xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

1deb3a29a0068a66f60f7a72e5a69387e26b00c4

2,777

py

Python

planning-and-learning/dyna.py

Sirius79/reinforcement-learning

96ea39260cd399c75278745365aad7e5babab0a0

[ "MIT" ]

null

planning-and-learning/dyna.py

Sirius79/reinforcement-learning

96ea39260cd399c75278745365aad7e5babab0a0

[ "MIT" ]

null

planning-and-learning/dyna.py

Sirius79/reinforcement-learning

96ea39260cd399c75278745365aad7e5babab0a0

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt from matplotlib import colors import itertools from collections import defaultdict import environment class Dyna(): def __init__(self, alpha, epsilon, gamma): ''' Q(s,a) -> action value model(s,a) -> R, s' ''' self.alpha = alpha self.epsilon = epsilon self.gamma = gamma self.Q = defaultdict(lambda: np.random.randint(5,size=4)) self.model_reward = defaultdict(lambda: np.random.randint(5,size=4)) self.model_obs = defaultdict(lambda : np.random.randint(5, size=400)) def greedy_policy(self, state): ''' returns action from state using epsilon greedy policy derived from Q ''' prob = np.random.random() if prob <= self.epsilon: return np.random.randint(4) else: return np.argmax(self.Q[state]) def play(self): score = np.zeros(episode_num) for episode in range(episode_num): env.reset() env.render() state = env.env.flatten() action = self.greedy_policy(tuple(state)) observation, reward, done = env.step(action) observation = observation.flatten() next_action = np.argmax(self.Q[tuple(observation)]) self.Q[tuple(state)][action] += self.alpha * (reward + (self.gamma * self.Q[tuple(observation)][next_action]) - self.Q[tuple(state)][action]) self.model_reward[tuple(state)][action] = reward self.model_obs[tuple(state)][action*100:(action*100)+100] = observation for step in range(20): random_state = list(self.Q.keys())[np.random.choice(len(list(self.Q.keys())))] random_action = np.random.randint(4) R = self.model_reward[tuple(random_state)][random_action] print("Reward and Action ",R, random_action) next_S = self.model_obs[tuple(random_state)][random_action*100: (random_action*100)+100] a = np.argmax(self.Q[tuple(next_S)]) self.Q[tuple(random_state)][random_action] += self.alpha * (R + (self.gamma * self.Q[tuple(next_S)][a]) - self.Q[tuple(random_state)][random_action]) env.render() if done: print("Finished episode", episode+1) score[episode] = step break print("Finished time step "+str(step)+"of episode"+str(episode+1)) return score env = env.Environment(10,10) agent = Dyna(0.1,0.1,0.95) episode_num = 100 score = agent.play() print(np.amax(score))

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0.565718

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2,777

4.553254

0.248521

0.038986

0.051982

0.05718

0.251462

0.160494

0.128005

0.061079

0

0.023983

0.309327

2,777

73

166

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0.039971

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0.075472

0

null

0

null

0

1

0

1deec6ae5dce012d14c4e9ed45f53515b88ecefc

4,734

py

Python

src/week4/partitioned_stream_producer.py

shamilbi/ru202

ffeb78616bcffe3c887ffbcd3d875358f9a11cf6

[ "MIT" ]

null

src/week4/partitioned_stream_producer.py

shamilbi/ru202

ffeb78616bcffe3c887ffbcd3d875358f9a11cf6

[ "MIT" ]

null

src/week4/partitioned_stream_producer.py

shamilbi/ru202

ffeb78616bcffe3c887ffbcd3d875358f9a11cf6

[ "MIT" ]

null

# Use Case: Partitioned Stream Example with Python # Usage: Part of Redis University RU202 courseware # # Simulates a temperature logging device that # continuously outputs new temperature readings, and # pushes them into a date-partitioned set of streams # in Redis. A new stream is created for each new # day, and set to expire a few days after creation # to ensure that memory usage is managed. # # The producer starts at a configurable date and # generates readings for a configurable number of days # at a configurable interval. By default it starts # on January 1st 2025, generating 10 days worth of # data at 1 second intervals. import random from datetime import datetime import util.constants as const from util.connection import get_connection ONE_DAY_SECONDS = 60 * 60 * 24 # Expire stream partitions 2 days after we finish # writing to them. PARTITION_EXPIRY_TIME = ONE_DAY_SECONDS * 2 # Record temperature readings every second. TEMPERATURE_READING_INTERVAL_SECONDS = 1 # Date that we'll start recording temperatures for - # using a future date so that all students get the # same dataset rather than using dates relative to # when the producer is run. So this timestamp # represents the oldest temperature reading that # will be generated. TIMESTAMP_START = 1735689600 # 01/01/2025 00:00:00 UTC # Number of days of data to generate. DAYS_TO_GENERATE = 10 # Utility class to produce wandering temperature range. class Measurement: def __init__(self): self.current_temp = 50 self.max_temp = 100 self.min_temp = 0 def get_next(self): if random.random() >= 0.5: if self.current_temp + 1 <= self.max_temp: self.current_temp += 1 elif self.current_temp - 1 >= self.min_temp: self.current_temp -= 1 return {'temp_f': self.current_temp} # To make this demonstration repeatable, running # the producer resets all the streams. def reset_state(): redis = get_connection() # Delete any old streams that have not yet expired. keys_to_delete = [] stream_timestamp = TIMESTAMP_START print("Deleting old streams:") for _ in range(DAYS_TO_GENERATE): s = datetime.utcfromtimestamp(stream_timestamp).strftime('%Y%m%d') stream_key_name = f"{const.STREAM_KEY_BASE}:{s}" print(stream_key_name) keys_to_delete.append(stream_key_name) stream_timestamp += ONE_DAY_SECONDS redis.delete(*keys_to_delete) # Return a string containing the UTC date for # the supplied timestamp in the format specified by # format_pattern. See http://strftime.org/ def format_timestamp_as_utc(timestamp, format_pattern): return datetime.utcfromtimestamp(timestamp).strftime(format_pattern) # Calculate key name for the stream partition that # the supplied timestamp belongs to. Each day has # its own stream key name, all times are in UTC. def get_stream_key_for_timestamp(timestamp): return f"{const.STREAM_KEY_BASE}:{format_timestamp_as_utc(timestamp, '%Y%m%d')}" # Entry point: clean up any old state and run the producer. def main(): reset_state() measurement = Measurement() previous_stream_key = "" current_timestamp = TIMESTAMP_START # End data production a configurable number of days after we began. end_timestamp = TIMESTAMP_START + (ONE_DAY_SECONDS * DAYS_TO_GENERATE) redis = get_connection() stream_key = "" while current_timestamp < end_timestamp: # Get the stream partition key name that this timestamp should # be written to. stream_key = get_stream_key_for_timestamp(current_timestamp) # Get a temperature reading. entry = measurement.get_next() # Publish to the current stream partition and set # or update expiry time on the stream partition. # This is done as a pipeline so that both commands are # executed with a single round trip to the Redis Server # for performance reasons. An alternative strategy might # be to only update the expiry time every 100th message # or similar. # Pipeline: https://redis.io/topics/pipelining pipe = redis.pipeline() pipe.xadd(stream_key, entry, current_timestamp) pipe.expireat(stream_key, current_timestamp + PARTITION_EXPIRY_TIME) pipe.execute() # Have we started a new stream? if stream_key != previous_stream_key: # A new day's stream started. print(f"Populating stream partition {stream_key}.") previous_stream_key = stream_key # Move on to the next timestamp value. current_timestamp += TEMPERATURE_READING_INTERVAL_SECONDS if __name__ == "__main__": main()

34.808824

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0.35303

0.049466

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0.019542

0.099542

0

0.016495

0.218842

4,734

135

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0

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1

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false

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0.051724

0

null

0

null

0

1

0

1df2df202426ece1c28a8eca0173d137a249a259

461

py

Python

api/urls.py

Somsubhra1/Django-rest-framework-todo

1f3d527987a835ea2f901f3bb778ecd3dd57c646

[ "MIT" ]

2

2021-05-16T18:51:02.000Z

2021-11-06T04:01:27.000Z

api/urls.py

Somsubhra1/Django-rest-framework-todo

1f3d527987a835ea2f901f3bb778ecd3dd57c646

[ "MIT" ]

null

api/urls.py

Somsubhra1/Django-rest-framework-todo

1f3d527987a835ea2f901f3bb778ecd3dd57c646

[ "MIT" ]

null

from django.urls import path from . import views urlpatterns = [ path("", views.apiOverView, name="api-overview"), path("task-list/", views.taskList, name="task-list"), path("task-detail/<int:pk>", views.taskDetail, name="task-detail"), path("task-create/", views.taskCreate, name="task-create"), path("task-update/<int:pk>", views.taskUpdate, name="task-update"), path("task-delete/<int:pk>", views.taskDelete, name="task-delete"), ]

35.461538

71

0.670282

61

461

5.065574

0.393443

0.12945

0.097087

0

0.121475

461

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false

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0

0.2

0

null

0

null

0

1

0

1df369bab97bebb47ebd600adec10c1f1a6a30eb

1,895

py

Python

link_start.py

povsister/yuanshen-trend

b03c21e720b1c196b5caf20f2f1b1b70ec3ad41a

[ "MIT" ]

4

2019-07-24T09:45:32.000Z

2020-10-11T07:18:40.000Z

link_start.py

kami-sama-dp/yuanshen-trend

b03c21e720b1c196b5caf20f2f1b1b70ec3ad41a

[ "MIT" ]

null

link_start.py

kami-sama-dp/yuanshen-trend

b03c21e720b1c196b5caf20f2f1b1b70ec3ad41a

[ "MIT" ]

1

2020-03-05T11:31:30.000Z

from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer import traceback from json import dumps from urllib.parse import urlparse, unquote from sourcelib.basiclib import get_query_as_dict from sourcelib.TapTap import SourceTapTap class YS_Factory: @staticmethod def factory(url, action): CLASS_DICT = { 'www.taptap.com': SourceTapTap } parsed_url = urlparse(url) selected_class = CLASS_DICT.get(parsed_url.netloc) return selected_class(parsed_url, action) class YS_HTTPHandler(BaseHTTPRequestHandler): __software_version = 'ys-trend/0.1' def __writeHeaders(self): self.send_header('Content-type', 'application/json') self.send_header('X-Powered-By', self.__software_version) self.end_headers() def __respond(self, code, js): self.send_response(code) self.send_header('Content-Length', str(len(js))) self.__writeHeaders() self.wfile.write(js) self.wfile.flush() def respond(self, js): self.__respond(200, js) def respondNotFond(self): js = dumps({ 'msg': 'not implemented' }).encode('utf8') self.__respond(404, js) def do_GET(self): # Incoming request like http://127.0.0.1:1551/?url=xxx&action=xxx # self.path looks like /?url=xxx&action=xxx print('[Query Path]:', unquote(self.path)) query = get_query_as_dict(self.path) try: if query.get('url') is not None: lib = YS_Factory.factory(query['url'], query) data = lib.getJSON() self.respond(data) else: self.respondNotFond() except Exception: traceback.print_exc() if __name__ == '__main__': server = ThreadingHTTPServer(('', 1571), YS_HTTPHandler) server.serve_forever()

28.712121

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0.629024

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1,895

5.171946

0.434389

0.027997

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0

0.016393

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1,895

65

74

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0

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0.122449

false

0

0.122449

0

0.326531

0.040816

0

null

0

null

0

1

0

1df400ab1fff54ddad11224dfcfcbbb9ca856386

6,729

py

Python

graphics/right_menu_options.py

VMS19/Inhalator

77ff3f063efa48e825d1c5ef648203b2d70b753e

[ "MIT" ]

9

2020-03-30T08:27:57.000Z

2020-04-11T12:37:28.000Z

graphics/right_menu_options.py

VMS19/Inhalator

77ff3f063efa48e825d1c5ef648203b2d70b753e

[ "MIT" ]

145

2020-03-25T20:41:24.000Z

2020-04-15T17:39:10.000Z

graphics/right_menu_options.py

VMS19/Inhalator

77ff3f063efa48e825d1c5ef648203b2d70b753e

[ "MIT" ]

4

2020-03-22T09:57:27.000Z

2020-04-15T18:10:48.000Z

import os import time from data.alerts import AlertCodes from graphics.alerts_history_screen import AlertsHistoryScreen from graphics.configure_alerts_screen import ConfigureAlarmsScreen from graphics.imagebutton import ImageButton from tkinter import * from graphics.themes import Theme THIS_DIRECTORY = os.path.dirname(__file__) RESOURCES_DIRECTORY = os.path.join(os.path.dirname(THIS_DIRECTORY), "resources") class BaseButton(object): def enable_button(self): self.button.configure( state="normal", ) def disable_button(self): self.button.configure( state="disabled", ) def update(self): pass class ClearAlertsButton(BaseButton): IMAGE_PATH = os.path.join(RESOURCES_DIRECTORY, "baseline_history_white_48dp.png") def __init__(self, parent, events): self.parent = parent self.root = parent.element self.events = events self.button = ImageButton( master=self.root, image_path=self.IMAGE_PATH, command=self.on_click, font=("Roboto", 9), text="Clear", pady=10, compound="top", state="normal", relief="flat", bg=Theme.active().RIGHT_SIDE_BUTTON_BG, fg=Theme.active().RIGHT_SIDE_BUTTON_FG, ) def on_click(self): self.events.alerts_queue.clear_alerts() def render(self): self.button.place(relx=0, rely=0.01, relwidth=1, relheight=0.2) class MuteAlertsButton(BaseButton): PATH_TO_MUTED = os.path.join(RESOURCES_DIRECTORY, "round_notifications_off_white_48dp.png") PATH_TO_UNMUTED = os.path.join(RESOURCES_DIRECTORY, "baseline_notifications_active_white_48dp.png") def __init__(self, parent, events): self.parent = parent self.root = parent.element self.events = events self.muted = False self.button = ImageButton( master=self.root, image_path=self.PATH_TO_UNMUTED, command=self.on_click, font=("Roboto", 9), relief="flat", text="Mute", pady=10, compound="top", bg=Theme.active().RIGHT_SIDE_BUTTON_BG, fg=Theme.active().RIGHT_SIDE_BUTTON_FG, activebackground=Theme.active().RIGHT_SIDE_BUTTON_BG_ACTIVE, activeforeground=Theme.active().RIGHT_SIDE_BUTTON_FG_ACTIVE, ) def on_click(self): self.events.mute_alerts.mute_alerts() self.update() def render(self): self.button.place(relx=0, rely=0.27, relwidth=1, relheight=0.2) def update(self): if self.events.mute_alerts._alerts_muted: self.button.set_image(self.PATH_TO_MUTED) self.button.configure(text="Unmute") else: self.button.set_image(self.PATH_TO_UNMUTED) self.button.configure(text="Mute") class LockThresholdsButton(BaseButton): UNLOCK_IMAGE_PATH = os.path.join(RESOURCES_DIRECTORY, "baseline_lock_open_white_48dp.png") LOCK_IMAGE_PATH = os.path.join(RESOURCES_DIRECTORY, "outline_lock_white_24dp.png") def __init__(self, parent): self.parent = parent self.root = parent.element self.button = ImageButton( master=self.root, image_path=self.UNLOCK_IMAGE_PATH, command=self.parent.lock_buttons, text="Lock", relief="flat", font=("Roboto", 9), pady=10, compound="top", bg=Theme.active().RIGHT_SIDE_BUTTON_BG, fg=Theme.active().RIGHT_SIDE_BUTTON_FG, activebackground=Theme.active().RIGHT_SIDE_BUTTON_BG_ACTIVE, activeforeground=Theme.active().RIGHT_SIDE_BUTTON_FG_ACTIVE, state="normal", ) def lock_button(self): self.button.configure( text="Lock" ) self.button.set_image( self.LOCK_IMAGE_PATH ) def unlock_button(self): self.button.configure( text = "Unlock" ) self.button.set_image(self.UNLOCK_IMAGE_PATH) def render(self): self.button.place(relx=0, rely=0.53, relwidth=1, relheight=0.2) class OpenConfigureAlertsScreenButton(BaseButton): IMAGE_PATH = os.path.join(RESOURCES_DIRECTORY, "baseline_settings_white_48dp.png") def __init__(self, parent, drivers, observer): self.parent = parent self.root = parent.element self.drivers = drivers self.observer = observer self.button = ImageButton( master=self.root, image_path=self.IMAGE_PATH, command=self.on_click, font=("Roboto", 9), relief="flat", text="Settings", pady=11, compound="top", bg=Theme.active().RIGHT_SIDE_BUTTON_BG, fg=Theme.active().RIGHT_SIDE_BUTTON_FG, activebackground=Theme.active().RIGHT_SIDE_BUTTON_BG_ACTIVE, activeforeground=Theme.active().RIGHT_SIDE_BUTTON_FG_ACTIVE, ) def on_click(self): master_frame = self.parent.parent.element screen = ConfigureAlarmsScreen(master_frame, drivers=self.drivers, observer=self.observer) screen.show() def render(self): self.button.place(relx=0, rely=0.79, relwidth=1, relheight=0.2) class OpenAlertsHistoryScreenButton(BaseButton): PATH_TO_HISTORY = os.path.join(RESOURCES_DIRECTORY, "baseline_history_white_24dp.png") def __init__(self, parent, events): self.parent = parent self.root = parent.element self.events = events self.button = ImageButton( master=self.root, image_path=self.PATH_TO_HISTORY, command=self.on_click, font=("Roboto", 9), relief="flat", bg=Theme.active().RIGHT_SIDE_BUTTON_BG, fg=Theme.active().RIGHT_SIDE_BUTTON_FG, activebackground=Theme.active().RIGHT_SIDE_BUTTON_BG_ACTIVE, activeforeground=Theme.active().RIGHT_SIDE_BUTTON_FG_ACTIVE, ) def on_click(self): master_frame = self.parent.parent.element screen = AlertsHistoryScreen(master_frame, events=self.events) screen.show() def render(self): self.button.place(relx=0, rely=0.79, relwidth=1, relheight=0.2)

31.009217

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6,729

5.208614

0.15074

0.05168

0.074419

0.093023

0.673902

0.650129

0.583204

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0.497158

0.448837

0

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6,729

216

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31.152778

0.806951

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false

0.005814

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0

0.238372

0

null

0

null

0

1

0

1df59c4741b3466781cd371a9af543304321f5c9

5,353

py

Python

2020/python/day-03-cleaned.py

tadhg-ohiggins/advent-of-code

d0f113955940e69cbe0953607f62862f8a8bb830

[ "CC0-1.0" ]

1

2021-12-04T18:09:44.000Z

2020/python/day-03-cleaned.py

tadhg-ohiggins/advent-of-code

d0f113955940e69cbe0953607f62862f8a8bb830

[ "CC0-1.0" ]

null

2020/python/day-03-cleaned.py

tadhg-ohiggins/advent-of-code

d0f113955940e69cbe0953607f62862f8a8bb830

[ "CC0-1.0" ]

null

from math import prod from typing import Any, List from tutils import ( lmap, splitstriplines, load_and_process_input, run_tests, ) DAY = "03" INPUT = f"input-{DAY}.txt" TEST = f"test-input-{DAY}.txt" TA1 = 7 TA2 = 336 ANSWER1 = 145 ANSWER2 = 3424528800 def process_one(data: List[str]) -> int: return count_trees(data, 3, 1) def process_two(data: List[str]) -> Any: ct = lambda x: count_trees(data, *x) vals = [ (1, 1), (3, 1), (5, 1), (7, 1), (1, 2, 2), ] return prod(lmap(ct, vals)) def count_trees( lines: List[str], rightstep: int, downstep: int, start: int = 1 ) -> int: lnlen = len(lines[0]) right, trees = 0, 0 for ln in lines[start::downstep]: right = right + rightstep if right > (lnlen - 1): right = abs(right - lnlen) if ln[right] == "#": trees = trees + 1 return trees def cli_main() -> None: input_funcs = [splitstriplines] data = load_and_process_input(INPUT, input_funcs) run_tests(TEST, TA1, TA2, ANSWER1, input_funcs, process_one, process_two) answer_one = process_one(data) assert answer_one == ANSWER1 print("Answer one:", answer_one) answer_two = process_two(data) assert answer_two == ANSWER2 print("Answer two:", answer_two) if __name__ == "__main__": cli_main() """ --- Day 3: Toboggan Trajectory --- With the toboggan login problems resolved, you set off toward the airport. While travel by toboggan might be easy, it's certainly not safe: there's very minimal steering and the area is covered in trees. You'll need to see which angles will take you near the fewest trees. Due to the local geology, trees in this area only grow on exact integer coordinates in a grid. You make a map (your puzzle input) of the open squares (.) and trees (#) you can see. For example: ..##....... #...#...#.. .#....#..#. ..#.#...#.# .#...##..#. ..#.##..... .#.#.#....# .#........# #.##...#... #...##....# .#..#...#.# These aren't the only trees, though; due to something you read about once involving arboreal genetics and biome stability, the same pattern repeats to the right many times: ..##.........##.........##.........##.........##.........##....... ---> #...#...#..#...#...#..#...#...#..#...#...#..#...#...#..#...#...#.. .#....#..#..#....#..#..#....#..#..#....#..#..#....#..#..#....#..#. ..#.#...#.#..#.#...#.#..#.#...#.#..#.#...#.#..#.#...#.#..#.#...#.# .#...##..#..#...##..#..#...##..#..#...##..#..#...##..#..#...##..#. ..#.##.......#.##.......#.##.......#.##.......#.##.......#.##..... ---> .#.#.#....#.#.#.#....#.#.#.#....#.#.#.#....#.#.#.#....#.#.#.#....# .#........#.#........#.#........#.#........#.#........#.#........# #.##...#...#.##...#...#.##...#...#.##...#...#.##...#...#.##...#... #...##....##...##....##...##....##...##....##...##....##...##....# .#..#...#.#.#..#...#.#.#..#...#.#.#..#...#.#.#..#...#.#.#..#...#.# ---> You start on the open square (.) in the top-left corner and need to reach the bottom (below the bottom-most row on your map). The toboggan can only follow a few specific slopes (you opted for a cheaper model that prefers rational numbers); start by counting all the trees you would encounter for the slope right 3, down 1: From your starting position at the top-left, check the position that is right 3 and down 1. Then, check the position that is right 3 and down 1 from there, and so on until you go past the bottom of the map. The locations you'd check in the above example are marked here with O where there was an open square and X where there was a tree: ..##.........##.........##.........##.........##.........##....... ---> #..O#...#..#...#...#..#...#...#..#...#...#..#...#...#..#...#...#.. .#....X..#..#....#..#..#....#..#..#....#..#..#....#..#..#....#..#. ..#.#...#O#..#.#...#.#..#.#...#.#..#.#...#.#..#.#...#.#..#.#...#.# .#...##..#..X...##..#..#...##..#..#...##..#..#...##..#..#...##..#. ..#.##.......#.X#.......#.##.......#.##.......#.##.......#.##..... ---> .#.#.#....#.#.#.#.O..#.#.#.#....#.#.#.#....#.#.#.#....#.#.#.#....# .#........#.#........X.#........#.#........#.#........#.#........# #.##...#...#.##...#...#.X#...#...#.##...#...#.##...#...#.##...#... #...##....##...##....##...#X....##...##....##...##....##...##....# .#..#...#.#.#..#...#.#.#..#...X.#.#..#...#.#.#..#...#.#.#..#...#.# ---> In this example, traversing the map using this slope would cause you to encounter 7 trees. Starting at the top-left corner of your map and following a slope of right 3 and down 1, how many trees would you encounter? Your puzzle answer was 145. --- Part Two --- Time to check the rest of the slopes - you need to minimize the probability of a sudden arboreal stop, after all. Determine the number of trees you would encounter if, for each of the following slopes, you start at the top-left corner and traverse the map all the way to the bottom: Right 1, down 1. Right 3, down 1. (This is the slope you already checked.) Right 5, down 1. Right 7, down 1. Right 1, down 2. In the above example, these slopes would find 2, 7, 3, 4, and 2 tree(s) respectively; multiplied together, these produce the answer 336. What do you get if you multiply together the number of trees encountered on each of the listed slopes? Your puzzle answer was 3424528800. """

33.248447

79

0.485896

627

5,353

4.090909

0.357257

0.015595

0.018713

0.05614

0.02807

0

0.019669

0.164207

5,353

160

80

33.45625

0.553643

0

0.049168

0

0.039216

1

0.078431

false

0

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0.019608

0.196078

0.039216

0

null

0

null

0

1

0

1df643918b3d787d3328fb2b2e1aa044930642a4

2,362

py

Python

Python3/Books/Douson/chapter10/movie_chooser.py

neon1ks/Study

5d40171cf3bf5e8d3a95539e91f5afec54d1daf3

[ "MIT" ]

null

Python3/Books/Douson/chapter10/movie_chooser.py

neon1ks/Study

5d40171cf3bf5e8d3a95539e91f5afec54d1daf3

[ "MIT" ]

null

Python3/Books/Douson/chapter10/movie_chooser.py

neon1ks/Study

5d40171cf3bf5e8d3a95539e91f5afec54d1daf3

[ "MIT" ]

null

# Movie Chooser # Demonstrates check buttons from tkinter import * class Application(Frame): """ GUI Application for favorite movie types. """ def __init__(self, master): super(Application, self).__init__(master) self.grid() self.create_widgets() def create_widgets(self): """ Create widgets for movie type choices. """ # create description label Label(self, text = "Choose your favorite movie types" ).grid(row = 0, column = 0, sticky = W) # create instruction label Label(self, text = "Select all that apply:" ).grid(row = 1, column = 0, sticky = W) # create Comedy check button self.likes_comedy = BooleanVar() Checkbutton(self, text = "Comedy", variable = self.likes_comedy, command = self.update_text ).grid(row = 2, column = 0, sticky = W) # create Drama check button self.likes_drama = BooleanVar() Checkbutton(self, text = "Drama", variable = self.likes_drama, command = self.update_text ).grid(row = 3, column = 0, sticky = W) # create Romance check button self.likes_romance = BooleanVar() Checkbutton(self, text = "Romance", variable = self.likes_romance, command = self.update_text ).grid(row = 4, column = 0, sticky = W) # create text field to display results self.results_txt = Text(self, width = 40, height = 5, wrap = WORD) self.results_txt.grid(row = 5, column = 0, columnspan = 3) def update_text(self): """ Update text widget and display user's favorite movie types. """ likes = "" if self.likes_comedy.get(): likes += "You like comedic movies.\n" if self.likes_drama.get(): likes += "You like dramatic movies.\n" if self.likes_romance.get(): likes += "You like romantic movies." self.results_txt.delete(0.0, END) self.results_txt.insert(0.0, likes) # main root = Tk() root.title("Movie Chooser") app = Application(root) root.mainloop()

31.918919

75

0.539797

257

2,362

4.856031

0.342412

0.064904

0.052083

0.05609

0.176282

0.067308

0

0.013219

0.359441

2,362

73

76

32.356164

0.811633

0.151566

0

0.170213

0

0.082448

0

1

0.06383

false

0

0.021277

0

0.106383

0

null

0

null

0

1

0

1dff4abd36bf5302e229243451841450c38b4467

6,102

py

Python

src/metrics/metric_computer.py

bnewm0609/refining-tse

db423f80246ba2d7c2fc602fc514943949cfaf71

[ "MIT" ]

null

src/metrics/metric_computer.py

bnewm0609/refining-tse

db423f80246ba2d7c2fc602fc514943949cfaf71

[ "MIT" ]

null

src/metrics/metric_computer.py

bnewm0609/refining-tse

db423f80246ba2d7c2fc602fc514943949cfaf71

[ "MIT" ]

null

"""Defines general structure of a metric computer, as well as some general utility functions. Attributes: example_aggregator_name_to_function: Maps name of an example aggregator to the corresponding function. """ import numpy as np from ..constants import INVALID_SCORE_VALUE, BATCH_SIZE def mean_example_aggregator(score_per_example, extra_info_per_example): """Aggregates example scores via mean. Returns invalid score if 0 scores are given.""" if len(score_per_example) == 0: return INVALID_SCORE_VALUE, {} else: return np.mean(score_per_example), {} example_aggregator_name_to_function = { "mean": mean_example_aggregator, } class MetricComputer(object): """Abstract class that computes a metric for each example, records some information, and later produces a summary. Functions to override: get_example_aggregator_from_name _compute Attributes: extra_info_per_example (list): List of per-example info needed for later aggregation, e.g. example weight. example_aggregator: Function that aggregates information. Args: score_per_example (List[float]): List of scores for each example. extra_info_per_example (list): List of extra information for each example. Returns: Tuple([float, dict]): Overall score and any supplementary information. """ def __init__(self, config): """Initializes metric computer. Args: config: Metric-level config dict. """ self._score_per_example = [] self.extra_info_per_example = [] # for more complicated aggregation functions, e.g. weights per example self.example_aggregator = self.get_example_aggregator_from_name(config["example_aggregator"]) self._summary_functions = self.initialize_summary_functions() self.use_custom_dataset = config.get("use_custom_dataset", False) @property def summary_functions(self): """list: List of functions to run during summarization. Returns: list: Update output `summary_dict` with more information to give to the `Logger`. Children should include `self.summary_functions.extend([func1, func2])` in their `__init__`. """ return self._summary_functions @property def score_per_example(self): """List[float]: List of scores for each example.""" return self._score_per_example def initialize_summary_functions(self): """Returns list of functions to run during summarization. Returns: list: List of functions to run. Returns: dict: Information to give to `Logger`. """ return [] def get_example_aggregator_from_name(self, aggregator_name): """Returns the example aggregator function given the aggregator name. Override me. Args: aggregator_name (str): Name of example aggregator. Returns: Function that aggregates information, as described in `MetricComputer` docstring. """ return example_aggregator_name_to_function[aggregator_name] def _compute(self, logits, label, template_id, word_to_index): """Computes score for a single example. Override me. Same Args as `compute`. Returns: Tuple[float, Any, dict]: The example score, any extra info for later example aggregation (e.g., weight), and a dict to update this example's `metrics_dict` with. """ raise NotImplementedError def compute(self, logits, label, template_id, word_to_index): """Computes metrics for a single example, and keeps some internal notes. Records whether the score was invalid. If not, tracks them. Args: logits (torch.Tensor): Predicted logits with shape (1, vocab_size) or just (vocab_size). label (..constants.Number): Correct singular/plural label of this example. template_id: Template ID of this example. word_to_index: Dict-like indexer object mapping a word to an index. Returns: dict: Information for the `Logger` to record for this example. Includes "score" key. """ score, extra_info, metrics_dict = self._compute(logits, label, template_id, word_to_index) if score == INVALID_SCORE_VALUE: metrics_dict["score"] = "INVALID_SCORE_VALUE" else: metrics_dict["score"] = score if isinstance(score, list): # used ony by ML metric valid_idxs, valid_scores, valid_extra_infos = zip(*[(i, s, ei) for i, (s, ei) in enumerate(zip(score, extra_info)) if s != INVALID_SCORE_VALUE]) metrics_dict["valid_idxs"] = valid_idxs self.score_per_example.extend(valid_scores) self.extra_info_per_example.extend(valid_extra_infos) else: # used by main metric, but main metric tracks its own state so # `score` is not meaningful self.score_per_example.append(score) self.extra_info_per_example.append(score) return metrics_dict def summarize(self): """Summarizes the model's score on the dataset by producing information for the `Logger`. Returns: dict: Summary information for the `Logger`, including an "Overall model score" key. """ overall_score, summary_dict = self.example_aggregator(self.score_per_example, self.extra_info_per_example) if overall_score == INVALID_SCORE_VALUE: summary_dict["Overall model score"] = "INVALID_SCORE_VALUE" else: summary_dict["Overall model score"] = overall_score summary_dict["Number of examples"] = len(self.score_per_example) # Iterate through other summary functions. for summary_function in self.summary_functions: summary_dict.update(summary_function()) return summary_dict

40.144737

160

0.660603

738

6,102

5.239837

0.246612

0.05172

0.042669

0.034394

0.228601

0.123093

0.116886

0.093613

0.075511

0.027929

0

0.001114

0.26434

6,102

151

161

40.410596

0.860325

0.476893

0

0.111111

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1

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false

0

0.037037

0

0.37037

0

null

0

null

0

1

0

1dff7b5e8e48ffd1098d3accc04a72edc1fd41bc

3,776

py

Python

pm4pymdl/algo/mvp/utils/build_graph.py

dorian1000/pm4py-mdl

71e0c2425abb183da293a58d31e25e50137c774f

[ "MIT" ]

5

2021-01-31T22:45:29.000Z

2022-02-22T14:26:06.000Z

pm4pymdl/algo/mvp/utils/build_graph.py

Javert899/pm4py-mdl

4cc875999100f3f1ad60b925a20e40cf52337757

[ "MIT" ]

3

2021-07-07T15:32:55.000Z

2021-07-07T16:15:36.000Z

pm4pymdl/algo/mvp/utils/build_graph.py

dorian1000/pm4py-mdl

71e0c2425abb183da293a58d31e25e50137c774f

[ "MIT" ]

9

2020-09-23T15:34:11.000Z

2022-03-17T09:15:40.000Z

import networkx from pm4py.objects.log.log import EventLog, Trace, Event from datetime import datetime from pm4py.objects.log.util import sorting from collections import Counter def apply(df, source_attr, target_attr, type_attr, timestamp_key, reverse=False): df[source_attr] = df[source_attr].astype(str) df[target_attr] = df[target_attr].astype(str) df[type_attr] = df[type_attr].astype(str) first_df = df.groupby(source_attr).first().reset_index() timestamps = first_df[[timestamp_key, source_attr]].to_dict('r') timestamps = {x[source_attr]: x[timestamp_key] for x in timestamps} map_types = df[[source_attr, type_attr]].to_dict('r') map_types = {x[source_attr]: x[type_attr] for x in map_types if x[type_attr] != 'None'} map_source_target = dict(df.groupby([source_attr, target_attr]).size()) map_source_target = list(map_source_target.keys()) map_source_target = [(map_types[x] + "=" + x, map_types[y] + "=" + y) for (x, y) in map_source_target if x in map_types and y in map_types] map_types = {y + "=" + x: y for x, y in map_types.items()} G = networkx.DiGraph() for k in map_types: G.add_node(k) for el in map_source_target: if reverse: G.add_edge(el[1], el[0]) else: G.add_edge(el[0], el[1]) conn_comp = sorted(list(networkx.connected_components(networkx.Graph(G))), key=lambda x: len(x), reverse=True) return G, conn_comp, timestamps def describe_graph(G, comp): SG = G.subgraph(comp) nodes = list(SG.nodes) edges = list(SG.edges) nodes = [(x, y) for x, y in Counter(n.split("=")[0] for n in nodes).items()] for i in range(len(nodes)): if nodes[i][1] > 1: nodes[i] = (nodes[i][0], "N") else: nodes[i] = (nodes[i][0], "1") source0 = {n[0]: [e for e in edges if e[0].split("=")[0] == n[0]] for n in nodes} target0 = {n[0]: [e for e in edges if e[1].split("=")[0] == n[0]] for n in nodes} source1 = {x: {} for x in source0} target1 = {x: {} for x in target0} for x in source0: ik = set(y[0] for y in source0[x]) for k in ik: source1[x][k.split("=")[1]] = Counter(y[1].split("=")[0] for y in source0[x] if y[0] == k) for x in target0: ik = set(y[1] for y in target0[x]) for k in ik: target1[x][k.split("=")[1]] = Counter(y[0].split("=")[0] for y in source0[x] if y[1] == k) print(source1) print(target1) # print(edges) def create_log(G, conn_comp, timestamps, max_comp_len=50, include_loops=False): log = EventLog() for i in range(len(conn_comp)): if len(conn_comp[i]) <= max_comp_len: trace = Trace() trace.attributes["concept:name"] = str(i) SG = G.subgraph(conn_comp[i]) SGG = networkx.DiGraph(SG) edges = list(SGG.edges) for e in edges: if e[0] == e[1]: SGG.remove_edge(e[0], e[1]) sorted_nodes = list(networkx.topological_sort(SGG)) for n in sorted_nodes: selfloop = 1 if (n, n) in SG.edges else 0 trace.append(Event({'time:timestamp': timestamps[n.split("=")[1]], 'concept:name': n.split("=")[0], 'value': n.split("=")[1], 'typevalue': n, 'selfloop': selfloop})) if include_loops and selfloop: trace.append(Event({'time:timestamp': timestamps[n.split("=")[1]], 'concept:name': n.split("=")[0], 'value': n.split("=")[1], 'typevalue': n, 'selfloop': selfloop})) log.append(trace) log = sorting.sort_timestamp_log(log, "time:timestamp") return log

41.494505

119

0.574947

575

3,776

3.648696

0.177391

0.038132

0.017159

0.00858

0.254051

0.198761

0.161106

0.15348

0.135367

0.097235

0

0.021583

0.263771

3,776

90

120

41.955556

0.733094

0.003178

0

0.105263

0

0.038809

0

1

0.039474

false

0

0.065789

0

0.131579

0.026316

0

null

0

null

0

1

0

380174a9e1c618e5d205ff702047439c04fbce30

50,176

py

Python

main.py

hiromin0627/discord-mlg

7d178d20983eba94dd1a1a209f5a1aa17987af0f

[ "MIT" ]

1

2020-03-05T03:56:15.000Z

main.py

hiromin0627/discord-mlg

7d178d20983eba94dd1a1a209f5a1aa17987af0f

[ "MIT" ]

4

2020-03-18T10:55:33.000Z

2020-03-18T12:25:13.000Z

main.py

hiromin0627/discord-mlg

7d178d20983eba94dd1a1a209f5a1aa17987af0f

[ "MIT" ]

3

2020-03-06T13:07:50.000Z

2020-03-08T03:51:58.000Z

#coding: utf-8 #created by @hiromin0627 #MilliShita Gacha v5 mlgbotver = '5.1.2' import glob import gettext import os import discord import asyncio import re,random import datetime from threading import (Event, Thread) from urllib import request import configparser import json ini = configparser.ConfigParser() ini.read('./config.ini', 'UTF-8') lang = ini['Language']['lang'] path_to_locale_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),'./locale')) if lang == 'cn': translang = 'zh_TW' elif lang == 'kr': translang = 'ko_KR' else: translang = 'ja_JP' translater = gettext.translation('messages',localedir=path_to_locale_dir,languages=[translang],fallback=True,codeset="utf8") translater.install() token = ini['tokens']['token'] bgm_id = int(ini['ids']['bgm-room']) log_id = int(ini['ids']['log-room']) version = ini['Data']['Version'] prefix = ini['Prefix']['commandprefix'] timeout = float(ini['Reaction']['timeout']) aftermsgdel = ini['Reaction']['aftermsgdel'] client = discord.Client() mlg_all = [[],[],[]] mlg_data = [[],[],[]] pickup_id = [[],[],[]] gacha_mode = ['','',''] current_ver = ['','',''] pickup_name = ['','',''] pickup_img = ['','',''] rarity_str = ['R','SR','SSR','FES'] langnamelist = ['ja','kr','cn'] timer = 0 @client.event async def on_ready(): print(strtimestamp() + '---Millishita Gacha ' + mlgbotver + '---') print(strtimestamp() + 'discord.py ver:' + discord.__version__) print(strtimestamp() + 'Logged in as ' + client.user.name + '(ID:' + str(client.user.id) + ')') print(strtimestamp() + 'Bot created by @hiromin0627') await gacha_reload(0,None,version) @client.event async def on_message(message): if message.author.bot: return if message.content.startswith("MLhelp"): if not aftermsgdel == 'false': await message.delete() print(strtimestamp() + 'Start MLhelp') await message.channel.send('ミリシタガシャシミュレーターDiscordボット ' + mlgbotver + '\n' +\ 'MLhelp：ヘルプコマンドです。ミリシタガシャの説明を見ることができます。\n' +\ prefix + 'update：ミリシタガシャデータベースをダウンロードして更新します。\n' +\ prefix + 'reset：全ユーザーのMLガシャを引いた回数をリセットします。\n' +\ prefix + 'cards：MLガシャで引いたカード名を確認することができます。\n' +\ prefix + 'pickup：現在のガシャ名とピックアップカードを確認できます。\n' +\ prefix + 'call：MLガシャで引いたカード画像を検索できます。スペースを挟んでカード名を入力してください。（制服シリーズはアイドル名も記入）\n' +\ prefix + 'ガシャ or ' + prefix + '轉蛋 or ' + prefix + '촬영 or ' + prefix + 'gacha：ミリシタガシャシミュレーターができます。' +\ '10を後ろに付け加えると、10連ガシャになります。jp（日本語版）、cn（中国語繁体字版）、kr（韓国語版）を後ろに付け加えると、その言語のガシャが引くことができます。') if message.content.startswith(prefix): global version if not aftermsgdel == 'false': if "change" in message.content or "update" in message.content or "uselatest" in message.content or "retention" in message.content or "cards" in message.content or "reset" in message.content or "pickup" in message.content or "call" in message.content or "ガシャ" in message.content or "gacha" in message.content or "轉蛋" in message.content or "촬영" in message.content: await message.delete() langint = 0 if not message.content == '': langint = langstrtoint(message.content[6:]) else: langint = langtoint() if message.content.startswith(prefix + "change"): try: mlgver = message.content.split()[1] except IndexError: await message.channel.send('コマンドが間違っています。バージョン名はスペースを空けて入力してください。（例：MLchange 20200101）') return if await gacha_check_available(mlgver): version = mlgver else: await message.channel.send('該当バージョンが見つかりませんでした。バージョン名を確認してください。（検索バージョン名：' + mlgver + '）') return current = await current_version_loader() if ini['Data']['Version'] == 'Latest': msgupdate = await message.channel.send('**現在のガシャデータベース**\n日本語版：' + current["version"][0] + '　アジア版：' + current["version"][1] + '\n**見つかったガシャデータベース**\n' + mlgver + '\n**ローカルバージョンを維持する設定に変更**し、バージョンを入れ替えますか？') await msgupdate.add_reaction('⭕') await msgupdate.add_reaction('❌') else: msgupdate = await message.channel.send('**現在のガシャデータベース**\n日本語版：' + current["version"][0] + '　アジア版：' + current["version"][1] + '\n**見つかったガシャデータベース**\n' + mlgver + '\nバージョンを入れ替えますか？') await msgupdate.add_reaction('⭕') await msgupdate.add_reaction('❌') while True: try: target_reaction, user = await client.wait_for('reaction_add', timeout=timeout) if target_reaction.emoji == '⭕' and user != msgupdate.author: await msgupdate.edit(content='入れ替えを開始します。') await msgupdate.clear_reactions() ini.set("Data","Version","Retention") ini.write(open('./config.ini', 'w'), 'UTF-8') await gacha_reload(1, message, mlgver) await msgupdate.edit(content='入れ替えが完了しました。') return if target_reaction.emoji == '❌' and user != msgupdate.author: await msgupdate.edit(content='入れ替えを中止します。') return except: await msgupdate.edit(content='コマンドに失敗しました。もう一度やり直してください。') return elif message.content.startswith(prefix + "uselatest"): ini.set("Data","Version","Latest") ini.write(open('./config.ini', 'w'), 'UTF-8') await message.channel.send('起動時に最新版をロードするように設定されました。') elif message.content.startswith(prefix + "retention"): ini.set("Data","Version","Retention") ini.write(open('./config.ini', 'w'), 'UTF-8') await message.channel.send('起動時に保存されているバージョンでロードするように設定されました。') elif message.content.startswith(prefix + "update"): latest = gacha_check_update() current = await current_version_loader() if latest["version"] == current["version"]: msgl = await message.channel.send('現在のガシャデータベースは最新のものが使われています。') return else: msgl = await message.channel.send('**最新のガシャデータベース**\n日本語版：' + latest["version"][0] + '　アジア版：' + latest["version"][1] + '\n**現在のガシャデータベース**\n日本語版：' + current["version"][0] + '　アジア版：' + current["version"][1] + '\nアップデートしますか？') await msgl.add_reaction('⭕') await msgl.add_reaction('❌') while True: try: target_reaction, user = await client.wait_for('reaction_add', timeout=timeout) if target_reaction.emoji == '⭕' and user != msgl.author: await msgl.edit(content='アップデートを開始します。') await msgl.clear_reactions() version = latest["version"] await gacha_reload(1, message) await msgl.edit(content='アップデートが完了しました。') return if target_reaction.emoji == '❌' and user != msgl.author: await msgl.edit(content='アップデートを中止します。') return except: await msgl.edit(content='コマンドに失敗しました。もう一度やり直してください。') return elif message.content.startswith(prefix + 'cards'): print(strtimestamp() + 'Start MLGacha[cards].') await gacha_note(message,langint) elif message.content.startswith(prefix + 'reset'): print(strtimestamp() + 'Start MLGacha[reset].') file_list = glob.glob("./gacha_count/*.txt") for file in file_list: os.remove(file) await message.channel.send(_('すべてのユーザーのガチャカウントをリセットしました。')) elif message.content.startswith(prefix + 'pickup'): print(strtimestamp() + 'Start MLGacha[pickup].') name = pickupcheck(langint) emb = discord.Embed(title=_('ピックアップカード一覧'), description=name) emb.set_image(url=pickup_img[langint]) emb.set_author(name=pickup_name[langint]) await message.channel.send('', embed=emb) elif message.content.startswith(prefix + 'call'): print(strtimestamp() + 'Start MLGacha[call].') await gacha_call(message,langint) elif message.content.startswith(prefix + "ガシャ") or message.content.startswith(prefix + "gacha") or message.content.startswith(prefix + "轉蛋") or message.content.startswith(prefix + "촬영"): if voicecheck(): await message.channel.send(_('他のユーザーがプレイ中です。終了までお待ちください。')) return elif gacha_mode[langint] == "skip": await message.channel.send('ガシャデータがありません。現在使われているバージョンにてこの言語のガシャ情報がありません。') return gacha_count = int() try: with open('./gacha_count/' + current_ver[langint] + '_' + str(message.author.id) + '.txt', 'r') as f: gacha_count = int(f.read()) except: with open('./gacha_count/' + current_ver[langint] + '_' + str(message.author.id) + '.txt', 'w') as f: f.write('0') if gacha_count >= 300 and (gacha_mode[langint] == "normal" or gacha_mode[langint] == "fes"): await gacha_prepare_select(message,langint) else: await gacha_prepare(message,langint,gacha_count) async def gacha_prepare_select(message,langint): try: vc_id = message.author.voice.channel.id channel = client.get_channel(vc_id) except: vc_id = None kind = '' result = [] count_emoji = ['1⃣','2⃣','3⃣','4⃣','5⃣','6⃣','7⃣','8⃣','9⃣','🔟'] pickup_counter = 0 pickup_alllist = list() name = pickupcheck(langint) for row in mlg_data[langint]: if row["id"] in pickup_id[langint]: pickup_alllist.append(row) pickup_counter += 1 mlgpickupemb = discord.Embed(title=_('交換カード一覧'), description=name) mlgpickupemb.set_author(name=message.author.name, icon_url=message.author.avatar_url) mlgpickupemb.set_footer(text=pickup_name[langint]) msgs = await message.channel.send(_('ドリームスターがカード交換数に達しているため、ガシャをご利用いただけません。カードを交換してください。\n該当番号のリアクションを返すと交換できます。'), embed=mlgpickupemb) for r in range(pickup_counter): await msgs.add_reaction(count_emoji[r]) kind = _('ドリームスター交換') + '「' + pickup_name[langint] + '」' pickup_num = int() numemoji_to_int = {'1⃣':0, '2⃣':1, '3⃣':2, '4⃣':3, '5⃣':4, '6⃣':5, '7⃣':6, '8⃣':7, '9⃣':8, '🔟':9} while True: target_reaction, user = await client.wait_for('reaction_add') if not user == msgs.author: if target_reaction.emoji in numemoji_to_int: pickup_num = numemoji_to_int[target_reaction.emoji] break result = [pickup_alllist[pickup_num]] await msgs.delete() print(strtimestamp() + 'Start MLChange[' + kind + '] by ' + str(message.author.id) + '.') try: with open('./gacha_count/' + current_ver[langint] + '_' + str(message.author.id) + '.txt', 'w') as f: f.write(str(0)) except: print(strtimestamp() + '[ERROR]Gacha count FAILED.') char_list = list() try: with open('./gacha/' + langnamelist[langint] + str(message.author.id) + '.txt', 'r') as f: listline = f.read() char_list = list(listline) except: pass with open('./gacha/' + langnamelist[langint] + str(message.author.id) + '.txt', 'w+') as f: try: char_list[result[0]["id"]] = '1' except: for n in range(500): char_list.append('0') char_list[result[0]["id"]] = '1' newlistline = ''.join(char_list) f.write(newlistline) if vc_id == None: vc = None botmsg = None else: if not bgm_id == 0: toBot = client.get_channel(bgm_id) botmsg = await toBot.send('ML' + str(vc_id)) vc = await channel.connect() await mlg_touch(message,result,kind,vc,botmsg,langint) return async def gacha_prepare(message,langint,gacha_count): try: vc_id = message.author.voice.channel.id channel = client.get_channel(vc_id) except: vc_id = None role = 0 if gacha_mode[langint] == "normal" or gacha_mode[langint] == "fes" or gacha_mode[langint] == "type": if '10' in message.content or '１０' in message.content: role = 10 else: role = 1 elif gacha_mode[langint] == "party": role = 10 else: role = 1 if gacha_mode[langint] == "normal" or gacha_mode[langint] == "fes": try: gacha_count += role with open('./gacha_count/' + current_ver[langint] + '_' + str(message.author.id) + '.txt', 'w') as f: f.write(str(gacha_count)) except: print(strtimestamp() + '[ERROR]Failed to count.') result = await gacha_emission(langint,role) print(strtimestamp() + 'Start MLGacha[' + pickup_name[langint] + '] by ' + message.author.name + '.') char_list = list() try: with open('./gacha/' + langnamelist[langint] + str(message.author.id) + '.txt', 'r') as f: listline = f.read() char_list = list(listline) except: pass for box in result: with open('./gacha/' + langnamelist[langint] + str(message.author.id) + '.txt', 'w+') as f: try: char_list[box["id"]] = '1' except: for n in range(500): char_list.append('0') char_list[box["id"]] = '1' newlistline = ''.join(char_list) f.write(newlistline) mess = random.randint(1,10) fes_flag = 0 ssr_flag = 0 sr_flag = 0 for val in result: if val["rarity"] == 3: fes_flag = 1 elif val["rarity"] == 2: ssr_flag = 1 elif val["rarity"] == 1: sr_flag = 1 phrase = [_('最高の一枚ができましたのでぜひご確認ください！'),_('みんなのいい表情が撮れました！'),_('楽しそうなところが撮れましたよ')] cameratxt = '' if mess >= 2 : if ssr_flag == 1 or fes_flag == 1: cameratxt = phrase[0] elif sr_flag == 1 or ssr_flag == 1 or fes_flag == 1: cameratxt = phrase[1] elif sr_flag == 1 or ssr_flag == 1 or fes_flag == 1: cameratxt = phrase[2] if vc_id == None: vc = None botmsg = None if not cameratxt == '': camera = await message.channel.send(cameratxt) await asyncio.sleep(3) await camera.delete() else: vc = await channel.connect() if not cameratxt == '': vc.play(discord.FFmpegPCMAudio('./resources/message.mp3')) camera = await message.channel.send(cameratxt) while vc.is_playing(): await asyncio.sleep(1) await camera.delete() if not bgm_id == 0: toBot = client.get_channel(bgm_id) botmsg = await toBot.send('ML' + str(vc_id)) await mlg_touch(message,result,pickup_name[langint],vc,botmsg,langint) async def gacha_emission(langint,role): #慣れてないのでメモ #gachaMode = [normal,fes,party,final,special,type,skip] result = [] ssr_rate = 9700 pick_rate = 99 if gacha_mode[langint] == "fes": ssr_rate = 9400 pick_rate = 198 if gacha_mode[langint] == "normal" or gacha_mode[langint] == "fes" or gacha_mode[langint] == "final": if gacha_mode[langint] == "final": role = 10 rpick = list() rcard = list() srpick = list() srcard = list() ssrpick = list() ssrcard = list() for row in mlg_data[langint]: if row["rarity"] == 0 and row["id"] in pickup_id[langint]: rpick.append(row) elif row["rarity"] == 0 and not row["id"] in pickup_id[langint]: rcard.append(row) elif row["rarity"] == 1 and row["id"] in pickup_id[langint]: srpick.append(row) elif row["rarity"] == 1 and not row["id"] in pickup_id[langint]: srcard.append(row) elif row["rarity"] >= 2 and row["id"] in pickup_id[langint]: ssrpick.append(row) elif row["rarity"] >= 2 and not row["id"] in pickup_id[langint]: ssrcard.append(row) if len(rpick) == 0: rpick = rcard if len(srpick) == 0: srpick = srcard for n in range(role): rand = random.randint(0, 9999) if n < 9: if rand >= 0 and rand < 850: if len(rpick) > 1: result.append(rpick[random.randrange(len(rpick) - 1)]) else: result.append(rpick[0]) elif rand >= 850 and rand < 8500: result.append(rcard[random.randrange(len(rcard) - 1)]) elif rand >= 8500 and rand <= 8740: if len(srpick) > 1: result.append(srpick[random.randrange(len(srpick) - 1)]) else: result.append(srpick[0]) elif rand >= 8740 and rand < ssr_rate: result.append(srcard[random.randrange(len(srcard) - 1)]) elif rand >= ssr_rate and rand <= ssr_rate + pick_rate: if len(ssrpick) > 1: result.append(ssrpick[random.randrange(len(ssrpick) - 1)]) else: result.append(ssrpick[0]) elif rand >= ssr_rate + pick_rate: result.append(ssrcard[random.randrange(len(ssrcard) - 1)]) elif n == 9: if gacha_mode[langint] == "normal" or gacha_mode[langint] == "fes": if rand >= 0 and rand <= 240: if len(srpick) > 1: result.append(srpick[random.randrange(len(srpick) - 1)]) else: result.append(srpick[0]) elif rand >= 240 and rand <= ssr_rate: result.append(srcard[random.randrange(len(srcard) - 1)]) elif rand >= ssr_rate and rand <= ssr_rate + pick_rate: if len(ssrpick) > 1: result.append(ssrpick[random.randrange(len(ssrpick) - 1)]) else: result.append(ssrpick[0]) elif rand >= ssr_rate + pick_rate: result.append(ssrcard[random.randrange(len(ssrcard) - 1)]) elif gacha_mode[langint] == "final": result.append(ssrpick[random.randrange(len(ssrpick) - 1)]) elif gacha_mode[langint] == "party": rcard = list() srcard = list() ssrcard = list() limcard = list() for row in mlg_data[langint]: if row["rarity"] == 0: rcard.append(row) elif row["rarity"] == 1: srcard.append(row) elif row["rarity"] >= 2 and not row["limited"]: ssrcard.append(row) elif row["rarity"] >= 2 and row["limited"]: ssrcard.append(row) limcard.append(row) for n in range(10): if n < 9: rand = random.randint(0, 9999) if rand >= 0 and rand < 8500: if len(rcard) > 1: result.append(rcard[random.randrange(len(rcard) - 1)]) else: result.append(rcard[0]) elif rand >= 8500 and rand <= 9700: if len(srcard) > 1: result.append(srcard[random.randrange(len(srcard) - 1)]) else: result.append(srcard[0]) elif rand >= 9700 and rand <= 9999: if len(ssrcard) > 1: result.append(ssrcard[random.randrange(len(ssrcard) - 1)]) else: result.append(ssrcard[0]) elif n == 9: result.append(limcard[random.randrange(len(limcard) - 1)]) elif gacha_mode[langint] == "special": rcard = list() srcard = list() ssrcard = list() limcard = list() for row in mlg_data[langint]: if row["rarity"] == 0: rcard.append(row) elif row["rarity"] == 1: srcard.append(row) elif row["rarity"] >= 2: ssrcard.append(row) if len(rcard) == 0: rcard = srcard if len(srcard) == 0: srcard = ssrcard rand = random.randint(0, 9999) if rand >= 0 and rand < 8500: if len(rcard) > 1: result.append(rcard[random.randrange(len(rcard) - 1)]) else: result.append(rcard[0]) elif rand >= 8500 and rand <= 9700: if len(srcard) > 1: result.append(srcard[random.randrange(len(srcard) - 1)]) else: result.append(srcard[0]) elif rand >= 9700 and rand <= 9999: if len(ssrcard) > 1: result.append(ssrcard[random.randrange(len(ssrcard) - 1)]) else: result.append(ssrcard[0]) return result async def gacha_call(message,langint): char_list = list() carddata = {} try: with open('./gacha/' + langnamelist[langint] + str(message.author.id) + '.txt', 'r') as f: listline = f.read() char_list = list(listline) except: print(strtimestamp() + '[ERROR]Failed to load gacha result file.') return if '制服シリーズ' in message.content[6:]: for data in mlg_all[langint]: if message.content[6:] in data["idol"] and data["name"] == '制服シリーズ': carddata = data break elif 'シアターデイズ' in message.content[6:] or '劇場時光' in message.content[6:] or '시어터 데이즈' in message.content[6:]: for data in mlg_all[langint]: if data["idol"] in message.content[6:] and (data["name"] == 'シアターデイズ' or data["name"] == '劇場時光' or data["name"] == '시어터 데이즈'): carddata = data break elif 'MILLION LIVE CLOSET!' in message.content[6:]: for data in mlg_all[langint]: if message.content[6:] in data["idol"] and data["name"] == 'MILLION LIVE CLOSET!': carddata = data break elif 'フォーチュンガール' in message.content[6:]: for data in mlg_all[langint]: if message.content[6:] in data["idol"] and data["name"] == 'フォーチュンガール': carddata = data break else: for data in mlg_all[langint]: if data["name"] in message.content[6:] and char_list[data["id"]] == '1': carddata = data break if len(carddata) == 0: msgn = await message.channel.send(_('カードが見つかりませんでした。\n「MLcheck」で自分が所持しているカード名を確認してください。')) await asyncio.sleep(10) await msgn.delete() return cardname = '[' + rarity_str[int(carddata["rarity"])] + ']' + carddata["name"] + ' ' + carddata["idol"] embmsg1 = discord.Embed(title=cardname, description='(CV.' + carddata["cv"] + ')', colour=int(carddata["color"], 0)) embmsg1.set_author(name=message.author.name + _('所持カード'), icon_url=message.author.avatar_url) embmsg1.set_image(url=carddata["image"]) msg = await message.channel.send('', embed=embmsg1) await msg.add_reaction('👆') while True: target_reaction, user = await client.wait_for('reaction_add') if target_reaction.emoji == '👆' and user != msg.author: if carddata["rarity"] >= 2: await msg.remove_reaction(target_reaction.emoji, user) cardname = '[' + rarity_str[int(carddata["rarity"])] + '+]' + carddata["name"] + ' ' + carddata["idol"] embmsg1 = discord.Embed(title=cardname, description='(CV.' + carddata["cv"] + ')', colour=int(carddata["color"], 0)) embmsg1.set_author(name=message.author.name + _('所持カード'), icon_url=message.author.avatar_url) embmsg1.set_image(url=carddata["imageAwake"]) await msg.edit(embed=embmsg1) while True: target_reaction, user = await client.wait_for('reaction_add') if target_reaction.emoji == '👆' and user != msg.author: await msg.delete() return else: target_reaction, user = await client.wait_for('reaction_add') if target_reaction.emoji == '👆' and user != msg.author: await msg.delete() return return def gacha_check_update(): url = "https://data.hiromin.xyz/latest" readObj = request.urlopen(url) response = readObj.read() data = json.loads(response) return data async def gacha_check_available(mlgver): try: response = request.urlopen('https://data.hiromin.xyz/gachadata/'+mlgver) if response.getcode() == 200: return True else: return False except: return False async def current_version_loader(): current = dict() try: with open('./gacha_data/version.json', 'r') as f: current = json.load(f) if "version" not in current: with open('./gacha_data/version.json', 'w') as f: pre = {"version": ["Nodata","Nodata"]} json.dump(pre, f) current = pre except: with open('./gacha_data/version.json', 'w') as f: pre = {"version": ["Nodata","Nodata"]} json.dump(pre, f) current = pre return current async def gacha_reload(flag,message,version="Latest"): global mlg_all, mlg_data, pickup_id, gacha_mode, current_ver print(strtimestamp() + '----------[MLG ' + mlgbotver + ' MLreload]----------') if flag == 1: msg = await message.channel.send('MLreload Start.') mlg_all = [[],[],[]] mlg_data = [[],[],[]] pickup_id = [[],[],[]] gacha_mode = ['','',''] name = ['','',''] print(strtimestamp() + 'MLG temporary data cleaned.') if flag == 1: await msg.edit(content='MLG temporary data cleaned.') url = "https://data.hiromin.xyz/" current = await current_version_loader() if version == "Retention": mlgver = current["version"] elif version == "Latest": readObj_latest = request.urlopen(url+"latest") response = readObj_latest.read() data = json.loads(response) mlgver = data["version"] with open('./gacha_data/version.json', 'w') as f: json.dump(data, f) else: try: if "ja" in version: mlgver = [version,current["version"][1]] with open('./gacha_data/version.json', 'w') as f: json.dump({"version":[version,current["version"][1]]}, f) else: mlgver = [current["version"][0],version] with open('./gacha_data/version.json', 'w') as f: json.dump({"version":[current[0],version]}, f) except: import traceback traceback.print_exc() current_ver = [mlgver[0],mlgver[1],mlgver[1]] print(strtimestamp() + 'Using version JP:"' + mlgver[0] + '", ASIA:"' + mlgver[1] + '". Start to load card datas.') info = list() for row in current_ver: readObj_gachadata = request.urlopen(url+"gachadata/"+row) response_gachadata = readObj_gachadata.read() info.append(json.loads(response_gachadata)) for langint,langname in enumerate(langnamelist): pickup_id[langint] = info[langint]["pickupIDs"] readObj_cards = request.urlopen(url+"cards") response_cards = readObj_cards.read() reader = json.loads(response_cards) for langint,langname in enumerate(langnamelist): count = [0,0,0,0] gacha_mode[langint] = info[langint]["gachaMode"] print('[Step ' + str(langint + 1) + '/3 (Lang:' + langname + ', Gacha mode is "' + gacha_mode[langint] + '")]') if flag == 1: await msg.edit(content='MLG Database Loading... \nStep ' + str(langint + 1) + '/3 (Lang:' + langname + ', Gacha mode is "' + gacha_mode[langint] + '")') if langint < 2: pickup_img[langint] = info[langint]["gachaImageUrl"] pickup_name[langint] = info[langint]["gachaName"] else: pickup_img[langint] = info[langint]["gachaImageUrlCN"] pickup_name[langint] = info[langint]["gachaNameCN"] mlg_all[langint] = reader[langname] #慣れてないのでメモ #gachaMode = [normal,fes,party,final,special,skip] if gacha_mode[langint] == 'skip': #skip ：スキップする name[langint] = "" continue elif gacha_mode[langint] == 'special': #special：スペシャルガチャ（pickupIDsで指定したidのカードしか出ない） pickup_id[langint] = info[langint]["pickupIDs"] for row in reader[langname]: if row["id"] in info[langint]["pickupIDs"]: mlg_data[langint].append(row) count[row["rarity"]] += 1 if info[langint]["lastIDs"] == row["id"]: break elif gacha_mode[langint] == 'party' or gacha_mode[langint] == 'type': #party ：打ち上げガシャ（pickupIDsで指定したアイドルidのキャラしか出ない）（3回目のガシャ仕様） #type ：タイプガシャ（pickupIDsで指定したアイドルidのキャラしか出ない） pickup_id[langint] = info[langint]["pickupIDs"] for row in reader[langname]: if row["idolNum"] in info[langint]["pickupIDs"]: mlg_data[langint].append(row) count[row["rarity"]] += 1 else: #final ：SSR確定ガシャ（10連目はpickupIDsで指定したidのSSRカードしか出ない） #normal ：通常のガシャ #fes ：ミリオンフェス（SSR確率が2倍） pickup_id[langint] = info[langint]["pickupIDs"] for row in reader[langname]: if not row["limited"]: mlg_data[langint].append(row) count[row["rarity"]] += 1 elif row["limited"] and row["id"] in pickup_id[langint]: mlg_data[langint].append(row) count[row["rarity"]] += 1 elif row["rarity"] == 3 and gacha_mode[langint] == "fes": mlg_data[langint].append(row) count[row["rarity"]] += 1 if info[langint]["lastIDs"] == row["id"]: break print('Gacha name is 「' + pickup_name[langint] + '」') if gacha_mode[langint] == 'party': name[langint] = '**```打ち上げガチャ3回目の仕様です。10枚目は期間限定SSRが確定で排出されます。以下のアイドルのみ排出されます。```**\n' elif gacha_mode[langint] == 'special': name[langint] = '**```以下のカードのみ排出されます。```**\n' elif gacha_mode[langint] == 'final': name[langint] = '**```10連目は以下のカードのみ排出されます。```**\n' elif gacha_mode[langint] == 'fes': name[langint] = '**```ミリオンフェス開催中！！SSR排出率が通常の2倍！```**\n' if gacha_mode[langint] == 'party' or gacha_mode[langint] == 'type': print('Pickup idols') idollist = [] for row in mlg_data[langint]: idollist.append(row["idol"]) idollist_set = set(idollist) for row in idollist_set: print(row) name[langint] += row + '・' else: print('Pickup cards') for row in mlg_data[langint]: if row["id"] in pickup_id[langint]: lim = _('限定') if row["limited"] == True else '' print('[' + lim + rarity_str[row["rarity"]] + ']' + row["name"] + ' ' + row["idol"] + ' (CV.' + row["cv"] + ')') name[langint] += '［' + lim + rarity_str[row["rarity"]] + '］' + row["name"] + ' ' + row["idol"] + ' (CV.' + row["cv"] + ')\n' print('Actived ' + str(len(mlg_data[langint])) + ' cards.([FES]' + str(count[3]) + ', [SSR]' + str(count[2]) + ', [SR]' + str(count[1]) + ', [R]' + str(count[0]) + ')') print('Loaded cards. (Japanese:' + str(len(mlg_all[0])) + ', Korea:' + str(len(mlg_all[1])) + ', China:' + str(len(mlg_all[2])) + ')') emb = discord.Embed(title='Pickup Cards') if not gacha_mode[0] == 'skip': emb.add_field(name='Japanese:' + pickup_name[0], value=name[0]) if not gacha_mode[1] == 'skip': emb.add_field(name='Korean:' + pickup_name[1], value=name[1]) if not gacha_mode[2] == 'skip': emb.add_field(name='Chinese:' + pickup_name[2], value=name[2]) emb.set_footer(text='Version JA:' + mlgver[0] + ', ASIA:' + mlgver[1]) if flag == 1: await msg.edit(content='All MLreload process completed successfully.', embed=emb) print(strtimestamp() + 'All MLreload process completed successfully.') print(strtimestamp() + '-----------------------------------------') return async def gacha_note(message,langint): char_list = list() try: with open('./gacha/' + langnamelist[langint] + str(message.author.id) + '.txt', 'r') as f: listline = f.read() char_list = list(listline) except: import traceback traceback.print_exc() await message.channel.send(message.author.mention + _('所持SSRの記録がないか、エラーが発生しました。')) return text = [''] cards = [] page = 0 count = 0 for n in range(4): for val in mlg_all[langint]: try: if char_list[val["id"]] == '1' and val["rarity"] == n: cards.insert(0, val) except: pass for val in cards: if count == 10: text.append('') page += 1 count = 0 text[page] += '\n[' + rarity_str[val["rarity"]] + ']' + val["name"] + ' ' + val["idol"] count += 1 gacha_count = str() try: with open('./gacha_count/' + current_ver[langint] + '_' + str(message.author.id) + '.txt', 'r') as f: gacha_count = f.read() except: gacha_count = '0' fotter_text = _('ドリームスター所持数：') + gacha_count now = 1 emb = discord.Embed(title=_('所持SSR一覧') + ' Page ' + str(now) + '/' + str(len(text)), description=text[now - 1]) emb.set_author(name=message.author.name, icon_url=message.author.avatar_url) emb.set_footer(text=fotter_text) msg = await message.channel.send(_('見終わったら×で消してね！'), embed=emb) await msg.add_reaction('◀') await msg.add_reaction('▶') await msg.add_reaction('❌') while True: try: target_reaction, user = await client.wait_for('reaction_add', timeout=timeout) if target_reaction.emoji == '◀' and user != msg.author: if not now == 1: now -= 1 else: now = len(text) emb = discord.Embed(title=_('所持SSR一覧') + ' Page ' + str(now) + '/' + str(len(text)), description=text[now - 1]) emb.set_author(name=message.author.name, icon_url=message.author.avatar_url) emb.set_footer(text=fotter_text) await msg.edit(embed=emb) await msg.remove_reaction(target_reaction.emoji, user) elif target_reaction.emoji == '▶' and user != msg.author: if not now == len(text): now += 1 else: now = 1 emb = discord.Embed(title=_('所持SSR一覧') + ' Page ' + str(now) + '/' + str(len(text)), description=text[now - 1]) emb.set_author(name=message.author.name, icon_url=message.author.avatar_url) emb.set_footer(text=fotter_text) await msg.edit(embed=emb) await msg.remove_reaction(target_reaction, user) elif target_reaction.emoji == '❌' and user != msg.author: await msg.delete() break else: pass except asyncio.TimeoutError: await msg.edit(content=_('しばらく操作がなかったため、タイムアウトしました。'),embed=None) await asyncio.sleep(10) await msg.delete() break async def mlg_touch(message,result,kind,vc,botmsg,langint): fes_flag = 0 ssr_flag = 0 sr_flag = 0 author = message.author if kind == 'ミリオンフェス' or kind == '百萬祭典' or kind == '밀리언 페스티벌': for val in result: if val["rarity"] == 3: fes_flag = 1 pink_flag = random.randint(1, 20) if pink_flag == 10: img = 'https://i.imgur.com/fGpfCgB.gif' elif pink_flag == 20: img = 'https://i.imgur.com/jWTTZ0d.gif' else: img = 'https://i.imgur.com/0DxyVhm.gif' break elif val["rarity"] == 2: ssr_flag = 1 elif val["rarity"] == 1: sr_flag = 1 if not fes_flag == 1: if ssr_flag == 1: img = 'https://i.imgur.com/jWTTZ0d.gif' elif sr_flag == 1 and not ssr_flag == 1: img = 'https://i.imgur.com/vF7fDn3.gif' else: img = 'https://i.imgur.com/hEHa49X.gif' else: for val in result: if val["rarity"] == 2: ssr_flag = 1 break if val["rarity"] == 1: sr_flag = 1 if ssr_flag == 1: img = 'https://i.imgur.com/jWTTZ0d.gif' elif sr_flag == 1 and not ssr_flag == 1: img = 'https://i.imgur.com/vF7fDn3.gif' else: img = 'https://i.imgur.com/hEHa49X.gif' await asyncio.sleep(0.7) waitemb = discord.Embed() if fes_flag == 1 and pink_flag == 10: waitemb.set_image(url='https://i.imgur.com/ZC8JK9i.gif') else: waitemb.set_image(url='https://i.imgur.com/da2w9YS.gif') waitemb.set_footer(text=pickup_name[langint]) msg = await message.channel.send(message.author.mention, embed=waitemb) await msg.add_reaction('👆') try: log = '' count = 0 ssr_skip = [] ssr_count = [] while True: target_reaction, user = await client.wait_for('reaction_add', timeout=timeout) if user == author and target_reaction.emoji == '👆': await msg.clear_reactions() openemb = discord.Embed() openemb.set_footer(text=kind) openemb.set_image(url=img) await msg.edit(embed=openemb) if not vc == None: await asyncio.sleep(0.4) if fes_flag == 1 and not pink_flag == 20: vc.play(discord.FFmpegPCMAudio('./resources/open_fes.mp3')) else: vc.play(discord.FFmpegPCMAudio('./resources/open.mp3')) while vc.is_playing(): await asyncio.sleep(1) else: await asyncio.sleep(6) break while count < len(result): result_10 = result[count] if result_10["rarity"] == 3: player_show = discord.FFmpegPCMAudio('./resources/fes.mp3') await msg.clear_reactions() elif result_10["rarity"] == 2: player_show = discord.FFmpegPCMAudio('./resources/ssr.mp3') await msg.clear_reactions() elif result_10["rarity"] <= 1: player_show = discord.FFmpegPCMAudio('./resources/normal.mp3') desc = rarity_str[result_10["rarity"]] + '　' + result_10["name"] + '　' + result_10["idol"] mlgnormalemb = discord.Embed(title=desc, description='(CV.' + result_10["cv"] + ')', colour=int(result_10["color"], 0)) footer_text = kind + ' ' + str((count + 1)) + '/' + str(len(result)) mlgnormalemb.set_author(name=author.name, icon_url=author.avatar_url) mlgnormalemb.set_footer(text=footer_text) mlgnormalemb.set_image(url=result_10["image"]) if not vc == None: vc.play(player_show) #カード表示（SSRの場合特訓前） await msg.edit(content=author.mention, embed=mlgnormalemb) if result_10["rarity"] >= 2: if not vc == None: while vc.is_playing(): await asyncio.sleep(1) vc.play(discord.FFmpegPCMAudio('./resources/ssr_talk.mp3')) line = result_10["ssrText"].replace("ProP", author.name + "P") mlgssremb = discord.Embed(title=desc, description='(CV.' + result_10["cv"] + ')', colour=int(result_10["color"], 0)) mlgssremb.set_footer(text=footer_text, icon_url=author.avatar_url) mlgssremb.set_image(url=result_10["imageAwake"]) await asyncio.sleep(4.2) await msg.edit(content=author.mention, embed=mlgssremb) await asyncio.sleep(3) await msg.edit(content=author.mention + ' ' + result_10["idol"] + '「' + line + '」', embed=mlgssremb) await msg.add_reaction('👆') await msg.add_reaction('⏭') while True: target_reaction2, user = await client.wait_for('reaction_add', timeout=timeout) if target_reaction2.emoji == '👆' and user == author: if not vc == None and vc.is_playing(): vc.stop() count += 1 log += '[' + rarity_str[result_10["rarity"]] + ']' + result_10["name"] + ' ' + result_10["idol"] + '\n' if count == len(result): if not vc == None: if not bgm_id == 0: await botmsg.add_reaction('⏹') await vc.disconnect() await msg.clear_reactions() await msg.delete() gacha_count = str() try: with open('./gacha_count/' + current_ver[langint] + '_' + str(message.author.id) + '.txt', 'r') as f: gacha_count = f.read() except: print(strtimestamp() + '[ERROR]Gacha count read FAILED.') toLog = client.get_channel(log_id) footer_text = kind mlglogemb = discord.Embed(title=_('ガシャ結果'), description=log + '\n' + _('ドリームスター所持数：') + gacha_count) mlglogemb.set_author(name=author.name, icon_url=author.avatar_url) mlglogemb.set_footer(text=footer_text) await toLog.send(embed=mlglogemb) break else: await msg.remove_reaction(target_reaction2.emoji, user) break elif target_reaction2.emoji == '⏭' and user == author and len(result) == 10: for n,box in enumerate(result): if count > n: continue log += '[' + rarity_str[box["rarity"]] + ']' + box["name"] + ' ' + box["idol"] + '\n' if box["rarity"] >= 2: ssr_skip.append(box) ssr_count.append(str(n+1)) if len(ssr_skip) > 0: for n,result_ssr in enumerate(ssr_skip): if result_ssr["rarity"] == 3: player_show = discord.FFmpegPCMAudio('./resources/fes.mp3') await msg.clear_reactions() elif result_ssr["rarity"] == 2: player_show = discord.FFmpegPCMAudio('./resources/ssr.mp3') await msg.clear_reactions() desc = rarity_str[result_ssr["rarity"]] + '　' + result_ssr["name"] + '　' + result_ssr["idol"] mlgnormalemb = discord.Embed(title=desc, description='(CV.' + result_ssr["cv"] + ')', colour=int(result_ssr["color"], 0)) footer_text = kind + ' ' + str(ssr_count[n]) + '/' + str(len(result)) mlgnormalemb.set_author(name=author.name, icon_url=author.avatar_url) mlgnormalemb.set_footer(text=footer_text) mlgnormalemb.set_image(url=result_ssr["image"]) if not vc == None and vc.is_playing(): vc.stop() vc.play(player_show) await msg.edit(content=author.mention, embed=mlgnormalemb) if not vc == None: while vc.is_playing(): await asyncio.sleep(1) vc.play(discord.FFmpegPCMAudio('./resources/ssr_talk.mp3')) line = result_ssr["ssrText"].replace('ProP', author.name + 'P') mlgssremb = discord.Embed(title=desc, description='(CV.' + result_ssr["cv"] + ')', colour=int(result_ssr["color"], 0)) mlgssremb.set_footer(text=footer_text, icon_url=author.avatar_url) mlgssremb.set_image(url=result_ssr["imageAwake"]) await asyncio.sleep(4.2) await msg.edit(content=author.mention, embed=mlgssremb) await asyncio.sleep(3) await msg.edit(content=author.mention + ' ' + result_ssr["idol"] + '「' + line + '」', embed=mlgssremb) await msg.add_reaction('👆') while True: target_reaction2, user = await client.wait_for('reaction_add') if target_reaction2.emoji == '👆' and user == author: if not vc == None and vc.is_playing(): vc.stop() count += 1 await msg.remove_reaction(target_reaction2.emoji, user) break if not vc == None: if not bgm_id == 0: await botmsg.add_reaction('⏹') await vc.disconnect() gacha_count = str() try: with open('./gacha_count/' + current_ver[langint] + '_' + str(message.author.id) + '.txt', 'r') as f: gacha_count = f.read() except: print(strtimestamp() + '[ERROR]Gacha count read FAILED.') count += 10 await msg.delete() toLog = client.get_channel(log_id) footer_text = kind mlglogemb = discord.Embed(title=_('ガシャ結果'), description=log + '\n' + _('ドリームスター所持数：') + gacha_count) mlglogemb.set_author(name=author.name, icon_url=author.avatar_url) mlglogemb.set_footer(text=footer_text) await toLog.send(embed=mlglogemb) break print(strtimestamp() + 'MLGacha complete. ' + author.name + '`s result\n' + log) except TimeoutError: await msg.delete() if not vc == None: await vc.disconnect() if not bgm_id == 0: await botmsg.add_reaction('⏹') await message.channel.send(_('しばらく操作がなかったため、タイムアウトしました。')) def voicecheck(): try: if not client.voice_clients[0] is None: return True except: return False def pickupcheck(langint): global pickup_id name = '' if gacha_mode[langint] == 'party': name = '**```打ち上げガチャ3回目の仕様です。10枚目は期間限定SSRが確定で排出されます。```**\n' elif gacha_mode[langint] == 'special' or gacha_mode[langint] == 'final': name = '**```以下のカードのみ排出されます。```**\n' elif gacha_mode[langint] == 'fes': name = '**```ミリオンフェス開催中！！SSR排出率が通常の2倍！```**\n' if gacha_mode[langint] == 'party' or gacha_mode[langint] == 'type': print('Pickup idols') idollist = [] for row in mlg_data[langint]: idollist.append(row["idol"]) idollist_set = set(idollist) for row in idollist_set: name += row + '・' else: for row in mlg_data[langint]: if row["id"] in pickup_id[langint]: lim = _('限定') if row["limited"] == True else '' name += '［' + lim + rarity_str[row["rarity"]] + '］' + row["name"] + ' ' + row["idol"] + ' (CV.' + row["cv"] + ')\n' print(name) return name def langtoint(): if lang == 'ja': return 0 elif lang == 'kr': return 1 elif lang == 'cn': return 2 else: return 0 def langtostr(langint): if langint == 0: return 'ja' elif langint == 1: return 'kr' elif langint == 2: return 'cn' else: return 0 def langstrtoint(langstr): if 'ja' in langstr: return 0 elif 'kr' in langstr: return 1 elif 'cn' in langstr: return 2 else: return 0 def strtimestamp(): date = datetime.datetime.now() timestamp = '[' + str(date.year) + '-' + str(date.month) + '-' + str(date.day) + ' ' + str(date.hour) + ':' + str(date.minute) + ':' + str(date.second) + ']' return timestamp client.run(token)

41.883139

374

0.526905

5,479

50,176

4.726227

0.09217

0.016335

0.024097

0.020429

0.620274

0.552655

0.508631

0.483066

0.448658

0.422514

0

0.016824

0.333068

50,176

1,198

375

41.883139

0.755379

0.008789

0

0.547317

0

0.116249

0.03407

0

1

0.006829

false

0.003902

0.012683

0

0.059512

0.033171

0

null

0

null

0

1

0

3803dc06e5ee974e517f2282aaeb71e8db1bf6ca

850

py

Python

LeetCode/2018-12-25-88-Merge-Sorted-Array.py

HeRuivio/-Algorithm

1fbe6256630758fda3af68f469471ee246730afc

[ "MIT" ]

5

2018-10-30T05:07:32.000Z

2019-06-18T08:11:38.000Z

LeetCode/2018-12-25-88-Merge-Sorted-Array.py

HeRuivio/-Algorithm

1fbe6256630758fda3af68f469471ee246730afc

[ "MIT" ]

1

2020-05-09T09:05:16.000Z

LeetCode/2018-12-25-88-Merge-Sorted-Array.py

HeRuivio/-Algorithm

1fbe6256630758fda3af68f469471ee246730afc

[ "MIT" ]

2

2020-05-09T09:02:22.000Z

2020-12-09T13:23:00.000Z

# -*- coding: utf-8 -*- # @Author: 何睿 # @Create Date: 2018-12-25 17:10:16 # @Last Modified by: 何睿 # @Last Modified time: 2018-12-25 17:10:16 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 = p-1 else: nums1[p+q+1] = nums2[q] q = q-1 nums1[:q+1] = nums2[:q+1] if __name__ == "__main__": so = Solution() nums1 = [1, 2, 3, 0, 0, 0] nums2 = [2, 5, 6] so.merge(nums1, 3, nums2, 3) print(nums1)

25

75

0.454118

126

850

3

0.436508

0.026455

0.042328

0.05291

0.074074

0

0.131021

0.389412

850

33

76

25.757576

0.597303

0.343529

0

0.016064

0

1

0.058824

false

0

0.117647

0.058824

0

null

0

null

0

1

0

380578ad7be33432df72be80883318068123c9d9

593

py

Python

stanCode projects/movie_review/interactive.py

dianapei/SC-projects

f6a9a7f5b20cc81a8dce9b94621bac58274669d5

[ "MIT" ]

null

stanCode projects/movie_review/interactive.py

dianapei/SC-projects

f6a9a7f5b20cc81a8dce9b94621bac58274669d5

[ "MIT" ]

null

stanCode projects/movie_review/interactive.py

dianapei/SC-projects

f6a9a7f5b20cc81a8dce9b94621bac58274669d5

[ "MIT" ]

null

""" File: interactive.py Name: Pei-Rung Yu ------------------------ This file uses the function interactivePrompt from util.py to predict the reviews input by users through Console. Remember to read the weights and build a Dict[str: float] """ import util import submission def main(): weights = {} # get weight from file weights.py with open('weights', 'r', encoding='utf-8') as w: for line in w: line = line.split() weights[line[0]] = float(line[1]) util.interactivePrompt(featureExtractor=submission.extractWordFeatures, weights=weights) if __name__ == '__main__': main()

22.807692

89

0.698145

82

593

4.95122

0.658537

0

0.005976

0.153457

593

26

90

22.807692

0.802789

0.453626

0

0.066246

0

1

0.090909

false

0

0.181818

0

0.272727

0

null

0

null

0

1

0

38062ad37c4e05eb3986f8c4f66b52ff5a11db16

875

py

Python

UMAP_analysis/visualization.py

seasonsOfTheSun/UMAPanalysis

d7f413af59e01aa5e9b1aa909d1fd5858019aa23

[ "MIT" ]

2

2022-02-27T19:19:36.000Z

2022-03-15T10:38:36.000Z

UMAP_analysis/visualization.py

menchelab/UMAPanalysis

09f9b4a7823f6eceb6b40e25ee21412f3bf1c7fe

[ "MIT" ]

null

UMAP_analysis/visualization.py

menchelab/UMAPanalysis

09f9b4a7823f6eceb6b40e25ee21412f3bf1c7fe

[ "MIT" ]

1

2021-02-09T12:39:42.000Z

import networkx as nx def kamada_kawaii(G): j = None G_h = G.copy() for s in nx.weakly_connected_components(G): i = s.pop() if j == None: pass else: G_h.add_edge(i, j) j = i return nx.layout.kamada_kawai_layout(G_h, weight=None) def draw(G): fig= plt.figure(figsize = [10,10]) ax = fig.add_axes([0,0,1,1]) ax.set_xticks([]) ax.set_yticks([]) simplices = [i for i in nx.clique.find_cliques(G.to_undirected()) if len(i) == 3] collec = PolyCollection([[layout[i],layout[j],layout[k]] for i,j,k in simplices], facecolors = ["#00000044" for i in range(len(simplices))]) ax.add_collection(collec) nx.draw_networkx(G, ax = ax, pos = layout, labels = {}, node_color = df.Species.map(d.get))

29.166667

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3.55814

0.511628

0.013072

0.026144

0

0.028333

0.314286

875

29

145

30.172414

0.736667

0

0.010286

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1

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false

0.04

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0.16

0

null

0

null

0

1

0

380911a4654d528400ce615872d6b5986205a46a

94,120

py

Python

eval/eval.py

grammatek/GreynirCorrect

4bdd006c0c866e06d5697772ff4aee34fdcae4f4

[ "MIT" ]

2

2022-02-15T19:58:34.000Z

2022-02-16T17:55:25.000Z

eval/eval.py

grammatek/GreynirCorrect

4bdd006c0c866e06d5697772ff4aee34fdcae4f4

[ "MIT" ]

null

eval/eval.py

grammatek/GreynirCorrect

4bdd006c0c866e06d5697772ff4aee34fdcae4f4

[ "MIT" ]

null

#!/usr/bin/env python """ Greynir: Natural language processing for Icelandic Evaluation of spelling and grammar correction Copyright (C) 2021 Miðeind ehf. This software is licensed under the MIT License: 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 program uses an Icelandic spelling & grammar error corpus (https://github.com/antonkarl/iceErrorCorpus) to evaluate the performance of the GreynirCorrect package. The program reads a development set of hand-annotated texts in TEI XML format and automatically annotates errors using GreynirCorrect. The machine-generated annotations are then compared with the hand-annotated gold reference. This program uses Python's multiprocessing.Pool() to perform the evaluation using all available CPU cores, simultaneously. A normal way to configure this program is to clone the iceErrorCorpus repository (from the above path) into a separate directory, and then place a symlink to it to the /eval directory. For example: $ cd github $ git clone https://github.com/antonkarl/iceErrorCorpus $ cd GreynirCorrect/eval $ ln -s ../../iceErrorCorpus/ . $ python eval.py An alternate method is to specify a glob path to the error corpus as an argument to eval.py: $ python eval.py ~/github/iceErrorCorpus/data/**/*.xml To measure GreynirCorrect's performance on the test set (by default located in ./iceErrorCorpus/testCorpus/): $ python eval.py -m To measure GreynirCorrect's performance on the test set excluding malformed sentences: $ python eval.py -m -x To run GreynirCorrect on the entire development corpus (by default located in ./iceErrorCorpus/data): $ python eval.py To run GreynirCorrect on 10 files in the development corpus: $ python eval.py -n 10 To run GreynirCorrect on a randomly chosen subset of 10 files in the development corpus: $ python eval.py -n 10 -r To get an analysis report of token comparisons: $ python eval.py -a """ from typing import ( TYPE_CHECKING, Dict, List, Optional, Set, Union, Tuple, Iterable, cast, Any, DefaultDict, Counter, ) import os from collections import defaultdict from datetime import datetime import glob import random import argparse import xml.etree.ElementTree as ET if TYPE_CHECKING: # For some reason, types seem to be missing from the multiprocessing module # but not from multiprocessing.dummy import multiprocessing.dummy as multiprocessing else: import multiprocessing from reynir import _Sentence from tokenizer import detokenize, Tok, TOK from reynir_correct.annotation import Annotation from reynir_correct.checker import AnnotatedSentence, check as gc_check # Disable Pylint warnings arising from Pylint not understanding the typing module # pylint: disable=no-member # pylint: disable=unsubscriptable-object # The type of a single error descriptor, extracted from a TEI XML file ErrorDict = Dict[str, Union[str, int, bool]] # The type of the dict that holds statistical information about sentences # within a particular content category SentenceStatsDict = DefaultDict[str, Union[float, int]] # The type of the dict that holds statistical information about # content categories CategoryStatsDict = DefaultDict[str, SentenceStatsDict] # This tuple should agree with the parameters of the add_sentence() function StatsTuple = Tuple[ str, int, bool, bool, int, int, int, int, int, int, int, int, int, int, int, int ] # Counter of tp, tn, right_corr, wrong_corr, right_span, wrong_span TypeFreqs = Counter[str] # Stats for each error type for each content category # tp, fn, right_corr, wrong_corr, right_span, wrong_span ErrTypeStatsDict = DefaultDict[str, TypeFreqs] CatResultDict = Dict[str, Union[int, float, str]] # Create a lock to ensure that only one process outputs at a time OUTPUT_LOCK = multiprocessing.Lock() # Content categories in iceErrorCorpus, embedded within the file paths GENRES = ( "essays", "onlineNews", "wikipedia", ) # Error codes in iceErrorCorpus that are considered out of scope # for GreynirCorrect, at this stage at least OUT_OF_SCOPE = { "act4mid", "act4pass", "adj4noun", "adjective-inflection", "agreement-pro", # samræmi fornafns við undanfara grammar ...vöðvahólf sem sé um dælinguna. Hann dælir blóðinu > Það dælir blóðinu "aux", # meðferð vera og verða, hjálparsagna wording mun verða eftirminnilegt > mun vera eftirminnilegt "bad-contraction", "bracket4square", # svigi fyrir hornklofa punctuation (Portúgal) > [Portúgal] "caps4low", "case-verb", "case-prep", "case-adj", "case-collocation", # "collocation-idiom", # fast orðasamband með ógagnsæja merkingu collocation hélt hvorki vindi né vatni > hélt hvorki vatni né vindi # "collocation", # fast orðasamband collocation fram á þennan dag > fram til þessa dags "comma4conjunction", # komma fyrir samtengingu punctuation ...fara með vald Guðs, öll löggjöf byggir... > ...fara með vald Guðs og öll löggjöf byggir... "comma4dash", # komma fyrir bandstrik punctuation , > - "comma4ex", # komma fyrir upphrópun punctuation Viti menn, almúginn... > Viti menn! Almúginn... "comma4period", # komma fyrir punkt punctuation ...kynnast nýju fólki, er á þrítugsaldri > ...kynnast nýju fólki. Hann er á þrítugsaldri "comma4qm", # komma fyrir spurningarmerki punctuation Höfum við réttinn, eins og að... > Höfum við réttinn? Eins og að... "conjunction", "conjunction4comma", # samtenging fyrir kommu punctuation ...geta orðið þröngvandi og erfitt getur verið... > ...geta orðið þröngvandi, erfitt getur verið... "conjunction4period", # samtenging fyrir punkt punctuation ...tónlist ár hvert og tónlistarstefnurnar eru orðnar... > ...tónlist ár hvert. Tónlistarstefnurnar eru orðnar... "context", # rangt orð í samhengi other "dash4semicolon", # bandstrik fyrir semíkommu punctuation núna - þetta > núna; þetta "def4ind", # ákveðið fyrir óákveðið grammar skákinni > skák "dem-pro", # hinn í stað fyrir sá; sá ekki til eða ofnotað grammar hinn > sá "dem4noun", # ábendingarfornafn í stað nafnorðs grammar hinn > maðurinn "dem4pers", # ábendingarfornafn í stað persónufornafns grammar þessi > hún "extra-comma", # auka komma punctuation stríð, við náttúruna > stríð við náttúruna "extra-dem-pro", "extra-number", # tölustöfum ofaukið other 139,0 > 139 "extra-period", # auka punktur punctuation á morgun. Og ... > á morgun og... "extra-punctuation", # auka greinarmerki punctuation ... að > að "extra-space", # bili ofaukið spacing 4 . > 4. "extra-sub", "extra-symbol", # tákn ofaukið other Dalvík + gaf... > Dalvík gaf... "extra-word", # orði ofaukið insertion augun á mótherja > augu mótherja "extra-words", # orðum ofaukið insertion ...ég fer að hugsa... > ...ég hugsa... "foreign-error", # villa í útlendu orði foreign Supurbowl > Super Bowl "foreign-name", # villa í erlendu nafni foreign Warwixk > Warwick "fw4ice", # erlent orð þýtt yfir á íslensku style Elba > Saxelfur "gendered", # kynjað mál, menn fyrir fólk exclusion menn hugsa oft > fólk hugsar oft "genitive", "geta", "have", "ice4fw", # íslenskt orð notað í stað erlends Demókrata öldungarþings herferðarnefndina > Democratic Senatorial Campaign Committee "ind4def", # óákveðið fyrir ákveðið grammar gítartakta > gítartaktana "ind4sub", # framsöguháttur fyrir vh. grammar Þrátt fyrir að konfúsíanismi er upprunninn > Þrátt fyrir að konfúsíanismi sé upprunninn "indef-pro", # óákveðið fornafn grammar enginn > ekki neinn "interr-pro", "it4nonit", # skáletrað fyrir óskáletrað Studdi Isma'il > Studdi Isma'il "loan-syntax", # lánuð setningagerð style ég vaknaði upp > ég vaknaði "low4caps", "marked4unmarked", "mid4act", "mid4pass", "missing-commas", # kommur vantar utan um innskot punctuation Hún er jafn verðmæt ef ekki verðmætari en háskólapróf > Hún er verðmæt, ef ekki verðmætari, en háskólapróf "missing-conjunction", # samtengingu vantar punctuation í Noregi suður að Gíbraltarsundi > í Noregi og suður að Gíbraltarsundi "missing-dem-pro", "missing-ex", # vantar upphrópunarmerki punctuation Viti menn ég komst af > Viti menn! Ég komst af "missing-fin-verb", "missing-obj", "missing-quot", # gæsalöpp vantar punctuation „I'm winning > „I'm winning“ "missing-quots", # gæsalappir vantar punctuation I'm winning > „I'm winning“ "missing-semicolon", # vantar semíkommu punctuation Haukar Björgvin Páll > Haukar; Björgvin Páll "missing-square", # vantar hornklofi punctuation þeir > [þeir] "missing-sub", "missing-symbol", # tákn vantar punctuation 0 > 0% "missing-word", # orð vantar omission í Donalda > í þorpinu Donalda "missing-words", # fleiri en eitt orð vantar omission því betri laun > því betri laun hlýtur maður "nominal-inflection", "nonit4it", # óskáletrað fyrir skáletrað orðið qibt > orðið qibt "noun4adj", "noun4dem", # nafnorð í stað ábendingarfornafns grammar stærsta klukkan > sú stærsta "noun4pro", # nafnorð í stað fornafns grammar menntun má nálgast > hana má nálgast "number4word", "numeral-inflection", "pass4act", "pass4mid", "passive", "past4pres", # sögn í þátíð í stað nútíðar grammar þegar hún leigði spólur > þegar hún leigir spólur "perfect4tense", "period4comma", # punktur fyrir kommu punctuation meira en áður. Hella meira í sig > meira en áður, hella meira í sig "period4conjunction", # punktur fyrir samtengingu punctuation ...maður vill gera. Vissulega > ...maður vill gera en vissulega "period4ex", # punktur fyrir upphrópun punctuation Viti menn. > Viti menn! "pers4dem", # persónufornafn í staðinn fyrir ábendingarf. grammar það > þetta "pres4past", # sögn í nútíð í stað þátíðar grammar Þeir fara út > Þeir fóru út "pro4noun", # fornafn í stað nafnorðs grammar þau voru spurð > parið var spurt "pro4reflexive", # persónufornafn í stað afturbeygðs fn. grammar Fólk heldur að það geri það hamingjusamt > Fólk heldur að það geri sig hamingjusamt "pro-inflection", "punctuation", # greinarmerki punctuation hún mætti og hann var ekki tilbúinn > hún mætti en hann var ekki tilbúinn "qm4ex", # spurningarmerki fyrir upphrópun punctuation Algjört hrak sjálf? > Algjört hrak sjálf! "reflexive4noun", # afturbeygt fornafn í stað nafnorðs grammar félagið hélt aðalfund þess > félagið hélt aðalfund sinn "reflexive4pro", # afturbeygt fornafn í stað persónufornafns grammar gegnum líkama sinn > gegnum líkama hans "simple4cont", # nútíð í stað vera að + nafnh. grammar ók > var að aka "square4bracket", # hornklofi fyrir sviga punctuation [börnin] > (börnin) "sub4ind", "style", # stíll style urðu ekkert frægir > urðu ekki frægir "syntax-other", "tense4perfect", "unicelandic", # óíslenskuleg málnotkun style ...fer eftir persónunni... > ...fer eftir manneskjunni... "upper4lower-proper", # stór stafur í sérnafni þar sem hann á ekki að vera capitalization Mál og Menning > Mál og menning "upper4lower-noninitial", "v3-subordinate", "wording", # orðalag wording ...gerðum allt í raun... > ...gerðum í raun allt... "word4number", "wrong-prep", "xxx", # unclassified unclassified "zzz", # to revisit unannotated } # Default glob path of the development corpus TEI XML files to be processed # Using a symlink (ln -s /my/location/of/iceErrorCorpus .) can be a good idea _DEV_PATH = "iceErrorCorpus/data/**/*.xml" # Default glob path of the test corpus TEI XML files to be processed _TEST_PATH = "iceErrorCorpus/testCorpus/**/*.xml" NAMES = { "tp": "True positives", "tn": "True negatives", "fp": "False positives", "fn": "False negatives", "true_positives": "True positives", "true_negatives": "True negatives", "false_positives": "False positives", "false_negatives": "False negatives", "right_corr": "Right correction", "wrong_corr": "Wrong correction", "ctp": "True positives - error correction", "ctn": "True negatives - error correction", "cfp": "False positives - error correction", "cfn": "False negatives - error correction", "right_span": "Right span", "wrong_span": "Wrong span", } # Three levels: Supercategories, subcategories and error codes # supercategory: {subcategory : [error code]} SUPERCATEGORIES: DefaultDict[str, DefaultDict[str, List[str]]] = defaultdict( lambda: defaultdict(list) ) GCtoIEC = { "A001": ["abbreviation-period"], "A002": ["abbreviation-period"], "Z001": ["upper4lower-common", "upper4lower-proper", "upper4lower-noninitial"], "Z002": ["lower4upper-initial", "lower4upper-proper", "lower4upper-acro"], "Z003": ["upper4lower-common"], "Z004": ["upper4lower-common"], "Z005": ["upper4lower-common"], "Z005/w": ["upper4lower-common"], "Z006": ["lower4upper-acro"], "E001": ["No responding iEC category"], "E002": ["No responding iEC category"], "E003": ["No responding iEC category"], "E004": ["fw"], "C001": ["repeat-word"], "C002": ["merged-words"], "C003": ["split-compound", "split-word", "split-words"], "C004": ["repeat-word"], "C004/w": ["repeat-word"], "C005": ["split-compound", "split-word", "split-words"], "C005/w": ["split-compound", "split-word", "split-words"], "Y001/w": ["style", "wording", "context"], "C006": ["compound-nonword"], "P_NT_Að/w": ["extra-conjunction"], "P_NT_AnnaðHvort": ["conjunction"], "P_NT_Annaðhvort": ["conjunction"], "P_NT_Annara": ["pro-inflection"], "P_NT_Annarar": ["pro-inflection"], "P_NT_Annari": ["pro-inflection"], "P_NT_Einkunn": ["agreement-concord"], "P_NT_EinnAf": ["agreement"], "P_NT_EndingANA": ["n4nn"], "P_NT_EndingIR": ["nominal-inflection"], "P_NT_FjöldiHluti": ["agreement"], "P_NT_FráÞvíAð": ["missing-conjunction"], "P_NT_FsMeðFallstjórn": ["case-prep"], "P_NT_Heldur/w": ["conjunction"], "P_NT_ÍTölu": ["plural4singular", "singular4plural"], "P_NT_Komma/w": ["extra-comma"], "P_NT_Né": ["conjunction"], "P_NT_Sem/w": ["extra-conjunction"], "P_NT_Síðan/w": ["extra-word"], "P_NT_SíðastLiðinn": ["split-compound"], "P_NT_SvigaInnihaldNl": ["case-verb", "case-prep", "case-adj"], "P_NT_TvípunkturFs": ["extra-colon"], "P_NT_VantarKommu": ["missing-comma"], "P_NT_VístAð": ["conjunction"], "P_VeraAð": ["cont4simple"], "P_NT_ÞóAð": ["conjunction"], "P_redundant_word": ["extra-word"], "P_wrong_person": ["verb-inflection"], "P_wrong_phrase": ["wording"], "P_wrong_word": ["wording"], "P_wrong_case": ["case-noun"], "P_wrong_gender": ["agreement-concord"], "P_wrong_number": ["agreement-concord"], "P_wrong_form": ["agreement-concord"], "P_transposition": ["swapped-letters"], "P_WRONG_CASE_nf_þf": ["case-verb"], "P_WRONG_CASE_nf_þgf": ["case-verb"], "P_WRONG_CASE_nf_ef": ["case-verb"], "P_WRONG_CASE_þf_nf": ["case-verb"], "P_WRONG_CASE_þf_þgf": ["case-verb"], "P_WRONG_CASE_þf_ef": ["case-verb"], "P_WRONG_CASE_þgf_nf": ["case-verb"], "P_WRONG_CASE_þgf_þf": ["case-verb"], "P_WRONG_CASE_þgf_ef": ["case-verb"], "P_WRONG_CASE_ef_nf": ["case-verb"], "P_WRONG_CASE_ef_þf": ["case-verb"], "P_WRONG_CASE_ef_þgf": ["case-verb"], "P_WRONG_NOUN_WITH_VERB": ["collocation"], "P_WRONG_OP_FORM": ["verb-inflection"], "P_WRONG_PLACE_PP": ["wrong-prep"], "P_aðaf": ["að4af"], "P_afað": ["af4að"], "P_kvhv": ["kv4hv"], "P_hvkv": ["hv4kv"], "P_nn": ["n4nn"], "P_n": ["nn4n"], "P_yi": ["y4i"], "P_iy": ["i4y"], "P_yyii": ["ý4í"], "P_iiyy": ["í4ý"], "P_WRONG_PREP_AÐ": ["að4af"], "P_WRONG_PREP_AF": ["af4að"], "P_WRONG_VERB_USE": ["collocation"], "P_DIR_LOC": ["dir4loc"], "P_MOOD_ACK": ["ind4sub-conj"], "P_MOOD_REL": ["ind4sub-conj"], "P_MOOD_TEMP": ["sub4ind-conj"], "P_MOOD_TEMP/w": ["sub4ind-conj"], "P_MOOD_COND": ["sub4ind-conj"], "P_MOOD_PURP": ["sub4ind-conj"], "P_DOUBLE_DEFINITE": ["extra-dem-pro"], "X_number4word": ["number4word"], "N001": ["wrong-quot"], "N002": ["extra-punctuation"], "N003": ["extra-punctuation"], "S001": ["nonword"], "S002": ["nonword"], "S003": ["nonword"], "S004": ["nonword"], "S005": ["nonword"], # No better information available, most likely this "S006": ["nonword"], "T001": ["taboo-word"], "T001/w": ["taboo-word"], "U001": ["fw"], "U001/w": ["fw"], "W001/w": ["nonword"], "1ORD42": ["nonword", "merged-words"], "2ORD41": ["nonword", "split-word", "split-words"], "ANDA4ENDA": ["nonword", "nominal-inflection"], "ARI4NI": ["nonword", "adjective-inflection"], "ASLAUKABRODD": ["nonword", "extra-accent", "wrong-accent"], "ASLAUKASTAF": ["nonword", "extra-letter", "extra-letters"], "ASLBRODDVANTAR": ["nonword", "missing-accent", "wrong-accent"], "ASLSTAFVANTAR": ["nonword", "missing-letter", "missing-letters"], "ASLVITLSTAF": ["nonword", "letter-rep"], "ASLVIXL": ["nonword", "swapped-letters"], "ASLVIXLBRODD": ["nonword", "wrong-accent"], "AUKAG": ["nonword", "extra-letter"], "AUKAJ": ["nonword", "adjective-inflection"], "AUKARÞFFT": ["nonword", "nominal-inflection", "extra-letter"], "B4P": ["nonword", "letter-rep"], "BAND-OF": ["nonword", "extra-hyphen"], "BAND-VANT": ["nonword", "missing-hyphen"], "BEYGSJALD": [ "nonword", "nominal-inflection", "verb-inflection", "adjective-inflection", "numeral-inflection", ], "BEYGVILLA": [ "nonword", "nominal-inflection", "verb-inflection", "adjective-inflection", "numeral-inflection", ], "CE4CJE": ["nonword", "nominal-inflection"], "CJE4CE": ["nonword", "extra-letter"], "CÉ4CE": ["nonword", "extra-accent"], "DN4NN": ["nonword", "letter-rep"], "E4EI": ["nonword", "missing-letter"], "EBEYG": ["nonword", "nominal-inflection"], "EFINGU": ["nonword", "nominal-inflection"], "EI4E": ["nonword", "extra-letter"], "EI4EY": ["nonword", "i4y"], "EKKIORD": ["nonword"], "ETVILLA": ["nonword", "nominal-inflection"], "EY4EI": ["nonword", "y4i"], "F4FF": ["nonword", "missing-letter"], "F4V": ["nonword", "letter-rep"], "FS4PS": ["nonword", "letter-rep"], "FT4PT": ["nonword", "letter-rep"], "FTVILLA": ["nonword", "nominal-inflection"], "G4J": ["nonword", "letter-rep"], "G4K": ["nonword", "letter-rep"], "GGÐ4GÐ": ["nonword", "extra-letter"], "GL4GGL": ["nonword", "missing-letter"], "GMVILLA": ["nonword", "verb-inflection"], "GN4GGN": ["nonword", "missing-letter"], "GN4NG": ["nonword", "swapped-letters"], "GST4GGST": ["nonword", "missing-letter"], "GV4GGV": ["nonword", "missing-letter"], "GVANTAR": ["nonword", "missing-letter"], "GÐ4GGÐ": ["nonword", "missing-letter"], "HA4LAG": ["nonword", "upper4lower-common"], "HK4KVK": ["nonword", "nominal-inflection"], "HV4KV": ["nonword", "hv4kv"], "I4Y": ["nonword", "i4y"], "I4Í": ["nonword", "i4í"], "J4G": ["nonword", "letter-rep"], "J4GJ": ["nonword", "extra-letter"], "JE4É": ["nonword", "extra-letter"], "JVANTAR": ["nonword", "missing-letter"], "JÉ4JE": ["nonword", "extra-accent"], "KKN4KN": ["nonword", "extra-letter"], "KKT4KT": ["nonword", "extra-letter"], "KN4KKN": ["nonword", "missing-letter"], "KT4GT": ["nonword", "letter-rep"], "KT4KKT": ["nonword", "missing-letter"], "KV4HV": ["nonword", "kv4hv"], "KV4HV-FNSP": ["nonword", "kv4hv"], "KVK4HK": ["nonword", "nominal-inflection"], "KVK4KK": ["nonword", "nominal-inflection"], "LAG4HA": ["nonword", "lower4upper-proper"], "LG4GL": ["nonword", "swapped-letters"], "LLJ4LJ": ["nonword", "extra-letter"], "LLST4LST": ["nonword", "extra-letter"], "LLT4LT": ["nonword", "extra-letter"], "LS4LLS": ["nonword", "missing-letter"], "LST4LLST": ["nonword", "missing-letter"], "LT4LLT": ["nonword", "missing-letter"], "M4FN": ["nonword", "pronun-writing"], "M4MM": ["nonword", "missing-letter"], "MIÐSTIGV": ["nonword", "adjective-inflection"], "MM4M": ["nonword", "extra-letter"], "N4NN-END": ["nonword", "nominal-inflection"], "N4NN-ORD": ["nonword", "nominal-inflection"], "N4NN-SAM": ["nonword", "nominal-inflection"], "NG4GN": ["nonword", "swapped-letters"], "NGNK": ["nonword", "swapped-letters"], "NN4N-END": ["nonword", "nominal-inflection", "adjective-inflection"], "NN4N-ORD": ["nonword", "nominal-inflection"], "NN4N-SAM": ["nonword", "nominal-inflection"], "O4Ó": ["nonword", "missing-accent"], "O4Ó-NGNK": ["nonword", "missing-accent"], "OF-U": ["nonword", "letter-rep"], "P4B": ["nonword", "letter-rep"], "P4Þ": ["nonword", "letter-rep"], "PL4FL": ["nonword", "letter-rep"], "PPL4PL": ["nonword", "extra-letter"], "PPN4PN": ["nonword", "extra-letter"], "PS4PPS": ["nonword", "missing-letter"], "PT4PPT": ["nonword", "missing-letter"], "R4RR": ["nonword", "missing-letter"], "RFTGR": ["nonword", "missing-letter"], "RN4RFN": ["nonword", "pronun-writing"], "RN4RÐN": ["nonword", "pronun-writing"], "RR4R": ["nonword", "extra-letter"], "RS4RFS": ["nonword", "pronun-writing"], "SAMS-V": ["nonword", "compound-collocation"], "SK4STK": ["nonword", "pronun-writing"], "SKSTV": ["nonword", "missing-hyphen"], "SL4RSL": ["nonword", "pronun-writing"], "SN-TALA-GR": ["nonword", "compound-collocation"], # "SO-ÞGF4ÞF": [""], "SPMYNDV": ["nonword", "bad-contraction"], "SST4ST": ["nonword", "pronun-writing"], "ST4RST": ["nonword", "pronun-writing"], "ST4SKT": ["nonword", "pronun-writing"], "S4AR-EF": ["nonword", "nominal-inflection"], "S-EFGR": ["nonword", "nominal-inflection"], "STAFAGERD": ["nonword"], # No corresponding category in iEC? "STAFS-ERL": ["nonword", "fw4ice"], "STAFSVVIXL": ["nonword", "swapped-letters"], "STK4SK": ["nonword", "pronun-writing"], "STN4SN": ["nonword", "pronun-writing"], "T4TT": ["nonword", "missing-letter"], "TOKV": ["nonword", "fw4ice"], # No corresponding category in iEC? "TTN4TN": ["nonword", "pronun-writing", "extra-letter"], "U4Y": ["nonword", "u4y"], "U4Ú": ["nonword", "missing-accent"], "V4F": ["nonword", "letter-rep"], "VANTAR-J-FT": ["nonword", "nominal-inflection"], "Y4I": ["nonword", "y4i"], "Z4S": ["nonword", "letter-rep"], "É4JE": ["nonword", "pronun-writing"], "Í4I": ["nonword", "í4i"], "Í4Ý": ["nonword", "í4ý"], "Ý4Y": ["nonword", "ý4y"], "Ý4Í": ["nonword", "ý4í"], # Malsnid errors "URE": ["style", "wording", "context"], "VILLA": ["style", "wording", "context"], # Einkunn errors "R000": ["style"], "R002": ["style"], "R003": ["style"], "R004": ["style"], "R005": ["style"], } # Value given to float metrics when there is none available # to avoid magic numbers NO_RESULTS = -1.0 GCSKIPCODES = frozenset(("E001", "C005", "Z002", "W001")) # Define the command line arguments parser = argparse.ArgumentParser( description=( "This program evaluates the spelling and grammar checking performance " "of GreynirCorrect on iceErrorCorpus" ) ) parser.add_argument( "path", nargs="?", type=str, help=f"glob path of XML files to process (default: {_DEV_PATH})", ) parser.add_argument( "-n", "--number", type=int, default=0, help="number of files to process (default=all)", ) parser.add_argument( "-c", "--cores", type=int, help=f"number of CPU cores to use (default=all, i.e. {os.cpu_count() or 1})", ) parser.add_argument( "-m", "--measure", action="store_true", help="run measurements on test corpus and output results only", ) parser.add_argument( "-r", "--randomize", action="store_true", help="process a random subset of files", ) parser.add_argument( "-q", "--quiet", default=None, action="store_true", help="output results only, not individual sentences", ) parser.add_argument( "-v", "--verbose", default=None, action="store_true", help="output individual sentences as well as results, even for the test corpus", ) parser.add_argument( "-x", "--exclude", action="store_true", help="Exclude sentences marked for exclusion", ) parser.add_argument( "-s", "--single", type=str, default="", help="Get results for a single error category", ) parser.add_argument( "-a", "--analysis", action="store_true", help="Create an analysis report for token results", ) parser.add_argument( "-f", "--catfile", type=str, default="iceErrorCorpus/errorCodes.tsv" ) # This boolean global is set to True for quiet output, # which is the default when processing the test corpus QUIET = False # This boolean global is set to True if only a single # error category should be analyzed SINGLE = False # This boolean global is set to True if sentences marked # with an exclusion flag should be excluded from processing EXCLUDE = False # This boolean global is set to True for token-level analysis ANALYSIS = False def element_text(element: ET.Element) -> str: """Return the text of the given element, including all its subelements, if any""" return "".join(element.itertext()) class Stats: """A container for key statistics on processed files and sentences""" def __init__(self) -> None: """Initialize empty defaults for the stats collection""" self._starttime = datetime.utcnow() self._files: Dict[str, int] = defaultdict(int) # We employ a trick to make the defaultdicts picklable between processes: # instead of the usual lambda: defaultdict(int), use defaultdict(int).copy self._sentences: CategoryStatsDict = CategoryStatsDict( SentenceStatsDict(int).copy ) self._errtypes: ErrTypeStatsDict = ErrTypeStatsDict(Counter) self._true_positives: DefaultDict[str, int] = defaultdict(int) self._false_negatives: DefaultDict[str, int] = defaultdict(int) self._tp: DefaultDict[str, int] = defaultdict(int) self._tn: DefaultDict[str, int] = defaultdict(int) self._fp: DefaultDict[str, int] = defaultdict(int) self._fn: DefaultDict[str, int] = defaultdict(int) self._right_corr: DefaultDict[str, int] = defaultdict(int) self._wrong_corr: DefaultDict[str, int] = defaultdict(int) self._ctp: DefaultDict[str, int] = defaultdict(int) self._ctn: DefaultDict[str, int] = defaultdict(int) self._cfp: DefaultDict[str, int] = defaultdict(int) self._cfn: DefaultDict[str, int] = defaultdict(int) self._right_span: DefaultDict[str, int] = defaultdict(int) self._wrong_span: DefaultDict[str, int] = defaultdict(int) # reference error code : freq - for hypotheses with the unparsable error code self._tp_unparsables: DefaultDict[str, int] = defaultdict(int) def add_file(self, category: str) -> None: """Add a processed file in a given content category""" self._files[category] += 1 def add_result( self, *, stats: List[StatsTuple], true_positives: Dict[str, int], false_negatives: Dict[str, int], ups: Dict[str, int], errtypefreqs: ErrTypeStatsDict, ) -> None: """Add the result of a process() call to the statistics collection""" for sent_result in stats: self.add_sentence(*sent_result) for k, v in true_positives.items(): self._true_positives[k] += v for k, v in false_negatives.items(): self._false_negatives[k] += v for k, v in ups.items(): self._tp_unparsables[k] += v for okey, d in errtypefreqs.items(): # okey = xtype; d = DefaultDict[str, int] for ikey, vv in d.items(): # ikey = tp, fn, ... self._errtypes[okey][ikey] += vv # v = freq for each metric def add_sentence( self, category: str, num_tokens: int, ice_error: bool, gc_error: bool, tp: int, tn: int, fp: int, fn: int, right_corr: int, wrong_corr: int, ctp: int, ctn: int, cfp: int, cfn: int, right_span: int, wrong_span: int, ) -> None: """Add a processed sentence in a given content category""" d = self._sentences[category] d["count"] += 1 d["num_tokens"] += num_tokens d["ice_errors"] += 1 if ice_error else 0 d["gc_errors"] += 1 if gc_error else 0 # True negative: neither iceErrorCorpus nor GC report an error true_negative = not ice_error and not gc_error d["true_negatives"] += 1 if true_negative else 0 # True positive: both iceErrorCorpus and GC report an error true_positive = ice_error and gc_error d["true_positives"] += 1 if true_positive else 0 # False negative: iceErrorCorpus reports an error where GC doesn't false_negative = ice_error and not gc_error d["false_negatives"] += 1 if false_negative else 0 # False positive: GC reports an error where iceErrorCorpus doesn't false_positive = gc_error and not ice_error d["false_positives"] += 1 if false_positive else 0 # Stats for error detection for sentence d["tp"] += tp d["tn"] += tn d["fp"] += fp d["fn"] += fn # Stats for error correction ratio d["right_corr"] += right_corr d["wrong_corr"] += wrong_corr # Stats for error correction for sentence d["ctp"] += ctp d["ctn"] += ctn d["cfp"] += cfp d["cfn"] += cfn # Stats for error span d["right_span"] += right_span d["wrong_span"] += wrong_span # Causes of unparsable sentences def output(self, cores: int) -> None: """Write the statistics to stdout""" # Accumulate standard output in a buffer, for writing in one fell # swoop at the end (after acquiring the output lock) if SINGLE: bprint(f"") num_sentences: int = sum( cast(int, d["count"]) for d in self._sentences.values() ) def output_duration() -> None: # type: ignore """Calculate the duration of the processing""" dur = int((datetime.utcnow() - self._starttime).total_seconds()) h = dur // 3600 m = (dur % 3600) // 60 s = dur % 60 # Output a summary banner bprint(f"\n" + "=" * 7) bprint(f"Summary") bprint(f"=" * 7 + "\n") # Total number of files processed, and timing stats bprint(f"Processing started at {str(self._starttime)[0:19]}") bprint(f"Total processing time {h}h {m:02}m {s:02}s, using {cores} cores") bprint(f"\nFiles processed: {sum(self._files.values()):6}") for c in GENRES: bprint(f" {c:<13}: {self._files[c]:6}") # Total number of tokens processed num_tokens = sum(d["num_tokens"] for d in self._sentences.values()) bprint(f"\nTokens processed: {num_tokens:6}") for c in GENRES: bprint(f" {c:<13}: {self._sentences[c]['num_tokens']:6}") # Total number of sentences processed bprint(f"\nSentences processed: {num_sentences:6}") for c in GENRES: bprint(f" {c:<13}: {self._sentences[c]['count']:6}") def perc(n: int, whole: int) -> str: """Return a percentage of total sentences, formatted as 3.2f""" if whole == 0: return "N/A" return f"{100.0*n/whole:3.2f}" def write_basic_value( val: int, bv: str, whole: int, errwhole: Optional[int] = None ) -> None: """Write basic values for sentences and their freqs to stdout""" if errwhole: bprint( f"\n{NAMES[bv]+':':<20} {val:6} {perc(val, whole):>6}% / {perc(val, errwhole):>6}%" ) else: bprint(f"\n{NAMES[bv]+':':<20} {val:6} {perc(val, whole):>6}%") for c in GENRES: bprint(f" {c:<13}: {self._sentences[c][bv]:6}") def calc_PRF( tp: int, tn: int, fp: int, fn: int, tps: str, tns: str, fps: str, fns: str, recs: str, precs: str, ) -> None: """Calculate precision, recall and F0.5-score""" # Recall if tp + fn == 0: result = "N/A" recall = 0.0 else: recall = tp / (tp + fn) result = f"{recall:1.4f}" bprint(f"\nRecall: {result}") for c in GENRES: d = self._sentences[c] denominator = d[tps] + d[fns] if denominator == 0: bprint(f" {c:<13}: N/A") else: rc = d[recs] = d[tps] / denominator bprint(f" {c:<13}: {rc:1.4f}") # Precision if tp + fp == 0: result = "N/A" precision = 0.0 else: precision = tp / (tp + fp) result = f"{precision:1.4f}" bprint(f"\nPrecision: {result}") for c in GENRES: d = self._sentences[c] denominator = d[tps] + d[fps] if denominator == 0: bprint(f" {c:<13}: N/A") else: p = d[precs] = d[tps] / denominator bprint(f" {c:<13}: {p:1.4f}") # F0.5 score if precision + recall > 0.0: f05 = 1.25 * (precision * recall) / (0.25 * precision + recall) result = f"{f05:1.4f}" else: f05 = 0.0 result = "N/A" bprint(f"\nF0.5 score: {result}") for c in GENRES: d = self._sentences[c] if recs not in d or precs not in d: bprint(f" {c:<13}: N/A") continue rc = d[recs] p = d[precs] if p + rc > 0.0: f05 = 1.25 * (p * rc) / (0.25 * p + rc) bprint(f" {c:<13}: {f05:1.4f}") else: bprint(f" {c:<13}: N/A") def calc_recall( right: int, wrong: int, rights: str, wrongs: str, recs: str ) -> None: """Calculate precision for binary classification""" # Recall if right + wrong == 0: result = "N/A" recall = 0.0 else: recall = right / (right + wrong) result = f"{recall:1.4f}" bprint(f"\nRecall: {result}") for c in GENRES: d = self._sentences[c] denominator = d[rights] + d[wrongs] if denominator == 0: bprint(f" {c:<13}: N/A") else: rc = d[recs] = d[rights] / denominator bprint(f" {c:<13}: {rc:1.4f}") def calc_error_category_metrics(cat: str) -> CatResultDict: """Calculates precision, recall and f0.5-score for a single error code N = Number of errors in category z in reference corpus, Nall = number of tokens TP = Errors correctly classified as category z FP = Errors (or non-errors) incorrectly classified as category z FN = Errors in category z in reference but not hypothesis Recall = TPz/(TPz+FPz) Precision = TPz/(TPz+FNz) F0.5-score = 1.25*(P*R)/(0.25*P+R) """ catdict: CatResultDict = {k: v for k, v in self._errtypes[cat].items()} tp = cast(int, catdict.get("tp", 0)) fn = cast(int, catdict.get("fn", 0)) fp = cast(int, catdict.get("fp", 0)) recall: float = NO_RESULTS precision: float = NO_RESULTS ctp = cast(int, catdict.get("ctp", 0)) cfn = cast(int, catdict.get("cfn", 0)) cfp = cast(int, catdict.get("cfp", 0)) crecall: float = NO_RESULTS cprecision: float = NO_RESULTS catdict["freq"] = tp + fn if tp + fn + fp == 0: # No values in category catdict["recall"] = NO_RESULTS catdict["precision"] = NO_RESULTS catdict["f05score"] = NO_RESULTS catdict["crecall"] = NO_RESULTS catdict["cprecision"] = NO_RESULTS catdict["cf05score"] = NO_RESULTS else: # Error detection metrics # Recall if tp + fn != 0: recall = catdict["recall"] = tp / (tp + fn) # Precision if tp + fp != 0: precision = catdict["precision"] = tp / (tp + fp) # F0.5 score if recall + precision > 0.0: catdict["f05score"] = ( 1.25 * (precision * recall) / (0.25 * precision + recall) ) else: catdict["f05score"] = NO_RESULTS # Error correction metrics # Recall if ctp + cfn != 0: crecall = catdict["crecall"] = ctp / (ctp + cfn) # Precision if ctp + cfp != 0: cprecision = catdict["cprecision"] = ctp / (ctp + cfp) # F0.5 score if crecall + cprecision > 0.0: catdict["cf05score"] = ( 1.25 * (cprecision * crecall) / (0.25 * cprecision + crecall) ) else: catdict["cf05score"] = NO_RESULTS # Correction recall (not used) right_corr = cast(int, catdict.get("right_corr", 0)) if right_corr > 0: catdict["corr_rec"] = right_corr / ( right_corr + cast(int, catdict.get("wrong_corr", 0)) ) else: catdict["corr_rec"] = -1.0 # Span recall right_span = cast(int, catdict.get("right_span", 0)) if right_span > 0: catdict["span_rec"] = right_span / ( right_span + cast(int, catdict.get("wrong_span", 0)) ) else: catdict["span_rec"] = NO_RESULTS return catdict def output_sentence_scores() -> None: # type: ignore """Calculate and write sentence scores to stdout""" # Total number of true negatives found bprint(f"\nResults for error detection for whole sentences") true_positives: int = sum( cast(int, d["true_positives"]) for d in self._sentences.values() ) true_negatives: int = sum( cast(int, d["true_negatives"]) for d in self._sentences.values() ) false_positives: int = sum( cast(int, d["false_positives"]) for d in self._sentences.values() ) false_negatives: int = sum( cast(int, d["false_negatives"]) for d in self._sentences.values() ) write_basic_value(true_positives, "true_positives", num_sentences) write_basic_value(true_negatives, "true_negatives", num_sentences) write_basic_value(false_positives, "false_positives", num_sentences) write_basic_value(false_negatives, "false_negatives", num_sentences) # Percentage of true vs. false true_results = true_positives + true_negatives false_results = false_positives + false_negatives if num_sentences == 0: result = "N/A" else: result = ( perc(true_results, num_sentences) + "%/" + perc(false_results, num_sentences) + "%" ) bprint(f"\nTrue/false split: {result:>16}") for c in GENRES: d = self._sentences[c] num_sents = d["count"] true_results = cast(int, d["true_positives"] + d["true_negatives"]) false_results = cast(int, d["false_positives"] + d["false_negatives"]) if num_sents == 0: result = "N/A" else: result = f"{100.0*true_results/num_sents:3.2f}%/{100.0*false_results/num_sents:3.2f}%" bprint(f" {c:<13}: {result:>16}") # Precision, recall, F0.5-score calc_PRF( true_positives, true_negatives, false_positives, false_negatives, "true_positives", "true_negatives", "false_positives", "false_negatives", "sentrecall", "sentprecision", ) # Most common false negative error types # total = sum(self._false_negatives.values()) # if total > 0: # bprint(f"\nMost common false negative error types") # bprint(f"--------------------------------------\n") # for index, (xtype, cnt) in enumerate( # heapq.nlargest( # 20, self._false_negatives.items(), key=lambda x: x[1] # ) # ): # bprint(f"{index+1:3}. {xtype} ({cnt}, {100.0*cnt/total:3.2f}%)") # Most common error types in unparsable sentences # tot = sum(self._tp_unparsables.values()) # if tot > 0: # bprint(f"\nMost common error types for unparsable sentences") # bprint(f"------------------------------------------------\n") # for index, (xtype, cnt) in enumerate( # heapq.nlargest(20, self._tp_unparsables.items(), key=lambda x: x[1]) # ): # bprint(f"{index+1:3}. {xtype} ({cnt}, {100.0*cnt/tot:3.2f}%)") def output_token_scores() -> None: # type: ignore """Calculate and write token scores to stdout""" bprint(f"\n\nResults for error detection within sentences") num_tokens = sum( cast(int, d["num_tokens"]) for d in self._sentences.values() ) bprint(f"\nTokens processed: {num_tokens:6}") for c in GENRES: bprint(f" {c:<13}: {self._sentences[c]['num_tokens']:6}") tp = sum(cast(int, d["tp"]) for d in self._sentences.values()) tn = sum(cast(int, d["tn"]) for d in self._sentences.values()) fp = sum(cast(int, d["fp"]) for d in self._sentences.values()) fn = sum(cast(int, d["fn"]) for d in self._sentences.values()) all_ice_errs = tp + fn write_basic_value(tp, "tp", num_tokens, all_ice_errs) write_basic_value(tn, "tn", num_tokens) write_basic_value(fp, "fp", num_tokens, all_ice_errs) write_basic_value(fn, "fn", num_tokens, all_ice_errs) calc_PRF( tp, tn, fp, fn, "tp", "tn", "fp", "fn", "detectrecall", "detectprecision", ) # Stiff: Of all errors in error corpora, how many get the right correction? # Loose: Of all errors the tool correctly finds, how many get the right correction? # Can only calculate recall. bprint(f"\nResults for error correction") right_corr = sum( cast(int, d["right_corr"]) for d in self._sentences.values() ) wrong_corr = sum( cast(int, d["wrong_corr"]) for d in self._sentences.values() ) write_basic_value(right_corr, "right_corr", num_tokens, tp) write_basic_value(wrong_corr, "wrong_corr", num_tokens, tp) calc_recall( right_corr, wrong_corr, "right_corr", "wrong_corr", "correctrecall" ) # Stiff: Of all errors in error corpora, how many get the right span? # Loose: Of all errors the tool correctly finds, how many get the right span? # Can only calculate recall. bprint(f"\nResults for error span") right_span = sum( cast(int, d["right_span"]) for d in self._sentences.values() ) wrong_span = sum( cast(int, d["wrong_span"]) for d in self._sentences.values() ) write_basic_value(right_span, "right_span", num_tokens, tp) write_basic_value(wrong_span, "wrong_span", num_tokens, tp) calc_recall( right_span, wrong_span, "right_span", "wrong_span", "spanrecall" ) def output_error_cat_scores() -> None: """Calculate and write scores for each error category to stdout""" bprint(f"\n\nResults for each error category in order by frequency") freqdict: Dict[str, int] = dict() microf05: float = 0.0 nfreqs: int = 0 resultdict: Dict[str, CatResultDict] = dict() # Iterate over category counts for cat in self._errtypes.keys(): # Get recall, precision and F0.5; recall for correction and span catdict = resultdict[cat] = calc_error_category_metrics(cat) # Collect micro scores, both overall and for in-scope categories freq = cast(int, catdict["freq"]) assert isinstance(freq, int) f05score = cast(float, catdict["f05score"]) assert isinstance(f05score, float) if cat not in OUT_OF_SCOPE: microf05 += f05score * freq nfreqs += freq # Create freqdict for sorting error categories by frequency freqdict[cat] = freq # Print results for each category by frequency for k in sorted(freqdict, key=freqdict.__getitem__, reverse=True): rk = resultdict[k] bprint("{} (in_scope={})".format(k, k not in OUT_OF_SCOPE)) bprint( "\tTP, FP, FN: {}, {}, {}".format( rk.get("tp", 0), rk.get("fp", 0), rk.get("fn", 0), ) ) bprint( "\tRe, Pr, F0.5: {:3.2f}, {:3.2f}, {:3.2f}".format( cast(float, rk.get("recall", 0.0)) * 100.0, cast(float, rk.get("precision", 0.0)) * 100.0, cast(float, rk.get("f05score", 0.0)) * 100.0, ) ) if ( rk.get("corr_rec", "N/A") == "N/A" or rk.get("span_rec", "N/A") == "N/A" ): bprint("\tCorr, span: N/A, N/A") else: bprint( "\tCorr, span: {:3.2f}, {:3.2f}".format( cast(float, rk.get("corr_rec", 0.0)) * 100.0, cast(float, rk.get("span_rec", 0.0)) * 100.0, ) ) # Micro F0.5-score # Results for in-scope categories and all categories if nfreqs != 0: bprint( "F0.5-score: {:3.2f}".format( microf05 / nfreqs * 100.0, ) ) else: bprint(f"F0.5-score: N/A") def output_supercategory_scores(): """Error detection results for each supercategory in iEC given in SUPERCATEGORIES, each subcategory, and error code""" bprint("Supercategory: frequency, F-score") bprint("\tSubcategory: frequency, F-score") bprint( "\t\tError code: frequency, (recall, precision, F-score), (tp, fn, fp)| correct recall" ) totalfreq = 0 totalf = 0.0 for supercat in SUPERCATEGORIES: # supercategory: {subcategory : error code} # entry = supercategory, catlist = {subcategory : error code} superblob = "" superfreq = 0 superf = 0.0 for subcat in SUPERCATEGORIES[supercat]: subblob = "" subfreq = 0 subf = 0.0 for code in SUPERCATEGORIES[supercat][subcat]: if code not in OUT_OF_SCOPE: et = calc_error_category_metrics(code) if et["f05score"] == "N/A": continue freq = cast(int, et["freq"]) fscore = cast(float, et["f05score"]) # codework subblob = subblob + "\t\t{} {} ({:3.2f}, {:3.2f}, {:3.2f}) ({},{},{})| {}\n".format( code, freq, cast(float, et["recall"]) * 100.0 if "recall" in et else 0.0, cast(float, et["precision"]) * 100.0 if "precision" in et else 0.0, fscore * 100.0, cast(int, et["tp"]) if "tp" in et else 0, cast(int, et["fn"]) if "fn" in et else 0, cast(int, et["fp"]) if "fp" in et else 0, cast(float, et["corr_rec"]) if "corr_rec" in et else 0.0, ) # subwork subfreq += freq subf += fscore * freq * 100.0 if subfreq != 0: subblob = ( "\t{} {} {}\n".format( subcat.capitalize(), subfreq, subf / subfreq ) + subblob ) else: subblob = ( "\t{} 0 N/A\n".format(subcat.capitalize()) + subblob ) # superwork # freq, f05 superblob += subblob superfreq += subfreq superf += subf # TODO is this correct? if superfreq != 0: superblob = ( "\n{} {} {}\n".format( supercat.capitalize(), superfreq, superf / superfreq ) + superblob ) else: superblob = ( "\n{} 0 N/A\n".format(supercat.capitalize()) + superblob ) totalfreq += superfreq totalf += superf # TODO is this correct? bprint("".join(superblob)) bprint("Total frequency: {}".format(totalfreq)) bprint("Total F-score: {}".format(totalf / totalfreq)) def output_all_scores(): """Results for each supercategory in iEC given in SUPERCATEGORIES, each subcategory, and error code, in tsv format.""" bprint( "Category\tfrequency\ttp\tfn\tfp\trecall\tprecision\tF-score\tctp\tcfn\tcfp\tcrecall\tcprecision\tCF-score" ) totalfreq = 0 totaltp = 0 totalfn = 0 totalfp = 0 totalrecall = 0.0 totalprecision = 0.0 totalf = 0.0 totalctp = 0 totalcfn = 0 totalcfp = 0 totalcrecall = 0.0 totalcprecision = 0.0 totalcf = 0.0 for supercat in SUPERCATEGORIES: # supercategory: {subcategory : error code} # entry = supercategory, catlist = {subcategory : error code} superfreq = 0 supertp = 0 superfn = 0 superfp = 0 superrecall = 0.0 superprecision = 0.0 superf = 0.0 superctp = 0 supercfn = 0 supercfp = 0 supercrecall = 0.0 supercprecision = 0.0 supercf = 0.0 superblob = "" for subcat in SUPERCATEGORIES[supercat]: subfreq = 0 subtp = 0 subfn = 0 subfp = 0 subrecall = 0.0 subprecision = 0.0 subf = 0.0 subctp = 0 subcfn = 0 subcfp = 0 subcrecall = 0.0 subcprecision = 0.0 subcf = 0.0 subblob = "" for code in SUPERCATEGORIES[supercat][subcat]: if code not in OUT_OF_SCOPE: et = calc_error_category_metrics(code) freq = cast(int, et["freq"]) fscore = cast(float, et["f05score"]) cfscore = cast(float, et["cf05score"]) # codework subblob = subblob + "{}\t{}\t{}\t{}\t{}\t{:3.2f}\t{:3.2f}\t{:3.2f}\t{}\t{}\t{}\t{:3.2f}\t{:3.2f}\t{:3.2f}\n".format( code, freq, cast(int, et["tp"]) if "tp" in et else 0, cast(int, et["fn"]) if "fn" in et else 0, cast(int, et["fp"]) if "fp" in et else 0, cast(float, et["recall"]) * 100.0 if ("recall" in et and et["recall"] > 0.0) else NO_RESULTS, # Or "N/A", but that messes with the f-string formatting cast(float, et["precision"]) * 100.0 if ("precision" in et and et["precision"] > 0.0) else NO_RESULTS, fscore * 100.0 if fscore > 0.0 else NO_RESULTS, cast(int, et["ctp"]) if "ctp" in et else 0, cast(int, et["cfn"]) if "cfn" in et else 0, cast(int, et["cfp"]) if "cfp" in et else 0, cast(float, et["crecall"]) * 100.0 if ("crecall" in et and et["crecall"] > 0.0) else NO_RESULTS, cast(float, et["cprecision"]) * 100.0 if ("cprecision" in et and et["cprecision"] > 0.0) else NO_RESULTS, cfscore * 100.0 if cfscore > 0.0 else NO_RESULTS, ) # subwork subfreq += freq subtp += cast(int, et["tp"]) if "tp" in et else 0 subfn += cast(int, et["fn"]) if "fn" in et else 0 subfp += cast(int, et["fp"]) if "fp" in et else 0 subrecall += ( cast(float, et["recall"]) * freq * 100.0 if ("recall" in et and et["recall"] > 0.0) else 0.0 ) subprecision += ( cast(float, et["precision"]) * freq * 100.0 if ("precision" in et and et["precision"] > 0.0) else 0.0 ) subf += fscore * freq * 100.0 if fscore > 0.0 else 0.0 subctp += cast(int, et["ctp"]) if "ctp" in et else 0 subcfn += cast(int, et["cfn"]) if "cfn" in et else 0 subcfp += cast(int, et["cfp"]) if "cfp" in et else 0 subcrecall += ( cast(float, et["crecall"]) * freq * 100.0 if ("crecall" in et and et["crecall"] > 0.0) else 0.0 ) subcprecision += ( cast(float, et["cprecision"]) * freq * 100.0 if ("cprecision" in et and et["cprecision"] > 0.0) else 0.0 ) subcf += cfscore * freq * 100.0 if cfscore > 0.0 else 0.0 if subfreq != 0: subblob = ( "\n{}\t{}\t{}\t{}\t{}\t{:3.2f}\t{:3.2f}\t{:3.2f}\t{}\t{}\t{}\t{:3.2f}\t{:3.2f}\t{:3.2f}\n".format( subcat.capitalize(), subfreq, subtp, subfn, subfp, subrecall / subfreq, subprecision / subfreq, subf / subfreq, subctp, subcfn, subcfp, subcrecall / subfreq, subcprecision / subfreq, subcf / subfreq, ) + subblob ) else: subblob = ( "\n{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format( subcat.capitalize(), subfreq, subtp, subfn, subfp, NO_RESULTS, NO_RESULTS, NO_RESULTS, subctp, subcfn, subcfp, NO_RESULTS, NO_RESULTS, NO_RESULTS, ) + subblob ) # superwork superblob += subblob superfreq += subfreq supertp += subtp superfn += subfn superfp += subfp superrecall += subrecall superprecision += subprecision superf += subf superctp += subctp supercfn += subcfn supercfp += subcfp supercrecall += subcrecall supercprecision += subcprecision supercf += subcf if superfreq != 0: superblob = ( "\n{}\t{}\t{}\t{}\t{}\t{:3.2f}\t{:3.2f}\t{:3.2f}\t{}\t{}\t{}\t{:3.2f}\t{:3.2f}\t{:3.2f}\n".format( supercat.capitalize(), superfreq, supertp, superfn, superfp, superrecall / superfreq, superprecision / superfreq, superf / superfreq, superctp, supercfn, supercfp, supercrecall / superfreq, supercprecision / superfreq, supercf / superfreq, ) + superblob ) else: superblob = ( "\n{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format( supercat.capitalize(), superfreq, supertp, superfn, superfp, NO_RESULTS, NO_RESULTS, NO_RESULTS, superctp, supercfn, supercfp, NO_RESULTS, NO_RESULTS, NO_RESULTS, ) + superblob ) totalfreq += superfreq totaltp += supertp totalfn += superfn totalfp += superfp totalrecall += superrecall totalprecision += superprecision totalf += superf totalctp += superctp totalcfn += supercfn totalcfp += supercfp totalcrecall += supercrecall totalcprecision += supercprecision totalcf += supercf bprint("".join(superblob)) bprint( "\n{}\t{}\t{}\t{}\t{}\t{:3.2f}\t{:3.2f}\t{:3.2f}\t{}\t{}\t{}\t{:3.2f}\t{:3.2f}\t{:3.2f}\n".format( "Total", totalfreq, totaltp, totalfn, totalfp, totalrecall / totalfreq, totalprecision / totalfreq, totalf / totalfreq, totalctp, totalcfn, totalcfp, totalcrecall / totalfreq, totalcprecision / totalfreq, totalcf / totalfreq, ) ) # output_duration() # output_sentence_scores() # output_token_scores() # output_error_cat_scores() bprint(f"\n\nResults for iEC-categories:") # output_supercategory_scores() output_all_scores() # Print the accumulated output before exiting for s in buffer: print(s) def correct_spaces(tokens: List[Tuple[str, str]]) -> str: """Returns a string with a reasonably correct concatenation of the tokens, where each token is a (tag, text) tuple.""" return detokenize( Tok(TOK.PUNCTUATION if tag == "c" else TOK.WORD, txt, None) for tag, txt in tokens ) # Accumulate standard output in a buffer, for writing in one fell # swoop at the end (after acquiring the output lock) buffer: List[str] = [] def bprint(s: str): """Buffered print: accumulate output for printing at the end""" buffer.append(s) def process(fpath_and_category: Tuple[str, str]) -> Dict[str, Any]: """Process a single error corpus file in TEI XML format. This function is called within a multiprocessing pool and therefore usually executes in a child process, separate from the parent process. It should thus not modify any global state, and arguments and return values should be picklable.""" # Unpack arguments fpath, category = fpath_and_category # Set up XML namespace stuff NS = "http://www.tei-c.org/ns/1.0" # Length of namespace prefix to cut from tag names, including { } nl = len(NS) + 2 # Namespace dictionary to be passed to ET functions ns = dict(ns=NS) # Statistics about processed sentences. These data will # be returned back to the parent process. stats: List[StatsTuple] = [] # Counter of iceErrorCorpus error codes (xtypes) encountered true_positives: Dict[str, int] = defaultdict(int) false_negatives: Dict[str, int] = defaultdict(int) # Counter of iceErrorCorpus error codes in unparsable sentences ups: Dict[str, int] = defaultdict(int) # Stats for each error code (xtypes) errtypefreqs: ErrTypeStatsDict = ErrTypeStatsDict(TypeFreqs().copy) try: if not QUIET: # Output a file header bprint("-" * 64) bprint(f"File: {fpath}") bprint("-" * 64) # Parse the XML file into a tree try: tree = ET.parse(fpath) except ET.ParseError as e: if QUIET: bprint(f"000: *** Unable to parse XML file {fpath} ***") else: bprint(f"000: *** Unable to parse XML file ***") raise e # Obtain the root of the XML tree root = tree.getroot() # Iterate through the sentences in the file for sent in root.findall("ns:text/ns:body/ns:p/ns:s", ns): # Skip sentence if find exclude if EXCLUDE: exc = sent.attrib.get("exclude", "") if exc: continue check = False # When args.single, checking single error code # Sentence identifier (index) index = sent.attrib.get("n", "") tokens: List[Tuple[str, str]] = [] errors: List[ErrorDict] = [] # A dictionary of errors by their index (idx field) error_indexes: Dict[str, ErrorDict] = {} dependencies: List[Tuple[str, ErrorDict]] = [] analysisblob: List[str] = [] # Error corpora annotations for sentences marked as unparsable # Enumerate through the tokens in the sentence for el in sent: tag = el.tag[nl:] if tag == "revision": # An error annotation starts here, eventually # spanning multiple tokens original = "" corrected = "" # Note the index of the starting token within the span start = len(tokens) # Revision id rev_id = el.attrib["id"] # Look at the original text el_orig = el.find("ns:original", ns) if el_orig is not None: # We have 0 or more original tokens embedded # within the revision tag orig_tokens = [ (subel.tag[nl:], element_text(subel)) for subel in el_orig ] tokens.extend(orig_tokens) original = " ".join(t[1] for t in orig_tokens).strip() # Calculate the index of the ending token within the span end = max(start, len(tokens) - 1) # Look at the corrected text el_corr = el.find("ns:corrected", ns) if el_corr is not None: corr_tokens = [element_text(subel) for subel in el_corr] corrected = " ".join(corr_tokens).strip() # Accumulate the annotations (errors) for el_err in el.findall("ns:errors/ns:error", ns): attr = el_err.attrib # Collect relevant information into a dict xtype: str = attr["xtype"].lower() error: ErrorDict = dict( start=start, end=end, rev_id=rev_id, xtype=xtype, in_scope=xtype not in OUT_OF_SCOPE, eid=attr.get("eid", ""), original=original, corrected=corrected, ) errors.append(error) # Temporarily index errors by the idx field idx = attr.get("idx") if idx: error_indexes[idx] = error # Accumulate dependencies that need to be "fixed up", # i.e. errors that depend on and refer to other errors # within the sentence if xtype == "dep": dep_id = attr.get("depId") if dep_id: # Note the fact that this error depends on the # error with idx=dep_id dependencies.append((dep_id, error)) else: if QUIET: bprint(f"In file {fpath}:") bprint( f"\n{index}: *** 'depId' attribute missing for dependency ***" ) if SINGLE and xtype == SINGLE: check = True else: tokens.append((tag, element_text(el))) # Fix up the dependencies, if any for dep_id, error in dependencies: if dep_id not in error_indexes: if QUIET: bprint(f"In file {fpath}:") bprint(f"\n{index}: *** No error has idx='{dep_id}' ***") else: # Copy the in_scope attribute from the original error error["in_scope"] = error_indexes[dep_id]["in_scope"] if SINGLE and not check: continue # Reconstruct the original sentence # TODO switch for sentence from original text file text = correct_spaces(tokens) if not text: # Nothing to do: drop this and go to the next sentence continue # print(text) # Pass it to GreynirCorrect pg = [list(p) for p in gc_check(text)] s: Optional[_Sentence] = None if len(pg) >= 1 and len(pg[0]) >= 1: s = pg[0][0] if len(pg) > 1 or (len(pg) == 1 and len(pg[0]) > 1): # if QUIET: # bprint(f"In file {fpath}:") # bprint( # f"\n{index}: *** Input contains more than one sentence *** {text}" # ) pass if s is None: # if QUIET: # bprint(f"In file {fpath}:") # bprint(f"\n{index}: *** No parse for sentence *** {text}") pass if not QUIET: # Output the original sentence bprint(f"\n{index}: {text}") if not index: if QUIET: bprint(f"In file {fpath}:") bprint("000: *** Sentence identifier is missing ('n' attribute) ***") def sentence_results( hyp_annotations: List[Annotation], ref_annotations: List[ErrorDict] ) -> Tuple[bool, bool]: gc_error = False ice_error = False unparsable = False # Output GreynirCorrect annotations for ann in hyp_annotations: if ann.is_error: gc_error = True if ann.code == "E001": unparsable = True if not QUIET: bprint(f">>> {ann}") # Output iceErrorCorpus annotations xtypes: Dict[str, int] = defaultdict(int) for err in ref_annotations: asterisk = "*" xtype = cast(str, err["xtype"]) if err["in_scope"]: # This is an in-scope error asterisk = "" ice_error = True # Count the errors of each xtype if xtype != "dep": xtypes[xtype] += 1 if unparsable: ups[xtype] += 1 if not QUIET: bprint( f"<<< {err['start']:03}-{err['end']:03}: {asterisk}{xtype}" ) if not QUIET: # Output true/false positive/negative result if ice_error and gc_error: bprint("=++ True positive") for xtype in xtypes: true_positives[xtype] += 1 elif not ice_error and not gc_error: bprint("=-- True negative") elif ice_error and not gc_error: bprint("!-- False negative") for xtype in xtypes: false_negatives[xtype] += 1 else: assert gc_error and not ice_error bprint("!++ False positive") return gc_error, ice_error assert s is not None assert isinstance(s, AnnotatedSentence) gc_error, ice_error = sentence_results(s.annotations, errors) def token_results( hyp_annotations: Iterable[Annotation], ref_annotations: Iterable[ErrorDict], ) -> Tuple[int, int, int, int, int, int, int, int, int, int]: """Calculate statistics on annotations at the token span level""" tp, fp, fn = 0, 0, 0 # tn comes from len(tokens)-(tp+fp+fn) later on right_corr, wrong_corr = 0, 0 ctp, cfp, cfn = ( 0, 0, 0, ) # ctn comes from len(tokens)-(ctp+cfp+cfn) later on right_span, wrong_span = 0, 0 if not hyp_annotations and not ref_annotations: # No need to go any further return ( tp, fp, fn, right_corr, wrong_corr, ctp, cfp, cfn, right_span, wrong_span, ) y = iter(hyp_annotations) # GreynirCorrect annotations x = iter(ref_annotations) # iEC annotations ytok: Optional[Annotation] = None xtok: Optional[ErrorDict] = None if ANALYSIS: analysisblob.append("\n{}".format(text)) analysisblob.append("\tiEC:") for iec_ann in ref_annotations: analysisblob.append("\t\t{}".format(iec_ann)) analysisblob.append("\tGC:") for gc_ann in hyp_annotations: analysisblob.append("\t\t{}".format(gc_ann)) xspanlast = set([-1]) try: ytok = next(y) xtok = next(x) while True: ystart, yend = ytok.start, ytok.end xstart, xend = cast(int, xtok["start"]), cast(int, xtok["end"]) samespan = False # 1. Error detection # Token span in GreynirCorrect annotation # TODO Usually ystart, yend+1, reset when secondary comparison works yspan = set(range(ystart, yend + 1)) # Token span in iEC annotation xspan = set(range(xstart, xend + 1)) yorig: Set[str] ysugg: Set[str] if ytok.original: yorig = set(ytok.original.split()) else: yorig = set() xorig = set(cast(str, xtok["original"]).split()) if ytok.suggest: ysugg = set(ytok.suggest.split()) else: ysugg = set() xsugg = set(cast(str, xtok["corrected"]).split()) if xspan & yspan: samespan = True # Secondary comparison: # Check if any common tokens # and relatively same span if abs(ystart - xstart) <= 5 or abs(yend - xend) <= 5: if yorig and xorig and yorig.intersection(xorig): samespan = True if ysugg and xsugg and ysugg.intersection(xsugg): samespan = True # iEC error code xtype = cast(str, xtok["xtype"]) # By default, use iEC error code # on the GreynirCorrect side as well ytype = xtype if ytok.code in GCtoIEC: # We have a mapping of the GC code if xtype not in GCtoIEC[ytok.code]: # The iEC code is not one that could # correspond to a GC code. # We select the iEC code that most commonly # corresponds to the GC code; # we're going to get an error for # a wrong annotation type anyway, as ytype != xtype. ytype = GCtoIEC[ytok.code][0] else: print("Error tag {} is not supported".format(ytok.code)) if ANALYSIS: analysisblob.append( "\tComparing:\n\t {}\n\t {} - {} ({})".format( xtok, ytok, ytok.text, ytype ) ) # analysisblob.append("\tXspans: {} | {}".format(xspanlast, xspan)) # Multiple tags for same error: Skip rest if xspan == xspanlast: if ANALYSIS: analysisblob.append( "\t Same span, skip: {}".format( cast(str, xtok["xtype"]) ) ) xtok = None xtok = next(x) continue if ytok.code in GCSKIPCODES or ytok.code.endswith("/w"): # Skip these errors, shouldn't be compared. if ANALYSIS: analysisblob.append( "\t Skip: {}".format(ytok.code) ) ytok = None ytok = next(y) continue if samespan: # The annotation spans overlap # or almost overlap and contain the same original value or correction tp += 1 errtypefreqs[xtype]["tp"] += 1 if ANALYSIS: analysisblob.append("\t TP: {}".format(xtype)) # 2. Span detection if xspan == yspan: right_span += 1 errtypefreqs[xtype]["right_span"] += 1 else: wrong_span += 1 errtypefreqs[xtype]["wrong_span"] += 1 # 3. Error correction ycorr = getattr(ytok, "suggest", "") if ycorr == xtok["corrected"]: right_corr += 1 errtypefreqs[xtype]["right_corr"] += 1 ctp += 1 errtypefreqs[xtype]["ctp"] += 1 else: wrong_corr += 1 errtypefreqs[xtype]["wrong_corr"] += 1 cfn += 1 errtypefreqs[xtype]["cfn"] += 1 xspanlast = xspan xtok, ytok = None, None xtok = next(x) ytok = next(y) continue # The annotation spans do not overlap if yend < xstart: # Extraneous GC annotation before next iEC annotation fp += 1 errtypefreqs[ytype]["fp"] += 1 cfp += 1 errtypefreqs[ytype]["cfp"] += 1 if ANALYSIS: analysisblob.append("\t FP: {}".format(ytype)) ytok = None ytok = next(y) continue if ystart > xend: # iEC annotation with no corresponding GC annotation fn += 1 errtypefreqs[xtype]["fn"] += 1 cfn += 1 errtypefreqs[xtype]["cfn"] += 1 if ANALYSIS: analysisblob.append("\t FN: {}".format(xtype)) xspanlast = xspan xtok = None xtok = next(x) continue # Should never get here assert False except StopIteration: pass # At least one of the iterators has been exhausted # Process the remainder if ANALYSIS and ytok: analysisblob.append("\tDumping rest of GC errors:") while ytok is not None: # This is a remaining GC annotation: false positive if ytok.code in GCSKIPCODES or ytok.code.endswith("/w"): # Skip these errors, shouldn't be a part of the results. if ANALYSIS: analysisblob.append( "\t Skip: {}".format(ytok.code) ) ytok = next(y, None) continue fp += 1 ytype = GCtoIEC[ytok.code][0] if ytok.code in GCtoIEC else ytok.code errtypefreqs[ytype]["fp"] += 1 cfp += 1 errtypefreqs[ytype]["cfp"] += 1 if ANALYSIS: analysisblob.append("\t FP: {}".format(ytype)) ytok = next(y, None) if ANALYSIS and xtok: analysisblob.append("\tDumping rest of iEC errors:") if not xtok: # In case try fails on ytok = next(y) xtok = next(x, None) while xtok is not None: # This is a remaining iEC annotation: false negative xstart, xend = cast(int, xtok["start"]), cast(int, xtok["end"]) xspan = set(range(xstart, xend + 1)) xtype = cast(str, xtok["xtype"]) if xspan == xspanlast: # Multiple tags for same error: Skip rest if ANALYSIS: analysisblob.append( "\t Same span, skip: {}".format(xtype) ) xtok = None xtok = next(x, None) else: if ANALYSIS: analysisblob.append("\t FN: {}".format(xtype)) fn += 1 errtypefreqs[xtype]["fn"] += 1 cfn += 1 errtypefreqs[xtype]["cfn"] += 1 xspanlast = xspan xtok = next(x, None) return ( tp, fp, fn, right_corr, wrong_corr, ctp, cfp, cfn, right_span, wrong_span, ) assert isinstance(s, AnnotatedSentence) ( tp, fp, fn, right_corr, wrong_corr, ctp, cfp, cfn, right_span, wrong_span, ) = token_results(s.annotations, errors) tn = len(tokens) - tp - fp - fn ctn = len(tokens) - ctp - cfp - cfn # Collect statistics into the stats list, to be returned # to the parent process if stats is not None: stats.append( ( category, len(tokens), ice_error, gc_error, tp, tn, fp, fn, right_corr, wrong_corr, ctp, ctn, cfp, cfn, right_span, wrong_span, ) ) if ANALYSIS: with open("analysis.txt", "a+") as analysis: analysis.write("\n".join(analysisblob)) analysisblob = [] except ET.ParseError: # Already handled the exception: exit as gracefully as possible pass finally: # Print the accumulated output before exiting with OUTPUT_LOCK: for txt in buffer: print(txt) print("", flush=True) # This return value will be pickled and sent back to the parent process return dict( stats=stats, true_positives=true_positives, false_negatives=false_negatives, ups=ups, errtypefreqs=errtypefreqs, ) def initialize_cats(catfile: str) -> None: first = True with open(catfile, "r") as cfile: for row in cfile: split = row.split("\t") if first: first = False else: s0, s1, s2 = [s.strip() for s in split[0:3]] SUPERCATEGORIES[s0][s1].append(s2) def main() -> None: """Main program""" # Parse the command line arguments args = parser.parse_args() # For a measurement run on the test corpus, the default is # quiet operation. We store the flag in a global variable # that is accessible to child processes. global QUIET QUIET = args.measure # Overriding flags if args.verbose is not None: QUIET = False # --quiet has precedence over --verbose if args.quiet is not None: QUIET = True global EXCLUDE EXCLUDE = args.exclude global SINGLE SINGLE = args.single global ANALYSIS ANALYSIS = args.analysis # Maximum number of files to process (0=all files) max_count = args.number # Initialize the statistics collector stats = Stats() # The glob path of the XML files to process path: str = args.path # When running measurements only, we use _TEST_PATH as the default, # otherwise _DEV_PATH initialize_cats(args.catfile) if path is None: path = _TEST_PATH if args.measure else _DEV_PATH def gen_files() -> Iterable[Tuple[str, str]]: """Generate tuples with the file paths and categories to be processed by the multiprocessing pool""" count = 0 it: Iterable[str] if args.randomize and max_count > 0: # Randomizing only makes sense if there is a max count as well it = glob.glob(path, recursive=True) it = random.sample(it, max_count) else: it = glob.iglob(path, recursive=True) for fpath in it: # Find out which genre the file belongs to by # inference from the file name for genre in GENRES: if genre in fpath: break else: assert False, f"File path does not contain a recognized genre: {fpath}" # Add the file to the statistics under its genre stats.add_file(genre) # Yield the file information to the multiprocessing pool yield fpath, genre count += 1 # If there is a limit on the number of processed files, # and we're done, stop the generator if max_count > 0 and count >= max_count: break # Use a multiprocessing pool to process the articles with multiprocessing.Pool(processes=args.cores) as pool: # Iterate through the TEI XML files in turn and call the process() # function on each file, in a child process within the pool for result in pool.imap_unordered(process, gen_files()): # Results come back as a dict of arguments that # we pass to Stats.add_result() stats.add_result(**result) # Done: close the pool in an orderly manner pool.close() pool.join() # Finally, acquire the output lock and write the final statistics with OUTPUT_LOCK: stats.output(cores=args.cores or os.cpu_count() or 1) print("", flush=True) if __name__ == "__main__": main()

41.664453

177

0.492

9,641

94,120

4.727518

0.163157

0.002808

0.002764

0.002808

0.260542

0.208105

0.159485

0.131269

0.107947

0.093137

0

0.018815

0.396898

94,120

2,258

178

41.682905

0.784071

0.218923

0

0.266898

0

0.008666

0.190147

0.014986

0

0.000886

0.004622

1

0.014443

false

0.005199

0.008088

0

0.028307

0.045061

0

null

0

null

0

1

0

380ed18d43976269469c3c2614242e47914c12c3

615

py

Python

software-updatable/script-agent/src/device_info.py

vigier/iot-suite-examples

ea3995188c7dc8c2c461ba2b1989fc72fda093a4

[ "BSD-Source-Code" ]

1

2021-02-19T10:40:48.000Z

software-updatable/script-agent/src/device_info.py

vigier/iot-suite-examples

ea3995188c7dc8c2c461ba2b1989fc72fda093a4

[ "BSD-Source-Code" ]

null

software-updatable/script-agent/src/device_info.py

vigier/iot-suite-examples

ea3995188c7dc8c2c461ba2b1989fc72fda093a4

[ "BSD-Source-Code" ]

2

2021-03-18T10:41:19.000Z

2021-03-29T09:43:41.000Z

import json class DeviceInfo: """An entity that represents the information provided by edge agent over edge/thing/response topic.""" def toJson(self): return json.dumps(self, default=lambda o: o.__dict__, sort_keys=True, indent=4) def compute(self,payload): # See https://docs.bosch-iot-suite.com/edge/index.html#109655.htm arr = payload["deviceId"].split(":") self.namespace = arr[0] self.deviceId = arr[1] self.hubTenantId = payload["tenantId"] self.policyId = payload["policyId"] print("Device information is \n" + self.toJson())

38.4375

106

0.650407

79

615

5

0.734177

0

0.018711

0.217886

615

15

107

41

0.802495

0.260163

0

0.109375

0

1

0.181818

false

0

0.090909

0.454545

0.090909

0

null

0

null

0

1

0

38103fdb2d64dfd907732198ccb8056945c45042

867

py

Python

test/test_intermediate_image.py

beesandbombs/coldtype

d02c7dd36bf1576fa37dc8c50d5c1a6e47b1c5ea

[ "Apache-2.0" ]

1

2021-04-04T15:25:06.000Z

test/test_intermediate_image.py

beesandbombs/coldtype

d02c7dd36bf1576fa37dc8c50d5c1a6e47b1c5ea

[ "Apache-2.0" ]

null

test/test_intermediate_image.py

beesandbombs/coldtype

d02c7dd36bf1576fa37dc8c50d5c1a6e47b1c5ea

[ "Apache-2.0" ]

null

from coldtype import * import coldtype.filtering as fl import skia co = Font.Cacheable("assets/ColdtypeObviously-VF.ttf") @animation(bg=hsl(0.65, l=0.83), rstate=1, storyboard=[15], timeline=Timeline(30)) def render(f, rstate): p = f.a.progress(f.i, loops=1, easefn="qeio").e return (StyledString("COLDTYPE", Style(co, 700, wdth=(p)*0.1, tu=-85+(p*50), r=1, ro=1, rotate=10*p)) .pens() .align(f.a.r) .f(1) .understroke(s=0, sw=30) .precompose(f.a.r) .attr(skp=dict( ImageFilter=skia.BlurImageFilter.Make(10, 10), ColorFilter=skia.LumaColorFilter.Make() )) .precompose(f.a.r) .attr(skp=dict( ColorFilter=fl.compose( fl.as_filter(fl.contrast_cut(200+p*30, 5)), fl.fill(hsl(0.9, l=0.5, s=0.7)), ))))

32.111111

82

0.560554

128

867

3.78125

0.570313

0.016529

0.018595

0.053719

0.099174

0

0.070093

0.259516

867

27

83

32.111111

0.683801

0

0.166667

0

0.049539

0.035714

0

1

0.041667

false

0

0.125

0

0.208333

0

null

0

null

0

1

0

3810de583777d6043b6fbc1b957266870a434a71

8,722

py

Python

cogs/stream.py

948guppy/Morrigan-Rewrite

e0ad2823b09945ee88f7978361903b84e92d8bd1

[ "MIT" ]

null

cogs/stream.py

948guppy/Morrigan-Rewrite

e0ad2823b09945ee88f7978361903b84e92d8bd1

[ "MIT" ]

13

2020-10-07T09:43:45.000Z

2020-12-29T02:10:15.000Z

cogs/stream.py

948guppy/Morrigan-Rewrite

e0ad2823b09945ee88f7978361903b84e92d8bd1

[ "MIT" ]

null

import textwrap import asyncio import discord from discord.ext import commands class StreamStatusIsNone(Exception): pass class Stream(commands.Cog): def __init__(self, bot): self.bot = bot @commands.Cog.listener() async def on_voice_state_update(self, member, before, after): channel_id = 758219068007776256 channel = member.guild.get_channel(channel_id) # 配信作成時の関数 def overwrites(streamer): overwrite = { streamer.guild.default_role: discord.PermissionOverwrite(create_instant_invite=False, manage_channels=False, manage_permissions=False, manage_roles=False, manage_webhooks=False, read_messages=False, send_messages=False, send_tts_messages=False, manage_messages=False, embed_links=False, attach_files=False, read_message_history=False, mention_everyone=False, external_emojis=False, use_external_emojis=False, add_reactions=False, view_channel=True, connect=True, speak=True, stream=True, mute_members=False, deafen_members=False, move_members=False, use_voice_activation=True, priority_speaker=False), streamer: discord.PermissionOverwrite(create_instant_invite=False, manage_channels=False, manage_permissions=False, manage_roles=False, manage_webhooks=False, read_messages=False, send_messages=False, send_tts_messages=False, manage_messages=False, embed_links=False, attach_files=False, read_message_history=False, mention_everyone=False, external_emojis=False, use_external_emojis=False, add_reactions=False, view_channel=True, connect=True, speak=True, stream=True, mute_members=True, deafen_members=True, move_members=False, use_voice_activation=True, priority_speaker=True) } return overwrite async def create_stream_channel(streamer): category_id = 733625569178157076 category = streamer.guild.get_channel(category_id) stream = await category.create_voice_channel(name=f"{streamer.display_name}", overwrites=overwrites(streamer)) await streamer.move_to(stream) def get_streaming_game(streamer): try: game = streamer.activities[0] except IndexError: game = None return game async def send_stream_started(streamer): e = discord.Embed() e.title = "配信が開始されました！" e.description = textwrap.dedent( f""" 配信者 : {streamer.mention}さん配信中のゲーム : {get_streaming_game(streamer).name if get_streaming_game(streamer) else '取得されませんでした'} """ ) e.colour = 0x99FFFF await channel.send(embed=e) async def send_error_message(streamer): e = discord.Embed() e.title = "エラーが発生しました！" e.description = textwrap.dedent( f""" 配信者 : {streamer.mention}さんによる配信情報パネルの取得ができませんでした。既にパネルが削除されているか、存在するパネル数が多すぎる可能性があります。このエラーは10秒後に削除されます。 """ ) e.colour = 0xFF0000 await channel.send(embed=e, delete_after=10) async def delete_stream_information(streamer): stream_information = None async for message in channel.history(limit=200): try: if message.embeds[0].title == "配信が開始されました！": if f"配信者 : {streamer.mention}さん" in message.embeds[0].description: stream_information = message break except IndexError: continue try: await stream_information.delete() except AttributeError: await send_error_message(streamer) # 配信終了時の関数 async def close_stream(listener, stream): try: if stream.channel.overwrites_for(listener).deafen_members: await stream.channel.delete() await delete_stream_information(member) except AttributeError: pass # 処理の実行 try: if after.channel.id == 733626787992567868: await send_stream_started(member) await create_stream_channel(member) except AttributeError: if not before.channel.id == 733626787992567868 and before.channel.category_id == 733625569178157076: await close_stream(member, before) @commands.Cog.listener() async def on_raw_reaction_add(self, payload): information_channel_id = 758219068007776256 information_channel = self.bot.get_channel(information_channel_id) channel = self.bot.get_channel(payload.channel_id) guild = self.bot.get_guild(payload.guild_id) message = await channel.fetch_message(payload.message_id) member = guild.get_member(payload.user_id) delete = [] def check(m): return m.author == member async def change_streaming_channel_name(listener, stream_name): state = listener.voice if state is None: delete.append(await channel.send("VCにいません")) else: if state.channel.category_id == 733625569178157076 and not state.channel.id == 733626787992567868: if state.channel.overwrites_for(listener).deafen_members: stream_channel_id = state.channel.id stream_channel = listener.guild.get_channel(stream_channel_id) await stream_channel.edit(name=stream_name) return True return False raise StreamStatusIsNone if member.bot: return else: try: if message.embeds[0].title == "配信編集パネル": if str(payload.emoji) == "1⃣": try: delete.append(await channel.send("配信の名前を入力してください")) msg = await self.bot.wait_for('message', timeout=60.0, check=check) delete.append(msg) except asyncio.TimeoutError: delete.append(await channel.send('タイムアウトしました')) else: try: if await change_streaming_channel_name(member, msg.content): delete.append(await channel.send(f"配信の名前を{msg.content}に変更しました")) else: delete.append(await channel.send("あなたの配信ではありません")) await (await self.bot.get_channel(payload.channel_id).fetch_message(payload.message_id)).remove_reaction( payload.emoji, self.bot.get_guild(payload.guild_id).get_member(payload.user_id)) except StreamStatusIsNone: pass except IndexError: pass await asyncio.sleep(5) await channel.delete_messages(delete) def setup(bot): bot.add_cog(Stream(bot))

47.145946

137

0.497822

729

8,722

5.751715

0.223594

0.025757

0.026711

0.028619

0.399237

0.318626

0.260911

0.231338

0.209874

0.184593

0

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0.437858

8,722

184

138

47.402174

0.820722

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0

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0

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0.021967

0

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0

1

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false

0.025641

0.00641

0.108974

0

null

0

null

0

1

0

3812f35ba24d9bbcbcb368fba353b59808d5476c

7,477

py

Python

secapr/phase_alleles.py

mftorres/seqcap_processor

ce8ff01b3918bd29105db7b3d91b1d12572f014e

[ "MIT" ]

null

secapr/phase_alleles.py

mftorres/seqcap_processor

ce8ff01b3918bd29105db7b3d91b1d12572f014e

[ "MIT" ]

null

secapr/phase_alleles.py

mftorres/seqcap_processor

ce8ff01b3918bd29105db7b3d91b1d12572f014e

[ "MIT" ]

null

""" Phase remapped reads form reference-based assembly into two separate alleles. Then produce consensus sequence for each allele. """ #author: Tobias Andermann, tobias.andermann@bioenv.gu.se import os import sys import re import glob import shutil import argparse import configparser import subprocess import subprocess import pickle from Bio import SeqIO from secapr.utils import CompletePath from secapr.reference_assembly import bam_consensus, join_fastas from secapr.helpers import CreateDir # Get arguments def add_arguments(parser): parser.add_argument( '--input', required=True, action=CompletePath, default=None, help='Call the folder that contains the results of the reference based assembly (output of reference_assembly function, containing the bam-files).' ) parser.add_argument( '--output', required=True, action=CreateDir, default=None, help='The output directory where results will be safed.' ) parser.add_argument( '--min_coverage', type=int, default=4, help='Set the minimum read coverage. Only positions that are covered by this number of reads will be called in the consensus sequence, otherwise the program will add an ambiguity at this position.' ) parser.add_argument( '--reference', required=True, action=CompletePath, default=None, help='Provide the reference that was used for read-mapping. If you used the alignment-consensus method, provide the joined_fasta_library.fasta which is found in the reference_seqs folder within the reference-assembly output.' ) def phase_bam(sorted_bam_file,sample_output_folder,min_cov,reference): # Phasing: bam_basename = re.sub('.bam$', '', sorted_bam_file) split_sample_path = re.split("/",sorted_bam_file) split_file_name = split_sample_path[-1] phasing_file_base_pre = re.sub('.sorted.bam$', '', split_file_name) if 'unphased' in phasing_file_base_pre: phasing_file_base_pre = re.sub('_unphased','',phasing_file_base_pre) phasing_out_dir = "%s/phased_bam_files" %(sample_output_folder) if not os.path.exists(phasing_out_dir): os.makedirs(phasing_out_dir) phasing_basename = "%s/%s_allele" %(phasing_out_dir,phasing_file_base_pre) phasing_cmd = [ "samtools", "phase", "-A", "-F", "-Q", "20", "-b", phasing_basename, sorted_bam_file ] try: print ("Phasing bam file..........") with open(os.path.join(phasing_out_dir, "phasing_screen_out.txt"), 'w') as phasing_screen: ph = subprocess.Popen(phasing_cmd, stdout=phasing_screen) ph.communicate() print ("Phasing completed.") except: print ("Phasing unsuccessful. Script terminated.") sys.exit() allele_0_file = "%s.0.bam" %phasing_basename allele_1_file = "%s.1.bam" %phasing_basename allele_0_sorted_base = "%s/%s_sorted_allele_0" %(phasing_out_dir,phasing_file_base_pre) allele_1_sorted_base = "%s/%s_sorted_allele_1" %(phasing_out_dir,phasing_file_base_pre) allele_0_sorted_file = "%s.bam" %allele_0_sorted_base allele_1_sorted_file = "%s.bam" %allele_1_sorted_base # Sorting phased bam files: sort_phased_0 = "samtools sort -o %s %s" %(allele_0_sorted_file,allele_0_file) sort_phased_1 = "samtools sort -o %s %s" %(allele_1_sorted_file,allele_1_file) #sort_phased_0 = "samtools sort -o %s %s" %(allele_0_sorted_file, allele_0_file) #sort_phased_1 = "samtools sort -o %s %s" %(allele_1_sorted_file,allele_1_file) os.system(sort_phased_0) os.system(sort_phased_1) # Creating index file for phased bam-files: index_allele0 = "samtools index %s" %(allele_0_sorted_file) index_allele1 = "samtools index %s" %(allele_1_sorted_file) os.system(index_allele0) os.system(index_allele1) print ("Creating consensus sequences from bam-files..........") allele0_stem = re.split("/", allele_0_sorted_base)[-1] allele0_stem = re.sub('_sorted', '', allele0_stem) allele1_stem = re.split("/", allele_1_sorted_base)[-1] allele1_stem = re.sub('_sorted', '', allele1_stem) fasta_allele0 = bam_consensus(reference,allele_0_sorted_file,allele0_stem,sample_output_folder,min_cov) fasta_allele1 = bam_consensus(reference,allele_1_sorted_file,allele1_stem,sample_output_folder,min_cov) # Cleaning up output directory output_files = [val for sublist in [[os.path.join(i[0], j) for j in i[2]] for i in os.walk(sample_output_folder)] for val in sublist] intermediate_files = "%s/intermediate_files" %sample_output_folder if not os.path.exists(intermediate_files): os.makedirs(intermediate_files) # check the names to make sure we're not deleting something improperly try: assert fasta_allele0 in output_files except: raise IOError("Output-files were not created properly.") for file in output_files: if file.endswith('.mpileup') or file.endswith('.vcf'): shutil.move(file,intermediate_files) return fasta_allele0,fasta_allele1 def manage_homzygous_samples(fasta_dir, sample_id): fasta_sequences = SeqIO.parse(open("%s/%s.sorted.fasta" %(fasta_dir,sample_id)),'fasta') with open('%s/%s_joined_homozygous_alleles.fasta'%(fasta_dir,sample_id), 'w') as outfile: for fasta in fasta_sequences: name = re.split(" ", fasta.description) name[0] += "_0" fasta.description = " ".join(name) fasta.id += "_0" SeqIO.write(fasta, outfile, "fasta") name = re.split(" ", fasta.description) allele_1_name = re.sub("_0$", "_1", name[0]) name[0] = allele_1_name fasta.description = " ".join(name) allele_1_id = re.sub("_0$", "_1", str(fasta.id)) fasta.id = allele_1_id SeqIO.write(fasta, outfile, "fasta") outfile.close def main(args): print('\n') min_cov = args.min_coverage reference = args.reference # Set working directory out_dir = args.output input_folder = args.input sample_out_list = [] # Iterate through all sample specific subfolders for subfolder in os.listdir(input_folder): path = os.path.join(input_folder,subfolder) if os.path.isdir(path): subfolder_path = os.path.join(input_folder,subfolder) if subfolder_path.endswith('_remapped') or subfolder_path.endswith('_locus_selection'): sample = '_'.join(subfolder.split('_')[:-1]) sample_output_folder = os.path.join(out_dir,'%s_phased' %sample) if not os.path.exists(sample_output_folder): os.makedirs(sample_output_folder) sample_out_list.append(sample_output_folder) tmp_folder = os.path.join(subfolder_path,'tmp') reference_pickle = os.path.join(tmp_folder,'%s_reference.pickle' %sample) #with open(reference_pickle, 'rb') as handle: # reference = pickle.load(handle) for file in os.listdir(subfolder_path): if file.endswith("sorted.bam"): sorted_bam = file sorted_bam_path = os.path.join(subfolder_path,sorted_bam) print(('\n'+"#" * 50)) print(('Processing sample %s' %sample)) allele_fastas = phase_bam(sorted_bam_path,sample_output_folder,min_cov,reference) # The following is for the case that no phased bam files were created, i.e. the individual is homozygous for all loci (happens when only looking at one locus or a very few) allele0 = "" allele1 = "" # testing if phasing files were created for file in allele_fastas: if file.endswith(".fasta"): if "allele_0" in file: allele0 = file if "allele_1" in file: allele1 = file if allele0 == 0: manage_homzygous_samples(allele_fastas,sample_id) os.remove(os.path.join(allele_fastas,allele0)) os.remove(os.path.join(allele_fastas,allele1)) join_fastas(out_dir,sample_out_list) print('\n')

37.199005

227

0.738933

1,108

7,477

4.724729

0.222924

0.022732

0.037822

0.024069

0.22999

0.160267

0.119007

0.084432

0.056543

0.041261

0

0.012965

0.143774

7,477

201

228

37.199005

0.804749

0.118095

0

0.159509

0

0.018405

0.202069

0.02252

0

0.006135

1

0.02454

false

0

0.08589

0

0.116564

0.04908

0

null

0

null

0

1

0

38131c7ecad19e2b5a5ed7d86da453eb8db2ed3b

1,201

py

Python

src/anaphylaxis_nlp/algo/observation.py

kpwhri/anaphylaxis-runrex

ee52f11f54d2034314abd3dfc3caf4770dcd8acb

[ "MIT" ]

null

src/anaphylaxis_nlp/algo/observation.py

kpwhri/anaphylaxis-runrex

ee52f11f54d2034314abd3dfc3caf4770dcd8acb

[ "MIT" ]

null

src/anaphylaxis_nlp/algo/observation.py

kpwhri/anaphylaxis-runrex

ee52f11f54d2034314abd3dfc3caf4770dcd8acb

[ "MIT" ]

null

from runrex.algo.pattern import Pattern from runrex.text import Document from runrex.algo.result import Status, Result from anaphylaxis_nlp.algo.epinephrine import hypothetical class ObsStatus(Status): NONE = -1 OBSERVATION = 1 MONITORING = 2 overnight = r'(observation|overnight|extended|hospital|unit|[i1]cm?u|neuro)' admit = r'\b(add?mit|admission|transfer|xfer)\w*' OBSERVATION = Pattern( rf'{admit} (\w+ )?((for|to) )?(\w+ )?{overnight}', negates=[hypothetical] ) MONITORING = Pattern( rf'(close|continu)\w* ((to|for) )?(\w+ )?(monitor|observ|check|follow|track)\w*', negates=[hypothetical] ) def _search_observation(document: Document): for sentence in document: for text, start, end in sentence.get_patterns(OBSERVATION): yield ObsStatus.OBSERVATION, text, start, end for text, start, end in sentence.get_patterns(MONITORING): yield ObsStatus.MONITORING, text, start, end def get_observation(document: Document, expected=None): for status, text, start, end in _search_observation(document): yield Result(status, status.value, expected=expected, text=text, start=start, end=end)

30.025

85

0.693589

150

1,201

5.5

0.393333

0.065455

0.072727

0.050909

0.087273

0

0.004061

0.17985

1,201

39

86

30.794872

0.833503

0

0.071429

0

0.035714

0.183181

0.115737

0

1

0.071429

false

0

0.142857

0

0.357143

0

null

0

null

0

1

0

38171d2911bdd197c075b3659dc77a7fa491cb09

2,757

py

Python

Otros/automatizacion-separador-de-direcciones -2.py

gustavoghp87/MiseriScraping

a9f19c1f45424107713a331d74e2a905404390c4

[ "Apache-2.0" ]

null

Otros/automatizacion-separador-de-direcciones -2.py

gustavoghp87/MiseriScraping

a9f19c1f45424107713a331d74e2a905404390c4

[ "Apache-2.0" ]

null

Otros/automatizacion-separador-de-direcciones -2.py

gustavoghp87/MiseriScraping

a9f19c1f45424107713a331d74e2a905404390c4

[ "Apache-2.0" ]

null

import os import xlsxwriter import pandas as pd ################################################################################################################################ data = pd.read_excel("te.xlsx", encoding='UTF8') wb = xlsxwriter.Workbook("nuevo2.xlsx") ws = wb.add_worksheet() ws.write(0, 0, "inner_id") ws.write(0, 1, "calle_id") ws.write(0, 2, "territorio") ws.write(0, 3, "manzana") ws.write(0, 4, "dirección") ws.write(0, 5, "dire-alt") ws.write(0, 6, "piso/depto") ws.write(0, 13, "teléfono") ws.write(0, 14, "estado") for i in data.index: inner_id = data['inner_id'][i] cuadra_id = data['cuadra_id'][i] territorio = data['territorio'][i] manzana = data['manzana'][i] direccion = data['dirección'][i] telefono = data['teléfono'][i] estado = data['estado'][i] excA = "A" + str(i+2) ws.write(excA, inner_id) excB = "B" + str(i+2) ws.write(excB, cuadra_id) excC = "C" + str(i+2) ws.write(excC, territorio) excD = "D" + str(i+2) ws.write(excD, manzana) excE = "E" + str(i+2) ws.write(excE, direccion) excF = "F" + str(i+2) excG = "G" + str(i+2) print("DIRECCION: " + direccion) lista = direccion.split(' ') if (lista[1].isdigit()): direccion2 = lista[0] + " " + lista[1] ws.write(excF, direccion2) try: pisodepto = lista[2] + " " + lista[3] + " " + lista[4] except: try: pisodepto = lista[2] + " " + lista[3] except: try: pisodepto = lista[2] except: pisodepto = "" ws.write(excG, pisodepto) elif (lista[2].isdigit()): direccion2 = lista[0] + " " + lista[1] + " " + lista[2] ws.write(excF, direccion2) try: pisodepto = lista[3] + " " + lista[4] + " " + lista[5] except: try: pisodepto = lista[3] + " " + lista[4] except: try: pisodepto = lista[3] except: pisodepto = "" ws.write(excG, pisodepto) elif (lista[3].isdigit()): direccion2 = lista[0] + " " + lista[1] + " " + lista[2] + " " + lista[3] ws.write(excF, direccion2) try: pisodepto = lista[4] + " " + lista[5] + " " + lista[6] except: try: pisodepto = lista[4] + " " + lista[5] except: try: pisodepto = lista[4] except: pisodepto = "" ws.write(excG, pisodepto) elif (lista[4].isdigit()): direccion2 = lista[0] + " " + lista[1] + " " + lista[2] + " " + lista[3] + " " + lista[4] ws.write(excF, direccion2) try: pisodepto = lista[5] + " " + lista[6] + " " + lista[7] except: try: pisodepto = lista[5] + " " + lista[6] except: try: pisodepto = lista[5] except: pisodepto = "" ws.write(excG, pisodepto) excN = "N" + str(i+2) ws.write(excN, telefono) excO = "O" + str(i+2) ws.write(excO, estado) wb.close() print("\n\n\n##################### Terminado con éxito! \n\n\n")

24.39823

128

0.540805

372

2,757

3.981183

0.22043

0.113437

0.137745

0.12424

0.551654

0.472654

0.405807

0.303174

0.055368

0

0.037241

0.211099

2,757

112

129

24.616071

0.643678

0

0.363636

0

0.098136

0.01027

0

1

0

false

0

0.030303

0

0.030303

0.020202

0

null

0

null

0

1

0

3817a39ebae3bec12eb2bb2bd3ccd37374c4d67e

15,930

py

Python

Scripts/logica_estado_smach.py

LiuSeeker/Robotica-projeto-1

425795d51232470ac840faf9dc7d97863d801554

[ "CECILL-B" ]

null

Scripts/logica_estado_smach.py

LiuSeeker/Robotica-projeto-1

425795d51232470ac840faf9dc7d97863d801554

[ "CECILL-B" ]

null

Scripts/logica_estado_smach.py

LiuSeeker/Robotica-projeto-1

425795d51232470ac840faf9dc7d97863d801554

[ "CECILL-B" ]

null

#! /usr/bin/env python # -*- coding:utf-8 -*- import smach import smach_ros import rospy import numpy as np import tf import math import cv2 import time from geometry_msgs.msg import Twist, Vector3, Pose from nav_msgs.msg import Odometry from sensor_msgs.msg import Image, CompressedImage, LaserScan, Imu from turtlebot3_msgs.msg import Sound from cv_bridge import CvBridge, CvBridgeError import smach import smach_ros import cormodule import transformations #xml do haarcascade com o treinamento dos rostos de gatos face_cascade = cv2.CascadeClassifier('haarcascade_frontalcatface.xml') bridge = CvBridge() global cv_image global dif_x global media global centro global imu global angulos global toca1 global toca2 bateu = False toca1 = True toca2 = True angulos = [0] cv_image = None dif_x = None media = 0 centro = 0 media0 = (0,0) imu = [] atraso = 1.5E9 delay_frame = 0.03 bateu = None p = False #-------------------------------------------------------------------------------------------------------------------------------------- #Converte o valor da medida do LaserScan para centímetros def converte(valor): return valor*44.4/0.501 #Função que recebe a imagem da câmera def roda_todo_frame(imagem): global cv_image global media global centro global dif_x global p global area1, area2 global media0, centro0, area0 #Calcula o lag de frames now = rospy.get_rostime() imgtime = imagem.header.stamp lag = now-imgtime delay = lag.nsecs if delay > atraso and check_delay==True: print("delay: {}".format(delay/1.0E9)) return cv_image = bridge.compressed_imgmsg_to_cv2(imagem, "bgr8") #Deixa a imagem da câmera preto e branco para ser utilizada na detecção de faces gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.3, 5) #Detecta os pontos extremos da face for(x,y,z,w) in faces: cv2.rectangle(cv_image, (x,y), (x+z, y+w), (255,0,0), 2) #Desenha um retângulo em volta da face roi_gray = gray[y:y+w, x:x+z] roi_color = cv_image[y:y+w, x:x+z] media = x+z/2 #Calcula a posição em x do centro da face centro = cv_image.shape[0]//1.5 #Calcula a posição em x do centro da imagem #Identificando a primeira interacao do 'for' (ao reconhecer gato) para calcular a area da figura if p == False: area1 = z*w area2 = 0 p = True #Calculando a area em todas as outras interacoes para definir a velocidade proporcional do robo elif p == True: area2 = z*w #Caso tenha achado uma face, calcula a diferença entre o centro da face e o centro da imagem if faces != (): dif_x = media-centro else: dif_x = None #Define o centro do objeto detectada por cor e o centro da imagem media0, centro0, area0 = cormodule.identifica_cor(cv_image) #Printa a imagem da câmera cv2.imshow("Camera", cv_image) cv2.waitKey(1) #Define a lista de distâncias do scan do LaserScan def scaneou(dado): global distancias distancias = np.array(dado.ranges) #Define o posicionamento pelo IMU def leu_imu(dado): global angulos, imu, imu_acele, imu_media, bateu quat = dado.orientation lista = [quat.x, quat.y, quat.z, quat.w] angulos = np.degrees(transformations.euler_from_quaternion(lista)) imu_acele = np.array(dado.linear_acceleration.x).round(decimals=2) imu.append(imu_acele) #Pegando a media da lista recebida if len(imu) >= 12: imu = imu[6:] imu_media = np.mean(imu) #Analisando se bateu if abs(imu[-1] - imu_media) >= 3.0: imu = [] bateu = True else: bateu = False #-------------------------------------------------------------------------------------------------------------------------------------- ## Classes para o state machine #Classe para procurar objetos de interesse class Procurar(smach.State): def __init__(self): smach.State.__init__(self, outcomes=['objeto_1', 'objeto_2', 'nada']) def execute(self, userdata): print("Procurar") global velocidade velocidade = Twist(Vector3(0, 0, 0), Vector3(0, 0, 0.4)) #Se acha a face: if dif_x != None: return 'objeto_1' #Se acha o objeto pela cor de interesse if media0[0] != 0 and media0[1] != 0 and area0 >= 3000: return 'objeto_2' #Se não acha nada else: velocidade_saida.publish(velocidade) rospy.sleep(0.01) return 'nada' #Apos encontrar o objeto, essa classe serve para seguir o objeto class Seguir(smach.State): def __init__(self): smach.State.__init__(self, outcomes=['seguir', 'desviar', 'perto', 'perdido', 'bateu']) def execute(self, userdata): print("Seguir") global velocidade_saida, bateu tolerancia = 25 desviar = False seguindo = False perdido = False perto = False l_fd = 0 l_fe = 0 l_d = 0 l_e = 0 if bateu: bateu = False return "bateu" #Utilizando as distancias recebidas na funcao scaneou() #A primeira distância na lista é a distância em frente ao robô, #e o scan segue sentido anti-horário a partir da primeira distância (a frente do robô) for i in range(len(distancias)): #Faixa de scan na parte frontal esquerda do robô if i <= 40: #Se houver algo nessa faixa a uma distância menor que 50cm, deixa a flag 'Desviar' como True if converte(distancias[i]) < 30 and converte(distancias[i]) != 0: l_fe += 1 #Faixa de scan na parte frontal direita do robô if i >= 320: #Se houver algo nessa faixa a uma distância menor que 30cm, deixa a flag 'Desviar' como True if converte(distancias[i]) < 30 and converte(distancias[i]) != 0: l_fd += 1 #Faixa de scan na parte esquerda do robô if i <= 70 and i > 40: #Se houver algo nessa faixa a uma distância menor que 20cm, deixa a flag 'Desviar' como True if converte(distancias[i]) < 20 and converte(distancias[i]) != 0: l_e += 1 #Faixa de scan na parte direita do robô if i < 320 and i >= 290: #Se houver algo nessa faixa a uma distância menor que 20cm, deixa a flag 'Desviar' como True if converte(distancias[i]) < 20 and converte(distancias[i]) != 0: l_d += 1 if l_fe >= 5: desviar = True if l_fd >= 5: desviar = True if l_e >= 5: desviar = True if l_d >= 5: desviar = True #Centralizando o rosto (no caso, do gato) de acordo com a tolerancia if dif_x > tolerancia: velocidade = Twist(Vector3(0,0,0), Vector3(0,0,-0.2)) rospy.sleep(delay_frame) seguindo = True if dif_x < -tolerancia and dif_x != None: velocidade = Twist(Vector3(0,0,0), Vector3(0,0,0.2)) rospy.sleep(delay_frame) seguindo = True #Uma vez centralizado, comparamos a area que o objeto ocupa do frame em relacao a area que ocupava inicialmente #para decidir a velocidade que o robo pode andar if dif_x >= -tolerancia and dif_x <= tolerancia: #Caso a area esteja muito grande (ou seja, o objeto esta perto do robo), ele para, de maneira que nao colida if area2 >= area1*1.1: velocidade = Twist(Vector3(0,0,0), Vector3(0,0,0)) rospy.sleep(delay_frame) perto = True #Se o objeto estiver longe, podemos acelerar o robo if area2 <= area1*1.1 and area2 > area1*0.6: velocidade = Twist(Vector3(0.1,0,0), Vector3(0,0,0)) rospy.sleep(delay_frame) seguindo = True #Caso esteja muito longe, sua velocidade é maior ainda if area2 <= area1*0.6: velocidade = Twist(Vector3(0.2,0,0), Vector3(0,0,0)) rospy.sleep(delay_frame) seguindo = True if bateu: bateu = False return "bateu" #Se a if dif_x == None: if area1 == 0 and area2 == 0: perdido = True if desviar: return 'desviar' if perdido: return 'perdido' if seguindo: velocidade_saida.publish(velocidade) rospy.sleep(0.01) return 'seguir' if perto: velocidade_saida.publish(velocidade) rospy.sleep(0.01) return 'perto' return 'perdido' #Classe utilizada para desviar de objetos perto do robo class Desviar(smach.State): def __init__(self): smach.State.__init__(self, outcomes=['desviado', 'desviando','bateu']) def execute(self, userdata): global bateu print("Desviar") desviando = False l_fd = 0 l_fdc = 0 l_fe = 0 l_fec = 0 l_d = 0 l_e = 0 l_t = 0 if bateu: bateu = False return "bateu" for i in range(len(distancias)): #Faixa de scan na parte frontal esquerda do robô if i <= 40: #Se houver algo nessa faixa a uma distância menor que 30cm, desvia if converte(distancias[i]) < 30 and converte(distancias[i]) >= 15: l_fe += 1 #Se houver algo nessa faixa a uma distância menor que 15cm, para e desvia elif converte(distancias[i]) < 15 and converte(distancias[i]) != 0: l_fec += 1 #Faixa de scan na parte frontal direita do robô if i >= 320: #Se houver algo nessa faixa a uma distância menor que 30cm, desvia if converte(distancias[i]) < 30 and converte(distancias[i]) >= 15: l_fd += 1 #Se houver algo nessa faixa a uma distância menor que 15cm, para e desvia elif converte(distancias[i]) < 15 and converte(distancias[i]) != 0: l_fdc += 1 #Faixa de scan na parte esquerda do robô if i <= 70 and i > 40: #Se houver algo nessa faixa a uma distância menor que 20cm, desvia if converte(distancias[i]) < 20 and converte(distancias[i]) != 0: l_e += 1 #Faixa de scan na parte direita do robô if i < 320 and i >= 290: #Se houver algo nessa faixa a uma distância menor que 20cm, desvia if converte(distancias[i]) < 20 and converte(distancias[i]) != 0: l_d += 1 #Faixa de scan na parte traseira do robô if i < 290 and i > 70: #Se houver algo nessa faixa a uma distância menor que 20cm, para e desvia if converte(distancias[i]) < 20 and converte(distancias[i]) != 0: l_t += 1 if l_t >= 10: velocidade = Twist(Vector3(0, 0, 0), Vector3(0, 0, 0.5)) desviando = True if l_d >= 5: velocidade = Twist(Vector3(0.1, 0, 0), Vector3(0, 0, 0.5)) desviando = True if l_e >= 5: velocidade = Twist(Vector3(0.1, 0, 0), Vector3(0, 0, -0.5)) desviando = True if l_fe > l_fd and l_fe >= 5: velocidade = Twist(Vector3(0.2, 0, 0), Vector3(0, 0, -0.7)) desviando = True elif l_fd > l_fe and l_fd >= 5: velocidade = Twist(Vector3(0.2, 0, 0), Vector3(0, 0, 0.7)) desviando = True if l_fec > l_fdc and l_fec >= 5: velocidade = Twist(Vector3(-0.1, 0, 0), Vector3(0, 0, -0.9)) desviando = True elif l_fdc > l_fec and l_fdc >= 5: velocidade = Twist(Vector3(-0.1, 0, 0), Vector3(0, 0, 0.9)) desviando = True #Se estiver desviando, publica a velocidade e retorna 'desviando' if desviando: velocidade_saida.publish(velocidade) rospy.sleep(0.01) return 'desviando' #Se já acabou de desviar, retorna 'desviado' else: return 'desviado' #As duas classes abaixo servem para tocar sons do robo class Som1(smach.State): def __init__(self): smach.State.__init__(self, outcomes=['tocado']) def execute(self, userdata): global saida_som global toca1 print("Som1") if toca1: saida_som.publish(3) velocidade = Twist(Vector3(0,0,0), Vector3(0, 0,0)) velocidade_saida.publish(velocidade) rospy.sleep(0.01) toca1 = False return 'tocado' class Som2(smach.State): def __init__(self): smach.State.__init__(self, outcomes=['tocado']) def execute(self, userdata): global saida_som global toca2 print("Som2") if toca2: saida_som.publish(2) velocidade = Twist(Vector3(0,0,0), Vector3(0,0,0)) velocidade_saida.publish(velocidade) rospy.sleep(0.01) toca2 = False return 'tocado' #Classe que, quando rodada, grava o angulo atual como o inicial e define o angulo final class Pos_ini(smach.State): def __init__(self): smach.State.__init__(self, outcomes=['pego']) def execute(self, userdata): print("Pos_ini") global ang_inicial global ang_final global ang_varia global angulos ang_inicial = angulos[0] ang_varia = 180 if ang_inicial < ang_varia and ang_inicial >= 0: ang_final = ang_inicial - ang_varia elif ang_inicial > -ang_varia and ang_inicial < 0: ang_final = ang_inicial + ang_varia return 'pego' #Essa classe vira até o ângulo final definido pela classe Pos_ini class Virar(smach.State): def __init__(self): smach.State.__init__(self, outcomes=['virando', 'virado', 'bateu']) def execute(self, userdata): print("Virar") global velocidade_saida global angulos global bateu ang_atual = angulos[0] if bateu: bateu = False return "bateu" velocidade = Twist(Vector3(0, 0, 0), Vector3(0, 0, -0.7)) #Se já acabou de virar, retorna 'virado' if ang_atual <= ang_final+3 and ang_atual >= ang_final-3: velocidade = Twist(Vector3(0, 0, 0), Vector3(0, 0, 0)) velocidade_saida.publish(velocidade) rospy.sleep(0.01) return 'virado' #Se estiver virando, publica a velocidade e retorna 'virando' else: velocidade_saida.publish(velocidade) rospy.sleep(0.01) return 'virando' class Bateu(smach.State): def __init__(self): smach.State.__init__(self,outcomes = ['perdido']) def execute(self,userdata): print("Bateu") global velocidade_saida velocidade = Twist(Vector3(0, 0, 0), Vector3(0, 0, 0)) velocidade_saida.publish(velocidade) rospy.sleep(2) return "perdido" def main(): global velocidade_saida global angulos global ang_inicial global ang_final global saida_som rospy.init_node('smach_example_state_machine') #Dá subscribe na função 'leu_imu' recebe_imu = rospy.Subscriber("/imu", Imu, leu_imu) #Publica o som saida_som = rospy.Publisher("/sound", Sound, queue_size = 2) #Dá subscribe na função 'roda_todo_frame' recebedor = rospy.Subscriber("/raspicam_node/image/compressed", CompressedImage, roda_todo_frame, queue_size=10, buff_size = 2**24) #Publica a velocidade velocidade_saida = rospy.Publisher("/cmd_vel", Twist, queue_size = 2) #Dá subscribe na função 'scaneou' recebe_scan = rospy.Subscriber("/scan", LaserScan, scaneou) # Create a SMACH state machine sm = smach.StateMachine(outcomes=['terminei']) # Open the container with sm: # Add states to the container smach.StateMachine.add('PROCURAR', Procurar(), transitions={'objeto_1':'SEGUIR', #se achar o objeto 1, retorna 'objeto_1' e executa 'SEGUIR' 'objeto_2':'POS_INI', #se achar o objeto 2, retorna 'objeto_2' e executa 'SOM_2' 'nada': 'PROCURAR'}) #se não achar nada, retorna 'nada' e executa "PROCURAR" smach.StateMachine.add('SEGUIR', Seguir(), transitions={'seguir':'SEGUIR', #se nao scnear nada perto, estiver longe do objeto, retorna 'longe' e executa 'SEGUIR' 'desviar': 'DESVIAR', #se scanear qq coisa perto (direção qualquer), retorna 'desviar' e executa 'DESVIAR' 'perto': 'SOM_1', #se estiver perto do objeto, retorna 'perto' e executa 'SOM_1' 'perdido': 'PROCURAR', 'bateu': 'BATEU'}) #perder o objeto, retorna 'perdido' e executa 'PROCURAR' smach.StateMachine.add('DESVIAR', Desviar(), transitions={'desviado': 'PROCURAR', #se desviou, retorna 'desviado' e executa 'PROCURAR' 'desviando': 'DESVIAR', 'bateu' : 'BATEU'}) #scaneia a direção e desvia, e retorna 'desviado' e executa 'PROCURAR' smach.StateMachine.add('SOM_1', Som1(), transitions={'tocado': 'SEGUIR'}) #toca o som1, e retorna 'tocado' e executa 'PROCURAR' smach.StateMachine.add('SOM_2', Som2(), transitions={'tocado': 'PROCURAR'}) #toca o som2, e retorna 'tocado' e executa 'PROCURAR' smach.StateMachine.add('POS_INI', Pos_ini(), transitions={'pego': 'VIRAR'}) #grava a posição atual, e retorna 'pego' e executa 'VIRAR' smach.StateMachine.add('VIRAR', Virar(), transitions={'virado': 'SOM_2', #se virou 180 graus, retorna 'virado' e executa 'PROCURAR' 'virando': 'VIRAR', 'bateu' : 'BATEU'}) #se está virado 180 graus, e retorna 'virando' e executa 'VIRAR' smach.StateMachine.add('BATEU', Bateu(), transitions={'perdido': 'PROCURAR' }) # Execute SMACH plan outcome = sm.execute() if __name__ == '__main__': main()

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0.346282

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15,930

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0.784943

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381af4f749698a944829d6fcd5c433427a798dde

44,048

py

Python

sfm/ui/forms.py

edsu/sfm-ui

d07d1e28262330f12b64f0ce68998c378ed76e43

[ "MIT" ]

null

sfm/ui/forms.py

edsu/sfm-ui

d07d1e28262330f12b64f0ce68998c378ed76e43

[ "MIT" ]

null

sfm/ui/forms.py

edsu/sfm-ui

d07d1e28262330f12b64f0ce68998c378ed76e43

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from django import forms from django.contrib.auth.models import Group from django.urls import reverse from django.utils import timezone from django.core.exceptions import ValidationError from django.conf import settings from crispy_forms.helper import FormHelper from crispy_forms.layout import Layout, Fieldset, Button, Submit, Div, HTML from crispy_forms.bootstrap import FormActions from datetimewidget.widgets import DateTimeWidget from .models import CollectionSet, Collection, Seed, Credential, Export, User from .utils import clean_token, clean_blogname import json import logging import re log = logging.getLogger(__name__) HISTORY_NOTE_LABEL = "Change Note" HISTORY_NOTE_HELP = "Explain why you made these changes at this time." HISTORY_NOTE_HELP_ADD = "Further information about this addition." HISTORY_NOTE_WIDGET = forms.Textarea(attrs={'rows': 4}) DATETIME_WIDGET = DateTimeWidget( usel10n=True, bootstrap_version=3, attrs={'data-readonly': 'false'}, options={ 'showMeridian': True } ) SCHEDULE_HELP = "How frequently you want data to be retrieved." INCREMENTAL_LABEL = "Incremental harvest" INCREMENTAL_HELP = "Only collect new items since the last data retrieval." GROUP_HELP = "Your default group is your username, unless the SFM team has added you to another group." class CollectionSetForm(forms.ModelForm): group = forms.ModelChoiceField(queryset=None) class Meta: model = CollectionSet fields = ['name', 'description', 'group', 'history_note'] exclude = [] widgets = { 'history_note': HISTORY_NOTE_WIDGET } localized_fields = None labels = { 'history_note': HISTORY_NOTE_LABEL } help_texts = { 'history_note': HISTORY_NOTE_HELP, } error_messages = {} def __init__(self, *args, **kwargs): request = kwargs.pop('request') super(CollectionSetForm, self).__init__(*args, **kwargs) # limiting groups in dropdown to user's and setting default if only 1 value. group_queryset = Group.objects.filter(pk__in=request.user.groups.all()) if len(group_queryset) == 1: self.initial['group'] = group_queryset[0] self.fields['group'].queryset = group_queryset self.fields['group'].help_text = GROUP_HELP # check whether it's a CreateView and offer different help text if self.instance.pk is None: self.fields['history_note'].help_text = HISTORY_NOTE_HELP_ADD # set up crispy forms helper self.helper = FormHelper(self) self.helper.layout = Layout( Fieldset( '', 'name', 'description', 'group', 'history_note' ), FormActions( Submit('submit', 'Save'), Button('cancel', 'Cancel', onclick="window.history.back()") ) ) class NameModelChoiceField(forms.ModelChoiceField): def label_from_instance(self, obj): return obj.name class BaseCollectionForm(forms.ModelForm): credential = NameModelChoiceField(None) link = forms.URLField(required=False, label="Public link", help_text="Link to a public version of this collection, e.g., in a data repository.") class Meta: model = Collection fields = ['name', 'description', 'link', 'collection_set', 'visibility', 'schedule_minutes', 'credential', 'end_date', 'history_note'] exclude = [] widgets = {'collection_set': forms.HiddenInput, 'history_note': HISTORY_NOTE_WIDGET, 'end_date': DATETIME_WIDGET} labels = { 'history_note': HISTORY_NOTE_LABEL, 'visibility': 'Sharing' } help_texts = { 'history_note': HISTORY_NOTE_HELP, 'schedule_minutes': SCHEDULE_HELP } error_messages = {} def __init__(self, *args, **kwargs): self.coll = kwargs.pop("coll", None) self.credential_list = kwargs.pop('credential_list', None) super(BaseCollectionForm, self).__init__(*args, **kwargs) # Set default if only 1 value. if self.credential_list and self.credential_list.count() == 1: self.initial['credential'] = self.credential_list[0] self.fields['credential'].queryset = self.credential_list # check whether it's a create view and offer different help text if self.instance.pk is None: self.fields['history_note'].help_text = HISTORY_NOTE_HELP_ADD cancel_url = reverse('collection_set_detail', args=[self.coll]) self.helper = FormHelper(self) self.helper.layout = Layout( Fieldset( '', 'name', 'description', 'link', 'credential', Div(css_id='credential_warning'), Div(), 'schedule_minutes', 'end_date', 'collection_set', 'visibility', 'history_note' ), FormActions( Submit('submit', 'Save'), Button('cancel', 'Cancel', onclick="window.location.href='{0}'".format(cancel_url)) ) ) def clean_end_date(self): data = self.cleaned_data.get('end_date', None) if data: if data < timezone.now(): raise forms.ValidationError( 'End date must be later than current date and time.') return data class CollectionTwitterUserTimelineForm(BaseCollectionForm): incremental = forms.BooleanField(initial=True, required=False, label=INCREMENTAL_LABEL, help_text=INCREMENTAL_HELP) deleted_accounts_option = forms.BooleanField(initial=False, required=False, label="Automatically delete seeds " "for deleted / not found " "accounts.") suspended_accounts_option = forms.BooleanField(initial=False, required=False, label="Automatically delete seeds " "for suspended accounts.") protected_accounts_options = forms.BooleanField(initial=False, required=False, label="Automatically delete seeds " "for protected accounts.") def __init__(self, *args, **kwargs): super(CollectionTwitterUserTimelineForm, self).__init__(*args, **kwargs) self.helper.layout[0][5].extend(('incremental', 'deleted_accounts_option', 'suspended_accounts_option', 'protected_accounts_options')) if self.instance and self.instance.harvest_options: harvest_options = json.loads(self.instance.harvest_options) if "incremental" in harvest_options: self.fields['incremental'].initial = harvest_options["incremental"] if "deactivate_not_found_seeds" in harvest_options: self.fields['deleted_accounts_option'].initial = harvest_options["deactivate_not_found_seeds"] if "deactivate_unauthorized_seeds" in harvest_options: self.fields['protected_accounts_options'].initial = harvest_options["deactivate_unauthorized_seeds"] if "deactivate_suspended_seeds" in harvest_options: self.fields['suspended_accounts_option'].initial = harvest_options["deactivate_suspended_seeds"] def save(self, commit=True): m = super(CollectionTwitterUserTimelineForm, self).save(commit=False) m.harvest_type = Collection.TWITTER_USER_TIMELINE harvest_options = { "incremental": self.cleaned_data["incremental"], "deactivate_not_found_seeds": self.cleaned_data["deleted_accounts_option"], "deactivate_unauthorized_seeds": self.cleaned_data["protected_accounts_options"], "deactivate_suspended_seeds": self.cleaned_data["suspended_accounts_option"] } m.harvest_options = json.dumps(harvest_options, sort_keys=True) m.save() return m class CollectionTwitterSearchForm(BaseCollectionForm): incremental = forms.BooleanField(initial=True, required=False, label=INCREMENTAL_LABEL, help_text=INCREMENTAL_HELP) def __init__(self, *args, **kwargs): super(CollectionTwitterSearchForm, self).__init__(*args, **kwargs) self.helper.layout[0][5].extend(('incremental',)) if self.instance and self.instance.harvest_options: harvest_options = json.loads(self.instance.harvest_options) if "incremental" in harvest_options: self.fields['incremental'].initial = harvest_options["incremental"] def save(self, commit=True): m = super(CollectionTwitterSearchForm, self).save(commit=False) m.harvest_type = Collection.TWITTER_SEARCH harvest_options = { "incremental": self.cleaned_data["incremental"], } m.harvest_options = json.dumps(harvest_options, sort_keys=True) m.save() return m class CollectionTwitterSampleForm(BaseCollectionForm): class Meta(BaseCollectionForm.Meta): exclude = ('schedule_minutes',) def __init__(self, *args, **kwargs): super(CollectionTwitterSampleForm, self).__init__(*args, **kwargs) def save(self, commit=True): m = super(CollectionTwitterSampleForm, self).save(commit=False) m.harvest_type = Collection.TWITTER_SAMPLE m.schedule_minutes = None m.save() return m class CollectionTwitterFilterForm(BaseCollectionForm): class Meta(BaseCollectionForm.Meta): exclude = ('schedule_minutes',) def __init__(self, *args, **kwargs): super(CollectionTwitterFilterForm, self).__init__(*args, **kwargs) def save(self, commit=True): m = super(CollectionTwitterFilterForm, self).save(commit=False) m.harvest_type = Collection.TWITTER_FILTER m.schedule_minutes = None m.save() return m class CollectionFlickrUserForm(BaseCollectionForm): incremental = forms.BooleanField(initial=True, required=False, label=INCREMENTAL_LABEL, help_text=INCREMENTAL_HELP) def __init__(self, *args, **kwargs): super(CollectionFlickrUserForm, self).__init__(*args, **kwargs) self.helper.layout[0][5].extend(('incremental',)) if self.instance and self.instance.harvest_options: harvest_options = json.loads(self.instance.harvest_options) if "incremental" in harvest_options: self.fields['incremental'].initial = harvest_options["incremental"] def save(self, commit=True): m = super(CollectionFlickrUserForm, self).save(commit=False) m.harvest_type = Collection.FLICKR_USER harvest_options = { "incremental": self.cleaned_data["incremental"], } m.harvest_options = json.dumps(harvest_options) m.save() return m class CollectionWeiboTimelineForm(BaseCollectionForm): incremental = forms.BooleanField(initial=True, required=False, label=INCREMENTAL_LABEL, help_text=INCREMENTAL_HELP) def __init__(self, *args, **kwargs): super(CollectionWeiboTimelineForm, self).__init__(*args, **kwargs) self.helper.layout[0][5].extend(('incremental',)) if self.instance and self.instance.harvest_options: harvest_options = json.loads(self.instance.harvest_options) if "incremental" in harvest_options: self.fields['incremental'].initial = harvest_options["incremental"] def save(self, commit=True): m = super(CollectionWeiboTimelineForm, self).save(commit=False) m.harvest_type = Collection.WEIBO_TIMELINE harvest_options = { "incremental": self.cleaned_data["incremental"], } m.harvest_options = json.dumps(harvest_options, sort_keys=True) m.save() return m class CollectionWeiboSearchForm(BaseCollectionForm): incremental = forms.BooleanField(initial=True, required=False, help_text=INCREMENTAL_HELP, label=INCREMENTAL_LABEL) def __init__(self, *args, **kwargs): super(CollectionWeiboSearchForm, self).__init__(*args, **kwargs) self.helper.layout[0][5].extend(('incremental',)) if self.instance and self.instance.harvest_options: harvest_options = json.loads(self.instance.harvest_options) if "incremental" in harvest_options: self.fields['incremental'].initial = harvest_options["incremental"] def save(self, commit=True): m = super(CollectionWeiboSearchForm, self).save(commit=False) m.harvest_type = Collection.WEIBO_SEARCH harvest_options = { "incremental": self.cleaned_data["incremental"], } m.harvest_options = json.dumps(harvest_options, sort_keys=True) m.save() return m class CollectionTumblrBlogPostsForm(BaseCollectionForm): incremental = forms.BooleanField(initial=True, required=False, label=INCREMENTAL_LABEL, help_text=INCREMENTAL_HELP) def __init__(self, *args, **kwargs): super(CollectionTumblrBlogPostsForm, self).__init__(*args, **kwargs) self.helper.layout[0][5].extend(('incremental',)) if self.instance and self.instance.harvest_options: harvest_options = json.loads(self.instance.harvest_options) if "incremental" in harvest_options: self.fields['incremental'].initial = harvest_options["incremental"] def save(self, commit=True): m = super(CollectionTumblrBlogPostsForm, self).save(commit=False) m.harvest_type = Collection.TUMBLR_BLOG_POSTS harvest_options = { "incremental": self.cleaned_data["incremental"], } m.harvest_options = json.dumps(harvest_options, sort_keys=True) m.save() return m class BaseSeedForm(forms.ModelForm): class Meta: model = Seed fields = ['collection', 'history_note'] exclude = [] widgets = { 'collection': forms.HiddenInput, 'history_note': HISTORY_NOTE_WIDGET } labels = { 'history_note': HISTORY_NOTE_LABEL } help_texts = { 'history_note': HISTORY_NOTE_HELP } def __init__(self, *args, **kwargs): self.collection = kwargs.pop("collection", None) # for createView and updateView self.view_type = kwargs.pop("view_type", None) # for updateView check the updates for the original token and uid self.entry = kwargs.pop("entry", None) super(BaseSeedForm, self).__init__(*args, **kwargs) cancel_url = reverse('collection_detail', args=[self.collection]) # check whether it's a create view and offer different help text if self.instance.pk is None: self.fields['history_note'].help_text = HISTORY_NOTE_HELP_ADD self.helper = FormHelper(self) self.helper.layout = Layout( Fieldset( '', Div(), 'history_note', 'collection' ), FormActions( Submit('submit', 'Save'), Button('cancel', 'Cancel', onclick="window.location.href='{0}'".format(cancel_url)) ) ) def clean_token(self): token_val = self.cleaned_data.get("token") return token_val.strip() def clean_uid(self): uid_val = self.cleaned_data.get("uid") return uid_val.strip() def clean(self): fields = self._meta.fields uid_val, token_val = '', '' uid_label, token_label = '', '' if "uid" in fields: uid_val = self.cleaned_data.get("uid") uid_label = self._meta.labels["uid"] if "token" in fields: token_val = self.cleaned_data.get("token") token_label = self._meta.labels["token"] # if has invalid error before, directly not check deep error if self.errors: return # should not both empty if has token or uid fields, the twitter filter should deal with separately if (uid_label or token_label) and (not uid_val and not token_val): or_text = 'or' * (1 if uid_label and token_label else 0) raise ValidationError( u'One of the following fields is required :{} {} {}.'.format(token_label, or_text, uid_label)) # for the update view if self.view_type == Seed.UPDATE_VIEW: # check updated seeds exist in db if changes # case insensitive match, and user can't add 'token:TeSt' or 'token:teSt', etc if 'token:test exist.', # but can update to 'token:TeSt' or other. if token_val.lower() != self.entry.token.lower() and \ token_val and Seed.objects.filter(collection=self.collection, token__iexact=token_val).exists(): raise ValidationError(u'{}: {} already exist.'.format(token_label, token_val)) # check updated uid whether exist in db if changes if uid_val.lower() != self.entry.uid.lower() and \ uid_val and Seed.objects.filter(collection=self.collection, uid__iexact=uid_val).exists(): raise ValidationError(u'{}: {} already exist.'.format(uid_label, uid_val)) else: if token_val and Seed.objects.filter(collection=self.collection, token__iexact=token_val).exists(): raise ValidationError(u'{}: {} already exist.'.format(token_label, token_val)) if uid_val and Seed.objects.filter(collection=self.collection, uid__iexact=uid_val).exists(): raise ValidationError(u'{}: {} already exist.'.format(uid_label, uid_val)) class SeedTwitterUserTimelineForm(BaseSeedForm): class Meta(BaseSeedForm.Meta): fields = ['token', 'uid'] fields.extend(BaseSeedForm.Meta.fields) labels = dict(BaseSeedForm.Meta.labels) labels["token"] = "Screen name" labels["uid"] = "User id" widgets = dict(BaseSeedForm.Meta.widgets) widgets["token"] = forms.TextInput(attrs={'size': '40'}) widgets["uid"] = forms.TextInput(attrs={'size': '40'}) def __init__(self, *args, **kwargs): super(SeedTwitterUserTimelineForm, self).__init__(*args, **kwargs) self.helper.layout[0][0].extend(('token', 'uid')) def clean_uid(self): uid_val = self.cleaned_data.get("uid") # check the format if uid_val and not uid_val.isdigit(): raise ValidationError('Uid should be numeric.', code='invalid') return uid_val def clean_token(self): token_val = clean_token(self.cleaned_data.get("token")) token_val = token_val.split(" ")[0] # check the format if token_val and token_val.isdigit(): raise ValidationError('Screen name may not be numeric.', code='invalid') return token_val class SeedTwitterSearchForm(BaseSeedForm): query = forms.CharField(required=False, widget=forms.Textarea(attrs={'rows': 4}), help_text='See <a href="https://developer.twitter.com/en/docs/tweets/search/guides/' 'standard-operators" target="_blank">' 'these instructions</a> for writing a query. ' 'Example: firefly OR "lightning bug"') geocode = forms.CharField(required=False, help_text='Geocode in the format latitude,longitude,radius. ' 'Example: 38.899434,-77.036449,50mi') def __init__(self, *args, **kwargs): super(SeedTwitterSearchForm, self).__init__(*args, **kwargs) self.helper.layout[0][0].extend(('query', 'geocode')) if self.instance and self.instance.token: try: token = json.loads(self.instance.token) # This except handling is for converting over old query tokens except ValueError: token = {'query': self.instance.token} if 'query' in token: self.fields['query'].initial = token['query'] if 'geocode' in token: self.fields['geocode'].initial = token['geocode'] def clean_query(self): query_val = self.cleaned_data.get("query") return query_val.strip() def clean_geocode(self): geocode_val = self.cleaned_data.get("geocode") return geocode_val.strip() def clean(self): # if do string strip in here, string ends an empty space, not sure why query_val = self.cleaned_data.get("query") geocode_val = self.cleaned_data.get("geocode") # should not all be empty if not query_val and not geocode_val: raise ValidationError(u'One of the following fields is required: query, geocode.') def save(self, commit=True): m = super(SeedTwitterSearchForm, self).save(commit=False) token = dict() if self.cleaned_data['query']: token['query'] = self.cleaned_data['query'] if self.cleaned_data['geocode']: token['geocode'] = self.cleaned_data['geocode'] m.token = json.dumps(token, ensure_ascii=False) m.save() return m class SeedWeiboSearchForm(BaseSeedForm): class Meta(BaseSeedForm.Meta): fields = ['token'] fields.extend(BaseSeedForm.Meta.fields) labels = dict(BaseSeedForm.Meta.labels) labels["token"] = "Topic" help_texts = dict(BaseSeedForm.Meta.help_texts) help_texts["token"] = u'See <a href="http://open.weibo.com/wiki/2/search/topics" target="_blank">' \ u'API documents</a> for query Weibo related on a topic. ' \ u'Example: "科技".' widgets = dict(BaseSeedForm.Meta.widgets) widgets["token"] = forms.TextInput(attrs={'size': '40'}) def __init__(self, *args, **kwargs): super(SeedWeiboSearchForm, self).__init__(*args, **kwargs) self.helper.layout[0][0].append('token') class SeedTwitterFilterForm(BaseSeedForm): track = forms.CharField(required=False, widget=forms.Textarea(attrs={'rows': 4}), help_text="""Separate keywords and phrases with commas. See Twitter <a target="_blank" href="https://developer.twitter.com/en/docs/tweets/filter-realtime/guides/ basic-stream-parameters#track"> track</a> for more information.""") follow = forms.CharField(required=False, widget=forms.Textarea(attrs={'rows': 4}), help_text="""Use commas to separate user IDs (e.g. 1233718,6378678) of accounts whose tweets, retweets, and replies will be collected. See Twitter <a target="_blank" href="https://developer.twitter.com/en/docs/tweets/filter-realtime/guides/ basic-stream-parameters#follow"> follow</a> documentation for a full list of what is returned. User <a target="_blank" href="https://tweeterid.com/">TweeterID</a> to get the user ID for a screen name.""") locations = forms.CharField(required=False, widget=forms.Textarea(attrs={'rows': 4}), help_text="""Provide a longitude and latitude (e.g. -74,40,-73,41) of a geographic bounding box. See Twitter <a target="blank" href="https://developer.twitter.com/en/docs/tweets/filter-realtime/guides/basic-stream-parameters#locations"> locations</a> for more information.""") def __init__(self, *args, **kwargs): super(SeedTwitterFilterForm, self).__init__(*args, **kwargs) self.helper.layout[0][0].extend(('track', 'follow', 'locations')) if self.instance and self.instance.token: token = json.loads(self.instance.token) if 'track' in token: self.fields['track'].initial = token['track'] if 'follow' in token: self.fields['follow'].initial = token['follow'] if 'locations' in token: self.fields['locations'].initial = token['locations'] def clean_track(self): track_val = self.cleaned_data.get("track").strip() if len(track_val.split(",")) > 400: raise ValidationError("Can only track 400 keywords.") return track_val def clean_locations(self): return self.cleaned_data.get("locations").strip() def clean_follow(self): follow_val = self.cleaned_data.get("follow").strip() if len(follow_val.split(",")) > 5000: raise ValidationError("Can only follow 5000 users.") return follow_val def clean(self): # if do string strip in here, string ends an empty space, not sure why track_val = self.cleaned_data.get("track") follow_val = self.cleaned_data.get("follow") locations_val = self.cleaned_data.get("locations") # should not all be empty if not track_val and not follow_val and not locations_val: raise ValidationError(u'One of the following fields is required: track, follow, locations.') # check follow should be number uid if re.compile(r'[^0-9, ]').search(follow_val): raise ValidationError('Follow must be user ids', code='invalid_follow') token_val = {} if track_val: token_val['track'] = track_val if follow_val: token_val['follow'] = follow_val if locations_val: token_val['locations'] = locations_val token_val = json.dumps(token_val, ensure_ascii=False) # for the update view if self.view_type == Seed.UPDATE_VIEW: # check updated seeds exist in db if changes # case insensitive match, and user can update seed `tack:Test` to 'tack:test' if token_val.lower() != self.entry.token.lower() and \ token_val and Seed.objects.filter(collection=self.collection, token__iexact=token_val).exists(): raise ValidationError(u'Seed: {} already exist.'.format(token_val)) else: if token_val and Seed.objects.filter(collection=self.collection, token__iexact=token_val).exists(): raise ValidationError(u'Seed: {} already exist.'.format(token_val)) def save(self, commit=True): m = super(SeedTwitterFilterForm, self).save(commit=False) token = dict() if self.cleaned_data['track']: token['track'] = self.cleaned_data['track'] if self.cleaned_data['follow']: token['follow'] = self.cleaned_data['follow'] if self.cleaned_data['locations']: token['locations'] = self.cleaned_data['locations'] m.token = json.dumps(token, ensure_ascii=False) m.save() return m class SeedFlickrUserForm(BaseSeedForm): class Meta(BaseSeedForm.Meta): fields = ['token', 'uid'] fields.extend(BaseSeedForm.Meta.fields) labels = dict(BaseSeedForm.Meta.labels) labels["token"] = "Username" labels["uid"] = "NSID" help_texts = dict(BaseSeedForm.Meta.help_texts) help_texts["token"] = 'A string name for the user account. Finding this on the Flickr website can be ' \ 'confusing, so see NSID below.' help_texts["uid"] = 'An unchanging identifier for a user account, e.g., 80136838@N05. To find the NSID for a ' \ 'user account, use <a href="http://www.webpagefx.com/tools/idgettr/">idGettr</a>.' widgets = dict(BaseSeedForm.Meta.widgets) widgets["token"] = forms.TextInput(attrs={'size': '40'}) widgets["uid"] = forms.TextInput(attrs={'size': '40'}) def __init__(self, *args, **kwargs): super(SeedFlickrUserForm, self).__init__(*args, **kwargs) self.helper.layout[0][0].extend(('token', 'uid')) class SeedTumblrBlogPostsForm(BaseSeedForm): class Meta(BaseSeedForm.Meta): fields = ['uid'] fields.extend(BaseSeedForm.Meta.fields) labels = dict(BaseSeedForm.Meta.labels) labels["uid"] = "Blog hostname" help_texts = dict(BaseSeedForm.Meta.help_texts) help_texts["uid"] = 'Please provide the standard blog hostname, eg. codingjester or codingjester.tumblr.com.' \ 'If blog hostname is codingjester.tumblr.com, it would be considered as codingjester. ' \ 'To better understand standard blog hostname, See ' \ '<a target="_blank" href="https://www.tumblr.com/docs/en/api/v2#hostname">' \ 'these instructions</a>.' widgets = dict(BaseSeedForm.Meta.widgets) widgets["uid"] = forms.TextInput(attrs={'size': '40'}) def __init__(self, *args, **kwargs): super(SeedTumblrBlogPostsForm, self).__init__(*args, **kwargs) self.helper.layout[0][0].append('uid') def clean_uid(self): return clean_blogname(self.cleaned_data.get("uid")) class BaseBulkSeedForm(forms.Form): TYPES = (('token', 'Username'), ('uid', 'NSID')) seeds_type = forms.ChoiceField(required=True, choices=TYPES, widget=forms.RadioSelect) tokens = forms.CharField(required=True, widget=forms.Textarea(attrs={'rows': 20}), help_text="Enter each seed on a separate line.", label="Bulk Seeds") history_note = forms.CharField(label=HISTORY_NOTE_LABEL, widget=HISTORY_NOTE_WIDGET, help_text=HISTORY_NOTE_HELP, required=False) def __init__(self, *args, **kwargs): self.collection = kwargs.pop("collection", None) super(BaseBulkSeedForm, self).__init__(*args, **kwargs) self.fields['history_note'].help_text = HISTORY_NOTE_HELP_ADD cancel_url = reverse('collection_detail', args=[self.collection]) self.helper = FormHelper(self) self.helper.layout = Layout( Fieldset( '', 'seeds_type', 'tokens', 'history_note' ), FormActions( Submit('submit', 'Save'), Button('cancel', 'Cancel', onclick="window.location.href='{0}'".format(cancel_url)) ) ) class BulkSeedTwitterUserTimelineForm(BaseBulkSeedForm): def __init__(self, *args, **kwargs): super(BulkSeedTwitterUserTimelineForm, self).__init__(*args, **kwargs) self.fields['seeds_type'].choices = (('token', 'Screen Name'), ('uid', 'User id')) def clean_tokens(self): seed_type = self.cleaned_data.get("seeds_type") tokens = self.cleaned_data.get("tokens") splittoken = ''.join(tokens).splitlines() numtoken, strtoken, finaltokens = [], [], [] for t in splittoken: clean_t = clean_token(t) clean_t = clean_t.split(" ")[0] if clean_t and clean_t.isdigit(): numtoken.append(clean_t) elif clean_t and not clean_t.isdigit(): strtoken.append(clean_t) finaltokens.append(clean_t + "\n") if seed_type == 'token' and numtoken: raise ValidationError( 'Screen names may not be numeric. Please correct the following seeds: ' + ', '.join(numtoken) + '.') elif seed_type == 'uid' and strtoken: raise ValidationError( 'UIDs must be numeric. Please correct the following seeds: ' + ', '.join(strtoken) + '.') return ''.join(finaltokens) class BulkSeedFlickrUserForm(BaseBulkSeedForm): def __init__(self, *args, **kwargs): super(BulkSeedFlickrUserForm, self).__init__(*args, **kwargs) class BulkSeedTumblrBlogPostsForm(BaseBulkSeedForm): def __init__(self, *args, **kwargs): super(BulkSeedTumblrBlogPostsForm, self).__init__(*args, **kwargs) self.fields['seeds_type'].choices = (('uid', 'Blog hostnames'),) self.fields['seeds_type'].initial = 'uid' class BaseCredentialForm(forms.ModelForm): class Meta: model = Credential fields = ['name', 'history_note'] exclude = [] widgets = { 'history_note': HISTORY_NOTE_WIDGET } localized_fields = None labels = { 'history_note': HISTORY_NOTE_LABEL } help_texts = { 'history_note': HISTORY_NOTE_HELP } error_messages = {} def __init__(self, *args, **kwargs): # for createView and updateView self.view_type = kwargs.pop("view_type", None) # for updateView check the updates for the original token self.entry = kwargs.pop("entry", None) super(BaseCredentialForm, self).__init__(*args, **kwargs) # check whether it's a create view and offer different help text if self.instance.pk is None: self.fields['history_note'].help_text = HISTORY_NOTE_HELP_ADD # set up crispy forms helper self.helper = FormHelper(self) # set up crispy forms helper self.helper.layout = Layout( Fieldset( '', 'name', Div(), 'history_note' ), FormActions( Submit('submit', 'Save'), Button('cancel', 'Cancel', onclick="window.history.back()") ) ) def clean(self): cleaned_data = super(BaseCredentialForm, self).clean() token = json.dumps(self.to_token()) # for the update view if self.view_type == Credential.UPDATE_VIEW: # check updated Credential exist in db if changes if token != self.entry.token and Credential.objects.filter(token=token).exists(): raise ValidationError(u'This is a duplicate of an existing credential!') else: if Credential.objects.filter(token=token).exists(): raise ValidationError(u'This is a duplicate of an existing credential!') return cleaned_data class CredentialFlickrForm(BaseCredentialForm): key = forms.CharField(required=True) secret = forms.CharField(required=True) def __init__(self, *args, **kwargs): super(CredentialFlickrForm, self).__init__(*args, **kwargs) self.helper.layout[0][1].extend(['key', 'secret']) if self.instance and self.instance.token: token = json.loads(self.instance.token) self.fields['key'].initial = token.get('key') self.fields['secret'].initial = token.get('secret') def to_token(self): return { "key": self.cleaned_data.get("key", "").strip(), "secret": self.cleaned_data.get("secret", "").strip(), } def save(self, commit=True): m = super(CredentialFlickrForm, self).save(commit=False) m.platform = Credential.FLICKR m.token = json.dumps(self.to_token()) m.save() return m class CredentialTwitterForm(BaseCredentialForm): consumer_key = forms.CharField(required=True) consumer_secret = forms.CharField(required=True) access_token = forms.CharField(required=True) access_token_secret = forms.CharField(required=True) def __init__(self, *args, **kwargs): super(CredentialTwitterForm, self).__init__(*args, **kwargs) self.helper.layout[0][1].extend(['consumer_key', 'consumer_secret', 'access_token', 'access_token_secret']) if self.instance and self.instance.token: token = json.loads(self.instance.token) self.fields['consumer_key'].initial = token.get('consumer_key') self.fields['consumer_secret'].initial = token.get('consumer_secret') self.fields['access_token'].initial = token.get('access_token') self.fields['access_token_secret'].initial = token.get('access_token_secret') def to_token(self): return { "consumer_key": self.cleaned_data.get("consumer_key", "").strip(), "consumer_secret": self.cleaned_data.get("consumer_secret", "").strip(), "access_token": self.cleaned_data.get("access_token", "").strip(), "access_token_secret": self.cleaned_data.get("access_token_secret", "").strip(), } def save(self, commit=True): m = super(CredentialTwitterForm, self).save(commit=False) m.platform = Credential.TWITTER m.token = json.dumps(self.to_token()) m.save() return m class CredentialTumblrForm(BaseCredentialForm): api_key = forms.CharField(required=True) def __init__(self, *args, **kwargs): super(CredentialTumblrForm, self).__init__(*args, **kwargs) self.helper.layout[0][1].extend(['api_key']) if self.instance and self.instance.token: token = json.loads(self.instance.token) self.fields['api_key'].initial = token.get('api_key') def to_token(self): return { "api_key": self.cleaned_data.get("api_key", "").strip(), } def save(self, commit=True): m = super(CredentialTumblrForm, self).save(commit=False) m.platform = Credential.TUMBLR m.token = json.dumps(self.to_token()) m.save() return m class CredentialWeiboForm(BaseCredentialForm): access_token = forms.CharField(required=True) def __init__(self, *args, **kwargs): super(CredentialWeiboForm, self).__init__(*args, **kwargs) self.helper.layout[0][1].extend(['access_token']) if self.instance and self.instance.token: token = json.loads(self.instance.token) self.fields['access_token'].initial = token.get('access_token') def to_token(self): return { "access_token": self.cleaned_data.get("access_token", "").strip(), } def save(self, commit=True): m = super(CredentialWeiboForm, self).save(commit=False) m.platform = Credential.WEIBO m.token = json.dumps(self.to_token()) m.save() return m class SeedChoiceField(forms.ModelMultipleChoiceField): def label_from_instance(self, obj): return obj.label() class ExportForm(forms.ModelForm): seeds = SeedChoiceField(None, required=False, widget=forms.SelectMultiple, label="") seed_choice = forms.ChoiceField(choices=(('ALL', 'All seeds'), ('ACTIVE', 'Active seeds only'), ('SELECTED', 'Selected seeds only'),), initial='ALL', widget=forms.RadioSelect) class Meta: model = Export fields = ['seeds', 'seed_choice', 'export_format', 'export_segment_size', 'dedupe', 'item_date_start', 'item_date_end', 'harvest_date_start', 'harvest_date_end'] localized_fields = None error_messages = {} widgets = { 'item_date_start': DATETIME_WIDGET, 'item_date_end': DATETIME_WIDGET, 'harvest_date_start': DATETIME_WIDGET, 'harvest_date_end': DATETIME_WIDGET } labels = { 'dedupe': "Deduplicate (remove duplicate posts)", 'export_segment_size': "Maximum number of items per file" } def __init__(self, *args, **kwargs): self.collection = Collection.objects.get(pk=kwargs.pop("collection")) super(ExportForm, self).__init__(*args, **kwargs) self.fields["seeds"].queryset = self.collection.seeds.all() cancel_url = reverse('collection_detail', args=[self.collection.pk]) self.helper = FormHelper(self) self.helper.layout = Layout( Fieldset( '', 'seed_choice', Div('seeds', css_class="longseed"), 'export_format', 'export_segment_size', 'dedupe', Div( HTML("""<h4>Limit by item date range</h4>"""), 'item_date_start', 'item_date_end', HTML("""<p class="help-block">The timezone for dates entered here are {}. Adjustments will be made to match the time zone of the items. For example, dates in tweets are UTC.</p>""".format(settings.TIME_ZONE)), css_class="panel panel-default panel-body"), Div( HTML("""<h4>Limit by harvest date range</h4>"""), 'harvest_date_start', 'harvest_date_end', css_class="panel panel-default panel-body"), ), FormActions( Submit('submit', 'Export'), Button('cancel', 'Cancel', onclick="window.location.href='{0}'".format(cancel_url)) ) ) if len(self.fields["seeds"].queryset) < 2: del self.fields["seeds"] del self.fields["seed_choice"] self.helper.layout[0].pop(0) self.helper.layout[0].pop(0) def clean_seeds(self): seeds = self.cleaned_data["seeds"] if self.data.get("seed_choice") == "SELECTED" and not seeds: raise ValidationError("At least one seed must be selected") if self.data.get("seed_choice", "ALL") == "ALL": seeds = [] elif self.data["seed_choice"] == "ACTIVE": seeds = list(self.collection.seeds.filter(is_active=True)) return seeds def save(self, commit=True): m = super(ExportForm, self).save(commit=False) m.export_type = self.collection.harvest_type if self.cleaned_data.get("seed_choice", "ALL") == "ALL": m.collection = self.collection m.save() self.save_m2m() return m class UserProfileForm(forms.ModelForm): class Meta: model = User fields = ['username', 'email', 'email_frequency', 'harvest_notifications'] widgets = { "username": forms.TextInput(attrs={'size': '40'}), "email": forms.TextInput(attrs={'size': '40'}) } help_texts = { 'harvest_notifications': "Receive an email when there is a problem with a harvest.", } def __init__(self, *args, **kwargs): super(UserProfileForm, self).__init__(*args, **kwargs) # set up crispy forms helper self.helper = FormHelper(self) # set up crispy forms helper self.helper.layout = Layout( Fieldset( '', 'username', 'email', 'email_frequency', 'harvest_notifications', Div() ), FormActions( Submit('submit', 'Save'), Button('cancel', 'Cancel', onclick="window.history.back()") ) )

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0.603024

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44,048

5.427244

0.10792

0.021792

0.029185

0.020313

0.586155

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0.458129

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0

0.004768

0.280966

44,048

1,055

142

41.751659

0.806612

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0.00939

0.223005

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null

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null

0

1

0

381b84b00335b790806debd10772e523f8081f3d

10,583

py

Python

gritsbot/firmware.py

robotarium/gritsbot_2

43d3d32ac3dbb960301b45f6addbcad6c2195a6f

[ "MIT" ]

1

2020-11-24T19:32:22.000Z

gritsbot/firmware.py

robotarium/gritsbot_2

43d3d32ac3dbb960301b45f6addbcad6c2195a6f

[ "MIT" ]

6

2018-10-02T22:33:04.000Z

2018-10-09T20:20:41.000Z

gritsbot/firmware.py

robotarium/gritsbot_2

43d3d32ac3dbb960301b45f6addbcad6c2195a6f

[ "MIT" ]

null

import gritsbot.gritsbotserial as gritsbotserial import json import vizier.node as node import time import argparse import queue import netifaces import vizier.log as log global logger logger = log.get_logger() # Constants MAX_QUEUE_SIZE = 100 def get_mac(): """Gets the MAC address for the robot from the network config info. Returns: str: A MAC address for the robot. Example: >>> print(get_mac()) AA:BB:CC:DD:EE:FF """ interface = [x for x in netifaces.interfaces() if 'wlan' in x][0] return netifaces.ifaddresses(interface)[netifaces.AF_LINK][0]['addr'] def create_node_descriptor(end_point): """Returns a node descriptor for the robot based on the end_point. The server_alive link is for the robot to check the MQTT connection periodically. Args: end_point (str): The ID of the robot. Returns: dict: A node descriptor of the vizier format for the robot. Example: >>> node_descriptor(1) """ node_descriptor = \ { 'end_point': end_point, 'links': { '/status': {'type': 'DATA'}, }, 'requests': [ { 'link': 'matlab_api/'+end_point, 'type': 'STREAM', 'required': False }, ] } return node_descriptor # Responses # Battery voltage response # response = {'status': 1, 'body': {'bat_volt': 4.3}} class Request: """Represents serial requests to the microcontroller. The serial communications operate on a request/response architecture. For example, the request is of a form (when JSON encoded) .. code-block:: python {'request': ['read', 'write', 'read'], 'iface': [iface1, iface2, iface3], body: [body1, body2, body3]} Attributes: request (list): A list of requests (or actions) to perform. Must be 'read' or 'write'. iface (list): A list of interfaces on which to perform the request body (list): A list of bodies for the requests. These are empty if the request is a read. """ def __init__(self): """Initializes a request with optional iface, request, and body parameters. Returns: The created request. """ self.iface = [] self.request = [] self.body = [] def add_write_request(self, iface, body): """Adds a write to the request. Args: iface (str): The interface to write. body (dict): A JSON-encodable body to be written. Returns: The modified request containing the new interface and body. Examples: >>> r = Request().add_write_request('motor', {'v': 0.1, 'w': 0.0}) """ self.iface.append(iface) self.request.append('write') self.body.append(body) return self def add_read_request(self, iface): """Adds a read to the request. Args: iface (str): Interface from which to read. Returns: The request with the added read. """ self.iface.append(iface) self.request.append('read') self.body.append({}) return self def to_json_encodable(self): """Turns the request into a JSON-encodable dict. Raises: Exception: If an underlying body element is not JSON-encodable. Returns: dict: A JSON-encodable dict representing the request. """ req = {'request': self.request, 'iface': self.iface} if(self.body): req['body'] = self.body return req def handle_write_response(status, body): return {} def handle_read_response(iface, status, body): if(iface in body): return {iface: body[iface]} else: logger.critical('Request for ({0}) not in body ({1}) after request.'.format(iface, body)) return {} def main(): parser = argparse.ArgumentParser() parser.add_argument("mac_list", help="JSON file containing MAC to id mapping") parser.add_argument("-port", type=int, help="MQTT Port", default=8080) parser.add_argument("-host", help="MQTT Host IP", default="localhost") parser.add_argument('-update_rate', type=float, help='Update rate for robot main loop', default=0.016) parser.add_argument('-status_update_rate', type=float, help='How often to check status info', default=1) # Retrieve the MAC address for the robot mac_address = get_mac() # Parser and set CLI arguments args = parser.parse_args() update_rate = args.update_rate status_update_rate = args.status_update_rate # Retrieve the MAC list file, containing a mapping from MAC address to robot ID try: f = open(args.mac_list, 'r') mac_list = json.load(f) except Exception as e: print(repr(e)) print('Could not open file ({})'.format(args.node_descriptor)) if(mac_address in mac_list): robot_id = mac_list[mac_address] else: print('MAC address {} not in supplied MAC list file'.format(mac_address)) raise ValueError() logger.info('This is robot: ({0}) with MAC address: ({1})'.format(robot_id, mac_address)) # Create node descriptor for robot and set up links node_descriptor = create_node_descriptor(mac_list[mac_address]) status_link = robot_id + '/status' input_link = 'matlab_api/' + robot_id started = False robot_node = None while (not started): robot_node = node.Node(args.host, args.port, node_descriptor) try: robot_node.start() started = True except Exception as e: logger.critical('Could not start robot node.') logger.critical(repr(e)) robot_node.stop() # Don't try to make nodes too quickly time.sleep(1) logger.info('Started robot node.') started = False serial = None while (not started): serial = gritsbotserial.GritsbotSerial(serial_dev='/dev/ttyACM0', baud_rate=500000) try: serial.start() started = True except Exception as e: # This class stops itself if the device cannot be initially acquired, so we don't need to stop it. logger.critical('Could not acquire serial device.') logger.critical(repr(e)) # Don't try to acquire the serial device too quickly time.sleep(1) logger.info('Acquired serial device.') # Queues for STREAM links inputs = robot_node.subscribe(input_link) # Initialize times for various activities start_time = time.time() print_time = time.time() status_update_time = time.time() # Initialize data status_data = {'batt_volt': -1, 'charge_status': False} last_input_msg = {} # Main loop for the robot while True: start_time = time.time() # Serial requests request = Request() handlers = [] # Retrieve status data: battery voltage and charging status if((start_time - status_update_time) >= status_update_rate): request.add_read_request('batt_volt').add_read_request('charge_status') handlers.append(lambda status, body: handle_read_response('batt_volt', status, body)) handlers.append(lambda status, body: handle_read_response('charge_status', status, body)) status_update_time = start_time # Process input commands input_msg = None # Make sure that the queue has few enough messages if(inputs.qsize() > MAX_QUEUE_SIZE): logger.critical('Queue of motor messages is too large.') try: # Clear out the queue while True: input_msg = inputs.get_nowait() except queue.Empty: pass if(input_msg is not None): try: input_msg = json.loads(input_msg.decode(encoding='UTF-8')) except Exception as e: logger.warning('Got malformed JSON motor message ({})'.format(input_msg)) logger.warning(e) # Set this to None for the next checks input_msg = None # If we got a valid JSON input msg, look for appropriate commands if(input_msg is not None): last_input_msg = input_msg if('v' in input_msg and 'w' in input_msg): # Handle response? request.add_write_request('motor', {'v': input_msg['v'], 'w': input_msg['w']}) handlers.append(handle_write_response) if('left_led' in input_msg): request.add_write_request('left_led', {'rgb': input_msg['left_led']}) handlers.append(handle_write_response) if('right_led' in input_msg): request.add_write_request('right_led', {'rgb': input_msg['right_led']}) handlers.append(handle_write_response) # Write to serial port response = None if(len(handlers) > 0): try: response = serial.serial_request(request.to_json_encodable()) except Exception as e: logger.critical('Serial exception.') logger.critical(e) # Call handlers # We'll have a status and body for each request if(response is not None and 'status' in response and 'body' in response and len(response['status']) == len(handlers) and len(response['body']) == len(handlers)): status = response['status'] body = response['body'] # Ensure the appropriate handler gets each response for i, handler in enumerate(handlers): status_data.update(handler(status[i], body[i])) else: # If we should have responses, but we don't if(len(handlers) > 0): logger.critical('Malformed response ({})'.format(response)) robot_node.put(status_link, json.dumps(status_data)) # Print out status data if((start_time - print_time) >= status_update_rate): logger.info('Status data ({})'.format(status_data)) logger.info('Last input message received ({})'.format(last_input_msg)) print_time = time.time() # Sleep for whatever time is left at the end of the loop time.sleep(max(0, update_rate - (time.time() - start_time))) if __name__ == '__main__': main()

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0.034091

0

null

0

null

0

1

0

381c68068132361d222183fa84b228cba0959f89

5,963

py

Python

crypto/code_crypto_py_book/enigmaM3.py

gcjordi/tests

f917d0b77d0363ddb1abeea1eb172ea7c8a764d7

[ "Unlicense" ]

null

crypto/code_crypto_py_book/enigmaM3.py

gcjordi/tests

f917d0b77d0363ddb1abeea1eb172ea7c8a764d7

[ "Unlicense" ]

null

crypto/code_crypto_py_book/enigmaM3.py

gcjordi/tests

f917d0b77d0363ddb1abeea1eb172ea7c8a764d7

[ "Unlicense" ]

null

# ENIGMA M3 # Octubre 2016 # David Arboledas # Dominio Público import re, pyperclip LETRAS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' ################################################# ### Parámetros de configuración de Enigma M3 ### ################################################# # Grundstellung inicio = ('A','A','A') # Walzenlage rotores = (1,2,3) # Umkehrwalze reflector = 'B' # Ringstellung posicion_interna = ('A','A','A') # Steckerverbindungen clavijero = [('Z','N'),('Y','O'),('Q','B'),('E','R'),('D','K'),('X','U'),('G','P'),('T','V'),('S','J'),('L','M')] cableado_rotor =('EKMFLGDQVZNTOWYHXUSPAIBRCJ', 'AJDKSIRUXBLHWTMCQGZNPYFVOE', 'BDFHJLCPRTXVZNYEIWGAKMUSQO', 'ESOVPZJAYQUIRHXLNFTGKDCMWB', 'VZBRGITYUPSDNHLXAWMJQOFECK', 'JPGVOUMFYQBENHZRDKASXLICTW', 'NZJHGRCXMYSWBOUFAIVLPEKQDT', 'FKQHTLXOCBJSPDZRAMEWNIUYGV') cableado_reflector = ('EJMZALYXVBWFCRQUONTSPIKHGD', 'YRUHQSLDPXNGOKMIEBFZCWVJAT', 'FVPJIAOYEDRZXWGCTKUQSBNMHL') def main(): print("""\n**** Enigma M3 ****\n 1. Cifrar 2. Descifrar""") opcion = int(input('\nOpcion (1, 2)> ')) if opcion == 1: # Cifrado mensaje = input('Mensaje > ') texto = cifrar(mensaje) print('\n',texto) pyperclip.copy(texto) elif opcion == 2: # Descifrado mensaje = input('Mensaje > ') texto = descifrar(mensaje) print('\n', texto) pyperclip.copy(texto) def vuelta(cableado): # Sigue el recorrido de vuelta de la señal # desde el reflector para un rotor salida = '' for i in LETRAS: salida += LETRAS[cableado.find(i)] return salida def cableado_inverso(): # Devuelve una tupla con el recorrido # inverso de los 8 rotores inverso=[] for i in range(len(cableado_rotor)): inverso.append(vuelta(cableado_rotor[i])) inverso = tuple(inverso) return inverso inverso = cableado_inverso() inicio = list(inicio) # inicio se actualiza con cada caracter rotores = tuple([q-1 for q in rotores]) # Las muescan indican dónde se produce el giro del siguiente rotor # en el momento en que el anterior pasa por la muesca. # Los rotores 6, 7 y 8 tienen dos muesca = (('Q',),('E',),('V',),('J',),('Z',),('Z','M'),('Z','M'),('Z','M')) def numero(car): # Devulve el número correspondiente a cada carácter car = car.upper() arr = {'A':0,'B':1,'C':2, 'D':3,'E':4, 'F':5, 'G':6, 'H':7, 'I':8, 'J':9, 'K':10, 'L':11, 'M':12, 'N':13, 'O':14, 'P':15, 'Q':16, 'R':17, 'S':18,'T':19, 'U':20, 'V':21, 'W':22,'X':23, 'Y':24, 'Z':25} return arr[car] reflector = numero(reflector) def rotor(letra,veces,cableado_rotor): # letra - el carácter de entrada que se cifrará # veces - cuántas veces ha girado el rotor # cableado_rotor - cableado del rotor letra = sustituye(letra,cableado_rotor,veces) # Letra de salida por el rotor return sustituye(letra,veces=-veces) def refleja(letra): # Realiza la sustitución del reflector. # El reflector se representa por un entero 0-2 return sustituye(letra,cableado_reflector[reflector]) def sustituye(letra,alfabeto=LETRAS,veces=0): # sustituye una letra de acuerdo a la clave indice = (numero(letra)+veces)%26 return alfabeto[indice] def aplicar_clavijero(letra): for i in clavijero: if letra == i[0]: return i[1] if letra == i[1]: return i[0] return letra def rotor_avanza(): # Los rotores mueven al siguiente rotor dependiendo de su posicion if inicio[1] in muesca[rotores[1]]: # Incrementa la posición 1 letra inicio[0] = sustituye(inicio[0],veces=1) inicio[1] = sustituye(inicio[1],veces=1) if inicio[2] in muesca[rotores[2]]: inicio[1] = sustituye(inicio[1],veces=1) inicio[2] = sustituye(inicio[2],veces=1) def cifrar_caracter(letra): # Con cada letra los rotores avanzan rotor_avanza() # Entrada al clavijero letra = aplicar_clavijero(letra) # Camino de ida de la señal for i in [2,1,0]: veces = ord(inicio[i])-ord(posicion_interna[i]) letra = rotor(letra,veces,cableado_rotor[rotores[i]]) # Entrada y salida por el refletor letra = refleja(letra) # Camino de vuelta for i in [0,1,2]: veces = ord(inicio[i])-ord(posicion_interna[i]) letra = rotor(letra,veces,inverso[rotores[i]]) # Salida por el clavijero letra = aplicar_clavijero(letra) return letra def descifrar(texto): # cifrar y descifrar son la misma operación return cifrar(texto) def cifrar(texto): texto = eliminar_puntuacion(texto).upper() salida = '' for c in texto: if c.isalpha(): salida += cifrar_caracter(c) else: salida += c return salida def eliminar_puntuacion(texto): return re.sub('[^A-Z]','',texto.upper()) if __name__ == "__main__": main() ''' Entra clavijero A Sale clavijero A Rotor 3 Entra letra A Rotor 3 Sale letra C Rotor 2 Entra letra C Rotor 2 Sale letra D Rotor 1 Entra letra D Rotor 1 Sale letra F Entra reflector F Sale reflector S Rotor 1 Entra letra S Rotor 1 Sale letra S Rotor 2 Entra letra S Rotor 2 Sale letra E Rotor 3 Entra letra E Rotor 3 Sale letra B Entra clavijero B Sale clavijero Q '''

27.734884

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null

0

1

0

381c9c9f8a9d7896ecd6ae03b9d5931ad71791d9

2,586

py

Python

max_connect.py

passionzhan/LeetCode

c4d33b64b9da15ca7a9b0d41e645d86a697694fe

[ "MIT" ]

1

2019-08-29T01:12:47.000Z

max_connect.py

passionzhan/LeetCode

c4d33b64b9da15ca7a9b0d41e645d86a697694fe

[ "MIT" ]

null

max_connect.py

passionzhan/LeetCode

c4d33b64b9da15ca7a9b0d41e645d86a697694fe

[ "MIT" ]

null

# -*- coding: utf-8 -*-# #------------------------------------------------------------------------------- # PROJECT_NAME: design_pattern # Name: max_connect.py # Author: 9824373 # Date: 2020-08-19 21:03 # Contact: 9824373@qq.com # Version: V1.0 # Description: #------------------------------------------------------------------------------- ''' 0 1 1 1 0 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 0 1 1 int getMaxLinked(int[][] matrix,int n,int m) { } 我给定任意一个矩阵n*m,其中只有 0 1 两个数字,1表示可以走通,0表示不能走通,只能上下左右,求矩阵的最长联通长度假如说 m和n如果是无限大情况,应该怎么处理? ''' # from queue import Queue from collections import deque import copy def neighbors(matrix, iRow, iClo): for (nr, nc) in ((iRow - 1, iClo), (iRow + 1, iClo), (iRow, iClo - 1), (iRow, iClo + 1)): if 0 <= nr < len(matrix) and 0 <= nc < len(matrix[0]): yield (nr, nc) # 深度优先搜索 def dfs(matrix, seen, i, j): res_len = 1 rst_lst = [0, ] for nei_i, nei_j in neighbors(matrix,i,j,): if matrix[nei_i][nei_j]==1 and (nei_i,nei_j) not in seen: next_seen = copy.deepcopy(seen) next_seen.add((nei_i,nei_j)) rst_lst.append(dfs(matrix, next_seen, nei_i, nei_j)) res_len += max(rst_lst) return res_len def max_linked(matrix): n = len(matrix) m = len(matrix[0]) untraveled = set([(i,j) for i in range(n) for j in range(m) if matrix[i][j]==1]) max_len = 0 for i,j in untraveled: # for i, j in ((0,5),(1,0),): cur_seen = set() cur_seen.add((i,j)) cur_len = dfs(matrix, cur_seen, i, j) # for n_i, n_j in neighbors(matrix, i, j): # if matrix[n_i][n_j] == 1 and (n_i, n_j) not in cur_seen: # # cur_len += dfs(matrix, next_cur_seen, i, j) # max_len = max(max_len, cur_len) return max_len - 1 # def max_linked(matrix): # n = len(matrix) # m = len(matrix[0]) # # for i in range(n): # for j in range(j): # if matrix[i][j]: # cur_deque = deque() # cur_deque.append(matrix[i][j]) # seen = set([matrix[i][j]],) # for ii,jj in neighbors(matrix,i,j): # if matrix[ii][jj] and matrix[ii][jj] not in seen: # cur_deque.append(matrix[ii][jj]) # if __name__ == '__main__': a = [ [0, 1, 1, 0, 1, 1, 1, 1,], [1, 0, 1, 0, 1, 0, 1, 0,], [1, 1, 1, 0, 1, 0, 1, 1,], ] rst = max_linked(a) print(rst)

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null

0

null

0

1

0

381d46f30a7018770a9715e4227a6f4b622412b6

786

py

Python

tests/wipy/sd.py

sebastien-riou/micropython

116c15842fd48ddb77b0bc016341d936a0756573

[ "MIT" ]

13,648

2015-01-01T01:34:51.000Z

2022-03-31T16:19:53.000Z

tests/wipy/sd.py

sebastien-riou/micropython

116c15842fd48ddb77b0bc016341d936a0756573

[ "MIT" ]

7,092

2015-01-01T07:59:11.000Z

2022-03-31T23:52:18.000Z

tests/wipy/sd.py

sebastien-riou/micropython

116c15842fd48ddb77b0bc016341d936a0756573

[ "MIT" ]

4,942

2015-01-02T11:48:50.000Z

2022-03-31T19:57:10.000Z

""" SD card test for the CC3200 based boards. """ from machine import SD import os mch = os.uname().machine if "LaunchPad" in mch: sd_pins = ("GP16", "GP17", "GP15") elif "WiPy" in mch: sd_pins = ("GP10", "GP11", "GP15") else: raise Exception("Board not supported!") sd = SD(pins=sd_pins) print(sd) sd.deinit() print(sd) sd.init(sd_pins) print(sd) sd = SD(0, pins=sd_pins) sd = SD(id=0, pins=sd_pins) sd = SD(0, sd_pins) # check for memory leaks for i in range(0, 1000): sd = sd = SD(0, pins=sd_pins) # next ones should raise try: sd = SD(pins=()) except Exception: print("Exception") try: sd = SD(pins=("GP10", "GP11", "GP8")) except Exception: print("Exception") try: sd = SD(pins=("GP10", "GP11")) except Exception: print("Exception")

17.086957

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786

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null

0

null

0

1

0

381fc709361b0418ede89f17181ce6266425a268

6,001

py

Python

data/Criteo/forOtherModels/dataPreprocess_TensorFlow.py

wangweitong/recommend_system

635a772c5d6097527ab446bf69c8e9e2bb7f16da

[ "MIT" ]

2

2021-09-22T09:47:37.000Z

2022-02-07T07:30:47.000Z

data/Criteo/forOtherModels/dataPreprocess_TensorFlow.py

wangweitong/recommend_system

635a772c5d6097527ab446bf69c8e9e2bb7f16da

[ "MIT" ]

null

data/Criteo/forOtherModels/dataPreprocess_TensorFlow.py

wangweitong/recommend_system

635a772c5d6097527ab446bf69c8e9e2bb7f16da

[ "MIT" ]

null

import numpy as np from data.Criteo.util import * """ Data Process for FM, PNN, and DeepFM. [1] PaddlePaddle implementation of DeepFM for CTR prediction https://github.com/PaddlePaddle/models/blob/develop/PaddleRec/ctr/deepfm/data/preprocess.py """ def get_train_test_file(file_path, feat_dict_, split_ratio=0.9): train_label_fout = open('train_label', 'w') train_value_fout = open('train_value', 'w') train_idx_fout = open('train_idx', 'w') test_label_fout = open('test_label', 'w') test_value_fout = open('test_value', 'w') test_idx_fout = open('test_idx', 'w') categorical_range_ = range(1, 11) continuous_range_ = range(11, 19) cont_min_ = [1.00000000e+00, 0.00000000e+00, -1.00000000e+00, 1.38888889e-03, -8.70709083e+03, -9.62673000e+05, -1, -4.00000000e+00] cont_max_ = [3.02320000e+04, 4.04000000e+02, 9.00000000e+00, 9.85688611e+03, 8.72573694e+03, 2.19570650e+06, 9.00000000e+00, 4.00000000e+00] cont_diff_ = [cont_max_[i] - cont_min_[i] for i in range(len(cont_min_))] # 分割并获取索引以及特征值这些 def process_line_(line): # 用,分割获取每行元素 features = line.rstrip('\n').split(',') feat_idx, feat_value, label = [], [], [] # MinMax Normalization 对于连续特征最大最小标准化 for idx in continuous_range_: # 如果特征的值为空则索引为0 if features[idx] == '': # print(idx,features[idx],"0000000000000000000000") feat_idx.append(0) feat_value.append(0.0) else: # 否则用索引值代替 feat_idx.append(feat_dict_[idx]) # 原来的值 feat_value.append(features[idx]) # 处理分类型数据 for idx in categorical_range_: if features[idx] == '' or features[idx] not in feat_dict_: feat_idx.append(0) feat_value.append(0.0) else: feat_idx.append(feat_dict_[features[idx]]) feat_value.append(1.0) return feat_idx, feat_value, [int(features[0])] # 打开训练文件train.txt with open(file_path, 'r') as fin: # 遍历行号和每行的值 for line_idx, line in enumerate(fin): # print(line_idx,"----------",line) if line_idx % 1000000 == 0: print(line_idx) # 数据超过某个界限就停止 if line_idx >= EACH_FILE_DATA_NUM * 10: break # 给每一行赋值 feat_idx, feat_value, label = process_line_(line) feat_value = ','.join([str(v) for v in feat_value]) + '\n' feat_idx = ','.join([str(idx) for idx in feat_idx]) + '\n' label = ','.join([str(idx) for idx in label]) + '\n' # print("feat_value",feat_value, "feat_idx", feat_idx, "label", label) if np.random.random() <= split_ratio: train_label_fout.write(label) train_idx_fout.write(feat_idx) train_value_fout.write(feat_value) else: test_label_fout.write(label) test_idx_fout.write(feat_idx) test_value_fout.write(feat_value) fin.close() train_label_fout.close() train_idx_fout.close() train_value_fout.close() test_label_fout.close() test_idx_fout.close() test_value_fout.close() def get_feat_dict(): freq_ = 10 dir_feat_dict_ = 'feat_dict_' + str(freq_) + '.pkl2' # 确定连续和分类特征的列 categorical_range_ = range(1, 11) continuous_range_ = range(11, 19) if os.path.exists(dir_feat_dict_): feat_dict = pickle.load(open(dir_feat_dict_, 'rb')) else: # 设置全局的特征索引，用counter来整 # print('generate a feature dict') # Count the number of occurrences of discrete features feat_cnt = Counter() with open('../train.txt', 'r') as fin: for line_idx, line in enumerate(fin): # for test # print("line_idx---", line_idx, "line---", line) if line_idx >= EACH_FILE_DATA_NUM * 10: break if line_idx % EACH_FILE_DATA_NUM == 0: print('generating feature dict', line_idx / 45000000) features = line.rstrip('\n').split(',') for idx in categorical_range_: if features[idx] == '': continue # print(features[idx], "----categorical_range_333", idx) feat_cnt.update([features[idx]]) # Only retain discrete features with high frequency # 仅保留高频离散特征 dis_feat_set = set() for feat, ot in feat_cnt.items(): if ot >= freq_: dis_feat_set.add(feat) # Create a dictionary for continuous and discrete features # 创建连续和离散特征的字典 feat_dict = {} tc = 1 # Continuous features # 1 到14 就赋值为原来的列号 for idx in continuous_range_: feat_dict[idx] = tc tc += 1 # Discrete features cnt_feat_set = set() with open('../train.txt', 'r') as fin: for line_idx, line in enumerate(fin): # get mini-sample for test if line_idx >= EACH_FILE_DATA_NUM * 10: break features = line.rstrip('\n').split(',') for idx in categorical_range_: if features[idx] == '' or features[idx] not in dis_feat_set: continue if features[idx] not in cnt_feat_set: cnt_feat_set.add(features[idx]) feat_dict[features[idx]] = tc tc += 1 # Save dictionary with open(dir_feat_dict_, 'wb') as fout: pickle.dump(feat_dict, fout) print('args.num_feat ', len(feat_dict) + 1) return feat_dict if __name__ == '__main__': feat_dict = get_feat_dict() get_train_test_file('../train.txt', feat_dict) print('Done!')

35.3

95

0.557074

738

6,001

4.257453

0.234417

0.050923

0.017823

0.01655

0.292171

0.204328

0.192871

0.176321

0.146722

0

0.062221

0.327779

6,001

169

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35.508876

0.716658

0.125146

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false

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0.037037

0

null

0

null

0

1

0

38278d88790447c7025e49fc709ca332d9b8da93

6,106

py

Python

data/spellhelper.py

patronical/disaster_pipeline

a6b699f76c7174389905cdff57d651d5f398bb58

[ "MIT" ]

null

data/spellhelper.py

patronical/disaster_pipeline

a6b699f76c7174389905cdff57d651d5f398bb58

[ "MIT" ]

null

data/spellhelper.py

patronical/disaster_pipeline

a6b699f76c7174389905cdff57d651d5f398bb58

[ "MIT" ]

null

# spellhelper.py # spelling helper class for text cleaning # this is a wrapper class for frequency dictionary # that complements peter norvig spell check routines ''' Implementation Notes For default case Download the frequency dictionary and put adjacent to this file https://raw.githubusercontent.com/mammothb/symspellpy/master/symspellpy/frequency_dictionary_en_82_765.txt License: Apache Software License (MIT) ''' import regex as re import numpy as np from collections import Counter class Spellhelper: ''' A class for frequency dictionary based spell correction and segmentation. ''' import peter_norvig_spelling as pns def __init__(self, filename = 'data/frequency_dictionary_en_82_765.txt'): ''' initialize counter by loading frequency dictionary file ''' def LoadFD(file): ''' extract frequency dictionary file transform data and load into counter return counter ''' # extract with open(file, 'r') as myfile: DICT=myfile.read().replace('\n', '') # transform DICT2 = re.sub(r'(\d+)(\w+)', r'\1 \2', DICT.lower()) d_tup = re.findall(r'(\w+)\s(\d+)', DICT2) # load C = Counter() for pair in d_tup: C[pair[0]] = int(pair[1]) return C self.filename = filename self.counter = LoadFD(self.filename) def spellcheck(self, token): ''' input token string check spelling return best effort spell check ''' self.pns.COUNTS = self.counter spell_check = self.pns.correct(token) return spell_check def segcheck(self, token): ''' input token string check segmentation return list of words as best effort segmentation check ''' self.pns.COUNTS = self.counter segs = self.pns.segment(token) return segs def updatefreq(self, C_corpus): ''' import corpus frequency counters build interpolation intervals convert corpus frequencies update english frequencies set counter to updated values ''' C_english = self.counter # range variables eng_max = C_english.most_common()[0][1]+1 eng_min = C_english.most_common()[-1][1] cor_max = C_corpus.most_common()[0][1]+1 cor_min = C_corpus.most_common()[-1][1] # interpolation intervals eng_int = np.linspace(eng_min,eng_max,1000) cor_int = np.linspace(cor_min,cor_max,1000) # interpolator def IntFreq(token): ''' import token lookup token frequency in corpus interpolate frequency in english round float frequency into integer assign updated counter ''' cf = C_corpus[token] idx = np.max(np.where(cor_int<=cf)) c_base = cor_int[idx] e_base = eng_int[idx] cspan = cor_int[idx+1]-c_base espan = eng_int[idx+1]-e_base f_intp = (cf - c_base)*(espan/cspan) + e_base return int(round(f_intp,0)) # sort entries tok_updates = [token for token in C_corpus if token in C_english] # update frequencies C_eng = C_english.copy() for token in tok_updates: tf = IntFreq(token) C_eng[token] = tf self.counter = C_eng print('Frequency dictionary updated.') def addwords(self, C_corpus, new_word_list): ''' import english and corpus frequency counters import words not in English Dict but in Corpus build interpolation intervals convert corpus frequencies add new words to english dict set counter to updated values ''' C_english = self.counter # range variables eng_max = C_english.most_common()[0][1]+1 eng_min = C_english.most_common()[-1][1] cor_max = C_corpus.most_common()[0][1]+1 cor_min = C_corpus.most_common()[-1][1] # interpolation intervals eng_int = np.linspace(eng_min,eng_max,1000) cor_int = np.linspace(cor_min,cor_max,1000) # interpolator def IntFreq(token): ''' import token lookup token frequency in corpus interpolate frequency in english round float frequency into integer return english frequency ''' cf = C_corpus[token] idx = np.max(np.where(cor_int<=cf)) c_base = cor_int[idx] e_base = eng_int[idx] cspan = cor_int[idx+1]-c_base espan = eng_int[idx+1]-e_base f_intp = (cf - c_base)*(espan/cspan) + e_base return int(round(f_intp,0)) # input new words C_eng = C_english.copy() news = [word for word in new_word_list if word in C_corpus] for word in news: tf = IntFreq(word) C_eng[word] = tf self.counter = C_eng print('New words added to frequency dictionary.') def savefreqdict(self, filename): ''' import filename format counter as frequency dictionary save the Counter as frequency dictionary ''' C = self.counter #Format Counter for Output Cout = sorted(C.items(), key=lambda pair: pair[1], reverse=True) # Save for later re-run from start in place of generic dictionary with open(filename, encoding='utf-8', mode='w') as f: for tag, count in Cout: f.write('{} {}\n'.format(tag, count)) print('Spell Check Counter saved to ' + filename)

31.312821

107

0.557648

726

6,106

4.548209

0.263085

0.063295

0.014537

0.021805

0.425197

0.415506

0.317989

0

0.015322

0.358664

6,106

194

108

31.474227

0.827886

0.290043

0

0.475

0

0.0496

0.010747

0

1

0.1125

false

0

0.05

0

0.2375

0.0375

0

null

0

null

0

1

0

38293665d011cbbd211e0920b3368185cc35abc1

1,362

py

Python

UNIOA/algs/unioaCSA.py

Huilin-Li/UNIOA

0f2527eac955a7193406775e5b71fab35f064422

[ "MIT" ]

null

UNIOA/algs/unioaCSA.py

Huilin-Li/UNIOA

0f2527eac955a7193406775e5b71fab35f064422

[ "MIT" ]

null

UNIOA/algs/unioaCSA.py

Huilin-Li/UNIOA

0f2527eac955a7193406775e5b71fab35f064422

[ "MIT" ]

null

from UNIOA.NatureOpt import NatureOpt # This class implements CSA-Optimizer in the UNIOA framework. # E is sync # G is sync class CSA_UNIOA(NatureOpt): def __init__(self, func ,hyperparams_set, budget_factor=1e4): super().__init__(func, budget_factor) self.M = hyperparams_set.get('popsize',50) self.w1 = hyperparams_set.get('w1', 0.1) self.w2 = hyperparams_set.get('w2', 2) def __call__(self): t = 0 # iteration counter X = self.Init_X.Init_X(M=self.M, n=self.n, lb_x=self.lb_x, ub_x=self.ub_x) X_Fit = self.Evaluate_X(X=X) # Evaluate X X_p, X_p_Fit = self.Init_Delta_X.Personal_best(new_X=X,new_X_Fit=X_Fit) # Optimizing while not self.stop: # OOpt temp_X(t+1) temp_X = self.Opt_X.csa(old_X=X, X_p=X_p, w1=self.w1, w2=self.w2, lb_x=self.lb_x, ub_x=self.ub_x) # Evaluate temp_X_Fit = self.Evaluate_X(X=temp_X) # Selection new_X, new_X_Fit = self.Selection.same_type(temp_X=temp_X, temp_X_Fit=temp_X_Fit) # ---------------------------- t = t + 1 ########################## X_p, X_p_Fit = self.Opt_Delta_X.Personal_best(new_X=new_X, new_X_Fit=new_X_Fit, old_X_p=X_p, old_X_p_Fit=X_p_Fit) X = new_X X_Fit = new_X_Fit

34.923077

125

0.589574

230

1,362

3.121739

0.265217

0.061281

0.034819

0.022284

0.261838

0.197772

0.05571

0

0.017982

0.265051

1,362

38

126

35.842105

0.699301

0.135095

0

0.009649

0

1

0.1

false

0

0.05

0

0.2

0

null

0

null

0

1

0

382bc908de3c182c09ebf8232f4505e77a202c6f

417

py

Python

code/genric/torch_ext/segment_indirect.py

wendazhou/reversible-inductive-construction

14815d1b5ef5a35a569c0793888bc5548acd64be

[ "MIT" ]

31

2019-07-22T23:34:43.000Z

2021-09-10T14:28:53.000Z

code/genric/torch_ext/segment_indirect.py

wendazhou/reversible-inductive-construction

14815d1b5ef5a35a569c0793888bc5548acd64be

[ "MIT" ]

null

code/genric/torch_ext/segment_indirect.py

wendazhou/reversible-inductive-construction

14815d1b5ef5a35a569c0793888bc5548acd64be

[ "MIT" ]

3

2019-12-03T14:12:45.000Z

2020-07-17T14:50:18.000Z

import torch def segment_index_add_python(values, scopes, indices, out=None): if out is None: out = values.new_zeros([scopes.shape[0]] + list(values.shape[1:])) scopes = scopes.long() values_dup = values.index_select(0, indices) idx_global = torch.repeat_interleave(scopes[:, 1]) out.index_add_(0, idx_global, values_dup) return out segment_index_add = segment_index_add_python

23.166667

74

0.709832

61

417

4.57377

0.459016

0.114695

0.16129

0.150538

0

0.014577

0.177458

417

17

75

24.529412

0.798834

0

1

0.1

false

0

0.1

0

0.3

0

null

0

null

0

1

0

382cc28dba3c76c364eeb160a5426a562bfbf3bf

1,580

py

Python

bot/conversations/statistics/graphs.py

borissimkin/moneykeeper-bot

45f7ed92be187db71d28c5326a5b62cb587c88bf

[ "MIT" ]

2

2021-08-04T08:04:31.000Z

2022-01-21T13:00:28.000Z

bot/conversations/statistics/graphs.py

borissimkin/moneykeeper-bot

45f7ed92be187db71d28c5326a5b62cb587c88bf

[ "MIT" ]

2

2021-06-08T21:07:54.000Z

2021-09-08T01:46:50.000Z

bot/conversations/statistics/graphs.py

borissimkin/moneykeeper-bot

45f7ed92be187db71d28c5326a5b62cb587c88bf

[ "MIT" ]

null

# -*- coding: utf-8 -*- import os import tempfile import matplotlib.pyplot as plt import numpy as np from bot.conversations.statistics.type_transacation_graph import TypeTransaction from bot.conversations.statistics.utils import remove_emoji def make_pie_graph(data, labels, title, type_transactions: TypeTransaction): labels = [remove_emoji(label) for label in labels] explode = [0.01 for _ in labels] data = [int(x) for x in data] sum_data = sum(data) patches, texts, _ = plt.pie(data, labels=labels, startangle=90, pctdistance=0.85, explode=explode, autopct=lambda x: '{}р.'.format(int(sum_data / 100 * x))) centre_circle = plt.Circle((0, 0), 0.70, fc='white') make_center_text(type_transactions, sum_data) fig = plt.gcf() plt.title(title, fontsize=20) fig.gca().add_artist(centre_circle) # Equal aspect ratio ensures that pie is drawn as a circle plt.axis('equal') plt.tight_layout() path_to_graph = os.path.join(tempfile.mkdtemp(), 'figure.jpg') plt.savefig(path_to_graph) plt.close() return path_to_graph def make_center_text(type_transactions, amount_money): if type_transactions == type_transactions.CONSUMPTION: text = 'Расходы\n{} р.'.format(amount_money) color = 'red' elif type_transactions == type_transactions.EARNING: text = 'Доходы\n{} р.'.format(amount_money) color = 'green' else: text = 'Неопределено' color = 'black' plt.annotate(text, xy=(0, 0), fontsize=18, ha="center", color=color)

32.916667

102

0.678481

216

1,580

4.800926

0.476852

0.108004

0.031823

0.057859

0.104147

0.046287

0

0.018239

0.201899

1,580

47

103

33.617021

0.804124

0.049367

0

0.054813

0

1

0.057143

false

0

0.171429

0

0.257143

0

null

0

null

0

1

0

382d17974be4712bef8cb069dbd3f2c26b182c0d

2,182

py

Python

weibo_search.py

py1120/weibo_wordcloud

1e182709cab1dbf632eaf6746d3d4859f13bd7ba

[ "MIT" ]

197

2018-01-24T05:15:28.000Z

2022-03-24T15:09:24.000Z

weibo_search.py

MonteLex/weibo_wordcloud

1e182709cab1dbf632eaf6746d3d4859f13bd7ba

[ "MIT" ]

1

2020-06-15T17:27:38.000Z

weibo_search.py

MonteLex/weibo_wordcloud

1e182709cab1dbf632eaf6746d3d4859f13bd7ba

[ "MIT" ]

71

2018-01-25T03:35:08.000Z

2022-03-01T06:31:29.000Z

# coding: utf-8 import re import json import requests # 基于 m.weibo.cn 抓取少量数据，无需登陆验证 url_template = "https://m.weibo.cn/api/container/getIndex?type=wb&queryVal={}&containerid=100103type=2%26q%3D{}&page={}" def clean_text(text): """清除文本中的标签等信息""" dr = re.compile(r'(<)[^>]+>', re.S) dd = dr.sub('', text) dr = re.compile(r'#[^#]+#', re.S) dd = dr.sub('', dd) dr = re.compile(r'@[^ ]+ ', re.S) dd = dr.sub('', dd) return dd.strip() def fetch_data(query_val, page_id): """抓取关键词某一页的数据""" resp = requests.get(url_template.format(query_val, query_val, page_id)) card_group = json.loads(resp.text)['data']['cards'][0]['card_group'] print('url：', resp.url, ' --- 条数:', len(card_group)) mblogs = [] # 保存处理过的微博 for card in card_group: mblog = card['mblog'] blog = {'mid': mblog['id'], # 微博id 'text': clean_text(mblog['text']), # 文本 'userid': str(mblog['user']['id']), # 用户id 'username': mblog['user']['screen_name'], # 用户名 'reposts_count': mblog['reposts_count'], # 转发 'comments_count': mblog['comments_count'], # 评论 'attitudes_count': mblog['attitudes_count'] # 点赞 } mblogs.append(blog) return mblogs def remove_duplication(mblogs): """根据微博的id对微博进行去重""" mid_set = {mblogs[0]['mid']} new_blogs = [] for blog in mblogs[1:]: if blog['mid'] not in mid_set: new_blogs.append(blog) mid_set.add(blog['mid']) return new_blogs def fetch_pages(query_val, page_num): """抓取关键词多页的数据""" mblogs = [] for page_id in range(1 + page_num + 1): try: mblogs.extend(fetch_data(query_val, page_id)) except Exception as e: print(e) print("去重前：", len(mblogs)) mblogs = remove_duplication(mblogs) print("去重后：", len(mblogs)) # 保存到 result.json 文件中 fp = open('result_{}.json'.format(query_val), 'w', encoding='utf-8') json.dump(mblogs, fp, ensure_ascii=False, indent=4) print("已保存至 result_{}.json".format(query_val)) if __name__ == '__main__': fetch_pages('谷歌', 50)

29.093333

120

0.572411

285

2,182

4.203509

0.424561

0.046745

0.040067

0.03005

0.136895

0.096828

0.058431

0.040067

0

0.012173

0.247021

2,182

74

121

29.486486

0.716981

0.067369

0

0.076923

0

0.019231

0.179641

0

1

0.076923

false

0

0.057692

0

0.192308

0.096154

0

null

0

null

0

1

0

382d234f18f176eef58325d0092f70dabe6db06d

5,482

py

Python

stix_shifter_modules/arcsight/stix_transmission/api_client.py

priti-patil/stix-shifter

26954598fb79dde4506987388592ec391ff8a10b

[ "Apache-2.0" ]

33

2018-05-25T17:07:28.000Z

2019-09-30T10:08:53.000Z

stix_shifter_modules/arcsight/stix_transmission/api_client.py

priti-patil/stix-shifter

26954598fb79dde4506987388592ec391ff8a10b

[ "Apache-2.0" ]

54

2018-06-01T18:17:24.000Z

2019-09-30T18:36:15.000Z

stix_shifter_modules/arcsight/stix_transmission/api_client.py

subbyte/stix-shifter

36d71c172a5fc5b97d872e623753b0dd1bf4fe6c

[ "Apache-2.0" ]

37

2018-07-24T13:29:46.000Z

2019-09-29T19:06:27.000Z

import time from datetime import datetime, timedelta import json from stix_shifter_utils.stix_transmission.utils.RestApiClient import RestApiClient from stix_shifter_utils.utils.error_response import ErrorResponder class APIClient: TOKEN_ENDPOINT = 'core-service/rest/LoginService/login' STATUS_ENDPOINT = 'server/search/status' QUERY_ENDPOINT = 'server/search' RESULT_ENDPOINT = 'server/search/events' DELETE_ENDPOINT = 'server/search/close' def __init__(self, connection, configuration): self.connector = __name__.split('.')[1] self.auth = configuration.get('auth') headers = {'Accept': 'application/json'} self.client = RestApiClient(connection.get('host'), connection.get('port'), headers, cert_verify=connection.get('selfSignedCert', True) ) def ping_data_source(self): data, headers = dict(), dict() data['search_session_id'] = int(round(time.time() * 1000)) data['user_session_id'] = self.get_user_session_id() data['start_time'] = self.get_current_time()['start_time'] data['end_time'] = self.get_current_time()['end_time'] headers['Content-Type'] = 'application/json' headers['Accept-Charset'] = 'utf-8' return self.client.call_api(self.QUERY_ENDPOINT, 'POST', headers, data=json.dumps(data)) def create_search(self, query_expression): return_obj = dict() auth = dict() auth['search_session_id'] = int(round(time.time() * 1000)) auth['user_session_id'] = self.get_user_session_id() try: query = json.loads(query_expression) query.update(auth) headers = {'Content-Type': 'application/json', 'Accept-Charset': 'utf-8'} response = self.client.call_api(self.QUERY_ENDPOINT, 'POST', headers, data=json.dumps(query)) raw_response = response.read() response_code = response.code if 199 < response_code < 300: response_dict = json.loads(raw_response) if response_dict.get('sessionId'): return_obj['success'] = True return_obj['search_id'] = str(auth['search_session_id']) + ':' + str(auth['user_session_id']) # arcsight logger error codes - currently unavailable state elif response_code in [500, 503]: response_string = raw_response.decode() ErrorResponder.fill_error(return_obj, response_string, ['message'], connector=self.connector) elif isinstance(json.loads(raw_response), dict): response_error = json.loads(raw_response) response_dict = response_error['errors'][0] ErrorResponder.fill_error(return_obj, response_dict, ['message'], connector=self.connector) else: raise Exception(raw_response) return return_obj except Exception as err: raise err def get_search_status(self, search_session_id, user_session_id): headers, params = dict(), dict() params['search_session_id'] = int(search_session_id) params['user_session_id'] = user_session_id headers['Content-Type'] = 'application/json' headers['Accept-Charset'] = 'utf-8' return self.client.call_api(self.STATUS_ENDPOINT, 'POST', headers, data=json.dumps(params)) def get_search_results(self, search_session_id, user_session_id, range_start=None, range_end=None): headers, params = dict(), dict() params['search_session_id'] = int(search_session_id) params['user_session_id'] = user_session_id params['offset'] = int(range_start) params['length'] = int(range_end) headers['Content-Type'] = 'application/json' headers['Accept-Charset'] = 'utf-8' return self.client.call_api(self.RESULT_ENDPOINT, 'POST', headers, data=json.dumps(params)) def delete_search(self, search_session_id, user_session_id): headers, params = dict(), dict() params['search_session_id'] = int(search_session_id) params['user_session_id'] = user_session_id headers['Content-Type'] = 'application/json' headers['Accept-Charset'] = 'utf-8' return self.client.call_api(self.DELETE_ENDPOINT, 'POST', headers, data=json.dumps(params)) def get_user_session_id(self): try: response = self.client.call_api(self.TOKEN_ENDPOINT, 'POST', data=self.auth) if response.code == 200: response_text = json.loads(response.read()) token = response_text['log.loginResponse']['log.return'] elif response.read().decode("utf-8") == '': return_dict = 'Request error or authentication failure.' raise Exception(return_dict) else: raise Exception(response) return token except Exception as err: raise err @staticmethod def get_current_time(): ping_time = dict() end_time = datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%S.%f')[:-3] + 'Z' start_time = (datetime.utcnow() - timedelta(minutes=5)).strftime('%Y-%m-%dT%H:%M:%S.%f')[:-3] + 'Z' ping_time['start_time'] = start_time ping_time['end_time'] = end_time return ping_time

46.457627

113

0.620394

632

5,482

5.139241

0.221519

0.074815

0.060037

0.031404

0.416872

0.393165

0.339901

0.330049

0.275554

0.248461

0

0.008339

0.256293

5,482

117

114

46.854701

0.788325

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false

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0

0.252427

0

null

0

null

0

1

0

382d7124361bb294df0980d102709ff2f1a3f049

5,010

py

Python

app/routers/v1/api_listeners.py

d3vzer0/reternal-backend

aeeb613c820759212e7aef9150738a66b2882d50

[ "MIT" ]

6

2019-01-01T23:38:12.000Z

2021-07-27T03:43:11.000Z

app/routers/v1/api_listeners.py

d3vzer0/reternal-backend

aeeb613c820759212e7aef9150738a66b2882d50

[ "MIT" ]

1

2020-08-02T00:21:41.000Z

app/routers/v1/api_listeners.py

d3vzer0/reternal-backend

aeeb613c820759212e7aef9150738a66b2882d50

[ "MIT" ]

1

2021-07-27T03:43:24.000Z

from app.utils import celery from app.utils.depends import validate_worker, decode_token, validate_token from fastapi import Depends, Body, APIRouter, Security from app.schemas.listeners import ListenersOut from app.schemas.generic import CeleryTask from typing import List, Dict from celery import Signature from celery.result import AsyncResult router = APIRouter() # Get active listeners and currently configured options @router.get('/listeners/{worker_name}', response_model=CeleryTask, dependencies=[Security(validate_token)]) async def get_listeners(worker_name: str, context: dict = Depends(validate_worker), current_user: dict = Depends(decode_token)): ''' Get configuration options for all potential listeners by worker name / c2 framework ''' get_listeners = celery.send_task(context[worker_name]['listeners']['get'], chain=[ Signature('api.websocket.result.transmit', kwargs={ 'user': current_user['sub'], 'task_type': 'getListeners' }) ]) return {'task': str(get_listeners)} @router.get('/state/listeners/get/{job_uuid}', response_model=List[ListenersOut], dependencies=[Security(validate_token)]) async def get_listeners_result(job_uuid: str): ''' Get configuration options for all potential listeners by worker name / c2 framework ''' get_workers = AsyncResult(id=job_uuid, app=celery) workers_result = get_workers.get() if get_workers.state == 'SUCCESS' else None print(workers_result['response']) return workers_result['response'] # Enable a new listener @router.post('/listeners/{worker_name}/{listener_type}', response_model=CeleryTask, dependencies=[Security(validate_token, scopes=['write:integrations'])]) async def create_listener(worker_name: str, listener_type: str, listener_opts: dict = Body(...), context: dict = Depends(validate_worker), current_user: dict = Depends(decode_token)): ''' Enable listener for a specific c2 framework ''' create_listener = celery.send_task(context[worker_name]['listeners']['create'], args=(listener_type, listener_opts,), chain=[ Signature('api.websocket.result.transmit', kwargs={ 'user': current_user['sub'], 'task_type': 'createListener' }) ]) return {'task': str(create_listener)} @router.get('/state/listeners/create/{job_uuid}', dependencies=[Security(validate_token)]) async def create_listener_result(job_uuid: str): ''' Enable listener for a specific c2 framework ''' get_workers = AsyncResult(id=job_uuid, app=celery) workers_result = get_workers.get() if get_workers.state == 'SUCCESS' else None return workers_result['response'] # Disable a running listener @router.delete('/listener/{worker_name}/{listener_name}', response_model=CeleryTask, dependencies=[Security(validate_token, scopes=['write:integrations'])]) async def delete_listener(worker_name: str, listener_name: str, context: dict = Depends(validate_worker), current_user: dict = Depends(decode_token)): ''' Disable enabled listener option by c2 framework and active listener name ''' delete_listener = celery.send_task(context[worker_name]['listeners']['delete'], args=(listener_name,), chain=[ Signature('api.websocket.result.transmit', kwargs={ 'user': current_user['sub'], 'task_type': 'deleteListener' }) ]) return {'task': str(delete_listener)} @router.get('/state/listener/delete/{job_uuid}', dependencies=[Security(validate_token, scopes=['write:integrations'])]) async def delete_listener_result(job_uuid: str): ''' Disable enabled listener option by c2 framework and active listener name ''' get_workers = AsyncResult(id=job_uuid, app=celery) workers_result = get_workers.get() if get_workers.state == 'SUCCESS' else None return workers_result['response'] # Get options available for all listeners @router.get('/listeneroptions/{worker_name}', response_model=CeleryTask, dependencies=[Security(validate_token)]) async def get_listener_options(worker_name: str, context: dict = Depends(validate_worker), current_user: dict = Depends(decode_token)): ''' Get all available listeners to run by specific c2 framework ''' get_listeners = celery.send_task(context[worker_name]['listeners']['options'], chain=[ Signature('api.websocket.result.transmit', kwargs={ 'user': current_user['sub'], 'task_type': 'getListenerOptions' }) ]) return {'task': str(get_listeners)} @router.get('/state/listeneroptions/get/{job_uuid}', response_model=Dict[str, Dict], dependencies=[Security(validate_token)]) async def get_listener_options_result(job_uuid: str): ''' Get all available listeners to run by specific c2 framework ''' get_workers = AsyncResult(id=job_uuid, app=celery) workers_result = get_workers.get() if get_workers.state == 'SUCCESS' else None return workers_result['response']

53.297872

156

0.719561

606

5,010

5.757426

0.155116

0.040126

0.064202

0.075666

0.730582

0.682431

0.666667

0.650903

0.582115

0.560906

0

0.001892

0.155888

5,010

93

157

53.870968

0.823126

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0

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0

1

0

false

0

0.115942

0

0.231884

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0

null

0

null

0

1

0

382e3eda425327d2b534d7a2a0cc8ec6e1f59ecf

2,827

py

Python

metrics/classification_metrics.py

Karthik-Ragunath/DDU

b9daae9304bdeb222857884ef8cb3b6b3d004d33

[ "MIT" ]

43

2021-05-20T14:07:53.000Z

2022-03-23T12:58:26.000Z

metrics/classification_metrics.py

Karthik-Ragunath/DDU

b9daae9304bdeb222857884ef8cb3b6b3d004d33

[ "MIT" ]

3

2021-09-19T20:49:21.000Z

2022-03-07T10:25:47.000Z

metrics/classification_metrics.py

Karthik-Ragunath/DDU

b9daae9304bdeb222857884ef8cb3b6b3d004d33

[ "MIT" ]

8

2021-06-26T15:28:45.000Z

2022-02-19T02:07:05.000Z

""" Metrics to measure classification performance """ import torch from torch import nn from torch.nn import functional as F from utils.ensemble_utils import ensemble_forward_pass from sklearn.metrics import accuracy_score from sklearn.metrics import confusion_matrix def get_logits_labels(model, data_loader, device): """ Utility function to get logits and labels. """ model.eval() logits = [] labels = [] with torch.no_grad(): for data, label in data_loader: data = data.to(device) label = label.to(device) logit = model(data) logits.append(logit) labels.append(label) logits = torch.cat(logits, dim=0) labels = torch.cat(labels, dim=0) return logits, labels def test_classification_net_softmax(softmax_prob, labels): """ This function reports classification accuracy and confusion matrix given softmax vectors and labels from a model. """ labels_list = [] predictions_list = [] confidence_vals_list = [] confidence_vals, predictions = torch.max(softmax_prob, dim=1) labels_list.extend(labels.cpu().numpy()) predictions_list.extend(predictions.cpu().numpy()) confidence_vals_list.extend(confidence_vals.cpu().numpy()) accuracy = accuracy_score(labels_list, predictions_list) return ( confusion_matrix(labels_list, predictions_list), accuracy, labels_list, predictions_list, confidence_vals_list, ) def test_classification_net_logits(logits, labels): """ This function reports classification accuracy and confusion matrix given logits and labels from a model. """ softmax_prob = F.softmax(logits, dim=1) return test_classification_net_softmax(softmax_prob, labels) def test_classification_net(model, data_loader, device): """ This function reports classification accuracy and confusion matrix over a dataset. """ logits, labels = get_logits_labels(model, data_loader, device) return test_classification_net_logits(logits, labels) def test_classification_net_ensemble(model_ensemble, data_loader, device): """ This function reports classification accuracy and confusion matrix over a dataset for a deep ensemble. """ for model in model_ensemble: model.eval() softmax_prob = [] labels = [] with torch.no_grad(): for data, label in data_loader: data = data.to(device) label = label.to(device) softmax, _, _ = ensemble_forward_pass(model_ensemble, data) softmax_prob.append(softmax) labels.append(label) softmax_prob = torch.cat(softmax_prob, dim=0) labels = torch.cat(labels, dim=0) return test_classification_net_softmax(softmax_prob, labels)

29.447917

96

0.691546

342

2,827

5.502924

0.192982

0.052604

0.078108

0.05101

0.534538

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0.442614

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0.286929

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0

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0.223912

2,827

95

97

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0.17439

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0

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0

0.280702

0

null

0

null

0

1

0

382e6e3b04b4482534ee076317c8dbc1adf788e7

1,351

py

Python

CH04/coin_flip_streaks.py

kaifee-haque/Automate-the-Boring-Stuff-Solutions

5acbf9a397dc4aa000ebd9e8f6d79d0ee5287fef

[ "MIT" ]

null

CH04/coin_flip_streaks.py

kaifee-haque/Automate-the-Boring-Stuff-Solutions

5acbf9a397dc4aa000ebd9e8f6d79d0ee5287fef

[ "MIT" ]

null

CH04/coin_flip_streaks.py

kaifee-haque/Automate-the-Boring-Stuff-Solutions

5acbf9a397dc4aa000ebd9e8f6d79d0ee5287fef

[ "MIT" ]

null

#! python3 """Using a given number of trials, calculates the probability that a streak of 6 heads or 6 tails will occur in a series of 100 coin tosses.""" import random def streaks_in_list(): """Determines whether or not a streak of 6 heads of 6 tails occurs in a given series. returns: A boolean indicating the presence of a streak. """ flip_list = [] for i in range(100): flip_list.append(random.choice(["H", "T"])) #For each item in the list of tosses, up until the 94th item, determines #whether or not the next 5 tosses produced the same result (making a streak). for i in range(95): for j in range(6): if flip_list[i + j] != flip_list[i]: return False if j == 5: return True def percent_of_streaks(trials): """Calls the streaks_in_list function for each trial and determines the probability of a streak occurring. Args: trials: An integer representing the number of trials to perform. Returns: The probability of a streak of 6 occurring in a series of 100 coin tosses. """ streaks = 0 for i in range(trials): if streaks_in_list() == True: streaks += 1 return streaks / trials print(f"Chance of streak: {percent_of_streaks(10000) * 100}%")

27.02

82

0.632124

203

1,351

4.137931

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0.032143

0.035714

0.147619

0.057143

0

0.033438

0.291636

1,351

49

83

27.571429

0.844305

0.523316

0

0.092466

0.044521

0

1

0.111111

false

0

0.055556

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0.333333

0.055556

0

null

0

null

0

1

0

38319015f478f01fd850ca28cd8def2b6dfa7969

2,287

py

Python

testdsc.py

aligirayhanozbay/pydscpack

48d1df0775e4b063cf387b3884b8b463b3660e89

[ "BSD-3-Clause" ]

null

testdsc.py

aligirayhanozbay/pydscpack

48d1df0775e4b063cf387b3884b8b463b3660e89

[ "BSD-3-Clause" ]

null

testdsc.py

aligirayhanozbay/pydscpack

48d1df0775e4b063cf387b3884b8b463b3660e89

[ "BSD-3-Clause" ]

null

import pydscpack import numpy as np import matplotlib.pyplot as plt import multiprocessing #python3 -m numpy.f2py -c Src/Dp/src.f -m dsc def complexify(x): return np.array([complex(zs) for zs in x]) def check_laplacian(c, spacing, u, *wdsc_args): spacing = np.real(spacing) if not isinstance(c, np.ndarray): c = np.array(c) if not (c.dtype == np.complex128 or c.dtype==np.complex64): c = np.complex(*c) zcoords = np.array([c, c+spacing, c-spacing, c+1j*spacing, c-1j*spacing], dtype = c.dtype) wcoords = map_wdsc(zcoords,u,*wdsc_args) wnorm = np.real(wcoords * np.conj(wcoords)) soln = 1-(1/np.log(u))*np.log(wnorm**0.5) laplacian = (np.sum(soln[1:]) - 4*soln[0])/(spacing**2) return laplacian if __name__ == '__main__': nptq = 64 #No of Gauss-Jacobi integration pts tol = 1e-10 #tolerance for iterative process iguess = 1 #initial guess type. see line 770 is src.f - not sure what this does yet. ishape = 0 #0 for no vertices at infinity linearc = 1 #outer_coords = 5*complexify(['1.5+1.5j', '-1.5+1.5j', '-1.5-1.5j', '1.5-1.5j']) #coordinates of the outer polygon #inner_coords = -1.0*complexify(['0.5', '-0.5+0.5j', '-0.5-0.5j']) #coordinates of the inner polygon q = np.sqrt(2) q = 0.25 outer_coords = 1.5*complexify(['1+j', '-1+j', '-1-j', '1-j']) #coordinates of the outer polygon inner_coords = complexify([f'{q}+0.0j', f'0.0+{q}j', f'-{q}+0.0j', f'0.0-{q}j']) #coordinates of the inner polygon amap = pydscpack.AnnulusMap(outer_coords, inner_coords) n_plotpts = (50,200) r = np.linspace(amap.mapping_params['inner_radius'],1.0-(1e-5),n_plotpts[0]) #important to not evaluate map at r=1.0 (outer annulus ring) theta = np.linspace(0,2*np.pi,n_plotpts[1]) a = np.exp(theta*1j) #import pdb; pdb.set_trace() wplot = np.einsum('i,j->ij', r, a) wnorm = np.real(wplot * np.conj(wplot)) wangle = np.angle(wplot) amap.plot_map('norm', 'argument', w=wplot, save_path='/tmp/radius_and_argument.png') zplot = np.array([0.65+0.10*1j,0.75+0.1*1j,0.65-0.10*1j,0.75-0.1*1j]) wplot = amap.backward_map(zplot) amap.plot_map(np.ones(zplot.shape),z=zplot, plot_type='scatter', save_path='/tmp/triangle_sensors_z.png')

38.762712

141

0.640577

406

2,287

3.524631

0.366995

0.006988

0.008386

0.013976

0.148148

0.109015

0.103424

0.048917

0.034941

0

0.063034

0.18146

2,287

58

142

39.431034

0.701389

0.249235

0

0.087008

0.032334

0

1

0.05

false

0

0.1

0.025

0.2

0

null

0

null

0

1

0

38342d037b3b1d7b13402937a3a8f685102c37a8

1,852

py

Python

test/TestLog.py

nrejack/redi

51dea9a217d52fec278adba2fb794fa7620f9ba7

[ "BSD-3-Clause" ]

7

2015-07-16T03:03:24.000Z

2021-02-05T14:26:32.000Z

test/TestLog.py

nrejack/redi

51dea9a217d52fec278adba2fb794fa7620f9ba7

[ "BSD-3-Clause" ]

87

2015-01-08T16:21:47.000Z

2021-12-22T14:37:53.000Z

test/TestLog.py

nrejack/redi

51dea9a217d52fec278adba2fb794fa7620f9ba7

[ "BSD-3-Clause" ]

15

2015-04-01T19:05:55.000Z

2021-03-02T22:23:16.000Z

#!/usr/bin/env python # Contributors: # Christopher P. Barnes <senrabc@gmail.com> # Andrei Sura: github.com/indera # Mohan Das Katragadda <mohan.das142@gmail.com> # Philip Chase <philipbchase@gmail.com> # Ruchi Vivek Desai <ruchivdesai@gmail.com> # Taeber Rapczak <taeber@ufl.edu> # Nicholas Rejack <nrejack@ufl.edu> # Josh Hanna <josh@hanna.io> # Copyright (c) 2014-2015, University of Florida # All rights reserved. # # Distributed under the BSD 3-Clause License # For full text of the BSD 3-Clause License see http://opensource.org/licenses/BSD-3-Clause ''' @author : Radha email : rkandula@ufl.edu This file tests if the `configure_logging` properly creates a log file Note: the created file is destroyed at the end ''' import unittest import os import sys from redi import redi file_dir = os.path.dirname(os.path.realpath(__file__)) goal_dir = os.path.join(file_dir, "../") proj_root = os.path.abspath(goal_dir)+'/' DEFAULT_DATA_DIRECTORY = os.getcwd() class TestLog(unittest.TestCase): def setUp(self): # initialize log file name self.file_name = proj_root + 'log/redi.log' def test_log(self): import os.path file_name = self.file_name sys.path.append('log') # remove any existing log file in log/ folder #if os.path.isfile(file_name): # with open(file_name): #print "here" # os.remove(file_name) #print os.path.isfile(file_name) # call the configure logging function redi.configure_logging(DEFAULT_DATA_DIRECTORY) #print 'checking if log file was created: ' + file_name # check if the file is created assert os.path.isfile(file_name) == True # remove the file created through testing os.remove(file_name) if __name__ == '__main__': unittest.main()

26.84058

91

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1,852

4.685824

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0.071954

0.02453

0.039248

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0

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0.215983

1,852

68

92

27.235294

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0.562095

0

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0

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0.1

false

0

0.25

0

0.4

0

null

0

null

0

1

0

3835c2b5cedc5df64dfd33405ffd3071075936ab

2,330

py

Python

data/cbct2ct_dataset.py

CommanderCero/DJRevGAN

bb2a79acc951eb01c5427a7a08e82b1e40348a53

[ "BSD-3-Clause" ]

2

2022-03-16T15:13:30.000Z

2022-03-16T15:13:37.000Z

data/cbct2ct_dataset.py

CommanderCero/DJRevGAN

bb2a79acc951eb01c5427a7a08e82b1e40348a53

[ "BSD-3-Clause" ]

null

data/cbct2ct_dataset.py

CommanderCero/DJRevGAN

bb2a79acc951eb01c5427a7a08e82b1e40348a53

[ "BSD-3-Clause" ]

null

import os.path from data.base_dataset import BaseDataset, get_transform from data.image_folder import make_dataset from PIL import Image import random import torch import numpy as np def normalize(image, minimum, maximum): # https://stats.stackexchange.com/questions/178626/how-to-normalize-data-between-1-and-1 image = (image - minimum) / (maximum - minimum) return 2 * image - 1 class cbct2ctDataset(BaseDataset): @staticmethod def modify_commandline_options(parser, is_train): return parser def initialize(self, opt): self.opt = opt self.root = opt.dataroot self.cbct_dir = os.path.join(opt.dataroot, opt.phase + 'CBCT') self.ct_dir = os.path.join(opt.dataroot, opt.phase + 'CT') self.cbct_paths = make_dataset(self.cbct_dir) self.ct_paths = make_dataset(self.ct_dir) # Do we really need to sort the paths? #self.cbct_paths = sorted(self.cbct_paths) #self.ct_paths = sorted(self.ct_paths) self.cbct_size = len(self.cbct_paths) self.ct_size = len(self.ct_paths) print('len(CBCT),len(CT)=', self.cbct_size, self.ct_size) #self.transform = get_transform(opt) def __getitem__(self, index): # Calculate indices index_cbct = index % self.cbct_size cbct_path = self.cbct_paths[index_cbct] if self.opt.serial_batches: index_ct = index % self.ct_size else: index_ct = random.randint(0, self.ct_size - 1) ct_path = self.ct_paths[index_ct] # Load data cbct_img = np.load(cbct_path) ct_img = np.load(ct_path) # Convert to tensors cbct_tensor = torch.from_numpy(cbct_img).view(1, *cbct_img.shape) ct_tensor = torch.from_numpy(ct_img).view(1, *ct_img.shape) # Normalize data # We didnt do this in the preprocessing step so we dont have to store our data as floats cbct_tensor = normalize(cbct_tensor, -1000.0, 7000.0) ct_tensor = normalize(ct_tensor, -1024.0, 3072.0) return {'A': cbct_tensor, 'B': ct_tensor, 'A_paths': cbct_path, 'ct_paths': ct_path} def __len__(self): return max(self.cbct_size, self.ct_size) def name(self): return 'cbct2ctDataset'

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0.31003

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0.045582

0.018233

0.102384

0.075736

0.044881

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0.020761

0.255794

2,330

69

97

33.768116

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0.159091

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0.431818

0.022727

0

null

0

null

0

1

0

38388cbdae4f3ce7f30294f51a48514831405b20

1,222

py

Python

savor_code/pipelines.py

tobias-fyi/savor_data

b06cbef44f23048fa01f9ad7fb36b8fa715d63b2

[ "MIT" ]

null

savor_code/pipelines.py

tobias-fyi/savor_data

b06cbef44f23048fa01f9ad7fb36b8fa715d63b2

[ "MIT" ]

null

savor_code/pipelines.py

tobias-fyi/savor_data

b06cbef44f23048fa01f9ad7fb36b8fa715d63b2

[ "MIT" ]

null

""" Savor Data :: Various pipeline functions """ from os import environ from pathlib import Path import pandas as pd def extract_and_concat_airtable_data(records: dict) -> pd.DataFrame: """Extracts fields from the airtable data and concatenates them with airtable id. Uses pyjanitor to clean up column names. """ # Load and clean/fix names df = ( pd.DataFrame.from_records(records) .clean_names() .rename_column("id", "airtable_id") ) df2 = pd.concat( # Extract `fields` and concat to `airtable_id` [df["airtable_id"], df["fields"].apply(pd.Series)], axis=1 ) return df2 def convert_datetime_cols(data: pd.DataFrame, dt_cols: list) -> pd.DataFrame: """If datetime columns exist in dataframe, convert them to datetime. :param data (pd.DataFrame) : DataFrame with datetime cols to be converted. :param dt_cols (list) : List of potential datetime cols. :return (pd.DataFrame) : DataFrame with datetime cols converted. """ data = data.copy() # Don't change original dataframe for col in dt_cols: if col in data.columns: # Make sure column exists data[col] = pd.to_datetime(data[col]) return data

31.333333

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1,222

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0.368421

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null

0

null

0

1

0

383966bec51a678c93133bca8d324981bf23e90d

7,526

py

Python

sdd-db/cronjobs/db_upload_energy.py

socialdistancingdashboard/virushack

6ef69d26c5719d0bf257f4594ed2488dd73cdc40

[ "Apache-2.0" ]

29

2020-03-21T00:47:51.000Z

2021-07-17T15:50:33.000Z

sdd-db/cronjobs/db_upload_energy.py

socialdistancingdashboard/virushack

6ef69d26c5719d0bf257f4594ed2488dd73cdc40

[ "Apache-2.0" ]

7

2020-03-21T14:04:26.000Z

2022-03-02T08:05:40.000Z

sdd-db/cronjobs/db_upload_energy.py

socialdistancingdashboard/virushack

6ef69d26c5719d0bf257f4594ed2488dd73cdc40

[ "Apache-2.0" ]

13

2020-03-21T01:08:08.000Z

2020-04-08T17:21:11.000Z

""" Uploads Corona data from Zeit online Note: infected numbers are known infections on a particular day. Dead and Recovered numbers were summed up until today. """ import os import pandas as pd from datetime import datetime, timedelta import pytz # compatibility with ipython try: __IPYTHON__ os.chdir(os.path.dirname(__file__)) except: pass import json import pymysql from pymysql.constants import CLIENT from sqlalchemy import create_engine from sqlalchemy.pool import NullPool import requests from hashlib import md5 # connect to aws database with sqlalchemy (used for pandas connections) config = json.load(open("../../credentials/credentials-aws-db.json", "r")) aws_engine = create_engine( ("mysql+pymysql://" + config["user"] + ":" + config["password"] + "@" + config["host"] + ":" + str(config["port"]) + "/" + config["database"]), poolclass=NullPool, # dont maintain a pool of connections pool_recycle=3600 # handles timeouts better, I think... ) # aws database connection used for normal queries because sqlalchemy doesnt support on duplicate key queries pymysql_con = pymysql.connect( config["host"], config["user"], config["password"], config["database"], client_flag=CLIENT.MULTI_STATEMENTS) charts_whitelist = [ "import balance", "load", "day ahead auction", "intraday continuous average price", "intraday continuous id3 price", "intraday continuous id1 price" ] description_lookup = { "import balance": { "desc_short": "Netto Stromimporte", "desc_long": "Netto Stromimporte", "unit": "Gigawatt", "unit_agg": "Gigawatt", "agg_mode": "sum" }, "load": { "desc_short": "Stromverbrauch", "desc_long": "Stromverbrauch", "unit": "Gigawatt", "unit_agg": "Gigawatt", "agg_mode": "sum" }, "day ahead auction": { "desc_short": "Day-ahead Strompreis", "desc_long": "Day-ahead Strompreise", "unit": "EUR/MWh", "unit_agg": "Prozent", "agg_mode": "avg-percentage-of-normal" }, "intraday continuous average price": { "desc_short": "Strompreis Index IDFull", "desc_long": "The IDFull index is the weighted average price of all continuous trades executed during the full trading session of any EPEX SPOT continuous contract. This index includes the entire market liquidity and thus represents the obvious continuous market price references for each contract.", "unit": "EUR/MWh", "unit_agg": "Prozent", "agg_mode": "avg-percentage-of-normal" }, "intraday continuous id3 price": { "desc_short": "Strompreis Index ID3", "desc_long": "The ID3 index is the weighted average price of all continuous trades executed within the last 3 trading hours of a contract (up to 30min before delivery start).This index focuses on the most liquid timeframe of a continuous contract trading session. As such, this index presents large business interest for EPEX SPOT customers to market their offers or challenge their trading activity.", "unit": "EUR/MWh", "unit_agg": "Prozent", "agg_mode": "avg-percentage-of-normal" }, "intraday continuous id1 price": { "desc_short": "Strompreis Index ID1", "desc_long": "The ID1 index is the weighted average price of all continuous trades executed within the last trading hour of a contract up to 30min before delivery start. This index catches the market last minute imbalance needs, reflecting amongst other the increasing REN breakthrough and system balancing flexibility.", "unit": "EUR/MWh", "unit_agg": "Prozent", "agg_mode": "avg-percentage-of-normal" } } # retrieve data from fraunhofer ise def upload_week(week): url = f"https://www.energy-charts.de/price/week_2020_{week}.json" r = requests.get(url) data = r.json() for chart in data: chart_key = chart["key"][0]["en"].lower().replace("-", " ") if not chart_key in charts_whitelist: continue print(f"current chart_key {chart_key}") source_id = ("score fraunhofer " + chart_key).replace(" ", "_") source = { "id": source_id, "desc_short": description_lookup[chart_key]["desc_short"], "desc_long": description_lookup[chart_key]["desc_long"] , "contributors": "Fraunhofer ISI, 50 Hertz, Amprion, Tennet, TransnetBW, EEX, EPEX SPOT", "unit": description_lookup[chart_key]["unit"], "unit_long": description_lookup[chart_key]["unit"], "unit_agg_long": description_lookup[chart_key]["unit_agg"], "sample_interval": "hourly", "agg_mode": description_lookup[chart_key]["agg_mode"], "has_reference_values": 0, "spatial_level": "country" } q = """ REPLACE INTO sources ( id, desc_short, desc_long, contributors, unit, unit_long, unit_agg_long, sample_interval, agg_mode, has_reference_values, spatial_level ) VALUES ( %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s) """ with pymysql_con.cursor() as cur: cur.execute(q, list(source.values())) pymysql_con.commit() country_id = "DE" unique_index = source_id + country_id station = { "source_id": source_id, "description": "country-level data", "source_station_id": "country-level data", "country_id": country_id, "unique_index": md5(unique_index.encode("utf-8")).hexdigest() } q = """ INSERT INTO stations ( source_id, description, source_station_id, country_id, unique_index ) VALUES ( %s, %s, %s, %s, %s ) ON DUPLICATE KEY UPDATE source_id = VALUES(source_id), description = VALUES(description), source_station_id = VALUES(source_station_id), country_id = VALUES(country_id) """ with pymysql_con.cursor() as cur: cur.execute(q, list(station.values())) pymysql_con.commit() q = """ SELECT id AS station_id FROM stations WHERE source_id = '%s' """ % source_id scores_stations_foreign_key = pd.read_sql(q, aws_engine)["station_id"].iloc[0] # remove trailing zeros drop_index = len(chart["values"]) while chart["values"][drop_index-1][1] == 0: drop_index = drop_index - 1 df_scores = pd.DataFrame(chart["values"][:drop_index], columns=["dt", "score_value"]) df_scores.dropna(inplace=True) df_scores.dt = df_scores.dt.apply(lambda x: datetime.fromtimestamp(x / 1000)) df_scores['dt'] = df_scores['dt'].astype(str) df_scores["source_id"] = source_id df_scores["station_id"] = scores_stations_foreign_key q = """ INSERT INTO scores ( dt, score_value, source_id, station_id ) VALUES (%s, %s, %s, %s) ON DUPLICATE KEY UPDATE score_value = VALUES(score_value) """ with pymysql_con.cursor() as cur: cur.executemany(q, df_scores[["dt", "score_value", "source_id", "station_id"]].values.tolist()) pymysql_con.commit() print("uploaded week %s done" % week) def upload_all(): """ Drop all fraunhofer data before reuploading """ q = """ DELETE FROM sources WHERE id LIKE '%fraunhofer%'; DELETE FROM stations WHERE source_id LIKE '%fraunhofer%'; DELETE FROM scores WHERE source_id LIKE '%fraunhofer%'; """ with pymysql_con.cursor() as cur: cur.execute(q) pymysql_con.commit() start = datetime(2020,1,1) week = start.isocalendar()[1] current_week = datetime.now().isocalendar()[1] while week <= current_week: upload_week(str(week).zfill(2)) week = week + 1 upload_all() # upload for today #current_week = datetime.now().isocalendar()[1] #upload_week(str(week).zfill(2)) pymysql_con.close()

34.054299

406

0.680973

992

7,526

4.99496

0.295363

0.027447

0.008476

0.00888

0.344097

0.240161

0.185671

0.18002

0.155399

0.131584

0

0.008026

0.188812

7,526

221

407

34.054299

0.803604

0.083577

0

0.207865

0

0.022472

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0.02676

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false

0.016854

0.08427

0

0.095506

0.011236

0

null

0

null

0

1

0

383b03761e542f44270f90f959691b44c38bf91a

1,499

py

Python

celery_worker.py

ceephoen/celery-flask-example

b92febfdd29dc2431c3c39940d3e54d4cc677134

[ "MIT" ]

null

celery_worker.py

ceephoen/celery-flask-example

b92febfdd29dc2431c3c39940d3e54d4cc677134

[ "MIT" ]

null

celery_worker.py

ceephoen/celery-flask-example

b92febfdd29dc2431c3c39940d3e54d4cc677134

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ celery work file """ from start import app from datum.datum import Invitation, Account from public.initial import db from celery import Celery def create_celery(app): celery = Celery( app.import_name, backend=app.config['CELERY_RESULT_BACKEND'], broker=app.config['CELERY_BROKER_URL'] ) celery.conf.update(app.config) class ContextTask(celery.Task): def __call__(self, *args, **kwargs): with app.app_context(): return self.run(*args, **kwargs) celery.Task = ContextTask return celery # init celery celery_app = create_celery(app) celery_app.config_from_object(app.config) @celery_app.task() def invite_record(uid, code): """invite_record""" invitation = Invitation() invitation.uid = uid invitation.share_code = code try: db.session.add(invitation) db.session.commit() except Exception as err: print(err) db.session.rollback() @celery_app.task() def init_account(uid, mobile): """init_account""" account = Account() account.uid = uid account.acc_no = mobile try: db.session.add(account) db.session.commit() except Exception as err: print(err) db.session.rollback() """ 1 start celery by following command celery -A celery_worker.celery_app worker -l info 2 use it in views by from celery_worker import invite_record invite_record.delay(uid, invite_code) """

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1,499

4.974093

0.367876

0.075

0.046875

0.04375

0.125

0

0.0026

0.230153

1,499

73

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20.534247

0.829289

0.052035

0

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0

0.031405

0.017355

0

1

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false

0

0.121951

0

0.292683

0.04878

0

null

0

null

0

1

0

383dc005e8248bd773075f0ffebcc3744799a8e7

1,672

py

Python

invoice/urls.py

seba6m6/django_invoice_shipment

0cfa76176e40916c6cf33ff6a95d3ed40cc244f1

[ "MIT" ]

null

invoice/urls.py

seba6m6/django_invoice_shipment

0cfa76176e40916c6cf33ff6a95d3ed40cc244f1

[ "MIT" ]

10

2019-12-04T22:51:43.000Z

2022-02-10T08:28:31.000Z

invoice/urls.py

seba6m6/django_invoice_shipment

0cfa76176e40916c6cf33ff6a95d3ed40cc244f1

[ "MIT" ]

null

from django.urls import path, re_path from . import views urlpatterns = [ path('invoice-list-api/', views.invoice_list_api ), path('invoice-list/', views.InvoiceList.as_view(), name='invoice_list' ), path('invoice-list-unpaid/', views.UnpaidInvoice.as_view(), name='unpaid_invoice_list' ), path('invoice-list-partially-paid/', views.PartiallyPaidInvoice.as_view(), name='partially_paid_invoice_list' ), path('invoice-list-batch/', views.BatchInvoice.as_view(), name='batch_invoice_list' ), re_path(r'^invoice/(?P<pk>[0-9]+)/$', views.invoice_detail, name='invoice_detail' ), re_path(r'^invoice-pdf/(?P<pk>[0-9]+)/$', views.generate_pdf, name='generate_pdf'), re_path(r'^shipments-list-pdf/(?P<pk>[0-9]+)/$', views.generate_shipment_pdf, name='generate_shipment_pdf'), path('invoice-create/', views.CreateInvoice.as_view(), name='create_invoice'), re_path(r'^invoice-update/(?P<pk>[0-9]+)/$', views.UpdateInvoice.as_view(), name='update_invoice'), path('invoice-items-ship/', views.ShipmentView.as_view(), name='ship_items'), re_path(r'^invoice-items-ship-update/(?P<pk>[0-9]+)/$', views.ShipmentUpdate.as_view(), name='ship_items_update'), re_path(r'^invoice-items-ship-simple-update/(?P<pk>[0-9]+)/$', views.ShipmentSimpleUpdate.as_view(), name='ship_items_simple_update'), path('invoice-items-to-ship/', views.ItemsToSendList.as_view(), name='items_to_send'), path('invoice-items-sent/', views.ItemsSent.as_view(), name='items_sent'), path('shipments-list/', views.shipment_lists, name='shipment_lists'), re_path(r'^shipment-detail/(?P<pk>[0-9]+)/$', views.shipment_detail, name='shipment_detail'), ]

72.695652

138

0.705742

239

1,672

4.719665

0.200837

0.058511

0.097518

0.031028

0.257979

0.120567

0.037234

0

0.009126

0.082536

1,672

23

139

72.695652

0.726206

0

0.411835

0.22116

0

1

0

false

0

0.095238

0

0.095238

0

null

0

null

0

1

0

383fc49df8fbfef72b69486d6d3c48fe6421c587

1,114

py

Python

migrations/versions/06bfb5c8f270_.py

jingshu-fk/FXTest_copy

150012f87021b6b8204fd342c62538c10d8dfa85

[ "MIT" ]

1

2019-12-31T01:53:04.000Z

migrations/versions/06bfb5c8f270_.py

fuyang123/FXTest

ccbc5e986f4d0f9d3145a857674529380d873719

[ "MIT" ]

2

2021-03-26T00:24:28.000Z

2022-03-22T22:06:39.000Z

migrations/versions/06bfb5c8f270_.py

jingshu-fk/FXTest_copy

150012f87021b6b8204fd342c62538c10d8dfa85

[ "MIT" ]

null

"""empty message Revision ID: 06bfb5c8f270 Revises: 2488b2a0a217 Create Date: 2019-04-23 08:54:15.345663 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '06bfb5c8f270' down_revision = '2488b2a0a217' branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.add_column('interfacetests', sa.Column('is_ci', sa.Boolean(), nullable=True)) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_constraint(None, 'users', type_='foreignkey') op.drop_constraint(None, 'tstresults', type_='foreignkey') op.add_column('tasks', sa.Column('taskdesc', sa.TEXT(length=252), nullable=True)) op.drop_constraint(None, 'tasks', type_='foreignkey') op.drop_constraint(None, 'tasks', type_='foreignkey') op.drop_constraint(None, 'projects', type_='unique') op.drop_constraint(None, 'interfacetests', type_='foreignkey') op.drop_column('interfacetests', 'is_ci') # ### end Alembic commands ###

30.108108

85

0.702873

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1,114

5.569343

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0.055046

0.125819

0.157274

0.288336

0.243775

0.12844

0

0.057956

0.148115

1,114

36

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false

0

0.117647

0

0.235294

0

null

0

null

0

1

0

384710fe7a2d234e8e57b874e247e8de75288b44

5,057

py

Python

data/read_xml.py

guillaume-chevalier/Wikipedia-XML-Markup-Code-to-Plain-Text-Parser

b2b41b9786e676e6f855c993f938a1345b9591c1

[ "BSD-3-Clause" ]

1

2019-01-15T11:00:21.000Z

data/read_xml.py

guillaume-chevalier/Wikipedia-XML-Markup-Code-to-Plain-Text-Parser

b2b41b9786e676e6f855c993f938a1345b9591c1

[ "BSD-3-Clause" ]

3

2019-01-14T10:46:03.000Z

2019-01-14T10:47:14.000Z

data/read_xml.py

guillaume-chevalier/Wikipedia-XML-Markup-Code-to-Plain-Text-Parser-of-Hell

b2b41b9786e676e6f855c993f938a1345b9591c1

[ "BSD-3-Clause" ]

null

import time import os import codecs import csv import xml.etree.ElementTree as etree import re import mwparserfromhell from data.config import WIKIPEDIA_XML, WIKIPEDIA_WORKING_DIR, WIKIPEDIA_OUTPUT_UTF8_TXT_RAW_FILE from data.read_write_txt import FilesReaderBinaryUTF8, FilesWriterBinaryUTF8 def mwparserfromhell_remove_templates(text): """ Removes templates from wikicode parsed tree. Note: I'm not even sure what this does or if it does something. """ wikicode = mwparserfromhell.parse(text) for template in reversed(wikicode.filter_templates()): wikicode.remove(template) return wikicode def wikicode_to_txt(wikicode): """ I couldn't find a properly-licensed tool to do this, I created my own. This mostly crushes wikicode to plain text, but it's not perfect. """ # removes wiki markup: https://en.wikipedia.org/wiki/Wikipedia:Tutorial/Formatting#Wiki_markup # matches everything but a `<`, a `}`, or a `}`. everything_but_closing_parenthesis = '[^\]\<\}]' # matches a `[`, a `{`, or a `<ref>`: begin = '(\{|\[|\<ref\>)+' # matches anywhing and then a final `:` or a final `|`: preceded = '(' + everything_but_closing_parenthesis + '+(\||:))?' # matches a `]`, a `}`, or a `</ref>.: closing = '(\]|\}|\</ref\>)+' # matches the last thing between "begin" and "closing" which is preceded by "preceded".: RE = begin + preceded + '(?P<text>' + everything_but_closing_parenthesis + '*)' + closing RE = re.compile(RE) # Keep the "<text>" group found before closing parenthesis. keep_right_group = r'\g<text>' out = re.sub(RE, keep_right_group, wikicode) # remove remaining refs and xml comments. RE = "((</?ref)[^\>]*\>|(<!--)[^\>]*--\>)" out = re.sub(RE, "", out) # remove a whole lot of crap out = out.replace("'''", "").replace("''", "").replace(" ", " ").replace("--", "").replace("~~", "").replace("\t", " ").replace(" ", " ") out = out.replace("\n*", "\n").replace("\n#", "\n").replace(" \n", "\n").replace("\n ", "\n").replace(" \n", "\n") out = out.replace("\n\n", "\n").replace("\n\n", "\n").replace("\n\n", "\n").replace("\n\n", "\n") return out def beautify_text_to_sentences_that_ends_with_dots(text): """ Make sentences end with dots whenever possible or where there is a dot missing. """ symbols_to_avoid = ["^", "\n", "\.", "\!", ",", "-", ":", ";", "\?"] not_an_empty_nor_starting_nor_punctuated_line = "" for symb in symbols_to_avoid: not_an_empty_nor_starting_nor_punctuated_line += '(?<!' + symb + ')' #not_an_empty_nor_starting_nor_punctuated_line = "(?<!^)(?<!\n)" RE = not_an_empty_nor_starting_nor_punctuated_line + "(?P<eos>(\n|$))" RE = re.compile(RE) # Keep the "<text>" group found before closing parenthesis. keep = r'.\g<eos>' out = re.sub(RE, keep, text) return out def remove_titles_and_insert_newline_instead(text): """ A title will now be 2 new lines, and an article separation will now be 3 newlines. """ RE = re.compile("(((?<=^)|(?<=\n)))((=|_).*(=|_)\s?\.?\s?)(\n|$)") out = re.sub(RE, "\n", text) out = out.strip().rstrip("\n") + "\n\n\n" return out def title_tag_name_to_clean_title(title_tag): i = title_tag.rfind("}") if i != -1: return title_tag[i + 1:] else: return title_tag def convert_wikipedia_to_plain_txt(WIKIPEDIA_OUTPUT_UTF8_TXT_RAW_FILE, WIKIPEDIA_XML): # With a writer on text files with FilesWriterBinaryUTF8(WIKIPEDIA_OUTPUT_UTF8_TXT_RAW_FILE, chunk_size=int(1024**2), verbose=True) as fswr: title = "" # For every note in Wikipedia's XML dump for event, elem in etree.iterparse(WIKIPEDIA_XML, events=('start', 'end')): tname = title_tag_name_to_clean_title(elem.tag) # Retrieve pages just under certain conditions. if event == 'start': if tname == 'title': title = elem.text elif ( tname == 'text' # Discard if no title or no text: and elem.text is not None and title is not None # Discard if page is a redirection to another page: and "{{redirect" not in elem.text.lower() and "#redirect" not in elem.text.lower() # Discard if the page is not a regular article (ex type: page, list, talk, book): and ":" not in title ): # Will save this page. text = elem.text wikicode = mwparserfromhell_remove_templates(text) text = wikicode_to_txt(str(wikicode)) text = beautify_text_to_sentences_that_ends_with_dots(text) text = remove_titles_and_insert_newline_instead(text) fswr.write(text) elem.clear()

35.612676

143

0.584932

637

5,057

4.452119

0.324961

0.011284

0.028561

0.024683

0.324048

0.295134

0.238011

0.190056

0.13646

0.106135

0

0.004025

0.263002

5,057

141

144

35.865248

0.756909

0.25786

0

0.068493

0

0.085699

0.02238

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0.082192

false

0

0.123288

0

0.287671

0

null

0

null

0

1

0

384823b968310ae6bd298d652f5063804c7c94d4

6,779

py

Python

hathor/mining/block_template.py

mbnunes/hathor-core

e5e0d4a627341e2a37ee46db5c9354ddb7f8dfb8

[ "Apache-2.0" ]

51

2019-12-28T03:33:27.000Z

2022-03-10T14:03:03.000Z

hathor/mining/block_template.py

mbnunes/hathor-core

e5e0d4a627341e2a37ee46db5c9354ddb7f8dfb8

[ "Apache-2.0" ]

316

2019-09-10T09:20:05.000Z

2022-03-31T20:18:56.000Z

hathor/mining/block_template.py

mbnunes/hathor-core

e5e0d4a627341e2a37ee46db5c9354ddb7f8dfb8

[ "Apache-2.0" ]

19

2020-01-04T00:13:18.000Z

2022-02-08T21:18:46.000Z

# Copyright 2021 Hathor Labs # # 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. """ Module for abstractions around generating mining templates. """ from typing import Dict, Iterable, List, NamedTuple, Optional, Set, Tuple, Type, Union from hathor.transaction import BaseTransaction, Block, MergeMinedBlock from hathor.transaction.storage import TransactionStorage from hathor.util import Random class BlockTemplate(NamedTuple): versions: Set[int] reward: int # reward unit value, 64.00 HTR is 6400 weight: float # calculated from the DAA timestamp_now: int # the reference timestamp the template was generated for timestamp_min: int # min valid timestamp timestamp_max: int # max valid timestamp parents: List[bytes] # required parents, will always have a block and at most 2 txs parents_any: List[bytes] # list of extra parents to choose from when there are more options height: int # metadata score: float # metadata def generate_minimaly_valid_block(self) -> BaseTransaction: """ Generates a block, without any extra information that is valid for this template. No random choices.""" from hathor.transaction import TxOutput, TxVersion return TxVersion(min(self.versions)).get_cls()( timestamp=self.timestamp_min, parents=self.parents[:] + sorted(self.parents_any)[:(3 - len(self.parents))], outputs=[TxOutput(self.reward, b'')], weight=self.weight, ) def generate_mining_block(self, rng: Random, merge_mined: bool = False, address: Optional[bytes] = None, timestamp: Optional[int] = None, data: Optional[bytes] = None, storage: Optional[TransactionStorage] = None, include_metadata: bool = False, ) -> Union[Block, MergeMinedBlock]: """ Generates a block by filling the template with the given options and random parents (if multiple choices). Note that if a timestamp is given it will be coerced into the [timestamp_min, timestamp_max] range. """ # XXX: importing these here to try to contain hathor dependencies as much as possible from hathor.transaction import TransactionMetadata, TxOutput from hathor.transaction.scripts import create_output_script parents = list(self.get_random_parents(rng)) output_script = create_output_script(address) if address is not None else b'' base_timestamp = timestamp if timestamp is not None else self.timestamp_now block_timestamp = min(max(base_timestamp, self.timestamp_min), self.timestamp_max) tx_outputs = [TxOutput(self.reward, output_script)] cls: Union[Type['Block'], Type['MergeMinedBlock']] = MergeMinedBlock if merge_mined else Block block = cls(outputs=tx_outputs, parents=parents, timestamp=block_timestamp, data=data or b'', storage=storage, weight=self.weight) if include_metadata: block._metadata = TransactionMetadata(height=self.height, score=self.score) block.get_metadata(use_storage=False) return block def get_random_parents(self, rng: Random) -> Tuple[bytes, bytes, bytes]: """ Get parents from self.parents plus a random choice from self.parents_any to make it 3 in total. Return type is tuple just to make it clear that the length is always 3. """ assert 1 <= len(self.parents) <= 3 more_parents = rng.ordered_sample(self.parents_any, 3 - len(self.parents)) p1, p2, p3 = self.parents[:] + more_parents return p1, p2, p3 def to_dict(self) -> Dict: return { 'data': self.generate_minimaly_valid_block().get_struct_without_nonce().hex(), 'versions': sorted(self.versions), 'reward': self.reward, 'weight': self.weight, 'timestamp_now': self.timestamp_now, 'timestamp_min': self.timestamp_min, 'timestamp_max': self.timestamp_max, 'parents': [p.hex() for p in self.parents], 'parents_any': [p.hex() for p in self.parents_any], 'height': self.height, 'score': self.score, } @classmethod def from_dict(cls, data: Dict) -> 'BlockTemplate': return cls( versions=set(data['versions']), reward=int(data['reward']), weight=float(data['weight']), timestamp_now=int(data['timestamp_now']), timestamp_min=int(data['timestamp_min']), timestamp_max=int(data['timestamp_max']), parents=[bytes.fromhex(p) for p in data['parents']], parents_any=[bytes.fromhex(p) for p in data['parents_any']], height=int(data['height']), score=int(data['score']), ) class BlockTemplates(List[BlockTemplate]): def __init__(self, templates: Iterable[BlockTemplate], storage: Optional[TransactionStorage] = None): super().__init__(templates) self.storage = storage assert len(self) > 0, 'This class requires at least one block template.' def choose_random_template(self, rng: Random) -> BlockTemplate: """ Randomly choose and return a template and use that for generating a block, see BlockTemplate""" return rng.choice(self) def generate_mining_block(self, rng: Random, merge_mined: bool = False, address: Optional[bytes] = None, timestamp: Optional[int] = None, data: Optional[bytes] = None, storage: Optional[TransactionStorage] = None, include_metadata: bool = False, ) -> Union[Block, MergeMinedBlock]: """ Randomly choose a template and use that for generating a block, see BlockTemplate.generate_mining_block""" return self.choose_random_template(rng).generate_mining_block(rng, merge_mined=merge_mined, address=address, timestamp=timestamp, data=data, storage=storage or self.storage, include_metadata=include_metadata)

50.969925

118

0.648031

813

6,779

5.289053

0.260763

0.02814

0.024419

0.018837

0.173488

0.159535

0.136279

0.122326

0

0.005986

0.260658

6,779

132

119

51.356061

0.851955

0.245906

0

0.089888

0

0.052013

0

0.022472

1

0.089888

false

0

0.078652

0.022472

0.382022

0

null

0

null

0

1

0

38482a9c89f54e16cf60b0ec6e3262e31f7db32b

1,124

py

Python

baselines/ADJ/adj_rerank.py

sordonia/HierarchicalEncoderDecoder

b217a94387eca1e37975d7dd770d94981ec7eac1

[ "BSD-3-Clause" ]

116

2015-11-01T19:37:25.000Z

2022-02-03T10:26:41.000Z

baselines/ADJ/adj_rerank.py

sordonia/HierarchicalEncoderDecoder

b217a94387eca1e37975d7dd770d94981ec7eac1

[ "BSD-3-Clause" ]

10

2015-11-02T14:17:03.000Z

2019-06-12T08:37:20.000Z

baselines/ADJ/adj_rerank.py

sordonia/HierarchicalEncoderDecoder

b217a94387eca1e37975d7dd770d94981ec7eac1

[ "BSD-3-Clause" ]

52

2015-11-02T06:40:42.000Z

2021-07-07T11:47:39.000Z

import os import argparse import cPickle import operator import itertools from Common.psteff import * def rerank(model_file, ctx_file, rnk_file, \ score=False, no_normalize=False, fallback=False): pst = PSTInfer() pst.load(model_file) output_file = open(rnk_file + "_ADJ" + (".f" if score else ".gen"), "w") begin = True for ctx_line, rnk_line in itertools.izip(open(ctx_file), open(rnk_file)): suffix = ctx_line.strip().split('\t') candidates = rnk_line.strip().split('\t') candidates, scores = pst.rerank( suffix, candidates, no_normalize=no_normalize, fallback=fallback) if not score: reranked = [x[0] for x in sorted(zip(candidates, scores), key=operator.itemgetter(1), reverse=False)] print >> output_file, '\t'.join(reranked) else: if begin: print >> output_file, 'ADJ' begin=False for s in scores: print >> output_file, s output_file.close()

35.125

77

0.563167

132

1,124

4.643939

0.431818

0.081566

0.073409

0.04894

0.081566

0

0.002653

0.329181

1,124

31

78

36.258065

0.810345

0

0.017794

0

1

0.034483

false

0

0.206897

0

0.241379

0.103448

0

null

0

null

0

1

0

3849cad925c322f4e12bb86a7774aad131406491

474

py

Python

2015/2/paper.py

lvaughn/advent

ff3f727b8db1fd9b2a04aad5dcda9a6c8d1c271e

[ "CC0-1.0" ]

null

2015/2/paper.py

lvaughn/advent

ff3f727b8db1fd9b2a04aad5dcda9a6c8d1c271e

[ "CC0-1.0" ]

null

2015/2/paper.py

lvaughn/advent

ff3f727b8db1fd9b2a04aad5dcda9a6c8d1c271e

[ "CC0-1.0" ]

null

#!/usr/bin/env python3 paper_needed = 0 ribbon_needed = 0 with open('input.txt', 'r') as f: for line in f: [h,w,l] = [int(a) for a in line.split('x')] side_a = h*w side_b = h*l side_c = w*l paper_needed += min(side_a, side_b, side_c) + 2*side_a + 2*side_b + 2*side_c smallest_perimiter = 2*(h+w+l-max(h,w,l)) ribbon_needed += h*w*l + smallest_perimiter print("Paper", paper_needed) print("Ribbon", ribbon_needed)

29.625

84

0.594937

87

474

3.045977

0.390805

0.037736

0.045283

0

0.019608

0.246835

474

16

85

29.625

0.722689

0.044304

0

0.048565

0

1

0

false

0

0.153846

0

null

0

null

0

1

0

384b99fd8b2faa37e3b775a159b4d21eb26af8c2

20,467

py

Python

gym/envs/robotics/hand/move.py

carlo-/gym

7e7575601a0df5476ab9b15072c8b65693ce3071

[ "Python-2.0", "OLDAP-2.7" ]

1

2021-01-08T18:18:43.000Z

gym/envs/robotics/hand/move.py

carlo-/gym

7e7575601a0df5476ab9b15072c8b65693ce3071

[ "Python-2.0", "OLDAP-2.7" ]

null

gym/envs/robotics/hand/move.py

carlo-/gym

7e7575601a0df5476ab9b15072c8b65693ce3071

[ "Python-2.0", "OLDAP-2.7" ]

1

2019-07-31T18:40:26.000Z

import os import pickle from typing import Sequence import numpy as np from gym import utils, error from gym.envs.robotics import rotations, hand_env from gym.envs.robotics.utils import robot_get_obs, reset_mocap_welds, reset_mocap2body_xpos try: import mujoco_py except ImportError as e: raise error.DependencyNotInstalled("{}. (HINT: you need to install mujoco_py, " "and also perform the setup instructions here: " "https://github.com/openai/mujoco-py/.)".format(e)) HAND_PICK_AND_PLACE_XML = os.path.join('hand', 'pick_and_place.xml') HAND_MOVE_AND_REACH_XML = os.path.join('hand', 'move_and_reach.xml') FINGERTIP_BODY_NAMES = [ 'robot0:ffdistal', 'robot0:mfdistal', 'robot0:rfdistal', 'robot0:lfdistal', 'robot0:thdistal', ] OBJECTS = dict( original=dict(type='ellipsoid', size='0.03 0.03 0.04'), small_box=dict(type='box', size='0.022 0.022 0.022'), box=dict(type='box', size='0.03 0.03 0.03'), sphere=dict(type='ellipsoid', size='0.028 0.028 0.028'), small_sphere=dict(type='ellipsoid', size='0.024 0.024 0.024'), teapot=dict(type='mesh', mesh='object_mesh:teapot_vhacd_m', mesh_parts=6, mass=[0.01, 0.01, 0.01, 0.5, 0.01, 0.01]), ) def _goal_distance(goal_a, goal_b, ignore_target_rotation): assert goal_a.shape == goal_b.shape assert goal_a.shape[-1] == 7 delta_pos = goal_a[..., :3] - goal_b[..., :3] d_pos = np.linalg.norm(delta_pos, axis=-1) d_rot = np.zeros_like(goal_b[..., 0]) if not ignore_target_rotation: quat_a, quat_b = goal_a[..., 3:], goal_b[..., 3:] # Subtract quaternions and extract angle between them. quat_diff = rotations.quat_mul(quat_a, rotations.quat_conjugate(quat_b)) angle_diff = 2 * np.arccos(np.clip(quat_diff[..., 0], -1., 1.)) d_rot = angle_diff assert d_pos.shape == d_rot.shape return d_pos, d_rot def _check_range(a, a_min, a_max, include_bounds=True): if include_bounds: return np.all((a_min <= a) & (a <= a_max)) else: return np.all((a_min < a) & (a < a_max)) class MovingHandEnv(hand_env.HandEnv, utils.EzPickle): def __init__(self, model_path, reward_type, initial_qpos=None, relative_control=False, has_object=False, randomize_initial_arm_pos=False, randomize_initial_object_pos=True, ignore_rotation_ctrl=False, distance_threshold=0.05, rotation_threshold=0.1, n_substeps=20, ignore_target_rotation=False, success_on_grasp_only=False, grasp_state=None, grasp_state_reset_p=0.0, target_in_the_air_p=0.5, object_id='original', object_cage=False, cage_opacity=0.1, weld_fingers=False): self.target_in_the_air_p = target_in_the_air_p self.has_object = has_object self.ignore_target_rotation = ignore_target_rotation self.randomize_initial_arm_pos = randomize_initial_arm_pos self.randomize_initial_object_pos = randomize_initial_object_pos self.ignore_rotation_ctrl = ignore_rotation_ctrl self.distance_threshold = distance_threshold self.rotation_threshold = rotation_threshold self.reward_type = reward_type self.success_on_grasp_only = success_on_grasp_only self.object_id = object_id self.forearm_bounds = (np.r_[0.65, 0.3, 0.42], np.r_[1.75, 1.2, 1.0]) self.table_safe_bounds = (np.r_[1.10, 0.43], np.r_[1.49, 1.05]) self._initial_arm_mocap_pose = np.r_[1.05, 0.75, 0.65, rotations.euler2quat(np.r_[0., 1.59, 1.57])] if isinstance(grasp_state, bool) and grasp_state: suffix = "" if object_id == 'original' else f'_{object_id}' p = os.path.join(os.path.dirname(__file__), f'../assets/states/grasp_state{suffix}.pkl') if not os.path.exists(p): raise IOError('File {} does not exist'.format(p)) grasp_state = pickle.load(open(p, 'rb')) if grasp_state is not None and grasp_state_reset_p <= 0.0: raise ValueError('grasp_state_reset_p must be greater than zero if grasp_state is specified!') self.grasp_state = grasp_state self.grasp_state_reset_p = grasp_state_reset_p if ignore_rotation_ctrl and not ignore_target_rotation: raise ValueError('Target rotation must be ignored if arm cannot rotate! Set ignore_target_rotation=True') if success_on_grasp_only: if reward_type != 'sparse': raise ValueError('Parameter success_on_grasp_only requires sparse rewards!') if not has_object: raise ValueError('Parameter success_on_grasp_only requires object to be grasped!') default_qpos = dict() xml_format = None if self.has_object: default_qpos['object:joint'] = [1.25, 0.53, 0.4, 1., 0., 0., 0.] xml_format = dict( object_geom='<geom name="{name}" {props} material="material:object" condim="4"' 'friction="1 0.95 0.01" solimp="0.99 0.99 0.01" solref="0.01 1"/>', target_geom='<geom name="{name}" {props} material="material:target" condim="4" group="2"' 'contype="0" conaffinity="0"/>', ) obj = dict(OBJECTS[object_id]) if 'mass' not in obj.keys(): obj['mass'] = 0.2 mesh_parts = obj.get('mesh_parts') if mesh_parts is not None and isinstance(mesh_parts, int): del obj['mesh_parts'] object_geom, target_geom = '', '' if isinstance(obj['mass'], Sequence): masses = list(obj['mass']) assert len(masses) == mesh_parts del obj['mass'] else: masses = [obj['mass']] * mesh_parts for i in range(mesh_parts): obj_part = dict(obj) obj_part['mesh'] += f'_part{i}' obj_part['mass'] = masses[i] props = " ".join([f'{k}="{v}"' for k, v in obj_part.items()]) object_geom += xml_format['object_geom'].format(name=f'object_part{i}', props=props) target_geom += xml_format['target_geom'].format(name=f'target_part{i}', props=props) xml_format = dict(object_geom=object_geom, target_geom=target_geom) else: props = " ".join([f'{k}="{v}"' for k, v in obj.items()]) xml_format = dict( object_geom=xml_format['object_geom'].format(name='object', props=props), target_geom=xml_format['target_geom'].format(name='target', props=props), ) if object_cage: rgba = f"1 0 0 {cage_opacity}" xml_format['cage'] = ''' <geom pos="0 0.55 0.4" quat="0.924 0.3826 0 0" size="1 0.75 0.01" type="box" mass="200" rgba="{}" solimp="0.99 0.99 0.01" solref="0.01 1"/> <geom pos="0 -0.55 0.4" quat="0.924 -0.3826 0 0" size="1 0.75 0.01" type="box" mass="200" rgba="{}" solimp="0.99 0.99 0.01" solref="0.01 1"/> <geom pos="-0.45 0 0.4" quat="0.924 0 0.3826 0" size="0.75 1 0.01" type="box" mass="200" rgba="{}" solimp="0.99 0.99 0.01" solref="0.01 1"/> <geom pos="0.45 0 0.4" quat="0.924 0 -0.3826 0" size="0.75 1 0.01" type="box" mass="200" rgba="{}" solimp="0.99 0.99 0.01" solref="0.01 1"/> '''.format(*[rgba]*4) else: xml_format['cage'] = '' if weld_fingers: mocap_tp = ''' <body mocap="true" name="{}:mocap" pos="0 0 0"> <geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0.5 0 0.7" size="0.005 0.005 0.005" type="box"/> <geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0.5 0 0.1" size="1 0.005 0.005" type="box"/> <geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0.5 0 0.1" size="0.005 1 0.001" type="box"/> <geom conaffinity="0" contype="0" pos="0 0 0" rgba="0 0.5 0 0.1" size="0.005 0.005 1" type="box"/> </body> ''' weld_tp = '<weld body1="{}:mocap" body2="{}" solimp="0.9 0.95 0.001" solref="0.02 1"/>' fingertip_names = [x.replace('robot0:', '') for x in FINGERTIP_BODY_NAMES] xml_format['finger_mocaps'] = '\n'.join([mocap_tp.format(n) for n in fingertip_names]) xml_format['finger_welds'] = '\n'.join([weld_tp.format(m, f) for m, f in zip(fingertip_names, FINGERTIP_BODY_NAMES)]) else: xml_format['finger_welds'] = '' xml_format['finger_mocaps'] = '' initial_qpos = initial_qpos or default_qpos hand_env.HandEnv.__init__(self, model_path, n_substeps=n_substeps, initial_qpos=initial_qpos, relative_control=relative_control, arm_control=True, xml_format=xml_format) utils.EzPickle.__init__(self) def get_object_contact_points(self, other_body='robot0:'): if not self.has_object: raise NotImplementedError("Cannot get object contact points in an environment without objects!") sim = self.sim object_name = 'object' object_pos = self.sim.data.get_body_xpos(object_name) object_rot = self.sim.data.get_body_xmat(object_name) contact_points = [] # Partially from: https://gist.github.com/machinaut/209c44e8c55245c0d0f0094693053158 for i in range(sim.data.ncon): # Note that the contact array has more than `ncon` entries, # so be careful to only read the valid entries. contact = sim.data.contact[i] body_name_1 = sim.model.body_id2name(sim.model.geom_bodyid[contact.geom1]) body_name_2 = sim.model.body_id2name(sim.model.geom_bodyid[contact.geom2]) if other_body in body_name_1 and body_name_2 == object_name or \ other_body in body_name_2 and body_name_1 == object_name: c_force = np.zeros(6, dtype=np.float64) mujoco_py.functions.mj_contactForce(sim.model, sim.data, i, c_force) # Compute contact point position wrt the object rel_contact_pos = object_rot.T @ (contact.pos - object_pos) contact_points.append(dict( body1=body_name_1, body2=body_name_2, relative_pos=rel_contact_pos, force=c_force )) return contact_points def _get_body_pose(self, body_name, no_rot=False, euler=False): if no_rot: rot = np.zeros(4) else: rot = self.sim.data.get_body_xquat(body_name) if euler: rot = rotations.quat2euler(rot) return np.r_[self.sim.data.get_body_xpos(body_name), rot] def _get_site_pose(self, site_name, no_rot=False): if no_rot: quat = np.zeros(4) else: # this is very inefficient, avoid computation when possible quat = rotations.mat2quat(self.sim.data.get_site_xmat(site_name)) return np.r_[self.sim.data.get_site_xpos(site_name), quat] def _get_palm_pose(self, no_rot=False): return self._get_site_pose('robot0:palm_center', no_rot) def _get_grasp_center_pose(self, no_rot=False): return self._get_site_pose('robot0:grasp_center', no_rot) def _get_object_pose(self): return self._get_body_pose('object') def _get_achieved_goal(self): palm_pose = self._get_palm_pose(no_rot=self.ignore_target_rotation) if self.has_object: pose = self._get_object_pose() else: pose = palm_pose if self.ignore_target_rotation: pose[3:] = 0.0 if self.success_on_grasp_only: d = np.linalg.norm(palm_pose[:3] - pose[:3]) return np.r_[pose, d] return pose def _set_arm_pose(self, pose: np.ndarray): assert pose.size == 7 or pose.size == 3 reset_mocap2body_xpos(self.sim) self.sim.data.mocap_pos[0, :] = np.clip(pose[:3], *self.forearm_bounds) if pose.size == 7: self.sim.data.mocap_quat[0, :] = pose[3:] def get_table_surface_pose(self): pose = np.r_[ self.sim.data.get_body_xpos('table0'), self.sim.data.get_body_xquat('table0'), ] geom = self.sim.model.geom_name2id('table0_geom') size = self.sim.model.geom_size[geom].copy() pose[2] += size[2] return pose # GoalEnv methods # ---------------------------- def compute_reward(self, achieved_goal: np.ndarray, goal: np.ndarray, info: dict): if self.reward_type == 'sparse': success = self._is_success(achieved_goal, goal).astype(np.float32) weights = (info or dict()).get('weights') if weights is not None: success *= weights return success - 1. else: d_pos, d_rot = _goal_distance(achieved_goal, goal, self.ignore_target_rotation) # We weigh the difference in position to avoid that `d_pos` (in meters) is completely # dominated by `d_rot` (in radians). return -(10. * d_pos + d_rot) # RobotEnv methods # ---------------------------- def _set_action(self, action): assert action.shape == self.action_space.shape hand_ctrl = action[:20] forearm_ctrl = action[20:] * 0.1 # set hand action hand_env.HandEnv._set_action(self, hand_ctrl) # set forearm action assert self.sim.model.nmocap == 1 pos_delta = forearm_ctrl[:3] quat_delta = forearm_ctrl[3:] if self.ignore_rotation_ctrl: quat_delta *= 0.0 new_pos = self.sim.data.mocap_pos[0] + pos_delta new_quat = self.sim.data.mocap_quat[0] + quat_delta self._set_arm_pose(np.r_[new_pos, new_quat]) def _is_success(self, achieved_goal: np.ndarray, desired_goal: np.ndarray): d_pos, d_rot = _goal_distance(achieved_goal[..., :7], desired_goal[..., :7], self.ignore_target_rotation) achieved_pos = (d_pos < self.distance_threshold).astype(np.float32) achieved_rot = (d_rot < self.rotation_threshold).astype(np.float32) achieved_all = achieved_pos * achieved_rot if self.success_on_grasp_only: assert achieved_goal.shape[-1] == 8 d_palm = achieved_goal[..., 7] achieved_grasp = (d_palm < 0.08).astype(np.float32) achieved_all *= achieved_grasp return achieved_all def _env_setup(self, initial_qpos): for name, value in initial_qpos.items(): self.sim.data.set_joint_qpos(name, value) reset_mocap_welds(self.sim) self.sim.forward() # Move end effector into position. self._set_arm_pose(self._initial_arm_mocap_pose.copy()) for _ in range(10): self.sim.step() if self.has_object: self.height_offset = self.sim.data.get_site_xpos('object:center')[2] def _viewer_setup(self): body_id = self.sim.model.body_name2id('table0') #'robot0:forearm') lookat = self.sim.data.body_xpos[body_id] for idx, value in enumerate(lookat): self.viewer.cam.lookat[idx] = value self.viewer.cam.distance = 1.4 self.viewer.cam.azimuth = 180. #132. self.viewer.cam.elevation = -38. def _reset_sim(self): reset_to_grasp_state = self.grasp_state_reset_p > self.np_random.uniform() while True: if reset_to_grasp_state: assert self.has_object self.sim.set_state(self.grasp_state) # Fix hand ctrl so that fingers stay close while we update the arm position later rel_ctrl = self.relative_control self.relative_control = True self._set_action(np.zeros(self.action_space.shape)) self.relative_control = rel_ctrl else: self.sim.set_state(self.initial_state) # Reset initial position of arm. new_arm_pose = self._initial_arm_mocap_pose.copy() if self.randomize_initial_arm_pos: new_arm_pose[:2] += self.np_random.uniform(-0.2, 0.2, size=2) self._set_arm_pose(new_arm_pose) for _ in range(10): self.sim.step() # Randomize initial position of object. if self.has_object and not reset_to_grasp_state: object_qpos = self.sim.data.get_joint_qpos('object:joint').copy() if self.randomize_initial_object_pos: object_xpos = self.np_random.uniform(*self.table_safe_bounds) else: object_xpos = self._get_palm_pose(no_rot=True)[:2] object_xpos += self.np_random.uniform(-0.005, 0.005, size=2) # always add small amount of noise object_qpos[:2] = object_xpos self.sim.data.set_joint_qpos('object:joint', object_qpos) self.sim.forward() if not self.has_object: break else: object_pos = self._get_object_pose()[:3] object_vel = self.sim.data.get_joint_qvel('object:joint') object_still = np.linalg.norm(object_vel) < 0.8 if reset_to_grasp_state: palm_pos = self._get_palm_pose(no_rot=True)[:3] object_on_palm = np.linalg.norm(object_pos - palm_pos) < 0.08 if object_still and object_on_palm: break else: object_on_table = _check_range(object_pos[:2], *self.table_safe_bounds) if object_still and object_on_table: break return True def _sample_goal(self): goal = np.r_[self.np_random.uniform(*self.table_safe_bounds), 0.0] if self.has_object: goal[2] = self.height_offset if self.np_random.uniform() < self.target_in_the_air_p: goal[2] += self.np_random.uniform(0, 0.45) goal = np.r_[goal, np.zeros(4)] if self.success_on_grasp_only: goal = np.r_[goal, 0.] return goal def _render_callback(self): # Assign current state to target object but offset a bit so that the actual object # is not obscured. goal = self.goal.copy()[:7] assert goal.shape == (7,) self.sim.data.set_joint_qpos('target:joint', goal) self.sim.data.set_joint_qvel('target:joint', np.zeros(6)) self.sim.forward() def _get_obs(self): robot_qpos, robot_qvel = robot_get_obs(self.sim) dt = self.sim.nsubsteps * self.sim.model.opt.timestep forearm_pose = self._get_body_pose('robot0:forearm', euler=True) forearm_velp = self.sim.data.get_body_xvelp('robot0:forearm') * dt palm_pos = self._get_palm_pose(no_rot=True)[:3] object_pose = np.zeros(0) object_vel = np.zeros(0) object_rel_pos = np.zeros(0) if self.has_object: object_vel = self.sim.data.get_joint_qvel('object:joint') object_pose = self._get_body_pose('object', euler=True) object_rel_pos = object_pose[:3] - palm_pos observation = np.concatenate([ forearm_pose, forearm_velp, palm_pos, object_rel_pos, robot_qpos, robot_qvel, object_pose, object_vel ]) return { 'observation': observation, 'achieved_goal': self._get_achieved_goal().ravel(), 'desired_goal': self.goal.ravel().copy(), } class HandPickAndPlaceEnv(MovingHandEnv): def __init__(self, reward_type='sparse', **kwargs): super(HandPickAndPlaceEnv, self).__init__( model_path=HAND_PICK_AND_PLACE_XML, reward_type=reward_type, has_object=True, **kwargs ) class MovingHandReachEnv(MovingHandEnv): def __init__(self, reward_type='sparse', **kwargs): super(MovingHandReachEnv, self).__init__( model_path=HAND_MOVE_AND_REACH_XML, reward_type=reward_type, has_object=False, **kwargs )

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384d14f0e35bddd98ff67e0f7591aca683112f67

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py

Python

paas-ce/paas/esb/components/generic/templates/cmsi/get_msg_type.py

canway-bk/bk-PaaS

7a6fe1ef38a7e4e2bd11a6c2efa871a967ac2a3c

[ "Apache-2.0" ]

767

2019-03-25T06:35:43.000Z

2022-03-30T08:57:51.000Z

paas-ce/paas/esb/components/generic/templates/cmsi/get_msg_type.py

canway-bk/bk-PaaS

7a6fe1ef38a7e4e2bd11a6c2efa871a967ac2a3c

[ "Apache-2.0" ]

194

2019-03-29T07:16:41.000Z

2022-03-30T06:17:49.000Z

paas-ce/paas/esb/components/generic/templates/cmsi/get_msg_type.py

canway-bk/bk-PaaS

7a6fe1ef38a7e4e2bd11a6c2efa871a967ac2a3c

[ "Apache-2.0" ]

381

2019-03-25T07:19:54.000Z

2022-03-29T03:22:42.000Z

# -*- coding: utf-8 -*- """ Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community Edition) available. Copyright (C) 2017-2018 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT 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. """ # noqa from components.component import Component, SetupConfMixin from common.constants import API_TYPE_Q, HTTP_METHOD from common.base_utils import str_bool from .toolkit import configs class GetMsgType(Component, SetupConfMixin): suggest_method = HTTP_METHOD.GET label = u'查询消息发送类型' label_en = 'Get message type' sys_name = configs.SYSTEM_NAME api_type = API_TYPE_Q def handle(self): bk_language = self.request.headers.get('Blueking-Language', 'en') msg_type = [] for mt in configs.msg_type: is_active = mt.get('is_active', str_bool(getattr(self, mt['type'], False))) msg_type.append({ 'type': mt['type'], 'label': mt['label_en'] if bk_language == 'en' else mt['label'], 'is_active': is_active, }) self.response.payload = { "result": True, "data": msg_type, }

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null

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697355181e1f710ba412694ba7696f2baa4d4985

4,646

py

Python

Compression.py

rafaaseddik/minimalistic-search-engine

a9cb14b7eb49b8990c93c913f9465d831a30b189

[ "Apache-2.0" ]

null

Compression.py

rafaaseddik/minimalistic-search-engine

a9cb14b7eb49b8990c93c913f9465d831a30b189

[ "Apache-2.0" ]

null

Compression.py

rafaaseddik/minimalistic-search-engine

a9cb14b7eb49b8990c93c913f9465d831a30b189

[ "Apache-2.0" ]

null

import pickle ## GAP def GapEncoding(decoded): result = {} for key in decoded.keys(): # Get The token's posting toEncode = decoded[key] # Initialise token's result encoded = [toEncode[0],toEncode[1],[]] encoded[2] = toEncode[2] # Gap Encoding Algorithm lastIndex = toEncode[2][0] for doc in toEncode[3:]: encoded.append((doc[0]-lastIndex,doc[1])) lastIndex = doc[0] result[key] = encoded return result def GapDecoding(encoded): result = {} for key in encoded.keys(): # Get The token's posting toDecode = encoded[key] # Initialise token's result decoded = [toDecode[0],toDecode[1],[]] decoded[2] = toDecode[2] # Gap Encoding Algorithm lastIndex = toDecode[2][0] for doc in toDecode[3:]: decoded.append((doc[0]+lastIndex,doc[1])) lastIndex = doc[0]+lastIndex result[key] = decoded return result def PostingToArray(posting): result = posting[:2] for element in posting[2:]: result.append(element[0]) result.append(element[1]) return result def GammaEncoding(number): binaryRep = bin(number)[2:] length = ''.join(['1' for i in range(len(binaryRep)-1)])+'0' return length + binaryRep[1:] def GammaDecoding(binary): result = [] while(len(binary)): firstZeroIndex = binary.find('0') decoded = int('1'+binary[firstZeroIndex+1:2*firstZeroIndex+1],2) result.append(decoded) binary = binary[2*firstZeroIndex+1:] return result def EncodeArrayGamma(array): return ''.join([GammaEncoding(e) for e in array]) def encodePostingToFile(allData,filename): with open('original'+filename, 'wb') as f: pickle.dump(allData, f, protocol=pickle.HIGHEST_PROTOCOL) result = {} for key in allData['index'].keys(): gapEncoded = GapEncoding( allData['index'])[key] binary_encoded = EncodeArrayGamma(PostingToArray(gapEncoded)) bit_strings = [binary_encoded[i:i + 8] for i in range(0, len(binary_encoded), 8)] byte_list = [int(b, 2) for b in bit_strings] if(key=="damage"): print(key , byte_list) result[key]= byte_list allData['index'] = result with open(filename, 'wb') as f: pickle.dump(allData, f, protocol=pickle.HIGHEST_PROTOCOL) return result def encodePostingToFileNew(allData,filename): with open('original'+filename, 'wb') as f: pickle.dump(allData, f, protocol=pickle.HIGHEST_PROTOCOL) result = {} for key in allData['index'].keys(): gapEncoded = GapEncoding( allData['index'])[key] binary_encoded = EncodeArrayGamma(PostingToArray(gapEncoded)) result[key]= binary_encoded allData['index'] = result with open(filename, 'wb') as f: pickle.dump(allData, f, protocol=pickle.HIGHEST_PROTOCOL) return result def fromBinaryToIntArray(binary): #binary_coded = ''.join([bin(e)[2:] for e in binary]) binary_string = '' for i in range(len(binary)): binary_code = bin(binary[i])[2:] if(i is not len(binary)-1 and len(binary_code) <8): # must add leading 0 binary_code = ''.join(['0' for i in range(8-len(binary_code))]) + binary_code binary_string = binary_string + binary_code return binary_string def loadIndex(filename): with open(filename, 'rb') as f: return pickle.load(f) def DecompressIndex(encodedIndex): result = {} for key in list(encodedIndex.keys())[:10]: decompressedList = GammaDecoding(fromBinaryToIntArray(encodedIndex[key])) node = decompressedList[:2] decompressedList = decompressedList[2:] while(len(decompressedList)>=2): node.append((decompressedList[0],decompressedList[1])) if(len(decompressedList) is 2): break else: decompressedList = decompressedList[2:] result[key] = node print(result) return GapDecoding(result) def DecompressIndexNew(encodedIndex): result = {} for key in list(encodedIndex.keys())[:10]: decompressedList = GammaDecoding(encodedIndex[key]) node = decompressedList[:2] decompressedList = decompressedList[2:] while(len(decompressedList)>=2): node.append((decompressedList[0],decompressedList[1])) if(len(decompressedList) is 2): break else: decompressedList = decompressedList[2:] result[key] = node return GapDecoding(result)

35.465649

89

0.620103

530

4,646

5.388679

0.181132

0.047619

0.02521

0.029412

0.520308

0.464986

0.44888

0.423669

0

0.019048

0.254197

4,646

131

90

35.465649

0.805195

0.047353

0

0.442478

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0.00885

0.221239

0.017699

0

null

0

null

0

1

0

69761b2e063701aea3c8b8d4a06a737b92dca98b

557

py

Python

app/ext/database.py

jonatasoli/sapu

b0b796a53fecd9ab77eb0ece31d2a0113c656fc5

[ "BSD-3-Clause" ]

1

2020-10-29T01:48:36.000Z

app/ext/database.py

jonatasoli/sapu

b0b796a53fecd9ab77eb0ece31d2a0113c656fc5

[ "BSD-3-Clause" ]

10

2020-10-29T23:46:13.000Z

2020-10-29T23:48:00.000Z

app/ext/database.py

jonatasoli/sapu

b0b796a53fecd9ab77eb0ece31d2a0113c656fc5

[ "BSD-3-Clause" ]

null

from tortoise import Tortoise from dynaconf import settings async def init(): # Here we connect to a Postgres. # also specify the app name of "models" # which contain models from "app.models" await Tortoise.init( db_url=settings.DATABASE_URL, modules={'models': ['app.models', "aerich.models"]} ) TORTOISE_ORM = { "connections": {"default": settings.DATABASE_URL}, "apps": { "models": { "models": ["models", "aerich.models"], "default_connection": "default", }, }, }

25.318182

59

0.599641

60

557

5.483333

0.566667

0.054711

0.115502

0

0.263914

557

21

60

26.52381

0.802439

0.192101

0

0.23991

0

1

0

false

0

0.125

0

0.125

0

null

0

null

0

1

0

69766d6af00c62b98d16c76cbde72ead241d0e17

628

py

Python

leetcode/python/medium/p1105_minHeightShelves.py

kefirzhang/algorithms

549e68731d4c05002e35f0499d4f7744f5c63979

[ "Apache-2.0" ]

null

leetcode/python/medium/p1105_minHeightShelves.py

kefirzhang/algorithms

549e68731d4c05002e35f0499d4f7744f5c63979

[ "Apache-2.0" ]

null

leetcode/python/medium/p1105_minHeightShelves.py

kefirzhang/algorithms

549e68731d4c05002e35f0499d4f7744f5c63979

[ "Apache-2.0" ]

null

class Solution: def minHeightShelves(self, books, shelf_width: int) -> int: length = len(books) dp = [1000000] * (length + 1) dp[0] = 0 for i in range(1, length + 1): tmp_width, h, j = 0, 0, i while j > 0: h = max(h, books[j - 1][1]) tmp_width += books[j - 1][0] if tmp_width > shelf_width: break dp[i] = min(dp[i], dp[j - 1] + h) j -= 1 return dp[-1] slu = Solution() print(slu.minHeightShelves([[1, 1], [2, 3], [2, 3], [1, 1], [1, 1], [1, 1], [1, 2]], 4))

31.4

88

0.420382

90

628

2.877778

0.366667

0.061776

0.057915

0.061776

0.027027

0

0.09973

0.409236

628

19

89

33.052632

0.598383

0

1

0.058824

false

0

0.176471

0.058824

0

null

0

null

0

1

0

6976ccafe60900afb7a75bc8b7218bc5457673b2

2,509

py

Python

frappe_notification/frappe_notification/controllers/templates/tests/test_get_templates.py

leam-tech/frappe_notification

79e40f2c541d86d714a0b8d48b87f32b2f85076a

[ "MIT" ]

null

frappe_notification/frappe_notification/controllers/templates/tests/test_get_templates.py

leam-tech/frappe_notification

79e40f2c541d86d714a0b8d48b87f32b2f85076a

[ "MIT" ]

null

frappe_notification/frappe_notification/controllers/templates/tests/test_get_templates.py

leam-tech/frappe_notification

79e40f2c541d86d714a0b8d48b87f32b2f85076a

[ "MIT" ]

null

from unittest import TestCase import frappe from frappe_notification import ( NotificationClientFixtures, NotificationTemplateFixtures, NotificationClientNotFound, # NotificationTemplateNotFound, set_active_notification_client) from ..get_templates_list import get_templates class TestGetTemplates(TestCase): clients = NotificationClientFixtures() templates = NotificationTemplateFixtures() @classmethod def setUpClass(cls): cls.clients.setUp() cls.templates.setUp() @classmethod def tearDownClass(cls): cls.templates.tearDown() cls.clients.tearDown() def setUp(self) -> None: frappe.set_user("Guest") set_active_notification_client(None) def tearDown(self) -> None: frappe.set_user("Administrator") set_active_notification_client(None) def test_simple(self): """ - Login as a Manager Client - Ask for all templates he can access """ client = self.clients.get_manager_client().name set_active_notification_client(client) templates = get_templates() self.assertGreater(len(templates), 0) for t in templates: self.assertIsNotNone(t.name) self.assertIsNotNone(t.key) self.assertIsNotNone(t.subject) self.assertEqual(t.created_by, client) def test_admin(self): """ - Login as Administrator (not a Client) - He will not have access to any templates """ frappe.set_user("Administrator") _template = self.templates[0] self.assertIsNotNone(_template) with self.assertRaises(NotificationClientNotFound): get_templates() def test_subordinate(self): """ - Login as a subordinate - Ask for all templates he can access - He should get templates that his manager allowed him access """ manager_1 = self.clients.get_manager_client().name client_m1 = self.clients.get_clients_managed_by(manager_1)[0].name set_active_notification_client(client_m1) templates = get_templates() self.assertGreater(len(templates), 0) for t in templates: self.assertIsNotNone(t.name) self.assertIsNotNone(t.key) self.assertIsNotNone(t.subject) self.assertTrue(any([ t.created_by == client_m1, t.created_by == manager_1 ]))

28.191011

74

0.64169

259

2,509

6.03861

0.285714

0.085038

0.076726

0.086317

0.375959

0.349105

0.221228

0.184143

0

0.005504

0.275807

2,509

88

75

28.511364

0.855256

0.121164

0

0.327273

0

0.014741

0

0.218182

1

0.127273

false

0

0.072727

0

0.254545

0

null

0

null

0

1

0

69803440f4ec5474a3d41e644bcbc330b3047a85

1,249

py

Python

examples/sms_example.py

twizoapi/lib-api-python

115d0f574518fbe03f74801d743972c7593bf30e

[ "MIT" ]

null

examples/sms_example.py

twizoapi/lib-api-python

115d0f574518fbe03f74801d743972c7593bf30e

[ "MIT" ]

null

examples/sms_example.py

twizoapi/lib-api-python

115d0f574518fbe03f74801d743972c7593bf30e

[ "MIT" ]

null

import pprint from examples.api_key import api_key, api_host from models.parameters import SmsAdvanceParams, SmsParams from twizo import Twizo if __name__ == '__main__': twizo = Twizo(api_key=api_key, api_host=api_host) params = SmsParams(['12345600000', '432543254'], "A SMS Message", '01023456789') params.resultType = 2 pprint = pprint.PrettyPrinter(indent=4) controller = twizo.sms_controller result = controller.send_simple(params) for sms in result: pprint.pprint(vars(sms)) print("\n One recipient: \n") params = SmsParams(['12345600000'], "A SMS Message", '01023456789') result = controller.send_simple(params) pprint.pprint(vars(result[0])) print("\n Advanced sms: \n") params = SmsAdvanceParams(params.recipients, params.body, params.sender) params.udh = "0A" params.dcs = 2 result = controller.send_advanced(params) pprint.pprint(vars(result[0])) print("\n get sms status: \n") result = controller.get_status(result[0].messageId) pprint.pprint(vars(result)) print("\n get delivery report: \n") result = controller.get_delivery_report() for sms in result: pprint.pprint(vars(sms))

26.020833

84

0.670136

154

1,249

5.292208

0.331169

0.088344

0.09816

0.080982

0.238037

0.166871

0

0.060852

0.210568

1,249

47

85

26.574468

0.76572

0

0.258065

0

0.152922

0

1

0

false

0

0.129032

0

0.129032

0.354839

0

null

0

null

0

1

0

698319c4eb2ec5e438aeef63de23d5f2cd5920f3

1,577

py

Python

censys_subdomains.py

ctixsystems/censys-recon-ng

fded032044796b5893a572d00b05f2f03843eeb5

[ "Apache-2.0" ]

23

2019-05-07T02:27:22.000Z

2022-02-17T02:14:40.000Z

censys_subdomains.py

ctixsystems/censys-recon-ng

fded032044796b5893a572d00b05f2f03843eeb5

[ "Apache-2.0" ]

3

2021-09-17T14:25:13.000Z

2022-03-08T14:32:44.000Z

censys_subdomains.py

ctixsystems/censys-recon-ng

fded032044796b5893a572d00b05f2f03843eeb5

[ "Apache-2.0" ]

13

2019-08-30T09:48:39.000Z

2022-02-13T01:32:35.000Z

from recon.core.module import BaseModule from censys.certificates import CensysCertificates from censys.base import CensysException class Module(BaseModule): meta = { 'name': 'Censys subdomains by company', 'author': 'J Nazario', 'version': '1.1', 'description': 'Retrieves certificates for companies, and \'domains\' tables \ with the results.', 'query': 'SELECT DISTINCT company FROM companies WHERE company IS NOT NULL', 'dependencies': ['censys'], 'required_keys': ['censysio_id', 'censysio_secret'], } def module_run(self, companies): api_id = self.get_key('censysio_id') api_secret = self.get_key('censysio_secret') c = CensysCertificates( api_id, api_secret, timeout=self._global_options['timeout'] ) SEARCH_FIELDS = [ 'parsed.subject.organization', 'parsed.subject.organizational_unit', ] CERT_FIELDS = [ 'parsed.names', ] for company in companies: self.heading(company, level=0) try: query = ' OR '.join( ['{0}:"{1}"'.format(x, company) for x in SEARCH_FIELDS] ) payload = c.search(query, CERT_FIELDS) except CensysException: continue for result in payload: for name in result.get('parsed.names', []): if name.startswith('*.'): self.insert_domains(name.replace('*.', ''))

35.044444

86

0.557387

155

1,577

5.541935

0.522581

0.023283

0.041909

0

0.004721

0.328472

1,577

44

87

35.840909

0.806421

0

0.235891

0.038681

0

1

0.025

false

0

0.075

0

0.15

0

null

0

null

0

1

0

6984c4e5f95ca2a2f02ce82bd452911e9c17f8a9

20,746

py

Python

rul_pm/results/results.py

lucianolorenti/rul_pm

da9dfad79129dd47d24923cfd6c833869ef7b6a7

[ "MIT" ]

1

2021-09-01T13:13:10.000Z

rul_pm/results/results.py

lucianolorenti/rul_pm

da9dfad79129dd47d24923cfd6c833869ef7b6a7

[ "MIT" ]

3

2021-08-24T15:23:52.000Z

2021-11-09T10:28:51.000Z

rul_pm/results/results.py

lucianolorenti/rul_pm

da9dfad79129dd47d24923cfd6c833869ef7b6a7

[ "MIT" ]

1

2021-12-25T14:00:16.000Z

"""Compute evaluating results of fitted models The main structure used on this functions is a dictionary in which each the keys are the model name, and the elements are list of dictionaries. Each of the dictionaries contain two keys: true, predicted. Those elements are list of the predictions """ import logging from dataclasses import dataclass from typing import Callable, Dict, List, Optional, Tuple, Union import numpy as np import pandas as pd from rul_pm.results.picewise_regression import ( PiecewesieLinearFunction, PiecewiseLinearRegression, ) from sklearn.metrics import mean_absolute_error as mae from sklearn.metrics import mean_squared_error as mse logger = logging.getLogger(__name__) @dataclass class MetricsResult: mae: float mse: float fitting_time: float = 0 prediction_time: float = 0 @dataclass class PredictionResult: name: str true_RUL: np.ndarray predicted_RUL: np.ndarray metrics: MetricsResult = MetricsResult(0, 0) def compute_metrics(self): self.metrics.mae = mae(self.true_RUL, self.predicted_RUL) self.metrics.mse = mse(self.true_RUL, self.predicted_RUL) def compute_sample_weight(sample_weight, y_true, y_pred, c: float = 0.9): if sample_weight == "relative": sample_weight = np.abs(y_true - y_pred) / (np.clip(y_true, c, np.inf)) else: sample_weight = 1 return sample_weight def compute_rul_line(rul: float, n: int, tt: Optional[np.array] = None): if tt is None: tt = -np.ones(n) z = np.zeros(n) z[0] = rul for i in range(len(tt) - 1): z[i + 1] = max(z[i] + tt[i], 0) if z[i + 1] - 0 < 0.0000000000001: break return z class CVResults: def __init__( self, y_true: List[List], y_pred: List[List], nbins: int = 5, bin_edges: Optional[np.array] = None, ): """ Compute the error histogram Compute the error with respect to the RUL considering the results of different folds Parameters ---------- y_true: List[List] List with the true values of each hold-out set of a cross validation y_pred: List[List] List with the predictions of each hold-out set of a cross validation nbins: int Number of bins to compute the histogram """ if bin_edges is None: max_value = np.max([np.max(y) for y in y_true]) bin_edges = np.linspace(0, max_value, nbins + 1) self.n_folds = len(y_true) self.n_bins = len(bin_edges) - 1 self.bin_edges = bin_edges self.mean_error = np.zeros((self.n_folds, self.n_bins)) self.mae = np.zeros((self.n_folds, self.n_bins)) self.mse = np.zeros((self.n_folds, self.n_bins)) self.errors = [] for i, (y_pred, y_true) in enumerate(zip(y_pred, y_true)): self._add_fold_result(i, y_pred, y_true) def _add_fold_result(self, fold: int, y_pred: np.array, y_true: np.array): y_pred = np.squeeze(y_pred) y_true = np.squeeze(y_true) for j in range(len(self.bin_edges) - 1): mask = (y_true >= self.bin_edges[j]) & (y_true <= self.bin_edges[j + 1]) indices = np.where(mask)[0] if len(indices) == 0: continue errors = y_true[indices] - y_pred[indices] self.mean_error[fold, j] = np.mean(errors) self.mae[fold, j] = np.mean(np.abs(errors)) self.mse[fold, j] = np.mean((errors) ** 2) self.errors.append(errors) def model_cv_results( results: List[PredictionResult], nbins: Optional[int] = None, bin_edges: Optional[np.ndarray] = None, ) -> CVResults: if nbins is None and bin_edges is None: raise ValueError("nbins and bin_edges cannot be both None") if nbins is None: nbins = len(bin_edges) - 1 if bin_edges is None: max_y_value = np.max([r.true_RUL.max() for r in results]) bin_edges = np.linspace(0, max_y_value, nbins + 1) trues = [] predicted = [] for results in results: trues.append(results.true_RUL) predicted.append(results.predicted_RUL) return CVResults(trues, predicted, nbins=nbins, bin_edges=bin_edges) def models_cv_results( results_dict: Dict[str, List[PredictionResult]], nbins: int ) -> Tuple[np.ndarray, Dict[str, CVResults]]: """Create a dictionary with the result of each cross validation of the model""" max_y_value = np.max( [ r.true_RUL.max() for model_name in results_dict.keys() for r in results_dict[model_name] ] ) bin_edges = np.linspace(0, max_y_value, nbins + 1) model_results = {} for model_name in results_dict.keys(): model_results[model_name] = model_cv_results( results_dict[model_name], bin_edges=bin_edges ) return bin_edges, model_results class FittedLife: """Represent a Fitted Life Parameters ---------- y_true: np.array The true RUL target y_pred: np.array The predicted target time: Optional[Union[np.array, int]] Time feature fit_line_not_increasing: Optional[bool] = False, Wether the fitted line can increase or not. RUL_threshold: Optional[float] Indicates the thresholding value used during de fit, By default None """ def __init__( self, y_true: np.array, y_pred: np.array, time: Optional[Union[np.array, int]] = None, fit_line_not_increasing: Optional[bool] = False, RUL_threshold: Optional[float] = None, ): self.fit_line_not_increasing = fit_line_not_increasing y_true = np.squeeze(y_true) y_pred = np.squeeze(y_pred) if time is not None: self.degrading_start = FittedLife._degrading_start(y_true, RUL_threshold) if isinstance(time, np.ndarray): self.time = time else: self.time = np.array(np.linspace(0, y_true[0], n=len(y_true))) else: self.degrading_start, self.time = FittedLife.compute_time_feature( y_true, RUL_threshold ) #self.y_pred_fitted_picewise = self._fit_picewise_linear_regression(y_pred) #self.y_true_fitted_picewise = self._fit_picewise_linear_regression(y_true) self.RUL_threshold = RUL_threshold self.y_pred = y_pred self.y_true = y_true self.y_pred_fitted_coefficients = np.polyfit(self.time, self.y_pred, 1) p = np.poly1d(self.y_pred_fitted_coefficients) self.y_pred_fitted = p(self.time) self.y_true_fitted_coefficients = np.polyfit(self.time, self.y_true, 1) p = np.poly1d(self.y_true_fitted_coefficients) self.y_true_fitted = p(self.time) @staticmethod def compute_time_feature(y_true: np.array, RUL_threshold: Optional[float] = None): degrading_start = FittedLife._degrading_start(y_true, RUL_threshold) time = FittedLife._compute_time(y_true, degrading_start) return degrading_start, time @staticmethod def _degrading_start( y_true: np.array, RUL_threshold: Optional[float] = None ) -> float: """Obtain the index when the life value is lower than the RUL_threshold Parameters ---------- y_true : np.array Array of true values of the RUL of the life RUL_threshold : float Returns ------- float if RUL_threshold is None, the degradint start if the first index. Otherwise it is the first index in which y_true < RUL_threshold """ degrading_start = 0 if RUL_threshold is not None: degrading_start_i = np.where(y_true < RUL_threshold) if len(degrading_start_i[0]) > 0: degrading_start = degrading_start_i[0][0] return degrading_start @staticmethod def _compute_time(y_true: np.array, degrading_start: int) -> np.array: """Compute the passage of time from the true RUL The passage of time is computed as the cumulative sum of the first difference of the true labels. In case there are tresholded values, the time steps of the thresholded zone is assumed to be as the median values of the time steps computed of the zones of the life in which we have information. Parameters ---------- y_true : np.array The true RUL labels degrading_start : int The index in which the true RUL values starts to be lower than the treshold Returns ------- np.array [description] """ time_diff = np.diff(np.squeeze(y_true)[degrading_start:][::-1]) time = np.zeros(len(y_true)) if degrading_start > 0: if len(time_diff) > 0: time[0 : degrading_start + 1] = np.median(time_diff) else: time[0 : degrading_start + 1] = 1 time[degrading_start + 1 :] = time_diff return np.cumsum(time) def _fit_picewise_linear_regression(self, y: np.array) -> PiecewesieLinearFunction: """Fit the array trough a picewise linear regression Parameters ---------- y : np.array Points to be fitted Returns ------- PiecewesieLinearFunction The Picewise linear function fitted """ pwlr = PiecewiseLinearRegression(not_increasing=self.fit_line_not_increasing) for j in range(len(y)): pwlr.add_point(self.time[j], y[j]) line = pwlr.finish() return line def rmse(self, sample_weight=None) -> float: N = len(self.y_pred) sw = compute_sample_weight(sample_weight, self.y_true[:N], self.y_pred) return np.sqrt(np.mean(sw * (self.y_true[:N] - self.y_pred) ** 2)) def mae(self, sample_weight=None) -> float: N = len(self.y_pred) sw = compute_sample_weight(sample_weight, self.y_true[:N], self.y_pred) return np.mean(sw * np.abs(self.y_true[:N] - self.y_pred)) def noisiness(self) -> float: """How much the predictions resemble a line This metric is computed as the mse of the fitted values with respect to the least squares fitted line of this values """ return mae(self.y_pred_fitted, self.y_pred) def slope_resemblance(self): m1 = self.y_true_fitted_coefficients[0] m2 = self.y_pred_fitted_coefficients[0] d = np.arctan((m1-m2)/(1+m1*m2)) d = d / (np.pi/2) return 1-np.abs((d / (np.pi/2))) def predicted_end_of_life(self): z = np.where(self.y_pred == 0)[0] if len(z) == 0: return self.time[len(self.y_pred) - 1] + self.y_pred[-1] else: return self.time[z[0]] def end_of_life(self): z = np.where(self.y_true == 0)[0] if len(z) == 0: return self.time[len(self.y_pred) - 1] + self.y_true[-1] else: return self.time[z[0]] def maintenance_point(self, m: float = 0): """Compute the maintenance point The maintenance point is computed as the predicted end of life - m Parameters ----------- m: float, optional Fault horizon Defaults to 0. Returns -------- float Time of maintenance """ return self.predicted_end_of_life() - m def unexploited_lifetime(self, m: float = 0): """Compute the unexploited lifetime given a fault horizon window Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., & Beghi, A. (2015). Parameters ---------- m: float, optional Fault horizon windpw. Defaults to 0. Returns: float: unexploited lifetime """ if self.maintenance_point(m) < self.end_of_life(): return self.end_of_life() - self.maintenance_point(m) else: return 0 def unexpected_break(self, m: float = 0, tolerance: float = 0): """Compute wether an unexpected break will produce using a fault horizon window of size m Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., & Beghi, A. (2015). Parameters ---------- m: float, optional Fault horizon windpw. Defaults to 0. Returns ------- bool Unexploited lifetime """ if self.maintenance_point(m) - tolerance < self.end_of_life(): return False else: return True def split_lives_indices(y_true: np.array): """Obtain a list of indices for each life Parameters ---------- y_true : np.array True vector with the RUL Returns ------- List[List[int]] A list with the indices belonging to each life """ assert len(y_true) >= 2 lives_indices = ( [0] + (np.where(np.diff(np.squeeze(y_true)) > 0)[0]+1).tolist() + [len(y_true)] ) indices = [] for i in range(len(lives_indices) - 1): r = range(lives_indices[i], lives_indices[i + 1]) if len(r) <= 1: continue indices.append(r) return indices def split_lives( results: PredictionResult, RUL_threshold: Optional[float] = None, fit_line_not_increasing: Optional[bool] = False, time: Optional[int] = None, ) -> List[FittedLife]: """Divide an array of predictions into a list of FittedLife Object Parameters ---------- y_true : np.array The true RUL target y_pred : np.array The predicted RUL fit_line_not_increasing : Optional[bool], optional Wether the fit line can increase, by default False time : Optional[int], optional A vector with timestamps. If omitted wil be computed from y_true, by default None Returns ------- List[FittedLife] FittedLife list """ lives = [] for r in split_lives_indices(results.true_RUL): if np.any(np.isnan(results.predicted_RUL[r])): continue lives.append( FittedLife( results.true_RUL[r], results.predicted_RUL[r], RUL_threshold=RUL_threshold, fit_line_not_increasing=fit_line_not_increasing, time=time, ) ) return lives def split_lives_from_results(d: dict) -> List[FittedLife]: y_true = d["true"] y_pred = d["predicted"] return split_lives(y_true, y_pred) def unexploited_lifetime(d: dict, window_size: int, step: int): bb = [split_lives_from_results(cv) for cv in d] return unexploited_lifetime_from_cv(bb, window_size, step) def unexploited_lifetime_from_cv( lives: List[List[FittedLife]], window_size: int, n: int ): qq = [] windows = np.linspace(0, window_size, n) for m in windows: jj = [] for r in lives: ul_cv_list = [life.unexploited_lifetime(m) for life in r] mean_ul_cv = np.mean(ul_cv_list) std_ul_cv = np.std(ul_cv_list) jj.append(mean_ul_cv) qq.append(np.mean(jj)) return windows, qq def unexpected_breaks( d: dict, window_size: int, step: int ) -> Tuple[np.ndarray, np.ndarray]: """Compute the risk of unexpected breaks with respect to the maintenance window size Parameters ---------- d : dict Dictionary with the results window_size : int Maximum size of the maintenance windows step : int Number of points in which compute the risks. step different maintenance windows will be used. Returns ------- Tuple[np.ndarray, np.ndarray] * Maintenance window size evaluated * Risk computed for every window size used """ bb = [split_lives_from_results(cv) for cv in d] return unexpected_breaks_from_cv(bb, window_size, step) def unexpected_breaks_from_cv( lives: List[List[FittedLife]], window_size: int, n: int ) -> Tuple[np.ndarray, np.ndarray]: """Compute the risk of unexpected breaks given a Cross-Validation results Parameters ---------- lives : List[List[FittedLife]] Cross validation results. window_size : int Maximum size of the maintenance window n : int Number of points to evaluate the risk of unexpected breaks Returns ------- Tuple[np.ndarray, np.ndarray] * Maintenance window size evaluated * Risk computed for every window size used """ qq = [] windows = np.linspace(0, window_size, n) for m in windows: jj = [] for r in lives: ul_cv_list = [life.unexpected_break(m) for life in r] mean_ul_cv = np.mean(ul_cv_list) std_ul_cv = np.std(ul_cv_list) jj.append(mean_ul_cv) qq.append(np.mean(jj)) return windows, np.array(qq) def metric_J_from_cv(lives: List[List[FittedLife]], window_size: int, n: int, q1, q2): J = [] windows = np.linspace(0, window_size, n) for m in windows: J_of_m = [] for r in lives: ub_cv_list = np.array([life.unexpected_break(m) for life in r]) ub_cv_list = (ub_cv_list / (np.max(ub_cv_list) + 0.0000000001)) * q1 ul_cv_list = np.array([life.unexploited_lifetime(m) for life in r]) ul_cv_list = (ul_cv_list / (np.max(ul_cv_list) + 0.0000000001)) * q2 values = ub_cv_list + ul_cv_list mean_J = np.mean(values) std_ul_cv = np.std(values) J_of_m.append(mean_J) J.append(np.mean(J_of_m)) return windows, J def metric_J(d, window_size: int, step: int): lives_cv = [split_lives_from_results(cv) for cv in d] return metric_J_from_cv(lives_cv, window_size, step) def cv_regression_metrics_single_model(results: List[PredictionResult], threshold: float = np.inf): errors = { 'MAE': [], 'MAE SW': [], 'MSE': [], 'MSE SW': [], 'Noisiness': [], 'Slope': [] } for result in results: y_mask = np.where(result.true_RUL <= threshold)[0] y_true = np.squeeze(result.true_RUL[y_mask]) y_pred = np.squeeze(result.predicted_RUL[y_mask]) sw = compute_sample_weight( "relative", y_true, y_pred, ) try: MAE_SW = mae( y_true, y_pred, sample_weight=sw, ) except: MAE_SW = np.nan MAE = mae(y_true, y_pred) try: MSE_SW = mse( y_true, y_pred, sample_weight=sw, ) except: MSE_SW = np.nan MSE = mse(y_true, y_pred) lives = split_lives(result) errors['Noisiness'].extend([life.noisiness() for life in lives ]) errors['Slope'].extend([life.slope_resemblance() for life in lives ]) errors['MAE'].append(MAE) errors['MAE SW'].append(MAE_SW) errors['MSE'].append(MSE) errors['MSE SW'].append(MSE_SW) errors1 = {} for k in errors.keys(): errors1[(k, 'mean')] = np.mean(errors[k]) errors1[(k, 'std')] = np.std(errors[k]) return errors1 def cv_regression_metrics( results_dict: Dict[str, List[PredictionResult]], threshold: float = np.inf ) -> dict: """Compute regression metrics for each model Parameters ---------- data : dict Dictionary with the model predictions. The dictionary must conform the results specification of this module threshold : float, optional Compute metrics errors only in RUL values less than the threshold, by default np.inf Returns ------- dict A dictionary with the following format: { 'MAE': { 'mean': 'std': }, 'MAE SW': { 'mean': 'std': }, 'MSE': { 'mean': 'std': }, } """ out = {} for model_name in results_dict.keys(): out[model_name] = cv_regression_metrics_single_model(results_dict['model_name'], threshold) return out

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6984f4887fd119cbb2dc7ba7d8a3f67d1de12e78

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py

Python

src/readthermal.py

wemakerspace/MLX90640_Serial_Processing_Python

66936a08e3c3864d267d214c8ab1e0bce34c0a19

[ "MIT" ]

4

2020-04-06T17:01:12.000Z

2022-01-08T21:32:29.000Z

src/readthermal.py

wemakerspace/MLX90640_Serial_Processing_Python

66936a08e3c3864d267d214c8ab1e0bce34c0a19

[ "MIT" ]

null

src/readthermal.py

wemakerspace/MLX90640_Serial_Processing_Python

66936a08e3c3864d267d214c8ab1e0bce34c0a19

[ "MIT" ]

2

2020-05-01T04:42:16.000Z

2020-10-09T16:04:44.000Z

import serial import numpy as np ser = serial.Serial("/dev/ttyACM0", 115200) def getData(): frames = 0; while True: frames+=1 recv = ser.readline() recv = recv.rstrip() #strip the return character if(frames > 1):#ditch the first frame in case it is incomplete data = np.fromstring(recv, dtype=float, count=-1, sep=',') #get the data print(data) try: if ser.is_open == False: ser.open() getData() except KeyboardInterrupt: # Ctrl+C if ser != None: ser.close()

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69854ff5a0a84afda144b80a24cdaa422d21b158

1,219

py

Python

setup.py

dmentipl/exact-astro

9099159b497ec9fa8b6089250c923e06e1e08691

[ "MIT" ]

3

2020-07-30T02:27:28.000Z

2020-08-20T05:05:21.000Z

setup.py

dmentipl/exact-astro

9099159b497ec9fa8b6089250c923e06e1e08691

[ "MIT" ]

null

setup.py

dmentipl/exact-astro

9099159b497ec9fa8b6089250c923e06e1e08691

[ "MIT" ]

null

"""exact-astro setup.py.""" import io import pathlib import re from setuptools import setup __version__ = re.search( r'__version__\s*=\s*[\'"]([^\'"]*)[\'"]', # It excludes inline comment too io.open('exact/__init__.py', encoding='utf_8_sig').read(), ).group(1) install_requires = ['numpy', 'scipy'] packages = ['exact'] description = 'Exact solutions to astrophysical problems.' long_description = (pathlib.Path(__file__).parent / 'README.md').read_text() setup( name='exact', version=__version__, author='Daniel Mentiplay', author_email='d.mentiplay@gmail.com', url='https://github.com/dmentipl/exact-astro', description=description, long_description=long_description, long_description_content_type='text/markdown', packages=packages, license='MIT', install_requires=install_requires, classifiers=[ "Development Status :: 2 - Pre-Alpha", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Operating System :: OS Independent", "Topic :: Scientific/Engineering :: Astronomy", ], )

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69871248278ff33bccd0670c2fde318aba91024c

4,712

py

Python

training/run_experiment.py

yezhengkai/im2latex

cd01d705c70e756a3ec205e6f067034407c943c1

[ "MIT" ]

3

2021-04-14T19:41:19.000Z

2022-02-07T08:44:32.000Z

training/run_experiment.py

yezhengkai/im2latex

cd01d705c70e756a3ec205e6f067034407c943c1

[ "MIT" ]

null

training/run_experiment.py

yezhengkai/im2latex

cd01d705c70e756a3ec205e6f067034407c943c1

[ "MIT" ]

1

2021-07-01T02:40:23.000Z

"""Experiment-running framework.""" import argparse import importlib import os import shutil import pytorch_lightning as pl import wandb from im2latex import lit_models # In order to ensure reproducible experiments, we must set random seeds. # https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html#reproducibility pl.seed_everything(42, workers=True) def _import_class(module_and_class_name: str) -> type: """Import class from a module, e.g. 'text_recognizer.models.MLP'""" module_name, class_name = module_and_class_name.rsplit(".", 1) module = importlib.import_module(module_name) class_ = getattr(module, class_name) return class_ def _setup_parser(): """Set up Python's ArgumentParser with data, model, trainer, and other arguments.""" parser = argparse.ArgumentParser(add_help=False) # Add Trainer specific arguments, such as --max_epochs, --gpus, --precision trainer_parser = pl.Trainer.add_argparse_args(parser) trainer_parser._action_groups[1].title = "Trainer Args" # pylint: disable=protected-access parser = argparse.ArgumentParser(add_help=False, parents=[trainer_parser]) # Basic arguments parser.add_argument("--wandb", action="store_true", default=False) parser.add_argument("--data_class", type=str, default="Im2Latex100K") parser.add_argument("--model_class", type=str, default="ResnetTransformer") parser.add_argument("--load_checkpoint", type=str, default=None) # Get the data and model classes, so that we can add their specific arguments temp_args, _ = parser.parse_known_args() data_class = _import_class(f"im2latex.data.{temp_args.data_class}") model_class = _import_class(f"im2latex.models.{temp_args.model_class}") # Get data, model, and LitModel specific arguments data_group = parser.add_argument_group("Data Args") data_class.add_to_argparse(data_group) model_group = parser.add_argument_group("Model Args") model_class.add_to_argparse(model_group) lit_model_group = parser.add_argument_group("LitModel Args") lit_models.BaseLitModel.add_to_argparse(lit_model_group) parser.add_argument("--help", "-h", action="help") return parser def main(): """ Run an experiment. Sample command: ``` python training/run_experiment.py --max_epochs=3 --gpus='0,' --num_workers=0 --model_class=ResnetTransformer --data_class=Im2Latex100K ``` """ parser = _setup_parser() args = parser.parse_args() data_class = _import_class(f"im2latex.data.{args.data_class}") model_class = _import_class(f"im2latex.models.{args.model_class}") data = data_class(args) model = model_class(data_config=data.config(), args=args) lit_model_class = lit_models.BaseLitModel if args.load_checkpoint is not None: lit_model = lit_model_class.load_from_checkpoint(args.load_checkpoint, args=args, model=model) else: lit_model = lit_model_class(args=args, model=model) logger = pl.loggers.TensorBoardLogger("training/logs") if args.wandb: logger = pl.loggers.WandbLogger() logger.watch(model) logger.log_hyperparams(vars(args)) early_stopping_callback = pl.callbacks.EarlyStopping(monitor="val_loss", mode="min", patience=10) model_checkpoint_callback = pl.callbacks.ModelCheckpoint( filename="{epoch:03d}-{val_loss:.3f}-{val_bleu:.3f}-{val_cer:.3f}-{val_edit:.3f}", monitor="val_loss", mode="min", ) callbacks = [early_stopping_callback, model_checkpoint_callback] args.weights_summary = "full" # Print full summary of the model trainer = pl.Trainer.from_argparse_args(args, callbacks=callbacks, logger=logger, weights_save_path="training/logs") # pylint: disable=no-member trainer.tune(lit_model, datamodule=data) # If passing --auto_lr_find, this will set learning rate trainer.fit(lit_model, datamodule=data) trainer.test(lit_model, datamodule=data) # pylint: enable=no-member best_model_path = model_checkpoint_callback.best_model_path if best_model_path: print("Best model saved at:", best_model_path) if args.wandb: # https://github.com/wandb/client/issues/1370 wandb_ckpt_dir = os.path.join( wandb.run.dir, "training", "logs", wandb.run.project, wandb.run.id, "checkpoints" ) os.makedirs(wandb_ckpt_dir, exist_ok=True) shutil.copy( best_model_path, os.path.join(wandb_ckpt_dir, os.path.basename(best_model_path),), ) wandb.save(best_model_path) print("Best model also uploaded to W&B") if __name__ == "__main__": main()

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null

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698a2f9d16f8e551807581f0095f4680980a32ea

2,875

py

Python

lib/importer.py

pe1te3son/colect-dotfiles

4a9fcbc25d684d26bee3568032a7c22b65f7b630

[ "MIT" ]

null

lib/importer.py

pe1te3son/colect-dotfiles

4a9fcbc25d684d26bee3568032a7c22b65f7b630

[ "MIT" ]

null

lib/importer.py

pe1te3son/colect-dotfiles

4a9fcbc25d684d26bee3568032a7c22b65f7b630

[ "MIT" ]

null

#!python3 from sys import exit import errno from shutil import copyfile from socket import gethostname from os import makedirs, path from lib import utils def import_configs(path_to_config_file, show_select_menu=False): settings = utils.get_config_file(path_to_config_file) files_to_import = settings['colect_files'] current_config_dir = get_current_config_dir(settings) if show_select_menu: menu_selection = print_select_menu(files_to_import) if menu_selection == "c": exit() elif type(menu_selection) is list: for idx in menu_selection: import_single(files_to_import[idx], current_config_dir) return for conf in files_to_import: import_single(conf, current_config_dir) def get_current_config_dir(settings): config_dir = utils.parse_home_path(settings['dest_dir']) return path.join(config_dir, gethostname()) def print_select_menu(files_to_import): print("\nSelect files to import; example: 1,2,3\n") for idx, val in enumerate(files_to_import): print(str(idx + 1) + ": " + val[0]) print("\n\t(a)ll\t(c)ancel\n") user_selection = None while not valid_user_selection(user_selection, files_to_import): user_selection = input("select: ") return parse_user_selection(user_selection) def parse_user_selection(user_selection): if user_selection == "a" or user_selection == "c": return user_selection return [int(x) - 1 for x in user_selection.split(",")] def valid_user_selection(user_selection, files_to_import): if not user_selection: return False if user_selection == "a" or user_selection == "c": return True selected_values = user_selection.split(",") for val in selected_values: if val.isnumeric() and int(val) - 1 < len(files_to_import): continue else: print("Invalid option(s)") return False return True def import_single(conf, current_config_dir): conf[0] = utils.parse_home_path(conf[0]) failed_to_copy = [] if not path.exists(path.dirname(conf[0])): try: makedirs(path.dirname(conf[0])) except OSError as exc: # Guard against race condition if exc.errno != errno.EEXIST: raise try: if len(conf) > 2: opt_dir = path.join(current_config_dir, conf[2]) copyfile(path.join(opt_dir, conf[1]), conf[0]) else: copyfile(path.join(current_config_dir, conf[1]), conf[0]) print("[ OK ] " + conf[0]) except FileNotFoundError: failed_to_copy.append(conf[0]) print("[ FAILED! ] " + conf[0]) if len(failed_to_copy): print("Failed to import following:") for failed_file in failed_to_copy: print("\t1: " + failed_file)

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0.138889

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null

0

null

0

1

0

698a52940330a24b1d2b110e4cd07e015cdf98c4

6,907

py

Python

usaspending_api/recipient/tests/integration/test_recipient_list.py

g4brielvs/usaspending-api

bae7da2c204937ec1cdf75c052405b13145728d5

[ "CC0-1.0" ]

217

2016-11-03T17:09:53.000Z

2022-03-10T04:17:54.000Z

usaspending_api/recipient/tests/integration/test_recipient_list.py

g4brielvs/usaspending-api

bae7da2c204937ec1cdf75c052405b13145728d5

[ "CC0-1.0" ]

622

2016-09-02T19:18:23.000Z

2022-03-29T17:11:01.000Z

usaspending_api/recipient/tests/integration/test_recipient_list.py

g4brielvs/usaspending-api

bae7da2c204937ec1cdf75c052405b13145728d5

[ "CC0-1.0" ]

93

2016-09-07T20:28:57.000Z

2022-02-25T00:25:27.000Z

# Stdlib imports import datetime import pytest # Core Django imports # Third-party app imports from rest_framework import status from model_mommy import mommy # Imports from your apps from usaspending_api.common.helpers.fiscal_year_helpers import generate_fiscal_year from usaspending_api.recipient.models import RecipientProfile from usaspending_api.recipient.v2.views.list_recipients import get_recipients # Getting relative dates as the 'latest'/default argument returns results relative to when it gets called TODAY = datetime.datetime.now() OUTSIDE_OF_LATEST = TODAY - datetime.timedelta(365 + 2) CURRENT_FISCAL_YEAR = generate_fiscal_year(TODAY) def list_recipients_endpoint(): return "/api/v2/recipient/duns/" @pytest.mark.django_db def test_one_recipient(): """Verify error on bad autocomplete request for budget function.""" mommy.make( RecipientProfile, recipient_level="A", recipient_hash="00077a9a-5a70-8919-fd19-330762af6b84", recipient_unique_id="000000123", recipient_name="WILSON AND ASSOC", last_12_months=-29470313.00, ) filters = {"limit": 10, "page": 1, "order": "desc", "sort": "amount", "award_type": "all"} results, meta = get_recipients(filters=filters) assert meta["total"] == 1 @pytest.mark.django_db def test_ignore_special_case(): """Verify error on bad autocomplete request for budget function.""" mommy.make( RecipientProfile, recipient_level="R", recipient_hash="00077a9a-5a70-8919-fd19-330762af6b85", recipient_unique_id=None, recipient_name="MULTIPLE RECIPIENTS", last_12_months=-29470313.00, ) filters = {"limit": 10, "page": 1, "order": "desc", "sort": "amount", "award_type": "all"} results, meta = get_recipients(filters=filters) assert meta["total"] == 0 @pytest.mark.django_db def test_filters_with_two_recipients(): """Verify error on bad autocomplete request for budget function.""" mommy.make( RecipientProfile, recipient_level="A", recipient_hash="00077a9a-5a70-8919-fd19-330762af6b84", recipient_unique_id="000000123", recipient_name="WILSON AND ASSOC", last_12_months=-29470313.00, ) mommy.make( RecipientProfile, recipient_level="B", recipient_hash="c8f79139-38b2-3063-b039-d48172abc710", recipient_unique_id="000000444", recipient_name="DREW JORDAN INC.", last_12_months=99705.97, ), filters = {"limit": 1, "page": 1, "order": "desc", "sort": "amount", "award_type": "all"} results, meta = get_recipients(filters=filters) # Ensure pagination metadata meets API Contract assert meta["total"] == 2 assert meta["page"] == 1 assert meta["limit"] == 1 assert len(results) == 1 assert results[0]["recipient_level"] == "B" assert float(results[0]["amount"]) == float(99705.97) assert results[0]["id"] == "c8f79139-38b2-3063-b039-d48172abc710-B" filters = {"limit": 1, "page": 1, "order": "asc", "sort": "amount", "award_type": "all"} results, meta = get_recipients(filters=filters) assert results[0]["recipient_level"] == "A" filters = {"limit": 10, "page": 1, "order": "asc", "sort": "amount", "keyword": "JOR", "award_type": "all"} results, meta = get_recipients(filters=filters) assert len(results) == 1 assert results[0]["recipient_level"] == "B" @pytest.mark.django_db def test_state_metadata_with_no_results(client): resp = client.post(list_recipients_endpoint()) assert resp.status_code == status.HTTP_200_OK assert resp.data.get("page_metadata", False) assert resp.data["page_metadata"].get("next", True) is None @pytest.mark.django_db def test_award_type_filter(): """Verify error on bad autocomplete request for budget function.""" mommy.make( RecipientProfile, recipient_level="A", recipient_hash="00077a9a-5a70-8919-fd19-330762af6b84", recipient_unique_id="000000123", recipient_name="SHOES AND SOCKS INC.", last_12_months=2400.00, last_12_contracts=400.00, last_12_grants=500.00, last_12_loans=0.00, last_12_other=700.00, last_12_direct_payments=800.00, award_types=["contract", "grant", "direct payment", "other"], ) mommy.make( RecipientProfile, recipient_level="B", recipient_hash="c8f79139-38b2-3063-b039-d48172abc710", recipient_unique_id="000000444", recipient_name="SPORT SHORTS", last_12_months=2000.00, last_12_contracts=700.00, last_12_grants=600.00, last_12_loans=0.00, last_12_other=400.00, last_12_direct_payments=300.00, award_types=["contract", "grant", "direct payment", "other"], ) mommy.make( RecipientProfile, recipient_level="C", recipient_hash="5770e860-0f7b-69f1-182f-4d6966ebaa62", recipient_unique_id="000000555", recipient_name="JUST JERSEYS", last_12_months=99.99, last_12_contracts=0.00, last_12_grants=0.00, last_12_loans=99.99, last_12_other=0.00, last_12_direct_payments=0.00, award_types=["loans"], ) filters = {"limit": 10, "page": 1, "order": "desc", "sort": "amount", "award_type": "all"} results, meta = get_recipients(filters=filters) # "all" assert len(results) == 3 assert results[0]["recipient_level"] == "A" assert float(results[0]["amount"]) == float(2400) assert results[0]["id"] == "00077a9a-5a70-8919-fd19-330762af6b84-A" # Test "grants" filters["award_type"] = "grants" results, meta = get_recipients(filters=filters) assert len(results) == 2 assert results[0]["recipient_level"] == "B" assert float(results[0]["amount"]) == float(600) assert results[0]["id"] == "c8f79139-38b2-3063-b039-d48172abc710-B" # Test "contracts" filters["award_type"] = "contracts" results, meta = get_recipients(filters=filters) assert len(results) == 2 assert results[0]["recipient_level"] == "B" assert float(results[0]["amount"]) == float(700) assert results[0]["id"] == "c8f79139-38b2-3063-b039-d48172abc710-B" # Test "direct_payments" filters["award_type"] = "direct_payments" results, meta = get_recipients(filters=filters) assert len(results) == 2 assert results[0]["recipient_level"] == "A" assert float(results[0]["amount"]) == float(800) assert results[0]["id"] == "00077a9a-5a70-8919-fd19-330762af6b84-A" # Test "loans" filters["award_type"] = "loans" results, meta = get_recipients(filters=filters) assert len(results) == 1 assert results[0]["recipient_level"] == "C" assert float(results[0]["amount"]) == float(99.99) assert results[0]["id"] == "5770e860-0f7b-69f1-182f-4d6966ebaa62-C"

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111

0.665267

865

6,907

5.123699

0.217341

0.029783

0.044224

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0.092105

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null

0

null

0

1

0

698acb3b25a009de312a19b1bc58ff4564a8e4ce

8,988

py

Python

tracardi/service/kasuj.py

Tracardi/tracardi

1505c38e43e6e69595212d2a3f6917edc5841390

[ "MIT" ]

153

2021-11-02T00:35:41.000Z

2022-03-25T16:37:44.000Z

tracardi/service/kasuj.py

Tracardi/tracardi

1505c38e43e6e69595212d2a3f6917edc5841390

[ "MIT" ]

243

2021-10-17T17:00:22.000Z

2022-03-28T10:13:34.000Z

tracardi/service/kasuj.py

Tracardi/tracardi

1505c38e43e6e69595212d2a3f6917edc5841390

[ "MIT" ]

14

2021-10-17T11:39:04.000Z

2022-03-14T14:44:02.000Z

from collections import defaultdict from pprint import pprint from typing import Tuple from dictdiffer import diff import deepdiff from deepdiff import Delta mappings_prev = { "dynamic": False, "date_detection": False, "properties": { "id": { "type": "keyword" }, "metadata": { "dynamic": False, "properties": { "time": { "properties": { "insert": { "type": "date" }, "visit": { "properties": { "last": { "type": "date" }, "current": { "type": "date" }, "count": { "type": "integer" } } } } } } }, "stats": { "dynamic": True, "type": "object" }, "traits": { "properties": { "private": { "dynamic": True, "type": "object" }, "public": { "dynamic": True, "type": "object" } } }, "pii": { "dynamic": False, "properties": { "name": { "type": "text" }, "surname": { "type": "text" }, "birthDate": { "type": "date" }, "email": { "type": "keyword" }, "telephone": { "type": "keyword" }, "twitter": { "type": "keyword" }, "facebook": { "type": "keyword" }, "whatsapp": { "type": "keyword" }, "other": { "dynamic": True, "type": "object" } } }, "segments": { "type": "keyword", "ignore_above": 64 }, "consents": { "dynamic": True, "type": "object" }, "active": { "type": "boolean" } } } mappings_head = { "dynamic": False, "date_detection": False, "properties": { "id": { "type": "keyword" }, "metadata": { "type": "object", "dynamic": True, "enabled": False, }, "stats": { "dynamic": True, "type": "int" }, "traits": { "properties": { "private": { "dynamic": True, "type": "object" }, "public": { "dynamic": True, "type": "object" } } }, "pii": { "dynamic": False, "properties": { "name": { "type": "keyword" }, "surname": { "type": "text" }, "birthDate": { "type": "date" }, "email": { "type": "keyword" }, "telephone": { "type": "keyword" }, "twitter": { "type": "keyword" }, "facebook": { "type": "keyword" }, "whatsapp": { "type": "keyword" }, "other": { "dynamic": True, "type": "object" } } }, "segments": { "type": "keyword", "ignore_above": 64 }, "consents": { "dynamic": True, "type": "object" }, } } class FieldMetaData: def __init__(self, type, sub_field): self.type = type self.sub_field = sub_field def __repr__(self): return f"FieldMetaData(type={self.type}, sub_field={self.sub_field})" def __eq__(self, other): return self.sub_field == other.sub_field and self.type == other.type class FieldTypes(dict): def has(self, field, meta): return field in self and self[field] == meta def has_sub_path(self, path): pass class ScriptGenerator: def reindex(self, prev_index, head_index, script): print({ "endpoint": f"_reindex/{prev_index}/{head_index}", "body": { "script": script } }) def alias(self, prev_index, head_index): pass def skip(self): print('skip') class MigrationRuleEngine: def __init__(self, prev_index, head_index, head_schema: FieldTypes, prev_schema: FieldTypes, diff): self.prev_index = prev_index self.head_index = head_index self._diff = diff self._head_schema = head_schema self._prev_schema = prev_schema self._generator = ScriptGenerator() def add(self, data): for field, new_mata in data: # type: str, FieldMetaData if 'remove' not in self._diff: # This field was added and no other field was removed. # This is an empty field. No migration needed self._generator.skip() elif new_mata.type == 'object': # If some sub path was removed and replaced by object then simple copy is OK. if self._prev_schema.has_sub_path(field): pass else: self._generator.reindex(self.prev_index, self.head_index, f""" ctx.{field} = "SOME_VALUE || FILED" """) def change(self, data): for field, changes in data: # type: str, Tuple[FieldMetaData, FieldMetaData] prev_meta, head_meta = changes print(field, prev_meta, head_meta) def remove(self, data): for field, old_meta in data: # type: str, FieldMetaData print(field, old_meta) class SchemaChangeManager: def __init__(self, prev_index, head_index, prev_schema: FieldTypes, head_schema: FieldTypes): self.head_index = head_index self.prev_index = prev_index self.head_schema = head_schema self.prev_schema = prev_schema _diff = sorted(self._standardize(diff(self.prev_schema, self.head_schema)), key=lambda item: item[0]) self._diff = defaultdict(list) for _mode, __field, __meta in _diff: self._diff[_mode].append((__field, __meta)) @staticmethod def _standardize(source): for _mode, _field, _change in source: if _mode == 'add' or _mode == 'remove': for __field, __meta in _change: yield _mode, __field, __meta elif _mode == 'change': if isinstance(_field, list): for __field in _field: yield _mode, __field, _change else: yield _mode, _field, _change def make_change_script(self): _rules_engine = MigrationRuleEngine(self.prev_index, self.head_index, head_schema, prev_schema, self._diff) for _mode, _list_of_tuples in self._diff.items(): getattr(_rules_engine, _mode)(_list_of_tuples) class MappingConverter: def __init__(self): self.fields = {} def schema(self, mappings) -> FieldTypes: self.fields = {} return FieldTypes(self._loop(mappings)) def _loop(self, mappings, path=None): if path is None: path = [] for _key, _object in mappings.items(): if 'properties' in _object: self._loop(_object['properties'], path=path + [_key]) if 'type' in _object: field_path = path + [_key] self.fields[".".join(field_path)] = FieldMetaData( type=_object['type'].lower(), sub_field='field' in _object ) return self.fields converter = MappingConverter() prev_schema = converter.schema(mappings_prev['properties']) head_schema = converter.schema(mappings_head['properties']) diff_master = SchemaChangeManager("prev_i", "head_i", prev_schema, head_schema) diff_master.make_change_script()

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8,988

5.084605

0.210818

0.045008

0.040917

0.051555

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0.26132

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0.001031

0.46028

8,988

315

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0.011029

0.128676

0.018382

0

null

0

null

0

1

0

698dd56244de111b1e33a880a1994bf163002e6f

1,706

py

Python

code/data_provider.py

ky-zhou/MMFDA

41b4668c3c4ccdbe2625b77d9f0f9ce30218c187

[ "MIT" ]

null

code/data_provider.py

ky-zhou/MMFDA

41b4668c3c4ccdbe2625b77d9f0f9ce30218c187

[ "MIT" ]

null

code/data_provider.py

ky-zhou/MMFDA

41b4668c3c4ccdbe2625b77d9f0f9ce30218c187

[ "MIT" ]

null

import numpy as np import pandas as pd from options import MODEL_DIR, opt, Debug_Index from sklearn.preprocessing import normalize import pyreadr test_size = opt.test_size a = 0 def load_csv(which): gedf = pd.read_csv('../data/%s/%s-ge.csv' % (which, which), sep=",").to_numpy() medf = pd.read_csv('../data/%s/%s-me.csv' % (which, which), sep=",").to_numpy() midf = pd.read_csv('../data/%s/%s-mi.csv' % (which, which), sep=",").to_numpy() sample, ge, me, mi = gedf[:, 0], gedf[:, 1:], medf[:, 1:], midf[:, 1:] ge, me, mi = normalize(X=ge, axis=a, norm="max"), normalize(X=me, axis=a, norm="max"), normalize(X=mi, axis=a, norm="max") label_info = pd.read_csv('../data/%s/%s-label.csv' % (which, which), header=None).to_numpy() label = label_info[:, 1].astype(np.int32) - 1 indices = np.arange(sample.shape[0]) np.random.seed(opt.seed) np.random.shuffle(indices) ge, me, mi = ge[indices], me[indices], mi[indices] sample, label = sample[indices], label[indices] label0, label1 = sum([1 for x in label if x==0]), sum([1 for x in label if x==1]) print('Data dimensions: ', ge.shape, me.shape, mi.shape, label0, label1) return ge, me, mi, sample, label if __name__ == '__main__': # tuple = load_h5("../data_process/snn_data.h5") # x, y, s1, s2 = get_batch(tuple, False) load_csv(opt.input_type) # load_geo('gbm') # load_raw() # save_sample() # load_rdata(opt.input_type) # gen_rdata(opt.input_type) # gen_csv(opt.input_type) # load_rdata4cluster(opt.input_type) # load_pr_processed(opt.input_type) # load_pr(opt.input_type) # calc_pins_label(opt.input_type) # define_label(opt.input_type)

37.911111

126

0.639508

276

1,706

3.771739

0.322464

0.069164

0.103746

0.049952

0.310279

0.200768

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0

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0.174091

1,706

44

127

38.772727

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0

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0.28

0.04

0

null

0

null

0

1

0

698e6a08964652d821060c7ce13a449d85e194c7

6,119

py

Python

src/draft.py

ScottNicholsonKurland/Capstone

139df7255e8e09366fb67eaa2256f5dd97f1cd54

[ "BSD-2-Clause" ]

1

2018-01-22T15:37:51.000Z

src/draft.py

ScottNicholsonKurland/Capstone

139df7255e8e09366fb67eaa2256f5dd97f1cd54

[ "BSD-2-Clause" ]

null

src/draft.py

ScottNicholsonKurland/Capstone

139df7255e8e09366fb67eaa2256f5dd97f1cd54

[ "BSD-2-Clause" ]

null

# coding: utf-8 # In[7]: import pandas as pd import numpy as np import matplotlib.pyplot as plt get_ipython().run_line_magic('matplotlib', 'inline') import seaborn as sns from sklearn import datasets from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split # In[8]: df=pd.read_csv('Issued_Construction_Permits.csv') # In[23]: ls # In[9]: dfa=df[df['TotalNewAddSQFT']>0] iris=dfa[['OriginalZip','Longitude','Latitude','TotalNewAddSQFT', 'TotalJobValuation','NumberOfFloors']] iris['TotalJobValuation']=iris['TotalJobValuation'].str.lstrip('$') iris.fillna(value=0) iris['TotalJobValuation']=iris['TotalJobValuation'].astype(float) iris.dropna(inplace=True) # In[ ]: #dfa['TotalJobValuation']=dfa['TotalJobValuation'].str.lstrip('$') #dfa.fillna(value=0) #dfa['TotalJobValuation']=dfa['TotalJobValuation'].astype(float) #dfa.dropna(inplace=True) # In[11]: dfa.groupby(['IssuedDate']).mean() # In[10]: dfa.info() # In[20]: y=iris['TotalNewAddSQFT'] #y=y.iloc[1:] x=iris.drop(labels='TotalNewAddSQFT',axis=1) #x=x.iloc[0:-1] X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=28) rf = RandomForestRegressor() rf.fit(X_train,y_train) rf.score(X_test,y_test) # In[ ]: dfa.drop(['IssuedInLast30Days','IssuanceMethod','Location','TCAD_ID','CalendarYearIssued','FiscalYearIssued','DayIssued','OriginalState','CouncilDistrict','Jurisdiction'],axis=1,inplace=True) # Build a random forest to predict total square footage on by month and by zip code basis for 2017 in Austin. # In[12]: dfa['IssuedDate']=pd.to_datetime(dfa['IssuedDate']) # In[21]: dfa.groupby(['IssuedDate','OriginalZip'])['TotalNewAddSQFT'].mean() tbz=dfa.groupby(['OriginalZip'])['TotalNewAddSQFT'].sum() tbz.nlargest(55) # In[ ]: dfa.std() # In[19]: df.shape # In[ ]: plt.figure(figsize=(15,6)) zip_counts = dfa['OriginalZip'].value_counts() ax1 = zip_counts.plot(kind='bar'); labels = [] for i, label in enumerate(zip_counts.index): labels.append('{} - ({})'.format(label, zip_counts[label])) ax1.set_xticklabels(labels); #Downtown - 78701 - had a tenth the *residential* development of 78702; huh. # In[ ]: plt.figure(figsize=(15,6)) floor_counts = dfa['NumberOfFloors'].value_counts() ax1 = floor_counts.plot(kind='bar'); labels = [] for i, label in enumerate(floor_counts.index): labels.append('{} - ({})'.format(label, floor_counts[label])) ax1.set_xticklabels(labels); # What the heck is a zero story project?! And two-story projects are half again as common as one-story. # In[ ]: plt.figure(figsize=(15,6)) desc_counts = dfa['PermitClass'].value_counts() ax1 = desc_counts.plot(kind='bar'); labels = [] for i, label in enumerate(desc_counts.index): labels.append('{} - ({})'.format(label, desc_counts[label])) ax1.set_xticklabels(labels); # In[ ]: plt.figure(figsize=(15,6)) jv_counts = dfa['TotalJobValuation'].value_counts() ax1 = jv_counts.plot(kind='hist'); labels = [] for i, label in enumerate(jv_counts.index): labels.append('{} - ({})'.format(label, jv_counts[label])) ax1.set_xticklabels(labels); #Most projects are $150k-$250k # In[ ]: dfa['PermitClass'].unique() # In[ ]: dfa[dfa['Condominium']=='Yes'].count() # In[ ]: print(dfa[dfa['Condominium']=='Yes']['TotalNewAddSQFT'].sum()) print(dfa.sort_values('TotalNewAddSQFT', ascending=False)) howmany=dfa['ProjectName'].unique() howmany.shape # In[ ]: print(dfa['OriginalZip'].unique()) dfa.sort_values('HousingUnits', ascending=False) # In[ ]: dfa['TotalNewAddSQFT'].sum() #Over 1.3 billion square feet; seems low for over a hundred new people per day, #but a lot of construction is likely unpermitted and many move into Austin metro #- 4300 square miles and 2 million people, not Austin, 272 square miles and 800K people. # In[ ]: plt.figure(figsize=(22,11)) plt.ylabel('Latitude') plt.xlabel('Longitude') plt.title('Austin new square footage') plt.scatter(dfa['Longitude'],dfa['Latitude'], s=dfa['TotalNewAddSQFT']/1000,c='g') # In[ ]: ''' This uses the Bokeh package so you will have to make sure it is loaded''' from bokeh.models import ( GMapPlot, GMapOptions, ColumnDataSource, Circle, Range1d, PanTool, WheelZoomTool, BoxSelectTool ) from bokeh.io import output_file, show, output_notebook from bokeh.io import export_png # Uncomment out this line to work within the jupyter notebook. # If left commented out map should show in new window output_notebook() # In[3]: #creating the plot ## Set up the center point zoom and location for a given map map_options = GMapOptions(lat=30.307182, lng=-97.76, map_type="roadmap", zoom=11) plot = GMapPlot( x_range=Range1d(), y_range=Range1d(), map_options=map_options ) plot.title.text = "Austin" ''' This is someone else's API key so you should get your own at https://console.developers.google.com/apis/ You will need to make sure you have set up the permissions and services so you can use this. I believe you need at least Google Maps JavaScript API Google Static Maps API Maybe more''' plot.api_key = "AIzaSyBl6OOSxtxS7qmNgkm6EXdY6MSdVrHW8h4" '''Here you will pass a list of the locations you want to show on the graph The lats and longs are broken up into 2 lists''' completed_lats = dfa['Latitude'].values completed_longs = dfa['Longitude'].values size=(dfa['TotalNewAddSQFT'].values**.25)/5 '''Setup for displaying the cordinates''' completed_source = ColumnDataSource( data=dict( lat=completed_lats, lon=completed_longs, alpha=size)) '''The dots are put together and added to the plot''' completed_dots = Circle(x="lon", y="lat", size='alpha', fill_color="red", fill_alpha=.5, line_color=None) plot.add_glyph(completed_source, completed_dots) '''This line setups up what functionality to allow (zoom etc)''' plot.add_tools(PanTool(), WheelZoomTool(), BoxSelectTool()) '''Show the map''' show(plot) export_png(plot, filename="plot.png") # In[ ]: import scipy.stats as scs import statsmodels.api as sm pd.plotting.scatter_matrix(dfa, figsize=(15, 10)) plt.show() # In[ ]: dfa.describe().T

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191

0.718091

879

6,119

4.913538

0.390216

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null

0

null

0

1

0

698faf78aa6c63752705207447b7c63891ba7586

4,274

py

Python

lib/util/calc_iou.py

kemaloksuz/aLRPLoss-AblationExperiments

2624afeeb272d45c96d266c8f0b331e2cb286e5e

[ "MIT" ]

4

2020-11-18T09:16:02.000Z

2021-09-30T12:08:13.000Z

lib/util/calc_iou.py

BigHeartDB/aLRPLoss

49d3eb7a01ae60380e46b8c0ef496d6c53ac40b9

[ "MIT" ]

1

2021-03-19T11:18:13.000Z

lib/util/calc_iou.py

BigHeartDB/aLRPLoss

49d3eb7a01ae60380e46b8c0ef496d6c53ac40b9

[ "MIT" ]

1

2020-11-18T09:16:05.000Z

import numpy as np import torch import torch.nn as nn import pdb def calc_iou(a, b): a=a.type(torch.cuda.DoubleTensor) b=b.type(torch.cuda.DoubleTensor) area = (b[:, 2] - b[:, 0]+1) * (b[:, 3] - b[:, 1]+1) iw = torch.min(torch.unsqueeze(a[:, 2], dim=1), b[:, 2]) - torch.max(torch.unsqueeze(a[:, 0], 1), b[:, 0])+1 ih = torch.min(torch.unsqueeze(a[:, 3], dim=1), b[:, 3]) - torch.max(torch.unsqueeze(a[:, 1], 1), b[:, 1])+1 iw = torch.clamp(iw, min=0) ih = torch.clamp(ih, min=0) ua = torch.unsqueeze((a[:, 2] - a[:, 0]+1) * (a[:, 3] - a[:, 1]+1), dim=1) + area - iw * ih #ua = torch.clamp(ua, min=1e-8) intersection = iw * ih IoU = intersection / ua return IoU def bbox_overlaps(bboxes1, bboxes2): ''' Calculates IoU between model prediction and target to compute IoULoss. Inputs: pred -> NX4 Bounding-boxes from model prediction. target -> Nx4 Target bounding boxes. Returns: ious -> Nx1 IoU value between prediction and target bounding boxes. ''' # check if prediction and target samples equal num_pred = bboxes1.size(0) num_target = bboxes2.size(0) assert num_pred == num_target # calculate max-top-left, min-bottom-right points top_left = torch.max(bboxes1[:,:2], bboxes2[:,:2]) bottom_right = torch.min(bboxes1[:,2:], bboxes2[:,2:]) wh = (bottom_right - top_left + 1).clamp(min=0) overlap = wh[:,0] * wh[:,1] # calculate area for box1 area1 = (bboxes1[:,2] - bboxes1[:,0] + 1) *( bboxes1[:,3] - bboxes1[:,1] + 1) # calculate area for box2 area2 = (bboxes2[:,2] - bboxes2[:,0] + 1) *( bboxes2[:,3] - bboxes2[:,1] + 1) # calculate ious ious = overlap / (area1 + area2 - overlap) return ious def compute_giou(pred, target, eps=1e-7): num_pred = pred.size(0) num_target = target.size(0) assert num_pred == num_target # calculate max-top-left and min-bottom-right points for overlap top_left = torch.max(pred[:,:2], target[:,:2]) bottom_right = torch.min(pred[:, 2:], target[:, 2:]) wh = (bottom_right - top_left + 1).clamp(min=0) # overlap overlap = wh[:,0] * wh[:,1] # calculate union area_pred = (pred[:, 2]-pred[:,0] + 1) * (pred[:,3] - pred[:,1]+1) area_target = (target[:, 2]-target[:, 0]+1) * (target[:,3]-target[:,1]+1) union = area_pred + area_target - overlap + eps # calculate iou ious = overlap/union # min. enclosing box enclose_x1y1 = torch.min(pred[:, :2], target[:, :2]) enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:]) enclose_wh = (enclose_x2y2 - enclose_x1y1 + 1).clamp(min=0) enclose_area = enclose_wh[:,0] * enclose_wh[: ,1] + eps # giou diff_term = (enclose_area - union) / enclose_area gious = ious - diff_term return gious def compute_diou(pred, target, eps=1e-7): num_pred = pred.size(0) num_target = target.size(0) assert num_pred == num_target # calculate max-top-left and min-bottom-right points for overlap top_left = torch.max(pred[:,:2], target[:,:2]) bottom_right = torch.min(pred[:, 2:], target[:, 2:]) wh = (bottom_right - top_left + 1).clamp(min=0) # overlap overlap = wh[:,0] * wh[:,1] # get pred and gt centers pred_c_x = (pred[:, 0] + pred[:, 2]) / 2 pred_c_y = (pred[:, 1] + pred[:, 3]) / 2 gt_c_x = (target[:, 0] + target[:, 2]) / 2 gt_c_y = (target[:, 1] + target[:, 3]) / 2 # calculate union area_pred = (pred[:, 2]-pred[:,0] + 1) * (pred[:,3] - pred[:,1]+1) area_target = (target[:, 2]-target[:, 0]+1) * (target[:,3]-target[:,1]+1) union = area_pred + area_target - overlap + eps # calculate iou ious = overlap/union # min. enclosing box enclose_x1y1 = torch.min(pred[:, :2], target[:, :2]) enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:]) enclose_c = ((enclose_x2y2[:, 0] - enclose_x1y1[:, 0]) ** 2) + \ ((enclose_x2y2[:, 1] - enclose_x1y1[:,1]) **2) + eps box_d = ((pred_c_x - gt_c_x) ** 2) + \ ((pred_c_y - gt_c_y) ** 2) # diou diff_term = box_d / enclose_c dious = ious - diff_term return dious

30.098592

112

0.565278

626

4,274

3.738019

0.153355

0.023504

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0.041026

0.492308

0.438034

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4,274

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0.162162

0

null

0

null

0

1

0

698fbdf71330808f397600f3ce9dfb44188c0cfc

1,871

py

Python

problems/336_palindrome_pairs.py

apoorvkk/LeetCodeSolutions

1c3461cfc05deb930d0866428eb00362b4338aab

[ "MIT" ]

1

2018-02-03T14:17:18.000Z

problems/336_palindrome_pairs.py

apoorvkk/LeetCodeSolutions

1c3461cfc05deb930d0866428eb00362b4338aab

[ "MIT" ]

null

problems/336_palindrome_pairs.py

apoorvkk/LeetCodeSolutions

1c3461cfc05deb930d0866428eb00362b4338aab

[ "MIT" ]

null

''' URL: https://leetcode.com/problems/palindrome-pairs Time complexity: O(k^2 * n) where n is number of words and k is length of word Space complexity: O(n) ''' class Solution(object): def _is_palindrome(self, word): return word == word[::-1] def palindromePairs(self, words): """ :type words: List[str] :rtype: List[List[int]] """ word_to_index = {} for i, word in enumerate(words): word_to_index[word] = i palindrome_pairs = [] # full word reversals case for word, i in word_to_index.iteritems(): if word[::-1] in word_to_index and word_to_index[word[::-1]] != i: palindrome_pairs.append([i, word_to_index[word[::-1]]]) # empty string case if "" in word_to_index: i = word_to_index[""] for j, word in enumerate(words): if self._is_palindrome(word) and j != i: palindrome_pairs.append([i,j]) palindrome_pairs.append([j,i]) # prefix/suffix case for i in range(len(words)): curr_word = words[i] for j in range(1, len(curr_word)): prefix = curr_word[:j] suffix = curr_word[j:] reversed_suffix = suffix[::-1] reversed_prefix = prefix[::-1] if self._is_palindrome(prefix): if reversed_suffix in word_to_index and i != word_to_index[reversed_suffix]: palindrome_pairs.append([word_to_index[reversed_suffix], i]) if self._is_palindrome(suffix): if reversed_prefix in word_to_index and i != word_to_index[reversed_prefix]: palindrome_pairs.append([i, word_to_index[reversed_prefix]]) return palindrome_pairs

35.301887

96

0.55799

236

1,871

4.190678

0.237288

0.084934

0.155713

0.065723

0.252781

0.159757

0.139535

0.072801

0

0.00641

0.332977

1,871

52

97

35.980769

0.786058

0.140567

0

1

0.064516

false

0

0.032258

0.16129

0

null

0

null

0

1

0

6991fbe18b2fd17fe844d70133264da95dbb60b1

1,518

py

Python

DDSP/DataReceiver.py

CharKwayTeow/ddsp

307ac3d5b0c0bac121c3ac7cef6f1d7ab6f23e1e

[ "MIT" ]

null

DDSP/DataReceiver.py

CharKwayTeow/ddsp

307ac3d5b0c0bac121c3ac7cef6f1d7ab6f23e1e

[ "MIT" ]

null

DDSP/DataReceiver.py

CharKwayTeow/ddsp

307ac3d5b0c0bac121c3ac7cef6f1d7ab6f23e1e

[ "MIT" ]

null

import os import socket import random class DataReceiver: """docstring for DataReceiver""" def __init__(self): self.selectPort() def selectPort(self): while True: self.port = random.randint(8192, 65535) try: # Detect whether the port has been occupied. s = socket.socket(socket.AF_INET,socket.SOCK_STREAM) s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) s.bind(('', self.port)) s.close() return except: # Do nothing here to repick another port pass def receive(self, path): rc = 0 s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) s.bind(('', self.port)) s.settimeout(10) try: s.listen(1) conn, addr = s.accept() f = open(path, 'wb') while True: data = conn.recv(4096) if data: f.write(data) else: f.close() break except: rc = -1 finally: s.close() return rc """Write the test code here""" if __name__ == '__main__': receiver = DataReceiver() receiver.port = 11111 print (receiver.receive('../../received/missfont.log')) print ("DataReceiver class should work if you see this")

28.641509

71

0.506588

163

1,518

4.595092

0.503067

0.096128

0.034713

0.050734

0.264352

0

0.027056

0.391304

1,518

53

72

28.641509

0.78355

0.071805

0

0.318182

0

0.060452

0.019665

0

1

0.068182

false

0.022727

0.068182

0

0.204545

0.045455

0

null

0

null

0

1

0

6992c1a60967636a99cd90b5996be62a2f93aea7

2,648

py

Python

scripts/main.py

akj127/Robotics-project

4cbe212281462a5cef8a6b0042915e57394c57c2

[ "MIT" ]

3

2020-11-15T10:11:54.000Z

2020-11-16T06:02:47.000Z

scripts/main.py

akj127/Robotics-project

4cbe212281462a5cef8a6b0042915e57394c57c2

[ "MIT" ]

null

scripts/main.py

akj127/Robotics-project

4cbe212281462a5cef8a6b0042915e57394c57c2

[ "MIT" ]

1

2021-01-26T17:16:11.000Z

#!/usr/bin/env python from sensor_msgs.msg import NavSatFix, Imu,Image from geometry_msgs.msg import Twist, TwistStamped from std_msgs.msg import Float64 from mavros_msgs.msg import OverrideRCIn import rospy import matplotlib.pyplot as plt import math import sys, random, math from math import sqrt,cos,sin,atan2 import os import roslib import numpy as np import csv import time import tf from cv_bridge import CvBridge import cv2 bridge = CvBridge() vx=0 vy=0 v=0 rover_x = 0 rover_y = 0 marker_size = 10 #Load the dictionary that was used to generate the markers. dictionary = cv2.aruco.Dictionary_get(cv2.aruco.DICT_6X6_250) # Initialize the detector parameters using default values parameters = cv2.aruco.DetectorParameters_create() def processimage(inp) : ## cv_image is a cv2 image object. proceed forward with merging your code global bridge cv_image = bridge.imgmsg_to_cv2(inp, "bgr8") # Detect markers markerCorners, markerIds, rejectedCandidates = cv2.aruco.detectMarkers(frame, dictionary, parameters=parameters) if markerIds != None: ret = aruco.estimatePoseSingleMarkers(markerCorners, marker_size, camera_matrix, camera_distortion) #-- Unpack the output, get only the first rvec, tvec = ret[0][0,0,:], ret[1][0,0,:] str_position = "MARKER Position x=%4.0f y=%4.0f z=%4.0f"%(tvec[0], tvec[1], tvec[2]) print(str_position) # bottom_left_corner = tuple(markerCorners[0][0][0]) # top_right_corner = tuple(markerCorners[0][0][2]) # rover_x = (bottom_left_corner[0] + top_right_corner[0]) / 2 # rover_y = (bottom_left_corner[1] + top_right_corner[1]) / 2 return cv_bridge def getVelocity(measurement): global vx,vy,v x=measurement.twist.linear.x y=measurement.twist.linear.y vx=math.sqrt(x ** 2) vy=math.sqrt(y ** 2) v = math.sqrt((vx**2) + (vy**2)) def dist(a,b): return math.sqrt((a[1]-b[1])**2+(a[0]-b[0])**2) def letstrack() : ## add whatever you want to add while True: pub = rospy.Publisher('mavros/rc/override', OverrideRCIn, queue_size=1000) control = OverrideRCIn() def listener(): rospy.init_node('listener', anonymous=True) rate = rospy.Rate(10) # 10hz #r = rosrate(0.05) # rospy.Subscriber("/mavros/global_position/global", NavSatFix, plot_gps_measurements) #rospy.Subscriber("/mavros/global_position/global", NavSatFix, getGPS) rospy.Subscriber("/mavros/global_position/raw/gps_vel", TwistStamped, getVelocity) rospy.Subscriber("/webcam/image_raw",Image,getimage) letsdetect() rospy.spin() #rospy.Subscriber("/mavros/imu/data", Imu, getPitch) #rospy.Subscriber("/mavros/global_position/rel_alt", Float64, getAltitude if __name__=='__main__' : listener()

29.098901

113

0.744713

400

2,648

4.7925

0.4225

0.00626

0.054773

0.056338

0.115806

0.052165

0

0.031561

0.126511

2,648

90

114

29.422222

0.797233

0.305514

0

0.071978

0.019231

0

1

0.086207

false

0

0.293103

0.017241

0.413793

0.017241

0

null

0

null

0

1

0

699b2d94c39de0d54b340886a431ea3e1379d79d

1,550

py

Python

test/test_weighted_avg_peak.py

stungkit/TradingBot

177400c1ee664817b199815cca85e6682a7686ab

[ "MIT" ]

218

2018-10-07T19:16:01.000Z

2022-03-29T10:59:22.000Z

test/test_weighted_avg_peak.py

stungkit/TradingBot

177400c1ee664817b199815cca85e6682a7686ab

[ "MIT" ]

123

2018-10-15T11:29:03.000Z

2022-03-30T16:03:11.000Z

test/test_weighted_avg_peak.py

stungkit/TradingBot

177400c1ee664817b199815cca85e6682a7686ab

[ "MIT" ]

27

2019-02-09T08:20:18.000Z

2022-03-29T14:31:16.000Z

from pathlib import Path import pytest from common.MockRequests import ( ig_request_confirm_trade, ig_request_login, ig_request_market_info, ig_request_prices, ig_request_set_account, ig_request_trade, ) from tradingbot.components import Configuration, TradeDirection from tradingbot.components.broker import Broker, BrokerFactory from tradingbot.strategies import WeightedAvgPeak @pytest.fixture def config(): config = Configuration.from_filepath(Path("test/test_data/trading_bot.toml")) config.config["strategies"]["active"] = "weighted_avg_peak" return config @pytest.fixture def broker(config, requests_mock): """ Initialise the strategy with mock services """ ig_request_login(requests_mock) ig_request_set_account(requests_mock) return Broker(BrokerFactory(config)) def test_find_trade_signal(config, broker, requests_mock): ig_request_login(requests_mock) ig_request_set_account(requests_mock) ig_request_prices(requests_mock) ig_request_trade(requests_mock) ig_request_confirm_trade(requests_mock) ig_request_market_info(requests_mock) strategy = WeightedAvgPeak(config, broker) # Need to use a mock enum as the requests_mock expect "mock" as interval market = broker.get_market_info("mock") prices = strategy.fetch_datapoints(market) tradeDir, limit, stop = strategy.find_trade_signal(market, prices) assert tradeDir is not None assert limit is None assert stop is None assert tradeDir == TradeDirection.NONE

28.181818

81

0.771613

198

1,550

5.742424

0.333333

0.110818

0.086192

0.129288

0.145998

0.100264

0

0.159355

1,550

54

82

28.703704

0.872602

0.073548

0

0.157895

0

0.047887

0.021831

0

0.105263

1

0.078947

false

0

0.157895

0

0.289474

0

null

0

null

0

1

0

699ca3785181d97033e4bc0c45ba1c016dc9742d

9,702

py

Python

simulation/soft_body.py

mahdidolati/elementary-graph-algorithms

08a90c021f766c2d30368112bc3b7f37b9a3b513

[ "MIT" ]

null

simulation/soft_body.py

mahdidolati/elementary-graph-algorithms

08a90c021f766c2d30368112bc3b7f37b9a3b513

[ "MIT" ]

null

simulation/soft_body.py

mahdidolati/elementary-graph-algorithms

08a90c021f766c2d30368112bc3b7f37b9a3b513

[ "MIT" ]

null

from manim import * import numpy as np import networkx as nx from util.util import Geometry def to_coord(v): return v[0] * RIGHT + v[1] * UP class Polygon: def __init__(self): self.convex_box = {'left-x': None, 'right-x': None, 'up-y': None, 'down-y': None} self.shape = nx.Graph() def update_convex_box(self, p): if self.convex_box['left-x'] is None: self.convex_box['left-x'] = p[0] self.convex_box['right-x'] = p[0] self.convex_box['up-x'] = p[1] self.convex_box['down-x'] = p[1] return if p[0] < self.convex_box['left-x']: self.convex_box['left-x'] = p[0] if p[0] > self.convex_box['right-x']: self.convex_box['right-x'] = p[0] if p[1] < self.convex_box['down-x']: self.convex_box['down-x'] = p[1] if p[1] > self.convex_box['up-x']: self.convex_box['up-x'] = p[1] def add_edge(self, x, y): if x not in self.shape.nodes(): self.shape.add_node(x) self.update_convex_box(x) if y not in self.shape.nodes(): self.shape.add_node(y) self.update_convex_box(y) if (x, y) not in self.shape.edges(): self.shape.add_edge(x, y, q=set(), e=[np.array(x).reshape((2, 1)), np.array(y).reshape((2, 1))]) def add_edges(self, edge_list): for e in edge_list: self.add_edge(e[0], e[1]) def draw(self, q_screen): for e in self.shape.edges(): l = Line(to_coord(e[0]), to_coord(e[1])).set_color(BLUE) self.shape.edges[e]['q'].add(l) q_screen.add(l) def point_inside(self, p): if p[0] <= self.convex_box['left-x'] or p[0] >= self.convex_box['right-x']: return False if p[1] <= self.convex_box['down-x'] or p[1] >= self.convex_box['up-x']: return False x = p[0, 0] y = p[1, 0] n = 0 corner_meet = 0 for e in self.shape.edges(): e0 = self.shape.edges[e]['e'][0] e1 = self.shape.edges[e]['e'][1] if e0[0, 0] < x < e1[0, 0] or e1[0, 0] < x < e0[0, 0]: m = (e1[1, 0] - e0[1, 0]) / (e1[0, 0] - e0[0, 0]) yp = (x - e0[0, 0]) * m + e0[1, 0] if np.abs(yp - y) < 0.0001: return False if yp < y: n += 1 if x == e0[0, 0]: if e0[1, 0] < y: corner_meet += 1 if x == e1[0, 0]: if e1[1, 0] < y: corner_meet += 1 return (n + corner_meet // 2) % 2 != 0 def get_intersection(self, p1, p2): g = Geometry() candidates = None hl = None for e in self.shape.edges(): r = g.get_line_segment_intersection(self.shape.edges[e]['e'], [p1, p2]) if r is not None: if candidates is None: candidates = r hl = self.shape.edges[e]['e'] if np.linalg.norm(r - p1) < np.linalg.norm(candidates - p1): candidates = r hl = self.shape.edges[e]['e'] return candidates, hl class Point: def __init__(self, x, y): self.id = (x, y) self.position = np.array([x, y], dtype='float64').reshape((2, 1)) self.velocity = np.zeros(2, dtype='float64').reshape((2, 1)) self.force = np.zeros(2, dtype='float64').reshape((2, 1)) self.latent_force = np.zeros(2, dtype='float64').reshape((2, 1)) self.mass = 1.0 def set_force_gravity(self): self.force[1, 0] = self.force[1, 0] - self.mass * 0.15 def shift(self, dx, dy): self.position += np.array([dx, dy], dtype='float64').reshape((2, 1)) def reset_velocity(self, direction=None): if direction is None: self.velocity = np.zeros(2, dtype='float64').reshape((2, 1)) else: d = direction / np.linalg.norm(direction) t = np.dot(np.transpose(self.velocity), d) * d self.velocity -= t def set_velocity(self, dt): self.velocity = self.velocity + self.force * dt / self.mass def set_position(self, dt): self.position = self.position + self.velocity * dt def reset_force(self): self.force = self.latent_force self.latent_force = np.zeros(2, dtype='float64').reshape((2, 1)) def apply_force(self, fd, f): unit_x = np.array([1, 0], dtype='float64').reshape((2, 1)) unit_y = np.array([0, 1], dtype='float64').reshape((2, 1)) self.force[0, 0] = self.force[0, 0] + np.dot(np.transpose(fd), unit_x) * f self.force[1, 0] = self.force[1, 0] + np.dot(np.transpose(fd), unit_y) * f def get_next_position(self, dt): v = self.velocity + self.force * dt / self.mass return self.position + v * dt class Spring: def __init__(self, A, B, ks): self.A = A self.B = B self.L0 = np.linalg.norm(self.A.position - self.B.position) self.ks = ks self.kd = 0.9 def set_force_spring(self): norm1 = np.linalg.norm(self.A.position - self.B.position) abu = (self.B.position - self.A.position) / norm1 bau = -1 * abu f1 = self.ks * (norm1 - self.L0) self.A.apply_force(abu, f1) self.B.apply_force(bau, f1) fa = np.dot(np.transpose(abu), self.B.velocity - self.A.velocity) * self.kd self.A.apply_force(abu, fa) fb = np.dot(np.transpose(bau), self.A.velocity - self.B.velocity) * self.kd self.B.apply_force(bau, fb) class SoftRectangle: def __init__(self, w, h, ks): self.lines = set() self.g = nx.Graph() for i in range(w): for j in range(h): c = Circle(radius=0.1) x = 0.5 * i y = 0.5 * j c.move_to(x * RIGHT + y * UP) c.set_fill(PINK, opacity=0.5) self.g.add_node((i, j), p=Point(x, y), c=c) if i % 2 == 0: neighbors = [(i-1, j), (i, j-1), (i-1, j-1)] else: neighbors = [(i - 1, j), (i, j - 1), (i - 1, j + 1)] for n in neighbors: if n[0] >= 0 and 0 <= n[1] < h: self.g.add_edge((i, j), n, s=Spring(self.g.nodes[(i, j)]['p'], self.g.nodes[n]['p'], ks)) def shift(self, dx, dy): for n in self.g.nodes(): self.g.nodes[n]['p'].shift(dx, dy) def step(self): for n in self.g.nodes(): self.g.nodes[n]['p'].reset_force() self.g.nodes[n]['p'].set_force_gravity() for e in self.g.edges(): self.g.edges[e]['s'].set_force_spring() def update_positions(self, dt, p): for n in self.g.nodes(): c_p = self.g.nodes[n]['p'].position n_p = self.g.nodes[n]['p'].get_next_position(dt) if p.point_inside(n_p): t, hl = p.get_intersection(c_p, n_p) g = Geometry() force = self.g.nodes[n]['p'].force p_dir = g.get_force_reflection(hl) f = -1 * np.dot(np.transpose(force), p_dir) * p_dir self.g.nodes[n]['p'].latent_force = f self.g.nodes[n]['p'].reset_velocity(p_dir) self.g.nodes[n]['p'].position = t else: self.g.nodes[n]['p'].latent_force = np.zeros(2, dtype='float64').reshape((2, 1)) self.g.nodes[n]['p'].set_velocity(dt) self.g.nodes[n]['p'].set_position(dt) def move_atom(self, n, d): self.g.nodes[n]['p'].position += d def move_anim(self, q_screen): for l in self.lines: q_screen.remove(l) for e in self.g.edges(): e1 = self.g.nodes[e[0]]['p'].position[0, 0] * RIGHT + self.g.nodes[e[0]]['p'].position[1, 0] * UP e2 = self.g.nodes[e[1]]['p'].position[0, 0] * RIGHT + self.g.nodes[e[1]]['p'].position[1, 0] * UP l = Line(e1, e2).set_color(PINK).set_opacity(0.5) self.lines.add(l) q_screen.add(l) for n in self.g.nodes(): self.g.nodes[n]['c'].move_to( self.g.nodes[n]['p'].position[0, 0] * RIGHT + self.g.nodes[n]['p'].position[1, 0] * UP ) class SoftBody(Scene): def draw_body(self, body): for n in body.g.nodes(): self.add(body.g.nodes[n]['c']) body.move_anim(self) def move_body(self, body, dt, p): body.step() body.update_positions(dt, p) body.move_anim(self) def construct(self): bodies = [SoftRectangle(3, 3, 72), SoftRectangle(3, 3, 24), SoftRectangle(3, 3, 8)] body_shifts = [(-4, 1.5), (-1, 1.5), (2, 1.5)] for i in range(len(bodies)): bodies[i].shift(*body_shifts[i]) self.draw_body(bodies[i]) p = Polygon() p.add_edges([ # [(-1, -2), (3, -2)], [(3, -2), (-1, 2)], [(-1, 2), (-1, -2)] [(-5, -2), (4, -2)], [(4, -2), (4, -1)], [(4, -1), (-5, -1)], [(-5, -1), (-5, -2)] ]) p.draw(self) self.wait(1) # s.move_atom((0, 0), np.array([-0.5, 0], dtype='float64').reshape((2, 1))) # s.move_atom((1, 0), np.array([0.5, 0], dtype='float64').reshape((2, 1))) # s.move_anim(self) # self.wait(1) dt = 0.05 for _ in range(40): for b in bodies: self.move_body(b, dt, p) self.wait(2 * dt) if __name__ == "__main__": pass

36.066914

113

0.49093

1,500

9,702

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0.11

0.034647

0.056301

0.040494

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0.289952

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0.331375

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0.201794

0

null

0

null

0

1

0

699efb78c06b896f75ab6b08619347efc68e02be

3,576

py

Python

pyroomacoustics/tests/tests_libroom/test_is_inside_2d_polygon.py

HemaZ/pyroomacoustics

c401f829c71ff03a947f68f9b6b2f48346ae84b2

[ "MIT" ]

1

2019-08-04T07:34:02.000Z

pyroomacoustics/tests/tests_libroom/test_is_inside_2d_polygon.py

HemaZ/pyroomacoustics

c401f829c71ff03a947f68f9b6b2f48346ae84b2

[ "MIT" ]

null

pyroomacoustics/tests/tests_libroom/test_is_inside_2d_polygon.py

HemaZ/pyroomacoustics

c401f829c71ff03a947f68f9b6b2f48346ae84b2

[ "MIT" ]

1

2021-03-07T09:46:58.000Z

# Test of polygon point inclusion test # Copyright (C) 2019 Robin Scheibler, Cyril Cadoux, Sidney Barthe # # 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. # # You should have received a copy of the MIT License along with this program. If # not, see <https://opensource.org/licenses/MIT>. from __future__ import division import numpy as np import pyroomacoustics as pra polygons = [ np.array([ # this one is clockwise [0, 4, 4, 0,], [0, 0, 4, 4,], ]), np.array([ # this one is clockwise! [0, 0, 1, 1, 3, 3], [0, 1, 1, 2, 2, 0], ]), np.array([ # this one is clockwise! [0, 1, 1, 3, 3], [0, 1, 2, 2, 0], ]), ] cases = { 'inside' : { 'pol' : 0, 'p' : [2,2], 'ret' : 0, }, 'on_border' : { 'pol' : 0, 'p' : [0,2], 'ret' : 1, }, 'on_corner' : { 'pol' : 0, 'p' : [4,4], 'ret' : 1, }, 'outside' : { 'pol' : 0, 'p' : [5,5], 'ret' : -1, }, # horizontal wall aligned with point 'horiz_wall_align' : { 'pol' : 1, 'p' : [2,1], 'ret' : 0, }, # ray is going through vertex 'ray_through_vertex' : { 'pol' : 2, 'p' : [2,1], 'ret' : 0, }, # point is at the same height as top of polygon, but outside 'top_outside' : { 'pol' : 2, 'p' : [4,2], 'ret' : -1, }, } def run_inside_pol(lbl): pol = polygons[cases[lbl]['pol']] p = cases[lbl]['p'] r_exp = cases[lbl]['ret'] ret = pra.libroom.is_inside_2d_polygon(p, pol) assert ret == r_exp, '{} : returned={} expected={}'.format(lbl, ret, r_exp) def test_inside(): run_inside_pol('inside') def test_on_border(): run_inside_pol('on_border') def test_on_corner(): run_inside_pol('on_corner') def test_outside(): run_inside_pol('outside') def test_horiz_wall_align(): run_inside_pol('horiz_wall_align') def test_ray_through_vertex(): run_inside_pol('ray_through_vertex') def test_top_outside(): run_inside_pol('top_outside') if __name__ == '__main__': test_inside() test_on_border() test_on_corner() test_outside() test_top_outside() test_horiz_wall_align() test_ray_through_vertex()

27.29771

80

0.568233

473

3,576

4.135307

0.357294

0.041411

0.04908

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0.053681

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false

0

0.036585

0

0.134146

0

null

0

null

0

1

0

699f551d304f7026352ad9206f00d1322df3dcbb

267

py

Python

sphinx-sources/Examples/Commands/GaussScreen.py

jccmak/lightpipes

1a296fe08bdd97fc9a0e11f92bab25c85f68e57d

[ "BSD-3-Clause" ]

132

2017-03-15T15:28:46.000Z

2022-03-09T00:28:25.000Z

sphinx-sources/Examples/Commands/GaussScreen.py

jccmak/lightpipes

1a296fe08bdd97fc9a0e11f92bab25c85f68e57d

[ "BSD-3-Clause" ]

63

2017-01-26T15:46:55.000Z

2022-01-25T04:50:59.000Z

sphinx-sources/Examples/Commands/GaussScreen.py

jccmak/lightpipes

1a296fe08bdd97fc9a0e11f92bab25c85f68e57d

[ "BSD-3-Clause" ]

37

2017-02-17T16:11:38.000Z

2022-01-25T18:03:47.000Z

from LightPipes import * import matplotlib.pyplot as plt wavelength=500*nm size=5.0*mm N=100 w=0.2*mm T=2 z=1*m dx=1.0*mm dy=1.0*mm F=Begin(size,wavelength,N) F=GaussScreen(w,dx,dy,T,F) F=Forvard(z,F) I=Intensity(2,F) plt.imshow(I) #plt.plot(I[N/2][:N]) plt.show()

13.35

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0.043011

0

0.07438

0.093633

267

19

32

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0.694215

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0

1

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false

0

0.125

0

0.125

0

null

0

null

0

1

0

69a04b1c505bb845a7403a07e74c3d06f3debfa0

14,063

py

Python

head-ensembles/legacy/extract_trees.py

Tom556/BERTHeadEnsembles

aeb6ec37d24f30bb1d224f38fd4928488a215ed7

[ "MIT" ]

null

head-ensembles/legacy/extract_trees.py

Tom556/BERTHeadEnsembles

aeb6ec37d24f30bb1d224f38fd4928488a215ed7

[ "MIT" ]

3

2020-09-26T01:00:29.000Z

2021-05-21T16:25:33.000Z

head-ensembles/legacy/extract_trees.py

tomlimi/BERTHeadEnsembles

aeb6ec37d24f30bb1d224f38fd4928488a215ed7

[ "MIT" ]

null

from tools import dependency, sentence_attentions import argparse from collections import defaultdict, namedtuple import numpy as np RelData = namedtuple('RelData','layers heads transpose d2p') # soft pos mask (BEST) # relation_rules = {'adj-clause-p2d': RelData([3, 4, 7, 6, 5, 7], [3, 5, 6, 6, 9, 10],False, False), # 'adj-modifier-d2p': RelData([3, 7, 4, 5], [9, 2, 5, 0],False, True), # 'adv-clause-p2d': RelData([5, 3, 4, 5, 1], [8, 3, 8, 11, 5],False, False), # 'adv-modifier-p2d': RelData([7, 5, 6, 4, 9, 4], [7, 1, 9, 7, 3, 10],False, True), # 'apposition-p2d': RelData([3], [3],False, False), # 'auxiliary-d2p': RelData([7, 3, 5, 4, 1, 1], [2, 9, 9, 11, 6, 1],False, True), # 'clausal-p2d': RelData([5, 7, 7, 7, 4, 3, 5, 0, 6, 1], [8, 10, 1, 6, 5, 3, 11, 8, 6, 5],False, False), # 'clausal-d2p': RelData([6, 5, 7, 7], [2, 3, 0, 9],False, True), # 'compound-d2p': RelData([3, 7], [9, 2],False, True), # 'conjunct-d2p': RelData([6, 9, 8, 4, 3, 11], [0, 6, 4, 10, 11, 9],False, True), # 'determiner-d2p': RelData([7, 3, 4], [10, 9, 5],False, True), # # 'i object-d2p': RelData([5], [1],False, True), # 'noun-modifier-d2p': RelData([6, 3, 7, 6], [9, 11, 9, 2],False, True), # 'num-modifier-p2d': RelData([7, 4, 0, 6, 5], [11, 7, 2, 9, 9],False, False), # 'object-d2p': RelData([6, 3, 6], [9, 11, 10],False, True), # 'other-d2p': RelData([5, 4, 8, 6, 3, 5, 7, 7, 7, 5, 1, 1], [0, 5, 5, 6, 9, 7, 1, 2, 6, 9, 6, 1],False, True), # 'punctuation-d2p': RelData([7, 8, 8, 5, 7, 8], [1, 5, 0, 7, 6, 4],False, True), # 'subject-p2d': RelData([7, 4, 6], [4, 10, 4],False, False)} # diagonal mask # relation_rules = {'adj-clause-p2d': RelData([4, 7, 6, 0], [5, 6, 5, 8],False, False), # 'adj-modifier-d2p': RelData([3, 7, 6, 5, 7, 8, 0, 2], [9, 10, 5, 7, 6, 5, 8, 11],False, True), # 'adv-clause-d2p': RelData([4, 4, 5, 4, 2, 0], [9, 3, 4, 4, 7, 7],False, True), # 'adv-modifier-d2p': RelData([7, 5, 6, 8, 7, 3, 10, 0, 6, 0], [6, 7, 5, 5, 10, 10, 10, 8, 4, 11],False, True), # 'apposition-p2d': RelData([0, 9], [8, 0],False, False), # 'auxiliary-d2p': RelData([3, 8, 7, 5, 4, 7, 10], [9, 5, 6, 0, 5, 10, 10],False, True), # 'clausal subject-p2d': RelData([8, 0, 0, 0], [10, 8, 5, 1],False, False), # 'clausal-d2p': RelData([7, 6, 4, 8, 5, 0, 0, 1, 0], [0, 2, 6, 8, 4, 5, 9, 11, 7],False, True), # 'compound-d2p': RelData([3, 7, 6, 7, 0], [9, 6, 5, 10, 8],False, True), # 'conjunct-d2p': RelData([4, 6, 4, 9, 5, 1, 0, 4, 6], [3, 0, 9, 6, 4, 10, 1, 4, 8],False, True), # 'determiner-d2p': RelData([7, 3, 4, 8], [10, 9, 5, 10],False, True), # #'i object-d2p': RelData([6], [9],False, True), # 'noun-modifier-p2d': RelData([4, 0, 9, 5, 3, 0, 0], [5, 8, 1, 8, 3, 1, 5],False, False), # 'num-modifier-d2p': RelData([7, 6, 3, 8, 7, 6, 0, 10], [10, 5, 10, 5, 6, 4, 11, 10],False, True), # 'object-d2p': RelData([7, 6, 4, 5, 3], [9, 9, 6, 3, 8],False, True), # 'other-d2p': RelData([7, 4, 8, 6, 3, 0], [10, 5, 5, 5, 10, 8],False, True), # 'punctuation-p2d': RelData([11, 10, 2, 11, 7, 7], [6, 7, 2, 2, 8, 7],False, False), # 'subject-p2d': RelData([7, 4], [11, 10],False, False)} # diagonal mask2 relation_rules = {'adj-clause-p2d': RelData([4, 7, 6, 0], [5, 6, 5, 8],False, False), 'adj-modifier-d2p': RelData([3, 7, 6, 5, 7, 8, 0, 2], [9, 10, 5, 7, 6, 5, 8, 11],False, True), 'adv-clause-p2d': RelData([4, 5, 5, 0, 4, 5, 11, 8, 3, 0], [3, 4, 5, 8, 9, 8, 8, 7, 1, 4],False, False), 'adv-modifier-d2p': RelData([7, 5, 6, 8, 7, 3, 10, 0, 6, 0], [6, 7, 5, 5, 10, 10, 10, 8, 4, 11],False, True), 'apposition-p2d': RelData([0, 9], [8, 0],False, False), 'auxiliary-d2p': RelData([3, 8, 7, 5, 4, 7, 10], [9, 5, 6, 0, 5, 10, 10],False, True), 'clausal subject-p2d': RelData([8, 0, 0, 0], [10, 8, 5, 1],False, False), 'clausal-p2d': RelData([5, 4, 7, 5, 0, 7, 4], [7, 5, 6, 8, 8, 1, 8],False, False), 'compound-d2p': RelData([3, 7, 6, 7, 0], [9, 6, 5, 10, 8],False, True), 'conjunct-d2p': RelData([4, 6, 4, 9, 5, 1, 0, 4, 6], [3, 0, 9, 6, 4, 10, 1, 4, 8],False, True), 'determiner-d2p': RelData([7, 3, 4, 8], [10, 9, 5, 10],False, True), #'i object-d2p': RelData([6], [9],False, True), 'noun-modifier-p2d': RelData([4, 0, 9, 5, 3, 0, 0], [5, 8, 1, 8, 3, 1, 5],False, False), 'num-modifier-d2p': RelData([7, 6, 3, 8, 7, 6, 0, 10], [10, 5, 10, 5, 6, 4, 11, 10],False, True), 'object-d2p': RelData([7, 6, 4, 5, 3], [9, 9, 6, 3, 8],False, True), 'other-d2p': RelData([7, 4, 8, 6, 3, 0], [10, 5, 5, 5, 10, 8],False, True), 'punctuation-d2p': RelData([4, 8, 3, 7, 3], [5, 5, 10, 5, 9],False, True), 'subject-p2d': RelData([7, 4], [11, 10],False, False) } # based on tiny set 10 examples # relation_rules = {'adj-clause-p2d': RelData([1], [1],False, False), # 'adj-modifier-d2p': RelData([3], [9],False, True), # 'adv-clause-p2d': RelData([1], [10],False, False), # 'adv-modifier-p2d': RelData([2], [10],False, False), # 'apposition-p2d': RelData([2], [1],False, False), # 'auxiliary-p2d': RelData([3], [8],False, False), # 'compound-d2p': RelData([5], [11],False, True), # 'conjunct-d2p': RelData([11, 4], [9, 7],False, True), # 'determiner-p2d': RelData([7], [9],False, False), # 'noun-modifier-p2d': RelData([3], [9],False, False), # 'num-modifier-p2d': RelData([11], [11],False, False), # 'object-d2p': RelData([9], [3],False, True), # 'other-d2p': RelData([6, 3, 5, 5], [6, 9, 9, 7],False, True), # 'punctuation-d2p': RelData([6, 5], [6, 0],False, True), # 'subject-p2d': RelData([4, 6], [10, 4],False, False)} # more detailed relationship labels # relation_rules = {'acl-p2d': RelData([3, 4, 7, 6, 5, 7], [3, 5, 6, 6, 9, 10],False, False), # 'advcl-p2d': RelData([5, 3, 4, 5, 1], [8, 3, 8, 11, 5],False, False), # 'advmod-p2d': RelData([7, 5, 6, 4, 9, 4], [7, 1, 9, 7, 3, 10],False, False), # 'amod-d2p': RelData([3, 7, 4, 5], [9, 2, 5, 0],False, True), # 'appos-p2d': RelData([3], [3],False, False), # 'aux-d2p': RelData([7, 3, 5, 4, 1, 1], [2, 9, 9, 11, 6, 1],False, True), # 'case-p2d': RelData([0, 5, 3, 9, 1], [3, 10, 11, 3, 4],False, False), # 'cc-p2d': RelData([5, 3, 0, 6, 7, 7], [10, 11, 3, 9, 9, 4],False, False), # 'ccomp-d2p': RelData([6, 5, 7, 7], [2, 3, 0, 9],False, True), # 'compound-d2p': RelData([3, 7], [9, 2],False, True), # 'conj-p2d': RelData([5, 9, 7, 3, 10, 4, 11], [5, 6, 8, 3, 5, 8, 8],False, False), # 'csubj-p2d': RelData([7, 7], [1, 10],False, False), # 'det-p2d': RelData([0, 6, 2, 6, 1], [3, 9, 1, 3, 4],False, False), # 'discourse-p2d': RelData([11], [11],False, False), # 'expl-p2d': RelData([5], [9],False, False), # 'fixed-d2p': RelData([8], [6],False, True), # 'flat-p2d': RelData([8], [5],False, False), # 'iobj-p2d': RelData([3], [9],False, False), # 'mark-p2d': RelData([7, 6, 5, 0, 6, 7, 8], [9, 9, 6, 3, 10, 4, 11],False, False), # 'nmod-d2p': RelData([6, 3, 7, 6], [9, 11, 9, 2],False, True), # 'nsubj-d2p': RelData([7, 6, 5, 5, 1, 7, 5, 7, 4], [2, 6, 9, 0, 6, 1, 11, 8, 11],False, True), # 'nummod-p2d': RelData([7, 4, 0, 6, 5], [11, 7, 2, 9, 9],False, False), # 'obj-d2p': RelData([6, 3, 6], [9, 11, 10],False, True), # 'parataxis-p2d': RelData([5], [5],False, False), # 'punct-p2d': RelData([5, 6], [10, 11],False, False), # 'vocative-p2d': RelData([6], [9],False, False), # 'xcomp-d2p': RelData([6, 3, 5, 5], [9, 11, 6, 3],False, True)} def rewrite_conllu(conllu_file, conllu_out_pred, conllu_out_gold, break_after=1000): CONLLU_ID = 0 CONLLU_LABEL = 7 CONLLU_HEAD = 6 reverse_label_map = {value: key for key, value in dependency.label_map.items()} reverse_label_map['other'] = 'dep' print(reverse_label_map) lengths = [] uas= [] length_sent = 0 out_lines = [] out_lines_gold =[] with open(conllu_file, 'r') as in_conllu: sentid = 0 for line in in_conllu: if sentid > break_after: break if line == '\n': out_lines.append(line.strip()) out_lines_gold.append(line.strip()) uas_sent = (np.array(list(map(int, gold.ravel() != 'no edge'))) * np.array( list(map(int, pred.ravel() != 'no edge')))).sum() / np.array(list(map(int,gold.ravel()!='no edge'))).sum() uas.append(uas_sent) lengths.append(length_sent) print(f"Processed sentence {sentid}", flush=True) sentid += 1 elif line.startswith('#'): if line.startswith('# sent_id'): out_lines.append(line.strip() + '/pred') out_lines_gold.append(line.strip() + '/gold') pred, gold = multigraph_aborescene(sentid) length_sent = 0 else: out_lines_gold.append(line.strip()) out_lines.append(line.strip()) else: fields = line.strip().split('\t') out_lines_gold.append(line.strip()) if fields[CONLLU_ID].isdigit(): length_sent += 1 if fields[CONLLU_LABEL].strip() != 'root': col = pred.transpose()[int(fields[CONLLU_ID]) - 1] x = np.argwhere(col != 'no edge') x = x.item() lab = reverse_label_map[col[x][:-4]] fields[CONLLU_HEAD] = str(x + 1) fields[CONLLU_LABEL] = lab out_lines.append('\t'.join(fields)) with open(conllu_out_pred, 'w') as out_conllu: out_conllu.write('\n'.join(out_lines)) with open(conllu_out_gold, 'w') as out_conllu: out_conllu.write('\n'.join(out_lines_gold)) print("mena uas:") print(np.mean(np.array(uas))) print("length uas corr coef:") print(np.corrcoef(np.array(uas), np.array(lengths))) def multigraph_aborescene(sentence_index, dependency): matrices, sentence_id = next(attention_gen) assert sentence_index == sentence_id words_list = common_tokens[sentence_index] words = ' '.join(words_list) edge_labeled = {(h, d): l for d, h, l, p in dependency_rels[sentence_index] if l != 'root'} root_ord = 0 for d, h, l, p in dependency_rels[sentence_index]: if l == 'root': root_ord = d break DG = nx.DiGraph() DG.add_edges_from(edge_labeled.keys()) labels = {} for node in DG.nodes(): labels[node] = words_list[node] MultiAttention = nx.MultiDiGraph() MultiAttention.add_nodes_from(DG.nodes()) multi_edge2label = dict() for relation, rules in relation_rules.items(): aggr_matrix = np.mean(np.array(matrices)[rules.layers, rules.heads, :, :], axis=0) if rules.d2p == True: aggr_matrix = aggr_matrix.transpose() aggr_matrix[:, root_ord] = 0.001 np.fill_diagonal(aggr_matrix, 0.001) aggr_matrix = np.log(aggr_matrix/(1-aggr_matrix)) AG = nx.from_numpy_matrix(aggr_matrix, create_using=nx.DiGraph) for u, v, d in AG.edges(data=True): multi_edge2label[(u, v, d['weight'])] = relation # incldue statistical info about pos: MultiAttention.add_edges_from(AG.edges(data=True), label=relation) AttentionAborescene = tree.branchings.maximum_spanning_arborescence(MultiAttention) espanning = AttentionAborescene.edges(data=True) weights = [max(d['weight'] * 20, 1) for _, _, d in espanning] attention_labels = {(u, v): multi_edge2label[(u, v, d['weight'])] for u, v, d in espanning} espanning = [(u, v) for (u, v, d) in espanning] posA = nx.spring_layout(AttentionAborescene) alabelm = np.full((len(aggr_matrix), len(aggr_matrix)), 'no edge', dtype='U24') dlabelm = np.full((len(aggr_matrix), len(aggr_matrix)), 'no edge', dtype='U24') for aedge, ael in attention_labels.items(): alabelm[aedge[0], aedge[1]] = ael # else: # alabelm[aedge[1],aedge[0]] = ael for dedge, deel in edge_labeled.items(): deel = dependency.transform_label(deel) if deel + '-d2p' in relation_rules: dlabelm[dedge[0], dedge[1]] = deel + '-d2p' elif deel + '-p2d' in relation_rules: dlabelm[dedge[0], dedge[1]] = deel + '-p2d' elif 'other-d2p' in relation_rules: dlabelm[dedge[0], dedge[1]] = 'other-d2p' elif 'other-p2d' in relation_rules: dlabelm[dedge[1], dedge[0]] = 'other-p2d' return alabelm, dlabelm if __name__ == '__main__': ap = argparse.ArgumentParser() ap.add_argument("-a", "--attentions", required=True, help="NPZ file with attentions") ap.add_argument("-t", "--tokens", required=True, help="Labels (tokens) separated by spaces") ap.add_argument("-T", "--train-conllu", help="Conllu file for training POS", default='/net/projects/LSD/attention_tomasz/lsd/attention-analysis/naacl2019/graph-extraction/entrain.conllu') ap.add_argument("-c", "--conllu", help="Eval against the given conllu file") ap.add_argument("-o", "--output-pred") ap.add_argument("-g", "--output-gold") ap.add_argument("-s", "--sentences", nargs='*', type=int, default=None, help="Only use the specified sentences; 0-based") ap.add_argument("-m", "--maxlen", type=int, default=1000, help="Skip sentences longer than this many words. A word split into several wordpieces is counted as one word. EOS is not counted.") ap.add_argument("-e", "--eos", action="store_true", help="Attentions contain EOS") ap.add_argument("-n", "--no-softmax", action="store_true", help="Whether not to use softmax for attention matrices, use with bert metrices") args = ap.parse_args() attentions_loaded = np.load(args.attentions) sentences_count = len(attentions_loaded.files) layers_count = attentions_loaded['arr_0'].shape[0] heads_count = attentions_loaded['arr_0'].shape[1] with open(args.tokens) as tokens_file: tokens_loaded = [l.split() for l in tokens_file] # in dependency_rels for each sentece there is a lists of tuples (token, token's head) # in dependency_rels_rev tuples are reversed. dependency_rels = dependency.read_conllu_labeled(args.conllu) grouped_tokens, common_tokens = dependency.group_wordpieces(tokens_loaded, args.conllu) attention_gen = sentence_attentions.generate_matrices(attentions_loaded, grouped_tokens, args.eos, args.no_softmax, args.maxlen, args.sentences) pos_frame = dependency.conllu2freq_frame(args.train_conllu) rewrite_conllu(args.conllu, args.output_pred, args.output_gold, break_after=300)

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69a2448f8373ef1e9a09551af005d5fb310a079a

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py

Python

klue_baseline/models/relation_extraction.py

tucan9389/KLUE-baseline

add61158e61f86adfca65087237443828b650090

[ "Apache-2.0" ]

71

2021-07-29T11:34:50.000Z

2022-03-21T08:17:21.000Z

klue_baseline/models/relation_extraction.py

tucan9389/KLUE-baseline

add61158e61f86adfca65087237443828b650090

[ "Apache-2.0" ]

3

2021-08-20T14:19:58.000Z

2021-12-03T06:42:27.000Z

klue_baseline/models/relation_extraction.py

tucan9389/KLUE-baseline

add61158e61f86adfca65087237443828b650090

[ "Apache-2.0" ]

16

2021-08-01T02:29:11.000Z

2022-02-25T07:51:03.000Z

import argparse from typing import Dict, List, Tuple import torch from overrides import overrides from .mode import Mode from .sequence_classification import SCTransformer class RETransformer(SCTransformer): mode: str = Mode.RelationExtraction def __init__(self, hparams: argparse.Namespace, metrics: dict = {}) -> None: super().__init__(hparams, metrics=metrics) self.label_list = hparams.label_list @overrides def validation_epoch_end( self, outputs: List[Dict[str, torch.Tensor]], data_type: str = "valid", write_predictions: bool = False ) -> None: labels = torch.cat([output["labels"] for output in outputs], dim=0) preds, probs = self._convert_outputs_to_preds(outputs) if write_predictions is True: self.predictions = preds self._set_metrics_device() micro_f1 = self.metrics["micro_f1"] micro_f1(preds, labels, self.label_list) self.log(f"{data_type}/micro_f1", micro_f1, on_step=False, on_epoch=True, logger=True) auprc = self.metrics["auprc"] auprc(probs, labels) self.log(f"{data_type}/auprc", auprc, on_step=False, on_epoch=True, logger=True) @overrides def _convert_outputs_to_preds(self, outputs: List[Dict[str, torch.Tensor]]) -> Tuple[torch.Tensor, torch.Tensor]: # logits: (B, num_labels) logits = torch.cat([output["logits"] for output in outputs], dim=0) return torch.argmax(logits, dim=1), torch.softmax(logits, dim=1)

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69a2f926c0e745aa76abcf2f7851f4911e10c02a

816

py

Python

examples/chart-types/word_tree.py

tcbegley/dash-google-charts

b8b22e5b6bac533167f218e3610697dec0c3e4ca

[ "Apache-2.0" ]

6

2019-01-23T17:37:09.000Z

2020-11-17T16:12:27.000Z

examples/chart-types/word_tree.py

tcbegley/dash-google-charts

b8b22e5b6bac533167f218e3610697dec0c3e4ca

[ "Apache-2.0" ]

9

2019-01-25T11:09:17.000Z

2022-02-26T09:10:04.000Z

examples/chart-types/word_tree.py

tcbegley/dash-google-charts

b8b22e5b6bac533167f218e3610697dec0c3e4ca

[ "Apache-2.0" ]

1

2019-01-23T17:37:12.000Z

import dash from dash_google_charts import WordTree app = dash.Dash() app.layout = WordTree( data=[ ["Phrases"], ["cats are better than dogs"], ["cats eat kibble"], ["cats are better than hamsters"], ["cats are awesome"], ["cats are people too"], ["cats eat mice"], ["cats meowing"], ["cats in the cradle"], ["cats eat mice"], ["cats in the cradle lyrics"], ["cats eat kibble"], ["cats for adoption"], ["cats are family"], ["cats eat mice"], ["cats are better than kittens"], ["cats are evil"], ["cats are weird"], ["cats eat mice"], ], options={"wordtree": {"format": "implicit", "word": "cats"}}, ) if __name__ == "__main__": app.run_server()

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69a4cf62f85e10cc538b2079b12a30a64505ab66

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py

Python

context_processors/utils.py

urchinpro/L2-forms

37f33386984efbb2d1e92c73d915256247801109

[ "MIT" ]

null

context_processors/utils.py

urchinpro/L2-forms

37f33386984efbb2d1e92c73d915256247801109

[ "MIT" ]

null

context_processors/utils.py

urchinpro/L2-forms

37f33386984efbb2d1e92c73d915256247801109

[ "MIT" ]

null

import clients.models as Clients import simplejson as json from appconf.manager import SettingManager def card_bases(request): card_bases_vars = [] for b in Clients.CardBase.objects.filter(hide=False).order_by("pk"): card_bases_vars.append(dict(title=b.title, code=b.short_title, pk=b.pk, history_number=b.history_number)) return {"card_bases": json.dumps(card_bases_vars)} def ws(request): from laboratory import settings return {"ws_url": json.dumps(settings.WS_URL), "ws_enabled": json.dumps(settings.WS_ENABLED)} def menu(request): from laboratory import VERSION data = [] if request.user.is_authenticated and not request.is_ajax(): from laboratory import settings groups = [str(x) for x in request.user.groups.all()] pages = [ {"url": "/mainmenu/", "title": "Начальная страница", "nt": False, "access": ["*"], "not_show_home": True}, {"url": "/logout", "title": "Выход из профиля", "nt": False, "access": ["*"], "not_show_home": True}, {"hr": True, "access": ["*"]}, {"url": "/mainmenu/directions", "title": "Направления", "nt": False, "access": ["Лечащий врач", "Оператор лечащего врача"]}, {"url": "/mainmenu/direction/info", "title": "История направления", "nt": False, "access": ["Лечащий врач", "Оператор лечащего врача", "Лаборант", "Врач-лаборант", "Просмотр журнала"]}, {"url": "/mainmenu/directions/multiprint", "title": "Печать направлений", "nt": False, "access": ["*"]}, {"url": "/mainmenu/results_fastprint", "title": "Печать результатов", "nt": False, "access": ["Лечащий врач", "Оператор лечащего врача"]}, {"url": "/mainmenu/biomaterial/get", "title": "Забор биоматериала", "nt": False, "access": ["Заборщик биоматериала"]}, {"url": "/mainmenu/receive", "title": "Приём биоматериала", "nt": False, "access": ["Получатель биоматериала"]}, {"url": "/mainmenu/statistics-tickets", "title": "Статталоны", "nt": False, "access": [ "Оформление статталонов", "Лечащий врач", "Оператор лечащего врача", ]}, {"url": "/mainmenu/receive/one_by_one", "title": "Приём биоматериала по одному", "nt": False, "access": ["Получатель биоматериала"]}, {"url": "/mainmenu/receive/journal_form", "title": "Журнал приёма", "nt": False, "access": ["Получатель биоматериала"]}, {"url": "/results/enter", "title": "Ввод результатов", "nt": False, "access": ["Врач-лаборант", "Лаборант", "Сброс подтверждений результатов"]}, {"url": "/construct/menu", "title": "Конструктор справочника", "nt": False, "access": ["Конструктор: Лабораторные исследования", "Конструктор: Параклинические (описательные) исследования", "Конструктор: Консультации", "Конструктор: Ёмкости для биоматериала", "Конструктор: Настройка УЕТов", "Конструктор: Группировка исследований по направлениям"]}, {"url": "/statistic", "title": "Статистика", "nt": False, "access": ["Просмотр статистики", "Врач-лаборант"]}, {"url": "/mainmenu/results_history", "title": "Поиск", "nt": False, "access": ["Лечащий врач", "Оператор лечащего врача", "Врач-лаборант", "Лаборант", "Врач параклиники"]}, {"url": "/mainmenu/results_report", "title": "Отчёт по результатам", "nt": False, "access": ["Лечащий врач", "Оператор лечащего врача", "Врач-лаборант", "Лаборант", "Врач параклиники"]}, {"url": "/mainmenu/discharge", "title": "Выписки", "nt": False, "access": ["Загрузка выписок", "Поиск выписок"], "module": "discharge_module"}, {"url": "/mainmenu/create_user", "title": "Создать пользователя", "nt": False, "access": ["Создание и редактирование пользователей"]}, {"url": "/mainmenu/change_password", "title": "Настройка профилей пользователей", "nt": False, "access": ["Создание и редактирование пользователей"]}, {"url": "/mainmenu/create_podr", "title": "Управление подразделениями", "nt": False, "access": ["Создание и редактирование пользователей"]}, {"url": "/mainmenu/view_log", "title": "Просмотр журнала", "nt": False, "access": ["Просмотр журнала"]}, # {"url": "/reports", "title": "Отчёты", "nt": False, "access": []}, {"url": "/admin", "title": "Администрирование L2", "nt": False, "access": []}, {"url": "/silk/", "title": "Профилирование", "nt": False, "access": []}, {"url": '/mainmenu/cards', "title": "Картотека L2", "nt": True, "access": ["Картотека L2"], "module": "mis_module"}, {"url": "/mainmenu/direction_visit", "title": "Посещения по направлениям", "nt": False, "access": ["Посещения по направлениям", "Врач параклиники"], "module": "paraclinic_module"}, {"url": "/mainmenu/results/paraclinic", "title": "Ввод описательных результатов", "nt": False, "access": ["Врач параклиники"], "module": "paraclinic_module"}, {"url": '/mainmenu/hosp', "title": "Госпитализация", "nt": True, "access": ["Госпитализация"], "module": "hosp_module"}, {"url": '/mainmenu/rmis_confirm', "title": "Подтверждение отправки результатов в РМИС", "nt": False, "access": ["Подтверждение отправки результатов в РМИС"]}, {"url": '/cases/', "title": "Случаи обслуживания", "nt": False, "access": []}, ] if settings.LDAP and settings.LDAP["enable"]: pages.append({"url": "/mainmenu/ldap_sync", "title": "Синхронизация с LDAP", "nt": False, "access": []}) if settings.RMQ_ENABLED: pages.append({"url": "/mainmenu/rmq", "title": "Rabbit MQ", "nt": False, "access": []}) pages.append({"url": "/mainmenu/utils", "title": "Инструменты", "nt": False, "access": []}) if SettingManager.get("home_page", default="false") != "false": pages.append( {"url": SettingManager.get(key="home_page", default="http://home"), "title": "Домашняя страница", "nt": True, "access": ["*"]}) if SettingManager.get("support", default="false") != "false": pages.append( {"url": SettingManager.get(key="support", default="false"), "title": "Техническая поддержка", "nt": True, "access": ["*"]}) data = make_menu(pages, groups, request.user.is_superuser, request.path) return {"mainmenu": data, "version": VERSION} def make_menu(pages, groups, superuser, current_path=None): menu = [] groups_set = set(groups) for page in pages: if (not superuser and "*" not in page["access"] and len(groups_set & set(page["access"])) == 0) or ( page.get("module") and not SettingManager.get(page["module"], default='false', default_type='b')): continue page["active"] = current_path == page.get("url") menu.append(page) return menu def profile(request): if not request.user.is_authenticated: return {} return {"specialities": [x.title for x in request.user.doctorprofile.specialities.all() if not x.hide]}

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null

0

null

0

1

0

69a7f0433ba3a569747e0d7771759a3942aab2f1

729

py

Python

api/configfile.py

giorno420/pyDashactyl

e8adac76e3d73c332a723bcc577faeaf779fa72b

[ "MIT" ]

null

api/configfile.py

giorno420/pyDashactyl

e8adac76e3d73c332a723bcc577faeaf779fa72b

[ "MIT" ]

null

api/configfile.py

giorno420/pyDashactyl

e8adac76e3d73c332a723bcc577faeaf779fa72b

[ "MIT" ]

null

import json """ Python config file. This file is used by every single other file in this project, just for convenience. It gets config data and user data from `settings.json` and `users.json`, and assigns them to Pythonic variables Make a pull request if you think anything can be improved. """ with open('settings.json', 'r') as cfg: settings = json.load(cfg) discordsettings = settings["discord"] pterosettings = settings["pterodactyl"] clientID = discordsettings['application_id'] clientSecret = discordsettings['secret_key'] redirectURI = discordsettings['redirect_uri'] pteroURL = pterosettings['url'] pteroAppKey = pterosettings['key'] with open('api\\users.json') as usrs: users = json.load(usrs)

24.3

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96

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0.677083

0.066915

0

0.152263

729

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1

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0

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0.083333

0

null

0

null

0

1

0

69a8daf782d0eb6dfe40db0dd5af7fc8869ecb45

1,099

py

Python

gcn.py

Aveek-Saha/Graph-Conv-Net

16c4608d8ff3c2d2d97c1f2b4aecddece456684b

[ "MIT" ]

1

2021-09-16T07:03:49.000Z

gcn.py

Aveek-Saha/Graph-Conv-Net

16c4608d8ff3c2d2d97c1f2b4aecddece456684b

[ "MIT" ]

null

gcn.py

Aveek-Saha/Graph-Conv-Net

16c4608d8ff3c2d2d97c1f2b4aecddece456684b

[ "MIT" ]

1

2021-02-16T14:08:59.000Z

import tensorflow as tf import numpy as np import networkx as nx # import matplotlib.pyplot as plt def norm_adjacency_matrix(A): I = tf.eye(tf.shape(A)[0]) A_hat = A + I D_inv = tf.linalg.tensor_diag( tf.pow(tf.reduce_sum(A_hat, 0), tf.cast(-0.5, tf.float32))) D_inv = tf.where(tf.math.is_inf(D_inv), tf.zeros_like(D_inv), D_inv) A_hat = D_inv @ A_hat @ D_inv return A_hat class GraphConvolutionLayer(tf.keras.layers.Layer): def __init__(self, units, A, activation=tf.identity, rate=0.0, l2=0.0): super(GraphConvolutionLayer, self).__init__() self.activation = activation self.units = units self.rate = rate self.l2 = l2 self.A = A def build(self, input_shape): self.W = self.add_weight( shape=(input_shape[1], self.units), dtype=self.dtype, initializer='glorot_uniform', regularizer=tf.keras.regularizers.l2(self.l2) ) def call(self, X): X = tf.nn.dropout(X, self.rate) X = self.A @ X @ self.W return self.activation(X)

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75

0.619654

170

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1,099

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null

0

null

0

1

0

69a94585dad8923405082850f97610b33b356cf8

18,173

py

Python

mlrun/frameworks/_common/mlrun_interface.py

george0st/mlrun

6467d3a5ceadf6cd35512b84b3ddc3da611cf39a

[ "Apache-2.0" ]

null

mlrun/frameworks/_common/mlrun_interface.py

george0st/mlrun

6467d3a5ceadf6cd35512b84b3ddc3da611cf39a

[ "Apache-2.0" ]

null

mlrun/frameworks/_common/mlrun_interface.py

george0st/mlrun

6467d3a5ceadf6cd35512b84b3ddc3da611cf39a

[ "Apache-2.0" ]

null

import copy import functools import inspect from abc import ABC from types import MethodType from typing import Any, Dict, Generic, List, Tuple from .utils import MLRunInterfaceableType RestorationInformation = Tuple[ Dict[str, Any], # Interface properties. Dict[str, Any], # Replaced properties. List[str], # Replaced methods and functions. ] class MLRunInterface(ABC, Generic[MLRunInterfaceableType]): """ An abstract class for enriching an object interface with the properties, methods and functions written below. A class inheriting MLRun interface should insert what ever it needs to be inserted to the object to the following attributes: '_PROPERTIES', '_METHODS' and '_FUNCTIONS'. Then it should implement 'add_interface' and call 'super'. In order to replace object's attributes, the attributes to replace are needed to be added to the attributes: '_REPLACED_PROPERTIES', '_REPLACED_METHODS' and '_REPLACED_FUNCTIONS'. The original attribute will be kept in a backup attribute with the prefix noted in '_ORIGINAL_ATTRIBUTE_NAME'. Replacing functions / methods will be the one located by looking for the prefix noted in '_REPLACING_ATTRIBUTE_NAME'. The replacing function / method can be a MLRunInterface class method that return a function / method. For example: if "x" is in the list then the method "object.x" will be stored as "object.original_x" and "object.x" will then point to the method "MLRunInterface.mlrun_x". If "mlrun_x" is a class method, it will point to the function returned from MLRunInterface.mlrun_x()". """ # Attributes to be inserted so the MLRun interface will be fully enabled. _PROPERTIES = {} # type: Dict[str, Any] _METHODS = [] # type: List[str] _FUNCTIONS = [] # type: List[str] # Attributes to replace so the MLRun interface will be fully enabled. _REPLACED_PROPERTIES = {} # type: Dict[str, Any] _REPLACED_METHODS = [] # type: List[str] _REPLACED_FUNCTIONS = [] # type: List[str] # Name template for the replaced attribute to be stored as in the object. _ORIGINAL_ATTRIBUTE_NAME = "original_{}" # Name template of the function / method to look for to replace the original function / method. _REPLACING_ATTRIBUTE_NAME = "mlrun_{}" @classmethod def add_interface( cls, obj: MLRunInterfaceableType, restoration_information: RestorationInformation = None, ): """ Enrich the object with this interface properties, methods and functions so it will have this framework MLRun's features. :param obj: The object to enrich his interface. :param restoration_information: Restoration information tuple as returned from 'remove_interface' in order to add the interface in a certain state. """ # Set default value to the restoration data: if restoration_information is None: restoration_information = (None, None, None) # Add the MLRun properties: cls._insert_properties( obj=obj, properties=restoration_information[0], ) # Replace the object's properties in MLRun's properties: cls._replace_properties(obj=obj, properties=restoration_information[1]) # Add the MLRun functions: cls._insert_functions(obj=obj) # Replace the object's functions / methods in MLRun's functions / methods: cls._replace_functions(obj=obj, functions=restoration_information[2]) @classmethod def remove_interface(cls, obj: MLRunInterfaceableType) -> RestorationInformation: """ Remove the MLRun features from the given object. The properties and replaced attributes found in the object will be returned. :param obj: The object to remove the interface from. :return: A tuple of interface restoration information: [0] = The interface properties. [1] = The replaced properties. [2] = The replaced methods and functions. """ # Get the interface properties from the element: properties = { attribute: getattr(obj, attribute) for attribute in cls._PROPERTIES if hasattr(obj, attribute) # Later it will be asserted. } # Get the replaced properties from the object: replaced_properties = { attribute: getattr(obj, attribute) for attribute in cls._REPLACED_PROPERTIES if hasattr(obj, cls._ORIGINAL_ATTRIBUTE_NAME.format(attribute)) } # Get the replaced methods and functions from the object: replaced_functions = [ function_name for function_name in [*cls._REPLACED_METHODS, *cls._REPLACED_FUNCTIONS] if hasattr(obj, cls._ORIGINAL_ATTRIBUTE_NAME.format(function_name)) ] # Restore the replaced attributes: for attribute_name in [ *replaced_properties, *replaced_functions, ]: cls._restore_attribute(obj=obj, attribute_name=attribute_name) # Remove the interface from the object: for attribute_name in [*cls._PROPERTIES, *cls._METHODS, *cls._FUNCTIONS]: assert hasattr( obj, attribute_name ), f"Can't remove the attribute '{attribute_name}' as the object doesn't has it." # Mark it first as None so the actual object won't be deleted: setattr(obj, attribute_name, None) delattr(obj, attribute_name) return properties, replaced_properties, replaced_functions @classmethod def is_applied(cls, obj: MLRunInterfaceableType) -> bool: """ Check if the given object has MLRun interface attributes in it. Interface is applied if all of its attributes are found in the object. If only replaced attributes are configured in the interface, then the interface is applied if some of at least one is found in the object. :param obj: The object to check. :return: True if the MLRun interface is applied on the object and False if not. """ # Check for the attributes: attributes = [*cls._PROPERTIES, *cls._METHODS, *cls._FUNCTIONS] if attributes: return all(hasattr(obj, attribute) for attribute in attributes) # The interface has only replaced attributes, check if at least one is in the object: replaced_attributes = [ *cls._REPLACED_PROPERTIES, *cls._REPLACED_METHODS, *cls._REPLACED_FUNCTIONS, ] return any(hasattr(obj, attribute) for attribute in replaced_attributes) @classmethod def _insert_properties( cls, obj: MLRunInterfaceableType, properties: Dict[str, Any] = None, ): """ Insert the properties of the interface to the object. The properties default values are being copied (not deep copied) into the object. :param obj: The object to enrich. :param properties: Properties to set in the object. """ # Set default dictionary if there is no properties to restore: if properties is None: properties = {} # Verify the provided properties are supported by this interface (noted in the interface '_PROPERTIES'): error_properties = [ property_name for property_name in properties if property_name not in cls._PROPERTIES ] assert not error_properties, ( f"The following properties provided to insert to the object are not supported by this interface: " f"{error_properties}" ) # Insert the properties, copy only default values: for property_name, default_value in cls._PROPERTIES.items(): # Verify there is no property with the same name in the object: assert not hasattr(obj, property_name), ( f"Can't insert the property '{property_name}' as the object already have an attribute with the same " f"name." ) # Insert the property to the object prioritizing given values over default ones: value = ( properties[property_name] if property_name in properties else copy.copy(default_value) ) setattr(obj, property_name, value) @classmethod def _insert_functions(cls, obj: MLRunInterfaceableType): """ Insert the functions / methods of the interface to the object. :param obj: The object to enrich. """ # Insert the functions / methods: for function_name in [*cls._METHODS, *cls._FUNCTIONS]: # Verify there is no function / method with the same name in the object: assert not hasattr(obj, function_name), ( f"Can't insert the function / method '{function_name}' as the object already have a function / method " f"with the same name. To replace a function / method, add the name of the function / method to the " f"'_REPLACED_METHODS' / '_REPLACED_METHODS' list and follow the instructions documented." ) # Get the function / method: func = getattr(cls, function_name) # If the function is a method and not a function (appears in '_METHODS' and not '_FUNCTIONS'), set the # 'self' to the object: if function_name in cls._METHODS: func = MethodType(func, obj) # Insert the function / method to the object: setattr(obj, function_name, func) @classmethod def _replace_properties( cls, obj: MLRunInterfaceableType, properties: Dict[str, Any] = None ): """ Replace the properties of the given object according to the configuration in the MLRun interface. :param obj: The object to replace its properties. :param properties: The properties to replace in the object. Defaulted to all the properties in the interface '_REPLACE_PROPERTIES' dictionary. """ # Set default replacing properties if there are no properties given: if properties is None: properties = { property_name: copy.copy(property_value) for property_name, property_value in cls._REPLACED_PROPERTIES.items() } else: # Verify the provided properties are supported by this interface (noted in the interface's # '_REPLACED_PROPERTIES' dictionary): error_properties = [ property_name for property_name in properties if property_name not in cls._REPLACED_PROPERTIES ] assert not error_properties, ( f"The following properties provided to be replace in the object are not supported by this " f"interface: {error_properties}" ) # Replace the properties in the object: for property_name, property_value in properties.items(): # Verify there is a property with this name in the object to replace: assert hasattr( obj, property_name ), f"Can't replace the property '{property_name}' as the object doesn't have a property with this name." # Replace the property: cls._replace_property( obj=obj, property_name=property_name, property_value=property_value, include_none=True, ) @classmethod def _replace_functions( cls, obj: MLRunInterfaceableType, functions: List[str] = None ): """ Replace the functions / methods of the given object according to the configuration in the MLRun interface. :param obj: The object to replace its functions / methods. :param functions: The functions / methods to replace in the object. Defaulted to all the functions / methods in the interface '_REPLACE_METHODS' and '_REPLACE_FUNCTIONS' lists. """ # Set default list if there are no functions / methods to restore: if functions is None: functions = [*cls._REPLACED_METHODS, *cls._REPLACED_FUNCTIONS] else: # Verify the provided functions / methods are supported by this interface (noted in the interface's # '_REPLACED_METHODS' or '_REPLACE_FUNCTIONS' lists): error_functions = [ function_name for function_name in functions if function_name not in [*cls._REPLACED_METHODS, *cls._REPLACED_FUNCTIONS] ] assert not error_functions, ( f"The following functions / methods provided to be replace in the object are not supported by this " f"interface: {error_functions}" ) # Replace the functions / methods in the object: for function_name in functions: # Verify there is a function / method with this name in the object to replace: assert hasattr(obj, function_name), ( f"Can't replace the function / method '{function_name}' as the object doesn't have a function / method " f"with this name." ) # Replace the method: cls._replace_function(obj=obj, function_name=function_name) @classmethod def _replace_property( cls, obj: MLRunInterfaceableType, property_name: str, property_value: Any = None, include_none: bool = False, ): """ Replace the property in the object with the configured property in this interface. The original property will be stored in a backup attribute with the prefix noted in '_ORIGINAL_ATTRIBUTE_NAME' and the replacing property will be the one with the prefix noted in '_REPLACING_ATTRIBUTE_NAME'. If the property value should be None, set 'include_none' to True, otherwise the interface default will be copied if 'property_value' is None. :param obj: The object to replace its property. :param property_name: The property name to replace. :param property_value: The value to set. If not provided, the interface's default will be copied. :param include_none: Whether to enable the property value to be None. """ # Get the original property from the object: original_property = getattr(obj, property_name) # Set a backup attribute with for the original property: original_property_name = cls._ORIGINAL_ATTRIBUTE_NAME.format(property_name) setattr(obj, original_property_name, original_property) # Check if a value is provided, if not copy the default value in this interface if None should not be included: if not include_none and property_value is None: property_value = copy.copy(cls._REPLACED_PROPERTIES[property_name]) # Replace the property: setattr(obj, property_name, property_value) @classmethod def _replace_function(cls, obj: MLRunInterfaceableType, function_name: str): """ Replace the method / function in the object with the configured method / function in this interface. The original method / function will be stored in a backup attribute with the prefix noted in '_ORIGINAL_ATTRIBUTE_NAME' and the replacing method / function will be the one with the prefix noted in '_REPLACING_ATTRIBUTE_NAME'. :param obj: The object to replace its method. :param function_name: The method / function name to replace. """ # Get the original function from the object: original_function = getattr(obj, function_name) # Set a backup attribute with for the original function: original_function_name = cls._ORIGINAL_ATTRIBUTE_NAME.format(function_name) setattr(obj, original_function_name, original_function) # Get the function to replace from the interface: replacing_function_name = cls._REPLACING_ATTRIBUTE_NAME.format(function_name) replacing_function = getattr(cls, replacing_function_name) # Check if the replacing function is a class method (returning a function to use as the replacing function): if inspect.ismethod(replacing_function): replacing_function = replacing_function() # Wrap the replacing function with 'functools.wraps' decorator so the properties of the original function will # be passed to the replacing function: replacing_function = functools.wraps(original_function)(replacing_function) # If the replacing function is a method and not a function (appears in the _REPLACED_METHODS and not # _REPLACED_FUNCTIONS), set the 'self' to the object: if function_name in cls._REPLACED_METHODS: replacing_function = MethodType(replacing_function, obj) # Replace the function: setattr(obj, function_name, replacing_function) @classmethod def _restore_attribute(cls, obj: MLRunInterfaceableType, attribute_name: str): """ Restore the replaced attribute (property, method or function) in the object, removing the backup attribute as well. :param obj: The object to restore its method. :param attribute_name: The method to restore. """ # Get the original attribute: original_attribute_name = cls._ORIGINAL_ATTRIBUTE_NAME.format(attribute_name) original_attribute = getattr(obj, original_attribute_name) # Set the attribute to point back to the original attribute: setattr(obj, attribute_name, original_attribute) # Remove the original backup attribute: setattr(obj, original_attribute_name, None) delattr(obj, original_attribute_name)

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0.092421

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0.014664

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0.298715

0.234711

0.193048

0.163461

0.12982

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null

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69a9471a68dd58de8072e2825adc0e140124a1f0

3,658

py

Python

better_work_data/better_work_data/db/db_helper.py

JackDan9/miniProgram

6c9b2714042a21fa8aa60d90800a903fbd51c0f5

[ "MIT" ]

null

better_work_data/better_work_data/db/db_helper.py

JackDan9/miniProgram

6c9b2714042a21fa8aa60d90800a903fbd51c0f5

[ "MIT" ]

6

2021-12-13T20:50:22.000Z

2022-03-21T06:42:46.000Z

better_work_data/better_work_data/db/db_helper.py

JackDan9/miniProgram

6c9b2714042a21fa8aa60d90800a903fbd51c0f5

[ "MIT" ]

null

# -*- coding:utf-8 -*- from datetime import datetime import pymysql from twisted.enterprise import adbapi from scrapy.utils.project import get_project_settings # 导入settings配置 class DBHelper(): '''这个类是读取settings中的配置，自行修改代码进行操作 Twisted是用Python实现的基于事件驱动的网络引擎框架; Twisted支持许多常见的传输及应用层协议, 包括TCP、UDP、SSL/TLS、HTTP、IMAP、SSH、IRC以及FTP。就像Python一样, Twisted也具有“内置池”(batteries-included)的特点。 Twisted对于其支持的所有协议都带有客户端和服务器实现, 同时附带有基于命令行的工具, 使得配置和部署产品级的Twisted应用变得非常方便。 ''' def __init__(self): settings = get_project_settings() # 获取settings配置, 设置需要的信息 db_params = dict( host=settings['MYSQL_HOST'], db=settings['MYSQL_DBNAME'], user=settings['MYSQL_USER'], passwd=settings['MYSQL_PASSWD'], charset='utf8', # 编码要加上, 否则可能出现中文乱码问题 cursorclass=pymysql.cursors.DictCursor, use_unicode=False ) db_pool = adbapi.ConnectionPool('pymysql', **db_params) self.db_pool = db_pool # 连接数据库 def connect(self): return self.db_pool # 插入数据到数据库 def insert_parent_news(self, parent_news_item): parent_news_sql = "INSERT INTO `parent_news`(`order`, `title`, `summary`, `source_type`, `source_name`, `publish_on`, `created_on`, `updated_on`) VALUES(%s, %s, %s, %s, %s, %s, %s, %s);" # 调用插入的方法 query = self.db_pool.runInteraction(self._conditional_parent_news_insert, sql=parent_news_sql, parent_news_item=parent_news_item) # 调用一场处理方法 query.addErrback(self._handle_error) return parent_news_item def insert_child_news(self, child_news_item): child_news_sql = "INSERT INTO `child_news`(`parent_id`, `language`, `author_name`, `site_name`, `title`, `summary`, `url`, `mobile_url`, `is_attachment`, `attachment_name`, `attachment_url`, `publish_on`, `created_on`, `updated_on`) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)" query = self.db_pool.runInteraction(self._conditional_child_news_insert, sql=child_news_sql, child_news_item=child_news_item) query.addErrback(self._handle_error) return child_news_item def _conditional_parent_news_insert(self, tx, sql=None, parent_news_item=None): if parent_news_item.get('created_on') is None: parent_news_item['created_on'] = datetime.now() if parent_news_item.get('updated_on') is None: parent_news_item['updated_on'] = datetime.now() params = (parent_news_item['order'], parent_news_item['title'], parent_news_item['summary'], parent_news_item['source_type'], parent_news_item['source_name'], parent_news_item['publish_on'], parent_news_item['created_on'], parent_news_item['updated_on']) tx.execute(sql, params) def _conditional_child_news_insert(self, tx, sql=None, child_news_item=None): if child_news_item.get('created_on') is None: child_news_item['created_on'] = datetime.now() if child_news_item.get('updated_on') is None: child_news_item['updated_on'] = datetime.now() params = (child_news_item['parent_id'], child_news_item['language'], child_news_item['author_name'], child_news_item['site_name'], child_news_item['title'], child_news_item['summary'], child_news_item['url'], child_news_item['mobile_url'], child_news_item['is_attachment'], child_news_item['attachment_name'], child_news_item['attachment_url'], child_news_item['publish_on']) tx.execute(sql, params) def _handle_error(self, failure): print('--------------database operation exception!!-----------------') print(failure)

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null

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null

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1

0

69a94db0c5b7ee171f18c600e534c664386f496a

19,335

py

Python

f3dasm/simulator/abaqus/abaqus_src/modelling/step.py

gawelk/F3DAS

4a4e7233add608820de9ee0fd1c369c2fa1d24c1

[ "BSD-3-Clause" ]

45

2019-10-15T06:08:23.000Z

2020-08-01T03:15:11.000Z

f3dasm/simulator/abaqus/abaqus_src/modelling/step.py

gawelk/F3DAS

4a4e7233add608820de9ee0fd1c369c2fa1d24c1

[ "BSD-3-Clause" ]

1

2020-02-28T10:35:41.000Z

2020-03-09T13:19:54.000Z

f3dasm/simulator/abaqus/abaqus_src/modelling/step.py

gawelk/F3DAS

4a4e7233add608820de9ee0fd1c369c2fa1d24c1

[ "BSD-3-Clause" ]

10

2020-01-10T09:42:58.000Z

2020-07-20T19:57:15.000Z

''' Created on 2020-04-08 14:55:21 Last modified on 2020-04-22 16:06:23 Python 2.7.16 v0.1 @author: L. F. Pereira (lfpereira@fe.up.pt) Main goal --------- Define abaqus steps. References ---------- 1. Simulia (2015). ABAQUS 2016: Scripting Reference Guide ''' #%% imports # abaqus from abaqusConstants import (LANCZOS, DEFAULT, SOLVER_DEFAULT, OFF, AUTOMATIC, NONE, DIRECT, RAMP, FULL_NEWTON, PROPAGATED, LINEAR, ALL, DISPLACEMENT, ON, AC_ON) # standard library import abc #%% abstract classes class Step(object): __metaclass__ = abc.ABCMeta def __init__(self, name, previous, model=None): ''' Parameters ---------- name : str previous : str Previous step model : abaqus mdb object ''' self.name = name self.previous = previous # computations if model: self.create_step(model) def create_step(self, model): # get method create_step = getattr(model, self.method_name) # create step create_step(name=self.name, previous=self.previous, **self.args) #%% particular step definition class StaticStep(Step): method_name = 'StaticStep' def __init__(self, name, previous='Initial', model=None, description='', timePeriod=1., nlgeom=OFF, stabilizationMethod=NONE, stabilizationMagnitude=2e-4, adiabatic=OFF, timeIncrementationMethod=AUTOMATIC, maxNumInc=100, initialInc=None, minInc=None, maxInc=None, matrixSolver=DIRECT, matrixStorage=SOLVER_DEFAULT, amplitude=RAMP, extrapolation=LINEAR, fullyPlastic='', noStop=OFF, maintainAttributes=False, useLongTermSolution=OFF, solutionTechnique=FULL_NEWTON, reformKernel=8, convertSDI=PROPAGATED, adaptiveDampingRatio=0.05, continueDampingFactors=OFF): ''' Parameters ---------- description : str Step description. timePeriod : float Total time period. nlgeom : bool Whether to allow for geometric nonlinearity. stabilizationMethod : abaqus constant Stabilization type. stabilizationMagnitude : float Damping intensity of the automatic damping algorithm. Ignored if stabilizationMethod=None. adiabatic : bool Whether to perform an adiabatic stress analysis. timeIncrementationMethod : abaqus constant maxNumInc : int Number of incrementations in a step. initalInc : float Initial time increment. minInc : float Minimum tome increment allowed. maxInc : float Maximum time increment allowed. matrixSolver : abaqus constant Type of solver. matrixStorage : abaqus constant Type of matrix storage. amplitude : abaqus constant Amplitude variation for loading magnitudes during the step. extrapolation : abaqus constant Type of extrapolation to use in determining the incremental solution for a nonlinear analysis. fullyPlastic : str Region being monitored for fully plastic behavior. noStop : bool Whether to accept the solution to an increment after the maximum number of interations allowed has been completed, even if the equilibrium tolerances are not satisfied. maintainAttributes : bool Whether to retain attributes from an existing step with the same name. useLongTermSolution : bool Whether to obtain the fully relaxed long-term elastic solution with time-domain viscoelasticity or the long-term elastic-plastic solution for two-layer viscoplasticity. solutionTechnique : abaqus constant Technique used for solving nonlinear equations. reformKernel : int Number of quasi-Newton iterations allowed before the kernel matrix is reformed. convertSDI : abaqus constant Whether to force a new iteration if severe discontinuities occur during an iteration. adaptiveDampingRatio : float Maximum allowable ratio of the stabilization energy to the total strain energy. Ignored if stabilizationMethod=None. continueDampingFactors : bool Whether this step will carry over the damping factors from the results of the preceding general step. Notes ----- -for further informations see p49-134 of [1]. ''' # computations initialInc = timePeriod if initialInc is None else initialInc minInc = min(initialInc, timePeriod * 1e-5) if minInc is None else minInc maxInc = timePeriod if maxInc is None else maxInc # create args dict self.args = {'description': description, 'timePeriod': timePeriod, 'nlgeom': nlgeom, 'stabilizationMethod': stabilizationMethod, 'stabilizationMagnitude': stabilizationMagnitude, 'adiabatic': adiabatic, 'timeIncrementationMethod': timeIncrementationMethod, 'maxNumInc': maxNumInc, 'initialInc': initialInc, 'minInc': minInc, 'maxInc': maxInc, 'matrixSolver': matrixSolver, 'matrixStorage': matrixStorage, 'amplitude': amplitude, 'extrapolation': extrapolation, 'fullyPlastic': fullyPlastic, 'noStop': noStop, 'maintainAttributes': maintainAttributes, 'useLongTermSolution': useLongTermSolution, 'solutionTechnique': solutionTechnique, 'reformKernel': reformKernel, 'convertSDI': convertSDI, 'adaptiveDampingRatio': adaptiveDampingRatio, 'continueDampingFactors': continueDampingFactors} # initialize parent Step.__init__(self, name, previous, model=model) class StaticRiksStep(Step): method_name = 'StaticRiksStep' def __init__(self, name, previous='Initial', model=None, description='', nlgeom=OFF, adiabatic=OFF, maxLPF=None, nodeOn=OFF, maximumDisplacement=0., dof=0, region=None, timeIncrementationMethod=AUTOMATIC, maxNumInc=100, totalArcLength=1., initialArcInc=None, minArcInc=None, maxArcInc=None, matrixStorage=SOLVER_DEFAULT, extrapolation=LINEAR, fullyPlastic='', noStop=OFF, maintainAttributes=False, useLongTermSolution=OFF, convertSDI=PROPAGATED): ''' Parameters ---------- description : str Step description. nlgeom : bool Whether to allow for geometric nonlinearity. adiabatic : bool Whether to perform an adiabatic stress analysis. maxLPF : float Maximum value of the load proportionality factor. nodeOn : bool Whether to monitor the finishing displacement value at a node. maximumDisplacement : float Value of the total displacement (or rotation) at the node node and degree of freedom that, if crossed during an increment, ends the step at the current increment. Only applicable when nodeOn=ON. dof : int Degree of freedom being monitored. Only applicable when nodeOn=ON region : abaqus region object Vertex at which the finishing displacement value is being monitored. Only applicable when nodeOn=ON. timeIncrementationMethod : abaqus constant maxNumInc : int Number of incrementations in a step. totalArcLength : float Total load proportionality factor associated with the load in this step. initialArcInc : float Initial load proportionality factor. minArcInc : float Minimum arc length increment allowed. maxArcInc : Maximum arc length increment allowed. matrixStorage : abaqus constant Type of matrix storage. extrapolation : abaqus constant Type of extrapolation to use in determining the incremental solution for a nonlinear analysis. fullyPlastic : str Region being monitored for fully plastic behavior. noStop : bool Whether to accept the solution to an increment after the maximum number of interations allowed has been completed, even if the equilibrium tolerances are not satisfied. maintainAttributes : bool Whether to retain attributes from an existing step with the same name. useLongTermSolution : bool Whether to obtain the fully relaxed long-term elastic solution with time-domain viscoelasticity or the long-term elastic-plastic solution for two-layer viscoplasticity. convertSDI : abaqus constant Whether to force a new iteration if severe discontinuities occur during an iteration. Notes ----- -for further informations see p49-128 of [1]. ''' # computations initialArcInc = totalArcLength if initialArcInc is None else initialArcInc minArcInc = min(initialArcInc, 1e-5 * totalArcLength) if minArcInc is None else minArcInc maxArcInc = totalArcLength if maxArcInc is None else maxArcInc # create arg dict self.args = {'description': description, 'nlgeom': nlgeom, 'adiabatic': adiabatic, 'maxLPF': maxLPF, 'nodeOn': nodeOn, 'maximumDisplacement': maximumDisplacement, 'dof': dof, 'timeIncrementationMethod': timeIncrementationMethod, 'maxNumInc': maxNumInc, 'totalArcLength': totalArcLength, 'initialArcInc': initialArcInc, 'minArcInc': minArcInc, 'maxArcInc': maxArcInc, 'matrixStorage': matrixStorage, 'extrapolation': extrapolation, 'fullyPlastic': fullyPlastic, 'noStop': noStop, 'maintainAttributes': maintainAttributes, 'useLongTermSolution': useLongTermSolution, 'convertSDI': convertSDI, } if nodeOn is ON and region: self.args['region'] = region # initialize parent Step.__init__(self, name, previous, model=model) class BuckleStep(Step): method_name = 'BuckleStep' def __init__(self, name, previous='Initial', model=None, numEigen=20, description='', eigensolver=LANCZOS, minEigen=None, maxEigen=None, vectors=None, maxIterations=30, blockSize=DEFAULT, maxBlocks=DEFAULT, matrixStorage=SOLVER_DEFAULT, maintainAttributes=False): ''' Parameters ---------- numEigen : int Number of eigenvalues to be estimated. description : str Step description. eigensolver : abaqus constant minEigen : float Minimum eigenvalue of interest. Ignored if eigensolver!=LANCZOS. maxEigen : float Maximum eigenvalue of interest. vectors : int Number of vectors used in each iteration. maxIterations : int Maximum number of iterations. blockSize : abaqus constant or int Size of the Lanczos block steps. Ignored if eigensolver!=LANCZOS. maxBlocks : abaqus constant or int Maximum number of Lanczos block steps within each Lanczos run. Ignored if eigensolver!=LANCZOS. matrixStorage : abaqus constant Type of matrix storage. maintainAttributes : bool Whether to retain attributes from an existing step with the same name. Notes ----- -for further informations see p49-10 of [1]. ''' # computations vectors = min(2 * numEigen, numEigen * 8) if vectors is None else vectors # create arg dict self.args = {'numEigen': numEigen, 'description': description, 'eigensolver': eigensolver, 'minEigen': minEigen, 'maxEigen': maxEigen, 'vectors': vectors, 'maxIterations': maxIterations, 'blockSize': blockSize, 'maxBlocks': maxBlocks, 'matrixStorage': matrixStorage, 'maintainAttributes': maintainAttributes} # initialize parent Step.__init__(self, name, previous, model=model) class FrequencyStep(Step): method_name = 'FrequencyStep' def __init__(self, name, previous='Initial', model=None, eigensolver=LANCZOS, numEigen=ALL, description='', shift=0., minEigen=None, maxEigen=None, vectors=None, maxIterations=30, blockSize=DEFAULT, maxBlocks=DEFAULT, normalization=DISPLACEMENT, propertyEvaluationFrequency=None, projectDamping=ON, acousticDamping=AC_ON, acousticRangeFactor=1., frictionDamping=OFF, matrixStorage=SOLVER_DEFAULT, maintainAttributes=False, simLinearDynamics=OFF, residualModes=OFF, substructureCutoffMultiplier=5., firstCutoffMultiplier=1.7, secondCutoffMultiplier=1.1, residualModeRegion=None, residualModeDof=None, limitSavedEigenVectorRegion=None): ''' Parameters ---------- eigensolver : abaqus constant Arguments ignored if eigenSolver!=LANCZOS: blockSize, maxBlocks, normalization, propertyEvaluationFrequency. Arguments ignored if eigenSolver!=LANCZOS or AMS: minEigen, maxEigen, acousticCoupling. Arguments ignored if eigenSolver!=AMS: projectDamping, acousticRangeFactor, substructureCutoffMultiplier, firstCutoffMultiplier, secondCutoffMultiplier, residualModeRegion, regionalModeDof, limitSavedEigenVectorRegion. numEigen : int or abaqus constant Number of eigenvalues to be estimated. description : str Step description. shift : float Shift point in cycles per time. minEigen : float Minimum eigenvalue of interest. maxEigen : float Maximum eigenvalue of interest. vectors : int Number of vectors used in each iteration. maxIterations : int Maximum number of iterations. blockSize : abaqus constant or int Size of the Lanczos block steps. maxBlocks : abaqus constant or int Maximum number of Lanczos block steps within each Lanczos run. Ignored if eigensolver!=LANCZOS. normalization : abaqus constant Method for normalizing eigenvectors. propertyEvaluationFrequency : float Frequency at which to evaluate frequency-dependent properties for viscoelasticity, springs, and dashpots during the eigenvalues extraction. projectDamping : bool Whether to include projection of viscous and structural damping operators during AMS eigenvalue extraction. acousticCoupling : abaqus constant Type of acoustic-structural coupling in models with acoustic and structural elements coupled using the *TIE option or in models with ASI-type elements. acousticRangeFactor : float Ratio of the maximum acoustic frequency to the maximum structural frequency. frictionDamping : bool Whether to add to the damping matrix contributions due to friction effects. matrixStorage : abaqus constant Type of matrix storage. maintainAttributes : bool Whether to retain attributes from an existing step with the same name. simLinearDynamics : bool Whether to activate the SIM-based linear dynamics procedure. residualModes : bool Whether to include residual modes from an immediately preceding Static, LinearPerturbation step. substructureCutoffMultiplier : float Cutoff frequency for substructure eigenproblems. firstCutoffMultiplier : float First cutoff frequency for a reduced eigenproblem. secondCutoffMultiplier : float Second cutoff freqency for a reduced eigenproblem. residualModeRegion : sequence of str Name of a region for which residual modes are requested. residualModeDof : sequence of int Degrees of freedom for which residual modes are requested. limitSavedEigenvectorRegion : region object Region for which eigenvectors should be saved. Notes ----- -for further informations see p49-65 of [1]. ''' # computations vectors = min(2 * numEigen, numEigen * 8) if vectors is None else vectors # create arg dict self.args = { 'eigensolver': eigensolver, 'numEigen': numEigen, 'description': description, 'shift': shift, 'minEigen': minEigen, 'maxEigen': maxEigen, 'vectors': vectors, 'maxIterations': maxIterations, 'blockSize': blockSize, 'maxBlocks': maxBlocks, 'normalization': normalization, 'propertyEvaluationFrequency': propertyEvaluationFrequency, 'projectDamping': projectDamping, 'acousticDamping': acousticDamping, 'acousticRangeFactor': acousticRangeFactor, 'frictionDamping': frictionDamping, 'matrixStorage': matrixStorage, 'maintainAttributes': maintainAttributes, 'simLinearDynamics': simLinearDynamics, 'residualModes': residualModes, 'substructureCutoffMultiplier': substructureCutoffMultiplier, 'firstCutoffMultiplier': firstCutoffMultiplier, 'secondCutoffMultiplier': secondCutoffMultiplier, 'residualModeRegion': residualModeRegion, 'residualModeDof': residualModeDof, 'limitSavedEigenVectorRegion': limitSavedEigenVectorRegion} # initialize parent Step.__init__(self, name, previous, model=model)

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69aa431f75da8c8368149a2a99fcbf41b5cc11bd

9,756

py

Python

Fuzzy_clustering/ver_tf2/scripts_for_tf2/RBFNN_model.py

joesider9/forecasting_library

db07ff8f0f2693983058d49004f2fc6f8849d197

[ "Apache-2.0" ]

null

Fuzzy_clustering/ver_tf2/scripts_for_tf2/RBFNN_model.py

joesider9/forecasting_library

db07ff8f0f2693983058d49004f2fc6f8849d197

[ "Apache-2.0" ]

null

Fuzzy_clustering/ver_tf2/scripts_for_tf2/RBFNN_model.py

joesider9/forecasting_library

db07ff8f0f2693983058d49004f2fc6f8849d197

[ "Apache-2.0" ]

null

import tensorflow as tf from tensorflow.python.ops import math_ops from tensorflow.python.keras import backend as K from tensorflow.python.framework import ops from sklearn.cluster import KMeans from sklearn.model_selection import train_test_split from clustering.algorithms import FCV from tqdm import tqdm import numpy as np import os import pandas as pd from Fuzzy_clustering.ver_0.utils_for_forecast import split_continuous from util_database import write_database from Fuzzy_clustering.ver_tf2.Forecast_model import forecast_model class RBF_model(tf.keras.Model): def __init__(self, num_centr): super(RBF_model, self).__init__() self.num_centr = num_centr def find_centers(self,X_train): self.N, self.D = X_train.shape self.batch_size = self.N try: centers = FCV(X_train, n_clusters=self.num_centr, r=4).optimize() c = centers.C except: c = KMeans(n_clusters=self.num_centr, random_state=0).fit(X_train) c = c.cluster_centers_ centroids = c.astype(np.float32) return centroids def initialize(self,inputs): centroids = self.find_centers(inputs) cnt = pd.DataFrame(centroids, index=['c' + str(i) for i in range(centroids.shape[0])], columns=['v' + str(i) for i in range(centroids.shape[1])]) var_init = pd.DataFrame(columns=['v' + str(i) for i in range(centroids.shape[1])]) for r in cnt.index: v = (cnt.loc[r] - cnt.drop(r)).min() v[v == 0] = 0.0001 v.name = r var_init = var_init.append(v) var_init = tf.convert_to_tensor(var_init.values, dtype=tf.float32, name='var_init') self.var = tf.Variable(var_init, dtype=tf.float32, name='RBF_variance') self.centroids = tf.convert_to_tensor(centroids, dtype=tf.float32, name='centroids') @tf.function def lin_out(self, x, y): return tf.linalg.lstsq(x, y, l2_regularizer=0) @tf.function def rbf_map(self, x): s = tf.shape(x) d1 = tf.transpose(tf.tile(tf.expand_dims(x, 0), [self.num_centr, 1, 1]), perm=[1, 0, 2]) - tf.tile( tf.expand_dims(self.centroids, 0), [s[0], 1, 1]) d = tf.sqrt( tf.reduce_sum(tf.pow(tf.multiply(d1, tf.tile(tf.expand_dims(self.var, 0), [s[0], 1, 1])), 2), axis=2)) return tf.cast(tf.exp(tf.multiply(tf.constant(-1, dtype=tf.float32), tf.square(d))), tf.float32) def call(self, inputs, training=None, mask=None): if training: x = inputs[:, :-1] y = tf.expand_dims(inputs[:, -1], 1) else: x=inputs phi = self.rbf_map(x) if training: self.w = self.lin_out(phi, y) h = tf.matmul(phi, self.w) else: h = tf.matmul(phi, self.w) return h class sum_square_loss(tf.keras.losses.Loss): def __init__(self, name='SSE', **kwargs): super(sum_square_loss, self).__init__(name=name, **kwargs) def call(self, y_true, y_pred, sample_weight=None): return math_ops.reduce_sum(math_ops.square(y_true - y_pred)) class RBF_train(): def __init__(self, path_model, rated=None, max_iterations=10000,): self.path_model = path_model self.rated = rated self.max_iterations = max_iterations def distance(self, obj_new, obj_old, obj_max, obj_min): if np.any(np.isinf(obj_old)): obj_old = obj_new.copy() obj_max = obj_new.copy() return True, obj_old, obj_max, obj_min if np.any(np.isinf(obj_min)) and not np.all(obj_max == obj_new): obj_min = obj_new.copy() d = 0 for i in range(obj_new.shape[0]): if obj_max[i] < obj_new[i]: obj_max[i] = obj_new[i] if obj_min[i] > obj_new[i]: obj_min[i] = obj_new[i] d += (obj_new[i] - obj_old[i]) / (obj_max[i] - obj_min[i]) if d < 0: obj_old = obj_new.copy() return True, obj_old, obj_max, obj_min else: return False, obj_old, obj_max, obj_min def train(self, X_train, y_train, X_val, y_val, X_test, y_test, num_centr, lr, gpu_id=[-1]): tf.config.experimental.set_visible_devices(gpu_id[0], 'GPU') tf.config.experimental.set_memory_growth(gpu_id[0], True) tf.config.set_soft_device_placement(True) tf.debugging.set_log_device_placement(True) self.N, self.D = X_train.shape X_val = np.vstack((X_val, X_test)) y_val = np.vstack((y_val, y_test)) X_train = X_train.astype('float32',casting='same_kind') X_val = X_val.astype('float32',casting='same_kind') y_train = y_train.astype('float32',casting='same_kind') y_val = y_val.astype('float32',casting='same_kind') batch_size = self.N model = RBF_model(num_centr) model.initialize(X_train) optimizer = tf.keras.optimizers.Adam(lr) batches = [np.random.choice(self.N, batch_size, replace=False) for _ in range(self.max_iterations)] obj_old = np.inf * np.ones(4) obj_max = np.inf * np.ones(4) obj_min = np.inf * np.ones(4) if self.rated is None: loss_fn = sum_square_loss() mae = tf.keras.metrics.MeanAbsolutePercentageError(name='mae') mse = tf.keras.metrics.MeanSquaredLogarithmicError(name='mse') rms = tf.keras.metrics.RootMeanSquaredError(name='rms') else: loss_fn = sum_square_loss() mae = tf.keras.metrics.MeanAbsolutePercentageError(name='mae') mse = tf.keras.metrics.MeanSquaredLogarithmicError(name='mse') rms = tf.keras.metrics.RootMeanSquaredError(name='rms') res = dict() self.best_weights = None best_iteration = 0 best_glob_iterations = 0 max_iterations = self.max_iterations ext_iterations = self.max_iterations train_flag = True patience = 20000 wait = 0 while train_flag: for i in tqdm(range(max_iterations)): if i % 500 == 0: with tf.GradientTape() as tape: predictions = model(np.hstack((X_train,y_train)), training=True) loss = loss_fn(y_train, predictions) gradients = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(gradients, model.trainable_variables)) pred_val = model(X_val, training=False) val_mae = mae(y_val, pred_val) val_mse = mse(y_val, pred_val) val_rms = rms(y_val, pred_val) val_sse = loss_fn(y_val, pred_val) obj_new = np.array([val_mae, val_mse, val_sse, val_rms]) flag, obj_old, obj_max, obj_min = self.distance(obj_new, obj_old, obj_max, obj_min) if flag: res[str(i)] = obj_old print(val_mae.numpy()) self.best_weights = model.get_weights() best_iteration = i wait = 0 else: wait += 1 if wait > patience: train_flag = False break else: with tf.GradientTape() as tape: predictions = model(np.hstack((X_train,y_train)), training=True) loss = loss_fn(y_train, predictions) gradients = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(gradients, model.trainable_variables)) wait += 1 best_glob_iterations = ext_iterations + best_iteration if (max_iterations - best_iteration) <= 10000: ext_iterations += 20000 max_iterations = 20000 else: best_glob_iterations = ext_iterations + best_iteration train_flag = False model.set_weights(self.best_weights) model.save_weights(self.path_model + '/rbf_model.h5') model_dict = dict() model_dict['centroids'] = model.centroids.numpy() model_dict['Radius'] = model.var.numpy() model_dict['n_vars'] = self.D model_dict['num_centr'] = num_centr model_dict['W'] = model.w.numpy() model_dict['best_iteration'] = best_glob_iterations model_dict['metrics'] = obj_old model_dict['error_func'] = res print("Total accuracy cnn: %s" % obj_old[0]) return obj_old[3], model.centroids.numpy(), model.var.numpy(), model.w.numpy(), model_dict if __name__ == '__main__': cluster_dir = 'D:/APE_net_ver2/Regressor_layer/rule.2' data_dir = 'D:/APE_net_ver2/Regressor_layer/rule.2/data' rated = None X = np.load(os.path.join(data_dir, 'X_train.npy')) y = np.load(os.path.join(data_dir, 'y_train.npy')) static_data = write_database() forecast = forecast_model(static_data, use_db=False) forecast.load() X = X[:, 0:-1] X = forecast.sc.transform(X) y = forecast.scale_y.transform(y) X_train, X_test, y_train, y_test = split_continuous(X, y, test_size=0.15, random_state=42) X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.15, random_state=42) rbf=RBF_train(cluster_dir+'/RBFNN',rated=rated, max_iterations=1000) rbf.train(X_train, y_train, X_val, y_val, X_test, y_test, 12, 0.0001, gpu_id=tf.config.experimental.list_physical_devices('GPU'))

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134

0.597581

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0.202186

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0.009804

0

null

0

null

0

1

0

69ab65349b051891a98e709bb7854ddb4d9f2d1d

14,159

py

Python

training/DeepJet_callbacks.py

AlexDeMoor/DeepJetCore

d3aa4a8b8cf4b203878feeb36c4b0a8cedd9c215

[ "Apache-2.0" ]

null

training/DeepJet_callbacks.py

AlexDeMoor/DeepJetCore

d3aa4a8b8cf4b203878feeb36c4b0a8cedd9c215

[ "Apache-2.0" ]

null

training/DeepJet_callbacks.py

AlexDeMoor/DeepJetCore

d3aa4a8b8cf4b203878feeb36c4b0a8cedd9c215

[ "Apache-2.0" ]

null

''' Created on 7 Apr 2017 @author: jkiesele ''' import matplotlib matplotlib.use('Agg') from .ReduceLROnPlateau import ReduceLROnPlateau from ..evaluation import plotLoss from ..evaluation import plotBatchLoss import matplotlib.pyplot as plt import numpy as np from keras.callbacks import Callback, EarlyStopping,History,ModelCheckpoint #, ReduceLROnPlateau # , TensorBoard # loss per epoch from time import time from pdb import set_trace import json from keras import backend as K import matplotlib import os matplotlib.use('Agg') class plot_loss_or_metric(Callback): def __init__(self,outputDir,metrics): self.metrics=metrics self.outputDir=outputDir def on_epoch_end(self,epoch, logs={}): lossfile=os.path.join( self.outputDir, 'full_info.log') allinfo_history=None with open(lossfile, 'r') as infile: allinfo_history=json.load(infile) nepochs=len(allinfo_history) allnumbers=[[] for i in range(len(self.metrics))] epochs=[] for i in range(nepochs): epochs.append(i) for j in range(len(self.metrics)): allnumbers[j].append(allinfo_history[i][self.metrics[j]]) import matplotlib.pyplot as plt for j in range(len(self.metrics)): f = plt.figure() plt.plot(epochs,allnumbers[j],'r',label=self.metrics[j]) plt.ylabel(self.metrics[j]) plt.xlabel('epoch') #plt.legend() f.savefig(self.outputDir+'/'+self.metrics[j]+'.pdf') plt.close() class newline_callbacks_begin(Callback): def __init__(self,outputDir,plotLoss=False): self.outputDir=outputDir self.loss=[] self.val_loss=[] self.full_logs=[] self.plotLoss=plotLoss def on_epoch_end(self,epoch, logs={}): if len(logs)<1: return import os lossfile=os.path.join( self.outputDir, 'losses.log') print('\n***callbacks***\nsaving losses to '+lossfile) # problem with new keras version calling callbacks even after exceptions if logs.get('loss') is None: return if logs.get('val_loss') is None: return self.loss.append(logs.get('loss')) self.val_loss.append(logs.get('val_loss')) f = open(lossfile, 'a') f.write(str(logs.get('loss'))) f.write(" ") f.write(str(logs.get('val_loss'))) f.write("\n") f.close() learnfile=os.path.join( self.outputDir, 'learn.log') try: with open(learnfile, 'a') as f: f.write(str(float(K.get_value(self.model.optimizer.lr)))+'\n') lossfile=os.path.join( self.outputDir, 'full_info.log') if os.path.isfile(lossfile): with open(lossfile, 'r') as infile: self.full_logs=json.load(infile) normed = {} for vv in logs: normed[vv] = float(logs[vv]) self.full_logs.append(normed) with open(lossfile, 'w') as out: out.write(json.dumps(self.full_logs)) except: pass if self.plotLoss: try: plotLoss(self.outputDir+'/losses.log',self.outputDir+'/losses.pdf',[]) except: pass class batch_callback_begin(Callback): def __init__(self,outputDir,plotLoss=False,plot_frequency=-1,batch_frequency=1): self.outputDir=outputDir self.loss=[] self.val_loss=[] self.full_logs=[] self.plotLoss=plotLoss self.plot_frequency=plot_frequency self.plotcounter=0 self.batch_frequency=batch_frequency self.batchcounter=0 def read(self): import os if not os.path.isfile(self.outputDir+'/batch_losses.log') : return blossfile=os.path.join( self.outputDir, 'batch_losses.log') f = open(blossfile, 'r') self.loss = [] for line in f: if len(line)<1: continue tl=float(line.split(' ')[0]) self.loss.append(tl) f.close() def on_batch_end(self,batch,logs={}): if len(logs)<1: return if logs.get('loss') is None: return self.batchcounter += 1 if not self.batch_frequency == self.batchcounter: return self.batchcounter=0 self.loss.append(logs.get('loss')) if self.plot_frequency<0: return self.plotcounter+=1 if self.plot_frequency == self.plotcounter: self.plot() self.plotcounter = 0 def _plot(self): if len(self.loss) < 2: return batches = [self.batch_frequency*i for i in range(len(self.loss))] plt.close() plt.plot(batches,self.loss,'r-',label='loss') def smooth(y, box_pts): box = np.ones(box_pts)/box_pts y_smooth = np.convolve(y, box, mode='same') return y_smooth if len(batches) > 50: smoothed = smooth(self.loss,50) #remove where the simple smoothing doesn't give reasonable results plt.plot(batches[25:-25],smoothed[25:-25],'g-',label='smoothed') plt.legend() plt.xlabel("# batches") plt.ylabel("training loss") plt.yscale("log") plt.savefig(self.outputDir+'/batch_losses.pdf') plt.close() def plot(self): self._plot() def save(self): import os blossfile=os.path.join( self.outputDir, 'batch_losses.log') f = open(blossfile, 'w') for i in range(len(self.loss)): f.write(str(self.loss[i])) f.write("\n") self.loss=[] self.val_loss=[] f.close() def on_epoch_end(self,epoch,logs={}): self.plot() self.save() def on_epoch_begin(self, epoch, logs=None): self.read() if len(self.loss): self.plot() class newline_callbacks_end(Callback): def on_epoch_end(self,epoch, logs={}): print('\n***callbacks end***\n') class Losstimer(Callback): def __init__(self, every = 50): self.points = [] self.every = every self.counter=0 def on_train_begin(self, logs): self.start = time() def on_batch_end(self, batch, logs={}): if (self.counter != self.every): self.counter+=1 return self.counter = 0 elapsed = time() - self.start cop = {} for i, j in logs.items(): cop[i] = float(j) cop['elapsed'] = elapsed self.points.append(cop) class checkTokens_callback(Callback): def __init__(self,cutofftime_hours=48): self.cutofftime_hours=cutofftime_hours def on_epoch_begin(self, epoch, logs=None): from .tokenTools import checkTokens checkTokens(self.cutofftime_hours) class saveCheckPointDeepJet(Callback): ''' Slight extension of the normal checkpoint to multiple checkpoints per epoch ''' def __init__(self,outputFile,model,check_n_batches=-1,nrotate=3): self.outputFile=outputFile self.djmodel=model self.counter=0 self.rotate_idx=0 self.rotations=[str(i) for i in range(nrotate)] self.check_n_batches=check_n_batches def on_batch_end(self,batch,logs={}): if self.check_n_batches < 1: return if self.counter < self.check_n_batches: self.counter+=1 return self.djmodel.save(self.outputFile[:-3]+'_rot_'+self.rotations[self.rotate_idx]+'.h5') self.djmodel.save(self.outputFile) self.counter=0 self.rotate_idx += 1 if self.rotate_idx >= len(self.rotations): self.rotate_idx=0 def on_epoch_end(self,epoch, logs={}): if len(logs)<1: return if logs.get('loss') is None: return if logs.get('val_loss') is None: return self.djmodel.save(self.outputFile) class DeepJet_callbacks(object): def __init__(self, model, stop_patience=-1, lr_factor=0.5, lr_patience=-1, lr_epsilon=0.001, lr_cooldown=4, lr_minimum=1e-5, outputDir='', minTokenLifetime=5, checkperiod=10, backup_after_batches=-1, checkperiodoffset=0, plotLossEachEpoch=True, additional_plots=None, batch_loss = False): self.nl_begin=newline_callbacks_begin(outputDir,plotLossEachEpoch) self.nl_end=newline_callbacks_end() self.callbacks=[self.nl_begin] if batch_loss: self.batch_callback=batch_callback_begin(outputDir,plotLossEachEpoch) self.callbacks.append(self.batch_callback) if minTokenLifetime>0: self.tokencheck=checkTokens_callback(minTokenLifetime) self.callbacks.append(self.tokencheck) if lr_patience>0: self.reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=lr_factor, patience=lr_patience, mode='min', verbose=1, epsilon=lr_epsilon, cooldown=lr_cooldown, min_lr=lr_minimum) self.callbacks.append(self.reduce_lr) self.modelbestcheck=ModelCheckpoint(outputDir+"/KERAS_check_best_model.h5", monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=False) self.callbacks.append(self.modelbestcheck) if checkperiod>0: self.modelcheckperiod=ModelCheckpoint(outputDir+"/KERAS_check_model_block_"+str(checkperiodoffset)+"_epoch_{epoch:02d}.h5", verbose=1,period=checkperiod, save_weights_only=False) self.callbacks.append(self.modelcheckperiod) self.modelcheck=saveCheckPointDeepJet(outputDir+"/KERAS_check_model_last.h5",model,backup_after_batches) self.callbacks.append(self.modelcheck) if stop_patience>0: self.stopping = EarlyStopping(monitor='val_loss', patience=stop_patience, verbose=1, mode='min') self.callbacks.append(self.stopping) if additional_plots: self.additionalplots = plot_loss_or_metric(outputDir,additional_plots) self.callbacks.append(self.additionalplots) self.history=History() self.timer = Losstimer() self.callbacks.extend([ self.nl_end, self.history,self.timer]) from DeepJetCore.TrainData import TrainData from DeepJetCore.dataPipeline import TrainDataGenerator class PredictCallback(Callback): def __init__(self, samplefile, function_to_apply=None, #needs to be function(counter,[model_input], [predict_output], [truth]) after_n_batches=50, batchsize=10, on_epoch_end=False, use_event=0, decay_function=None, offset=0 ): super(PredictCallback, self).__init__() self.samplefile=samplefile self.function_to_apply=function_to_apply self.counter=0 self.call_counter=offset self.decay_function=decay_function self.after_n_batches=after_n_batches self.run_on_epoch_end=on_epoch_end if self.run_on_epoch_end and self.after_n_batches>=0: print('PredictCallback: can only be used on epoch end OR after n batches, falling back to epoch end') self.after_n_batches=0 td=TrainData() td.readFromFile(samplefile) if use_event>=0: td.skim(use_event) self.batchsize = 1 self.td = td self.gen = TrainDataGenerator() self.gen.setBatchSize(batchsize) self.gen.setSkipTooLargeBatches(False) def reset(self): self.call_counter=0 def predict_and_call(self,counter): self.gen.setBuffer(self.td) predicted = self.model.predict_generator(self.gen.feedNumpyData(), steps=self.gen.getNBatches(), max_queue_size=1, use_multiprocessing=False, verbose=2) if not isinstance(predicted, list): predicted=[predicted] self.function_to_apply(self.call_counter,self.td.copyFeatureListToNumpy(), predicted,self.td.copyTruthListToNumpy()) self.call_counter+=1 def on_epoch_end(self, epoch, logs=None): self.counter=0 if not self.run_on_epoch_end: return self.predict_and_call(epoch) def on_batch_end(self, batch, logs=None): if self.after_n_batches<=0: return self.counter+=1 if self.counter>self.after_n_batches: self.counter=0 self.predict_and_call(batch) if self.decay_function is not None: self.after_n_batches=self.decay_function(self.call_counter)

31.817978

136

0.555689

1,567

14,159

4.854499

0.179324

0.029052

0.015775

0.024188

0.250296

0.184041

0.150519

0.121073

0.086368

0.066912

0

0.010539

0.343245

14,159

444

137

31.88964

0.807506

0.027121

0

0.317073

0

0.040742

0.008949

0

1

0.085366

false

0.006098

0.060976

0

0.222561

0.009146

0

null

0

null

0

1

0

69ae283d0ff5b55a3dfbfd1de32e6dac0789c024

9,028

py

Python

scripts/matriz_confusion.py

lmc00/tfg_en_desarrollo

30e61f4bb3f060f7468b1bb94930fcbe0d0f92ae

[ "Apache-2.0" ]

null

scripts/matriz_confusion.py

lmc00/tfg_en_desarrollo

30e61f4bb3f060f7468b1bb94930fcbe0d0f92ae

[ "Apache-2.0" ]

null

scripts/matriz_confusion.py

lmc00/tfg_en_desarrollo

30e61f4bb3f060f7468b1bb94930fcbe0d0f92ae

[ "Apache-2.0" ]

1

2020-11-25T15:16:36.000Z

# Método para generar la matriz de confusión. # Hay que editar el parámetro del TIMESTAMP para poner el del modelo que quieres evaluar. # El primer bloque de código está copiado del de Ignacio, pero con "INCISO" de vez en cuando, que son necesarios import os import json import numpy as np import matplotlib.pylab as plt import seaborn from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score from tensorflow.keras.models import load_model from imgclas.data_utils import load_image, load_data_splits, load_class_names from imgclas.test_utils import predict from imgclas import paths, plot_utils, utils, test_utils # User parameters to set TIMESTAMP = input("Indica el timestamp. Sin espacios. Mismo formato que en models: ") # timestamp of the model MODEL_NAME = 'final_model.h5' # model to use to make the prediction TOP_K = 2 # number of top classes predictions to save # Set the timestamp paths.timestamp = TIMESTAMP # Load the data print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++antes de class names") class_names = load_class_names(splits_dir=paths.get_ts_splits_dir()) # INCISO: Estas son las clases que había en el modelo # en el momento en el que estrenaste (dado por el timestamp). No las que tienes en data/dataset_files print("----------------------------------------------------------------------despues de class names") # Load training configuration conf_path = os.path.join(paths.get_conf_dir(), 'conf.json') with open(conf_path) as f: conf = json.load(f) # Load the model print("--------------------------------------------------------------------------------------------------------------------------------------------------------------------------antes") model = load_model(os.path.join(paths.get_checkpoints_dir(), MODEL_NAME)) #model = load_model(os.path.join(paths.get_checkpoints_dir(), MODEL_NAME), custom_objects=utils.get_custom_objects()) print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++después") # INCISO: Ahora la parte que continúa está basada en el predicting a datasplit txt file que incluye Ignacio en el notebook # 3.0 . Esta preparación previa es necesaria para computar la matriz de confusión. # # OJO: ahora lo que le vas a dar para testear el modelo dado por el timestamp SÍ se encuentra en data/dataset_files # Y ES CON LO QUE TÚ QUIERES TESTEAR EL MODELO. SPLIT_NAME = input("Indica el nombre del split con el que evaluas. Es de data/dataset_files. Ejemplos: val train ...: ") # Load the data X, y = load_data_splits(splits_dir=paths.get_ts_splits_dir(), im_dir=conf['general']['images_directory'], split_name=SPLIT_NAME) # Predict # Añade esto si quieres no usar aumentacion en la validacion: # print(conf['augmentation']['val_mode']) pred_lab, pred_prob = predict(model, X, conf, top_K=TOP_K, filemode='local') #Ahora guardamos las predicciones # Save the predictions pred_dict = {'filenames': list(X), 'pred_lab': pred_lab.tolist(), 'pred_prob': pred_prob.tolist()} if y is not None: pred_dict['true_lab'] = y.tolist() # No incluimos la parte de guardarlo en json porque lo vamos a utilizar ahora mismo. # Importamos el warning este porque Ignacio lo sugiere. import warnings warnings.filterwarnings("ignore") # To ignore UndefinedMetricWarning: [Recall/Precision/F-Score] is ill-defined and being set to 0.0 in labels with no [true/predicted] samples. # INCISO: Sacamos por pantalla distintas métricas relevantes SOBRE EL SPLIT SELECCIONADO true_lab, pred_lab = np.array(pred_dict['true_lab']), np.array(pred_dict['pred_lab']) top1 = test_utils.topK_accuracy(true_lab, pred_lab, K=1) top2 = test_utils.topK_accuracy(true_lab, pred_lab, K=2) # INCISO: LO COMENTO PORQUE QUIERO EL TOP 2 top5 = test_utils.topK_accuracy(true_lab, pred_lab, K=5) # INCISO: También vamos a guardarlo en un .txt, del que solicitaremos nombre al usuario nombre_metricas = input("Ponle nombre al fichero con las métricas relevantes. No especifiques formato, va a ser .txt por defecto: " ) nombre_metricas = nombre_metricas + ".txt" m = open(nombre_metricas,'w') print('Top1 accuracy: {:.1f} %'.format(top1 * 100)) m.write('Top1 accuracy: {:.1f} %'.format(top1 * 100) + '\n') print('Top2 accuracy: {:.1f} %'.format(top2 * 100)) m.write('Top2 accuracy: {:.1f} %'.format(top2 * 100) + '\n') # INCISO ESTO LO COMENTO PORQUE AHORA QUIERO TOP 2 print('Top5 accuracy: {:.1f} %'.format(top5 * 100)) #m.write('Top5 accuracy: {:.1f} %'.format(top5 * 100) + '\n') labels = range(len(class_names)) print('\n') m.write('\n') print('Micro recall: {:.1f} %'.format(100 * recall_score(true_lab, pred_lab[:, 0], labels=labels, average='micro'))) m.write('Micro recall: {:.1f} %'.format(100 * recall_score(true_lab, pred_lab[:, 0], labels=labels, average='micro')) + '\n') print('Macro recall: {:.1f} %'.format(100 * recall_score(true_lab, pred_lab[:, 0], labels=labels, average='macro'))) m.write('Macro recall: {:.1f} %'.format(100 * recall_score(true_lab, pred_lab[:, 0], labels=labels, average='macro')) + '\n') print('Macro recall (no labels): {:.1f} %'.format(100 * recall_score(true_lab, pred_lab[:, 0], average='macro'))) m.write('Macro recall (no labels): {:.1f} %'.format(100 * recall_score(true_lab, pred_lab[:, 0], average='macro')) + '\n') print('Weighted recall: {:.1f} %'.format(100 * recall_score(true_lab, pred_lab[:, 0], labels=labels, average='weighted'))) m.write('Weighted recall: {:.1f} %'.format(100 * recall_score(true_lab, pred_lab[:, 0], labels=labels, average='weighted')) + '\n') print('\n') m.write('\n') print('Micro precision: {:.1f} %'.format(100 * precision_score(true_lab, pred_lab[:, 0], labels=labels, average='micro'))) m.write('Micro precision: {:.1f} %'.format(100 * precision_score(true_lab, pred_lab[:, 0], labels=labels, average='micro')) + '\n') print('Macro precision: {:.1f} %'.format(100 * precision_score(true_lab, pred_lab[:, 0], labels=labels, average='macro'))) m.write('Macro precision: {:.1f} %'.format(100 * precision_score(true_lab, pred_lab[:, 0], labels=labels, average='macro')) + '\n') print('Macro precision (no labels): {:.1f} %'.format(100 * precision_score(true_lab, pred_lab[:, 0], average='macro'))) m.write('Macro precision (no labels): {:.1f} %'.format(100 * precision_score(true_lab, pred_lab[:, 0], average='macro')) + '\n') print('Weighted precision: {:.1f} %'.format(100 * precision_score(true_lab, pred_lab[:, 0], labels=labels, average='weighted'))) m.write('Weighted precision: {:.1f} %'.format(100 * precision_score(true_lab, pred_lab[:, 0], labels=labels, average='weighted')) + '\n') print('\n') m.write('\n') print('Micro F1 score: {:.1f} %'.format(100 * f1_score(true_lab, pred_lab[:, 0], labels=labels, average='micro'))) m.write('Micro F1 score: {:.1f} %'.format(100 * f1_score(true_lab, pred_lab[:, 0], labels=labels, average='micro')) + '\n') print('Macro F1 score: {:.1f} %'.format(100 * f1_score(true_lab, pred_lab[:, 0], labels=labels, average='macro'))) m.write('Macro F1 score: {:.1f} %'.format(100 * f1_score(true_lab, pred_lab[:, 0], labels=labels, average='macro')) + '\n') print('Macro F1 score (no labels): {:.1f} %'.format(100 * f1_score(true_lab, pred_lab[:, 0], average='macro'))) m.write('Macro F1 score (no labels): {:.1f} %'.format(100 * f1_score(true_lab, pred_lab[:, 0], average='macro')) + '\n') print('Weighted F1 score: {:.1f} %'.format(100 * f1_score(true_lab, pred_lab[:, 0], labels=labels, average='weighted'))) m.write('Weighted F1 score: {:.1f} %'.format(100 * f1_score(true_lab, pred_lab[:, 0], labels=labels, average='weighted')) + '\n') m.close() # INCISO: YA VAMOS CON LA MATRIZ DE CONFUSIÓN!! def plt_conf_matrix(conf_mat, labels=False): fig = plt.figure(figsize=(20, 20)) hm = seaborn.heatmap(conf_mat, annot=False, square=True, cbar_kws={'fraction':0.046, 'pad':0.04}, xticklabels=labels, yticklabels=labels, cmap="YlGnBu") fontsize = None hm.yaxis.set_ticklabels(hm.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize) hm.xaxis.set_ticklabels(hm.xaxis.get_ticklabels(), rotation=90, ha='right', fontsize=fontsize) plt.ylabel('True label') plt.xlabel('Predicted label') y_true, y_pred = np.array(pred_dict['true_lab']), np.array(pred_dict['pred_lab'])[:, 0] #Aqui, al poner [:,0] te asegura que de todas las probabilidades # tú coges la que más tienes como etiqueta predicha conf_mat = confusion_matrix(y_true, y_pred, labels=range(len(class_names)), sample_weight=None) normed_conf = conf_mat / np.sum(conf_mat, axis=1)[:, np.newaxis] # plt_conf_matrix(conf_mat) plt_conf_matrix(normed_conf, labels=class_names) nombre_confusion = input("Ponle nombre a la matriz de confusion. No especifiques formato: " ) plt.savefig(nombre_confusion,dpi = 50,format = "png", bbox_inches = 'tight')

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null

0

null

0

1

0

69afcb6111e2b727f9b4db4fba7fb9a04892dfe5

1,099

py

Python

time/plot.py

gray0018/Normal-integration-benchmark

3f4fff86e659ae2a3588c0960ebb0af39e4a1e21

[ "MIT" ]

null

time/plot.py

gray0018/Normal-integration-benchmark

3f4fff86e659ae2a3588c0960ebb0af39e4a1e21

[ "MIT" ]

null

time/plot.py

gray0018/Normal-integration-benchmark

3f4fff86e659ae2a3588c0960ebb0af39e4a1e21

[ "MIT" ]

null

import numpy as np import operator import matplotlib.pyplot as plt import json import os # directory = '.' # d = {} # for filename in os.listdir(directory): # if filename.endswith(".npy"): # t = np.load(filename) # d[filename[:-4]] = float(t) # j = json.dumps(d) # f = open("woloop.json","w") # f.write(j) # f.close() plt.style.use(['science','no-latex']) with open('withloop.json') as json_file: d_w = json.load(json_file) d_w = dict(sorted(d_w.items(), key=operator.itemgetter(1))) with open('woloop.json') as json_file: d_wo = json.load(json_file) d_wo = dict(sorted(d_wo.items(), key=operator.itemgetter(1))) fig, axes = plt.subplots(figsize=(5,5)) axes.scatter(d_w.keys(), d_w.values()) axes.scatter(d_wo.keys(), d_wo.values()) axes.legend(['With nested loops','W/O nested loops']) axes.set_ylabel('Time (s)', fontsize=16) axes.set_xlabel('Model-resolution', fontsize=16) chartBox = axes.get_position() axes.set_position([chartBox.x0, chartBox.y0*2, chartBox.width, chartBox.height]) plt.xticks(rotation=90) plt.show()

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69b249ca121767aa062b4c57848e58f48033a136

2,132

py

Python

python/video-tools/rename-toutv.py

bmaupin/graveyard

71d52fe6589ce13dfe7433906d1aa50df48c9f94

[ "MIT" ]

1

2019-11-23T10:44:58.000Z

python/video-tools/rename-toutv.py

bmaupin/graveyard

71d52fe6589ce13dfe7433906d1aa50df48c9f94

[ "MIT" ]

8

2020-07-16T07:14:12.000Z

2020-10-14T17:25:33.000Z

python/video-tools/rename-toutv.py

bmaupin/graveyard

71d52fe6589ce13dfe7433906d1aa50df48c9f94

[ "MIT" ]

1

2019-11-23T10:45:00.000Z

#!/usr/bin/env python # coding=utf8 import os import os.path import re import sys if sys.version_info < (3, 4): sys.exit('ERROR: Requires Python 3.4 or higher') from enum import Enum def main(): VideoTypes = Enum('VideoType', 'emission film') filename_chars = 'àÀâÂçÇéÉèÈêÊëîÎôÔ\w\-\'\.' pattern = re.compile('([{0}]+)\.(S([\d]+)E[\d]+)\.([{0}]+)\.[\d]+kbps\.ts'.format(filename_chars)) # pattern = re.compile('([{0}]+)\.(S[\d]+E[\d]+)\.([{0}]+)'.format(filename_chars)) files_to_rename = {} video_type = None def parse_filename(filename): nonlocal video_type match = pattern.search(filename) if match: show = match.group(1).replace('.', ' ') episode = match.group(2) season = match.group(3) title = match.group(4).replace('.', ' ') if show.lower() == title.lower(): video_type = VideoTypes.film else: video_type = VideoTypes.emission return show, episode, season, title else: sys.exit('ERROR: Unrecognized character in {0}\n'.format(filename)) for filename in sorted(os.listdir(os.getcwd())): if not filename.endswith('.ts'): continue show, episode, season, title = parse_filename(filename) if video_type == VideoTypes.film: renamed_filename = '{} ({}).mp4'.format(title, season) else: renamed_filename = '{} - {} - {}.mp4'.format(show, episode, title) print(filename) print('\t{}'.format(renamed_filename)) files_to_rename[filename] = renamed_filename response = input('Rename files? (y/n) ') if response == 'y': for filename in files_to_rename: os.rename( os.path.join( os.getcwd(), filename ), os.path.join( os.getcwd(), files_to_rename[filename] ) ) if __name__ == '__main__': main()

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0.037037

0

null

0

null

0

1

0

69b34c5fbd0dd31fc5ad9d5907ba03629ebdf899

6,582

py

Python

flask_resty/related.py

d-wysocki/flask-resty

2a5e7d7ea7e2130dce44b8f50625df72ad0dcd19

[ "MIT" ]

86

2015-11-25T07:09:10.000Z

2022-02-15T19:40:30.000Z

flask_resty/related.py

d-wysocki/flask-resty

2a5e7d7ea7e2130dce44b8f50625df72ad0dcd19

[ "MIT" ]

180

2015-11-24T23:02:53.000Z

2022-03-31T04:05:38.000Z

flask_resty/related.py

d-wysocki/flask-resty

2a5e7d7ea7e2130dce44b8f50625df72ad0dcd19

[ "MIT" ]

17

2015-12-28T11:05:47.000Z

2022-03-15T12:10:02.000Z

import functools from .exceptions import ApiError # ----------------------------------------------------------------------------- class RelatedId: """Resolve a related item by a scalar ID. :param view_class: The :py:class:`ModelView` corresponding to the related model. :param str field_name: The field name on request data. """ def __init__(self, view_class, field_name): self._view_class = view_class self.field_name = field_name def create_view(self): # Separating this out saves instantiating the view multiple times for # list fields. return self._view_class() def resolve_related_id(self, view, id): return view.resolve_related_id(id) class Related: """A component for resolving deserialized data fields to model instances. The `Related` component is responsible for resolving related model instances by ID and for constructing nested model instances. It supports multiple related types of functionality. For a view with:: related = Related( foo=RelatedId(FooView, "foo_id"), bar=BarView, baz=Related(models.Baz, qux=RelatedId(QuxView, "qux_id"), ) Given deserialized input data like:: { "foo_id": "3", "bar": {"id": "4"}, "baz": {name: "Bob", "qux_id": "5"}, "other_field": "value", } This component will resolve these data into something like:: { "foo": <Foo(id=3)>, "bar": <Bar(id=4)>, "baz": <Baz(name="Bob", qux=<Qux(id=5)>>, "other_field": "value", } In this case, the Foo, Bar, and Qux instances are fetched from the database, while the Baz instance is freshly constructed. If any of the Foo, Bar, or Qux instances do not exist, then the component will fail the request with a 422. Formally, in this specification: - A `RelatedId` item will retrieve the existing object in the database with the ID from the specified scalar ID field using the specified view. - A view class will retrieve the existing object in the database using the object stub containing the ID fields from the data field of the same name, using the specified view. This is generally used with the `RelatedItem` field class, and unlike `RelatedId`, supports composite IDs. - Another `Related` item will apply the same resolution to a nested dictionary. Additionally, if the `Related` item is given a callable as its positional argument, it will construct a new instance given that callable, which can often be a model class. `Related` depends on the deserializer schema to function accordingly, and delegates validation beyond the database fetch to the schema. `Related` also automatically supports cases where the fields are list fields or are configured with ``many=True``. In those cases, `Related` will iterate through the sequence and resolve each item in turn, using the rules as above. :param item_class: The SQLAlchemy mapper corresponding to the related item. :param dict kwargs: A mapping from related fields to a callable resolver. """ def __init__(self, item_class=None, **kwargs): self._item_class = item_class self._resolvers = kwargs def resolve_related(self, data): """Resolve the related values in the request data. This method will replace values in `data` with resolved model instances as described above. This operates in place and will mutate `data`. :param data object: The deserialized request data. :return: The deserialized data with related fields resolved. :rtype: object """ for field_name, resolver in self._resolvers.items(): if isinstance(resolver, RelatedId): data_field_name = resolver.field_name else: data_field_name = field_name if data_field_name not in data: # If this field were required, the deserializer would already # have raised an exception. continue # Remove the data field (in case it's different) so we can keep # just the output field. value = data.pop(data_field_name) if value is None: # Explicitly clear the related item if the value was None. data[field_name] = None continue try: resolved = self.resolve_field(value, resolver) except ApiError as e: pointer = f"/data/{data_field_name}" raise e.update({"source": {"pointer": pointer}}) data[field_name] = resolved if self._item_class: return self._item_class(**data) return data def resolve_field(self, value, resolver): """Resolve a single field value. :param value: The value corresponding to the field we are resolving. :param resolver: A callable capable of resolving the given `value`. :type resolver: :py:class:`Related` | :py:class:`RelatedId` | func """ # marshmallow always uses lists here. many = isinstance(value, list) if many and not value: # As a tiny optimization, there's no need to resolve an empty list. return value if isinstance(resolver, Related): resolve_item = resolver.resolve_related elif isinstance(resolver, RelatedId): view = resolver.create_view() resolve_item = functools.partial(resolver.resolve_related_id, view) else: resolve_item = resolver().resolve_related_item if many: return [resolve_item(item) for item in value] return resolve_item(value) def __or__(self, other): """Combine two `Related` instances. `Related` supports view inheritance by implementing the `|` operator. For example, `Related(foo=..., bar=...) | Related(baz=...)` will create a new `Related` instance with resolvers for each `foo`, `bar` and `baz`. Resolvers on the right-hand side take precedence where each `Related` instance has the same key. """ if not isinstance(other, Related): return NotImplemented return self.__class__( other._item_class or self._item_class, **{**self._resolvers, **other._resolvers}, )

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null

0

null

0

1

0

69b3ed41ddcf330b9785bd4810fc8e2f6713d932

6,489

py

Python

src/data/split.py

bathienle/master-thesis-code

58182f54a56c34fb4a33d67743ca515c80e33657

[ "Apache-2.0" ]

2

2021-06-22T13:43:40.000Z

2022-03-01T18:15:32.000Z

src/data/split.py

bathienle/master-thesis-code

58182f54a56c34fb4a33d67743ca515c80e33657

[ "Apache-2.0" ]

null

src/data/split.py

bathienle/master-thesis-code

58182f54a56c34fb4a33d67743ca515c80e33657

[ "Apache-2.0" ]

null

""" Split the dataset into train/val/test sets from a Cytomine dataset. """ import csv import logging import numpy as np import os from argparse import ArgumentParser from itertools import chain from cytomine import Cytomine from cytomine.models import ( AnnotationCollection, ImageInstanceCollection, TermCollection, UserCollection ) def parse_arguments(): """ Parse the arguments of the program. Return ------ args : class argparse.Namespace The parsed arguments. """ desc = "Split dataset into train/val/test sets or folds and test sets." parser = ArgumentParser(description=desc) parser.add_argument( '--host', help="The Cytomine host." ) parser.add_argument( '--public_key', help="The Cytomine public key." ) parser.add_argument( '--private_key', help="The Cytomine private key." ) parser.add_argument( '--project_id', help="The project from the dataset." ) parser.add_argument( '--path', help="Path to the dataset." ) parser.add_argument( '--term', help="The specific term of the annotations." ) parser.add_argument( '--ratio', type=float, default=0.8, help="The train/test split ratio of the dataset." ) parser.add_argument( '--cross', type=bool, default=False, help="Whether to split into folds for a cross-validation." ) parser.add_argument( '--cv', type=int, default=5, help="If cross is enabled, the number of folds to split the data." ) return parser.parse_args() if __name__ == '__main__': args = parse_arguments() # Connect to Cytomine Cytomine(args.host, args.public_key, args.private_key, logging.INFO) # Get the term ID terms = TermCollection().fetch_with_filter('project', args.project_id) term_id = next( (term.id for term in terms if term.name.lower() == args.term.lower()) ) # Get the users of Cytomine users = UserCollection().fetch() users = {user.id: user.username for user in users} # Get all images of a Cytomine project images = ImageInstanceCollection() images = images.fetch_with_filter('project', args.project_id) id_to_images = {image.id: image for image in images} # Get the all the annotations of all the images of the project annotations = AnnotationCollection( project=args.project_id, term=term_id, showMeta=True, ) annotations.fetch() id_to_annotations = { annotation.id: annotation for annotation in annotations } # Map the annotations to the corresponding image image_to_label = { image.id: [annot.id for annot in annotations if annot.image == image.id] for image in images } # Remove no label images image_to_label = { image: labels for image, labels in image_to_label.items() if labels } # Remove the unexisting or deleted images/annotations filenames = os.listdir(os.path.join(args.path, 'images')) annotations = list(chain.from_iterable( [annotation for annotation in image_to_label.values()] )) annotations = [f'{annotation}.jpg' for annotation in annotations] image_to_label = { image: list( set([f'{label}.jpg' for label in labels]).intersection(filenames) ) for image, labels in image_to_label.items() } # Sort the image by the number of annotations image_to_label = { image: labels for image, labels in sorted( image_to_label.items(), key=lambda item: len(item[1]), reverse=True ) } # Get the number of annotations n_labels = sum([len(v) for v in image_to_label.values()]) # Split the dataset with the ratio split_train_test = round(n_labels * args.ratio) split_train_val = round(split_train_test * 0.8) # Split 80/20 train_set = [] test_set = [] for image, labels in image_to_label.items(): if len(train_set) < split_train_test: train_set.extend(labels) else: test_set.extend(labels) # Split either into train/val/test or into folds if args.cross: folds = np.array_split(np.array(train_set), args.cv) categories = [f'fold{i+1}' for i in range(args.cv)] + ['test'] sets = [fold.tolist() for fold in folds] + [test_set] else: val_set = train_set[split_train_val:] train_set = train_set[:split_train_val] categories = ['train', 'val', 'test'] sets = [train_set, val_set, test_set] # Save details about the split into a CSV file detail_path = os.path.join(args.path, f'details-{args.project_id}.csv') header = ['Annotation ID', 'Image ID', 'Image filename', 'Term', 'User', 'Category'] with open(detail_path, 'w', newline='') as file: writer = csv.DictWriter(file, fieldnames=header) writer.writeheader() # Write the information of each set for category, filenames in zip(categories, sets): for filename in filenames: annotation_id = int(filename[:-4]) annotation = id_to_annotations[annotation_id] image = id_to_images[annotation.image] csv.writer(file).writerow([ annotation_id, annotation.image, image.filename, args.term, users[annotation.user], category ]) # Split the annotations to the corresponding directories subdirs = ['images', 'masks', 'inclusions', 'exclusions'] # Create the directories for category in categories: for subdir in subdirs: os.makedirs( os.path.join(args.path, category, subdir), exist_ok=True ) # Move the images and masks to the destination directory for dataset, name in zip(sets, categories): for filename in dataset: for subdir in subdirs: os.rename( os.path.join(args.path, subdir, filename), os.path.join(args.path, name, subdir, filename) ) # Delete empty directories for subdir in subdirs: os.rmdir(os.path.join(args.path, subdir))

29.22973

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0.034286

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6,489

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null

0

null

0

1

0

69b532a45670bfc1aba43201c129695282189089

14,886

py

Python

omsdk/typemgr/FieldType.py

DanielFroehlich/omsdk

475d925e4033104957fdc64480fe8f9af0ab6b8a

[ "Apache-2.0" ]

61

2018-02-21T00:02:20.000Z

2022-01-26T03:47:19.000Z

omsdk/typemgr/FieldType.py

DanielFroehlich/omsdk

475d925e4033104957fdc64480fe8f9af0ab6b8a

[ "Apache-2.0" ]

31

2018-03-24T05:43:39.000Z

2022-03-16T07:10:37.000Z

omsdk/typemgr/FieldType.py

DanielFroehlich/omsdk

475d925e4033104957fdc64480fe8f9af0ab6b8a

[ "Apache-2.0" ]

25

2018-03-13T10:06:12.000Z

2022-01-26T03:47:21.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # # Copyright © 2018 Dell Inc. or its subsidiaries. All rights reserved. # Dell, EMC, and other trademarks are trademarks of Dell Inc. or its subsidiaries. # Other trademarks may be trademarks of their respective owners. # # 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. # # Authors: Vaideeswaran Ganesan # from omsdk.sdkcenum import TypeHelper from omsdk.sdkcunicode import UnicodeHelper from omsdk.typemgr.TypeState import TypeState, TypeBase import sys PY2 = sys.version_info[0] == 2 PY3 = sys.version_info[0] == 3 import logging logger = logging.getLogger(__name__) # private # # def __init__(self, init_value, typename, fname, alias, parent=None, volatile=False) # def __eq__, __ne__, __lt__, __le__, __gt__, __ge__ # def __str__, __repr__ # def __getattr__ # def __delattr__ # def __setattr__ # # protected: # # def my_accept_value(self, value): # # public: # def is_changed(self) # def sanitized_value(self): # def copy(self, other) # def commit(self) # def reject(self) # def freeze(self) # def unfreeze(self) # def is_frozen(self) # def json_encode(self) # def child_state_changed(self, child, child_state) # def parent_state_changed(self, new_state) class FieldType(TypeBase): def __init__(self, init_value, typename, fname, alias, parent=None, volatile=False, modifyAllowed = True, deleteAllowed = True, rebootRequired=False, default_on_delete=''): if PY2: super(FieldType, self).__init__() else: super().__init__() self._type = typename self._alias = alias self._fname = fname self._volatile = volatile self._parent = parent self._composite = False self._index = 1 self._modifyAllowed = modifyAllowed self._deleteAllowed = deleteAllowed self._rebootRequired = rebootRequired self._default_on_delete = default_on_delete self._list = False self._freeze = False self.__dict__['_state'] = TypeState.UnInitialized self._value = init_value # Value APIs def __getattr__(self, name): if name in self.__dict__ and name not in ['_orig_value']: return self.__dict__[name] elif name == '_optimal' and self._composite: return tuple(sorted([i for i in self.__dict__['_value'] \ if i._value is not None])) raise AttributeError('Invalid attribute ' + name) @property def Value(self): return self._value @property def OptimalValue(self): return (self._optimal if self._composite else self._value) #deprecate this. replace with Property def get_value(self): return self._value # Value APIs def __setattr__(self, name, value): # Do not allow access to internal variables if name in ['_orig_value', '_state']: raise AttributeError('Invalid attribute ' + name) # Freeze mode: No sets allowed if '_freeze' in self.__dict__ and self._freeze: raise ValueError('object in freeze mode') # allow updates to other fields except _value # should we allow updates to '_type', '_alias', '_fname'? if name not in ['_value']: self.__dict__[name] = value return # Create-only attribute : No updates allowed if not self._modifyAllowed and \ self._state in [TypeState.Committed, TypeState.Changing]: raise ValueError('updates not allowed to this object') # CompositeField : sets not allowed in composite fields if self._composite: raise AttributeError('composite objects cannot be modified') # value is None, object was committed; ==> no change if value is None and \ self._state in [TypeState.Committed, TypeState.Precommit, TypeState.Changing]: return # Validate value and convert it if needed valid = False msg = None if value is None or TypeHelper.belongs_to(self._type, value): valid = True elif type(self) == type(value): value = value._value valid = True elif UnicodeHelper.is_string(value): value = UnicodeHelper.stringize(value) # expected value is int if self._type == int: value = int(value) valid = True # expected value is bool elif self._type == bool: value = bool(value) valid = True # expected value is str elif self._type == str: valid = True # expected value is enumeration elif TypeHelper.is_enum(self._type): newvalue = TypeHelper.convert_to_enum(value, self._type) if newvalue is not None: value = newvalue valid = True else: msg = str(value) + " is not " + str(self._type) else: msg = str(value) + " cannot be converted to " + str(self._type) else: msg = "No type conversion found for '" + str(value) + "'. "\ "Expected " + str(self._type.__name__) + ". Got " +\ type(value).__name__ if valid and not self.my_accept_value(value): msg = type(self).__name__ +" returned failure for " + str(value) valid = False # if invalid, raise ValueError exception if not valid: raise ValueError(msg) # same value - no change if name in self.__dict__ and self._value == value: return # List fields, simply append the new entry! if self._list and name in self.__dict__ and self.__dict__[name]: value = self.__dict__[name] + "," + value # modify the value self.__dict__[name] = value if self._state in [TypeState.UnInitialized, TypeState.Precommit, TypeState.Initializing]: self.__dict__['_state'] = TypeState.Initializing elif self._state in [TypeState.Committed, TypeState.Changing]: if self._orig_value == self._value: self.__dict__['_state'] = TypeState.Committed else: self.__dict__['_state'] = TypeState.Changing else: print("Should not come here") if self.is_changed() and self._parent: self._parent.child_state_changed(self, self._state) # Value APIs def __delattr__(self, name): # Do not allow access to internal variables if name in ['_orig_value', '_track', '_freeze', '_type', '_default_on_delete', '_value', '_volatile', '_composite']: raise AttributeError('Invalid attribute ' + name) # Freeze mode - don't allow any updates if '_freeze' in self.__dict__ and self._freeze: raise AttributeError('object in freeze mode') if name in self.__dict__: del self.__dict__[name] def set_value(self, value): self._value = value # nulls the value def nullify_value(self): if '_value' in self.__dict__: self.__dict__['_value'] = None if self._state in [TypeState.UnInitialized, TypeState.Precommit, TypeState.Initializing]: self.__dict__['_state'] = TypeState.Initializing elif self._state in [TypeState.Committed, TypeState.Changing]: if self._orig_value == self._value: self.__dict__['_state'] = TypeState.Committed else: self.__dict__['_state'] = TypeState.Changing else: print("Should not come here") if self.is_changed() and self._parent: self._parent.child_state_changed(self, self._state) # Value APIs def my_accept_value(self, value): return True # Representation APIs def __str__(self): return str(self._value) # Representation APIs def sanitized_value(self): if '_value' not in self.__dict__ or self._value is None: return None return TypeHelper.resolve(self._value) # State APIs: def is_changed(self): return self._state in [TypeState.Initializing, TypeState.Changing] def reboot_required(self): return self.is_changed() and self._rebootRequired # State : to Committed # allowed even during freeze def commit(self, loading_from_scp = False): if self.is_changed() or self._state == TypeState.Precommit: if not self._composite: self.__dict__['_orig_value'] = self._value if loading_from_scp: self.__dict__['_state'] = TypeState.Precommit else: self.__dict__['_state'] = TypeState.Committed return True # State : to Committed # allowed even during freeze def reject(self): if self.is_changed(): if not self._composite: if '_orig_value' not in self.__dict__: del self.__dict__['_value'] self.__dict__['_state'] = TypeState.UnInitialized else: self.__dict__['_value'] = self._orig_value self.__dict__['_state'] = TypeState.Committed return True # Does not have children - so not implemented def child_state_changed(self, child, child_state): not_implemented # what to do? def parent_state_changed(self, new_state): not_implemented # Object APIs def copy(self, other): if isinstance(other, type(self)): self._value = other._value return True return False def print_commit(self): print(self._state) # Compare APIs: def __lt__(self, other): if self._state is TypeState.UnInitialized: return False if other is None: return False myvalue = self._value if isinstance(other, type(self)): othervalue = other._value elif isinstance(other, self._type): othervalue = other else: raise TypeError('cannot compare with ' + type(other).__name__) if myvalue is None and othervalue is not None: return True if myvalue is None and othervalue is None: return False return myvalue < othervalue # Compare APIs: def __le__(self, other): if self._state is TypeState.UnInitialized: return False if self._value is None and other is None: return True if self._value is not None and other is None: return False myvalue = self._value if isinstance(other, type(self)): othervalue = other._value elif isinstance(other, self._type): othervalue = other else: raise TypeError('cannot compare with ' + type(other).__name__) if myvalue is not None and othervalue is None: return False if myvalue is None and othervalue is not None: return True return myvalue <= othervalue # Compare APIs: def __gt__(self, other): if self._state is TypeState.UnInitialized: return False if self._value is None: return False if self._value is not None and other is None: return True myvalue = self._value if isinstance(other, type(self)): othervalue = other._value elif isinstance(other, self._type): othervalue = other else: raise TypeError('cannot compare with ' + type(other).__name__) if myvalue is not None and othervalue is None: return True return myvalue > othervalue # Compare APIs: def __ge__(self, other): if self._state is TypeState.UnInitialized: return False if self._value is None and other is None: return True if self._value is not None and other is None: return True myvalue = self._value if isinstance(other, type(self)): othervalue = other._value elif isinstance(other, self._type): othervalue = other else: raise TypeError('cannot compare with ' + type(other).__name__) if myvalue is None and othervalue is None: return True if myvalue is None and othervalue is not None: return False return myvalue >= othervalue # Don't allow comparision with string ==> becomes too generic # Compare APIs: def __eq__(self, other): if self._state is TypeState.UnInitialized: return False if self._value is None and other is None: return True if self._value is not None and other is None: return False myvalue = self._value if isinstance(other, type(self)): othervalue = other._value elif isinstance(other, self._type): othervalue = other else: raise TypeError('cannot compare with ' + type(other).__name__) if myvalue is None and othervalue is None: return True if myvalue is None and othervalue is not None: return True return myvalue == othervalue # Compare APIs: def __ne__(self, other): return not self.__eq__(other) # Freeze APIs def freeze(self): self._freeze = True # Freeze APIs def unfreeze(self): self.__dict__['_freeze'] = False # Freeze APIs def is_frozen(self): return self.__dict__['_freeze'] def json_encode(self): return self._value def _clear_duplicates(self): pass def printx(self): print(str(type(self._value))+"<>"+str(self._type)+"::"+str(self._value))

34.780374

177

0.590555

1,682

14,886

4.938169

0.160523

0.031784

0.021671

0.029136

0.496749

0.444016

0.408981

0.367325

0.347821

0.333012

0

0.001805

0.329974

14,886

427

178

34.861827

0.83086

0.167809

0

0.510638

0

0.057203

0

1

0.113475

false

0.003546

0.017731

0.035461

0.308511

0.021277

0

null

0

null

0

1

0

69b853f40f346029e133948525fa18fe2b5354f4

16,785

py

Python

LinearOptimization/__init__.py

snickers2524/SFUMATH308LinearOptimization

47a6c030c5221857be483d0eca8dd712d8d7c155

[ "MIT" ]

null

LinearOptimization/__init__.py

snickers2524/SFUMATH308LinearOptimization

47a6c030c5221857be483d0eca8dd712d8d7c155

[ "MIT" ]

null

LinearOptimization/__init__.py

snickers2524/SFUMATH308LinearOptimization

47a6c030c5221857be483d0eca8dd712d8d7c155

[ "MIT" ]

null

import numpy as np def getIndexPositions(listOfElements, item): """ Returns the indexes of all occurrences of give element in the list- listOfElements """ indexPosList = [] for index in range(0, len(listOfElements)): if listOfElements[index] == item: indexPosList.insert(len(indexPosList), index) return indexPosList def getIndexPositionsThatStartWith(listOfElements, item): """ Returns the indexes of all occurrences of give element in the list- listOfElements """ indexPosList = [] for index in range(0, len(listOfElements)): if listOfElements[index].startswith(item): indexPosList.insert(len(indexPosList), index) return indexPosList class Optimizer: def __init__(self, A, primal_independent=[], primal_dependent=[]): self.A = np.array(A) # Tableau A self.A = self.A.astype('float64') self.n = len(A[0, :]) - 1 # Number of Columns self.m = len(A[:, 0]) - 1 # Number of Rows if primal_independent != [] and primal_dependent != []: # Both Independent and Dependent Vars are specified in the class call self.primal_ind = primal_independent + ["-1"] self.primal_dep = primal_dependent + ["f"] elif primal_independent == [] and primal_dependent == []: # Both Independent and Dependent Vars are not specified in the class call self.primal_ind = self.__variable_creator("x", self.n) + ["-1"] self.primal_dep = self.__variable_creator("t", self.m) + ["f"] elif not primal_independent: # Only Dependent Vars are specified in the class call self.primal_ind = self.__variable_creator("x", self.n) + ["-1"] self.primal_dep = primal_dependent + ["f"] elif not primal_independent: # Only Independent Vars are specified in the class call self.primal_ind = primal_independent + ["-1"] self.primal_dep = self.__variable_creator("t", self.m) + ["f"] self.blands_rule = self.primal_ind + self.primal_dep # Initializes Variable Order for Blands Anti Cycling Rule self.dual_ind = self.__dual_variable_creator("y", self.m, True) + ["-1"] self.dual_dep = self.__dual_variable_creator("s", self.n, False) + ["g"] self.primal_recorded_equations = [] self.dual_recorded_equations = [] if len(self.primal_ind) != self.n + 1 or len(self.primal_dep) != self.m + 1: raise Exception("ERROR, INCORRECT VARIABLES") def __dual_variable_creator(self, var, iterable, dual_primal): # Based on the constraints of the primal LP, it creates the variables and constrains of the Dual LP variables = [var] * iterable for j in range(0, iterable): if not dual_primal: if self.primal_ind[j].startswith("*"): # Testing for Equality in the Constraints variables[j] = '0' else: variables[j] = var + str(j + 1) else: if self.primal_dep[j] == '0': # Testing for Equality in the Constraints variables[j] = '*' + var + str(j + 1) else: variables[j] = var + str(j + 1) return variables def __variable_creator(self, var, iterable): variables = [var] * iterable for j in range(0, iterable): variables[j] = var + str(j + 1) return variables def print(self): # problem_complete = [""] # [problem_complete.insert(len(problem_complete),i) for i in self.primal_ind] # for i in range(0,self.m): # problem_complete.insert(len(problem_complete),self.dual_ind[i]) # [problem_complete.insert(len(problem_complete), elem) for elem in self.A[i,:]] # problem_complete.insert(len(problem_complete),self.primal_dep[i]) # [problem_complete.insert(len(problem_complete), j) for j in self.dual_dep] # # for i in range(0,self.m): # for j in range(0,self.n): # print(problem_complete[i+j]) # print() # print(problem_complete) print("A = ", self.A) print("Primal Independent Variables: ", self.primal_ind) print("Primal Dependent Variables: ", self.primal_dep) print("Dual Independent Variables: ", self.dual_ind) print("Dual Dependent Variables: ", self.dual_dep) print("Primal Stored Equations: ", self.primal_recorded_equations) print("Dual Stored Equations: ", self.dual_recorded_equations) def simplex_algorithm(self): self.print() print("************************************************************************************************************************") print("************************************************************************************************************************") primal_equality_constraints = getIndexPositions(self.primal_dep, '0') primal_unconstrained_variables = getIndexPositionsThatStartWith(self.primal_ind, "*") while len(primal_equality_constraints) > 0 and len(primal_unconstrained_variables) > 0: if self.A[primal_equality_constraints[0], primal_unconstrained_variables[0]] == 0: # Testing if it tries to pivot on a zero. If so it passes to the method that is equipped to deal with it self.removing_primal_unconstrained_variables(primal_unconstrained_variables) primal_equality_constraints = getIndexPositions(self.primal_dep, '0') primal_unconstrained_variables = getIndexPositionsThatStartWith(self.primal_ind, "*") continue self.__pivot([primal_equality_constraints[0], primal_unconstrained_variables[0]]) # Recording Rows and Columns of the Tableau self.primal_recorded_equations.insert(len(self.primal_recorded_equations), [self.A[primal_equality_constraints[0], :], self.primal_ind[:], self.primal_dep[primal_equality_constraints[0]]]) self.dual_recorded_equations.insert(len(self.dual_recorded_equations), [self.A[:, primal_unconstrained_variables[0]], self.dual_ind[:], self.dual_dep[primal_unconstrained_variables[0]]]) # Deleting the Corresponding Variables del self.primal_ind[primal_unconstrained_variables[0]] del self.primal_dep[primal_equality_constraints[0]] del self.dual_ind[primal_equality_constraints[0]] del self.dual_dep[primal_unconstrained_variables[0]] # Deleting Rows and Columns of the Tableau self.A = np.delete(self.A, primal_equality_constraints[0], axis=0) self.m = self.m - 1 self.A = np.delete(self.A, primal_unconstrained_variables[0], axis=1) self.n = self.n - 1 # Resetting the Variables primal_equality_constraints = getIndexPositions(self.primal_dep, '0') primal_unconstrained_variables = getIndexPositionsThatStartWith(self.primal_ind, "*") if len(primal_unconstrained_variables) > 0: self.removing_primal_unconstrained_variables(primal_unconstrained_variables) if len(primal_equality_constraints) > 0: self.removing_equality_constraints(primal_equality_constraints) while any(self.A[0:self.m, self.n] < 0): # Testing if the Tableau is Maximum Basic Feasible if not self.__max_basic_feasible(): return while any(self.A[self.m, 0:self.n] > 0): # Testing if the Tableau is Optimal if not self.__optimal(): return print("************************************************************************************************************************") print("************************************************************************************************************************") self.print() def removing_equality_constraints(self, pec): primal_equality_constraints = pec while len(primal_equality_constraints) > 0: try: pivot_row = int(np.where(self.A[primal_equality_constraints[0], 0:self.n] != 0)[0][0]) except IndexError as error: print(error) print('The LP as entered cannot be converted into canonical form, please check the tableau for incorrect entries. Look for a row of zeros in row: ', primal_equality_constraints[0]) self.__pivot([primal_equality_constraints[0], pivot_row]) # Recording Rows and Columns of the Tableau self.dual_recorded_equations.insert(len(self.dual_recorded_equations), [self.A[:, pivot_row], self.dual_ind[:], self.dual_dep[pivot_row]]) # Deleting the Corresponding Variables del self.primal_ind[pivot_row] del self.dual_dep[pivot_row] # Deleting Rows and Columns of the Tableau self.A = np.delete(self.A, pivot_row, axis=1) self.n = self.n - 1 # Resetting the Variables primal_equality_constraints = getIndexPositions(self.primal_dep, '0') print("************************************************************************************************************************") def removing_primal_unconstrained_variables(self, puc): primal_unconstrained_variables = puc while len(primal_unconstrained_variables) > 0: try: pivot_row = int(np.where(self.A[primal_unconstrained_variables[0], 0:self.n] != 0)[0][0]) except IndexError as error: print(error) print('The LP as entered cannot be converted into canonical form, please check the tableau for incorrect entries. Look for a column of zeros in row: ', primal_unconstrained_variables[0]) pivot_row = int(pivot_row) # Pivoting self.__pivot([pivot_row, primal_unconstrained_variables[0]]) # Recording Rows and Columns of the Tableau self.primal_recorded_equations.insert(len(self.primal_recorded_equations), [self.A[pivot_row, :], self.primal_ind[:], self.primal_dep[pivot_row]]) # Deleting the Corresponding Variables del self.primal_dep[pivot_row] del self.dual_ind[pivot_row] # Deleting Rows and Columns of the Tableau self.A = np.delete(self.A, pivot_row, axis=0) self.m = self.m - 1 # Reset Variables primal_unconstrained_variables = getIndexPositionsThatStartWith(self.primal_ind, "*") print("************************************************************************************************************************") def __final_result(self): print("Primal: ") for dep in range(0, self.m): print(" ", self.primal_dep[dep], " = ", self.A[dep, self.n]) print("Dual") for dep in range(0, self.n): print(" ", self.dual_dep[dep], " = ", self.A[self.m, dep]) def __optimal(self): possible_pivot_columns = np.where(self.A[self.m, 0:self.n] > 0) possible_pivot_columns = possible_pivot_columns[0] for j in possible_pivot_columns: # Testing if the Linear Program is Unbounded if not self.__unbounded_check(j): return False if len(possible_pivot_columns) > 1: # If there are more than one choice, this will pass to method to decide which one # self. __min_optimal([self.primal_dep[i] for i in possible_pivot_columns], False) pivot_col = self.__which_row_or_col_to_use([self.primal_dep[i] for i in possible_pivot_columns], False) print(pivot_col) else: # self.__min_optimal([self.primal_dep[i] for i in possible_pivot_columns], False) pivot_col = possible_pivot_columns[0] pivot_cell = [self.__min(pivot_col, np.where(self.A[0:self.m, pivot_col] > 0)[0][0]), pivot_col] # print(np.where(self.A[0:self.m, pivot_col] > 0)[0]) # return self.__pivot(pivot_cell) return True def __unbounded_check(self, j): # Method That tests if a Linear Program is Unbounded, by testing a specific c_j > 0 and a column j if all(self.A[0:self.m, j] <= 0): print("The Linear Program is Unbounded in column: ", j, " No Solution!") self.print() return False else: return True def __max_basic_feasible(self): # loop from the bottom up, testing to see if any b_i<0. for i in reversed(range(0, self.m)): # Going from bottom up for each b_i so i is maximal if self.A[i, self.n] < 0: # testing to see if a pivot is needed (i.e. is b_i < 0) if not self.__infeasible_check(i): # if the row is infeasible, automatically ends the method return False possible_pivot_columns = np.where(self.A[i, 0:self.n] < 0) if len(possible_pivot_columns[0]) > 1: # If there are more than one choice, this will pass to method to decide which one pivot_col = self.__which_row_or_col_to_use([self.primal_ind[i] for i in possible_pivot_columns[0]], True) else: pivot_col = possible_pivot_columns[0][0] pivot_cell = [self.__min(pivot_col, i), pivot_col] self.__pivot(pivot_cell) return True def __pivot(self, pivot_cell): print("The Pivot Position is: ", "( ", pivot_cell[0], " , ", pivot_cell[1], " ). This gives: ") temp_primal_dep = self.primal_ind[pivot_cell[1]] # Switching Variables of The Primal Linear Program self.primal_ind[pivot_cell[1]] = self.primal_dep[pivot_cell[0]] self.primal_dep[pivot_cell[0]] = temp_primal_dep temp_dual_dep = self.dual_ind[pivot_cell[0]] # Switching Variables of the Dual Linear Program self.dual_ind[pivot_cell[0]] = self.dual_dep[pivot_cell[1]] self.dual_dep[pivot_cell[1]] = temp_dual_dep temp = np.copy(self.A) for i in range(0, self.m + 1): # Going through each entry in the tableau for j in range(0, self.n + 1): if i == pivot_cell[0] and j != pivot_cell[1]: # Pivot if on the same row as the pivot cell (and is not the pivot cell) self.A[i, j] = temp[i, j] / temp[pivot_cell[0], pivot_cell[1]] elif j == pivot_cell[1] and i != pivot_cell[0]: # Pivot if on the same column as the pivot cell (and is not the pivot cell) self.A[i, j] = -1 * temp[i, j] / temp[pivot_cell[0], pivot_cell[1]] elif i == pivot_cell[0] and j == pivot_cell[1]: # Pivoting on the actual pivot cell self.A[i, j] = 1 / temp[i, j] else: # All other entries self.A[i, j] = (temp[i, j] * temp[pivot_cell[0], pivot_cell[1]] - temp[pivot_cell[0], j] * temp[i, pivot_cell[1]]) / temp[pivot_cell[0], pivot_cell[1]] self.print() def __min(self, col, start_row): min_ratio = self.A[start_row, self.n] / self.A[start_row, col] current_row = start_row # print(current_row) for i in range(start_row + 1, self.m): if self.A[i, col] <= 0: continue if min_ratio > self.A[i, self.n] / self.A[i, col] >= 0: min_ratio = self.A[i, self.n] / self.A[i, col] current_row = i elif self.A[i, self.n] / self.A[i, col] == min_ratio: current_row = self.__which_row_or_col_to_use([self.primal_dep[i] for i in [current_row, i]], False) min_ratio = self.A[current_row, self.n] / self.A[current_row, col] # print(current_row) return current_row def __which_row_or_col_to_use(self, choices, col): # Returns the Proper Choice According to the Order for Blands Anti Cycling Rule. # If choosing pivot according to columns, set col = True in the method call, else set it to false for bland in self.blands_rule: if bland in choices: if col: return self.primal_ind.index(bland) else: return self.primal_dep.index(bland) def __infeasible_check(self, i): # This method checks if given a row of the tableau with b_i<0, it will test if the other the entries in the row are neg/pos and return false if they are indicating # that the tableau is infeasible if all(self.A[i, 0:self.n] >= 0): print("Tableau is Infeasible in Row: ", i, " No Solution!") self.print() return False else: return True

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null

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null

0

1

0

69be35315cfb09e0f8fbf8c5b357eea847bb9220

4,253

py

Python

pilot/user/atlas/memory.py

anisyonk/pilot2

f06cbf903e3f235d6fc504b54faa02006b95256d

[ "Apache-2.0" ]

null

pilot/user/atlas/memory.py

anisyonk/pilot2

f06cbf903e3f235d6fc504b54faa02006b95256d

[ "Apache-2.0" ]

null

pilot/user/atlas/memory.py

anisyonk/pilot2

f06cbf903e3f235d6fc504b54faa02006b95256d

[ "Apache-2.0" ]

null

#!/usr/bin/env python # 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 # # Authors: # - Paul Nilsson, paul.nilsson@cern.ch, 2018 from .utilities import get_memory_values from pilot.common.errorcodes import ErrorCodes from pilot.util.auxiliary import get_logger, set_pilot_state from pilot.util.processes import kill_processes errors = ErrorCodes() def allow_memory_usage_verifications(): """ Should memory usage verifications be performed? :return: boolean. """ return True def get_ucore_scale_factor(job): """ Get the correction/scale factor for SCORE/4CORE/nCORE jobs on UCORE queues/ :param job: job object. :return: scale factor (int). """ log = get_logger(job.jobid) try: job_corecount = float(job.corecount) except Exception as e: log.warning('exception caught: %s (job.corecount=%s)' % (e, str(job.corecount))) job_corecount = None try: schedconfig_corecount = float(job.infosys.queuedata.corecount) except Exception as e: log.warning('exception caught: %s (job.infosys.queuedata.corecount=%s)' % (e, str(job.infosys.queuedata.corecount))) schedconfig_corecount = None if job_corecount and schedconfig_corecount: try: scale = job_corecount / schedconfig_corecount log.debug('scale=%f' % scale) except Exception as e: log.warning('exception caught: %s (using scale factor 1)' % e) scale = 1 else: log.debug('will use scale factor 1') scale = 1 return scale def memory_usage(job): """ Perform memory usage verification. :param job: job object :return: exit code (int), diagnostics (string). """ exit_code = 0 diagnostics = "" log = get_logger(job.jobid) # Get the maxPSS value from the memory monitor summary_dictionary = get_memory_values(job.workdir, name=job.memorymonitor) if not summary_dictionary: exit_code = errors.BADMEMORYMONITORJSON diagnostics = "Memory monitor output could not be read" return exit_code, diagnostics maxdict = summary_dictionary.get('Max', {}) maxpss_int = maxdict.get('maxPSS', -1) # Only proceed if values are set if maxpss_int != -1: maxrss = job.infosys.queuedata.maxrss if maxrss: # correction for SCORE/4CORE/nCORE jobs on UCORE queues scale = get_ucore_scale_factor(job) try: maxrss_int = 2 * int(maxrss * scale) * 1024 # Convert to int and kB except Exception as e: log.warning("unexpected value for maxRSS: %s" % e) else: # Compare the maxRSS with the maxPSS from memory monitor if maxrss_int > 0 and maxpss_int > 0: if maxpss_int > maxrss_int: diagnostics = "job has exceeded the memory limit %d kB > %d kB (2 * queuedata.maxrss)" % \ (maxpss_int, maxrss_int) log.warning(diagnostics) # Create a lockfile to let RunJob know that it should not restart the memory monitor after it has been killed #pUtil.createLockFile(False, self.__env['jobDic'][k][1].workdir, lockfile="MEMORYEXCEEDED") # Kill the job set_pilot_state(job=job, state="failed") job.piloterrorcodes, job.piloterrordiags = errors.add_error_code(errors.PAYLOADEXCEEDMAXMEM) kill_processes(job.pid) else: log.info("max memory (maxPSS) used by the payload is within the allowed limit: " "%d B (2 * maxRSS = %d B)" % (maxpss_int, maxrss_int)) else: if maxrss == 0 or maxrss == "0": log.info("queuedata.maxrss set to 0 (no memory checks will be done)") else: log.warning("queuedata.maxrss is not set") return exit_code, diagnostics

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0

0.164179

0

null

0

null

0

1

0

69bf52e942c0d69a50c6990eb95bacea62b42777

6,038

py

Python

validator/validator.py

iestynpryce/file-validator

c1a16c3ae41847d92c0a251fa0ae386be6f39766

[ "MIT" ]

null

validator/validator.py

iestynpryce/file-validator

c1a16c3ae41847d92c0a251fa0ae386be6f39766

[ "MIT" ]

null

validator/validator.py

iestynpryce/file-validator

c1a16c3ae41847d92c0a251fa0ae386be6f39766

[ "MIT" ]

null

from copy import deepcopy from os.path import basename import pickle import re import sys import xml.etree.ElementTree as ET class Validate(): # Store information about the fields field_mapping = dict() field_length = dict() field_type = dict() diefast = True database = None file_status = dict() def __init__(self, dblocation, diefast=True): self.database = dblocation self.diefast = diefast def store_results(self): out = open(self.database, 'wb') pickle.dump(self.file_status, out) out.close() def validate_header(self, header, field_config): fields = header.split('|') prefix = fields[0] if prefix != 'H': print("Error: header prefix is '" + prefix + "', 'H' expected.") return False required_fields = {k.text: True for k in field_config.findall('.//name')} for index, field in enumerate(fields[1:], start=1): if field in required_fields: self.field_mapping[field] = index del(required_fields[field]) else: print("Error: header: field '" + field + "' found in header, but not in the configuration") return False if len(required_fields) > 0: print("Error: header missing the following fields:") for k in required_fields.keys(): print(k) return False return True def validate_footer(self, footer, nlines, interface_id, filename_dt): fields = footer.split('|') prefix = fields[0] if prefix != 'F': print("Error: footer prefix is '" + prefix + "', 'F' expected.") return False interface = fields[1] if interface != interface_id: print("Error: footer file group is '" + interface + "', '" + interface_id + "' expected.") return False dt_stamp = fields[2] if dt_stamp != filename_dt: print("Error: footer datetimestamp is:", dt_stamp, "Expected:", filename_dt) return False read_obs = fields[3] if int(read_obs) != nlines: print("Error: footer", nlines, "lines read", read_obs, "expected.") return False return True def validate_line(self, line, linenum, field_config): fields = line.split('|') prefix = fields[0] if prefix != 'B': print("Error: line", format(linenum), "body prefix is '" + prefix + "', 'B' expected.") return False for f, i in self.field_mapping.items(): # Check if the field even exists try: field = fields[i] except IndexError: print("Error: line", linenum, "column", i+1, "- field not found. (Field name:", f + ")") return False # Check if it's within the length limits if len(field) > self.field_length[f]: print("Error: line", linenum, "column", i+1, "- field too long.", len(field), ">", self.field_length[f], "(Field name:", f + ")") return False # Check the type of the field self.check_type(field, f) return True def check_type(self, field, field_name): return True def validator_setup(self, fields): for field in fields.findall('field'): name = field.find('name').text length = int(field.find('length').text) ftype = field.find('type').text self.field_length[name] = length self.field_type[name] = ftype def validate_file(self, config, filename): tree = ET.parse(config) root = tree.getroot() error = False for interface in root.findall('interface'): fields = interface.find('fields') self.validator_setup(fields) group = interface.find('group').text name = interface.find('fileName').find('name').text m = re.match(name, basename(filename)) if m == None: print("Error: filename '" + basename(filename) +"' does not match the expected pattern:", name) return False interface_name = m.group(int(interface.find('fileName').find('fileInterface').text)) sub_group = m.group(int(interface.find('fileName').find('subGroup').text)) date_time = m.group(int(interface.find('fileName').find('dateTimeGroup').text)) try: with open(filename) as f: nlines = 0 first_line = f.readline().rstrip() last_line = f.readline().rstrip() ok = self.validate_header(first_line, fields) if not ok: error = True if self.diefast: sys.exit(-1) nlines += 1 for line in f: nlines +=1 ok = self.validate_line(last_line, nlines, fields) if not ok: error = True if self.diefast: sys.exit(-1) last_line = line.rstrip() nlines += 1 ok = self.validate_footer(last_line, nlines, group, date_time) if not ok: error = True if self.diefast: sys.exit(-1) except IOError as e: print("Error:", e.value) return False if not error: print("SUCCESS: validated", basename(filename), "from group:", group + ", subgroup:", sub_group + ", interface:", interface_name +". Timestamp:", date_time) return True else: return False

34.701149

145

0.51474

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0.224111

0.046931

0.031178

0.032819

0.189367

0.175583

0.09616

0.062685

0.040368

0

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6,038

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0

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false

0

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0.007407

0.288889

0.103704

0

null

0

null

0

1

0

69c12ea8e597124ef988d5e50b496e666fa30337

1,912

py

Python

models/encoders/encoder_transformer.py

tushar117/counter-neural-essay-length-copy

010aee9b4869c32472ec821f52ce6c9914410a2e

[ "MIT" ]

6

2021-11-13T02:20:42.000Z

2022-01-03T14:22:25.000Z

models/encoders/encoder_transformer.py

tushar117/counter-neural-essay-length-copy

010aee9b4869c32472ec821f52ce6c9914410a2e

[ "MIT" ]

2

2021-11-12T06:00:31.000Z

2022-03-29T15:11:31.000Z

models/encoders/encoder_transformer.py

tushar117/counter-neural-essay-length-copy

010aee9b4869c32472ec821f52ce6c9914410a2e

[ "MIT" ]

null

# -*- coding: utf-8 -*- import torch import torch.nn as nn import utils from utils import LONG, FLOAT from models.transformer.Models import Encoder as Transf_Encoder class Encoder_Transfomer(nn.Module): """ encoders class """ # def __init__(self, config, x_embed): super().__init__() self.use_gpu = config.use_gpu len_max_seq = 0 if config.pad_level == "doc": len_max_seq = config.max_len_doc else: len_max_seq = config.max_len_sent self.d_model = config.embed_size self.model = Transf_Encoder( x_embed=x_embed.x_embed, n_src_vocab=config.max_vocab_cnt, len_max_seq=len_max_seq, d_word_vec=config.embed_size, d_model=self.d_model, d_inner=config.d_inner_hid, n_layers=config.transf_n_layers, n_head=config.n_head, d_k=config.d_k, d_v=config.d_v, dropout=config.dropout ) #self.encoder_out_size = config.d_model self.encoder_out_size = self.d_model return # end def __init__ # generate positional input consists of order def gen_positional_input(self, seq_x): pos_x = [] for cur_batch in seq_x: cur_pos = [] for ind, val in enumerate(cur_batch): if val != 0: cur_pos.append(ind+1) else: cur_pos.append(0) pos_x.append(cur_pos) pos_input = torch.LongTensor(pos_x) pos_input = utils.cast_type(pos_input, LONG, self.use_gpu) return pos_input # end def_gen_positional_input # def forward(self, text_inputs, mask_input, len_seq, mode=""): pos_x = self.gen_positional_input(text_inputs) encoder_out, *_ = self.model(text_inputs, pos_x) encoder_out = encoder_out * mask_input.unsqueeze(2) return encoder_out # end forward

27.710145

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0.054845

0.041133

0.027422

0.038391

0

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0.28818

1,912

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0

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0

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0

1

0.073171

false

0

0.121951

0

0.292683

0

null

0

null

0

1

0

69c6a761f9ad45bb17ab0266ac95a1768e562491

1,760

py

Python

some_completed_notebooks/energy_levels_not_commented/gradient.py

Quantum-Computing-Cooperation/Quantum_Hackathon_2021

69bb50ebd93cbc2bdca59de360408a5a2ecd6f2e

[ "Apache-2.0" ]

4

2021-11-17T19:42:15.000Z

2022-03-05T18:36:59.000Z

some_completed_notebooks/energy_levels_not_commented/gradient.py

Quantum-Computing-Cooperation/Quantum_Hackathon_2021

69bb50ebd93cbc2bdca59de360408a5a2ecd6f2e

[ "Apache-2.0" ]

null

some_completed_notebooks/energy_levels_not_commented/gradient.py

Quantum-Computing-Cooperation/Quantum_Hackathon_2021

69bb50ebd93cbc2bdca59de360408a5a2ecd6f2e

[ "Apache-2.0" ]

6

2021-11-19T18:37:07.000Z

2021-11-20T22:26:17.000Z

from qiskit.opflow.state_fns import CircuitStateFn, StateFn from qiskit.opflow.expectations import PauliExpectation from qiskit.opflow.converters import CircuitSampler import numpy as np def ei(i, n): vi = np.zeros(n) vi[i] = 1.0 return vi[:] def gradient(n_qubits, n_layer, op, ansatz, params, shots, instance): n_params = len(params) wfn_circuits = [] op = StateFn(op, is_measurement=True) for i in range(n_params): wfn_circuits.append(CircuitStateFn( ansatz(params + ei(i, n_params) * np.pi / 2.0, n_spins=n_qubits, n_layer=n_layer, full_rotation=True))) wfn_circuits.append(CircuitStateFn( ansatz(params - ei(i, n_params) * np.pi / 2.0, n_spins=n_qubits, n_layer=n_layer, full_rotation=True))) # Now measure circuits results = [] for wfn in wfn_circuits: braket = op @ wfn # Simulate the sampling grouped = PauliExpectation().convert(braket) sampled_op = CircuitSampler(instance).convert(grouped) # Expectation value mean_value = sampled_op.eval().real est_err = 0 # If the simulations is not unitary evolution, return an error bar if (not instance.is_statevector): variance = PauliExpectation().compute_variance(sampled_op).real est_err = np.sqrt(variance / shots) results.append([mean_value, est_err]) g = np.zeros((n_params, 2)) for i in range(n_params): rplus = results[2 * i] rminus = results[2 * i + 1] # G = (Ep - Em)/2 # var(G) = var(Ep) * (dG/dEp)**2 + var(Em) * (dG/dEm)**2 g[i, :] = (rplus[0] - rminus[0]) / 2.0, np.sqrt(rplus[1] ** 2 + rminus[1] ** 2) / 2.0 return g

34.509804

115

0.614205

245

1,760

4.273469

0.346939

0.040115

0.045845

0.037249

0.225406

0.191022

0

0.018547

0.264773

1,760

51

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34.509804

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1

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false

0

0.117647

0

0.235294

0

null

0

null

0

1

0

69c80f5af3f6bc89594adb960d2a07c5548b7847

6,814

py

Python

src/ext/executor/compare_executor.py

terryyrliang/facenet

0f6c0074cef5f1254abfa2bfa0357db023f1908d

[ "MIT" ]

null

src/ext/executor/compare_executor.py

terryyrliang/facenet

0f6c0074cef5f1254abfa2bfa0357db023f1908d

[ "MIT" ]

null

src/ext/executor/compare_executor.py

terryyrliang/facenet

0f6c0074cef5f1254abfa2bfa0357db023f1908d

[ "MIT" ]

null

from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys import numpy as np import tensorflow as tf from scipy import misc app_path = os.environ['APP_PATH'] for p in app_path.split(';'): sys.path.append(p) import os import copy import facenet import align.detect_face from common import config_fetcher from kafka import KafkaConsumer from kafka import KafkaProducer import json from bean import event servers = config_fetcher.bootstrap_hosts group_id = config_fetcher.group_id compare_topic = config_fetcher.compare_topic aggregate_topic = config_fetcher.aggregate_topic model = config_fetcher.model # To consume latest messages and auto-commit offsets consumer = KafkaConsumer(compare_topic, group_id = group_id, bootstrap_servers = servers, value_deserializer=lambda m: json.loads(m.decode('ascii'))) producer = KafkaProducer(value_serializer=lambda v:json.dumps(v).encode('utf-8'), bootstrap_servers = servers) def execute(): # images = load_and_align_data(image_files, image_size, margin, gpu_memory_fraction) with tf.Graph().as_default(): with tf.Session() as sess: # Load the model facenet.load_model(model) ######################################################################################################### ############################################# Split Line ################################################ ######################################################################################################### print('load model done...') for message in consumer: try: request = message.value image_files = request['face_extract_path'] target_extract_path = request['target_extract_path'] image_files.insert(0, target_extract_path) print("get a request") images = load_and_align_data(image_files, config_fetcher.compare_is, config_fetcher.compare_margin, config_fetcher.compare_gmf) # Get input and output tensors images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0") embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0") phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0") # Run forward pass to calculate embeddings feed_dict = {images_placeholder: images, phase_train_placeholder: False} emb = sess.run(embeddings, feed_dict=feed_dict) nrof_images = len(image_files) print_target_images(nrof_images, image_files) print_result_matrix(np, nrof_images, emb) fr = extract_final_result(np, nrof_images, emb) result = False for r in fr: if r < 1: result = True break next_request = build_next_request(request, result, '') print('-----------------------------------') print(event.convert_to_dict(next_request)) producer.send(aggregate_topic, next_request) print('-----------------------------------') print("process one request done...") except Exception as e: print(e) def extract_final_result(np, nrof_images, emb): final_result = [] for j in range(1, nrof_images): dist = np.sqrt(np.sum(np.square(np.subtract(emb[0, :], emb[j, :])))) final_result.append(dist) return final_result def print_target_images(nrof_images, image_files): print('Images:') for i in range(nrof_images): print('%1d: %s' % (i, image_files[i])) print('') def print_result_matrix(np, nrof_images, emb): # Print distance matrix print('Distance matrix') print(' ', end='') for i in range(nrof_images): print(' %1d ' % i, end='') print('') for i in range(nrof_images): print('%1d ' % i, end='') for j in range(nrof_images): dist = np.sqrt(np.sum(np.square(np.subtract(emb[i, :], emb[j, :])))) print(' %1.4f ' % dist, end='') print('') def load_and_align_data(image_paths, image_size, margin, gpu_memory_fraction): minsize = 20 # minimum size of face threshold = [0.6, 0.7, 0.7] # three steps's threshold factor = 0.709 # scale factor print('Creating networks and loading parameters') with tf.Graph().as_default(): gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction) sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False)) with sess.as_default(): pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None) tmp_image_paths = copy.copy(image_paths) img_list = [] for image in tmp_image_paths: img = misc.imread(os.path.expanduser(image), mode='RGB') img_size = np.asarray(img.shape)[0:2] bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor) if len(bounding_boxes) < 1: image_paths.remove(image) print("can't detect face, remove ", image) continue det = np.squeeze(bounding_boxes[0, 0:4]) bb = np.zeros(4, dtype=np.int32) bb[0] = np.maximum(det[0] - margin / 2, 0) bb[1] = np.maximum(det[1] - margin / 2, 0) bb[2] = np.minimum(det[2] + margin / 2, img_size[1]) bb[3] = np.minimum(det[3] + margin / 2, img_size[0]) cropped = img[bb[1]:bb[3], bb[0]:bb[2], :] aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear') prewhitened = facenet.prewhiten(aligned) img_list.append(prewhitened) images = np.stack(img_list) return images def build_next_request(old_request, result, error_message): nr = event.AggregatorRequest(old_request['client_id'], old_request['session_id'], old_request['trace_id'], old_request['total_image_numbers'], old_request['request_order'], old_request['root_path'], old_request['extract_root_path'], old_request['face_image_path'], result, error_message) return nr if __name__ == "__main__": execute()

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148

0.567361

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0.016389

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0.157061

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149

43.96129

0.737477

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null

0

null

0

1

0

69ca51f9a5be7291a799b3ee485e4a0de181e34a

4,356

py

Python

tests/test_it2.py

gamis/flo

bcaf3856cc2dcef0ef223e3e8901dcfbce34d5a0

[ "MIT" ]

null

tests/test_it2.py

gamis/flo

bcaf3856cc2dcef0ef223e3e8901dcfbce34d5a0

[ "MIT" ]

null

tests/test_it2.py

gamis/flo

bcaf3856cc2dcef0ef223e3e8901dcfbce34d5a0

[ "MIT" ]

null

import random import pytest from flo.it2 import from_, for_each, unique_index, index from flo.lamb import e_ def test_usage_examples() -> None: mylist = ['pretty', 'cool', 'items', 'kiddo'] expected = {'P': 'PRETTY', 'I': 'ITEMS'} expected_repr = "It(list[4] * _.upper() / _ contains 'E')" it = from_(mylist).map(e_.upper()).filter(e_.has('E')) assert repr(it) == expected_repr assert it.collect(unique_index, key=e_[0]) == expected it = from_(mylist).map(e_.upper()).where(e_.has('E')) assert it.to(unique_index, key=e_[0]) == expected assert (for_each('pretty', 'cool', 'items', 'kiddo') * e_.upper() / e_.has('E') > list) == list(expected.values()) @pytest.mark.parametrize('src,mapper,rep,expected', [([3, 4], str, "It(list[2] * str)", ["3", "4"]), ([3, 4], e_ + 1, "It(list[2] * _+1)", [4, 5])]) def test_map(src, mapper, rep, expected): it = from_(src).map(mapper) assert repr(it) == rep assert it.to(list) == expected @pytest.mark.parametrize('src,mapper,rep,expected', [(["3 1", "a", "b c d"], str.split, "It(list[3] * split * flatten)", ["3", "1", "a", "b", "c", "d"]), (["3,1", "a", "b,c,d"], e_.split(','), "It(list[3] * _.split(',') * flatten)", ["3", "1", "a", "b", "c", "d"]), (["3,1", "a", "b,c,d"], (str.split, {'sep': ','}), "It(list[3] * split(_, sep=',') * flatten)", ["3", "1", "a", "b", "c", "d"]) ]) def test_flatmap(src, mapper, rep, expected): if isinstance(mapper, tuple): mapper, kwargs = mapper else: kwargs = {} it = from_(src).flatmap(mapper, **kwargs) assert repr(it) == rep assert it.to(list) == expected @pytest.mark.parametrize('src,f,rep,expected', [([3, 4], e_ > 3, "It(list[2] / _>3)", [4]), ([3, 4], e_.apply(str).not_in({'2'}), "It(list[2] / _.apply(str) not in {'2'})", [3, 4])]) def test_filter(src, f, rep, expected): it = from_(src).filter(f) assert repr(it) == rep assert it.to(list) == expected @pytest.mark.parametrize('src,f,rep,expected', [([3, 4], e_ > 3, "It(list[2] / not _>3)", [3]), ([3, 4], e_.apply(str).not_in({'2'}), "It(list[2] / not _.apply(str) not in {'2'})", [])]) def test_exclude(src, f, rep, expected): it = from_(src).exclude(f) assert repr(it) == rep assert it.to(list) == expected @pytest.mark.parametrize('src,f,expected', [([3, 4], (set,), {3, 4}), ([3, 4], (list, str), '[3, 4]') ]) def test_collect(src, f, expected): assert from_(src).collect(*f) == expected @pytest.mark.parametrize('src,expected', [([[1, 2, 3], [4]], [1, 2, 3, 4]), ([[1, 2, 3], [], [4]], [1, 2, 3, 4]), ([[1, 2, 3], None, [4]], TypeError), ([[1, 2, 3], 15, [4]], TypeError), ]) def test_flatten(src: list, expected: list): try: assert from_(src).flatten().to(list) == expected except TypeError: assert expected == TypeError def test_zip_with(): assert for_each(1, 2, 3).zip_with('abc').to(list) == [(1, 'a'), (2, 'b'), (3, 'c')] def test_chain(): assert for_each(1, 2, 3).chain('abc').to(list) == [1, 2, 3, 'a', 'b', 'c'] def test_dropwhile(): assert from_(range(10)).dropwhile(e_ < 3).to(list) == [3, 4, 5, 6, 7, 8, 9] def test_takewhile(): assert from_(range(10)).takewhile(e_ < 3).to(list) == [0, 1, 2] def test_cache(): rng = random.Random() original = for_each(1, 2, 3).map(lambda e: e + rng.random()) assert original.to(list) != original.to(list) cached = original.cache() first = cached.to(list) second = cached.to(list) assert first == second def test_index(): src = ['apples', 'bananas', 'apricots', 'grapes', 'grapefruit'] actual = from_(src).to(index, key=e_[0]) expected = dict(a=['apples', 'apricots'], b=['bananas'], g=['grapes', 'grapefruit']) assert actual == expected try: from_(src).to(unique_index, key=e_[0]) pytest.fail('Expected exception') except KeyError as e: pass

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153

0.504821

598

4,356

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0.177258

0.016909

0.014091

0.067637

0.387976

0.33396

0.267262

0.23861

0.202912

0

0.038305

0.268825

4,356

125

154

34.848

0.630141

0

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0

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0.01056

0

0.241758

1

0.142857

false

0.010989

0.043956

0

0.186813

0

null

0

null

0

1

0

69cc030fefacd641eb7ed7ab0afe45ef96bb7bd1

3,242

py

Python

foliant/backends/mdtopdf.py

foliant-docs/mdtopdf

c383af7b92779f2dca3644e127b0a2466e15afbc

[ "MIT" ]

null

foliant/backends/mdtopdf.py

foliant-docs/mdtopdf

c383af7b92779f2dca3644e127b0a2466e15afbc

[ "MIT" ]

null

foliant/backends/mdtopdf.py

foliant-docs/mdtopdf

c383af7b92779f2dca3644e127b0a2466e15afbc

[ "MIT" ]

null

import json import shutil from subprocess import run, PIPE, STDOUT, CalledProcessError from pathlib import PosixPath from foliant.utils import spinner from foliant.backends.base import BaseBackend class Backend(BaseBackend): _flat_src_file_name = '__all__.md' targets = ('pdf', 'site') required_preprocessors_after = [{ 'flatten': { 'flat_src_file_name': _flat_src_file_name }}, {'mdtopdf': {}} ] defaults = { 'mdtopdf_path': 'md-to-pdf', 'options': {}, } def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._flat_src_file_path = self.working_dir / self._flat_src_file_name config = self.config.get('backend_config', {}).get('mdtopdf', {}) self._mdtopdf_config = {**self.defaults, **config} self._slug = f'{self._mdtopdf_config.get("slug", self.get_slug())}' self._slug_for_commands = self._escape_control_characters(str(self._slug)) self._cachedir = self.project_path / '.mdtopdfcache' shutil.rmtree(self._cachedir, ignore_errors=True) self._cachedir.mkdir() self.logger = self.logger.getChild('mdtopdf') self.logger.debug(f'Backend inited: {self.__dict__}') def _escape_control_characters(self, source_string: str) -> str: escaped_string = source_string.replace('"', "\\\"").replace('$', "\\$").replace('`', "\\`") return escaped_string def _generate_config_file(self, options: dict) -> PosixPath: config_path = self._cachedir / 'config.json' config = {} for key, value in options.items(): config[key.replace('-', '_')] = value # md-to-pdf on unix requires --no-sandbox puppeteer flag to work properly launch_args = config.setdefault('launch_options', {}).setdefault('args', []) if '--no-sandbox' not in launch_args: launch_args.append('--no-sandbox') with open(config_path, 'w') as f: json.dump(config, f) return config_path def _get_html_command(self) -> str: pass def _get_pdf_command(self) -> str: components = [self._mdtopdf_config['mdtopdf_path']] config_path = self._generate_config_file(self._mdtopdf_config['options']) components.append(f'--config-file {config_path}') components.append(str(self._flat_src_file_path)) components.append(f'"{self._slug_for_commands}.pdf"') command = ' '.join(components) self.logger.debug(f'PDF generation command: {command}') return command def make(self, target: str) -> str: with spinner(f'Making {target} with md-to-pdf', self.logger, self.quiet, self.debug): try: command = self._get_pdf_command() self.logger.debug('Running the command.') run(command, shell=True, check=True, stdout=PIPE, stderr=STDOUT) return f'{self._slug}.{target}' except CalledProcessError as exception: raise RuntimeError(f'Build failed: {exception.output.decode()}') except Exception as exception: raise type(exception)(f'Build failed: {exception}')

33.081633

99

0.62554

373

3,242

5.158177

0.327078

0.02183

0.034304

0.031185

0.019751

0

0.239975

3,242

97

100

33.42268

0.780844

0.0219

0

0.16977

0.035342

0

1

0.089552

false

0.014925

0.089552

0

0.313433

0

null

0

null

0

1

0

69cd79a3c79726917baeb1caec48a1c929c2f4cf

12,859

py

Python

plottingScripts/PlotTweak.py

JaakkoAhola/LES-scripting

1ebe99ce4292e58581bf50615cb8e0aa3d0c0af2

[ "MIT" ]

null

plottingScripts/PlotTweak.py

JaakkoAhola/LES-scripting

1ebe99ce4292e58581bf50615cb8e0aa3d0c0af2

[ "MIT" ]

null

plottingScripts/PlotTweak.py

JaakkoAhola/LES-scripting

1ebe99ce4292e58581bf50615cb8e0aa3d0c0af2

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Jan 20 13:30:13 2020 @author: Jaakko Ahola, Finnish Meteorological Institute @licence: MIT licence Copyright """ import matplotlib import numpy import time from Data import Data from Simulation import Simulation class PlotTweak: # DELETE def hideXLabels(ax, xLabelListShowBoolean, xLabelListMajorLineBoolean): k = 0 for label in ax.xaxis.get_ticklabels(): label.set_visible(xLabelListShowBoolean[k]) k = k + 1 k = 0 for line in ax.xaxis.get_ticklines()[0::2]: if xLabelListMajorLineBoolean[k]: line.set_markersize(matplotlib.rcParams["xtick.major.size"]) else: line.set_markersize(matplotlib.rcParams["xtick.minor.size"]) k= k + 1 return ax # DELETE def hideYLabels(ax, param): k = 0 for label in ax.yaxis.get_ticklabels(): if numpy.mod(k,param) != 0: label.set_visible(False) k+=1 return ax # DELETE def hideColorbarXLabels(cbar, colorbarLabelListShowBoolean): k = 0 for label in cbar.ax.xaxis.get_ticklabels(): label.set_visible(colorbarLabelListShowBoolean[k]) k = k + 1 return cbar # DELETE def hideColorbarYLabels(cbar, colorbarLabelListShowBoolean): k = 0 for label in cbar.ax.yaxis.get_ticklabels(): label.set_visible(colorbarLabelListShowBoolean[k]) k = k + 1 # DELETE def setXTicksLabelsAsTimeOld(ax, timeHvalues, tickInterval = 16, unit = "h", startPoint = 0, xLabelListShow = None, xLabelListMajorLine = None, setXlabel = True): first = timeHvalues[0] last = timeHvalues[-1] xticks = numpy.arange(first,last+0.1, 0.5) #xticks = xticks[::tickInterval] xticklabels = [ str(int(round(elem,1))) for elem in list(xticks) ] #print(xticks) ax.set_xticks( xticks ) ax.set_xticklabels(xticklabels) timesize = numpy.shape(xticks)[0] ##################### if xLabelListShow is None: xLabelListShowBoolean = [False]*timesize #timesize = numpy.shape(timeHvalues)[0] for i in range(timesize): if numpy.mod(i, tickInterval) == 0: xLabelListShowBoolean[i] = True else: xLabelListShowBoolean = Data.getMaskedList( xticks, xLabelListShow) ############################# if xLabelListMajorLine is None: xLabelListMajorLineBoolean = [False]*timesize for i in range(timesize): if numpy.mod(i, tickInterval) == 0: xLabelListMajorLineBoolean[i] = True else: xLabelListMajorLineBoolean = Data.getMaskedList( xticks, xLabelListMajorLine) ax = PlotTweak.hideXLabels(ax, xLabelListShowBoolean, xLabelListMajorLineBoolean) if setXlabel: ax.set_xlabel(r"$\mathbf{time (" + unit + ")}$") else: ax.set_xlabel(None) ax.set_xlim( first ,last ) return ax #DELETE ENDS def setXticks(ax, ticks = None, start = 0, end = 8, interval = 0.5): if ticks is None: ticks = Data.getIntergerList( start, end, interval) PlotTweak._setTicks(ax.set_xticks, ticks) return ticks def setYticks(ax, ticks, start = 0, end = 1000, interval = 50): if ticks is None: ticks = Data.getIntergerList( start, end, interval) PlotTweak._setTicks(ax.set_yticks, ticks) return ticks # axset is in [ax.set_xticks, ax.set_yticks, ] def _setTicks(axset, ticks): axset(ticks) return ticks def setXLabels(ax, ticks, shownLabels = None, start = 0, end = 8, interval = 2): shownLabelsBoolean = PlotTweak._setLabels( ax.set_xticklabels, ax.xaxis, ticks, shownLabels, start, end, interval) return shownLabelsBoolean def setYLabels(ax, ticks, shownLabels = None, start = 0, end = 8, interval = 2): shownLabelsBoolean = PlotTweak._setLabels( ax.set_yticklabels, ax.yaxis, ticks, shownLabels, start, end, interval) return shownLabelsBoolean def _setLabels(axset, ax_axis, ticks, shownLabels = None, start = 0, end = 8, interval = 2): axset(ticks) if shownLabels is None: shownLabels = Data.getIntergerList( start, end, interval ) shownLabelsBoolean = Data.getMaskedList(ticks, shownLabels) PlotTweak._hideLabels(ax_axis, shownLabelsBoolean) return shownLabelsBoolean # ax_axis is eithery ax.yaxis or colorbar.ax.xaxis or colorbar.ax.yaxis def _hideLabels(ax_axis, shownLabelsBoolean): k = 0 for label in ax_axis.get_ticklabels(): label.set_visible(shownLabelsBoolean[k]) k = k + 1 def hideXTickLabels(ax): PlotTweak._hideAllTickLabels(ax.get_xticklabels) def hideYTickLabels(ax): PlotTweak._hideAllTickLabels(ax.get_yticklabels) def _hideAllTickLabels(axTicksGetter): matplotlib.pyplot.setp(axTicksGetter()[:], visible=False) def setXTickSizes(ax, labelListMajorLineBoolean, majorFontsize = matplotlib.rcParams["xtick.major.size"], minorFontSize = matplotlib.rcParams["xtick.minor.size"]): PlotTweak._setTickSizes(ax.xaxis, labelListMajorLineBoolean, majorFontsize, minorFontSize) def setYTickSizes(ax, labelListMajorLineBoolean, majorFontsize = matplotlib.rcParams["ytick.major.size"], minorFontSize = matplotlib.rcParams["ytick.minor.size"]): PlotTweak._setTickSizes(ax.yaxis, labelListMajorLineBoolean, majorFontsize, minorFontSize) # ax_axis is eithery ax.yaxis or colorbar.ax.xaxis or colorbar.ax.yaxis def _setTickSizes(ax_axis, labelListMajorLineBoolean, majorFontsize, minorFontSize): k = 0 for line in ax_axis.get_ticklines()[0::2]: if labelListMajorLineBoolean[k]: line.set_markersize( majorFontsize ) else: line.set_markersize(minorFontSize) k= k + 1 def getUnitLabel(label, unit, useBold = False): if useBold: boldingStart = "\mathbf{" boldingEnd = "}" else: boldingStart = "" boldingEnd = "" return r"$" +boldingStart + "{" + label + "}{\ } ( " + unit + ")" + boldingEnd + "$" def setXaxisLabel(ax, label, unit = None, useBold = False): PlotTweak._setLabel(ax.set_xlabel, label, unit, useBold) def setYaxisLabel(ax, label, unit = None, useBold = False): PlotTweak._setLabel(ax.set_ylabel, label, unit, useBold) def _setLabel(labelPointer, label, unit, useBold): if unit is not None: label = PlotTweak.getUnitLabel(label, unit, useBold) labelPointer(label) def getLogaritmicTicks(tstart, tend, includeFives = False): # tstart = -17 # tend = -9 logaritmicTicks = numpy.arange(tstart, tend) if includeFives: fives = numpy.arange(tstart+numpy.log10(5), tend) logaritmicTicks = numpy.sort(numpy.concatenate((logaritmicTicks,fives))) return logaritmicTicks def setXLim(ax, start = 0, end = 1): ax.set_xlim( start ,end ) def setYLim(ax, start = 0, end = 1): ax.set_ylim( start ,end ) def setAnnotation(ax, text, fontsize = None, xPosition = 0, yPosition = 0, bbox_props = dict(boxstyle="square,pad=0.1", fc="w", ec="0.5", alpha=0.9)): ax.annotate( text, xy=(xPosition, yPosition), size=fontsize, bbox = bbox_props) def setTightLayot(fig): fig.tight_layout() def setAxesOff(ax): ax.axis('off') def useLegend(ax = None, loc = "best"): if ax is None: matplotlib.pyplot.legend(loc = loc) else: ax.legend(loc = loc) def setLegendSimulation(ax, simulationList, loc = "center"): collectionOfLabelsColors = {} for simulation in simulationList: collectionOfLabelsColors[ simulation.getLabel() ] = simulation.getColor() PlotTweak.setLegend(ax, collectionOfLabelsColors, loc ) def setLegend(ax, collectionOfLabelsColors, loc = "center", fontsize = None): legend_elements = [] for label, color in collectionOfLabelsColors.items(): legend_elements.append( matplotlib.patches.Patch( label=label, facecolor=color)) ax.legend( handles=legend_elements, loc=loc, frameon = True, framealpha = 1.0, fontsize = fontsize ) def setArtist(ax, collectionOfLabelsColors, loc = "center", fontsize = None): legend_elements = [] for label, color in collectionOfLabelsColors.items(): legend_elements.append( matplotlib.patches.Patch( label=label, facecolor=color)) artist = ax.legend( handles=legend_elements, loc=loc, frameon = True, framealpha = 1.0, fontsize = fontsize ) ax.add_artist(artist) # setSuperWrapper # if xticks given, it overwrites xstart, xend, xinterval when getting ticks # if yticks given, it overwrites ystart, yend, yinterval when getting ticks def setSuperWrapper(ax, xstart = 0, xend = 8, xtickinterval = 0.5, xlabelinterval = 1, xticks = None, xShownLabels = None, xTickMajorFontSize = matplotlib.rcParams["xtick.major.size"], xTickMinorFontSize = matplotlib.rcParams["xtick.minor.size"], ystart = 0, yend = 100, ytickinterval = 50, ylabelinterval = 5, yticks = None, yShownLabels = None, yTickMajorFontSize = matplotlib.rcParams["ytick.major.size"], yTickMinorFontSize = matplotlib.rcParams["ytick.minor.size"], annotationText = None, annotationXPosition = 2, annotationYPosition = 30, xlabel = None, xunit = None, ylabel = None, yunit = None, setLegend = False, useBold = False ): # set xticks xticks = PlotTweak.setXticks(ax, ticks = xticks, start = xstart, end = xend, interval = xtickinterval) # set xlabels xShownLabelsBoolean = PlotTweak.setXLabels(ax, xticks, shownLabels = xShownLabels, start = xstart, end = xend, interval = xlabelinterval ) # set xtick sizes PlotTweak.setXTickSizes(ax, xShownLabelsBoolean, majorFontsize= xTickMajorFontSize, minorFontSize=xTickMinorFontSize) # set yticks yticks = PlotTweak.setYticks(ax, ticks = yticks, start = ystart, end = yend, interval = ytickinterval) # set ylabels yShownLabelsBoolean = PlotTweak.setYLabels(ax, yticks, shownLabels = yShownLabels, start = ystart, end = yend, interval = ylabelinterval ) # set ytick sizes PlotTweak.setYTickSizes(ax, yShownLabelsBoolean, majorFontsize= yTickMajorFontSize, minorFontSize=yTickMinorFontSize) # limit axes PlotTweak.setXLim(ax, start = xstart, end = xend) PlotTweak.setYLim(ax, start = ystart, end = yend) # set axes labels PlotTweak.setXaxisLabel( ax, xlabel, xunit, useBold) PlotTweak.setYaxisLabel( ax, ylabel, yunit, useBold) # set legend if setLegend: PlotTweak.setLegend( ax ) # set annotation for figure if annotationText is not None: PlotTweak.setAnnotation(ax, annotationText, xPosition = annotationXPosition, yPosition = annotationYPosition) def main(): pass if __name__ == "__main__": start = time.time() main() end = time.time() print("Script completed in " + str(round((end - start),0)) + " seconds")

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69cdbff0837f3d21e5dafd54a75dea3bef070906

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Python

So You Have Trained a Deep Learning Model/detectObjectsGPTool.py

esrinederland/DevSummit2020

09b14929552a6a5297ac7b117ebd5f21cf6eea37

[ "MIT" ]

3

2021-04-07T14:26:25.000Z

2021-04-15T14:56:12.000Z

So You Have Trained a Deep Learning Model/detectObjectsGPTool.py

esrinederland/DevSummit2021

09b14929552a6a5297ac7b117ebd5f21cf6eea37

[ "MIT" ]

null

So You Have Trained a Deep Learning Model/detectObjectsGPTool.py

esrinederland/DevSummit2021

09b14929552a6a5297ac7b117ebd5f21cf6eea37

[ "MIT" ]

null

import arcpy import arcgis import json ## Connect to your GIS Portal portalUrl = "https://pada.ad.local/" + "portal" gis = arcgis.gis.GIS(url=portalUrl, profile="portalAdmin", verify_cert=False) ## Get geometry from input parameter inputFC = arcpy.GetParameterAsText(0) with arcpy.da.SearchCursor(inputFC, ["SHAPE@JSON"]) as sCursor: for row in sCursor: inputGeometry = row[0] ## Get input raster and clip to geometry inputRaster = arcgis.raster.ImageryLayer("https://pada.ad.local/server/rest/services/Hosted/Imagery_Heerenveen/ImageServer", gis=gis) rasterClip = arcgis.raster.functions.clip(inputRaster, json.loads(inputGeometry)) rasterClip.save() ## Get Deep Learning Model modelObject = gis.content.get("054c037f7c1c47a99561410d5efe7b65") model = arcgis.learn.Model(modelObject) model.install() ## Set Model arguments modelArguments = { "padding": 100, "batch_size": 4, "threshold": 0.9, "return_bboxes": False } context = { "cellSize": 0.3, "processorType" : "CPU" } ## Inferencing outputFL = arcgis.learn.detect_objects( rasterClip, model, model_arguments=modelArguments, run_nms=True, confidence_score_field="Confidence", class_value_field="Class", max_overlap_ratio=0.3, context=context, gis=gis) ## Append new features to existing Feature Service featureService = arcgis.features.FeatureLayer("https://pada.ad.local/server/rest/services/Hosted/DetectObjects_test2/FeatureServer/0", gis=gis) result = featureService.edit_features(adds=outputFL.layers[0].query(where="1=1")) ## Delete temp Feature Service outputFL.delete() ## Set output parameter stringResult = json.dumps(result) arcpy.SetParameterAsText(1, stringResult)

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