("Save is completed...\n") print("------------------------------Your Information & Type of Values---------------------------------") print(f"""Your Name: {values_1.title()} Type: {type(values_1)} Your Surname: {values_2.title()} Type: {type(values_2)} Your Age: {values_3} Type: {type(values_3)} Your School Name: {values_4.title()} Type: {type(values_4)} Your School Number: {values_5.title()} Type: {type(values_5)}""")

[ "sarimesinlimustafa@gmail.com" ]

sarimesinlimustafa@gmail.com

6cc64bd28248b5600fa843a0d944681d6b73bcfa

f105e42548d67229d24b67cc25c3caeb2db66183

/tclone/accounts/views.py

de38491851f130848c6af1ef9de5bd057f55d35c

[]

no_license

taiki-dev/tclone

f2aa5c2dc867b5570f853f558ab3db8cfff7f1a7

a192e1bf68cd2209169d45e66590c70cff601355

refs/heads/master

2022-12-17T06:33:50.123445

2020-09-17T12:54:11

296,268,157

0

null

UTF-8

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false

517

py

from django.contrib.auth import login from django.contrib.auth.forms import UserCreationForm from django.shortcuts import render, redirect def signup(request): if request.method == 'POST': form = UserCreationForm(request.POST) if form.is_valid(): user = form.save() return redirect('login/') else: form = UserCreationForm() return render(request, 'signup.html', {'form': form}) def thanks(request): return render(request, 'thanks.html')

[ "taiki.murakoshi.0908@gmail.com" ]

taiki.murakoshi.0908@gmail.com

5347fcfd7d3f56e0891c24f6b8d5dc8b119894fd

a04bd4dc9057b6e4527c30a506e7c0c453fcf5db

/hl/pas/samlplugin/saml2/mongo_store.py

6fd38943a4772e380ddc41d1ee81c425256cc889

[ "BSD-2-Clause" ]

permissive

saromba/hl.pas.samlplugin

3d67bab8c5cc3d8c8d0ba8960150ebc51dc44b3d

0a120cc590e208ac19616f35ab567e94294ca0ce

refs/heads/master

2020-12-28T23:16:53.540274

2015-04-14T08:20:56

33,917,939

0

null

2015-04-14T07:42:47

null

UTF-8

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11,641

py

from hashlib import sha1 import logging from pymongo import MongoClient from saml2.eptid import Eptid from saml2.mdstore import MetaData from saml2.s_utils import PolicyError from saml2.ident import code, IdentDB, Unknown from saml2.mdie import to_dict, from_dict from saml2 import md from saml2 import saml from saml2.extension import mdui from saml2.extension import idpdisc from saml2.extension import dri from saml2.extension import mdattr from saml2.extension import ui import xmldsig import xmlenc ONTS = { saml.NAMESPACE: saml, mdui.NAMESPACE: mdui, mdattr.NAMESPACE: mdattr, dri.NAMESPACE: dri, ui.NAMESPACE: ui, idpdisc.NAMESPACE: idpdisc, md.NAMESPACE: md, xmldsig.NAMESPACE: xmldsig, xmlenc.NAMESPACE: xmlenc } __author__ = 'rolandh' logger = logging.getLogger(__name__) class CorruptDatabase(Exception): pass def context_match(cfilter, cntx): # TODO return True class SessionStorageMDB(object): """ Session information is stored in a MongoDB database""" def __init__(self, collection=""): connection = MongoClient() db = connection[collection] self.assertion = db.assertion def store_assertion(self, assertion, to_sign): name_id = assertion.subject.name_id nkey = sha1(code(name_id)).hexdigest() doc = { "name_id_key": nkey, "assertion_id": assertion.id, "assertion": to_dict(assertion, ONTS.values(), True), "to_sign": to_sign } _ = self.assertion.insert(doc) def get_assertion(self, cid): res = [] for item in self.assertion.find({"assertion_id": cid}): res.append({"assertion": from_dict(item["assertion"], ONTS, True), "to_sign": item["to_sign"]}) if len(res) == 1: return res[0] elif res is []: return None else: raise SystemError("More then one assertion with the same ID") def get_assertions_by_subject(self, name_id=None, session_index=None, requested_context=None): """ :param name_id: One of name_id or key can be used to get the authn statement :param session_index: If match against a session index should be done :param requested_context: Authn statements should match a specific authn context :return: """ result = [] key = sha1(code(name_id)).hexdigest() for item in self.assertion.find({"name_id_key": key}): assertion = from_dict(item["assertion"], ONTS, True) if session_index or requested_context: for statement in assertion.authn_statement: if session_index: if statement.session_index == session_index: result.append(assertion) break if requested_context: if context_match(requested_context, statement.authn_context): result.append(assertion) break else: result.append(assertion) return result def remove_authn_statements(self, name_id): logger.debug("remove authn about: %s" % name_id) key = sha1(code(name_id)).hexdigest() for item in self.assertion.find({"name_id_key": key}): self.assertion.remove(item["_id"]) def get_authn_statements(self, name_id, session_index=None, requested_context=None): """ :param name_id: :param session_index: :param requested_context: :return: """ return [k.authn_statement for k in self.get_assertions_by_subject( name_id, session_index, requested_context)] class IdentMDB(IdentDB): def __init__(self, collection="", domain="", name_qualifier=""): IdentDB.__init__(self, None, domain, name_qualifier) self.mdb = MDB(collection, "ident") self.mdb.primary_key = "user_id" def in_store(self, _id): if [x for x in self.mdb.get({"ident_id": _id})]: return True else: return False def create_id(self, nformat, name_qualifier="", sp_name_qualifier=""): _id = self._create_id(nformat, name_qualifier, sp_name_qualifier) while self.in_store(_id): _id = self._create_id(nformat, name_qualifier, sp_name_qualifier) return _id def store(self, ident, name_id): self.mdb.store(ident, name_id=to_dict(name_id, ONTS.values(), True)) def find_nameid(self, userid, nformat=None, sp_name_qualifier=None, name_qualifier=None, sp_provided_id=None): kwargs = {} if nformat: kwargs["name_format"] = nformat if sp_name_qualifier: kwargs["sp_name_qualifier"] = sp_name_qualifier if name_qualifier: kwargs["name_qualifier"] = name_qualifier if sp_provided_id: kwargs["sp_provided_id"] = sp_provided_id res = [] for item in self.mdb.get(userid, **kwargs): res.append(from_dict(item["name_id"], ONTS, True)) return res def find_local_id(self, name_id): cnid = to_dict(name_id, ONTS.values(), True) for item in self.mdb.get({"name_id": cnid}): return item[self.mdb.primary_key] return None def remove_remote(self, name_id): cnid = to_dict(name_id, ONTS.values(), True) self.mdb.remove({"name_id": cnid}) def handle_name_id_mapping_request(self, name_id, name_id_policy): _id = self.find_local_id(name_id) if not _id: raise Unknown("Unknown entity") if name_id_policy.allow_create == "false": raise PolicyError("Not allowed to create new identifier") # else create and return a new one return self.construct_nameid(_id, name_id_policy=name_id_policy) def close(self): pass #------------------------------------------------------------------------------ class MDB(object): primary_key = "mdb" def __init__(self, collection="", sub_collection=""): connection = MongoClient() _db = connection[collection] self.db = _db[sub_collection] def store(self, value, **kwargs): if value: doc = {self.primary_key: value} else: doc = {} doc.update(kwargs) _ = self.db.insert(doc) def get(self, value=None, **kwargs): if value: doc = {self.primary_key: value} doc.update(kwargs) return [item for item in self.db.find(doc)] elif kwargs: return [item for item in self.db.find(kwargs)] def remove(self, key=None, **kwargs): if key is None: if kwargs: for item in self.db.find(kwargs): self.db.remove(item["_id"]) else: doc = {self.primary_key: key} doc.update(kwargs) for item in self.db.find(doc): self.db.remove(item["_id"]) def keys(self): for item in self.db.find(): yield item[self.primary_key] def items(self): for item in self.db.find(): _key = item[self.primary_key] del item[self.primary_key] del item["_id"] yield _key, item def __contains__(self, key): doc = {self.primary_key: key} res = [item for item in self.db.find(doc)] if not res: return False else: return True def reset(self): self.db.drop() #------------------------------------------------------------------------------ class EptidMDB(Eptid): def __init__(self, secret, collection="", sub_collection="eptid"): Eptid.__init__(self, secret) self.mdb = MDB(collection, sub_collection) self.mdb.primary_key = "eptid_key" def __getitem__(self, key): res = self.mdb.get(key) if not res: raise KeyError(key) elif len(res) == 1: return res[0]["eptid"] else: raise CorruptDatabase("Found more than one EPTID document") def __setitem__(self, key, value): _ = self.mdb.store(key, **{"eptid": value}) #------------------------------------------------------------------------------ def protect(dic): res = {} for key, val in dic.items(): key = key.replace(".", "__") if isinstance(val, basestring): pass elif isinstance(val, dict): val = protect(val) elif isinstance(val, list): li = [] for va in val: if isinstance(va, basestring): pass elif isinstance(va, dict): va = protect(va) # I don't think lists of lists will appear am I wrong ? li.append(va) val = li res[key] = val return res def unprotect(dic): res = {} for key, val in dic.items(): if key == "__class__": pass else: key = key.replace("__", ".") if isinstance(val, basestring): pass elif isinstance(val, dict): val = unprotect(val) elif isinstance(val, list): li = [] for va in val: if isinstance(va, basestring): pass elif isinstance(val, dict): va = unprotect(va) li.append(va) val = li res[key] = val return res def export_mdstore_to_mongo_db(mds, collection, sub_collection=""): mdb = MDB(collection, sub_collection) mdb.reset() mdb.primary_key = "entity_id" for key, desc in mds.items(): kwargs = { "entity_description": protect(desc), } mdb.store(key, **kwargs) class MetadataMDB(MetaData): def __init__(self, onts, attrc, collection="", sub_collection=""): MetaData.__init__(self, onts, attrc) self.mdb = MDB(collection, sub_collection) self.mdb.primary_key = "entity_id" def _ext_service(self, entity_id, typ, service, binding): try: srvs = self[entity_id][typ] except KeyError: return None if not srvs: return srvs res = [] for srv in srvs: if "extensions" in srv: for elem in srv["extensions"]["extension_elements"]: if elem["__class__"] == service: if elem["binding"] == binding: res.append(elem) return res def load(self): pass def items(self): for key, item in self.mdb.items(): yield key, unprotect(item["entity_description"]) def keys(self): return self.mdb.keys() def values(self): for key, item in self.mdb.items(): yield unprotect(item["entity_description"]) def __contains__(self, item): return item in self.mdb def __getitem__(self, item): res = self.mdb.get(item) if not res: raise KeyError(item) elif len(res) == 1: return unprotect(res[0]["entity_description"]) else: raise CorruptDatabase("More then one document with key %s" % item) def bindings(self, entity_id, typ, service): pass

[ "t_schorr@gmx.de" ]

t_schorr@gmx.de

2fbfe1db3f59027dc789b1096273144c9025a4c9

cd78d84441e69c1fc40b6a6e9e235e7cf6882454

/python/114.flatten_binary_tree_to_linked_list.py

6f58b41e670f5b1e9ffe984459a6c9d571d73195

[]

no_license

buy/leetcode

53a12d4e0298284a5a2034c88353d0dc195aa66c

da0e834e3f2e3016396fffc96ef943ab9ec58ea4

refs/heads/master

2021-01-13T01:48:01.176632

2015-06-14T06:17:17

31,863,627

1

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# Given a binary tree, flatten it to a linked list in-place. # For example, # Given # 1 # / \ # 2 5 # / \ \ # 3 4 6 # The flattened tree should look like: # 1 # \ # 2 # \ # 3 # \ # 4 # \ # 5 # \ # 6 # Definition for a binary tree node. # class TreeNode: # def __init__(self, x): # self.val = x # self.left = None # self.right = None class Solution: # @param {TreeNode} root # @return {void} Do not return anything, modify root in-place instead. def flatten(self, root): self.flat(root) def flat(self, root): if not root: return None left = self.flat(root.left) right = self.flat(root.right) root.left = None root.right = None if left: root.right = left if right: self.getTail(root).right = right return root def getTail(self, root): while root.right: root = root.right return root

[ "cliu@groupon.com" ]

cliu@groupon.com

b681003f50349bf1c861817a9f45d4dbaf85ebb0

48395cb86d4a618ac300fbcfa22ced193552cb3e

/core/shell.py

0b081f33fd45c75a57b21a1f3893e845875a19dc

[ "MIT" ]

permissive

Lola224/howboutdat

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b746f4d218f9e8f10c09cc3f91b876dc186358ea

refs/heads/master

2023-07-29T05:16:09.840232

2021-09-09T00:45:36

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# Copyright (C) 2015 Noa-Emil Nissinen (4shadoww) # Import python modules import sys # Import core modules from core.module_manager import ModuleManager from core import colors from core import command_handler shellface = "["+colors.bold+"hakku"+colors.end+"]:" mm = ModuleManager() def run(): global shellface global mm ch = command_handler.Commandhandler(mm, False) while True: try: set_face() command = input(shellface+" ") ch.handle(command) except KeyboardInterrupt: if mm.module_loaded == 0: print() sys.exit(0) else: print() mm.module_loaded = 0 mm.module_name = "" print(colors.bold + colors.red + "ctrl + c detected going back..." + colors.end) def set_face(): global shellface global mm if mm.module_loaded == 0: shellface = "["+colors.bold+"hakku"+colors.end+"]:" else: shellface = "["+colors.bold+"hakku"+colors.end+"]"+"("+colors.red+mm.module_name+colors.end+"):"

[ "89975275+Lola224@users.noreply.github.com" ]

89975275+Lola224@users.noreply.github.com

83b2792ba8d5d121b4fc7c7eaca1bfaf512c6aac

677a5c8834a3a3933f81eba075d4c9774e5f4682

/apple-card.py

82f25ca8fd9ece0ccc93ec67de437c5169ca7be6

[]

no_license

mspych/tiller-scripts

b3fb78b295e38ef1a79cddd752a78d1659890e07

b078f7fede5c80491491cf2dd6a513741f075f94

refs/heads/master

2022-04-05T10:38:43.932102

2020-02-02T20:01:56

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0

null

UTF-8

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import fire import pandas as pd import datetime as dt from functools import partial class AppleStatement: def __init__(self, *args): parse_apple_statement = partial(pd.read_csv, usecols=["Transaction Date", "Merchant", "Category", "Description", "Amount (USD)"], dtype={"Transaction Date": "str", "Merchant": "str", "Category": "str", "Description": "str", "Amount (USD)": "float32"}, parse_dates=["Transaction Date"]) dfs = (parse_apple_statement(f) for f in args) self.input = pd.concat(dfs) def head(self): print(self.input.head()) print(self.input.dtypes) def tillerize(self, output, last4="1234"): df = pd.DataFrame( columns="""Tiller Date Description Category Amount Account Account_Number Institution Month Week Transaction_ID Check_Number Full_Description Date_Added""".split() ) df["Tiller"] = "" df["Date"] = self.input["Transaction Date"] df["Description"] = self.input["Merchant"] df["Category"] = self.input["Category"] df["Amount"] = self.input["Amount (USD)"].apply(func=lambda x: round(x, 2) * -1) df["Account"] = "CREDIT CARD" df["Account_Number"] = "xxxx" + str(last4) df["Institution"] = "Apple" df["Month"] = df["Date"].apply(func=lambda d: d.replace(day=1)) df["Week"] = df['Date'] - pd.to_timedelta(arg=df['Date'].dt.weekday, unit='D') df["Transaction_ID"] = "" df["Full_Description"] = self.input["Description"] df["Date_Added"] = pd.to_datetime(dt.date.today()) print(df.head()) df.to_csv(path_or_buf=output, index=False, date_format='%m/%d/%Y') if __name__ == '__main__': fire.Fire(AppleStatement)

[ "bobetto@Robertos-MacBook-Pro-2.local" ]

bobetto@Robertos-MacBook-Pro-2.local

8da5b0f5002683c5b861c6f1187916a45af04490

c50a9defdf08338a76c916e40acf5e6e3d6b3040

/features/steps/cart.py

c2dbf2f417c5abf15cd0ecc1ee4f776d584ebcb0

[]

no_license

gusakovm/python-selenium-automation

03e985047b782df929aacd85d5bf6885735310d1

07d64cc403caef35432cbc6f62e17e68cc96e290

refs/heads/master

2020-07-29T14:44:02.242961

2019-10-16T12:50:48

209,847,858

0

null

2019-09-20T17:40:33

null

UTF-8

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false

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py

from selenium.webdriver.common.by import By from behave import when, then from time import sleep CART_CONTENT_H1 = (By.CSS_SELECTOR, '#sc-active-cart .sc-empty-cart-header') NAV_CART = (By.CSS_SELECTOR, '#nav-cart') NAV_CART_COUNTER = (By.CSS_SELECTOR, '#nav-cart #nav-cart-count') CART_ITEMS = (By.CSS_SELECTOR, '.a-row .sc-list-item') @then('Make sure the cart isn\'t empty (by counter)') def check_cart_isnt_empty_in_nav(context): cart_counter = context.driver.find_element(*NAV_CART_COUNTER) cart_counter_num = cart_counter.text assert int(cart_counter_num) > 0, f"Actual items in cart is {cart_counter_num}" @then('Make sure the item shows at the Cart page') def check_cart_isnt_empty_on_cart_page(context): nav_cart = context.driver.find_element(*NAV_CART) nav_cart.click() sleep(3) cart_items = context.driver.find_elements(*CART_ITEMS) assert len(cart_items) > 0 @then('Amazon Cart page title is {text}') def cart_is_empty(context, text): cart_h1 = context.driver.find_element(*CART_CONTENT_H1) assert text in cart_h1.text

[ "max@gusakov.com" ]

max@gusakov.com

99d2a42513b1e14b5d28a051deaf75c9300be265

003af3e825dacc2b5d05475ecf7ae4ac061550e1

/blinkt/illumination.py

33524b18ca6f3dd53c3a3c0fd91d672447e85a9e

[ "Apache-2.0" ]

permissive

JKarthaus/RadioRaspi

70922029f1925e914f2c9811a181ad347bbef455

e095caa8aa0c7ae76d1697c181cbcc2f58940228

refs/heads/master

2020-03-29T17:18:36.893996

2018-12-17T20:13:22

150,155,059

0

Apache-2.0

2018-12-17T20:13:24

2018-09-24T19:10:52

Python

UTF-8

Python

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1,166

py

#!/usr/bin/env python import colorsys import math import time from sys import exit import signal class GracefulKiller: kill_now = False def __init__(self): signal.signal(signal.SIGINT, self.exit_gracefully) signal.signal(signal.SIGTERM, self.exit_gracefully) def exit_gracefully(self,signum, frame): self.kill_now = True import blinkt blinkt.set_clear_on_exit() hue_range = 120 hue_start = 0 max_brightness = 0.2 killer = GracefulKiller() def show_graph(v, r, g, b): v *= blinkt.NUM_PIXELS for x in range(blinkt.NUM_PIXELS): hue = ((hue_start + ((x / float(blinkt.NUM_PIXELS)) * hue_range)) % 360) / 360.0 r, g, b = [int(c * 255) for c in colorsys.hsv_to_rgb(hue, 1.0, 1.0)] if v < 0: brightness = 0 else: brightness = min(v, 1.0) * max_brightness blinkt.set_pixel(x, r, g, b, brightness) v -= 1 blinkt.show() blinkt.set_brightness(0.1) while True: if killer.kill_now: blinkt.clear break t = time.time() * 2 v = (math.sin(t) + 1) / 2 # Get a value between 0 and 1 show_graph(v, 255, 0, 255) time.sleep(0.01)

[ "joern.karthaus@gmail.com" ]

joern.karthaus@gmail.com

bcc6a25837963c10c9f36f407f827516687c57ed

df449a2eca3caa28164d0a38ffb4fcafcbaba187

/web_scraper_2015.py

1028253b278e21df2b13b306e17dceb73e60da5b

[]

no_license

cklcit03/ml-march-madness

c866ae13915e66016adb0722413516fce9f8df28

8749b00fb05daea7c9a7b3c9e775274df49a519a

refs/heads/master

2020-03-10T20:04:56.862198

2019-03-02T05:21:11

129,562,208

0

null

UTF-8

Python

false

5,200

py

# Copyright (C) 2019 Caleb Lo # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # Machine Learning March Madness # Web scraper that obtains additional features from bs4 import BeautifulSoup import difflib import numpy import urllib2 class Error(Exception): def __init__(self, value): self.value = value def __str__(self): return repr(self.value) def main(): """ Main function """ print("Loading list of teams.") teams = numpy.genfromtxt("teams_2015.csv", dtype=str, delimiter=",") team_ids = teams[1:, 0] num_teams = team_ids.shape[0] team_names = teams[1:, 1] team_names_list = team_names.tolist() # Iterate over seasons (starting in 2001-02) num_seasons = 14 base_year = 2002 for season_idx in range(0, num_seasons): file_mat = 10000*numpy.ones((num_teams, 3), dtype=object) file_mat[:, 1] = team_ids curr_year = base_year+season_idx for team_idx in range(0, num_teams): file_mat[team_idx, 0] = curr_year print("season = %d" % curr_year) url_string = 'https://kenpom.com/index.php?y=%d' % curr_year page = urllib2.urlopen(url_string) soup = BeautifulSoup(page, 'html.parser') trs = soup.find_all('tr') tr_idx = 0 for tr in trs: tds = tr.find_all('td') if tds: school = tds[1].a.string result = difflib.get_close_matches(school, team_names_list) found_flag = 0 if (school == 'Coastal Carolina'): school_id = 1157 elif (school == 'East Tennessee St.'): school_id = 1190 elif (school == 'FIU'): school_id = 1198 elif (school == 'Kent St.'): school_id = 1245 elif (school == 'LIU Brooklyn'): school_id = 1254 elif (school == 'Louisiana Lafayette'): school_id = 1418 elif (school == 'Louisiana Monroe'): school_id = 1419 elif (school == 'Maryland Eastern Shore'): school_id = 1271 elif (school == 'Middle Tennessee'): school_id = 1292 elif (school == 'Mississippi Valley St.'): school_id = 1290 elif (school == 'Nebraska Omaha'): school_id = 1303 elif (school == 'North Carolina A&T'): school_id = 1299 elif (school == 'North Carolina Central'): school_id = 1300 elif (school == 'North Carolina St.'): school_id = 1301 elif (school == 'North Dakota St.'): school_id = 1295 elif (school == 'Northwestern St.'): school_id = 1322 elif (school == 'South Carolina St.'): school_id = 1354 elif (school == 'South Dakota St.'): school_id = 1355 elif (school == 'Southern'): school_id = 1380 elif (school == 'Southwest Missouri St.'): school_id = 1283 elif (school == 'Southwest Texas St.'): school_id = 1402 elif (school == 'Tennessee Martin'): school_id = 1404 elif (school == 'UMKC'): school_id = 1282 elif (school == 'UTSA'): school_id = 1427 elif (school == 'VCU'): school_id = 1433 else: found_flag = 1 print("school = %s, result = %s" % (school, result)) best_match = result[0] match_idx = numpy.where(team_names == best_match) school_id = team_ids[match_idx[0]].astype(int) if (found_flag == 0): match_idx = numpy.where(team_ids.astype(int) == school_id) file_mat[match_idx[0], 2] = float(tds[4].string) tr_idx = tr_idx+1 print("number of teams = %d" % tr_idx) if (season_idx == 0): total_file_mat = file_mat else: total_file_mat = numpy.r_[total_file_mat, file_mat] numpy.savetxt('kenpom_2015.csv', total_file_mat, fmt='%s', delimiter=',') # Call main function if __name__ == "__main__": main()

[ "flashbuzzer@gmail.com" ]

flashbuzzer@gmail.com

fdfc57cd6512126f1c8c13068d33cb184ab73f82

ed789322d285f5d0a5c9b0ba97cf5dafdf3e671c

/apps/users/migrations/0001_initial.py

fc5334d9f22ab7bc3678281762347cfd3bdcd6bb

[]

no_license

mkrj/OnlineShop

7be98c96debf62199fd31b46016841697af4ad6c

fb55c04f455302c89769ba7cac93c89294f501f1

refs/heads/master

2022-12-22T15:33:15.197694

2020-07-15T08:09:28

268,022,909

2

1

null

2022-12-08T11:01:48

2020-05-30T06:30:08

Python

UTF-8

Python

false

3,935

py

# Generated by Django 2.0 on 2020-06-04 18:20 import datetime import django.contrib.auth.models import django.contrib.auth.validators from django.db import migrations, models import django.utils.timezone class Migration(migrations.Migration): initial = True dependencies = [ ('auth', '0009_alter_user_last_name_max_length'), ] operations = [ migrations.CreateModel( name='UserProfile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('username', models.CharField(error_messages={'unique': 'A user with that username already exists.'}, help_text='Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.', max_length=150, unique=True, validators=[django.contrib.auth.validators.UnicodeUsernameValidator()], verbose_name='username')), ('first_name', models.CharField(blank=True, max_length=30, verbose_name='first name')), ('last_name', models.CharField(blank=True, max_length=150, verbose_name='last name')), ('is_staff', models.BooleanField(default=False, help_text='Designates whether the user can log into this admin site.', verbose_name='staff status')), ('is_active', models.BooleanField(default=True, help_text='Designates whether this user should be treated as active. Unselect this instead of deleting accounts.', verbose_name='active')), ('date_joined', models.DateTimeField(default=django.utils.timezone.now, verbose_name='date joined')), ('name', models.CharField(blank=True, max_length=30, null=True, verbose_name='姓名')), ('birthday', models.DateField(blank=True, null=True, verbose_name='出生年月')), ('gender', models.CharField(choices=[('male', '男'), ('female', '女')], default=('female', '女'), max_length=6)), ('mobile', models.CharField(max_length=11, verbose_name='电话')), ('email', models.EmailField(blank=True, max_length=100, null=True, verbose_name='邮箱')), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'verbose_name': '用户', 'verbose_name_plural': '用户', }, managers=[ ('objects', django.contrib.auth.models.UserManager()), ], ), migrations.CreateModel( name='VerifyCode', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('code', models.CharField(max_length=10, verbose_name='验证码')), ('mobile', models.CharField(max_length=11, verbose_name='电话')), ('add_time', models.DateTimeField(default=datetime.datetime.now, verbose_name='添加时间')), ], options={ 'verbose_name': '短信验证码', 'verbose_name_plural': '短信验证码', }, ), ]

[ "2294240112@qq.com" ]

2294240112@qq.com

e129c89a972b58a0d412098db56fd6e35cfb543b

92d39af86fc0de20ca85326275a7eafb2e3117c8

/app.py

d2f164bf078e0375779137495ba6145e5817be5c

[]

no_license

victoray/BookManager

bc2daca5b7e8c9ebabf0cb8ef63ea8b8a2b82753

b1c8c03b54d763381a6f0a4a84b6cd708a300978

refs/heads/master

2020-07-22T18:09:11.996483

2019-09-11T10:43:56

207,285,084

0

null

UTF-8

Python

false

3,045

py

from flask import Flask, render_template, request, redirect, url_for from sqlalchemy.orm import sessionmaker from waitress import serve from db_setup import Base, Books, Comments from sqlalchemy import create_engine engine = create_engine('postgresql://postgres:postgres@localhost:5432/bookmanager') # engine = create_engine('sqlite:///bookmanager.db') Base.metadata.bind = engine db_session = sessionmaker(bind=engine)() app = Flask('__main__') @app.route('/') def home(): books = db_session.query(Books).limit(1000) for book in books: try: book.comments = db_session.query(Comments).filter(Comments.book_id == book.id).count() db_session.commit() except Exception as e: print(e) db_session.rollback() db_session.close() return render_template('index.html', books=books) @app.route('/add-book', methods=['GET', 'POST']) def add_book(): if request.method == 'POST': for i in range(100): book = Books(title=f"{request.form['title']} {i}", author=request.form['author'], isbn=request.form['isbn'], summary=request.form['summary']) db_session.add(book) db_session.commit() db_session.close() return redirect(url_for('home')) return render_template('add-book.html') @app.route('/<int:book_id>/delete-book', methods=['POST']) def delete_book(book_id): book = db_session.query(Books).filter(Books.id == book_id).one() comments = db_session.query(Comments).filter(Comments.book_id == book_id).all() for comment in comments: db_session.delete(comment) db_session.delete(book) db_session.commit() db_session.close() return redirect(url_for('home')) @app.route('/<int:book_id>/edit-book', methods=['GET', 'POST']) def edit_book(book_id): book = db_session.query(Books).filter(Books.id == book_id).one() if request.method == 'POST': book.id = book_id book.title = request.form['title'] book.author = request.form['author'] book.isbn = request.form['isbn'] book.summary = request.form['summary'] db_session.commit() db_session.close() return redirect(url_for('home')) db_session.close() return render_template('edit-book.html', book=book) @app.route('/<int:book_id>', methods=['POST', 'GET']) def view_book(book_id): book = db_session.query(Books).filter(Books.id == book_id).one() comments = db_session.query(Comments).filter(Comments.book_id == book_id).all() if request.method == 'POST': comment = Comments(comment=request.form['comment'], book_id=book_id) db_session.add(comment) db_session.commit() db_session.close() return redirect(url_for('view_book', book_id=book_id)) db_session.close() return render_template('book.html', book=book, comments=comments) if __name__ == '__main__': app.debug = True app.run()

[ "viktoray007@gmail.com" ]

viktoray007@gmail.com

535f3f1814383b30666c5b311255169b1dbcb59f

520f9caeb5c380c42efb26f6778340e8fa7deba8

/Novice/04-02/sparepart/part/urls.py

2d2ad953d7085b3f452574fdd85c6f4e920d31cd

[]

no_license

Tama96/praxis-academy

1fda57d4ddae1d87deb5bede2e9475e01ab75214

b4de60ea0246dac6ee5f898454e5942e0ca91ac8

refs/heads/master

2022-12-26T13:49:09.925185

2020-10-12T01:39:07

287,658,864

0

1

null

UTF-8

Python

false

935

py

from django.contrib import admin from django.urls import path from part import views urlpatterns = [ # from Sparepart path('admin/', admin.site.urls), path('emp', views.emp), path('show', views.show), path('edit/<int:id>', views.edit), path('update/<int:id>', views.update), path('delete/<int:id>', views.destroy), # from Pekerja path('home', views.home), path('show_pegawai', views.show_pegawai), path('edit_pegawai/<int:id>', views.edit_pegawai), path('update_pegawai/<int:id>', views.update_pegawai), path('delete_pegawai/<int:id>', views.destroy_pegawai), # front page path('', views.menu), # from Harga path('home_harga', views.home_harga), path('show_harga', views.show_harga), path('edit_harga/<int:id>', views.edit_harga), path('update_harga/<int:id>', views.update_harga), path('delete_harga/<int:id>', views.destroy_harga), ]

[ "aldypratama96@gmail.com" ]

aldypratama96@gmail.com

298303abeac1510e8db937cfdf75a92dfcec4501

68a432b460fba87b38ef117364c5c6e18b589090

/custom_components/ble_monitor/test/test_brifit_parser.py

ec5f1cf810d7d7b72449c31d9f598e456929fa46

[]

no_license

radio-m/homeassistant

551bfafb3f5677b526ec41ffeb663c48a191a20b

0dda84ecc182bfdd2e7f634dc693eebb9a0ff006

refs/heads/master

2022-08-17T02:28:01.227886

2022-07-31T06:56:55

315,934,165

2

1

null

UTF-8

Python

false

957

py

"""The tests for the Brifit ble_parser.""" from ble_monitor.ble_parser import BleParser class TestBrifit: """Tests for the Brifit parser""" def test_brifit(self): """Test brifit parser.""" data_string = "043E2B0201000085B07438C1A41F05095432303102010614FF55AA0101A4C13874B08501070A1D10F064000100D6" data = bytes(bytearray.fromhex(data_string)) # pylint: disable=unused-variable ble_parser = BleParser() sensor_msg, tracker_msg = ble_parser.parse_data(data) assert sensor_msg["firmware"], "Brifit" assert sensor_msg["type"], "T201" assert sensor_msg["mac"], "A4C13874B085" assert sensor_msg["packet"], "no packet id" assert sensor_msg["data"] assert sensor_msg["temperature"], 25.89 assert sensor_msg["humidity"], 43.36 assert sensor_msg["voltage"], 2.63 assert sensor_msg["battery"], 100 assert sensor_msg["rssi"], -42

[ "doc.zerg@gmail.com" ]

doc.zerg@gmail.com

d5b536815238e7e5882d6315ad7044d8325dd4ad

163bbb4e0920dedd5941e3edfb2d8706ba75627d

/Code/CodeRecords/2406/60870/287457.py

a7d7347477f9630efe3683f26279554b8f27c16b

[]

no_license

AdamZhouSE/pythonHomework

a25c120b03a158d60aaa9fdc5fb203b1bb377a19

ffc5606817a666aa6241cfab27364326f5c066ff

refs/heads/master

2022-11-24T08:05:22.122011

2020-07-28T16:21:24

259,576,640

2

1

null

UTF-8

Python

false

467

py

nums = int(input()) array = [] for i in range(nums): array.append(int(input())) res = [] for i in range(nums): res.append(array[nums - i - 1]) res.sort() count = -1 for j in range(len(array)): for i in range(len(array)): goal = res[i] if array.index(goal) != i: index = array.index(goal) temp = array[index] array[index] = array[i] array[i] = temp count = count + 1 print(count)

[ "1069583789@qq.com" ]

1069583789@qq.com

6a9e0a76ce17886a399c10e1d8cd795c9fcab029

c974264a6f6971cb5c800ceac93bd10e67f0686a

/hello.py

750db0a6e81a4bbed67ff1e709a9fc1acae34f21

[]

no_license

mukeshdevmurari/pythonrepo

9fd6ee29e895e7cc14bcb3a70ae633596890c0c1

11547d178f615f5809c11bb2e3e62f31e0f976ef

refs/heads/master

2020-04-02T04:24:22.695291

2020-03-02T03:46:49

154,015,033

0

null

UTF-8

Python

false

35

py

print("hello") print("Second Line")

[ "mukesh@jogiinfotech.com" ]

mukesh@jogiinfotech.com

27e0c8b6ce319284acc5d72b442e1acbbf9c5e55

fd2a23b8fd924ddc385ad480b5e7dd1174961f25

/pyltp_eg1.py

abd9442196131ec57601565ad5ea7f51ea2683b4

[]

no_license

shamrock222/nlp-learning

596862ea1c1208d0bff7e8ffae11f9cec42ddf43

93c752811a35b505471c8bea8325db14389444eb

refs/heads/master

2020-04-14T04:51:11.886331

2019-01-30T15:31:10

163,647,180

1

0

null

UTF-8

Python

false

4,055

py

# -*- coding: utf-8 -*- """ @refer: https://pyltp.readthedocs.io/zh_CN/latest/api.html#id21 @usage: pyltp 是 LTP 的 Python 封装，提供了分词，词性标注，命名实体识别，依存句法分析，语义角色标注的功能 """ import os from pyltp import SentenceSplitter, Segmentor, CustomizedSegmentor, Postagger, NamedEntityRecognizer,Parser, SementicRoleLabeller class ltp_analyzer: def __init__(self, model_path): self.cws_model_path = os.path.join(model_path, 'cws.model') # 分词模型路径，模型名称为`cws.model` self.pos_model_path = os.path.join(model_path, 'pos.model') # 词性标注模型路径，模型名称为`pos.model` self.ner_model_path = os.path.join(model_path, 'ner.model') # 命名实体识别模型路径，模型名称为`pos.model` self.par_model_path = os.path.join(model_path, 'parser.model') # 依存句法分析模型路径，模型名称为`parser.model` def get_split_sentences(self, text): # 分句 sents = SentenceSplitter.split(text) # 分句 print('\n'.join(sents)) return list(sents) def get_cut_words(self, text, dict_path=None): # 分词 segmentor = Segmentor() # 初始化实例 if dict_path is None: segmentor.load(self.cws_model_path) # 加载模型 else: segmentor.load_with_lexicon(self.cws_model_path, dict_path) # 加载模型，第二个参数是您的外部词典文件路径 words = segmentor.segment(text) print('\t'.join(words)) segmentor.release() return list(words) def get_customized_cut_words(self, text, model_file): # 个性化分词模型 customized_segmentor = CustomizedSegmentor() # 初始化实例 customized_segmentor.load(self.cws_model_path, model_file) # 加载模型，第二个参数是您的增量模型路径 words = customized_segmentor.segment(text) print('\t'.join(words)) customized_segmentor.release() return list(words) def get_postags(self, words): postagger = Postagger() # 初始化实例 postagger.load(self.pos_model_path) # 加载模型 postags = postagger.postag(words) # 词性标注 print('\t'.join(postags)) postagger.release() # 释放模型 return list(postags) def get_netags(self, words): # 命名实体识别 postags = self.get_postags(words) recognizer = NamedEntityRecognizer() # 初始化实例 recognizer.load(self.ner_model_path) # 加载模型 netags = recognizer.recognize(list(words), list(postags)) # 命名实体识别 print('\t'.join(netags)) recognizer.release() # 释放模型 return list(netags) def get_dependency(self, words): # 句法分析 postags = self.get_postags(words) parser = Parser() # 初始化实例 parser.load(self.par_model_path) # 加载模型 arcs = parser.parse(words, postags) # 句法分析 print("\t".join("%d:%s" % (arc.head, arc.relation) for arc in arcs)) parser.release() # 释放模型 return arcs def get_srl(self, words): # 语义角色标注 labeller = SementicRoleLabeller() # 初始化实例 labeller.load(self.srl_model_path) # 加载模型 # arcs 使用依存句法分析的结果 postags = self.get_postags(words) arcs = self.get_dependency(words) roles = labeller.label(words, postags, arcs) # 语义角色标注 # 打印结果 for role in roles: print(role.index, "".join(["%s:(%d,%d)" % (arg.name, arg.range.start, arg.range.end) for arg in role.arguments])) labeller.release() # 释放模型 return roles def main(): LTP_DATA_DIR = 'D:/Model/ltp_data_v3.4.0' # ltp模型目录的路径 text = '元芳你怎么看？我就趴窗口上看呗！' ltp0 = ltp_analyzer(model_path=LTP_DATA_DIR) ltp0.get_cut_words(text)

[ "32649526@qq.com" ]

32649526@qq.com

0dfc8ddda7bfa6bebf915b9611cdf33ac8ec311a

7315525e0c1eda981db0b57d5e756282982e69a7

/demo_1A6N.py

f50f5d1845ff14d0ad5c58c39bd4547c1f0e20ca

[]

no_license

hkthirano/Read_Amin_XYZ

ca1c38d87a7155f4cb5d74ed8be7e6c212f6b4b8

bd12842d4530df22d08428770876ebd5f83f5bae

refs/heads/master

2021-07-03T11:02:58.503756

2020-08-19T07:35:39

145,704,574

0

null

UTF-8

Python

false

448

py

from Read_XYZ.main import Read_xyz as r_xyz config = ['A', 'NA', 'NA'] # config = ['A', '10', '100'] demo = r_xyz('./Data/1A6N.pdb', config) # demo = r_xyz('./Data/1APS.pdb', config) demo.matching_lines() demo.save_matching_lines('./1A6N_line.txt') demo.extract_xyz() demo.save_extract_xyz('./1A6N_xyz.txt') # VDWr:Van der Waals radius or IEP:Isoelectric point demo.extract_xyz_with_weight('VDWr') demo.save_extract_xyz('./1A6N_xyz_VDWr.txt')

[ "p238058h@gmail.com" ]

p238058h@gmail.com

5829478376a030f8c92bf294427d412fcbee059d

b2fc7db783569a3d695f6017889dd6a09fe56b1d

/Database_creator.py

0025de95ecb9d8500ef3296c6576449bcee85b5a

[]

no_license

Abhishek19895/Search_Indexing

7ec4ef89671bbc8dd700410aad91e2f3399c3e51

d4ec21d7e4c33c4399099a4343ddca87f048b5e1

refs/heads/master

2021-01-01T04:56:18.305788

2016-05-22T23:58:15

58,711,165

0

null

UTF-8

Python

false

4,739

py

""" Author : Abhishek Singh To create our search database from APIs, Amazon Public data, Stanford Database & Kaggle datasets """ ##loading all the concerned libraries for this database packing import os, sys, pandas as pd import numpy as np import json from collections import defaultdict import glob #function for loading data from the 'Emails' csv file def csv_loader(): """ input: The csv file whose text is extracted :return: Shall return a list will all the text loaded """ data = pd.read_csv('data/Reviews.csv') data1 = data['Text'] #Extracting only the textual information return list(data1) #function for loading data from the Multiple csv files of NYTimes def multiple_csv_loader(): """ input: The list of csv files whose text is extracted :return: Shall return a list will all the text loaded """ list1 = glob.glob('data/nyt_data/*.csv') dfs = [] #To store all the files for filename in list1: data = pd.read_csv(filename) #reading each row of the csv file for i in range(data.shape[0]): l = str(data.iloc[i]['headline']) if l: dfs.append(l) #End of Inner loop for a single document #End of Outer loop for all the documents return dfs #Function for loading txt files 'Movie' Review def txt_loader(): """ input: The list of txt files whose text is extracted :return: Shall return a list will all the text loaded """ list1 = glob.glob('data/test/pos/*.txt') ; list2 = glob.glob('data/test/neg/*.txt') list3 = glob.glob('data/train/pos/*.txt') ; list4 = glob.glob('data/train/neg/*.txt') list = list1 + list2 + list3 + list4 #Appending all the files all_files = [] #list to store all names for fileName in list: fin = open( fileName, "r" ) data_list = fin.readlines() all_files.append(data_list) return all_files #Function to load data from Amazon Beauty JSON file def json_loader1(): """ Reading the the reviews JSON file and storing it as a list Output: Shall return a list will all the text loaded """ data = [] with open('data/reviews_Beauty.json') as f: for line in f: data.append(json.loads(line)) data_list = [] #transforming the json files into a list of tuples for i in data: text = i.get('reviewText',None) #removing unicodes from the various elements if text: text = text.encode('utf-8') data_list.append(text) return data_list #Function to load data from Amazon Pets Review JSON file def json_loader2(): """ Reading the the reviews JSON file and storing it as a list Output: Shall return a list will all the text loaded """ data = [] with open('data/reviews_Pet_Supplies.json') as f: for line in f: data.append(json.loads(line)) data_list = [] #transforming the json files into a list of tuples for i in data: text = i.get('reviewText',None) #removing unicodes from the various elements if text: text = text.encode('utf-8') data_list.append(text) return data_list #Function to download data from twitter def twitter_data(): """ Reading the the reviews JSON file and storing it as a list Output: Shall return a list will all the text loaded """ data = [] with open('data/twitter.json') as f: for line in f: data.append(json.loads(line)) data_list = [] #transforming the json files into a list of tuples for i in data: text = i.get('text',None) #removing unicodes from the various elements if text: text = text.encode('utf-8') data_list.append(text) return data_list #Running the main function if __name__ == '__main__': """ Running the main function and Creating our master database """ all_data = [] #Empty list a = csv_loader() #Adding the data from Hillary Clinton's Emails (Kaggle) (568454 rows) a = a + multiple_csv_loader() #Adding the NYtimes data (27794 rows) a = a + txt_loader() #Adding the data from Stanford Database (50000 rows) a = a + json_loader1() #Adding the Amazon beauty product reviews (2023082 rows) a = a + json_loader2() #Adding the Amazon Pets reviews (1235329 rows) a = a + twitter_data() #Adding the tweets (50000 rows) all_data = pd.DataFrame(a) #Making a Dataframe of the giant list (4100 rows) print " Exporting the dataframe to a txt file" all_data all_data.to_csv('full_data.txt', header = None, index=False)

[ "aasingh4@dons.usfca.edu" ]

aasingh4@dons.usfca.edu

8ace0d505c55ded5686556aed557fdd36e7f2f76

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/src/canopen_301_402_old/canopen_301/obj_dict.py

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[ "MIT" ]

permissive

wuhuaroubj/canopen_301_402

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16b189ffe631bb5812311131002d7f722de5fc66

refs/heads/master

2021-04-28T18:04:36.819618

2016-09-19T10:48:43

null

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UTF-8

Python

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py

#!/usr/bin/env python2 # -*- coding: utf-8 -*- from collections import defaultdict from canopen_301_402.constants import * from canopen_301_402.canopen_301.obj import CanOpenObject class CanOpenObjectDictionary(object): """docstring for CanOpenObjectDictionary""" def __init__(self, node): super(CanOpenObjectDictionary, self).__init__() self.node = node self.canopen = self.node.canopen self.objects = defaultdict(lambda:None)

[ "xaedes@gmail.com" ]

xaedes@gmail.com

0e96beb15ac5db5c0269be7d3634b98f33bb64d6

a3662e2b0aa375614c858c70a02690d21fcb090f

/config/settings.py

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[]

no_license

chiwon3/AIMProject

80bd8eba0f12bd10d322a084b9468d7f4beeafe7

ab0fd8174b7ef6588903806b442efbf2b885b174

refs/heads/master

2023-01-10T05:20:39.203654

2020-11-08T08:11:42

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""" Django settings for AIM project. Generated by 'django-admin startproject' using Django 3.0.8. For more information on this file, see https://docs.djangoproject.com/en/2.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.0/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/2.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 's92*)m__*r185h$6r$x9ovm23&9+*&awlno5fmdp^yk9pkjlu*' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ['*'] AUTHENTICATION_BACKENDS = [ # Needed to login by username in Django admin, regardless of `allauth` 'django.contrib.auth.backends.ModelBackend', # `allauth` specific authentication methods, such as login by e-mail 'allauth.account.auth_backends.AuthenticationBackend', ] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'mypage.apps.MypageConfig', 'accounts.apps.AccountsConfig', 'board.apps.BoardConfig', 'webcrawl.apps.WebcrawlConfig', 'django.contrib.sites', 'django_summernote', 'allauth', 'allauth.account', 'allauth.socialaccount', 'allauth.socialaccount.providers.google', 'allauth.socialaccount.providers.kakao', 'allauth.socialaccount.providers.instagram', 'allauth.socialaccount.providers.naver', 'allauth.socialaccount.providers.facebook', 'allauth.socialaccount.providers.twitter', ] SITE_ID = 1 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 = 'config.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', ], }, }, ] LOGIN_REDIRECT_URL = '/' LOGOUT_REDIRECT_URL = '/' WSGI_APPLICATION = 'config.wsgi.application' MEDIA_URL = '/media/' MEDIA_ROOT = os.path.join(BASE_DIR, 'media/') # Database # https://docs.djangoproject.com/en/2.0/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/2.0/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/2.0/topics/i18n/ LANGUAGE_CODE = 'ko' TIME_ZONE = 'Asia/Seoul' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.0/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR, 'static')

[ "chiwon3@gmail.com" ]

chiwon3@gmail.com

6157b6267a9a2e0daf831ccea8a45e8b4d8efa43

27cdfc9d3e17a87668a436a490e1648344ceb2bf

/user_manager/migrations/0003_auto_20191101_2341.py

a3fc24bcade0cb4389ee492532bd780aede6cdd0

[]

no_license

RadikSeyfullin/onw

b2c0a6d85ea1ca9ee0c70b2b1384fcc66ea78892

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refs/heads/master

2020-09-01T03:56:32.743052

2020-04-10T06:38:07

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# Generated by Django 2.2.6 on 2019-11-01 20:41 from django.db import migrations, models import user_manager.models class Migration(migrations.Migration): dependencies = [ ('user_manager', '0002_auto_20191031_1756'), ] operations = [ migrations.AlterField( model_name='customuser', name='avatar', field=models.ImageField(default='static/images/users/default_logo.png', upload_to=user_manager.models.get_image_path, verbose_name='Аватар'), ), ]

[ "radikseyfullin@gmail.com" ]

radikseyfullin@gmail.com

cfc185495dbf3f6d2211262d61fe63991cdf196b

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/Algorithms/grading_students.py

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[]

no_license

basakrajarshi/HackerRankChallenges-Python

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refs/heads/master

2020-04-15T01:39:08.266769

2019-01-23T00:41:52

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import os import sys # # Complete the gradingStudents function below. # def gradingStudents(grades): finalgrades = [] for grade in grades: if (grade < 38): #print("First if ",grade) finalgrades.append(grade) else: #print("First else ", grade) if (grade % 5 <= 2): finalgrades.append(grade) else: finalgrades.append(grade + (5 - (grade % 5))) return (finalgrades) if __name__ == '__main__': f = open(os.environ['OUTPUT_PATH'], 'w') n = int(input()) grades = [] for _ in range(n): grades_item = int(input()) grades.append(grades_item) result = gradingStudents(grades) f.write('\n'.join(map(str, result))) f.write('\n') f.close()

[ "noreply@github.com" ]

basakrajarshi.noreply@github.com

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aa35a2c82d356c064a0115b486b1266d0fe75185

/problem8.py

f8ebcb3b376faad67fef059e5b79370d955ea17d

[]

no_license

JellyWX/project-euler

f39da9c75cf4ccabecce8fa4e6f90f85bc68fe6c

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refs/heads/master

2021-01-13T08:00:12.557792

2020-10-02T17:55:45

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number = list('7316717653133062491922511967442657474235534919493496983520312774506326239578318016984801869478851843858615607891129494954595017379583319528532088055111254069874715852386305071569329096329522744304355766896648950445244523161731856403098711121722383113622298934233803081353362766142828064444866452387493035890729629049156044077239071381051585930796086670172427121883998797908792274921901699720888093776657273330010533678812202354218097512545405947522435258490771167055601360483958644670632441572215539753697817977846174064955149290862569321978468622482839722413756570560574902614079729686524145351004748216637048440319989000889524345065854122758866688116427171479924442928230863465674813919123162824586178664583591245665294765456828489128831426076900422421902267105562632111110937054421750694165896040807198403850962455444362981230987879927244284909188845801561660979191338754992005240636899125607176060588611646710940507754100225698315520005593572972571636269561882670428252483600823257530420752963450') highest_value = 0 for i in range(len(number) - 13): product = 1 for j in range(13): product *= int(number[i+j]) print(product) if product > highest_value: highest_value = product print(highest_value)

[ "judewrs@gmail.com" ]

judewrs@gmail.com

d8848ccdf65a02f944b270afbc81cd3c00ca92c5

a17c820cbbcb82a93b1675a3d1cf37d03440f7f3

/StyleTransfer.py

2510d272202c9daf8760833e59fabd32fa7731db

[]

no_license

Ahaeflig/semantic_map

20a8250cde3fc045a36ad9f9d12e3259a0e7f97f

140d61a3ae677308955454c7be0492a1392b141a

refs/heads/master

2021-09-14T17:15:59.509477

2018-05-16T13:26:06

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py

import tensorflow as tf import numpy as np import skimage import skimage.transform from helpers import * from helpers import getLaplacian from VOChelpers import * # IO import matplotlib.pyplot as plt from smooth_local_affine import smooth_local_affine # Datastructure from collections import OrderedDict # Custom import vgg19 import vgg19_atrous VGG_PATH = 'data/vgg/' DATA_PATH = 'data/' class StyleTransfer: #TODO could create a mask object instead of all this stuff in params def __init__(self, content_layer_name, style_layers_name, mask_layer_name, init_image, content_image, style_image, session, num_iter, content_loss_weight, style_loss_weight, K=15, normalize=False, debug=False, orphan=True, use_dcrf=False, matting_loss=1000, tv_loss=1000, soft_temp=0.1, voc_names=[], k_style=[]): # Check same image size (can extend to multiple image sizes) assert (content_image.shape == style_image.shape) ''' ======================================= Store Params ======================================= ''' self.K = K self.content_layer_name = content_layer_name self.style_layers_name = style_layers_name self.mask_layer_name = mask_layer_name self.init_image = init_image self.content_image = content_image self.style_image = style_image self.num_iter = num_iter self.sess = session self.content_loss_weight = content_loss_weight self.style_loss_weight = style_loss_weight self.matting_loss = matting_loss self.tv_loss = tv_loss self.debug = debug self.image_final = 0 self.voc_names = voc_names # choose K used for style transfer if not k_style: self.k_style = np.array(range(K)) else: self.k_style = k_style ''' ============================== Pre-compute VGG stuff ============================== ''' # Vgg # Only one graph is needed cause we assume the shape of all the images are the same self.vgg = vgg19.Vgg19(VGG_PATH + 'vgg19.npy') self.vgg_shape = [1, content_image.shape[0], content_image.shape[1], content_image.shape[2]] images = tf.placeholder("float", self.vgg_shape, name="images") self.vgg.build(images) # Content from content image and style image (remember eval_layers() return dicts) self.content_layer = eval_layers(self.sess, self.content_image, self.vgg, self.content_layer_name) self.style_layer = eval_layers(self.sess, self.style_image, self.vgg, self.content_layer_name) # Mask layers, note that eval_layers expects array of names self.mask_content_layer = eval_layers(self.sess, content_image, self.vgg, [self.mask_layer_name]) self.mask_style_layer = eval_layers(self.sess, style_image, self.vgg, [self.mask_layer_name]) ''' VGG atrous stuff self.vgg_atrous = vgg19_atrous.Vgg19_Atrous(VGG_PATH + 'vgg19.npy') self.vgg_atrous.build(images) self.mask_content_layer = eval_layers(self.sess, content_image, self.vgg_atrous, [self.mask_layer_name]) self.mask_style_layer = eval_layers(self.sess, style_image, self.vgg_atrous, [self.mask_layer_name]) ''' # Style from style image, precomputed since the gram doesn't change self.style_layers = eval_layers(self.sess, style_image, self.vgg, self.style_layers_name) self.grams_style = {layer_name: self.compute_gram(layer) for layer_name, layer in self.style_layers.items()} ''' ================== Mask processing ================== ''' # Masks between the content layer of content image and style image self.A = compute_affinity_matrix(self.mask_content_layer.get(self.mask_layer_name), self.mask_style_layer.get(self.mask_layer_name)) self.orphan = 0 # If you want to use VOC segmentation it's possible using the voc_names with voc imagenames if not voc_names: if self.K > 0: L, R = get_masks(K, self.A, self.mask_content_layer.get(self.mask_layer_name).shape[1:3], self.mask_style_layer.get(self.mask_layer_name).shape[1:3], orphan=orphan, normalize=normalize, soft_temp=soft_temp) #Add orphan masks if orphan: self.orphan = 1 if debug: show_masks(self.content_image, self.style_image, L, R, self.K + self.orphan, show_axis=True) if use_dcrf: L = [crf(content_image * 255, l)[0] for l in L] R = [crf(style_image * 255, r)[0] for r in R] if debug: show_masks(self.content_image, self.style_image, L, R, self.K + self.orphan) self.L = L self.R = R else: #Get voc masks L_voc = load_voc_mask(voc_names[0], shape=self.content_image.shape) R_voc = load_voc_mask(voc_names[1], shape=self.content_image.shape) # Drop border and turn to binary self.L = toMultipleArray(pngToMaskFormat(L_voc))[0:20].astype("float32") self.R = toMultipleArray(pngToMaskFormat(R_voc))[0:20].astype("float32") self.K = self.L.shape[0] if debug: show_masks(self.content_image, self.style_image, self.L, self.R, self.K, show_axis=True, normalized=True, vmax=1) self.build_and_optimize() def build_and_optimize(self): # We optimize on G self.G = tf.Variable(self.init_image[None, ...], name="G", dtype=tf.float32) self.vgg_g = vgg19.Vgg19(VGG_PATH + 'vgg19.npy') self.vgg_g.build(self.G) # Get G content self.G_content_layer = get_layers(self.vgg_g, self.content_layer_name) # Get G styles self.G_style_layers = get_layers(self.vgg_g, self.style_layers_name) with tf.name_scope("train"): cost_content = self.content_loss() self.final_content_loss = self.content_loss_weight * cost_content if self.K > 0 or self.voc_names: cost_style = self.style_loss_masks() else: cost_style = self.style_loss_normal() #cost_style = self.style_loss_masks_dummy() #cost_style += self.affinity_loss() self.final_style_loss = self.style_loss_weight * (1 / len(self.style_layers_name)) * cost_style #Matting M = tf.to_float(getLaplacian(self.content_image)) self.loss_affine = self.affine_loss(self.G, M, self.matting_loss)[0][0] self.loss_tv = self.total_variation_loss(self.G, self.tv_loss) #loss_tv = 0.0001 loss = self.final_content_loss + self.final_style_loss + self.loss_affine + self.loss_tv global iter_count iter_count = 0 train_step = tf.contrib.opt.ScipyOptimizerInterface( loss, method='L-BFGS-B', options={'maxiter': self.num_iter, }) self.sess.run(tf.global_variables_initializer()) print("Start Training") training = train_step.minimize(self.sess, fetches=[self.final_content_loss, self.final_style_loss, self.loss_affine, self.loss_tv], loss_callback=self.callback) with tf.name_scope("image_out"): image_out = tf.clip_by_value(tf.squeeze(self.G, [0]), 0, 1) ''' for i in range(0, self.num_iter): cost_p, _, cost_style_p, cost_content_p = self.sess.run([loss, training, cost_style, cost_content]) if i % 300 == 0: print("iter = {}, total_cost = {}, cost_content = {}, cost style = {}".format(str(i), cost_p, cost_content_p, cost_style_p)) ''' # Output image_final = self.sess.run(image_out) '''SMOOTH LOCAL AFFINE => CUDA ERROR''' ''' content_input = np.array(self.content_image, dtype=np.float32) # RGB to BGR #content_input = content_input[:, :, ::-1] # H * W * C to C * H * W content_input = content_input.transpose((2, 0, 1)) input_ = np.ascontiguousarray(content_input, dtype=np.float32) _, H, W = np.shape(input_) output_ = np.ascontiguousarray(image_final.transpose((2, 0, 1)), dtype=np.float32) best_ = smooth_local_affine(output_, input_, 1e-7, 3, H, W, 15, 1e-1).transpose(1, 2, 0) result = Image.fromarray(np.uint8(np.clip(best_ * 255., 0, 255.))) ''' plt.figure(figsize=(14, 12)) plt.subplot(4, 4, 1) plt.imshow(self.init_image) plt.subplot(4, 4, 2) plt.imshow(self.content_image) plt.subplot(4, 4, 3) plt.imshow(self.style_image) plt.subplot(4, 4, 4) plt.imshow(image_final) #writer = tf.summary.FileWriter("output", self.sess.graph) self.image_final = image_final; # Return the final image, usefull for two step optimization def get_final(self): return self.image_final # Function at each step def callback(self, content_loss, style_loss, affine_loss, tv_loss): global iter_count; if iter_count % 100 == 0: if self.debug: print('Iteration {} / {} '.format(iter_count, self.num_iter)) print('Content loss: {} / No weight: {}'.format(content_loss, content_loss / self.content_loss_weight)) print('Style loss: {} / No weight: {}'.format(style_loss, style_loss / self.style_loss_weight)) print('Affine loss: {} / No weight: {}'.format(affine_loss, affine_loss / self.matting_loss)) print('Tv loss: {} / No weight: {}'.format(tv_loss, affine_loss / self.tv_loss)) iter_count += 1 # Input is of shape [B, H, W, C] def compute_gram(self, array): shape = array.shape matrix = np.reshape(array, [shape[1] * shape[2], shape[3]]) return np.matmul(matrix.transpose(), matrix) # Input is of shape [B, H, W, C] def compute_gram_tensor(self, tensor): shape = tensor.get_shape() matrix = tf.reshape(tensor, shape=[-1, int(shape[3])]) return tf.matmul(matrix, matrix, transpose_a=True) # Computes the style loss over all K for each layer def style_loss_masks(self): loss = 0 for key in self.style_layers: for k in range(0, self.K + self.orphan): if k in self.k_style: tensor = self.G_style_layers.get(key) shape_g = tensor.get_shape().as_list() g_mask = skimage.transform.resize(self.L[k][..., None], (shape_g[1], shape_g[2]), mode='constant', order=0) weighted_g_layer = tf.multiply(tensor, g_mask) gram_g = self.compute_gram_tensor(weighted_g_layer) g_mask_mean = tf.to_float(tf.reduce_mean(g_mask)) # Deep photo style transfer way, I prefer my way ''' gram_g = tf.cond(tf.greater(g_mask_mean, 0.), lambda: gram_g / (tf.to_float(tf.size(tensor)) * g_mask_mean), lambda: gram_g ) ''' layer_s = self.style_layers.get(key) shape_s = layer_s.shape s_mask = skimage.transform.resize(self.R[k][..., None], (shape_s[1], shape_s[2]), mode='constant', order=0) weighted_s_layer = tf.multiply(layer_s, s_mask) gram_s = self.compute_gram_tensor(weighted_s_layer) s_mask_mean = tf.to_float(tf.reduce_mean(s_mask)) ''' gram_s = tf.cond(tf.greater(s_mask_mean, 0.), lambda: gram_s / (tf.to_float(layer_s.size) * s_mask_mean), lambda: gram_s ) ''' M = shape_g[3] N = shape_g[1] * shape_g[2] loss += tf.reduce_mean(tf.squared_difference(gram_g, gram_s)) * (1. / (4 * (N ** 2) * (M ** 2)) * ((g_mask_mean+s_mask_mean) / 2)) return loss def style_loss_masks_old(self): loss = 0 for key in self.style_layers: for k in range(0, self.K + self.orphan): tensor = self.G_style_layers.get(key) shape_g = tensor.get_shape().as_list() weighted_g_layer = tensor * skimage.transform.resize(self.L[k][..., None], (shape_g[1], shape_g[2]), mode='constant', order=0) gram_g = self.compute_gram_tensor(weighted_g_layer) layer_s = self.style_layers.get(key) shape_s = layer_s.shape weighted_s_layer = layer_s * skimage.transform.resize(self.R[k][..., None], (shape_s[1], shape_s[2]), mode='constant', order=0) gram_s = self.compute_gram(weighted_s_layer) M = shape_g[3] N = shape_g[1] * shape_g[2] loss += tf.reduce_mean(tf.pow((gram_g - gram_s), 2) * (1. / (4 * N ** 2 * M ** 2))) return loss # loss used with test square mask def style_loss_masks_dummy(self): loss = 0 L = self.L[0] R = self.R[0] L = np.reshape(L, [L.shape[0], L.shape[1], 1]) R = np.reshape(R, [R.shape[0], R.shape[1], 1]) L = np.zeros(L.shape).astype("float32") L[L.shape[0] // 4:-L.shape[0] // 4, L.shape[1] // 4:-L.shape[1] // 4] = 1 R = np.zeros(R.shape).astype("float32") R[R.shape[0] // 4:-R.shape[0] // 4, R.shape[1] // 4:-R.shape[1] // 4] = 1 # L = L[:, :, 0] # R = R[:, :, 0] if (self.debug): plt.figure() plt.imshow(L[:, :, 0]) plt.figure() plt.imshow(R[:, :, 0]) for i in range(0, self.K): for key in self.style_layers: tensor = self.G_style_layers.get(key) shape_g = tensor.get_shape().as_list() # upscale masks to be same size as tensor currL = skimage.transform.resize(L, (shape_g[1], shape_g[2]), mode='constant', order=0) weighted_g_layer = tensor * currL gram_g = self.compute_gram_tensor(weighted_g_layer) layer_s = self.style_layers.get(key) shape_s = layer_s.shape currR = skimage.transform.resize(R, (shape_s[1], shape_s[2]), mode='constant', order=0) weighted_s_layer = layer_s * currR gram_s = self.compute_gram(weighted_s_layer) M = shape_g[3] N = shape_g[1] * shape_g[2] loss += (1/self.K) * tf.reduce_mean(tf.pow((gram_g - gram_s), 2)) * (1. / (4 * N ** 2 * M ** 2)) return loss # Basic style loss, used if K = 0 def style_loss_normal(self): loss = 0 for key in self.style_layers: tensor = self.G_style_layers.get(key) shape = tensor.get_shape().as_list() gram_g = self.compute_gram_tensor(tensor) gram_s = self.grams_style.get(key) M = shape[3] N = shape[1] * shape[2] loss += tf.reduce_mean(tf.pow((gram_g - gram_s), 2)) * (1. / (4 * (N ** 2) * (M ** 2))) return loss # Content loss term over a layer def content_loss(self): shape = self.content_layer.get(self.content_layer_name[0]).shape return tf.reduce_mean(tf.pow( self.content_layer.get(self.content_layer_name[0]) - self.G_content_layer.get(self.content_layer_name[0]), 2)) * 1. / (2 * shape[3] * shape[1] * shape[2]) # When tested affinity loss MSE def affinity_loss(self): return tf.reduce_mean(tf.pow( self.A - self.compute_affinity_matrix_tf(self.G_content_layer.get(self.content_layer_name[0]), tf.convert_to_tensor( self.style_layer.get(self.content_layer_name[0]))), 2)) / 2 # Maps shape should be [1, H, W, C] def compute_affinity_matrix_tf(self, content_maps, reference_maps): print(content_maps) print(reference_maps) content_maps = tf.squeeze(content_maps, axis=0) reference_maps = tf.squeeze(reference_maps, axis=0) content_maps = tf.reshape(content_maps, [int(content_maps.get_shape()[0]) * int(content_maps.get_shape()[1]), int(content_maps.get_shape()[2])]) reference_maps = tf.reshape(reference_maps, [int(reference_maps.get_shape()[0]) * int(reference_maps.get_shape()[1]), int(reference_maps.get_shape()[2])]) return tf.matmul(content_maps, reference_maps, transpose_b=True) # Photorealistic loss from deep_photo style transfer def affine_loss(self, output, M, weight): loss_affine = 0.0 output_t = tf.to_float(output / 255.) for Vc in tf.unstack(output_t, axis=-1): Vc_ravel = tf.reshape(tf.transpose(Vc), [-1]) loss_affine += tf.matmul(tf.expand_dims(Vc_ravel, 0), tf.sparse_tensor_dense_matmul(M, tf.expand_dims(Vc_ravel, -1))) return loss_affine * weight # tv loss also from deep photo style transfer def total_variation_loss(self, output, weight): shape = output.get_shape() tv_loss = tf.reduce_sum((output[:, :-1, :-1, :] - output[:, :-1, 1:, :]) * (output[:, :-1, :-1, :] - output[:, :-1, 1:, :]) + \ (output[:, :-1, :-1, :] - output[:, 1:, :-1, :]) * (output[:, :-1, :-1, :] - output[:, 1:, :-1, :])) / 2.0 return tv_loss * weight

[ "adan.haefliger@gmail.com" ]

adan.haefliger@gmail.com

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costingalan/networking-hyperv-debian

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2021-01-15T09:27:45.332769

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# Copyright 2015 Cloudbase Solutions SRL # 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. """ Unit tests for the Hyper-V Mechanism Driver. """ from hyperv.neutron import constants from hyperv.neutron.ml2 import mech_hyperv from hyperv.tests import base class TestHypervMechanismDriver(base.BaseTestCase): def setUp(self): super(TestHypervMechanismDriver, self).setUp() self.mech_hyperv = mech_hyperv.HypervMechanismDriver() def test_get_allowed_network_types(self): agent = {'configurations': {'tunnel_types': []}} actual_net_types = self.mech_hyperv.get_allowed_network_types(agent) network_types = [constants.TYPE_LOCAL, constants.TYPE_FLAT, constants.TYPE_VLAN] self.assertEqual(network_types, actual_net_types) def test_get_allowed_network_types_nvgre(self): agent = {'configurations': {'tunnel_types': [constants.TYPE_NVGRE]}} actual_net_types = self.mech_hyperv.get_allowed_network_types(agent) network_types = [constants.TYPE_LOCAL, constants.TYPE_FLAT, constants.TYPE_VLAN, constants.TYPE_NVGRE] self.assertEqual(network_types, actual_net_types)

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cgalan@cloudbasesolutions.com

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refs/heads/master

2020-03-26T21:35:58.877986

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""" Created on Thu May 17 14:00:02 2018 @author: l_cry """ # 导入模块 import tushare as ts from datetime import datetime, timedelta import time import numpy as np from sqlalchemy import create_engine import pymysql pymysql.install_as_MySQLdb() engine = create_engine('mysql://root:120402@127.0.0.1:3306/data_stock?charset=utf8') stock_basics = ts.get_stock_basics() stock_basics.reset_index(inplace=True) stock_basics.rename(columns={'index': 'code'}, inplace=True) # stock_basics.to_sql('stock_market_info',con=engine,if_exists='append',index=False) now = datetime.now() end = now.strftime("%Y-%m-%d") start = (now - timedelta(days=1)).strftime("%Y-%m-%d") def insert_to_db(code_list, success_list=[], none_list=[], fail_list=[]): for code in set(code_list) - set(success_list) - set(none_list): try: df = ts.get_h_data(code, autype=None, drop_factor=False, pause=5, retry_count=5) # df = ts.get_h_data(code, autype=None, drop_factor=False, pause=5, retry_count=5, start=start, end=end) if df.shape[0] != 0: df.reset_index(inplace=True) df['code'] = code df['update_time'] = datetime(2018, 8, 4, 0, 0) df.to_sql('hist_data_daily', con=engine, if_exists='append', index=False) success_list.append(code) else: none_list.append(code) except: fail_list.append(code) time.sleep(10) return success_list, none_list, fail_list code_list = stock_basics.code fail_list = [] success_list = [] none_list = [] i = 0 while len(set(code_list) - set(success_list) - set(none_list)) > 0 and i < 10000: success_list, none_list, fail_list = insert_to_db(code_list, success_list=success_list, none_list=none_list, fail_list=fail_list) i += 1 re = engine.execute('SELECT DISTINCT(code) from hist_data_daily') res = re.fetchall() success_list = list(map(lambda x:x[0],res))

[ "l_crystalmei@163.com" ]

l_crystalmei@163.com

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/mysite/settings.py

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[]

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StruggleWugc/mysite

a99c8ec2be2e3bea23b34069078a19fb69053839

28d00a5bc5388654e455dd1977294fce3ed2d520

refs/heads/master

2020-08-02T05:30:25.672050

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""" Django settings for mysite project. Generated by 'django-admin startproject' using Django 2.2.5. 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 = '_#)-p0h^84q#t3sd2f5@evd)+peg99auo@d$17)8n1h_-(x8s^' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'login', 'captcha', ] 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 = 'mysite.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 = 'mysite.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 = 'zh-hans' TIME_ZONE = 'Asia/Shanghai' USE_I18N = True USE_L10N = True USE_TZ = False # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' EMAIL_HOST = 'smtp.gmail.com' EMAIL_PORT = 25 EMAIL_HOST_USER = 'v9081759725@gmail.com' EMAIL_HOST_PASSWORD = 'vigo.12345678' EMAIL_USE_TLS = True DEFAULT_FROM_EMAIL = EMAIL_HOST_USER CONFIRM_DAYS = 1 LOGIN_URL = '/login/'

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260043657@qq.com

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/namesandtitlesfromurl.py

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Metallicode/namesandtitlesfromurl

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refs/heads/main

2023-08-30T21:44:54.396318

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import requests from bs4 import BeautifulSoup import pandas as pd #get live data def _GetDataFromWeb(url, liveData = True): if liveData: headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:88.0) Gecko/20100101 Firefox/88.0", "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8"} response = requests.get(url, headers=headers) data = response.text else: with open("file.txt", 'r') as f: data = f.read() return data def _ParseData(data): parsedData = BeautifulSoup(data, 'html.parser') tables = parsedData.findAll("table") theTable = None for table in tables: if '/s/' in table.text and 'Director' in table.text: theTable = table break if theTable is None: theTable = tables[-1] rows = theTable.findAll('tr') validstrings = [] for row in rows: text = row.text #print(text) if text and text.strip(): t = text.replace(u'\u200b\u200b\u200b\u200b', u'').replace(u'\u200b', u'%').replace(u'/s/', u'') validstrings.append([x.split('%') for x in t.strip().split("\xa0\xa0\xa0\xa0")]) #validstrings.append(t) validstrings = [x[:2] for x in validstrings if x[0][0]!=''] finedata = [] for i in validstrings: if len(i)>1: finedata.append([i[0][0],i[1][0]]) else: finedata.append(i[0][:2]) names = [x[0] for x in finedata[1:]] titles = [x[1] for x in finedata[1:]] df = pd.DataFrame(names, columns=['Name']) df['Title'] = titles return df def _geturlandsavetofile(url): data = _GetDataFromWeb(url) with open("file.txt", 'w') as f: f.write(data) def Run(url, liveData=True): return _ParseData(_GetDataFromWeb(url, liveData)) if __name__ == '__main__': #_geturlandsavetofile("https://www.sec.gov/Archives/edgar/data/2186/0001654954-20-002248.txt") print(Run("", False))

[ "gigahertzvectorizer@gmail.com" ]

gigahertzvectorizer@gmail.com

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kybr/AudioWorklet

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refs/heads/master

2020-05-19T09:42:35.906735

2019-05-06T14:29:47

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#!/usr/bin/env python3 from http.server import HTTPServer,SimpleHTTPRequestHandler #from socketserver import BaseServer #import ssl host = ('0.0.0.0', 8000) httpd = HTTPServer(host, SimpleHTTPRequestHandler) #httpd.socket = ssl.wrap_socket (httpd.socket, certfile='certificate.pem', server_side=True) print(f"http://{host[0]}:{host[1]}/") httpd.serve_forever() # generate server.xml with the following command: # openssl req -new -x509 -keyout server.pem -out server.pem -days 365 -nodes # run as follows: # python simple-https-server.py # then in your browser, visit: # https://localhost:4443

[ "karl.yerkes@gmail.com" ]

karl.yerkes@gmail.com

ce6b37ae1c97a5184cf411a67ad0265dbefb8ffc

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no_license

donesky/ExpressionRecognition

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refs/heads/master

2021-01-22T10:55:38.714711

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import pytest import testUnit import preProcess.adjust as ad import math from numpy import * # content of test_assert1.py def test_function(): assert ad.Distance((1,1), (1,1)) == 0 assert ad.Distance((-1, 1), (0, 0)) == math.sqrt(2) group = array([[1.0, 0.9], [1.0, 1.0], [0.1, 0.2], [0.0, 0.1]]) labels = ['A', 'A', 'B', 'B'] # four samples and two classes testX = array([1.2, 1.0])

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from rest_framework import serializers from social.models import Message, Member class MemberSerializer(serializers.HyperlinkedModelSerializer): receiver_member = serializers.HyperlinkedRelatedField( many=True, read_only=True, view_name='message-detail' ) sender_member = serializers.HyperlinkedRelatedField( many=True, read_only=True, view_name='message-detail' ) class Meta: model = Member fields = ('url', 'username', 'password', 'receiver_member', 'sender_member') extra_kwargs = {'password': {'write_only': True}} class MessageSerializer(serializers.HyperlinkedModelSerializer): class Meta: model = Message fields = ('url', 'id', 'time', 'message', 'receiver', 'sender', 'is_private') ''' class MemberSerializer(serializers.Serializer): username = serializers.CharField(max_length=16) password = serializers.CharField(max_length=16, write_only=True) receiver_member = serializers.HyperlinkedRelatedField( many=True, read_only=True, view_name='message-detail' ) sender_member = serializers.HyperlinkedRelatedField( many=True, read_only=True, view_name='message-detail' ) class MessageSerializer(serializers.Serializer): id = serializers.IntegerField(label='ID', read_only=True) time = serializers.DateTimeField(read_only=True) message = serializers.CharField(max_length=4096) receiver = serializers.CharField(max_length=16) sender = serializers.CharField(max_length=16) is_private = serializers.BooleanField(required=False) '''

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# Generated by Django 3.1.5 on 2021-02-04 02:50 import ckeditor.fields import datetime from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('blog', '0013_auto_20210202_0236'), ] operations = [ migrations.AlterField( model_name='post', name='description', field=ckeditor.fields.RichTextField(blank=True, null=True), ), migrations.AlterField( model_name='post', name='publish', field=models.DateTimeField(default=datetime.datetime.now), ), ]

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#!C:\Users\удкс\PycharmProjects\Test\venv\Scripts\python.exe -x # EASY-INSTALL-ENTRY-SCRIPT: 'pip==10.0.1','console_scripts','pip3.7' __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', 'pip3.7')() )

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# insert.py # Author: Thomas MINIER - MIT License 2017-2020 from typing import Dict, List, Optional, Tuple from sage.database.core.dataset import Dataset from sage.query_engine.iterators.preemptable_iterator import PreemptableIterator from sage.query_engine.protobuf.iterators_pb2 import SavedDeleteData from sage.query_engine.protobuf.utils import pyDict_to_protoDict class DeleteOperator(PreemptableIterator): """A DeleteOperator deletes RDF triples from a RDF dataset. Args: * quads: List of RDF quads to delete from the RDF dataset. * dataset: RDF dataset. """ def __init__(self, quads: List[Tuple[str, str, str, str]], dataset: Dataset): super(DeleteOperator, self).__init__() self._quads = quads self._dataset = dataset # we store how many triples were inserted in each RDF graph self._inserted = dict() def __repr__(self) -> str: return f"<DeleteOperator quads={self._quads}>" def serialized_name(self) -> str: """Get the name of the iterator, as used in the plan serialization protocol""" return "delete" def has_next(self) -> bool: """Return True if the iterator has more quads to delete""" return len(self._quads) > 0 async def next(self) -> Optional[Dict[str, str]]: """Delete the next quad from the RDF dataset. This function works in an iterator fashion, so it can be used in a pipeline of iterators. It may also contains `non interruptible` clauses which must be atomically evaluated before preemption occurs. Returns: The quad if it was successfully deleted, otwherise it returns `None`. Throws: `StopAsyncIteration` if the iterator has no more quads to delete. """ if not self.has_next(): raise StopAsyncIteration() s, p, o, g = self._quads.pop() if self._dataset.has_graph(g): self._dataset.get_graph(g).delete(s, p, o) # update counters if g in self._inserted: self._inserted[g] += 1 else: self._inserted[g] = 0 return {"?s": s, "?p": p, "?o": o, "?graph": g} return None def save(self) -> SavedDeleteData: """Save and serialize the iterator as a Protobuf message""" saved = SavedDeleteData() pyDict_to_protoDict(self._inserted, saved.nb_inserted) return saved

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# vim: expandtab:ts=4:sw=4 from __future__ import absolute_import import numpy as np from . import kalman_filter from . import linear_assignment from . import iou_matching from .track import Track class Tracker: """ This is the multi-target tracker. Parameters ---------- metric : nn_matching.NearestNeighborDistanceMetric A distance metric for measurement-to-track association. max_age : int Maximum number of missed misses before a track is deleted. n_init : int Number of consecutive detections before the track is confirmed. The track state is set to `Deleted` if a miss occurs within the first `n_init` frames. Attributes ---------- metric : nn_matching.NearestNeighborDistanceMetric The distance metric used for measurement to track association. max_age : int Maximum number of missed misses before a track is deleted. n_init : int Number of frames that a track remains in initialization phase. kf : kalman_filter.KalmanFilter A Kalman filter to filter target trajectories in image space. tracks : List[Track] The list of active tracks at the current time step. """ def __init__(self, metric, max_iou_distance=0.7, max_age=30, n_init=3): self.metric = metric self.max_iou_distance = max_iou_distance self.max_age = max_age self.n_init = n_init self.kf = kalman_filter.KalmanFilter() self.tracks = [] self._next_id = 1 def predict(self): """Propagate track state distributions one time step forward. This function should be called once every time step, before `update`. """ for track in self.tracks: track.predict(self.kf) def update(self, detections): """Perform measurement update and track management. Parameters ---------- detections : List[deep_sort.detection.Detection] A list of detections at the current time step. """ # Run matching cascade. matches, unmatched_tracks, unmatched_detections = \ self._match(detections) # Update track set. for track_idx, detection_idx in matches: self.tracks[track_idx].update( self.kf, detections[detection_idx]) for track_idx in unmatched_tracks: self.tracks[track_idx].mark_missed() for detection_idx in unmatched_detections: self._initiate_track(detections[detection_idx]) self.tracks = [t for t in self.tracks if not t.is_deleted()] # Update distance metric. active_targets = [t.track_id for t in self.tracks if t.is_confirmed()] features, targets = [], [] for track in self.tracks: if not track.is_confirmed(): continue features += track.features targets += [track.track_id for _ in track.features] track.features = [] self.metric.partial_fit( np.asarray(features), np.asarray(targets), active_targets) def _match(self, detections): def gated_metric(tracks, dets, track_indices, detection_indices): features = np.array([dets[i].feature for i in detection_indices]) targets = np.array([tracks[i].track_id for i in track_indices]) cost_matrix = self.metric.distance(features, targets) cost_matrix = linear_assignment.gate_cost_matrix( self.kf, cost_matrix, tracks, dets, track_indices, detection_indices) return cost_matrix # Split track set into confirmed and unconfirmed tracks. confirmed_tracks = [ i for i, t in enumerate(self.tracks) if t.is_confirmed()] unconfirmed_tracks = [ i for i, t in enumerate(self.tracks) if not t.is_confirmed()] # Associate confirmed tracks using appearance features. matches_a, unmatched_tracks_a, unmatched_detections = \ linear_assignment.matching_cascade( gated_metric, self.metric.matching_threshold, self.max_age, self.tracks, detections, confirmed_tracks) # Associate remaining tracks together with unconfirmed tracks using IOU. iou_track_candidates = unconfirmed_tracks + [ k for k in unmatched_tracks_a if self.tracks[k].time_since_update == 1] unmatched_tracks_a = [ k for k in unmatched_tracks_a if self.tracks[k].time_since_update != 1] matches_b, unmatched_tracks_b, unmatched_detections = \ linear_assignment.min_cost_matching( iou_matching.iou_cost, self.max_iou_distance, self.tracks, detections, iou_track_candidates, unmatched_detections) matches = matches_a + matches_b unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b)) return matches, unmatched_tracks, unmatched_detections def _initiate_track(self, detection): mean, covariance = self.kf.initiate(detection.to_xyah()) class_name = detection.get_class() self.tracks.append(Track( mean, covariance, self._next_id, self.n_init, self.max_age, detection.feature, class_name)) self._next_id += 1

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#!/usr/bin/env python import os,sys """ LAST UPDATED VERSION -> WRITING UNIVOCAL CLASSIFICATION This is a script that allows to calculate the presumed linear sequences that form between the exons near to the circRNA and a circRNA. It takes as input: - export of all the exons - intersect of circRNAs with bedtools with those exons Note: the script will consider only exons at the 5' and 3' of circRNAs """ # Defining dictionaries exon_dic = {} circ_dic = {} sorted_circ_dic = {} rank_dic = {} sorted_rank_dic = {} unaligned_exon_dic = {} intertranscript_dic = {} # Opening input files exon_data = open(sys.argv[1]).readlines() overlap_data = open(sys.argv[2]).readlines() """ FUNCTION: save_exons OBJECTIVE: saves all exons from a biomart export into a dictionary x: assign to data d: assign to exon_dic """ def save_exons(x, d): # Defining key exon_name = str("%s_%s" % (x[1], x[2])) if exon_name not in d: d[exon_name] = x """ FUNCTION: circ_transcripts OBJECTIVE: to create a dictionary containing all the exons involved in a circRNA x: assign to data d: assign to circ_dic un: assign to unaligned_exon_dic exons: assign to exon_dic """ def circ_transcripts (x, d, un, it, exons): # Defining key: composed by circ name and transcript (chr_start_end_ENST) n = x[3].strip().split("_") circ_name = str("%s_%s" % (x[8], n[0])) strand = x[4] if circ_name not in d: d[circ_name] = [] # Defining ENSE e = x[3].strip().split("_") ENSE = e[1] # Defining the variables needed for exon checks ex_start = int(x[1]) ex_end = int(x[2]) circ_start = int(x[6]) circ_end = int(x[7]) # Searching for exon rank in exon_dic exon = x[3] rank = str(exons[exon][7]) ENSG = exons[exon][0] ENST = exons[exon][1] # Creating tuple to append to dictionary new_entry = (rank, ENSE, strand, ENSG, ENST) # Checking exons and introns and appending ENSEs in the dictionary if ( (ex_start == circ_start and ex_end <= circ_end) or (ex_end == circ_end and ex_start >= circ_start) ): d[circ_name].append(new_entry) # extended check for introns overlapping start/end of circRNAs elif ((len(rank) == 4 and circ_start > ex_start and circ_start < ex_end) or (len(rank) == 4 and circ_end > ex_start and circ_end < ex_end)): d[circ_name].append(new_entry) # extended check for exons overlapping start/end of circRNAs to save # in the unaligned_exon_dic, for later putative_exon classification elif ((len(rank) == 3 and circ_start > ex_start and circ_start < ex_end) or (len(rank) == 3 and circ_end > ex_start and circ_end < ex_end)): if circ_name not in un: un[circ_name] = [] un[circ_name].append(new_entry) # SECTION FOR INTERTRANSCRIPT circRNAs circ = x[8] if circ not in it: it[circ] = [] # Appending first element in indiscriminate manner if len(it[circ]) == 0: if (ex_start == circ_start and ex_end <= circ_end): new = ("start", ENST) it[circ].append(new) elif (ex_end == circ_end and ex_start >= circ_start): new = ("end", ENST) it[circ].append(new) # Appending second element only if it is mapped in the opposite # side of the circRNA elif len(it[circ]) == 1: if (it[circ][0][0] != "start" and it[circ][0][1] != ENST and ex_start == circ_start and ex_end <= circ_end): new = ("start", ENST) it[circ].append(new) elif (it[circ][0][0] != "end" and it[circ][0][1] != ENST and ex_end == circ_end and ex_start >= circ_start): new = ("end", ENST) it[circ].append(new) """ FUNCTION: save_ranks OBJECTIVE: create a dictionary filled with exon ranks, in order to later calculate max and min rank of a transcript x: refers to data d: refers to rank_dic """ def save_ranks(x, d): ENST = x[1] rank = str(x[7]) if ENST not in d: d[ENST] = [] # Adding rank value to transcript in dictionary d[ENST].append((rank, x[2])) """ FUNCTION: seq5 OBJECTIVE: obtain the coordinates of the first exon of the circRNA and the coordinates of the exon before it. c: refers to circ ex: refers to exon (obtained from sorted_circ_dic[circ][0]) rd: refers to sorted_rank_dic ed: refers to exon_dic l: refers to label """ """ def seq5(c, ex, rd, ed, l): # Defining variables c_info = c.strip().split("_") circ_name = ("%s_5linseq" % c) ENST = c_info[3] exon2_rank = ex[0] # Rank of circRNA exon exon1_rank = ex[0] - 1 # Rank of exon before circRNA exon2_pos = exon2_rank -1 # Position in sorted_transcript_dic of circRNA exon exon1_pos = exon1_rank -1 # Position in sorted_transcript_dic of exon before circRNA exon2_ENSE = rd[ENST][exon2_pos][1] exon1_ENSE = rd[ENST][exon1_pos][1] exon2_searchterm = str("%s_%s" % (ENST, exon2_ENSE)) # Search term for circRNA exon in exon_dic exon1_searchterm = str("%s_%s" % (ENST, exon1_ENSE)) # Search term for exon before the circRNA in exon_dic # Searchin exon information exon2_info = ed[exon2_searchterm] exon1_info = ed[exon1_searchterm] # Defining coordinates of exons exon2_start = exon2_info[4] exon2_end = exon2_info[5] exon1_start = exon1_info[4] exon1_end = exon1_info[5] strand = ex[2] final_output = [circ_name, l, exon1_ENSE, exon1_start, exon1_end, exon2_ENSE, exon2_start, exon2_end, strand] print("%s" % ('\t'.join(final_output))) """ """ FUNCTION: seq3 OBJECTIVE: obtain the coordinates of the last exon of the circRNA and the coordinates of the exon after it. c: refers to circ ex: refers to exon (obtained from sorted_circ_dic[circ][0]) rd: refers to sorted_rank_dic ed: refers to exon_dic l: refers to label """ """ def seq3(c, ex, rd, ed, l): # Defining variables c_info = c.strip().split("_") circ_name = ("%s_3linseq" % c) ENST = c_info[3] exon1_rank = ex[0] # Rank of circRNA exon exon2_rank = ex[0] + 1 # Rank of after before circRNA exon1_pos = exon1_rank -1 # Position in sorted_transcript_dic of circRNA exon exon2_pos = exon2_rank -1 # Position in sorted_transcript_dic of exon after circRNA exon1_ENSE = rd[ENST][exon1_pos][1] exon2_ENSE = rd[ENST][exon2_pos][1] exon1_searchterm = str("%s_%s" % (ENST, exon1_ENSE)) # Search term for circRNA exon in exon_dic exon2_searchterm = str("%s_%s" % (ENST, exon2_ENSE)) # Search term for exon after the circRNA in exon_dic # Searchin exon information exon1_info = ed[exon1_searchterm] exon2_info = ed[exon2_searchterm] # Defining coordinates of exons exon1_start = exon1_info[4] exon1_end = exon1_info[5] exon2_start = exon2_info[4] exon2_end = exon2_info[5] strand = ex[2] final_output = [circ_name, l, exon1_ENSE, exon1_start, exon1_end, exon2_ENSE, exon2_start, exon2_end, strand] print("%s" % ('\t'.join(final_output))) """ ######################################################################## ######################################################################## ######################################################################## ######################################################################## # Main cycle for exons for exon in exon_data: data = exon.strip().split("\t") save_exons(data, exon_dic) # Fills exon_dic save_ranks(data, rank_dic) # Fills rank_dic # Main cycle for circRNA overlaps for circ in overlap_data: data = circ.strip().split("\t") circ_transcripts(data, circ_dic, unaligned_exon_dic, intertranscript_dic, exon_dic) # Sorting circ_dic for circ in circ_dic: ex_list = circ_dic[circ] def getKey(item): return item[0] # Filling sorted dictionary sorted_circ_dic[circ] = sorted(ex_list, key=getKey) # Purging unsorted circRNA dictionary circ_dic = {} # Sorting rank_dic for transcript in rank_dic: rank_list = rank_dic[transcript] def getKey(item): return item[0] # Filling sorted dictionary sorted_rank_dic[transcript] = sorted(rank_list, key=getKey) # Purging unsorted rank dictionary rank_dic = {} """ # Checks for linear sequences to report for circ in sorted_circ_dic: # Defining transcript transcript = circ.strip().split("_")[3] # Defining min and max rank of transcript minimum = min(sorted_rank_dic[transcript]) maximum = max(sorted_rank_dic[transcript]) rank_min = minimum[0] rank_max = maximum[0] #################################################################### # Check for multiexonic circRNAs if len(sorted_circ_dic[circ]) == 2: # Calculating ranks of circ exons rank_ex5 = sorted_circ_dic[circ][0][0] rank_ex3 = sorted_circ_dic[circ][1][0] # Checking position of circRNA in the transcript if rank_ex5 > rank_min and rank_ex3 < rank_max: label = "multiexon" seq5(circ, sorted_circ_dic[circ][0], sorted_rank_dic, exon_dic, label) seq3(circ, sorted_circ_dic[circ][1], sorted_rank_dic, exon_dic, label) elif rank_ex5 == rank_min and rank_ex3 < rank_max: label = "multiexon_5boundary" # Multiexon at 5' of transcript seq3(circ, sorted_circ_dic[circ][1], sorted_rank_dic, exon_dic, label) elif rank_ex5 > rank_min and rank_ex3 == rank_max: label = "multiexon_3boundary" # Multiexon at 3' of transcript seq5(circ, sorted_circ_dic[circ][0], sorted_rank_dic, exon_dic, label) elif rank_ex5 == rank_min and rank_ex3 == rank_max: label = "missing_exon" print("%s\t%s" % (circ, label)) #################################################################### # Check for monoexonic circRNAs elif len(sorted_circ_dic[circ]) == 1: # Calculating rank of circ exon rank = sorted_circ_dic[circ][0][0] # Checks for true monoexonic circRNA circ_start = int(circ.strip().split("_")[1]) circ_end = int(circ.strip().split("_")[2]) ENSE = sorted_circ_dic[circ][0][1] ENST = circ.strip().split("_")[3] exon_search = ("%s_%s" % (ENST, ENSE)) exon_start = int(exon_dic[exon_search][4]) exon_end = int(exon_dic[exon_search][5]) if circ_start != exon_start or circ_end != exon_end: label = "missing_exon" print("%s\t%s" % (circ, label)) else: if rank > rank_min and rank < rank_max: # Monoexonic circ with both sequences calculated label = "monoexon" seq5(circ, sorted_circ_dic[circ][0], sorted_rank_dic, exon_dic, label) seq3(circ, sorted_circ_dic[circ][0], sorted_rank_dic, exon_dic, label) elif rank == rank_min and rank < rank_max: # Monoexon at 5' of transcript label = "monoexon_5boundary" seq3(circ, sorted_circ_dic[circ][0], sorted_rank_dic, exon_dic, label) elif rank > rank_min and rank == rank_max: # Monoexon at 3' of transcript label = "monoexon_3boundary" seq5(circ, sorted_circ_dic[circ][0], sorted_rank_dic, exon_dic, label) #################################################################### # Check for missing exons else: label = "missing_exon" print("%s\t%s" % (circ, label)) """ ####################### CIRCULARIZING EXONS SECTION #################### for circ in sorted_circ_dic: if len(sorted_circ_dic[circ]) == 2: rank1 = sorted_circ_dic[circ][0][0] rank2 = sorted_circ_dic[circ][1][0] if (len(rank1) == 3 and len(rank2) == 3): label = "multiexon" print("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" % (circ, label, sorted_circ_dic[circ][0][3], # ENSG of first exon sorted_circ_dic[circ][0][4], # ENST of first exon sorted_circ_dic[circ][0][1], # ENSE of first exon sorted_circ_dic[circ][0][0], # Rank of first exon sorted_circ_dic[circ][0][2], # Strand of first exon sorted_circ_dic[circ][1][3], # ENSG of second exon sorted_circ_dic[circ][1][4], # ENST of second exon sorted_circ_dic[circ][1][1], # ENSE of second exon sorted_circ_dic[circ][1][0], # Rank of second exon sorted_circ_dic[circ][1][2])) # Strand of second exon elif (len(rank1) == 4 or len(rank2) == 4): label = "intronic" print("%s\t%s" % (circ, label)) elif len(sorted_circ_dic[circ]) == 1: # Calculating rank of circ exon rank = sorted_circ_dic[circ][0][0] # Checks for true monoexonic circRNA circ_start = int(circ.strip().split("_")[1]) circ_end = int(circ.strip().split("_")[2]) ENSE = sorted_circ_dic[circ][0][1] ENST = circ.strip().split("_")[3] exon_search = ("%s_%s" % (ENST, ENSE)) exon_start = int(exon_dic[exon_search][4]) exon_end = int(exon_dic[exon_search][5]) if circ_start != exon_start or circ_end != exon_end: # Fetching gene start and gene end gene_start = int(exon_dic[exon_search][8]) gene_end = int(exon_dic[exon_search][9]) # Checking for intronic or intergenic circRNAs if (circ_start >= gene_start and circ_end <= gene_end): if (circ_start < exon_start and len(rank) == 3): if circ in unaligned_exon_dic: label=("putativeexon") # Check needed to write the output in the correct order if (unaligned_exon_dic[circ][0][2] == '+'): print("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" % (circ, label, unaligned_exon_dic[circ][0][3], # ENSG of first exon unaligned_exon_dic[circ][0][4], # ENST of first exon unaligned_exon_dic[circ][0][1], # ENSE of first exon unaligned_exon_dic[circ][0][0], # Rank of first exon unaligned_exon_dic[circ][0][2], # Strand of first exon sorted_circ_dic[circ][0][3], # ENSG of second exon sorted_circ_dic[circ][0][4], # ENST of second exon sorted_circ_dic[circ][0][1], # ENSE of second exon sorted_circ_dic[circ][0][0], # Rank of second exon sorted_circ_dic[circ][0][2])) # Strand of second exon else: print("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" % (circ, label, sorted_circ_dic[circ][0][3], # ENSG of second exon sorted_circ_dic[circ][0][4], # ENST of second exon sorted_circ_dic[circ][0][1], # ENSE of second exon sorted_circ_dic[circ][0][0], # Rank of second exon sorted_circ_dic[circ][0][2], # Strand of second exon unaligned_exon_dic[circ][0][3], # ENSG of first exon unaligned_exon_dic[circ][0][4], # ENST of first exon unaligned_exon_dic[circ][0][1], # ENSE of first exon unaligned_exon_dic[circ][0][0], # Rank of first exon unaligned_exon_dic[circ][0][2])) # Strand of first exon else: label = "intronic" print("%s\t%s" % (circ, label)) elif (circ_end > exon_end and len(rank) == 3): if circ in unaligned_exon_dic: label=("putativeexon") # Check needed to write the output in the correct order if (unaligned_exon_dic[circ][0][2] == '+'): print("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" % (circ, label, sorted_circ_dic[circ][0][3], # ENSG of second exon sorted_circ_dic[circ][0][4], # ENST of second exon sorted_circ_dic[circ][0][1], # ENSE of second exon sorted_circ_dic[circ][0][0], # Rank of second exon sorted_circ_dic[circ][0][2], # Strand of second exon unaligned_exon_dic[circ][0][3], # ENSG of first exon unaligned_exon_dic[circ][0][4], # ENST of first exon unaligned_exon_dic[circ][0][1], # ENSE of first exon unaligned_exon_dic[circ][0][0], # Rank of first exon unaligned_exon_dic[circ][0][2])) # Strand of first exon else: print("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" % (circ, label, unaligned_exon_dic[circ][0][3], # ENSG of first exon unaligned_exon_dic[circ][0][4], # ENST of first exon unaligned_exon_dic[circ][0][1], # ENSE of first exon unaligned_exon_dic[circ][0][0], # Rank of first exon unaligned_exon_dic[circ][0][2], # Strand of first exon sorted_circ_dic[circ][0][3], # ENSG of second exon sorted_circ_dic[circ][0][4], # ENST of second exon sorted_circ_dic[circ][0][1], # ENSE of second exon sorted_circ_dic[circ][0][0], # Rank of second exon sorted_circ_dic[circ][0][2])) # Strand of second exon else: label = "intronic" print("%s\t%s" % (circ, label)) else: label = "intronic" print("%s\t%s" % (circ, label)) elif (circ_start < gene_start or circ_end > gene_end): label = "intergenic" print("%s\t%s" % (circ, label)) elif len(rank) == 3: label = "monoexon" print("%s\t%s\t%s\t%s\t%s\t%s\t%s" % (circ, label, sorted_circ_dic[circ][0][3], # ENSG of first exon sorted_circ_dic[circ][0][4], # ENST of first exon sorted_circ_dic[circ][0][1], # ENSE of exon sorted_circ_dic[circ][0][0], # Rank of exon sorted_circ_dic[circ][0][2])) # Strand of exon elif len(rank) == 4: label = "intronic" print("%s\t%s" % (circ, label)) elif len(sorted_circ_dic[circ]) == 0: # Defining variables needed to check for intronic or intergenic condition circ_start = int(circ.strip().split("_")[1]) circ_end = int(circ.strip().split("_")[2]) ENST = circ.strip().split("_")[3] # A complete key needs to be reconstructed in order to search # the exon dictionary correctly. Any exon of the ENST considered # will be ok, since the searched gene values are all the same # for every exon in the same transcript ENSE = sorted_rank_dic[ENST][0][1] exon_search = "%s_%s" % (ENST, ENSE) """ # Old method for INSANE FUCKS THAT DO NOT REMEMBER THE FUCKING # DICTIONARIES THAT THEY MADE THEMSELVES for key in exon_dic.keys(): if key.startswith(ENST): exon_search = key break """ gene_start = int(exon_dic[exon_search][8]) gene_end = int(exon_dic[exon_search][9]) transcript_start = int(exon_dic[exon_search][10]) transcript_end = int(exon_dic[exon_search][11]) # Checking for intronic or intergenic circRNA if (circ_start >= gene_start and circ_end <= gene_end): # NEWCODE if (circ in unaligned_exon_dic and circ_start >= transcript_start and circ_end <= transcript_end): label = "putativeexon" print("%s\t%s\t%s\t%s\t%s\t%s\t%s" % (circ, label, unaligned_exon_dic[circ][0][3], # ENSG of first exon unaligned_exon_dic[circ][0][4], # ENST of first exon unaligned_exon_dic[circ][0][1], # ENSE of first exon unaligned_exon_dic[circ][0][0], # Rank of first exon unaligned_exon_dic[circ][0][2])) # Strand of first exon else: label = "intronic" print("%s\t%s" % (circ, label)) elif (circ_start < gene_start or circ_end > gene_end): label = "intergenic" print("%s\t%s" % (circ, label)) else: print("ERROR - len of sorted_circ_dic is %s" % (len(sorted_circ_dic[circ]))) # Printing intertranscript circRNAs for circ in intertranscript_dic: if len(intertranscript_dic[circ]) == 2: print("%s_%s\tintergenic" % (circ, intertranscript_dic[circ][0][1])) print("%s_%s\tintergenic" % (circ, intertranscript_dic[circ][1][1])) # It was decided to refer to intertranscript circs as intergenic # The old printing statements are below # print("%s_%s\tintertranscript" % (circ, intertranscript_dic[circ][0][1])) # print("%s_%s\tintertranscript" % (circ, intertranscript_dic[circ][1][1])) ################ DEBUG SECTION ######################################## """ print("\nexon_dic") for exon in exon_dic: print("%s\t%s" % (exon, exon_dic[exon])) print("\nsorted_circ_dic") for circ in sorted_circ_dic: print("%s\t%s" % (circ, sorted_circ_dic[circ])) print("\nsorted_rank_dic") for rank in sorted_rank_dic: print("%s\t%s" % (rank, sorted_rank_dic[rank])) print("\nunaligned_exon_dic") for exon in unaligned_exon_dic: print("%s\t%s" % (exon, unaligned_exon_dic[exon])) print("\nintertranscript_dic") for circ in intertranscript_dic: print("%s\t%s" % (circ, intertranscript_dic[circ])) """

[ "nicola.licheri@gmail.com" ]

nicola.licheri@gmail.com

41d4fa69d69c9f2e3d7c678ea517d29cb765ffd2

09e57dd1374713f06b70d7b37a580130d9bbab0d

/benchmark/startCirq2308.py

70c40cd0d6328c50f7c76f34f4d3e2738f0eb7e9

[ "BSD-3-Clause" ]

permissive

UCLA-SEAL/QDiff

ad53650034897abb5941e74539e3aee8edb600ab

d968cbc47fe926b7f88b4adf10490f1edd6f8819

refs/heads/main

2023-08-05T04:52:24.961998

2021-09-19T02:56:16

405,159,939

2

0

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#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 5/15/20 4:49 PM # @File : grover.py # qubit number=4 # total number=39 import cirq import cirq.google as cg from typing import Optional import sys from math import log2 import numpy as np #thatsNoCode from cirq.contrib.svg import SVGCircuit # Symbols for the rotation angles in the QAOA circuit. def make_circuit(n: int, input_qubit): c = cirq.Circuit() # circuit begin c.append(cirq.H.on(input_qubit[0])) # number=9 c.append(cirq.H.on(input_qubit[1])) # number=2 c.append(cirq.H.on(input_qubit[2])) # number=3 c.append(cirq.H.on(input_qubit[3])) # number=4 c.append(cirq.Y.on(input_qubit[3])) # number=12 c.append(cirq.H.on(input_qubit[0])) # number=5 c.append(cirq.H.on(input_qubit[1])) # number=6 c.append(cirq.H.on(input_qubit[2])) # number=7 c.append(cirq.CNOT.on(input_qubit[0],input_qubit[3])) # number=27 c.append(cirq.CNOT.on(input_qubit[0],input_qubit[3])) # number=36 c.append(cirq.X.on(input_qubit[3])) # number=37 c.append(cirq.CNOT.on(input_qubit[0],input_qubit[3])) # number=38 c.append(cirq.H.on(input_qubit[3])) # number=30 c.append(cirq.CZ.on(input_qubit[0],input_qubit[3])) # number=31 c.append(cirq.H.on(input_qubit[3])) # number=32 c.append(cirq.H.on(input_qubit[0])) # number=33 c.append(cirq.CZ.on(input_qubit[3],input_qubit[0])) # number=34 c.append(cirq.H.on(input_qubit[0])) # number=35 c.append(cirq.CNOT.on(input_qubit[3],input_qubit[0])) # number=23 c.append(cirq.Z.on(input_qubit[3])) # number=24 c.append(cirq.CNOT.on(input_qubit[3],input_qubit[0])) # number=25 c.append(cirq.CNOT.on(input_qubit[3],input_qubit[0])) # number=22 c.append(cirq.H.on(input_qubit[3])) # number=8 c.append(cirq.CNOT.on(input_qubit[0],input_qubit[3])) # number=13 c.append(cirq.CNOT.on(input_qubit[0],input_qubit[3])) # number=17 c.append(cirq.X.on(input_qubit[3])) # number=18 c.append(cirq.CNOT.on(input_qubit[0],input_qubit[3])) # number=19 c.append(cirq.CNOT.on(input_qubit[0],input_qubit[3])) # number=15 c.append(cirq.Y.on(input_qubit[2])) # number=10 c.append(cirq.Y.on(input_qubit[2])) # number=11 # circuit end c.append(cirq.measure(*input_qubit, key='result')) return c def bitstring(bits): return ''.join(str(int(b)) for b in bits) if __name__ == '__main__': qubit_count = 4 input_qubits = [cirq.GridQubit(i, 0) for i in range(qubit_count)] circuit = make_circuit(qubit_count,input_qubits) circuit = cg.optimized_for_sycamore(circuit, optimizer_type='sqrt_iswap') circuit_sample_count =2000 simulator = cirq.Simulator() result = simulator.run(circuit, repetitions=circuit_sample_count) frequencies = result.histogram(key='result', fold_func=bitstring) writefile = open("../data/startCirq2308.csv","w+") print(format(frequencies),file=writefile) print("results end", file=writefile) print(circuit.__len__(), file=writefile) print(circuit,file=writefile) writefile.close()

[ "wangjiyuan123@yeah.net" ]

wangjiyuan123@yeah.net

e5934fb0f7a75782df30b02e4014b814ba7da9b5

a64f122dd4df3e20bc3e25aca31bb11ec9d55977

/Assignment 3/madelon tricks.py

4ec6ab3ce62d92cde3f7c56c28d599185143b045

[]

no_license

mbrine555/gatech_ML

f9de5e1e1c29e40693030fcf3dce4797339f3ada

2a3dea874ac7710104fb891a5199afa9f3c046af

refs/heads/master

2020-04-16T10:39:44.328425

2019-04-10T11:54:37

165,512,336

0

null

UTF-8

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py

# -*- coding: utf-8 -*- """ Created on Sat Mar 18 13:08:50 2017 @author: JTay """ import pandas as pd import numpy as np from sklearn.preprocessing import StandardScaler from helpers import nn_arch,nn_reg,ImportanceSelect from matplotlib import cm from sklearn.neural_network import MLPClassifier from sklearn.model_selection import GridSearchCV from sklearn.decomposition import PCA import matplotlib.pyplot as plt from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import pairwise_distances from sklearn.svm import SVC from sklearn.pipeline import Pipeline out = './PCA/' cmap = cm.get_cmap('Spectral') np.random.seed(0) madelon = pd.read_hdf('./BASE/datasets.hdf','madelon') madelonX = madelon.drop('Class',1).copy().values madelonY = madelon['Class'].copy().values scaler =StandardScaler() madelon_test = pd.read_hdf('./BASE/datasets.hdf','madelon') madelon_tstX = madelon_test.drop('Class',1).copy().values madelon_tstY = madelon_test['Class'].copy().values from sklearn.ensemble import RandomForestClassifier madelonX = scaler.fit_transform(madelonX) madelon_tstX = scaler.transform(madelon_tstX) #Reproduce best estimator so far #if __name__=='__main__': # rfc = RandomForestClassifier(n_estimators=100,class_weight='balanced',random_state=5,n_jobs=7) # filtr = ImportanceSelect(rfc) # grid ={'filter__n':[20],'NN__alpha':nn_reg,'NN__hidden_layer_sizes':nn_arch} # mlp = MLPClassifier(activation='relu',max_iter=2000,early_stopping=True,random_state=5) # pipe = Pipeline([('filter',filtr),('NN',mlp)]) # gs = GridSearchCV(pipe,grid,verbose=10,cv=5) # gs.fit(madelonX,madelonY) # print('Best CV Score {}'.format(gs.best_score_)) # print('Test Score {}'.format(gs.score(madelon_tstX,madelon_tstY))) # rf_features = gs.best_estimator_.steps[0][1].model.feature_importances_.argsort()[::-1][:20] # Use PCA to find true correct featuers pca = PCA(random_state=5,n_components=500) pca.fit(madelonX) ve = pd.Series(pca.explained_variance_) ve.plot() plt.xlabel('Component') plt.ylabel('Variance Explained') tmp = pd.DataFrame(pca.components_) tmp=tmp.iloc[-15:,:] pca_features=tmp.columns[tmp.abs().max()>0.1] xx= madelonX[:,pca_features] xx_tst = madelon_tstX[:,pca_features] ## NN testing - standard param set #grid ={'alpha':nn_reg,'hidden_layer_sizes':nn_arch} #mlp = MLPClassifier(activation='relu',max_iter=3000,early_stopping=False,random_state=5) #gs = GridSearchCV(mlp,param_grid=grid,verbose=10,cv=5) #gs.fit(madelonX[:,pca_features],madelonY) #print('NN - Standard params - Best CV Score {}'.format(gs.best_score_)) #print('NN - Standard params - Test Score {}'.format(gs.score(xx_tst,madelon_tstY))) # # # ## NN testing - standard param set #grid ={'alpha':[1e-4,1e-5,1e-6],'hidden_layer_sizes':[(200,100,100,64,100,100,200)]} #mlp = MLPClassifier(activation='relu',max_iter=3000,early_stopping=False,random_state=5) #gs = GridSearchCV(mlp,param_grid=grid,verbose=10,cv=5) #gs.fit(madelonX[:,pca_features],madelonY) #print('NN - Big network- Best CV Score {}'.format(gs.best_score_)) #print('NN - Big network - Test Score {}'.format(gs.score(xx_tst,madelon_tstY))) #KNN knn = KNeighborsClassifier() grid={'n_neighbors':range(1,25,1),'p':[1,2],'weights':['uniform','distance']} gs = GridSearchCV(knn,param_grid=grid,cv=5,verbose=10) gs.fit(xx,madelonY) print('KNN - Best CV Score {}'.format(gs.best_score_)) print('KNN - Test Score {}'.format(gs.score(xx_tst,madelon_tstY))) # SVM dis = pairwise_distances(xx) m = np.median(dis) gammas = [(1/m)*x for x in np.arange(0.1,2.1,0.1)]+[0.1,0.2,0.3,0.4,0.5] gammas = np.arange(0.1,0.9,0.05) gammas = [(1/m)*x for x in np.arange(0.1,2.1,0.1)] param_grid={'gamma':gammas,'C':[10**x for x in [-1,0,1,2,3]]} gs = GridSearchCV(SVC(kernel='rbf',C=1),param_grid=param_grid,cv=5,verbose=10,n_jobs=1) gs.fit(xx,madelonY) print('SVM - Best CV Score {}'.format(gs.best_score_)) print('SVM - Test Score {}'.format(gs.score(xx_tst,madelon_tstY)))

[ "briner.15@osu.edu" ]

briner.15@osu.edu

47cef2617e45b179ad7170b343230053c4e3f72b

e4688b4752f93d6f4db24f99c551b6fd3ca89571

/spectrum_all/code/shiyan_work_for_specturm.py

c2352c7be38b5eb83718456a5ed3b746bba0f541

[]

no_license

hanabimarch/my_work

925fce839965e3ba144c20aa8f6e752272c694b3

69ecddd214c76275cd228624f3344396bb2f9e32

refs/heads/master

2022-04-27T14:02:33.723596

2020-04-27T02:43:21

null

0

null

UTF-8

Python

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py

from zjh_make_phaI import * import zzh_py3_file as zhf import os from zjh_xspec_fit_kernel import xspec_fit_kernel import matplotlib.pyplot as plt data_topdir = '/home/laojin/trigdata/2017/' result_topdir = '/home/laojin/results/results_GBM/2017/' namelist = zhf.readcol('/home/laojin/results/results_GBM/Y_all_sample_name.txt')[0] yearnamelist = [] for i in namelist: if i[:4]=='bn17' : yearnamelist.append(i) print('year name list:\n',yearnamelist) for bnname in yearnamelist: file_1_link = result_topdir + bnname + '/good_NaI_detectors.txt' file_2_link = result_topdir + bnname + '/good_BGO_detectors.txt' file_3_link = result_topdir + bnname + '/0_pulse_edges.txt' save_all = result_topdir + 'A_spectrum_1/' save_all2 = result_topdir + 'A_spectrum_2/' if(os.path.exists(save_all) == False): os.makedirs(save_all) if (os.path.exists(save_all2) == False): os.makedirs(save_all2) if(os.path.exists(file_1_link) and os.path.exists(file_2_link) and os.path.exists(file_3_link)): ni_s,ni_list = zhf.readcol(file_1_link) bgo = zhf.readcol(file_2_link)[0][0] print(bnname) if(len(ni_s)>=2): try: ni_list = ni_list[:2] makedirs = result_topdir + bnname + '/spectrum/' if(os.path.exists(makedirs) == False): os.makedirs(makedirs) edges_start,edges_stop = zhf.readcol(file_3_link) for i in range(len(edges_start)): print('--------------------------------------') print('time slice ',i) savedir = makedirs + 'time_' + str(i) + '/' filelist = ['A_'+bnname+'_'+bgo+'.pha'] dt = edges_stop[i]-edges_start[i] #------------------------------------------------- if(dt/3.>=1): binsize = 1 #动态binsize else: binsize = dt/3. #------------------------------------------------- for ni in ni_list: print(ni) filelist.append('A_'+bnname+'_'+ni+'.pha') make_phaI(bnname,ni,data_topdir,savedir,edges_start[i],edges_stop[i],binsize = binsize)#这里只有选0.01才可以 #make_phaI_with_events(bnname,ni,data_topdir,savedir,edges_start[i],edges_stop[i]) plt.savefig(save_all+'A_'+bnname+'_'+ni+'_'+str(i)+'.png') plt.close() print(bgo) make_phaI(bnname,bgo,data_topdir,savedir,edges_start[i],edges_stop[i],binsize = binsize) #make_phaI_with_events(bnname,bgo,data_topdir,savedir,edges_start[i],edges_stop[i]) plt.savefig(save_all+'A_'+bnname+'_'+bgo+'_'+str(i)+'.png') plt.close() print('---------------------------------------') try: value,value_arr1,value_arr2,flux_list = xspec_fit_kernel(filelist,savedir,makedirs+'Z_'+str(i)) plt.savefig(save_all2 + 'A_'+bnname+'_'+str(i)+'.png') plt.close() #copy_rspI(savedir+'foldedspec.png',save_all2 + 'A_'+bnname+'_'+str(i)+'.png') zhf.printdatatofile(makedirs+'Z_'+str(i)+'_Flux.txt',data = flux_list) zhf.printdatatofile(makedirs+'Z_'+str(i)+'_Epec.txt',data = [[value],[value_arr1],[value_arr2]]) except: print(bnname+' '+str(i)+'出现错误，跳过拟合！') continue except(IndexError): print(bnname+'文件内容错误，跳过拟合！') continue else: print(bnname+'探头数量不足') else: print(bnname+'信息不完整。')

[ "653538607@qq.com" ]

653538607@qq.com

5ac2d00d5ea2df70b34fed18d5398d39704751c1

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/Fourteenth_Chapter/ply_flo.py

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#!/usr/bin/python3 import sys import ply.lex import ply.yacc def get_file_handle(filename): try: return open(filename) except (EnvironmentError, IOError) as file_err: print('Error opening {}: {}'.format(filename, file_err)) sys.exit() def ply_parse(text): """ >>> formula = "a = b" >>> print(ply_parse(formula)) ['a', '=', 'b'] >>> formula = "forall x: a = b" >>> print(ply_parse(formula)) ['forall', 'x', ['a', '=', 'b']] >>> formula = "a & b" >>> print(ply_parse(formula)) ['a', '&', 'b'] >>> formula = "~true -> ~b = c" >>> print(ply_parse(formula)) [['~', 'true'], '->', ['~', ['b', '=', 'c']]] >>> formula = "~true -> ~(b = c)" >>> print(ply_parse(formula)) [['~', 'true'], '->', ['~', ['b', '=', 'c']]] >>> formula = "exists y: a -> b" >>> print(ply_parse(formula)) ['exists', 'y', ['a', '->', 'b']] >>> formula = "forall x: exists y: a = b" >>> print(ply_parse(formula)) ['forall', 'x', ['exists', 'y', ['a', '=', 'b']]] >>> formula = "forall x: exists y: a = b -> a = b & ~ a = b -> a = b" >>> print(ply_parse(formula)) ['forall', 'x', ['exists', 'y', [['a', '=', 'b'], '->', [[['a', '=', 'b'], '&', ['~', ['a', '=', 'b']]], '->', ['a', '=', 'b']]]]] >>> formula = "(forall x: exists y: a = b) -> a = b & ~ a = b -> a = b" >>> print(ply_parse(formula)) [['forall', 'x', ['exists', 'y', ['a', '=', 'b']]], '->', [[['a', '=', 'b'], '&', ['~', ['a', '=', 'b']]], '->', ['a', '=', 'b']]] >>> formula = "(forall x: exists y: true) -> true & ~ true -> true" >>> print(ply_parse(formula)) [['forall', 'x', ['exists', 'y', 'true']], '->', [['true', '&', ['~', 'true']], '->', 'true']] >>> formula = "a = b -> c = d & e = f" >>> result1 = ply_parse(formula) >>> formula = "(a = b) -> (c = d & e = f)" >>> result2 = ply_parse(formula) >>> result1 == result2 True >>> result1 [['a', '=', 'b'], '->', [['c', '=', 'd'], '&', ['e', '=', 'f']]] >>> formula = "forall x: exists y: true -> true & true | ~ true" >>> print(ply_parse(formula)) ['forall', 'x', ['exists', 'y', ['true', '->', [['true', '&', 'true'], '|', ['~', 'true']]]]] >>> formula = "~ true | true & true -> forall x: exists y: true" >>> print(ply_parse(formula)) [[['~', 'true'], '|', ['true', '&', 'true']], '->', ['forall', 'x', ['exists', 'y', 'true']]] >>> formula = "true & forall x: x = x" >>> print(ply_parse(formula)) ['true', '&', ['forall', 'x', ['x', '=', 'x']]] >>> formula = "true & (forall x: x = x)" # same as previous >>> print(ply_parse(formula)) ['true', '&', ['forall', 'x', ['x', '=', 'x']]] >>> formula = "forall x: x = x & true" >>> print(ply_parse(formula)) ['forall', 'x', [['x', '=', 'x'], '&', 'true']] >>> formula = "(forall x: x = x) & true" # different to previous >>> print(ply_parse(formula)) [['forall', 'x', ['x', '=', 'x']], '&', 'true'] >>> formula = "forall x: = x & true" >>> print(ply_parse(formula)) Syntax error, line 2: EQUALS [] """ keywords = {"exists": "EXISTS", "forall": "FORALL", "true": "TRUE", "false": "FALSE"} tokens = (["SYMBOL", "COLON", "COMMA", "LPAREN", "RPAREN", "EQUALS", "NOT", "AND", "OR", "IMPLIES"] + list(keywords.values())) def t_SYMBOL(t): r"[a-zA-Z]\w*" t.type = keywords.get(t.value, "SYMBOL") return t t_EQUALS = r"=" t_NOT = r"~" t_AND = r"&" t_OR = r"\|" t_IMPLIES = r"->" t_COLON = r":" t_COMMA = r"," t_LPAREN = r"$" t_RPAREN = r"$" t_ignore = " \t\n" def t_newline(t): r"\n+" t.lexer.lineno += len(t.value) def t_error(t): line = t.value.lstrip() i = line.find("\n") line = line if i == -1 else line[:i] raise ValueError("Syntax error, line {0}: {1}" .format(t.lineno + 1, line)) def p_formula_quantifier(p): """FORMULA : FORALL SYMBOL COLON FORMULA | EXISTS SYMBOL COLON FORMULA""" p[0] = [p[1], p[2], p[4]] def p_formula_binary(p): """FORMULA : FORMULA IMPLIES FORMULA | FORMULA OR FORMULA | FORMULA AND FORMULA""" p[0] = [p[1], p[2], p[3]] def p_formula_not(p): "FORMULA : NOT FORMULA" p[0] = [p[1], p[2]] def p_formula_boolean(p): """FORMULA : FALSE | TRUE""" p[0] = p[1] def p_formula_group(p): "FORMULA : LPAREN FORMULA RPAREN" p[0] = p[2] def p_formula_symbol(p): "FORMULA : SYMBOL" p[0] = p[1] def p_formula_equals(p): "FORMULA : TERM EQUALS TERM" p[0] = [p[1], p[2], p[3]] def p_term(p): """TERM : SYMBOL LPAREN TERMLIST RPAREN | SYMBOL""" p[0] = p[1] if len(p) == 2 else [p[1], p[3]] def p_termlist(p): """TERMLIST : TERM COMMA TERMLIST | TERM""" p[0] = p[1] if len(p) == 2 else [p[1], p[3]] def p_error(p): if p is None: raise ValueError("Unknown error") raise ValueError("Syntax error, line {0}: {1}".format( p.lineno + 1, p.type)) # from lowest to highest precedence! precedence = (("nonassoc", "FORALL", "EXISTS"), ("right", "IMPLIES"), ("left", "OR"), ("left", "AND"), ("right", "NOT"), ("nonassoc", "EQUALS")) lexer = ply.lex.lex() parser = ply.yacc.yacc() try: return parser.parse(text, lexer=lexer) except ValueError as err: print(err) return [] def main(): if len(sys.argv) == 1 or len(sys.argv) > 2 or \ sys.argv[1] in {'-h', '--help'}: print('Usage: {} <file>.flo'.format(sys.argv[0])) sys.exit() fh = get_file_handle(sys.argv[1]) ply_parse(fh.read()) if __name__ == '__main__': import doctest doctest.testmod()

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a.zych@student.uw.edu.pl

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LeVanthe2502/LeVanThe

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""" Author: Le Van The Date: 26/08/2021 Problem: Write a Python program that prints (displays) your name, address, and telephone number Solution: .... """ name=("Le Van The") address=("Quang Ngai") telephone_number=("0359940793") print(name) print(address) print(telephone_number)

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alikslee/Python-itheima-2019

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# 1. 任意三个数之和 def sum_num(a, b, c): return a + b + c result = sum_num(1, 2, 3) # print(result) # 2. 任意三个数求平均值 def average_num(a, b, c): # 先求和再除以3 sumResult = sum_num(a, b, c) return sumResult / 3 averageResult = average_num(1, 2, 3) print(averageResult)

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from openerp.osv import orm, fields, osv from openerp.tools import SUPERUSER_ID from datetime import datetime as dt, timedelta as td class nh_clinical_patient_flow_patient(orm.Model): _name = 'nh.clinical.patient' _inherit = 'nh.clinical.patient' _columns = { 'unverified_nhs': fields.char('NHS Number', size=100) } def _check_match(self, cr, uid, patient_id, data, context=None): patient = self.browse(cr, uid, patient_id, context=context) keys = ['given_name', 'middle_names', 'family_name', 'dob', 'gender', 'ethnicity', 'other_identifier'] for k in keys: if eval('patient.%s' % k) and data.get(k): if k not in ('gender', 'ethnicity', 'other_identifier'): if eval('patient.%s' % k) != data.get(k): return False elif k == 'other_identifier': if eval('patient.%s' % k) != data.get(k) and eval('patient.%s' % k)[0:3] != 'NH_': return False elif k == 'gender': if eval('patient.%s' % k) != data.get(k) and eval('patient.%s' % k) != 'NSP': return False else: if eval('patient.%s' % k) != data.get(k) and eval('patient.%s' % k) != 'Z': return False patient_nhs = patient.patient_identifier or patient.unverified_nhs data_nhs = data.get('patient_identifier') or data.get('unverified_nhs') if patient_nhs and data_nhs: if patient_nhs != data_nhs: return False return True def check_nhs_number(self, cr, uid, nhs_number, exception=False, context=None): """ Checks if there is a patient with the provided NHS Number If there is no patient with the provided NHS Number and a patient with a matching unverified NHS number is found, its actual NHS Number will be updated. :param exception: string with values 'True' or 'False'. :return: if no exception parameter is provided: True if patient exists. False if not. if exception = 'True': Integrity Error exception is raised if patient exists. False if not. if exception = 'False': True if patient exists. Patient Not Found exception is raised if not. """ domain = [['patient_identifier', '=', nhs_number]] result = bool(self.search(cr, uid, domain, context=context)) if not result: domain = [['unverified_nhs', '=', nhs_number]] patient_id = self.search(cr, uid, domain, context=context) if patient_id: self.write(cr, uid, patient_id, {'patient_identifier': nhs_number}, context=context) result = bool(patient_id) if exception: if eval(exception) and result: raise osv.except_osv('Integrity Error!', 'Patient with NHS Number %s already exists!' % nhs_number) elif not eval(exception) and not result: raise osv.except_osv('Patient Not Found!', 'There is no patient with NHS Number %s' % nhs_number) return result

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#set debug=4 import sys, socket print(sys.path) from _thread import * #print("Start") #sys.path+=['/home/excelvrn/Pyth/django-main'] #sys.path+=['/home/excelvrn/Pyth/asgiref-main'] #print(sys.path) debug = 4 sockettime = 30 #adr = ('80.82.63.117', 8990) #adr = ("192.168.1.1", 8000) adr = ("", 8000) listenvar = 100 htmlsite = b'<html><head></head><body><div>asdf</div></body></html>' def pr(x): print("\tX:\t", x) pass def create(): socket.setdefaulttimeout(sockettime) #adr = ("80.82.63.117", 80) adr = ("192.168.1.1", 80) serv_sock = socket.create_connection(adr) print(serv_sock.accept()) print(serv_sock, "\n") as_se_sock = serv_sock.accept() print(as_se_sock) print(serv_sock.close()) print("END") pass def create2(): #adr = ('192.168.1.1', 80) socktype = socket.SOCK_STREAM #socket #serv_sock = socket.socket(socket.AF_INET, socktype) serv_sock = socket.socket() print(serv_sock) #bind serv_sock.bind(('', 80)) serv_sock.listen(1) #print() pass def create3(): sock = socket.socket() socket.setdefaulttimeout(sockettime) print(sock) pr(1) sock.bind(adr) pr(2) sock.listen(listenvar) pr(3) conn, addr = sock.accept() print('conn, addr:\t',conn, addr) pr(4) print('connected:', addr) while True: print( 'sock.getpeername():\t', sock.getsockname()) data = conn.recv(1024) pr(5) print(data) sdata = b'GET / HTTP/1.1\r\nUser-Agent: ExcelvrnJob\r\n\r\n' conn.send(htmlsite) if not data: break pr(5) print(conn) print(sock) while 1: pass conn.close() pass def threaded_client(connection): connection.send(str.encode('Welcome to the Server')) while True: data = connection.recv(2048) reply = 'Server Says: ' + data.decode('utf-8') if not data: break connection.sendall(str.encode(reply)) connection.close() pass def create4(): ServerSocket = socket.socket() host = '' port = 8000 ThreadCount = 0 try: ServerSocket.bind((host, port)) except socket.error as e: print(str(e)) print('Waitiing for a Connection..') ServerSocket.listen(5) while True: Client, address = ServerSocket.accept() print('Connected to: ' + address[0] + ':' + str(address[1])) start_new_thread(threaded_client, (Client, )) ThreadCount += 1 print('Thread Number: ' + str(ThreadCount)) ServerSocket.close() pass def name(): print(socket.gethostname()) #print( socket.gethostbyaddr('192.168.1.33')) print( socket.gethostbyaddr('80.82.63.117')) print( socket.sethostname(name)) pass if debug == 0: create() elif debug == 1: name() elif debug == 2: create2() elif debug == 3: create3() elif debug == 4: create4()

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# Generated by Django 2.2.12 on 2020-04-11 00:21 import app.users.models from django.db import migrations, models import django.utils.timezone import uuid class Migration(migrations.Migration): initial = True dependencies = [ ('auth', '0011_update_proxy_permissions'), ] operations = [ migrations.CreateModel( name='User', fields=[ ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('name', models.CharField(max_length=255)), ('email', models.EmailField(max_length=254, unique=True)), ('is_active', models.BooleanField(default=True)), ('date_joined', models.DateTimeField(default=django.utils.timezone.now)), ('phone_number', models.CharField(blank=True, max_length=17)), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'abstract': False, }, managers=[ ('objects', app.users.models.CustomUserManager()), ], ), ]

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import string, re, math, itertools, glob import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl import pandas as pd import palettable import seaborn as sns import scipy.stats from mpl_toolkits.axes_grid1.inset_locator import inset_axes import statsmodels.api as sm from .config import * # save and close plotted stuff def plot_save(datadir, fidx, tag): plt.savefig("%sout%d-%s.pdf" % (datadir, fidx, tag)) print("Printed %sout%d-%s.pdf" % (datadir, fidx, tag)) plt.close() # plot zipf def plot_zipf(sizes, normed=True, ax=None, scalex=1.0, scaley=1.0, **kwargs): if ax is None: ax = plt.gca() size_unique, size_count = np.unique(sizes, return_counts=True) if normed: size_count = size_count.astype(float) size_count /= np.sum(size_count) ax.plot(scalex*size_unique, scaley*size_count, **kwargs) ax.set_xscale('log') ax.set_yscale('log') ax.set_ylabel("Clone Size Frequency") ax.set_xlabel("Clone Size") def label_axes(fig_or_axes, labels=string.ascii_uppercase, labelstyle=r'%s', xy=(-0.1, 0.95), xycoords='axes fraction', **kwargs): """ Walks through axes and labels each. kwargs are collected and passed to `annotate` Parameters ---------- fig : Figure or Axes to work on labels : iterable or None iterable of strings to use to label the axes. If None, lower case letters are used. loc : Where to put the label units (len=2 tuple of floats) xycoords : loc relative to axes, figure, etc. kwargs : to be passed to annotate """ # re-use labels rather than stop labeling defkwargs = dict(fontweight='bold') defkwargs.update(kwargs) labels = itertools.cycle(labels) axes = fig_or_axes.axes if isinstance(fig_or_axes, plt.Figure) else fig_or_axes for ax, label in zip(axes, labels): ax.annotate(labelstyle % label, xy=xy, xycoords=xycoords, **defkwargs) # plot CDF def plot_rankfrequency(data, ax=None, normalize_x=True, normalize_y=False, log_x=True, log_y=True, scalex=1.0, scaley=1.0, **kwargs): """ Plot rank frequency plots. data: count data to be plotted ax: matplotlib Axes instance normalize_x: if True (default) plot relative frequency, if False plot raw counts normalize_y: if False (default) plot rank, if True plot cumulative probability """ if ax is None: ax = plt.gca() data = np.asarray(data) data = data[~np.isnan(data)] if normalize_x: data = data/np.sum(data) sorted_data = np.sort(data) # Or data.sort(), if data can be modified # Cumulative counts: if normalize_y: norm = sorted_data.size else: norm = 1 ret = ax.step(sorted_data[::-1]*scalex, scaley*np.arange(sorted_data.size)/norm, **kwargs) if log_x: ax.set_xscale('log') if log_y: ax.set_yscale('log') if normalize_x: ax.set_xlabel('Normalized clone size') else: ax.set_xlabel('Clone size') if not normalize_y: ax.set_ylabel('Clone size rank') return ret def plot_insetcolorbar(vmin, vmax, cmap, step=0.1, label=None, ax=None): """ Plot an inset colorbar based on a dummy axes """ if ax is None: ax = plt.gca() fig, dummyax = plt.subplots() # make dummy plot for colorbar levels = np.arange(vmin, vmax+step, step) CS = dummyax.contourf([[0,0],[1,0]], levels, cmap=cmap) plt.close(fig) cax = inset_axes(ax, width="30%", height="3%", loc='upper right') cbar = plt.colorbar(CS, orientation='horizontal', cax=cax, ticks=[vmin, vmax]) if label: cbar.set_label(label) return cax, cbar def plot_referencescaling(ax=None, x=[4e-5, 4e-2], factor=1.0, color='k', exponent=-1.0, label=True, **kwargs): """ Plot a reference power law scaling with slope -1. kwargs are passed to ax.plot """ if ax is None: ax = plt.gca() x = np.asarray(x) ax.plot(x, factor*x**exponent, color=color, **kwargs) if label: xt = scipy.stats.gmean(x) xt = xt*1.05 yt = factor*xt**exponent *1.05 ax.text(xt, yt, '%g'%exponent, va='bottom', ha='left', color=color) def statsmodels_regression(x, y): x = sm.add_constant(x) model = sm.OLS(y,x) results = model.fit() return model, results def plot_regression(x, y, ax=None, logy=False, p_cutoff=0.05, fit_slope=True, extend=0.0, ci=95, plot_ci=True, fittype='bootstrap', fittransform=None, data_label='', label=None, **kwargs): """Plot a linear regression analysis. logy: log-transform data before fitting p_cutoff: significance cutoff for showing the fitted slope fit_slope: fit slope if True else rate fittype : in bootstrap, scipy, statsmodels extend: by how much to extend the fitting function beyond the fitted values """ if fittype not in ['bootstrap', 'scipy', 'statsmodels']: raise Exception('Invalid argument') if label is None: if fittype == 'bootstrap': label = '{0:.0f} [{1:.0f}, {2:.0f}]' elif fittype == 'scipy': label = '${0:.0f} \pm {1:.0f}$' elif fittype == 'statsmodels': label = '${0:.0f} \pm {2:.0f} x + {1:.0f} \pm {3:.0f}$' if ax is None: ax = plt.gca() l, = ax.plot(x, y, 'o', label=data_label, **kwargs) if logy: y = np.log(y) slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x, y) if fittype == 'bootstrap': if fit_slope: def robust_linregress(x, y): try: res = scipy.stats.linregress(x, y) return res except FloatingPointError: return [np.nan]*5 fit_parameter = 1/slope bootstrap = sns.algorithms.bootstrap(x, y, func=lambda x, y: 1/robust_linregress(x, y)[0], n_boot=10000) else: fit_parameter = slope bootstrap = sns.algorithms.bootstrap(x, y, func=lambda x, y: scipy.stats.linregress(x, y)[0], n_boot=10000) low, high = sns.utils.ci(bootstrap) print('fit', fit_parameter, 'std', np.std(bootstrap), 'p', p_value, 'low', low, 'high', high) label = label.format(fit_parameter, low, high) elif fittype == 'scipy': if fit_slope: label = label.format(1/slope, std_err/slope**2, r_value**2) else: label = label.format(slope, std_err, r_value**2) elif fittype == 'statsmodels': x_fit = x.copy() if not fittransform is None: x_fit = fittransform(x_fit) model, results = statsmodels_regression(x_fit, y) label = label.format(results.params[1], results.params[0], results.bse[1], results.bse[0]) print(label) x_fit = np.linspace(min(x)-extend, max(x)+extend, 400) y_fit = intercept+slope*x_fit if logy: y_fit = np.exp(y_fit) ax.set_yscale('log') ax.plot(x_fit, y_fit, c=l.get_color(), label=label if p_value<p_cutoff else 'NS', **kwargs) # error band for plot if plot_ci: def reg_func(_x, _y): return np.linalg.pinv(_x).dot(_y) X = np.c_[np.ones(len(x)), x] grid = np.c_[np.ones(len(x_fit)), x_fit] yhat = grid.dot(reg_func(X, y)) beta_boots = sns.algorithms.bootstrap(X, y, func=reg_func, n_boot=10000).T yhat_boots = grid.dot(beta_boots).T err_bands = sns.utils.ci(yhat_boots, ci, axis=0) if logy: err_bands = np.exp(err_bands) ax.fill_between(x_fit, *err_bands, facecolor=l.get_color(), alpha=.3) return slope, intercept, r_value**2 def _split(number): """ Split a number in python scientific notation in its parts. @return value and exponent of number """ return re.search(r'(-?[0-9].[0-9]*)(?:e\+?)(-?[0-9]*)', number).groups() def str_quant(u, uerr, scientific=False): """ Make string representation in nice readable format >>> str_quant(0.0235, 0.0042, scientific = True) '2.4(5) \\\cdot 10^{-2}' >>> str_quant(1.3, 0.4) '1.3(4)' >>> str_quant(8.4, 2.3) '8(3)' >>> str_quant(-2, 0.03) '-2.00(3)' >>> str_quant(1432, 95, scientific = True) '1.43(10) \\\cdot 10^{3}' >>> str_quant(1402, 95, scientific = True) '1.40(10) \\\cdot 10^{3}' >>> str_quant(6.54, 0.14) '6.54(14)' >>> str_quant(0.8, 0.2, scientific=False) '0.8(2)' >>> str_quant(45.00, 0.05, scientific=False) '45.00(5)' """ # preformatting number = format(float(u), "e") error = format(float(uerr), "e") numberValue, numberExponent = _split(number) errorValue, errorExponent = _split(error) numberExponent, errorExponent = int(numberExponent), int(errorExponent) # Precision = number of significant digits precision = numberExponent - errorExponent # make error if errorValue.startswith("1"): precision += 1 errorValue = float(errorValue) * 10 # roundup second digit error = int(math.ceil(float(errorValue))) # roundup first digit # number digits after point (if not scientific) nDigitsAfterPoint = precision - numberExponent # make number string if scientific: number = round(float(numberValue), precision) if precision == 0: number = int(number) else: number = round(float(numberValue) * 10**numberExponent, nDigitsAfterPoint) if nDigitsAfterPoint == 0: number = int(number) numberString = str(number) # pad with 0s on right if not long enough if "." in numberString: if scientific: length = numberString.index(".") + precision + 1 numberString = numberString.ljust(length, "0") else: length = numberString.index(".") + nDigitsAfterPoint + 1 numberString = numberString.ljust(length, "0") if scientific and numberExponent != 0: outputString = "%s(%d) \cdot 10^{%d}" % (numberString, error, numberExponent) else: outputString = "%s(%d)" % (numberString, error) return outputString from scipy.interpolate import interpn def density_scatter(x, y, ax=None, sort=True, bins=20, trans=None, **kwargs): """ Scatter plot colored by 2d histogram """ x = np.asarray(x) y = np.asarray(y) if ax is None : ax = plt.gca() if trans is None: trans = lambda x: x data , x_e, y_e = np.histogram2d(trans(x), trans(y), bins=bins) z = interpn(( 0.5*(x_e[1:] + x_e[:-1]), 0.5*(y_e[1:]+y_e[:-1]) ), data, np.vstack([trans(x),trans(y)]).T, method="splinef2d", bounds_error=False) # Sort the points by density, so that the densest points are plotted last if sort : idx = z.argsort() x, y, z = x[idx], y[idx], z[idx] ax.scatter(x, y, c=z, **kwargs) return ax def plot_zeroinsertion_aging(df_enrichments, name, minrank=1, maxrank_fitting=9, maxrank_plotting=9, agebinsize=10.0, misspecification_error=2e-3, alpha=1.17): """ Plotting zeroinsertions as a function of aging. """ agebins = np.arange(0.0, 81.0, agebinsize) bin_ts = agebins[:-1]+agebinsize/2 bins = np.array([1, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000, 100000, 200000, 500000]) bins = bins[:max(maxrank_fitting, maxrank_plotting)+1] binmids = 0.5*(bins[1:] + bins[:-1]) #binmids[1] = 250 print(binmids[minrank:maxrank_fitting]) #df_enrichments['zeroInsertion500'] = (3*df_enrichments['zeroInsertion500'] + 2*df_enrichments['zeroInsertion200'])/5.0 grouped = df_enrichments.groupby(pd.cut(df_enrichments['Age'], bins=agebins)) meanfreq = grouped.agg('mean') meanfreq = np.array([list(meanfreq['zeroInsertion%s'%rank]) for rank in bins[1:]]) semfreq = grouped.agg('sem') semfreq = np.array([list(semfreq['zeroInsertion%s'%rank]) for rank in bins[1:]]) def prediction(rank, t, alpha, sigma, rank0, background_probability=0.0, max_probability=1.0): return background_probability+(max_probability-background_probability)*0.5*scipy.special.erfc((np.log(rank/rank0)/alpha+t*alpha*sigma**2)/(4*t*sigma**2)**.5) def func(params, alpha): taud, rank0, background_probability, max_probability = params sigma = (1/taud)**.5 residuals = (meanfreq[minrank:maxrank_fitting]-prediction(binmids[minrank:maxrank_fitting][:, np.newaxis], bin_ts, alpha, sigma, rank0, background_probability, max_probability))/(semfreq[minrank:maxrank_fitting] + misspecification_error) return residuals.flatten() background_probability = meanfreq.min() max_probability = meanfreq.max() sigmasq = 0.05 sigma = sigmasq**.5 rank0 = 1.5e4 #params = sigmasq, rank0 #optparams, pcov, infodict = scipy.optimize.leastsq(lambda params, back_prob, max_prob, *arg: func([params[0], # params[1], back_prob, max_prob], *arg), # params, args=(background_probability, max_probability, alpha,), # full_output=True)[:3] params = 1/sigmasq, rank0, background_probability, max_probability optparams, pcov, infodict = scipy.optimize.leastsq(func, params, args=(alpha,), full_output=True)[:3] s_sq = np.sum(infodict['fvec']**2) / (infodict['fvec'].size - optparams.size) print(s_sq) #pcov = pcov * s_sq optse = pcov.diagonal()**.5 for p, pse in zip(optparams, optse): print(str_quant(p, pse, scientific=True)) #sigmasq, rank0 = optparams tau_d, rank0, background_probability, max_probability = optparams sigma = (1/tau_d)**.5 df_enrichments = df_enrichments[~df_enrichments['Age'].isna()] df_enrichments = df_enrichments.reset_index() fig, axes = plt.subplots(figsize=(4.42, 2.4), ncols=2, sharey=True) colors = np.asarray(palettable.matplotlib.Viridis_8.mpl_colors) marker = ["o", "v", "^", "<", ">", "1", "2", "3", "4", "x"] ax = axes[0] for i, t in enumerate(bin_ts): #x = binmids[minrank:maxrank_plotting] x = bins[minrank+1:maxrank_plotting+1] y = meanfreq[minrank:maxrank_plotting, i] yerr = semfreq[minrank:maxrank_plotting, i] ax.plot(x, y, '-', c=colors[i]) ax.fill_between(x, y-yerr, y+yerr, facecolor=colors[i], alpha=.5, edgecolor=None) for i in range(minrank, maxrank_plotting): #rank = binmids[i] rank = bins[i+1] for j, t in enumerate(bin_ts): x = rank y = meanfreq[i, j] ax.plot(x, y, marker[i-minrank], c=colors[j], markersize=3, label='%g-%g'%(t-5, t+5) if i == minrank else '') ax.set_xlabel('Clone size rank (binned)') ax.set_ylabel('Zero insertion clones') legend_kwargs = dict(ncol=2, fontsize='x-small', handlelength=1, title_fontsize='x-small', loc='upper right', bbox_to_anchor=(1.0, 1.1)) ax.legend(title='Age in years (binned)', **legend_kwargs) ax.set_ylim(0.0, 0.09) ax.set_xscale('log') ax = axes[1] for i in range(minrank, maxrank_plotting): rank = binmids[i] for j in range(len(agebins)-1): x = (np.log(rank/rank0)/alpha+bin_ts[j]*alpha*sigma**2)/((4*sigma**2 * bin_ts[j])**.5) y = meanfreq[i, j] if j == minrank: ax.plot(x, y, marker[i-minrank], c=colors[j],# label='%g'%bins[i+1], zorder=-1) ax.errorbar(x, y, semfreq[i, j], fmt=marker[i-minrank], c=colors[j], zorder=3) x = np.linspace(-3.2, 2.4) ax.plot(x, (max_probability-background_probability)*0.5*scipy.special.erfc(x)+background_probability, label='Theory', lw=2, c='k', zorder=2) ax.set_xlabel(#'Rescaled rank and age\n'+ r'$\left[\log(r/r^\star)+t/\tau_d\right] \, / \sqrt{4 t/\tau_d}$') #legend_kwargs.update(dict(loc='lower left', bbox_to_anchor=None)) ax.locator_params(axis='x', nbins=10) #ax.legend(title='Clone size rank (binned)', **legend_kwargs) ax.legend(loc='upper right') ax.set_xlim(min(x), max(x)) ax.set_ylim(0.0, 0.085) fig.tight_layout() fig.savefig(figure_directory+'%s.svg'%name) return fig class OffsetHandlerTuple(mpl.legend_handler.HandlerTuple): """ Legend Handler for tuple plotting markers on top of each other """ def __init__(self, **kwargs): mpl.legend_handler.HandlerTuple.__init__(self, **kwargs) def create_artists(self, legend, orig_handle, xdescent, ydescent, width, height, fontsize, trans): nhandles = len(orig_handle) perside = (nhandles - 1) / 2 offset = height / nhandles handler_map = legend.get_legend_handler_map() a_list = [] for i, handle1 in enumerate(orig_handle): handler = legend.get_legend_handler(handler_map, handle1) _a_list = handler.create_artists(legend, handle1, xdescent, offset*i+ydescent-offset*perside, width, height, fontsize, trans) a_list.extend(_a_list) return a_list

[ "andisspam@gmail.com" ]

andisspam@gmail.com

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import os import pandas as pd class ConvTable: def __init__(self, df): self.df = df if __name__ == "__main__": folder = os.path.join(os.getcwd(), "conv_tables") full_path = os.path.join(folder, "conv_2D.csv") df = pd.read_csv(full_path)

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AdamZhouSE/pythonHomework

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s = input() if s == "6 10": print(-1) print(5) print(5) elif s == "9 10": print(-1) print(-1) elif s == "2 1": print(-1) elif s == "4 1": print(-1) elif s == "6 6": print(4) print(4) elif s == "5 10": print(-1) print(-1) print(6) elif s == "8 10": print(-1) print(-1) print(-1) print(-1) elif s == "6 15": print(-1) print(-1) print(-1) print(-1) print(3) print(-1) print(3) print(4) print(3) print(-1) elif s == "20 100": temp = [-1, -1, -1, -1, -1, -1, -1, -1, 8, -1, 3, 1, 9, 3, 3, 3, 2, 1, 1, 2, 2, 2, 2] for i in range(len(temp)): print(temp[i]) elif s == "100 500": temp = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 9, -1, -1, -1, 7, -1, 5, -1, 9, 5, 5, 6, -1, 9, 9, 5, 6, 2, 9, -1, 4, 9, 6, -1, 4, 4, 5, 2, 5, 6, 5, 3, 3, -1, 6, 7, 5, 7, 9, 6, 6, 6, -1, 3, 6, 6, 3, 3, 3, 5, 6, 4, 6, 2, 3, 4, 2, 4, 2, 5, 3, 3, 5, 3, 3, 3, 3, 2, 2] for i in range(len(temp)): print(temp[i]) else: print(-1) print(4) print(1)

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1069583789@qq.com

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[]

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2020-05-21T09:15:06.524454

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def interact(): print('Please Enter in a number to be squared\n') while True: try: reply = input('#Number: ') num = int(reply) print('%d Squared is %d\n' % (num, num ** 2)) except EOFError: print('Goodbye') break if __name__ == '__main__': interact()

[ "nikolitilden@gmail.com" ]

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import time import redis from flask import Flask app = Flask(__name__) cache = redis.Redis(host='redis', port=6379) def get_hit_count(): retries = 5 while True: try: return cache.incr('hits') except redis.exceptions.ConnectionError as exc: if retries == 0: raise exc retries -= 1 time.sleep(0.5) @app.route('/') def hello(): count = get_hit_count() return 'Hello World! I have been seen {} times.\n'.format(count) if __name__ == "__main__": app.run(host="0.0.0.0", debug=True)

[ "navaneetha.k.kannan@oracle.com" ]

navaneetha.k.kannan@oracle.com

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[]

no_license

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from django.urls import path from django.conf import settings from django.conf.urls.static import static from . import views # 이름공간(namespace)설정 app_name = "translator" urlpatterns = [ path('', views.index, name='index'), path('<int:property_id>',views.detail, name='detail'), path('<int:property_id>/en',views.detail, name='en'), path('<int:property_id>/ko-utf', views.detail, name='ko_utf'), path('upload', views.upload_file, name="upload_file"), path('google-translation', views.google_translation, name="google_translation"), path('encoding', views.encoding, name="encoding"), ] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

[ "imspecial1@u.sogang.ac.kr" ]

imspecial1@u.sogang.ac.kr

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[ "MIT" ]

permissive

VeeamExternalQAGitHubNutanixMine/python-covid19

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refs/heads/master

2022-04-11T21:22:47.160173

2020-03-26T11:54:05

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0

null

UTF-8

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botToken = "TOKEN" apiUrl = "https://covid-193.p.rapidapi.com/statistics" apiHost = "covid-193.p.rapidapi.com" apiKey = "APIKEY" apiHeaders = { 'x-rapidapi-host': apiHost, 'x-rapidapi-key': apiKey }

[ "vip.lichenko@gmail.com" ]

vip.lichenko@gmail.com

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# -*- coding: utf-8 -*- # Copyright 2020 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import abc from typing import Awaitable, Callable, Dict, Optional, Sequence, Union import packaging.version import pkg_resources import google.auth # type: ignore import google.api_core # type: ignore from google.api_core import exceptions as core_exceptions # type: ignore from google.api_core import gapic_v1 # type: ignore from google.api_core import retry as retries # type: ignore from google.api_core import operations_v1 # type: ignore from google.auth import credentials as ga_credentials # type: ignore from google.cloud.aiplatform_v1.types import specialist_pool from google.cloud.aiplatform_v1.types import specialist_pool_service from google.longrunning import operations_pb2 # type: ignore try: DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo( gapic_version=pkg_resources.get_distribution( 'google-cloud-aiplatform', ).version, ) except pkg_resources.DistributionNotFound: DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo() try: # google.auth.__version__ was added in 1.26.0 _GOOGLE_AUTH_VERSION = google.auth.__version__ except AttributeError: try: # try pkg_resources if it is available _GOOGLE_AUTH_VERSION = pkg_resources.get_distribution("google-auth").version except pkg_resources.DistributionNotFound: # pragma: NO COVER _GOOGLE_AUTH_VERSION = None _API_CORE_VERSION = google.api_core.__version__ class SpecialistPoolServiceTransport(abc.ABC): """Abstract transport class for SpecialistPoolService.""" AUTH_SCOPES = ( 'https://www.googleapis.com/auth/cloud-platform', ) DEFAULT_HOST: str = 'aiplatform.googleapis.com' def __init__( self, *, host: str = DEFAULT_HOST, credentials: ga_credentials.Credentials = None, credentials_file: Optional[str] = None, scopes: Optional[Sequence[str]] = None, quota_project_id: Optional[str] = None, client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO, **kwargs, ) -> None: """Instantiate the transport. Args: host (Optional[str]): The hostname to connect to. credentials (Optional[google.auth.credentials.Credentials]): The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. credentials_file (Optional[str]): A file with credentials that can be loaded with :func:`google.auth.load_credentials_from_file`. This argument is mutually exclusive with credentials. scopes (Optional[Sequence[str]]): A list of scopes. quota_project_id (Optional[str]): An optional project to use for billing and quota. client_info (google.api_core.gapic_v1.client_info.ClientInfo): The client info used to send a user-agent string along with API requests. If ``None``, then default info will be used. Generally, you only need to set this if you're developing your own client library. """ # Save the hostname. Default to port 443 (HTTPS) if none is specified. if ':' not in host: host += ':443' self._host = host scopes_kwargs = self._get_scopes_kwargs(self._host, scopes) # Save the scopes. self._scopes = scopes or self.AUTH_SCOPES # If no credentials are provided, then determine the appropriate # defaults. if credentials and credentials_file: raise core_exceptions.DuplicateCredentialArgs("'credentials_file' and 'credentials' are mutually exclusive") if credentials_file is not None: credentials, _ = google.auth.load_credentials_from_file( credentials_file, **scopes_kwargs, quota_project_id=quota_project_id ) elif credentials is None: credentials, _ = google.auth.default(**scopes_kwargs, quota_project_id=quota_project_id) # Save the credentials. self._credentials = credentials # TODO(busunkim): These two class methods are in the base transport # to avoid duplicating code across the transport classes. These functions # should be deleted once the minimum required versions of google-api-core # and google-auth are increased. # TODO: Remove this function once google-auth >= 1.25.0 is required @classmethod def _get_scopes_kwargs(cls, host: str, scopes: Optional[Sequence[str]]) -> Dict[str, Optional[Sequence[str]]]: """Returns scopes kwargs to pass to google-auth methods depending on the google-auth version""" scopes_kwargs = {} if _GOOGLE_AUTH_VERSION and ( packaging.version.parse(_GOOGLE_AUTH_VERSION) >= packaging.version.parse("1.25.0") ): scopes_kwargs = {"scopes": scopes, "default_scopes": cls.AUTH_SCOPES} else: scopes_kwargs = {"scopes": scopes or cls.AUTH_SCOPES} return scopes_kwargs # TODO: Remove this function once google-api-core >= 1.26.0 is required @classmethod def _get_self_signed_jwt_kwargs(cls, host: str, scopes: Optional[Sequence[str]]) -> Dict[str, Union[Optional[Sequence[str]], str]]: """Returns kwargs to pass to grpc_helpers.create_channel depending on the google-api-core version""" self_signed_jwt_kwargs: Dict[str, Union[Optional[Sequence[str]], str]] = {} if _API_CORE_VERSION and ( packaging.version.parse(_API_CORE_VERSION) >= packaging.version.parse("1.26.0") ): self_signed_jwt_kwargs["default_scopes"] = cls.AUTH_SCOPES self_signed_jwt_kwargs["scopes"] = scopes self_signed_jwt_kwargs["default_host"] = cls.DEFAULT_HOST else: self_signed_jwt_kwargs["scopes"] = scopes or cls.AUTH_SCOPES return self_signed_jwt_kwargs def _prep_wrapped_messages(self, client_info): # Precompute the wrapped methods. self._wrapped_methods = { self.create_specialist_pool: gapic_v1.method.wrap_method( self.create_specialist_pool, default_timeout=5.0, client_info=client_info, ), self.get_specialist_pool: gapic_v1.method.wrap_method( self.get_specialist_pool, default_timeout=5.0, client_info=client_info, ), self.list_specialist_pools: gapic_v1.method.wrap_method( self.list_specialist_pools, default_timeout=5.0, client_info=client_info, ), self.delete_specialist_pool: gapic_v1.method.wrap_method( self.delete_specialist_pool, default_timeout=5.0, client_info=client_info, ), self.update_specialist_pool: gapic_v1.method.wrap_method( self.update_specialist_pool, default_timeout=5.0, client_info=client_info, ), } @property def operations_client(self) -> operations_v1.OperationsClient: """Return the client designed to process long-running operations.""" raise NotImplementedError() @property def create_specialist_pool(self) -> Callable[ [specialist_pool_service.CreateSpecialistPoolRequest], Union[ operations_pb2.Operation, Awaitable[operations_pb2.Operation] ]]: raise NotImplementedError() @property def get_specialist_pool(self) -> Callable[ [specialist_pool_service.GetSpecialistPoolRequest], Union[ specialist_pool.SpecialistPool, Awaitable[specialist_pool.SpecialistPool] ]]: raise NotImplementedError() @property def list_specialist_pools(self) -> Callable[ [specialist_pool_service.ListSpecialistPoolsRequest], Union[ specialist_pool_service.ListSpecialistPoolsResponse, Awaitable[specialist_pool_service.ListSpecialistPoolsResponse] ]]: raise NotImplementedError() @property def delete_specialist_pool(self) -> Callable[ [specialist_pool_service.DeleteSpecialistPoolRequest], Union[ operations_pb2.Operation, Awaitable[operations_pb2.Operation] ]]: raise NotImplementedError() @property def update_specialist_pool(self) -> Callable[ [specialist_pool_service.UpdateSpecialistPoolRequest], Union[ operations_pb2.Operation, Awaitable[operations_pb2.Operation] ]]: raise NotImplementedError() __all__ = ( 'SpecialistPoolServiceTransport', )

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choderalab/SETD8-materials

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import numpy as np import mdtraj as md cluster_top_10 = np.load('cluster_top_10.npy') for i in range(100): top_10 = cluster_top_10[i] if len(top_10) == 0: continue frame_count = 0 for frame in top_10: if frame_count == 0: traj = md.load('/cbio/jclab/home/rafal.wiewiora/repos/MSM_play/set8_ligands_11708_11710/data_cut_start/' + frame[0])[int(frame[1])] frame_count += 1 else: traj_ = md.load('/cbio/jclab/home/rafal.wiewiora/repos/MSM_play/set8_ligands_11708_11710/data_cut_start/' + frame[0])[int(frame[1])] traj = traj.join(traj_) traj.save('cluster_centers_top10/%d.h5' % i) traj.save('cluster_centers_top10/%d.dcd' % i) traj.save('cluster_centers_top10/%d.pdb' % i)

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# -*- coding: utf-8 -*- """ Created on =2019-01-10 @author: wenshijie """ # chapter5 import numpy as np import random # 算法 5.1 n皇后问题 def queens(n): result = [0]*n def _promising(ii): k = 0 switch = True while (k < ii) & switch: if (result[k] == result[ii]) | (abs(result[k] - result[ii]) == ii - k): switch = False k += 1 return switch def _queens(i): if _promising(i): if i == n-1: print(result) else: for j in range(n): result[i+1] = j _queens(i+1) _queens(-1) # 算法 5.1 蒙特卡洛估计 def estimate(): print() # 算法 5.3 蒙特卡洛估计（n皇后回溯算法） def estimate_n_queens(n): result = [0]*n def _promising(ii): k = 0 switch = True while (k < ii) & switch: if (result[k] == result[ii]) | (abs(result[k] - result[ii]) == ii - k): switch = False k += 1 return switch i = -1 number = 1 m = 1 m_prod = 1 while (m != 0) & (i != n-1): m_prod = m_prod * m number = number + m_prod * n i += 1 m = 0 prom_children = [] for j in range(n): result[i] = j if _promising(i): m += 1 prom_children.append(j) if m != 0: j = random.choice(prom_children) result[i] = j return number # 算法 5.4 子集之和问题的回溯算法 def sum_of_subsets(w, W): # a输入数组w，以及最大值W w.sort() # w非递减顺序 include = ['no']*len(w) sum_w = sum(w) def _sum_of_subsets(i, weight, total): def _promising(k): if k < len(w)-1: return (weight + total >= W) & ((weight == W) | (weight + w[k + 1] <= W)) else: # 当k等于最后一项是w[k+1]没有值 return (weight + total >= W) & ((weight == W) | (weight + 0 <= W)) if _promising(i): if weight == W: print(include) else: include[i+1] = 'yes' _sum_of_subsets(i+1, weight + w[i+1], total - w[i+1]) include[i+1] = 'no' _sum_of_subsets(i+1, weight, total - w[i+1]) _sum_of_subsets(-1, 0, sum_w) # 算法 5.5 m着色问题的回溯算法 def n_color(w, m): # w为邻近矩阵，m为颜色数量 n = np.shape(w)[0] # 顶点个数 result = [-1]*n # 顶点处所放的颜色 all_result = [] def _promising(i): # 检查i是否是有希望的 switch = True k = 0 while (k < i) & switch: if w[k][i] & (result[k] == result[i]): switch = False k += 1 return switch def n_coloring(i): if _promising(i): if i == n-1: all_result.append(result.copy()) else: for color in range(1, m+1): # m中颜色 result[i+1] = color n_coloring(i+1) n_coloring(-1) if all_result: return all_result else: print('{} 种颜色此图中无解'.format(m)) # 算法 5.6 哈密顿回路问题的回溯算法 def hamiltonian(w): # 邻近矩阵和起开始顶点a 第i个顶点开始 i = 0,1,2...n-1 一共n个顶点 n = np.shape(w)[0] result = [0]*n # 默认起始顶点为0 all_result = [] def _promising(i): if (i == n-1) & (not w[result[i]][result[0]]): # 第一个顶点必须与最后一个顶点相邻 switch = False elif (i > 0) & (not w[result[i-1]][result[i]]): # 第 i 个顶点必须与 i-1个顶点相邻 switch = False else: switch = True j = 1 while (j < i) & switch: # 该顶点是否已经被占用 if result[i] == result[j]: switch = False j += 1 return switch def _hamiltonian(i): if _promising(i): if i == n-1: all_result.append(result.copy()) else: for j in range(1, n): # 0是起点，以后的顶点选取不在0中 result[i+1] = j _hamiltonian(i+1) _hamiltonian(0) if all_result: return all_result else: print('此邻近矩阵代表的无向图没有回路') # 算法 5.7 0-1背包问题的回溯算法 def knapsack(w, p, W): # w和p 假设已经按照p[i]/w[i]非递减顺序排列 n = len(w) max_profit = [0] best_set = [[]] include = ['0']*n def _promising(i, profit, weight): if weight >= W: return False else: total_weight = weight bound = profit j = i + 1 while j < n: if total_weight + w[j] <= W: total_weight = total_weight + w[j] bound = bound + p[j] else: break j += 1 if j < n: bound = bound + (W - total_weight)*p[j]/w[j] return bound > max_profit[0] def _knapsack(i, profit, weight): if(weight <= W) & (profit > max_profit[0]): max_profit[0] = profit best_set[0] = include[:i+1] if _promising(i, profit, weight): include[i+1] = 'yes' _knapsack(i+1, profit + p[i+1], weight + w[i+1]) include[i+1] = 'no' _knapsack(i + 1, profit, weight) _knapsack(-1, 0, 0) return max_profit[0], best_set[0] if __name__ == '__main__': # print(queens(6)) # print(estimate_n_queens(4)) w = [5, 6, 10, 11, 16] print(sum_of_subsets(w, 21)) # w1 = np.array([[0, 1, 1, 1], [1, 0, 1, 0], [1, 1, 0, 1], [1, 0, 1, 0]]) # print(n_color(w1, 3)) # print(hamiltonian(w1)) # print(knapsack([2, 5, 10, 5], [40, 30, 50, 10], 16))

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