paulo037/starcoder · Datasets at Hugging Face

max_stars_repo_path stringlengths 4 237	max_stars_repo_name stringlengths 6 117	max_stars_count int64 0 95.2k	id stringlengths 1 7	content stringlengths 12 593k	input_ids sequencelengths 7 549k
FGR/utils/visual.py	weiyithu/FGR	27	119242	<filename>FGR/utils/visual.py<gh_stars>10-100 from .kitti_utils_official import * import traceback def draw_bbox_3d_to_2d_gt(img, corner, AverageValue_x, AverageValue_y): gt_box_3d = corner.copy() for point in gt_box_3d: point[0] = point[0] * 100 + 250 - AverageValue_x point[2] = point[2] * 100 + 250 - AverageValue_y cv2.line(img, (int(gt_box_3d[0][0]), int(gt_box_3d[0][2])), (int(gt_box_3d[1][0]), int(gt_box_3d[1][2])), (0, 255, 255), 1, 4) cv2.line(img, (int(gt_box_3d[1][0]), int(gt_box_3d[1][2])), (int(gt_box_3d[2][0]), int(gt_box_3d[2][2])), (0, 255, 255), 1, 4) cv2.line(img, (int(gt_box_3d[2][0]), int(gt_box_3d[2][2])), (int(gt_box_3d[3][0]), int(gt_box_3d[3][2])), (0, 255, 255), 1, 4) cv2.line(img, (int(gt_box_3d[3][0]), int(gt_box_3d[3][2])), (int(gt_box_3d[0][0]), int(gt_box_3d[0][2])), (0, 255, 255), 1, 4) return img def draw_frustum_lr_line(img, left_point, right_point, AverageValue_x, AverageValue_y): left_point_draw = np.array([0., 0.]) left_point_draw[0] = (left_point[0] * 20000 + 250 - AverageValue_x) left_point_draw[1] = (left_point[1] * 20000 + 250 - AverageValue_y) right_point_draw = np.array([0., 0.]) right_point_draw[0] = (right_point[0] * 20000 + 250 - AverageValue_x) right_point_draw[1] = (right_point[1] * 20000 + 250 - AverageValue_y) initial_point_draw = np.array([0., 0.]) initial_point_draw[0] = 250 - AverageValue_x initial_point_draw[1] = 250 - AverageValue_y cv2.line(img, tuple(initial_point_draw.astype(np.float32)), tuple(left_point_draw.astype(np.float32)), (255, 255, 0), 1, 4) cv2.line(img, tuple(initial_point_draw.astype(np.float32)), tuple(right_point_draw.astype(np.float32)), (255, 255, 0), 1, 4) return img def draw_bbox_3d_to_2d_psuedo_no_intersection(img, box_no_intersection, AverageValue_x, AverageValue_y): box_draw = box_no_intersection.copy() for var in box_draw: var[0] = var[0] * 100 + 250 - AverageValue_x var[1] = var[1] * 100 + 250 - AverageValue_y cv2.line(img, tuple(box_draw[0]), tuple(box_draw[1]), (255, 0, 255), 1, 4) cv2.line(img, tuple(box_draw[1]), tuple(box_draw[2]), (255, 0, 255), 1, 4) cv2.line(img, tuple(box_draw[2]), tuple(box_draw[3]), (255, 0, 255), 1, 4) cv2.line(img, tuple(box_draw[3]), tuple(box_draw[0]), (255, 0, 255), 1, 4) return img def draw_bbox_3d_to_2d_psuedo_with_key_vertex(img, FinalPoint, loc1, loc2, loc3, AverageValue_x, AverageValue_y): loc1_draw = np.array([0., 0.]) loc2_draw = np.array([0., 0.]) loc3_draw = np.array([0., 0.]) loc1_draw[0] = loc1[0] * 100 + 250 - AverageValue_x loc1_draw[1] = loc1[1] * 100 + 250 - AverageValue_y loc2_draw[0] = loc2[0] * 100 + 250 - AverageValue_x loc2_draw[1] = loc2[1] * 100 + 250 - AverageValue_y loc3_draw[0] = loc3[0] * 100 + 250 - AverageValue_x loc3_draw[1] = loc3[1] * 100 + 250 - AverageValue_y # draw key vertex with larger point than normal point cloud FinalPoint_draw = np.array([0., 0.]) FinalPoint_draw[0] = FinalPoint[0] * 100 + 250 - AverageValue_x FinalPoint_draw[1] = FinalPoint[1] * 100 + 250 - AverageValue_y cv2.circle(img, tuple(FinalPoint_draw.astype(np.float32)), 3, (0, 255, 0), 4) cv2.line(img, tuple(loc1_draw.astype(np.float32)), tuple(FinalPoint_draw.astype(np.float32)), (0, 0, 255), 1, 4) cv2.line(img, tuple(loc1_draw.astype(np.float32)), tuple(loc3_draw.astype(np.float32)), (0, 0, 255), 1, 4) cv2.line(img, tuple(loc3_draw.astype(np.float32)), tuple(loc2_draw.astype(np.float32)), (0, 0, 255), 1, 4) cv2.line(img, tuple(loc2_draw.astype(np.float32)), tuple(FinalPoint_draw.astype(np.float32)), (0, 0, 255), 1, 4) return img def draw_point_clouds(img, KeyPoint_for_draw, AverageValue_x, AverageValue_y): for point in KeyPoint_for_draw: a = point[0] * 100 + 250 - AverageValue_x b = point[1] * 100 + 250 - AverageValue_y cv2.circle(img, (int(a), int(b)), 1, (255, 255, 255), 2) return img def draw_2d_box_in_2d_image(KeyPoint_3d, box_2d, p2): img = np.zeros((1000, 1000, 3), 'f4') KeyPoint_for_draw = np.copy(KeyPoint_3d[:, [0, 2]]) KeyPoint = KeyPoint_3d[:, [0, 2]] AverageValue_x = np.mean(KeyPoint[:, 0]) * 100 AverageValue_y = np.mean(KeyPoint[:, 1]) * 100 for point in KeyPoint_for_draw: a = point[0] * 50 + 500 - AverageValue_x b = point[1] * 50 + 100 - AverageValue_y cv2.circle(img, (int(a), int(b)), 1, (255, 255, 255), 0) left_point = np.array([box_2d[0], 0, 1]) right_point = np.array([box_2d[2], 0, 1]) left_point = np.linalg.inv(p2[:, [0, 1, 2]]).dot(left_point.T) right_point = np.linalg.inv(p2[:, [0, 1, 2]]).dot(right_point.T) left_point = left_point[[0, 2]] right_point = right_point[[0, 2]] left_point_draw = np.array([0., 0.]) right_point_draw = np.array([0., 0.]) while True: zoom_factor = 60000 try: left_point_draw[0] = (left_point[0] * zoom_factor + 100 - AverageValue_x) left_point_draw[1] = (left_point[1] * zoom_factor + 100 - AverageValue_y) right_point_draw[0] = (right_point[0] * zoom_factor + 100 - AverageValue_x) right_point_draw[1] = (right_point[1] * zoom_factor + 100 - AverageValue_y) except OverflowError: zoom_factor /= 6 continue else: break initial_point_draw = np.array([0., 0.]) initial_point_draw[0] = 500 - AverageValue_x initial_point_draw[1] = 100 - AverageValue_y cv2.line(img, tuple(initial_point_draw.astype(np.float32)), tuple(left_point_draw.astype(np.float32)), (255, 255, 0), 1, 4) cv2.line(img, tuple(initial_point_draw.astype(np.float32)), tuple(right_point_draw.astype(np.float32)), (255, 255, 0), 1, 4) return img def draw_3d_box_in_2d_image(img, box): # draw 3D box in 2D RGB image, if needed cv2.line(img, (int(box[0][0]), int(box[0][1])), (int(box[1][0]), int(box[1][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[1][0]), int(box[1][1])), (int(box[2][0]), int(box[2][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[2][0]), int(box[2][1])), (int(box[3][0]), int(box[3][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[3][0]), int(box[3][1])), (int(box[0][0]), int(box[0][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[0][0]), int(box[0][1])), (int(box[4][0]), int(box[4][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[1][0]), int(box[1][1])), (int(box[5][0]), int(box[5][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[2][0]), int(box[2][1])), (int(box[6][0]), int(box[6][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[3][0]), int(box[3][1])), (int(box[7][0]), int(box[7][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[4][0]), int(box[4][1])), (int(box[5][0]), int(box[5][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[5][0]), int(box[5][1])), (int(box[6][0]), int(box[6][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[6][0]), int(box[6][1])), (int(box[7][0]), int(box[7][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[7][0]), int(box[7][1])), (int(box[4][0]), int(box[4][1])), (0, 0, 255), 1, 4) return img def show_velodyne_in_camera(loc0, loc1, loc2, loc3, y_min, y_max): # use mayavi to draw 3D bbox corners = np.array([[[loc0[0], loc1[0], loc2[0], loc3[0], loc0[0], loc1[0], loc2[0], loc3[0]], [loc0[1], loc1[1], loc2[1], loc3[1], loc0[1], loc1[1], loc2[1], loc3[1]], [y_min, y_min, y_min, y_min, y_max, y_max, y_max, y_max]]], dtype=np.float32) for i in range(corners.shape[0]): corner = corners[i] idx = np.array([0, 1, 2, 3, 0, 4, 5, 6, 7, 4, 5, 1, 2, 6, 7, 3]) x = corner[0, idx] y = corner[1, idx] z = corner[2, idx] mayavi.mlab.plot3d(x, y, z, color=(0.23, 0.6, 1), colormap='Spectral', representation='wireframe', line_width=5)	[ 1, 529, 9507, 29958, 29943, 14345, 29914, 13239, 29914, 20119, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 3166, 869, 29895, 986, 29875, 29918, 13239, 29918, 29877, 7880, 1053, 334, 13, 5215, 9637, 1627, 13, 13, 13, 1753, 4216, 29918, 29890, 1884, 29918, 29941, 29881, 29918, 517, 29918, 29906, 29881, 29918, 4141, 29898, 2492, 29892, 11155, 29892, 319, 19698, 1917, 29918, 29916, 29892, 319, 19698, 1917, 29918, 29891, 1125, 13, 268, 13, 1678, 330, 29873, 29918, 1884, 29918, 29941, 29881, 353, 11155, 29889, 8552, 580, 13, 1678, 363, 1298, 297, 330, 29873, 29918, 1884, 29918, 29941, 29881, 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tests/factorys.py	2h4dl/pymilvus	0	17967	# STL imports import random import logging import string import time import datetime import random import struct import sys from functools import wraps # Third party imports import numpy as np import faker from faker.providers import BaseProvider logging.getLogger('faker').setLevel(logging.ERROR) sys.path.append('.') # grpc from milvus.grpc_gen import milvus_pb2 def gen_vectors(num, dim): return [[random.random() for _ in range(dim)] for _ in range(num)] def gen_single_vector(dim): return [[random.random() for _ in range(dim)]] def gen_vector(nb, d, seed=np.random.RandomState(1234)): xb = seed.rand(nb, d).astype("float32") return xb.tolist() def gen_unique_str(str=None): prefix = "".join(random.choice(string.ascii_letters + string.digits) for _ in range(8)) return prefix if str is None else str + "_" + prefix def get_current_day(): return time.strftime('%Y-%m-%d', time.localtime()) def get_last_day(day): tmp = datetime.datetime.now() - datetime.timedelta(days=day) return tmp.strftime('%Y-%m-%d') def get_next_day(day): tmp = datetime.datetime.now() + datetime.timedelta(days=day) return tmp.strftime('%Y-%m-%d') def gen_long_str(num): string = '' for _ in range(num): char = random.choice('tomorrow') string += char def gen_one_binary(topk): ids = [random.randrange(10000000, 99999999) for _ in range(topk)] distances = [random.random() for _ in range(topk)] return milvus_pb2.TopKQueryResult(struct.pack(str(topk) + 'l', ids), struct.pack(str(topk) + 'd', distances)) def gen_nq_binaries(nq, topk): return [gen_one_binary(topk) for _ in range(nq)] def fake_query_bin_result(nq, topk): return gen_nq_binaries(nq, topk) class FakerProvider(BaseProvider): def collection_name(self): return 'collection_names' + str(random.randint(1000, 9999)) def name(self): return 'name' + str(random.randint(1000, 9999)) def dim(self): return random.randint(0, 999) fake = faker.Faker() fake.add_provider(FakerProvider) def collection_name_factory(): 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app/__init__.py	oOo0oOo/FoodWorld	0	42142	from flask import Flask, Blueprint from flask_ask import Ask from flask_sqlalchemy import SQLAlchemy from config import config # Extensions db = SQLAlchemy() alexa = Ask(route = '/') # Main blueprint main = Blueprint('main', __name__) from . import models, views def create_app(config_name = 'development'): app = Flask(__name__) app.config.from_object(config[config_name]) config[config_name].init_app(app) db.init_app(app) alexa.init_app(app) from . import main as main_blueprint app.register_blueprint(main_blueprint) return app	[ 1, 515, 29784, 1053, 2379, 1278, 29892, 10924, 2158, 13, 3166, 29784, 29918, 1278, 1053, 26579, 13, 13, 3166, 29784, 29918, 2850, 284, 305, 6764, 1053, 3758, 2499, 305, 6764, 13, 3166, 2295, 1053, 2295, 13, 13, 29937, 7338, 5580, 13, 2585, 353, 3758, 2499, 305, 6764, 580, 13, 744, 17367, 353, 26579, 29898, 13134, 353, 8207, 1495, 13, 13, 29937, 4241, 7254, 2158, 13, 3396, 353, 10924, 2158, 877, 3396, 742, 4770, 978, 1649, 29897, 13, 13, 3166, 869, 1053, 4733, 29892, 8386, 13, 13, 1753, 1653, 29918, 932, 29898, 2917, 29918, 978, 353, 525, 25431, 29374, 13, 1678, 623, 353, 2379, 1278, 22168, 978, 1649, 29897, 13, 1678, 623, 29889, 2917, 29889, 3166, 29918, 3318, 29898, 2917, 29961, 2917, 29918, 978, 2314, 13, 1678, 2295, 29961, 2917, 29918, 978, 1822, 2344, 29918, 932, 29898, 932, 29897, 13, 13, 1678, 4833, 29889, 2344, 29918, 932, 29898, 932, 29897, 13, 1678, 263, 2506, 29874, 29889, 2344, 29918, 932, 29898, 932, 29897, 13, 13, 1678, 515, 869, 1053, 1667, 408, 1667, 29918, 9539, 2158, 13, 1678, 623, 29889, 9573, 29918, 9539, 2158, 29898, 3396, 29918, 9539, 2158, 29897, 13, 13, 1678, 736, 623, 2 ]
Books/PythonCookBook/P02_String_Text.py	Tim232/Python-Things	2	89247	# Chapter 2. 문자열과 텍스트 # 2.1 여러 구분자로 문자열 나누기 # ▣ 문제 : 문자열을 필드로 나누고 싶지만 구분자가 문자열에 일관적이지 않다. # ▣ 해결 : 문자열 객체의 split() 메소드는 아주 간단한 상황에 사용하도록 설계되었고 여러 개의 구분자나 구분자 주변의 # 공백까지 고려하지는 않는다. 좀 더 유연해져야 할 필요가 있다면 re.split() 메소드를 사용한다. line = 'asdf fjdk; afed, fjek,asdf, foo' import re print(re.split(r'[;,\s]\s', line)) # ▣ 토론 : re.split() 함수는 분리 구문마다 여러 패턴을 명시할 수 있다는 점이 유리하다. # - re.split() 을 사용할 때는 괄호 안에 묶인 정규 표현식 패턴이 캡처 그룹이 된다. fields = re.split(r'(;\|,\|\s)\s', line) print(fields) # - 구분 문자만 출력하는 경우. values = fields[::2] delimiters = fields[1::2] + [''] print(values, delimiters) # - 동일한 구분자로 라인을 구성한다. print(''.join(v+d for v, d in zip(values, delimiters))) # - 분리 구문을 결과에 포함시키고 싶지 않지만 정규 표현식에 괄호를 사용해야 할 필요가 있다면 논캡처 그룹을 사용한다. print(re.split('(?:,\|;\|\s)\s', line)) # 2.2 문자열 처음이나 마지막에 텍스트 매칭 # ▣ 문제 : 문자열의 처음이나 마지막에 파일 확장자, URL scheme 등 특정 텍스트 패턴이 포함되었는지 검사하고 싶다. # ▣ 해결 : 문자열의 처음이나 마지막에 패턴이 포함되었는지 확인하는 간단한 방법으로 str.startswith() 나 str.endswith() 메소드가 있다. filename = 'spam.txt' print(filename.endswith('.txt')) print(filename.startswith('file:')) url = 'http://www.python.org' print(url.startswith('http:')) # - 여러 개의 선택지를 검사해야 한다면 검사하고 싶은 값을 튜플에 담아 startswith() 나 endswith() 에 전달한다. import os filenames = os.listdir('.') print(filenames) print([name for name in filenames if name.endswith(('.c', '.h'))]) from urllib.request import urlopen def read_data(name): if name.startswith(('http:', 'https:', 'ftp:')): return urlopen(name).read() else: with open(name) as f: return f.read() # - startswith() 메소드는 튜플만을 입력으로 받는다. choices = ['http:', 'ftp:'] url = 'http://www.python.org' url.startswith(choices) url.startswith(tuple(choices)) # ▣ 토론 : startswith() 와 endswith() 메소드는 접두어와 접미어를 검사할 때 매우 편리하다. # 슬라이스를 사용하면 비슷한 동작을 할 수 있지만 코드의 가독성이 많이 떨어진다. filename = 'spam.txt' print(filename[-4:] == '.txt') url = 'http://www.python.org' print(url[:5] == 'http:' or url[:6] == 'https:' or url[:4] == 'ftp:') # - 정규 표현식을 사용해도 된다. import re url = 'http://www.python.org' print(re.match('http:\|https:\|ftp:', url)) # - startswith() 와 endswith() 메소드는 일반적인 데이터 감소와 같은 다른 동작에 함께 사용하기에도 좋다. if any(name.endswith(('.c', '.h')) for name in os.listdir('.')): pass # 2.3 쉘 와일드카드 패턴으로 문자열 매칭 # ▣ 문제 : Unix 쉘에 사용하는 것과 동일한 와일드카드 패턴을 텍스트 매칭에 사용하고 싶다.(예: .py, Dat[0-9].csv 등) # ▣ 해결 : fnmatch 모듈에 두 함수 fnmatch() 와 fnmatchcase() 를 사용하면 된다. from fnmatch import fnmatch, fnmatchcase print(fnmatch('foo.txt', '.txt')) print(fnmatch('foo.txt', '?oo.txt')) print(fnmatch('Dat45.csv', 'Dat[0-9]')) names = ['Dat1.csv', 'Dat2.csv', 'config.ini', 'foo.py'] print([name for name in names if fnmatch(name, 'Dat.csv')]) # - 일반적으로 fnmatch() 는 시스템의 파일 시스템과 동일한 대소문자 구문 규칙을 따른다. print(fnmatch('foo.txt', '.TXT')) # Mac : True print(fnmatch('foo.txt', '.TXT')) # window : True # - 이런 차이점이 없는 것을 사용하려면 fnmatchcase() 를 사용한다. print(fnmatchcase('foo.txt', '.TXT')) # - 파일 이름이 아닌 데이터 프로세싱에도 사용할 수 있다. addresses = ['5412 N CLARK ST', '1060 W ADDISON ST', '1039 W GRANVILLE AVE', '2122 N CLARK ST', '4802 N BROADWAY'] from fnmatch import fnmatchcase print([addr for addr in addresses if fnmatchcase(addr, ' ST')]) print([addr for addr in addresses if fnmatchcase(addr, '54[0-9][0-9] CLARK')]) # ▣ 토론 : fnmatch 가 수행하는 매칭은 간단한 문자열 메소드의 기능과 정규 표현식의 중간쯤 위치하고 있다. # 데이터 프로세싱을 할 때 간단한 와일드카드를 사용할 생각이라면 이 함수를 사용하는 것이 괜찮은 선택이다. # 2.4 텍스트 패턴 매칭과 검색 # ▣ 문제 : 특정 패턴에 대한 텍스트 매칭이나 검색을 하고 싶다. # ▣ 해결 : 매칭하려는 텍스트가 간단하다면 str.find(), str.endswith(), str.startswith() 와 같은 기본적인 문자열 메소드만으로도 # 충분하다. text = 'yeah, but no, but yeah, but no, but yeah' # - 정확한 매칭 print(text == 'yeah') # - 처음이나 끝에 매칭 print(text.startswith('yeah')) print(text.endswith('no')) # - 처음 나타난 곳 검색 print(text.find('no')) # 해당 값이 처음 나온 인덱스를 리턴. # - 좀 더 복잡한 매칭을 위해 정규 표현식과 re 모듈을 사용한다. # match() : 항상 문자열 처음에서 찾기를 시도. # findall() : 텍스트 전체에 걸쳐 찾기를 시도. text1 = '11/27/2012' text2 = 'Nov 27, 2012' import re # - 간단한 매칭: \d+는 하나 이상의 숫자를 의미 if re.match(r'\d+/\d+/\d+', text1): print('yes') else: print('no') if re.match(r'\d+/\d+/\d+', text2): print('yes') else: print('no') # - 동일한 패턴으로 매칭을 많이 수행할 예정이라면 정규 표현식을 미리 컴파일해서 패턴 객체로 만들어 놓는다. datepat = re.compile(r'\d+/\d+/\d+') if datepat.match(text1): print('yes') else: print('no') if datepat.match(text2): print('yes') else: print('no') # - 텍스트 전체에 걸쳐 패턴을 찾으려면 findall() 메소드를 사용한다. text = 'Today is 11/27/2012. PyCon starts 3/13/2013.' print(datepat.findall(text)) # - 정규 표현식을 정의할 때 괄호를 사용해 캡처 그룹을 만드는 것이 일반적이다. datepat = re.compile(r'(\d+)/(\d+)/(\d+)') m = datepat.match('11/27/2012') print(m.group(0), m.group(1), m.group(2), m.group(3), m.groups()) # 그룹별로 출력 가능 (0 은 전체 출력) month, day, year = m.groups() for month, day, year in datepat.findall(text): print('{}-{}-{}'.format(year, month, day)) # - 한 번에 결과를 얻지 않고 텍스트를 순환하며 찾으려면 finditer() 를 사용한다. for m in datepat.finditer(text): print(m.groups()) # ▣ 토론 : 핵심이 되는 기능은 re.compile() 을 사용해 패턴을 컴파일하고 그것을 match(), findall(), finditer() 등에 사용한다. # 패턴을 명시할 때 r'(\d+)/(\d+)/(\d+)'와 같이 raw string 을 그대로 쓰는것이 일반적이다. # 이 형식은 백슬래시 문자를 해석하지 않고 남겨 두기 때문에 정규 표현식과 같은 곳에 유용하다. # - match() 메소드는 문자열의 처음만 확인하므로, 예상치 못한 것에 매칭할 확률도 있다. m = datepat.match('11/27/2012abcdef') print(m) print(m.group()) datepat = re.compile(r'(\d+)/(\d+)/(\d+)$') print(datepat.match('11/27/2012abcdef')) print(datepat.match('11/27/2012')) # 2.5 텍스트 검색과 치환 # ▣ 문제 : 문자열에서 텍스트 패턴을 검색하고 치환하고 싶다. # ▣ 해결 : 간단한 패턴이라면 str.replace() 메소드를 사용한다. # 조금 더 복잡한 패턴을 사용하려면 re 모듈의 sub() 함수/메소드를 사용한다. text = 'yeah, but no, but yeah, but no, but yeah' print(text.replace('yeah', 'yep')) text = 'Today is 11/27/2012. PyCon starts 3/13/2013.' import re print(re.sub(r'(\d+)/(\d+)/(\d+)', r'\3-\1-\2', text)) # - 동일한 패턴을 사용한 치환을 계속해야 한다면 성능 향상을 위해 컴파일링을 고려해 보는 것이 좋다. import re datepat = re.compile(r'(\d+)/(\d+)/(\d+)') print(datepat.sub(r'\3-\1-\2', text)) # - 더 복잡한 치환을 위한 콜백 함수 명시. from calendar import month_abbr def change_date(m): mon_name = month_abbr[int(m.group(1))] return '{} {} {}'.format(m.group(2), mon_name, m.group(3)) print(datepat.sub(change_date, text)) # - 치환이 몇 번 발생했는지 알고 싶다면 re.subn() 을 사용한다. newtext, n = datepat.subn(r'\3-\1-\2', text) print(newtext, n) # ▣ 토론 : 앞서 살펴본 sub() 메소드에 정규 표현식 검색과 치환 이외에 어려운 것은 없다. # 2.6 대소문자를 구별하지 않는 검색과 치환 # ▣ 문제 : 텍스트를 검색하고 치환할 때 대소문자를 구별하지 않고 싶다. # ▣ 해결 : 텍스트 관련 작업을 할 때 대소문자를 구별하지 않기 위해서는 re 모듈을 사용해야 하고 re.IGNORECASE 플래그를 지정해야 한다. text = 'UPPER PYTHON, lower python, Mixed Python' print(re.findall('python', text, flags=re.IGNORECASE)) print(re.sub('python', 'snake', text, flags=re.IGNORECASE)) def matchcase(word): def replace(m): text = m.group() if text.isupper(): return word.upper() elif text.islower(): return word.lower() elif text[0].isupper(): return word.capitalize() else: return word return replace print(re.sub('python', matchcase('snake'), text, flags=re.IGNORECASE)) # ▣ 토론 : 대개의 경우 re.IGNORECASE 를 사용하는 것만으로 대소문자를 무시한 텍스트 작업에 무리가 없다. # 하지만 유니코드가 포함된 작업을 하기에는 부족할 수도 있다. # 2.7 가장 짧은 매칭을 위한 정규 표현식 # ▣ 문제 : 정규 표현식을 사용한 텍스트 매칭을 하고 싶지만 텍스트에서 가장 긴 부분을 찾아낸다. # 만약 가장 짧은 부분을 찾아내고 싶다면 어떻게 해야 할까? # ▣ 해결 : * 뒤에 ? 를 붙이면 된다. import re str_pat = re.compile(r'\"(.)\"') text1 = 'Computer says "no."' print(str_pat.findall(text1)) text2 = 'Computer says "no." Phone says "yes."' print(str_pat.findall(text2)) str_pat = re.compile(r'\"(.?)\"') print(str_pat.findall(text2)) # ▣ 토론 : 패턴에서 점은 개행문을 제외한 모든 문제에 매칭하므로, ? 를 * 나 + 뒤에 붙여준다. # 2.8 여러 줄에 걸친 정규 표현식 사용 # ▣ 문제 : 여러 줄에 걸친 정규 표현식 매칭을 사용하고 싶다. # ▣ 해결 : 패턴에서 (?:.\|\n)인 논캡처 그룹을 명시한다. comment = re.compile(r'/\(.?)\/') text1 = '/ this is a comment /' text2 = '''/ this is a multiline comment / ''' print(comment.findall(text1)) print(comment.findall(text2)) comment = re.compile(r'/\((?:.\|\n)?)\/') print(comment.findall(text2)) # ▣ 토론 : re.compile() 함수에 re.DOTALL 이라는 유용한 플래그를 사용할 수 있다. comment = re.compile(r'/\(.?)\/', re.DOTALL) print(comment.findall(text2)) # 2.9 유니코드 텍스트 노멀화 # ▣ 문제 : 유니코드 모든 문자열에 동일한 표현식을 갖도록 보장해주자. # ▣ 해결 : unicodedata 모듈로 텍스트를 노멀화해서 표준 표현식으로 바꿔야 한다. s1 = 'Spicy Jalape\u00f1o' s2 = 'Spicy Jalapen\u0303o' print(s1, s2) print(s1 == s2, len(s1), len(s2)) # - 위의 경우 같은 문자이지만 표현 방식이 달라 다른 문자열로 인식하였다. # 따라서. 텍스트 노멀화를 통해 표준 표현식으로 변경해주어야 한다. import unicodedata t1 = unicodedata.normalize('NFC', s1) t2 = unicodedata.normalize('NFC', s2) print(t1 == t2) print(ascii(t1)) t3 = unicodedata.normalize('NFD', s1) t4 = unicodedata.normalize('NFD', s2) print(t3 == t4) print(ascii(t3)) # - 파이썬은 특정 문자를 다룰 수 있도록 추가적인 호환성을 부여하는 NFKC 와 NFKD 노멀화도 지원한다. s = '\ufb01' print(s) print(unicodedata.normalize('NFD', s)) print(unicodedata.normalize('NFKD', s)) # 개별 문자로 분리 print(unicodedata.normalize('NFKC', s)) # 개별 문자로 분리 # ▣ 토론 : 일관적이고 안전한 유니코드 텍스트 작업을 위해서 노멀화는 아주 중요하다. # 특히 인코딩을 조절할 수 없는 상황에서 사용자에게 문자열 입력을 받는 경우에는 특히 조심해야 한다. t1 = unicodedata.normalize('NFD', s1) print(''.join(c for c in t1 if not unicodedata.combining(c))) # combining() 함수는 문자가 결합 문자인지 확인한다. # 2.10 정규 표현식에 유니코드 사용 # ▣ 문제 : 텍스트 프로세싱에 정규 표현식을 사용 중이다. 하지만 유니코드 문자 처리가 걱정된다. # ▣ 해결 : 기본적인 유니코드 처리를 위한 대부분의 기능을 re 모듈이 제공한다. import re num = re.compile('\d+') print(num.match('123')) # 아스키 숫자 print(num.match('\u0661\u0662\u0663')) # 아라비아 숫자 # - 특정 유니코드 문자를 패턴에 포함하고 싶으면, 유니코드 문자에 이스케이프 시퀀스를 사용한다. arabic = re.compile('[\u0600-\u06ff\u0750-\u077f\u08a0-\u08ff]+') # - 대소문자를 무시하는 매칭에 대소문자 변환을 합친 코드는 다음과 같다. pat = re.compile('stra\u00dfe', re.IGNORECASE) s = 'straße' print(pat.match(s)) print(pat.match(s.upper()), s.upper()) # ▣ 토론 : 유니코드와 정규 표현식을 같이 사용하려면 서드파티 regex 라이브러리를 설치하고 유니코드 대소문자 변환 등을 기본으로 # 제공하는 많은 기능을 이용하는 것이 좋다. # 2.11 문자열에서 문자 잘라내기 # ▣ 문제 : 텍스트의 처음, 끝, 중간에서 원하지 않는 공백문 등을 잘라내고 싶다. # ▣ 해결 : strip() 메소드를 사용하면 문자열의 처음과 끝에서 문자를 잘라낼 수 있다. # 기본적으로 공백이나 \n 을 잘라내지만 원하는 문자를 지정할 수도 있다. s = ' hello world \n' print(s.strip()) print(s.lstrip()) print(s.rstrip()) t = '-----hello=====' print(t.strip('-')) print(t.strip('=')) print(t.strip('-=')) # ▣ 토론 : 데이터를 보기 좋게 만들기 위한 용도로 여러 strip() 메소드를 일반적으로 사용한다. # 하지만 텍스트의 중간에서 잘라내기를 할 수는 없다. s = ' hello world \n' print(s.strip()) # - 중간의 공백을 없애기 위해서는 replace() 메소드나 정규 표현식의 치환과 같은 다른 기술을 사용해야 한다. print(s.replace(' ', '')) import re print(re.sub('\s+', ' ', s).strip()) with open('files\\somefile.txt') as f: # 파일 전체 compile 해야 경로 인식. lines = (line.strip() for line in f) for line in lines: print(re.sub('\s+', ' ', line).strip()) # 2.12 텍스트 정리 # ▣ 문제 : 당신의 웹 페이지에 어떤 사람이 장난스럽게 "python" 이라는 특수문자를 입력했다. 이를 정리하고 싶다. # ▣ 해결 : 특정 범위의 문자나 발음 구별 구호를 없애려고 할 때는 str.translate() 메소드를 사용해야 한다. s = 'pýtĥöñ\fis\tawesome\r\n' print(s) # - 우선 문자열에서 공백문을 잘라내기 위해 작은 변환 테이블을 만들어 놓고 translate() 를 사용한다. remap = { ord('\t'): ' ', # ord() : 하나의 문자열에 대해 유니코드를 나타내는 의미로 변환. ord('\f'): ' ', ord('\r'): None # 삭제됨 } a = s.translate(remap) print(a) # - 결합 문자를 없애는 방법. import unicodedata import sys cmb_chrs = dict.fromkeys(c for c in range(sys.maxunicode) if unicodedata.combining(chr(c))) # combining() : 유니코드 중에 조합된 것을 추출. print(cmb_chrs) b = unicodedata.normalize('NFD', a) print(b) print(b.translate(cmb_chrs)) # - 유니코드 숫자 문자를 이와 관련 있는 아스키 숫자에 매핑하도록 변환 테이블을 작성한다. digitmap = { c: ord('0') + unicodedata.digit(chr(c)) for c in range(sys.maxunicode) if unicodedata.category(chr(c)) == 'Nd' } print(len(digitmap), digitmap) x = '\u0661\u0662\u0663' print(x.translate(digitmap)) # - 또 다른 텍스트 정리 기술로 I/O 인코딩, 디코딩 함수가 있다. 이 방식은 텍스트를 우선 정리해 놓고 encode() 나 decode() 를 # 실행해서 잘라내거나 변경한다. print(a) b = unicodedata.normalize('NFD', a) print(b.encode('ascii', 'ignore').decode('ascii')) # ascii 형태로 인코딩 및 디코딩 # ▣ 토론 : 텍스트 정리를 하다 보면 실행 성능 문제에 직면하기도 한다. # 간단한 치환을 위해서는 str.replace() 함수를 사용하는 것이 빠르고 복잡한 치환을 하는 경우에는 str.translate() 함수를 # 사용하는 것이 좋다. # 2.13 텍스트 정렬 # ▣ 문제 : 텍스트를 특정 형식에 맞추어 정렬하고 싶다. # ▣ 해결 : ljust(), rjust(), center() 등이 있다. text = 'Hello World' print(text.ljust(20)) print(text.rjust(20)) print(text.center(20)) # - 세 개의 메소드는 별도의 채워 넣기 문자를 사용할 수 있다. print(text.rjust(20, '=')) print(text.center(20, '')) # - format() 함수를 사용하면 인자로 <, >, ^ 를 적절하게 사용해 주면 된다. print(format(text, '>20')) # rjust 와 동일 print(format(text, '<20')) # ljust 와 동일 print(format(text, '^20')) # center 와 동일 # - 공백 대신 특정 문자를 채워 넣고 싶다면 정렬 문자 앞에 그 문자를 지정한다. print(format(text, '=>20')) print(format(text, '^20')) # - 포맷 코드는 format() 메소드에 사용해 여러 값을 서식화할 수도 있다. print('{:>10} {:>10}'.format('Hello', 'World')) # - format() 을 사용하면 문자열뿐만 아니라 숫자 값 등 모든 값에 동작한다. x = 1.2345 print(format(x, '>10')) # str 타입으로 변환 print(format(x, '^10.2f')) # 소수점 자리수 지정 가능 # ▣ 토론 : 오래된 코드를 보면 % 연산자를 사용해 텍스트를 서식화하기도 했다. print('%-20s ' % text) print('%20s ' % text) # 2.14 문자열 합치기 # ▣ 문제 : 작은 문자열 여러 개를 합쳐 하나의 긴 문자열을 만들고 싶다. # ▣ 해결 : 합치고자 하는 문자열이 시퀀스나 순환 객체 안에 있다면 join() 메소드를 사용하는 것이 가장 빠르다. parts = ['Is', 'Chicago', 'Not', 'Chicago?'] print(' '.join(parts)) print(','.join(parts)) print(''.join(parts)) # - 합치려고 하는 문자열의 수가 아주 적다면 + 를 사용하면 된다. a = 'Is Chicago' b = 'Not Chicago?' print(a + ' ' + b) # - + 연산자는 조금 더 복잡한 문자열 서식 연산에 사용해도 잘 동작한다. print('{} {}'.format(a, b)) print(a + ' ' + b) a = 'Hello' 'World' print(a) # ▣ 토론 : 명심해야 할 부분은, + 연산자로 많은 문자열을 합치려고 하면 메모리 복사와 가비지 컬렉션으로 인해 매우 비효율적이라는 점이다. s = '' for p in parts: s += p # - 생성자 표현식으로 합치는 방법이 있다. data = ['ACME', 50, 91.1] print(' '.join(str(v) for v in data)) # - 불필요한 문자열 합치기를 하고 있지 않은지도 주의하자. a = 'qwer' b = 'asdf' c = 'zxcv' print(a + ':' + b + ':' + c) # 좋지 않은 방식 print(':'.join([a, b, c])) # 개선된 방식 print(a, b, c, sep=':') # 좋은 방식 # - 수많은 짧은 문자열을 하나로 합쳐 문자열을 만드는 코드를 작성한다면, yeild 를 사용한 생성자 함수를 고려하자. def sample(): yield 'Is' yield 'Chicago' yield 'Not' yield 'Chicago?' text = ''.join(sample()) print(text) # - 문자열을 입출력(I/O)으로 리다이렉트 할 수 있다. for part in sample(): f.write(part) # - 입출력을 조합한 하이브리드 방식 구현도 가능하다. f = open('D:\\KYH\\02.PYTHON\\data\\combine.txt', 'a') def combine(source, maxsize): parts = [] size = 0 for part in source: parts.append(part) size += len(part) if size >= maxsize: yield ''.join(parts) parts = [] size = 0 yield ''.join(parts) for part in combine(sample(), 32768): f.write(part) # 2.15 문자열에 변수 사용 # ▣ 문제 : 문자열에 변수를 사용하고 이 변수에 맞는 값을 채우고 싶다. # ▣ 해결 : 파이썬 문자열에 변수 값을 치환하는 간단한 방법은 존재하지 않는다. # 하지만 format() 메소드를 사용하면 비슷하게 흉내 낼 수 있다. s = '{name} hash {n} messages.' print(s.format(name='Guido', n=37)) # - 치환할 값이 변수에 들어 있다면 format_map() 과 vars() 를 함께 사용하면 된다. name = 'Guido' n = 37 print(s.format_map(vars())) # - vars() 에는 인스턴스를 사용할 수도 있다. class Info: def __init__(self, name, n): self.name = name self.n = n a = Info('Guido', 37) print(s.format_map(vars(a))) # - format() 또는 format_map() 사용시 빠진 값은 __missing__() 메소드가 있는 딕셔너리 클래스를 정의해서 피할 수 있다. class safesub(dict): def __missing__(self, key): return '{' + key + '}' del n print(s.format_map(safesub(vars()))) # - 코드에서 변수 치환을 빈번히 사용할 것 같다면 치환하는 작업을 유틸리티 함수에 모아 놓고 소위 "프레임 핵(frame hack)"으로 사용할 수 있다. import sys def sub(text): return text.format_map(safesub(sys._getframe(1).f_locals)) name = 'Guido' n = 37 print(sub('Hello {name}')) print(sub('You have {n} messages.')) print(sub('Your favorite color is {color}')) # ▣ 토론 : 파이썬 자체에서 변수 보간법이 존재하지 않아서 다양한 대안이 생겼다. name = 'Guido' n = 37 print('%(name) has %(n) messages.' % vars()) import string s = string.Template('$name has $n messages.') print(s.substitute(vars())) # 2.16 텍스트 열의 개수 고정 # ▣ 문제 : 긴 문자열의 서식을 바꿔 열의 개수를 조절하고 싶다. # ▣ 해결 : textwrap 모듈을 사용해서 텍스트를 재서식화 한다. s = 'Look into my eyes, look into my eyes, the eyes, the eyes,' \ "the eyes, not around the eyes, don't look around the eyes," \ "look into my eyes, you're under." import textwrap print(textwrap.fill(s, 70)) print(textwrap.fill(s, 40)) print(textwrap.fill(s, 40, initial_indent=' ')) print(textwrap.fill(s, 40, subsequent_indent=' ')) # ▣ 토론 : 텍스트를 출력하기 전에 textwrap 모듈을 사용하면 깔끔하게 서식을 맞출 수 있다. # 특히 터미널에 사용할 텍스트에 적합하다. # 터미널의 크기를 얻으려면 os.get_terminal_size() 를 사용한다. import os print(os.get_terminal_size().columns) # 2.17 HTML 과 XML 엔티티 처리 # ▣ 문제 : &entity; 나 &#code; 와 같은 HTML, XML 엔터티를 이에 일치하는 문자로 치환하고 싶다. # 혹은 텍스트를 생성할 때 특정 문자(<, >, & 등)를 피하고 싶다. # ▣ 해결 : 텍스트를 생성할 때 <나>와 같은 특수 문자를 치환하는 것은 html.escape() 함수를 사용하면 상대적으로 간단히 처리할 수 있다. s = 'Elements are written as "<tag>text</tag>".' import html print(s) print(html.escape(s)) # - 따옴표는 남겨 두도록 지정 print(html.escape(s, quote=False)) # - 텍스트를 아스키로 만들고 캐릭터 코드를 아스키가 아닌 문자에 끼워 넣고 싶으면 errors='xmlcharrefreplace' 인자를 입출력 관련 함수에 사용한다. s = 'Spicy Jalapeño' print(s.encode('ascii', errors='xmlcharrefreplace')) # - 수동으로 치환을 해야 한다면 HTML, XML 파서에 내장되어 있는 여러 유틸리티 함수나 메소드를 사용한다. s = 'Spicy "Jalapeño&quot.' from html.parser import HTMLParser p = HTMLParser() print(p.unescape(s)) # 파이썬 3.5 버전부터 deprecated 됨. print(html.unescape(s)) t = 'The prompt is >>>' from xml.sax.saxutils import unescape print(unescape(t)) # ▣ 토론 : HTML, XML 을 생성할 때 특수 문자를 제대로 이스케이핑하는 과정을 간과하기 쉽다. # print() 로 결과물을 생성하거나 기본적인 문자열 서식 기능을 사용할 때 특히 더 그렇다. # 가장 쉬운 해결책은 html.escape() 와 같은 유틸리티 함수를 사용하는 것이다. # 2.18 텍스트 토큰화 # ▣ 문제 : 문자열을 파싱해서 토큰화하고 싶다. # ▣ 해결 : 정규 표현식과 scanner() 메소드를 사용한다. # - 정규 표현식을 사용.(이름 있는 캡처 그룹) text = 'foo = 23 + 42 10' import re NAME = r'(?P<NAME>[a-zA-Z_][a-zA-Z_0-9])' NUM = r'(?P<NUM>\d+)' PLUS = r'(?P<PLUS>\+)' TIMES = r'(?P<TIMES>\)' EQ = r'(?P<EQ>=)' WS = r'(?P<WS>\s+)' master_pat = re.compile('\|'.join([NAME, NUM, PLUS, TIMES, EQ, WS])) # - scanner() 메소드를 사용. scanner = master_pat.scanner('foo = 42') scanner.match() print(_.lastgroup, _.group()) # _ 는 파이썬 2.x 버전에서 사용 가능함. scanner.match() print(_.lastgroup, _.group()) scanner.match() print(_.lastgroup, _.group()) scanner.match() print(_.lastgroup, _.group()) scanner.match() print(_.lastgroup, _.group()) # - 이 기술을 사용해 간결한 생성자를 만들 수 있다. from collections import namedtuple Token = namedtuple('Token', ['type', 'value']) def generate_tokens(pat, text): scanner = pat.scanner(text) for m in iter(scanner.match, None): yield Token(m.lastgroup, m.group()) for tok in generate_tokens(master_pat, 'foo = 42'): print(tok) tokens = (tok for tok in generate_tokens(master_pat, text) if tok.type != 'WS') for tok in tokens: print(tok) # ▣ 토론 : 보통 더 복잡한 텍스트 파싱이나 처리를 하기 전에 토큰화를 한다. # 매칭할 때 re 는 명시한 순서대로 패턴을 매칭한다. 따라서 한 패턴이 다른 패턴의 부분이 되는 경우가 있다면 # 항상 더 긴 패턴을 먼저 넣어야 한다. LT = r'(?P<LT><)' LE = r'(?P<LE><=)' EQ = r'(?P<EQ>=)' master_pat = re.compile('\|'.join([LE, LT, EQ])) # 올바름 master_pat = re.compile('\|'.join([LT, LE, EQ])) # 틀림 for tok in generate_tokens(master_pat, '<='): print(tok) # - 패턴이 부분 문자열을 형성하는 경우도 조심해야 한다. PRINT = r'(?P<PRINT>print)' NAME = r'(?P<NAME>[a-zA-Z_][a-zA-Z_0-9])' master_pat = re.compile('\|'.join([PRINT, NAME])) for tok in generate_tokens(master_pat, 'printer'): print(tok) # 2.19 간단한 재귀 파서 작성 # ▣ 문제 : 주어진 문법 규칙에 따라 텍스트를 파싱하고 동작을 수행하거나 입력된 텍스트를 추상 신택스 트리로 나타내야 한다. # 문법은 간단하지만 프레임워크를 사용하지 않고 파서를 직접 작성하고 싶다. # ▣ 해결 : 이 문제는 특정 문법에 따라 텍스트를 파싱하는 데 집중한다. # 우선 문법의 정규 스펙을 BNF 나 EBNF 로 하는 데서 시작한다. import re import collections # - 토큰 스펙화. NUM = r'(?P<NUM>\d+)' PLUS = r'(?P<PLUS>\+)' MINUS = r'(?P<MINUS>-)' TIMES = r'(?P<TIMES>\)' DIVIDE = r'(?P<DIVIDE>/)' LPAREN = r'(?P<LPAREN>\()' RPAREN = r'(?P<RPAREN>\))' WS = r'(?P<WS>\s+)' master_pat = re.compile('\|'.join([NUM, PLUS, MINUS, TIMES, DIVIDE, LPAREN, RPAREN, WS])) # - 토큰화. Token = collections.namedtuple('Token', ['type', 'value']) def generate_tokens(text): scanner = master_pat.scanner(text) for m in iter(scanner.match, None): tok = Token(m.lastgroup, m.group()) if tok.type != 'WS': yield tok # - 파서. class ExpressionEvaluator: ''' 재귀 파서 구현, 모든 메소드는 하나의 문법 규칙을 구현한다. 현재 룩어헤드 토큰을 받고 테스트하는 용도로 ._accept()를 사용한다. 입력 받은 내역에 완벽히 매칭하고 다음 토큰을 무시할 때는 ._expect()를 사용한다. (혹시 매칭하지 않는 경우에는 SyntaxError 를 발생한다.) ''' def parse(self, text): self.tokens = generate_tokens(text) self.tok = None # 마지막 심볼 소비 self.nexttok = None # 다음 심볼 토큰화 self._advance() # 처음 룩어헤드 토큰 불러오기 return self.expr() # generate_tokens() 메서드에서 가져온 토큰을 순차적으로 설정하는 함수 def _advance(self): 'Advance one token ahead' self.tok, self.nexttok = self.nexttok, next(self.tokens, None) # next 함수는 iterator 를 순차적으로 리턴시켜주는 함수 # 다음 토큰이 원하는 토큰인 경우 self.tok 에 다음 토큰을 담고 True 를 리턴하는 함수 def _accept(self, toktype): 'Test and consume the next token if it matches toktype' if self.nexttok and self.nexttok.type == toktype: self._advance() return True else: return False def _expect(self, toktype): 'Consume next token if it matches toktype or raise SyntaxError' if not self._accept(toktype): raise SyntaxError('Expected ' + toktype) # - 문법 규칙. def expr(self): "expression ::= term { ('+'\|'-') term }" exprval = self.term() while self._accept('PLUS') or self._accept('MINUS'): op = self.tok.type right = self.term() if op == 'PLUS': exprval += right elif op == 'MINUS': exprval -= right return exprval def term(self): "term ::= factor { (''\|'/') factor }" termval = self.factor() while self._accept('TIMES') or self._accept('DIVIDE'): op = self.tok.type right = self.factor() if op == 'TIMES': termval = right elif op == 'DIVIDE': termval /= right return termval def factor(self): "factor ::= NUM \| ( expr )" if self._accept('NUM'): return int(self.tok.value) elif self._accept('LPAREN'): exprval = self.expr() self._expect('RPAREN') return exprval else: raise SyntaxError('Expected NUMBER or LPAREN') e = ExpressionEvaluator() print(e.parse('2')) print(e.parse('2 + 3')) print(e.parse('2 + 3 * 4')) print(e.parse('2 + (3 + 4) * 5')) # print(e.parse('2 + (3 + * 4)')) class ExpressionTreeBuilder(ExpressionEvaluator): def expr(self): "expression ::= term { ('+'\|'-') term }" exprval = self.term() while self._accept('PLUS') or self._accept('MINUS'): op = self.tok.type right = self.term() if op == 'PLUS': exprval = ('+', exprval, right) elif op == 'MINUS': exprval = ('-', exprval, right) return exprval def term(self): "term ::= factor { (''\|'/') factor }" termval = self.factor() while self._accept('TIMES') or self._accept('DIVIDE'): op = self.tok.type right = self.factor() if op == 'TIMES': termval = ('', termval, right) elif op == 'DIVIDE': termval = ('/', termval, right) return termval def factor(self): 'factor ::= NUM \| ( expr )' if self._accept('NUM'): return int(self.tok.value) elif self._accept('LPAREN'): exprval = self.expr() self._expect('RPAREN') return exprval else: raise SyntaxError('Expected NUMBER or LPAREN') e = ExpressionTreeBuilder() print(e.parse('2 + 3')) print(e.parse('2 + 3 * 4')) print(e.parse('2 + (3 + 4) * 5')) print(e.parse('2 + 3 + 4')) # ▣ 토론 : 파싱은 컴파일러 과목에서 3주 이상을 할애해서 배우는 쉽지 않은 주제이다. # 파싱 알고리즘이나 문법과 같은 기본적인 지식을 좀 더 알고 싶다면 우선 컴파일러 책을 한 권 읽어야 한다. # 재귀 파서의 한 가지 제약으로 좌측 재귀가 포함된 어떠한 문법 규칙에 대해서도 사용할 수 없다. # - 정말 복잡한 문법이 있다면 PyParsing 이나 PLY 와 같은 파싱 도구를 사용하는 것이 더 좋다. from ply.lex import lex from ply.yacc import yacc # 토큰 리스트 tokens = ['NUM', 'PLUS', 'MINUS', 'TIMES', 'DIVIDE', 'LPAREN', 'RPAREN'] # 무시 문자 t_ignore = '\t\n' # 토큰 스펙 (정규 표현식으로) t_PLUS = r'\+' t_MINUS = r'-' t_TIMES = r'\' t_DIVIDE = r'/' t_LPAREN = r'\(' t_RPAREN = r'\)' # 토큰화 함수 def t_NUM(t): r'\d+' t.value = int(t.value) return t # 에러 핸들러 def t_error(t): print('Bad character: {!r}'.format(t.value[0])) t.skip(1) # 렉서(lexer) 만들기 lexer = lex() # 문법 규칙과 핸들러 함수 def p_expr(p): ''' expr : expr PLUS term \| expr MINUS term ''' if p[2] == '+': p[0] = p[1] + p[3] elif p[2] == '-': p[0] = p[1] - p[3] def p_expr_term(p): ''' expr : term ''' p[0] = p[1] def p_term(p): ''' term : term TIMES factor \| term DIVIDE factor ''' if p[2] == '': p[0] = p[1] * p[3] elif p[2] == '/': p[0] = p[1] / p[3] def p_term_factor(p): ''' term : factor ''' p[0] = p[1] def p_factor(p): ''' factor : NUM ''' p[0] = p[1] def p_factor_group(p): ''' factor : LPAREN expr RPAREN ''' p[0] = p[2] def p_error(p): print('Syntax error') parser = yacc() print(parser.parse('2')) # 2.20 바이트 문자열에 텍스트 연산 수행 # ▣ 문제 : 바이트 문자열(byte string)에 일반적인 텍스트 연산(잘라내기, 검색, 치환 등)을 수행하고 싶다. # ▣ 해결 : 바이트 문자열도 텍스트 문자열과 마찬가지로 대부분의 연산을 내장하고 있다. data = b'Hello World' print(data[0:5]) print(data.startswith(b'Hello')) print(data.split()) print(data.replace(b'Hello', b'Hello Cruel')) # - 바이트 배열에도 사용 가능하다. data = bytearray(b'Hello World') print(data[0:5]) print(data.startswith(b'Hello')) print(data.split()) print(data.replace(b'Hello', b'Hello Cruel')) # - 바이트 배열에서도 정규 표현식이 가능하다. data = b'FOO:BAR,SPAM' import re print(re.split(b'[:,]', data)) # 패턴도 바이트로 나타내야 한다. # ▣ 토론 : 대개의 경우 텍스트 문자열에 있는 연산 기능은 바이트 문자열에도 내장되어 있다. # 하지만 주의해야 할 차이점이 몇 가지 있다. # - 첫째. 바이트 문자열에 인덱스를 사용하면 개별 문자가 아니라 정수를 가리킨다. a = 'Hello World' print(a[0], a[1]) b = b'Hello World' print(b[0], b[1]) # - 둘째. 바이트 문자열은 보기 좋은 표현식을 지원하지 않으며, 텍스트 문자열로 변환하지 않으면 깔끔하게 출력할 수도 없다. s = b'Hello World' print(s) print(s.decode('ascii')) # - 셋째. 바이트 문자열은 서식화를 지원하지 않는다. # print(b'%10s %10d %10.2f' %(b'ACME', 100, 490.1)) # print(b'{} {} {}'.format(b'ACME', 100, 490.1)) print('{:10s} {:10d} {:10.2f}'.format('ACME', 100, 490.1).encode('ascii')) # - 넷째. 바이트 문자열을 사용하면 특정 연산의 문법에 영향을 주기도 한다. with open('files/somefile.txt', 'w') as f: f.write('spicy') import os print(os.listdir('.')) print(os.listdir(b'.')) # ※ 바이트 데이터가 성능상 더 빠르더라도, 코드가 매우 지저분하고 이해하기 어려워지므로 텍스트 데이터를 사용하는 것이 좋다.	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backend/app/services.py	lfvilella/b2blue-challenge	2	59842	""" Services This module is reponsible to handle all interactions to the database and bussiness rules """ import typing import environs import dotenv import requests import sqlalchemy.orm from . import models, schemas, cache env = environs.Env() dotenv.load_dotenv() SECRET_KEY_RECAPTCHA = env("RECAPTCHA_SECRET_KEY", "") VALIDATE_RECAPTCHA = env.bool("VALIDATE_RECAPTCHA", True) class ServiceException(Exception): """ Service Exception This error is raised when data passed to the function is not valid """ class ValidationError(ServiceException): pass _cache = cache.get_cache() class CityService: """ City Service Service class designed to provide reusable functionalities relate to city """ _db: sqlalchemy.orm.Session def __init__(self, db: sqlalchemy.orm.Session): """ This constructor set database to others methods """ self._db = db def __getstate__(self): """ Prevent database connection to be cached """ state = self.__dict__.copy() state.pop("_db") # do not pickle _db session return state def get_city_by_id(self, name: str, state: str) -> models.City: """ Get City By ID This method is used to get City Args: name (str): City's name state (str): City's state Returns: Instance of models.City """ db_city = ( self._db.query(models.City) .filter_by(id=models.City.generate_id(name=name, state=state)) .first() ) return db_city def create_city(self, city: schemas.CityBase) -> models.City: """ Create City This method is used to create a City Args: city (schemas.CityInput): City's fields Returns: models.City """ if None in city.dict().values(): raise ValidationError("Invalid Post") db_city = self.get_city_by_id(name=city.name, state=city.state) if db_city: raise ValidationError("City already exist") city = models.City(**city.dict()) city.id = models.City.generate_id(name=city.name, state=city.state) self._db.add(city) self._db.commit() self._db.flush() self.cached_filter_city.invalidate_all() return city def filter_city(self, name: str) -> typing.List[models.City]: """ Filter City This method is used to filter a Cities Args: name (str): City's name Returns: list of cities """ query = self._db.query(models.City).filter( models.City.name.contains(name) ) return query.all() @_cache.cache(ttl=60) def cached_filter_city(self, name: str): """ Cached Filter City Cached version of filter_city it prevents from hitting database for alredy cached queries Args: name (str): City's name Returns: list of cities """ return self.filter_city(name) class GoogleService: _RECAPTCHA_SITEVERIFY_URL = ( "https://www.google.com/recaptcha/api/siteverify" ) def validate_recaptcha(self, response_token: str) -> bool: if not VALIDATE_RECAPTCHA: return True data = { "response": response_token, "secret": SECRET_KEY_RECAPTCHA, } response = requests.post(self._RECAPTCHA_SITEVERIFY_URL, data=data) if response.json().get("success") is not True: return False return True	[ 1, 9995, 15538, 13, 13, 4013, 3883, 338, 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pylighter/annotation.py	infinex/PyLighter	0	147837	<reponame>infinex/PyLighter import os import threading import pandas as pd import spacy from pylighter import config from pylighter import display as display_helper from pylighter import utils from pylighter.chunk_models import Chunk, Chunks from pylighter.shortcut_helper import shortcut_helper from pylighter.tokenizer import SpacyTokenizer class Annotation: """ Class used to annotate datasets. """ # -------------------------------------------- # Initialization # -------------------------------------------- def __init__( self, corpus, labels=None, start_index=0, save_path=config.ANNOTATION_SAVE_PATH, labels_names=config.LABELS_NAMES, labels_colors=config.DEFAULT_COLORS, additional_infos=None, additional_outputs_values=None, additional_outputs_elements=None, standard_shortcuts=config.SHORTCUTS, labels_shortcuts=None, char_params=config.CHAR_PARAMS, ): """ Class that starts the user interface to annotate the given corpus. Parameters ---------- corpus : List[str] The corpus to annotate. labels : List[List[str]], optional The labels of the documents. It must have the same length as documents. Moreover, the i-th label must have the same length as the number of characters of the i-th document. By default, none of the documents are annotated. start_index : int, optional The index of the document to start on. Default value is 0. save_path : str, optional Path to store the annotated corpus into a csv when clicking the save button. By default, the (document, labels) are stored in config.ANNOTATION_SAVE_PATH. labels_names : List[str], optional The names of your labels. Default value is config.LABELS_NAMES. labels_colors : List[str], optional The colors for your labels in hex format (ex: "#2DA9D5"). The first label in labels_names will have the first color in labels_colors. By default, the labels colors are defined in config.DEFAULT_COLORS. labels_shortcuts : List[Shortcut], optional Keyboard shortcuts to use to select a given label. A shortcut is linked to one of the label button only if they share the same name. By default, none of the labels buttons have keyboard shortcuts. additional_infos : pd.DataFrame, optional Dataframe of size (size of the corpus, n) containing additional infos to display for each document. By default, no additional infos are displayed. additional_outputs_elements : List[AdditionalOutputElement], optional List containing additional elements to display. 5 elements type are supported: checkbox, int_text, float_text, text, text_area. By default, there won't be any additional outputs. additional_outputs_values : pandas.DataFrame, optional DataFrame of size (size of the corpus, n) containing the values of additional outputs for each document. If additional_outputs_elements are given, then the columns of this DataFrame must match the names of the elements given. By default, it will be initialized with default values given by the additional_outputs_elements if it exists. shortcuts : List[Shortcut], optional Keyboard shortcuts for the different buttons (Next, Previous, Skip and Save). By default, it uses the keyboard shortcuts defined in config.SHORTCUTS char_params : Dict[str, str], optional Parameters to modify the display of the characters in the document. The correct values are: min_width_between_chars -- Min distance between two chars. Expects a css value as string (ex: "4px"). width_white_space -- Size of a white space. Expects a css value as string (ex: "10px"). font_size -- Size of the font. Expects a css value as string (ex:"large"). """ # Check input consistency utils.assert_input_consistency(corpus, labels, start_index) # Init "global" variables self.start_index = start_index self.current_index = start_index self.corpus = corpus self.tk = SpacyTokenizer() self.toggle_click_annotation = False self.labels = self._init_labels(labels) self.save_path = save_path self.char_params = char_params self.additional_infos = additional_infos self._init_additional_outputs( additional_outputs_values, additional_outputs_elements ) self.labels_names = labels_names self.labels_colors = self._init_labels_colors(labels_colors) self.threads = [] display_helper.start_display() all_shortcuts = standard_shortcuts[:] if labels_shortcuts: all_shortcuts += labels_shortcuts self.shortcuts_displays_helpers = ( shortcut_helper.create_shortcuts_displays_helpers(all_shortcuts) ) display_helper.define_keyboard_shortcuts( all_shortcuts, self.shortcuts_displays_helpers ) self.preloaded_displays = utils.PreloadedDisplays() # Start annotating self._annotate() def _init_labels(self, labels): """ Init labels as "empty" if not labels are given """ if not labels: labels = [] for document in self.corpus: labels.append(["O"] * len(document)) return labels def _init_additional_outputs( self, additional_outputs_values, additional_outputs_elements ): self.additional_outputs_elements = additional_outputs_elements if ( additional_outputs_elements is not None and additional_outputs_values is None ): # Create empty dataframe if none is given columns = [element.name for element in additional_outputs_elements] self.additional_outputs_values = pd.DataFrame( columns=columns, index=range(len(self.corpus)) ) else: # Use given data self.additional_outputs_values = additional_outputs_values def _init_labels_colors(self, labels_colors_hex): labels_colors = [] for index, label_name in enumerate(self.labels_names): labels_colors.append( utils.LabelColor( name=label_name, text_color="white", background_color=labels_colors_hex[index % len(labels_colors_hex)], ) ) return labels_colors # -------------------------------------------- # Main # -------------------------------------------- def _annotate(self): """ Method to annotate the current document. It is responsible for setting up the user interface and the variables needed on buttons clicked. """ # Init variables specific to the current document self.document = self.corpus[self.current_index] _, token_spans_lookup = self.tk.word_tokenizer(self.document) self.token_spans_lookup = token_spans_lookup self.chunks = Chunks(labels=self.labels[self.current_index]) self.selected_labeliser = self.labels_names[0] self.label_start_index = None self.additional_outputs_elements_displays = None # Display loader until the display is finished loader = display_helper.display_loader() # Load current core if not preloaded self._async_load(self.preloaded_displays.current, 0) # Define custom styles display_helper.define_custom_styles(self.labels_colors, self.char_params) # Display header display_helper.display_header( self.current_index, len(self.corpus), self._change_document, self.additional_infos, ) # Display moving buttons display_helper.display_top_buttons( self._change_document, self._clear_current, self.shortcuts_displays_helpers, ) # Display document, toolbox and chunk area utils.wait_for_threads(self.threads) display_helper.display_core(self.preloaded_displays.current["core_display"]) self.char_buttons = self.preloaded_displays.current["char_buttons"] self.labels_buttons = self.preloaded_displays.current["labels_buttons"] self.toggle_button = self.preloaded_displays.current["toggle_button"] # Display current chunks for chunk in self.chunks.chunks: self._sync_chunks( chunk, self.document[chunk.start_index : chunk.end_index + 1] # noqa ) if self.additional_outputs_elements: self.additional_outputs_elements_displays = ( display_helper.display_additional_outputs( self.additional_outputs_elements, self.additional_outputs_values.iloc[self.current_index], ) ) # Display footer display_helper.display_footer( self._save, self._quit, self.shortcuts_displays_helpers, ) # Remove loader display_helper.remove_loader(loader) # Prepare toast for on_save display_helper.prepare_toast() # Preload missing displays self._async_load(self.preloaded_displays.next, 1) self._async_load(self.preloaded_displays.previous, -1) # sync ui with data self._sync_toggle_labeliser() def _async_load(self, preloaded, direction): """ Compute the core display (ie toolbox, document and chunk area) and stores it in the preloaded object if not already precomputed. The displays are computed in other threads, so make sure to join the threads before accessing/updating any value in the preloaded object. Parameters ---------- preloaded : Dict[str, ...] Object for storing the values returned by the display if the dict is empty. Values are: - core_display {ipywidgets.Box}: The widget that represents the core display. - char_buttons {List[ipywidgets.Button]}: The buttons for every char. - labels_buttons {List[ipywidgets.Button]}: The buttons for every label. direction : int Represents the direction (and the distance) to the next document. For instance, if the direction equals 2 then it preloads the document at current index + 2. """ if ( not preloaded and self.current_index + direction < len(self.corpus) and self.current_index + direction >= 0 ): thread = threading.Thread( target=display_helper.preload_core, kwargs={ "obj": preloaded, "document": self.corpus[self.current_index + direction], "char_params": self.char_params, "char_on_click": self._labelise, "labels_names": self.labels_names, "selected_labeliser": self.labels_names[0], "shortcuts_displays_helpers": self.shortcuts_displays_helpers, "label_on_click": self._select_new_labeliser, "label_on_toggle": self._toggle_labeliser, }, ) thread.start() self.threads.append(thread) # -------------------------------------------- # On click functions # -------------------------------------------- def _sync_toggle_labeliser(self): description = (self.toggle_button.description or self.toggle_button.icon) if not self.toggle_click_annotation: self.toggle_button.add_class(f"{description}_color_selected") else: self.toggle_button.remove_class(f"{description}_color_selected") def _toggle_labeliser(self, button, button_index): description = (self.toggle_button.description or self.toggle_button.icon) self.toggle_button.remove_class(f"{description}_color_selected") self.toggle_click_annotation = not self.toggle_click_annotation self._sync_toggle_labeliser() def _select_new_labeliser(self, button, button_index): # Remove selected class from all buttons for labeliser_button in self.labels_buttons: labeliser_button.remove_class( f"{labeliser_button.description or labeliser_button.icon}_color_selected" ) labeliser_button.add_class( f"{labeliser_button.description or labeliser_button.icon}_color" ) # Add selected class to the current selected labeliser description = ( self.labels_buttons[button_index].description or self.labels_buttons[button_index].icon ) self.labels_buttons[button_index].add_class(f"{description}_color_selected") self.selected_labeliser = self.labels_buttons[button_index].description if button_index == len(self.labels_buttons) - 1: # Selects the eraser self.selected_labeliser = None # Restart the index to none self.label_start_index = None def _labelise(self, button, char_index): # Selecting the first part and create chunks accordingly if not self.toggle_click_annotation: if self.label_start_index is None: chunk = Chunk( start_index=char_index, end_index=char_index, label=self.selected_labeliser, ) self.label_start_index = char_index # Selecting the second part and create chunks accordingly else: start_index = self.label_start_index end_index = char_index if end_index < start_index: start_index, end_index = end_index, start_index chunk = Chunk( start_index=start_index, end_index=end_index, label=self.selected_labeliser, ) self.label_start_index = None else: start_end_index = self.token_spans_lookup.get(char_index) if start_end_index: chunk = Chunk( start_index=start_end_index[0], end_index=start_end_index[1] - 1, label=self.selected_labeliser, ) self.label_start_index = None else: return # Update chunks with the new chunk chunk_text = self.document[chunk.start_index : chunk.end_index + 1] # noqa updated_chunks, removed_chunks = self.chunks.add_new_chunk_and_update(chunk) # Remove the freshly created chunk if the eraser is selected display_new_chunk = True if self.selected_labeliser is None: self.chunks.remove_chunk_by_id(chunk.id) display_new_chunk = False chunk.display_id = None # Sync the display with the chunks self._sync_chunks( chunk, chunk_text, updated_chunks, removed_chunks, display_new_chunk ) def _sync_chunks( self, chunk, chunk_text, updated_chunks=[], removed_chunks=[], display_chunk=True, ): """ Sync the given chunk display with the current state of the chunks. """ # Display new char highlight display_helper.highlight_chars( char_buttons=self.char_buttons[ chunk.start_index : chunk.end_index + 1 # noqa ], selected_labeliser=chunk.label, labels_names=self.labels_names, undo=False, ) # Remove chunks from display for chunk_removed in removed_chunks: display_helper.remove_chunk(chunk_removed) # Update chunks text for chunk_updated in updated_chunks: display_helper.update_chunk_text( self.document[ chunk_updated.start_index : chunk_updated.end_index + 1 # noqa ], chunk_updated, delete_chunk_on_click=self._delete_chunk, ) # Display new chunk if display_chunk: display_helper.display_chunk( chunk, chunk_text, self._delete_chunk, ) def _delete_chunk(self, button, chunk): # Remove chunk from chunks self.chunks.remove_chunk_by_id(chunk.id) # Remove chunk display_helper.remove_chunk(chunk) # Remove char chunk highlight display_helper.highlight_chars( char_buttons=self.char_buttons[ chunk.start_index : chunk.end_index + 1 # noqa ], selected_labeliser=chunk.label, labels_names=self.labels_names, undo=True, ) def _change_document(self, button, direction, skip=False): """ Go from the current document at index i to the document at index i + direction. If skip is True, the current annotation is not added to the labels. """ # Add current annotation to the labels if skip is False if not skip: self.labels[self.current_index] = self.chunks.to_labels() # Update additional outputs values if self.additional_outputs_elements: for index, element in enumerate(self.additional_outputs_elements): self.additional_outputs_values.iloc[self.current_index][ element.name ] = self.additional_outputs_elements_displays[index].value if self.current_index + direction < 0: return # Move to the next document self.current_index += direction # Clear the current display display_helper.clear_display() if self.current_index >= len(self.corpus): # All done self._save() self._quit() else: # Continue annotation utils.wait_for_threads(self.threads) self.preloaded_displays.update(direction) self._annotate() def _clear_current(self, button): # Clearing the current document <=> Using the eraser on the whole document. current_labeliser = self.selected_labeliser self.selected_labeliser = None self.label_start_index = 0 self._labelise(None, len(self.document) - 1) self.selected_labeliser = current_labeliser def _save(self, button=None, file_path=None): """ Saves the current state of the corpus and the labels into a csv. """ try: # Save file in path if not file_path: file_path = self.save_path file_path = os.path.abspath(file_path) utils.annotation_to_csv( self.corpus, self.labels, self.additional_outputs_values, file_path ) # Display success toast display_helper.show_toast( msg=f"File successfully saved in <b>{file_path}</b> !", success=True ) except Exception as err: # Display error toast with error message display_helper.show_toast(msg=str(err), success=False) def _quit(self, button=None): # Display end screen display_helper.clear_display() display_helper.display_quit_text( self.current_index, len(self.corpus), self.start_index )	[ 1, 529, 276, 1112, 420, 29958, 7192, 457, 29916, 29914, 19737, 29931, 14643, 13, 5215, 2897, 13, 5215, 3244, 292, 13, 13, 5215, 11701, 408, 10518, 13, 5215, 805, 4135, 13, 13, 3166, 282, 2904, 14643, 1053, 2295, 13, 3166, 282, 2904, 14643, 1053, 2479, 408, 2479, 29918, 20907, 13, 3166, 282, 2904, 14643, 1053, 3667, 29879, 13, 3166, 282, 2904, 14643, 29889, 29812, 29918, 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beanstalktop.py	OldhamMade/beanstalktop	4	1600019	<reponame>OldhamMade/beanstalktop #!/bin/python import curses import optparse import select import sys import errno import beanstalkc class BeanstalkTopUI(object): def __init__(self, win, options): self.win = win self.options = options self.resize() try: curses.use_default_colors() curses.start_color() curses.curs_set(0) except curses.error: pass self._connection = None self.default_overview = dict( (i, '-') for i in ( 'pid', 'total-jobs', 'current-connections', 'current-producers', 'current-workers', 'current-tubes', 'current-jobs-ready', 'current-jobs-urgent', 'current-jobs-buried', 'current-jobs-reserved' )) self.default_row = dict( (i, '-') for i in ( 'current-jobs-buried', 'current-jobs-delayed', 'current-jobs-ready', 'current-jobs-reserved', 'current-jobs-urgent' )) self.default_row.update({'name': 'default'}) def _get_connection(self): return beanstalkc.Connection(host=self.options.host, port=int(self.options.port)) if not self._connection: try: self._connection = beanstalkc.Connection(host=self.options.host, port=int(self.options.port)) except beanstalkc.SocketError: self.win.erase() raise SystemExit('Host {0} not contactable on port {1}'.format( self.options.host, self.options.port )) return self._connection connection = property(_get_connection) def _format_uptime(self, seconds): hours, remainder = divmod(seconds, 3600) minutes, seconds = divmod(remainder, 60) return '{0}h {1}m {2}s'.format(hours, minutes, seconds) def run(self): poll = select.poll() poll.register(sys.stdin.fileno(), select.POLLIN \| select.POLLPRI) while 1: self.resize() self.refresh_display() try: events = poll.poll(self.options.delay_seconds * 1000.0) except select.error as e: if e.args and e.args[0] == errno.EINTR: events = 0 else: raise except KeyboardInterrupt: break if events: key = self.win.getch() self.handle_key(key) def handle_key(self, key): key_bindings = { ord('q'): lambda: sys.exit(0), ord('Q'): lambda: sys.exit(0), } action = key_bindings.get(key, lambda: None) action() def resize(self): self.height, self.width = self.win.getmaxyx() def refresh_display(self): self.win.erase() titles = ( 'TUBE', 'READY', 'URGENT', 'RESRVD', 'DELAYD', 'BURIED', ) overview, lines = self.get_data() try: overview['uptime'] = self._format_uptime(overview.get('uptime', 0)) except: overview['uptime'] = self._format_uptime(0) summary_items = [item.format(*overview) for item in ( 'PID: {pid}', 'Uptime: {uptime}', 'Total Jobs: {total-jobs}', 'Connections: {current-connections} ({current-producers}:{current-workers})', 'Cur. Tubes: {current-tubes}', 'Cur. Ready: {current-jobs-ready}', 'Cur. Urgent: {current-jobs-urgent}', 'Cur. Buried: {current-jobs-buried}', 'Cur. Reserved: {current-jobs-reserved}', )] summary_lines = [ summary_items[0:3], summary_items[3:6], summary_items[6:], ] summarywidth = self.width // max(len(i) for i in summary_lines) for item in summary_lines: line = ''.join(s.ljust(summarywidth) for s in item) self.win.addstr(line.ljust(self.width)) self.win.addstr(' ' self.width) colwidth = self.width // len(titles) + 1 titlelen = 0 for i in range(len(titles)): attr = curses.A_REVERSE if i is 0: title = (' ' + titles[i]).ljust(colwidth - 1) else: title = (titles[i] + ' ').rjust(colwidth - 1) titlelen += len(title) self.win.addstr(title, attr) self.win.addstr(' ' * (self.width - titlelen), curses.A_REVERSE) columns = ( 'name', 'current-jobs-ready', 'current-jobs-urgent', 'current-jobs-reserved', 'current-jobs-delayed', 'current-jobs-buried', ) max_lines = self.height - (len(summary_lines) + 1) sortedlines = sorted(lines, key=lambda x: x['current-jobs-ready'])[::-1] for i in range(max_lines): try: line = sortedlines[i] except IndexError: break row = '' for c, column in enumerate(columns): try: if c is 0: row += (' ' + str(line[column])).ljust(colwidth - 1) else: row += (str(line[column]) + ' ').rjust(colwidth - 1) except curses.error: pass try: self.win.addstr(row.ljust(self.width)) except curses.error: pass self.win.refresh() def get_data(self): """ Main statistics { 'binlog-current-index': 0, 'binlog-max-size': 10485760, 'binlog-oldest-index': 0, 'binlog-records-migrated': 0, 'binlog-records-written': 0, 'cmd-bury': 580, 'cmd-delete': 15291, 'cmd-ignore': 3, 'cmd-kick': 0, 'cmd-list-tube-used': 0, 'cmd-list-tubes': 1, 'cmd-list-tubes-watched': 0, 'cmd-pause-tube': 0, 'cmd-peek': 0, 'cmd-peek-buried': 0, 'cmd-peek-delayed': 0, 'cmd-peek-ready': 0, 'cmd-put': 19623, 'cmd-release': 0, 'cmd-reserve': 0, 'cmd-reserve-with-timeout': 15873, 'cmd-stats': 1, 'cmd-stats-job': 22719, 'cmd-stats-tube': 0, 'cmd-touch': 0, 'cmd-use': 10603, 'cmd-watch': 5, 'current-connections': 8, 'current-jobs-buried': 580, 'current-jobs-delayed': 0, 'current-jobs-ready': 3750, 'current-jobs-reserved': 2, 'current-jobs-urgent': 0, 'current-producers': 3, 'current-tubes': 8, 'current-waiting': 0, 'current-workers': 3, 'job-timeouts': 0, 'max-job-size': 65535, 'pid': 78938, 'rusage-stime': 2.585616, 'rusage-utime': 1.005601, 'total-connections': 8, 'total-jobs': 19623, 'uptime': 156, 'version': 1.6, } Tube-specific statistics { 'cmd-delete': 892, 'cmd-pause-tube': 0, 'current-jobs-buried': 685, 'current-jobs-delayed': 0, 'current-jobs-ready': 1001, 'current-jobs-reserved': 1, 'current-jobs-urgent': 0 'current-using': 0, 'current-waiting': 0, 'current-watching': 1, 'name': 'default', 'pause': 0, 'pause-time-left': 0, 'total-jobs': 2579, } """ try: return self.connection.stats(), [self.connection.stats_tube(tube) for tube in self.connection.tubes()] except (TypeError, beanstalkc.SocketError, beanstalkc.CommandFailed): return self.default_overview, [self.default_row] def run_beanstalktop_window(win, options): ui = BeanstalkTopUI(win, options) ui.run() def run_beanstalktop(options): return curses.wrapper(run_beanstalktop_window, options) def main(): parser = optparse.OptionParser() parser.add_option('--host', dest="host", default="0.0.0.0", help="beanstalkd host [0.0.0.0]" ) parser.add_option('-p', '--port', dest="port", default=11300, help="beanstalkd port [11300]" ) parser.add_option('-d', '--delay', dest="delay_seconds", default=1, metavar='NUM', help="delay between refreshes [1s]" ) options, args = parser.parse_args() if args: parser.error('Unexpected arguments: ' + ' '.join(args)) main_loop = lambda: run_beanstalktop(options) main_loop() if __name__ == '__main__': main()	[ 1, 529, 276, 1112, 420, 29958, 21648, 3391, 29924, 1943, 29914, 14471, 11195, 6883, 13, 29937, 14708, 2109, 29914, 4691, 13, 13, 5215, 18580, 267, 13, 5215, 3523, 5510, 13, 5215, 1831, 13, 5215, 10876, 13, 5215, 4589, 1217, 13, 5215, 367, 16220, 2235, 29883, 13, 13, 13, 1990, 1522, 16220, 2235, 7031, 3120, 29898, 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tinyq/__init__.py	mozillazg/tinyq	14	19808	# -- coding: utf-8 -- from tinyq.app import Application # noqa __version__ = '0.3.0' __author__ = 'mozillazg' __license__ = 'MIT' __copyright__ = 'Copyright (c) 2017 mozillazg'	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 3166, 21577, 29939, 29889, 932, 1053, 8427, 29871, 396, 694, 25621, 13, 13, 1649, 3259, 1649, 353, 525, 29900, 29889, 29941, 29889, 29900, 29915, 13, 1649, 8921, 1649, 353, 525, 13025, 29920, 29887, 29915, 13, 1649, 506, 1947, 1649, 353, 525, 26349, 29915, 13, 1649, 8552, 1266, 1649, 353, 525, 11882, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29955, 286, 2112, 2911, 29920, 29887, 29915, 13, 2 ]
mic/hej.py	safl/offload	0	1611093	#!/usr/bin/env python import numpy as np import time if "flush" in dir(np): np.flush() begin = time.time() #a = np.sum(((np.ones(100)+1.0)*2.0)/2.0) a = np.sum(np.random.random(50000000)) #a = np.multiply.accumulate(np.ones((8,8), dtype=np.float32)) print(a) if "flush" in dir(np): np.flush() end = time.time() - begin print(end)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 5215, 12655, 408, 7442, 13, 5215, 931, 13, 13, 361, 376, 23126, 29908, 297, 4516, 29898, 9302, 1125, 13, 1678, 7442, 29889, 23126, 580, 13, 463, 353, 931, 29889, 2230, 580, 13, 13, 29937, 29874, 353, 7442, 29889, 2083, 3552, 29898, 9302, 29889, 2873, 29898, 29896, 29900, 29900, 7240, 29896, 29889, 29900, 11877, 29906, 29889, 29900, 6802, 29906, 29889, 29900, 29897, 13, 29874, 353, 7442, 29889, 2083, 29898, 9302, 29889, 8172, 29889, 8172, 29898, 29945, 29900, 29900, 29900, 29900, 29900, 29900, 29900, 876, 13, 29937, 29874, 353, 7442, 29889, 18056, 368, 29889, 5753, 398, 5987, 29898, 9302, 29889, 2873, 3552, 29947, 29892, 29947, 511, 26688, 29922, 9302, 29889, 7411, 29941, 29906, 876, 13, 2158, 29898, 29874, 29897, 13, 13, 361, 376, 23126, 29908, 297, 4516, 29898, 9302, 1125, 13, 1678, 7442, 29889, 23126, 580, 13, 13, 355, 353, 931, 29889, 2230, 580, 448, 3380, 13, 13, 2158, 29898, 355, 29897, 13, 2 ]
catalog/bindings/gmd/vertical_cs_2.py	NIVANorge/s-enda-playground	0	101559	from dataclasses import dataclass from bindings.gmd.vertical_csproperty_type import VerticalCspropertyType __NAMESPACE__ = "http://www.opengis.net/gml" @dataclass class VerticalCs2(VerticalCspropertyType): """ gml:verticalCS is an association role to the vertical coordinate system used by this CRS. """ class Meta: name = "verticalCS" namespace = "http://www.opengis.net/gml"	[ 1, 515, 848, 13203, 1053, 848, 1990, 13, 3166, 7868, 886, 29889, 29887, 3487, 29889, 18575, 29918, 2395, 6799, 29918, 1853, 1053, 11198, 936, 29907, 29879, 6799, 1542, 13, 13, 1649, 5813, 5550, 11538, 1649, 353, 376, 1124, 597, 1636, 29889, 459, 996, 275, 29889, 1212, 29914, 29887, 828, 29908, 13, 13, 13, 29992, 1272, 1990, 13, 1990, 11198, 936, 29907, 29879, 29906, 29898, 29270, 29907, 29879, 6799, 1542, 1125, 13, 1678, 9995, 13, 1678, 330, 828, 29901, 18575, 9295, 338, 385, 15477, 6297, 304, 278, 11408, 14821, 1788, 13, 1678, 1304, 491, 445, 315, 12445, 29889, 13, 1678, 9995, 13, 13, 1678, 770, 20553, 29901, 13, 4706, 1024, 353, 376, 18575, 9295, 29908, 13, 4706, 7397, 353, 376, 1124, 597, 1636, 29889, 459, 996, 275, 29889, 1212, 29914, 29887, 828, 29908, 13, 2 ]
nwswx/exceptions.py	stacybrock/nws-wx-client	1	95565	""" WxAPI Exceptions """ class WxAPIException(Exception): """Base class for exceptions in this module""" def __init__(self, message): self.message = message class InvalidFormat(WxAPIException): """The format provided is invalid""" class FormatNotAllowed(WxAPIException): """The format provided is not allowed by this endpoint""" class APIError(WxAPIException): """The API returned an error"""	[ 1, 9995, 13, 29956, 29916, 8787, 8960, 29879, 13, 15945, 29908, 13, 13, 1990, 399, 29916, 8787, 2451, 29898, 2451, 1125, 13, 1678, 9995, 5160, 770, 363, 15283, 297, 445, 3883, 15945, 29908, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 2643, 1125, 13, 4706, 1583, 29889, 4906, 353, 2643, 13, 13, 1990, 21403, 5809, 29898, 29956, 29916, 8787, 2451, 1125, 13, 1678, 9995, 1576, 3402, 4944, 338, 8340, 15945, 29908, 13, 13, 1990, 19191, 3664, 15930, 287, 29898, 29956, 29916, 8787, 2451, 1125, 13, 1678, 9995, 1576, 3402, 4944, 338, 451, 6068, 491, 445, 16248, 15945, 29908, 13, 13, 1990, 3450, 2392, 29898, 29956, 29916, 8787, 2451, 1125, 13, 1678, 9995, 1576, 3450, 4133, 385, 1059, 15945, 29908, 13, 2 ]
cryptoPortfolioManager.py	srivathsanvenkateswaran/CryptoPortfolioManager	5	189664	import csv from termcolor import colored from pycoingecko import CoinGeckoAPI import sys # Handling system args if(len(sys.argv) > 1): if(sys.argv[1] in ['-h', '--help']): print( '''Usage: python3 cryptoPortfolioManager.py [OPTION]... Crypto Portfolio Manager - A CLI Tool to keep track of your crypto Portfolio. List of supported long options and short options. -h, --help Display the Help command. -w, --weightage Show the crypto Portfolio along with Coin Weightage. ''' ) sys.exit() cg = CoinGeckoAPI() sheetPath = '/home/srivathsan/Documents/Important Documents/crypto_portfolio.csv' fiatCurrency = 'inr' def getUSTaxBacket(totalProfits): if totalProfits <= 40400: return 1 elif totalProfits >= 40401 and totalProfits <= 445850: return 0.85 elif totalProfits >= 445851: return 0.8 def getTaxMultiplier(totalProfits): if fiatCurrency == 'inr': taxMultiplier = 0.4 elif fiatCurrency == 'usd': taxMultiplier = getUSTaxBacket(totalProfits=totalProfits) def getPrice(cryptoId): return cg.get_price(ids=cryptoId, vs_currencies=fiatCurrency) fields = [] rows = [] with open(sheetPath, 'r') as file: csvreader = csv.reader(file) fields = next(csvreader) fields[0] = 'Coin' for row in csvreader: rows.append(row) colors = ['yellow', 'green', 'red', 'white', 'cyan'] spaces = [] fields.append('Invested') fields.append('Current Value') fields.append('Gains (%)') for field in fields: if len(field) < 5: spaces.append(5) elif len(field) < 10: spaces.append(10) else: spaces.append(15) for field in fields: if field == 'Coingecko ID': continue print(colored(" "(spaces[fields.index(field)]-len(field)) + field + " \|", colors[0]), end='') print('\n') ticker = [] coins = [] quantity = [] buyingAverage = [] for row in rows: ticker.append(row[0]) coins.append(row[1]) quantity.append(float(row[2])) buyingAverage.append(float(row[3])) current = [round(quantity[i]getPrice(coins[i])[coins[i]][fiatCurrency], 2) for i in range(len(coins))] invested = [round(quantity[i]buyingAverage[i], 2) for i in range(len(coins))] profits = [current[i]-invested[i] for i in range(len(coins))] gains = [round(profits[i]/invested[i] 100, 2) for i in range(len(coins))] for i in range(len(coins)): if profits[i]>0: color = colors[1] elif profits[i] == 0: color = colors[3] else: color = colors[2] print(colored(ticker[i] + " "(spaces[0]-len(ticker[i])) + ' \|' + " "(spaces[2]-len(str(quantity[i]))) + str(quantity[i]) + ' \|' + " "(spaces[3]-len(str(buyingAverage[i]))) + str(buyingAverage[i]) + ' \|' + " "(spaces[4]-len(str(invested[i]))) + str(invested[i]) + ' \|' + " "(spaces[5]-len(str(current[i]))) + str(current[i]) + ' \|' + " "(spaces[6]-len(str(gains[i]) + '%')) + str(gains[i]) + '%' + ' \|', color=color)) print('') totalInvested = round(sum(invested), 2) totalCurrent = round(sum(current), 2) totalProfits = round(totalCurrent - totalInvested, 2) print(colored(' '20 + f'Total Invested: {totalInvested}', colors[0])) print(colored(' '20 + f'Total Current: {totalCurrent}', colors[0])) if totalProfits > 0: clr = colors[1] upOrDown = 'up' else: clr = colors[2] upOrDown = 'down' print(colored(' '20 + f'Total Profits: {totalProfits}', clr)) print(colored(' '20 + f'Portfolio {upOrDown} by ', colors[0]), end="") print(colored(f'{round(totalProfits/totalInvested * 100, 2)} %', clr)) print() netProfit = totalProfits if upOrDown == 'up': netProfit = getTaxMultiplier(totalProfits=totalProfits) print(colored(' '20 + f'Net Profits: {netProfit}', clr)) print(colored(' '20 + f'Net Portfolio {upOrDown} by ', colors[0]), end="") print(colored(f'{round(netProfit/totalInvested 100, 2)} %', clr)) print() if(len(sys.argv) > 1): if(sys.argv[1] in ['-w', '--weightage']): investedDict = {} for i in range(0, len(ticker)): investedDict[ticker[i]] = invested[i] investedDict = dict(sorted(investedDict.items(), key=lambda item: item[1])) investedDict = dict(reversed(list(investedDict.items()))) print(colored(' '10 + 'Portfolio Weightage: ', colors[1])) print() print(' '10 + 'Coin \| Invested \| Share ') for key, value in investedDict.items(): coinShare = str(round(value/totalInvested * 100, 2)) print(colored(' '10 + key + ' '(6-len(key)), colors[0]) + '\|' + colored(' '(10-len(str(value))) + str(value), colors[0]), end = " ") print('\|' + colored(' '(7-len(coinShare)) + coinShare + ' %', colors[4]))	[ 1, 1053, 11799, 13, 3166, 1840, 2780, 1053, 28684, 13, 3166, 11451, 1111, 19144, 27604, 1053, 3189, 262, 7999, 27604, 8787, 13, 5215, 10876, 13, 13, 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src/waldur_mastermind/marketplace_openstack/migrations/0006_change_billing_type_for_volumes.py	geant-multicloud/MCMS-mastermind	26	69264	from django.db import migrations INSTANCE_TYPE = 'OpenStackTenant.Instance' VOLUME_TYPE = 'OpenStackTenant.Volume' def change_billing_types(apps, schema_editor): Offering = apps.get_model('marketplace', 'Offering') OfferingComponent = apps.get_model('marketplace', 'OfferingComponent') for offering in Offering.objects.filter( type__in=(INSTANCE_TYPE, VOLUME_TYPE) ).all(): components = offering.components.filter(type__startswith='gigabytes_') if components: for component in components: component.type = OfferingComponent.BillingTypes.FIXED component.save(update_fields=['type']) class Migration(migrations.Migration): dependencies = [ ('marketplace_openstack', '0005_change_private_offerings_customers') ] operations = [migrations.RunPython(change_billing_types)]	[ 1, 515, 9557, 29889, 2585, 1053, 9725, 800, 13, 13, 25580, 23219, 29918, 11116, 353, 525, 6585, 7264, 29911, 27153, 29889, 4998, 29915, 13, 13, 29963, 5607, 29965, 2303, 29918, 11116, 353, 525, 6585, 7264, 29911, 27153, 29889, 24679, 29915, 13, 13, 13, 1753, 1735, 29918, 29890, 8873, 29918, 8768, 29898, 13371, 29892, 10938, 29918, 15204, 1125, 13, 1678, 4587, 571, 292, 353, 11446, 29889, 657, 29918, 4299, 877, 28549, 6689, 742, 525, 2776, 571, 292, 1495, 13, 1678, 4587, 571, 292, 5308, 353, 11446, 29889, 657, 29918, 4299, 877, 28549, 6689, 742, 525, 2776, 571, 292, 5308, 1495, 13, 13, 1678, 363, 27032, 297, 4587, 571, 292, 29889, 12650, 29889, 4572, 29898, 13, 4706, 1134, 1649, 262, 7607, 25580, 23219, 29918, 11116, 29892, 478, 5607, 29965, 2303, 29918, 11116, 29897, 13, 1678, 13742, 497, 7295, 13, 4706, 7117, 353, 27032, 29889, 14036, 29889, 4572, 29898, 1853, 1649, 27382, 2541, 2433, 29887, 335, 10798, 2167, 29918, 1495, 13, 4706, 565, 7117, 29901, 13, 9651, 363, 4163, 297, 7117, 29901, 13, 18884, 4163, 29889, 1853, 353, 4587, 571, 292, 5308, 29889, 29933, 8873, 10562, 29889, 25634, 3352, 13, 18884, 4163, 29889, 7620, 29898, 5504, 29918, 9621, 29922, 1839, 1853, 11287, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 28549, 6689, 29918, 3150, 1429, 742, 525, 29900, 29900, 29900, 29945, 29918, 3167, 29918, 9053, 29918, 974, 571, 886, 29918, 6341, 414, 1495, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 26983, 800, 29889, 6558, 11980, 29898, 3167, 29918, 29890, 8873, 29918, 8768, 4638, 13, 2 ]
Desafio 70.py	MoomenEltelbany/PythonDesafios	0	191999	<reponame>MoomenEltelbany/PythonDesafios print('-' * 30) print(f'{"Lojas do Moomen":^30}') print('-' * 30) soma = totmais = menor = cont = 0 while True: produto = str(input('Nome do produto: ')) preco = float(input('Preço: R$')) soma += preco cont += 1 if cont == 1 or preco < menor: menor = preco prodmenor = produto if preco > 1000: totmais += 1 resp = ' ' while resp not in 'SN': resp = str(input('Quer Continuar [S/N]: ')).upper().strip()[0] print('-' * 40) if resp == 'N': break print(f'{"Fim do Programa":-^40}') print(f'O total de compra foi R${soma:.2f}') print(f'Temos {totmais} produtos que custam mais de R$1000.00') print(f'O produto mais barato foi a/o {prodmenor} que custa R${menor:.2f}')	[ 1, 529, 276, 1112, 420, 29958, 22638, 2770, 29923, 1896, 295, 29890, 1384, 29914, 11980, 4002, 2142, 2363, 13, 2158, 877, 29899, 29915, 334, 29871, 29941, 29900, 29897, 13, 2158, 29898, 29888, 29915, 6377, 3410, 14196, 437, 4546, 2770, 1115, 29985, 29941, 29900, 29913, 1495, 13, 2158, 877, 29899, 29915, 334, 29871, 29941, 29900, 29897, 13, 29879, 4125, 353, 2025, 655, 275, 353, 26764, 353, 640, 353, 29871, 29900, 13, 8000, 5852, 29901, 13, 1678, 11859, 3066, 353, 851, 29898, 2080, 877, 29940, 608, 437, 11859, 3066, 29901, 525, 876, 13, 1678, 758, 1111, 353, 5785, 29898, 2080, 877, 6572, 6102, 29901, 390, 29938, 8785, 13, 1678, 1047, 29874, 4619, 758, 1111, 13, 1678, 640, 4619, 29871, 29896, 13, 1678, 565, 640, 1275, 29871, 29896, 470, 758, 1111, 529, 26764, 29901, 13, 4706, 26764, 353, 758, 1111, 13, 4706, 11859, 1527, 272, 353, 11859, 3066, 13, 1678, 565, 758, 1111, 1405, 29871, 29896, 29900, 29900, 29900, 29901, 13, 4706, 2025, 655, 275, 4619, 29871, 29896, 13, 1678, 4613, 353, 525, 525, 13, 1678, 1550, 4613, 451, 297, 525, 19296, 2396, 13, 4706, 4613, 353, 851, 29898, 2080, 877, 2182, 261, 2866, 8675, 279, 518, 29903, 29914, 29940, 5387, 525, 8106, 21064, 2141, 17010, 580, 29961, 29900, 29962, 13, 1678, 1596, 877, 29899, 29915, 334, 29871, 29946, 29900, 29897, 13, 1678, 565, 4613, 1275, 525, 29940, 2396, 13, 4706, 2867, 13, 2158, 29898, 29888, 29915, 6377, 29943, 326, 437, 7835, 29874, 1115, 29899, 29985, 29946, 29900, 29913, 1495, 13, 2158, 29898, 29888, 29915, 29949, 3001, 316, 752, 336, 4732, 390, 5303, 29879, 4125, 29901, 29889, 29906, 29888, 29913, 1495, 13, 2158, 29898, 29888, 29915, 5776, 359, 426, 4260, 655, 275, 29913, 11859, 20864, 712, 25387, 314, 3503, 316, 390, 29938, 29896, 29900, 29900, 29900, 29889, 29900, 29900, 1495, 13, 2158, 29898, 29888, 29915, 29949, 11859, 3066, 3503, 2594, 1219, 4732, 263, 29914, 29877, 426, 10633, 1527, 272, 29913, 712, 25387, 29874, 390, 5303, 1527, 272, 29901, 29889, 29906, 29888, 29913, 1495, 13, 13, 13, 13, 13, 13, 13, 13, 13, 2 ]
solutions/Interview-04.12-Paths-with-Sum-LCCI/0412.py	Wonz5130/LeetCode-Solutions	12	91941	<gh_stars>10-100 # Definition for a binary tree node. # class TreeNode: # def __init__(self, x): # self.val = x # self.left = None # self.right = None class Solution: def pathSum(self, root: TreeNode, sum: int) -> int: if not root: return 0 return self.dfs(root, sum) + self.pathSum(root.left, sum) + self.pathSum(root.right, sum) def dfs(self, root, sum): # 要特判为空，否则下面 sum == root.val 会报错 if not root: return 0 res = 0 if sum == root.val: res += 1 res += self.dfs(root.left, sum - root.val) res += self.dfs(root.right, sum - root.val) return res	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 29937, 21940, 363, 263, 7581, 5447, 2943, 29889, 13, 29937, 770, 15472, 4247, 29901, 13, 29937, 268, 822, 4770, 2344, 12035, 1311, 29892, 921, 1125, 13, 29937, 308, 1583, 29889, 791, 353, 921, 13, 29937, 308, 1583, 29889, 1563, 353, 6213, 13, 29937, 308, 1583, 29889, 1266, 353, 6213, 13, 13, 1990, 24380, 29901, 13, 1678, 822, 2224, 11139, 29898, 1311, 29892, 3876, 29901, 15472, 4247, 29892, 2533, 29901, 938, 29897, 1599, 938, 29901, 13, 4706, 565, 451, 3876, 29901, 13, 9651, 736, 29871, 29900, 13, 4706, 736, 1583, 29889, 29069, 29898, 4632, 29892, 2533, 29897, 718, 1583, 29889, 2084, 11139, 29898, 4632, 29889, 1563, 29892, 2533, 29897, 718, 1583, 29889, 2084, 11139, 29898, 4632, 29889, 1266, 29892, 2533, 29897, 13, 308, 13, 1678, 822, 4489, 29879, 29898, 1311, 29892, 3876, 29892, 2533, 1125, 13, 4706, 396, 29871, 30698, 31141, 31791, 30573, 30816, 30214, 31191, 31403, 30557, 30806, 2533, 1275, 3876, 29889, 791, 29871, 30437, 233, 141, 168, 31745, 13, 4706, 565, 451, 3876, 29901, 13, 9651, 736, 29871, 29900, 13, 13, 4706, 620, 353, 29871, 29900, 13, 4706, 565, 2533, 1275, 3876, 29889, 791, 29901, 13, 9651, 620, 4619, 29871, 29896, 13, 4706, 620, 4619, 1583, 29889, 29069, 29898, 4632, 29889, 1563, 29892, 2533, 448, 3876, 29889, 791, 29897, 13, 4706, 620, 4619, 1583, 29889, 29069, 29898, 4632, 29889, 1266, 29892, 2533, 448, 3876, 29889, 791, 29897, 13, 4706, 736, 620, 2 ]
examples/vault-consul-ami/auth/sign-request.py	crowd-ai/terraform-aws-vault	651	157093	#!/usr/bin/env python # -What------------------------------------------------------------------------- # This script creates a request to the AWS Security Token Service API # with the action "GetCallerIdentity" and then signs the request using the # AWS credentials. It was modified from the python 2.x example published by # <NAME>, the author of the Vault IAM auth method, at the vault support # mailing list. https://groups.google.com/forum/#!topic/vault-tool/Mfi3O-lW60I # -Why-------------------------------------------------------------------------- # We are using python here instead of bash to take advantage of the boto3 library # which facilitates this work by an order of magnitude # -What-for--------------------------------------------------------------------- # This is useful for authenticating to Vault, because a client can use # this script to generate this request and this request is sent with the # login attempt to the Vault server. Vault then executes this request and gets # the response from GetCallerIdentity, which tells who is trying to authenticate # ------------------------------------------------------------------------------ import botocore.session from botocore.awsrequest import create_request_object import json import base64 import sys def headers_to_go_style(headers): retval = {} for k, v in headers.iteritems(): retval[k] = [v] return retval def generate_vault_request(awsIamServerId): session = botocore.session.get_session() client = session.create_client('sts') endpoint = client._endpoint operation_model = client._service_model.operation_model('GetCallerIdentity') request_dict = client._convert_to_request_dict({}, operation_model) request_dict['headers']['X-Vault-AWS-IAM-Server-ID'] = awsIamServerId request = endpoint.create_request(request_dict, operation_model) return { 'iam_http_request_method': request.method, 'iam_request_url': base64.b64encode(request.url), 'iam_request_body': base64.b64encode(request.body), 'iam_request_headers': base64.b64encode(json.dumps(headers_to_go_style(dict(request.headers)))), # It's a CaseInsensitiveDict, which is not JSON-serializable } if __name__ == "__main__": awsIamServerId = sys.argv[1] print json.dumps(generate_vault_request(awsIamServerId))	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 5618, 2683, 2683, 2683, 2683, 1378, 29899, 13, 29937, 910, 2471, 10017, 263, 2009, 304, 278, 15540, 14223, 25159, 6692, 3450, 13, 29937, 411, 278, 3158, 376, 2577, 5594, 261, 18415, 29908, 322, 769, 18906, 278, 2009, 773, 278, 13, 29937, 15540, 16140, 29889, 739, 471, 9120, 515, 278, 3017, 29871, 29906, 29889, 29916, 1342, 6369, 491, 13, 29937, 529, 5813, 10202, 278, 4148, 310, 278, 478, 1292, 306, 5194, 4817, 1158, 29892, 472, 278, 325, 1292, 2304, 13, 29937, 611, 6504, 1051, 29889, 2045, 597, 13155, 29889, 3608, 29889, 510, 29914, 23343, 8484, 29991, 13010, 29914, 29894, 1292, 29899, 10154, 29914, 29924, 7241, 29941, 29949, 29899, 29880, 29956, 29953, 29900, 29902, 13, 29937, 448, 11008, 2683, 2683, 2683, 2683, 28400, 13, 29937, 1334, 526, 773, 3017, 1244, 2012, 310, 10891, 304, 2125, 10631, 310, 278, 289, 3747, 29941, 3489, 13, 29937, 607, 16089, 277, 1078, 445, 664, 491, 385, 1797, 310, 18497, 13, 29937, 448, 5618, 29899, 1454, 2683, 2683, 2683, 2683, 23648, 13, 29937, 910, 338, 5407, 363, 15585, 1218, 304, 478, 1292, 29892, 1363, 263, 3132, 508, 671, 13, 29937, 445, 2471, 304, 5706, 445, 2009, 322, 445, 2009, 338, 2665, 411, 278, 13, 29937, 6464, 4218, 304, 278, 478, 1292, 1923, 29889, 478, 1292, 769, 24138, 445, 2009, 322, 4947, 13, 29937, 278, 2933, 515, 3617, 5594, 261, 18415, 29892, 607, 10603, 1058, 338, 1811, 304, 15585, 403, 13, 29937, 448, 2683, 2683, 2683, 2683, 9072, 29899, 13, 13, 5215, 9225, 542, 487, 29889, 7924, 13, 3166, 9225, 542, 487, 29889, 10467, 3827, 1053, 1653, 29918, 3827, 29918, 3318, 13, 5215, 4390, 13, 5215, 2967, 29953, 29946, 13, 5215, 10876, 13, 13, 1753, 9066, 29918, 517, 29918, 1484, 29918, 3293, 29898, 13662, 1125, 13, 1678, 3240, 791, 353, 6571, 13, 1678, 363, 413, 29892, 325, 297, 9066, 29889, 1524, 7076, 7295, 13, 4706, 3240, 791, 29961, 29895, 29962, 353, 518, 29894, 29962, 13, 1678, 736, 3240, 791, 13, 13, 1753, 5706, 29918, 29894, 1292, 29918, 3827, 29898, 10467, 29902, 314, 6004, 1204, 1125, 13, 1678, 4867, 353, 9225, 542, 487, 29889, 7924, 29889, 657, 29918, 7924, 580, 13, 1678, 3132, 353, 4867, 29889, 3258, 29918, 4645, 877, 303, 29879, 1495, 13, 1678, 16248, 353, 3132, 3032, 29734, 13, 1678, 5858, 29918, 4299, 353, 3132, 3032, 5509, 29918, 4299, 29889, 16453, 29918, 4299, 877, 2577, 5594, 261, 18415, 1495, 13, 1678, 2009, 29918, 8977, 353, 3132, 3032, 13441, 29918, 517, 29918, 3827, 29918, 8977, 3319, 1118, 5858, 29918, 4299, 29897, 13, 13, 1678, 2009, 29918, 8977, 1839, 13662, 16215, 29990, 29899, 29963, 1292, 29899, 29909, 7811, 29899, 29902, 5194, 29899, 6004, 29899, 1367, 2033, 353, 25879, 29902, 314, 6004, 1204, 13, 13, 1678, 2009, 353, 16248, 29889, 3258, 29918, 3827, 29898, 3827, 29918, 8977, 29892, 5858, 29918, 4299, 29897, 13, 13, 1678, 736, 426, 13, 4706, 525, 2829, 29918, 1124, 29918, 3827, 29918, 5696, 2396, 2009, 29889, 5696, 29892, 13, 4706, 525, 2829, 29918, 3827, 29918, 2271, 2396, 308, 2967, 29953, 29946, 29889, 29890, 29953, 29946, 12508, 29898, 3827, 29889, 2271, 511, 13, 4706, 525, 2829, 29918, 3827, 29918, 2587, 2396, 4706, 2967, 29953, 29946, 29889, 29890, 29953, 29946, 12508, 29898, 3827, 29889, 2587, 511, 13, 4706, 525, 2829, 29918, 3827, 29918, 13662, 2396, 268, 2967, 29953, 29946, 29889, 29890, 29953, 29946, 12508, 29898, 3126, 29889, 29881, 17204, 29898, 13662, 29918, 517, 29918, 1484, 29918, 3293, 29898, 8977, 29898, 3827, 29889, 13662, 4961, 511, 396, 739, 29915, 29879, 263, 11733, 797, 23149, 3321, 21533, 29892, 607, 338, 451, 4663, 29899, 15550, 13902, 13, 1678, 500, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 25879, 29902, 314, 6004, 1204, 353, 10876, 29889, 19218, 29961, 29896, 29962, 13, 1678, 1596, 4390, 29889, 29881, 17204, 29898, 17158, 29918, 29894, 1292, 29918, 3827, 29898, 10467, 29902, 314, 6004, 1204, 876, 13, 2 ]
bitquant/sql/stmt.py	rosspalmer/bitQuant	27	67123	<gh_stars>10-100 from ..data import tools from sqlalchemy.sql import select def auth(): txt = open('auth_sql', 'r') typ = int(txt.readline().rstrip('\n')) if typ == 1: file_path = txt.readline().rstrip('\n') engine_str = 'sqlite+pysqlite:///%s' % file_path if typ == 2: host = txt.readline().rstrip('\n') user = txt.readline().rstrip('\n') password = txt.readline().rstrip('\n') name = txt.readline().rstrip('\n') engine_str = 'mysql+pymysql://%s:%s@%s/%s' % (user, password, host, name) return engine_str def insert(tbl, sql_type): if sql_type == 'mysql': stmt = tbl.insert().prefix_with('IGNORE') if sql_type == 'sqlite': stmt = tbl.insert().prefix_with('OR IGNORE') return stmt #\|Currently broken def select(tbl, exchange='', symbol='', start='', end='', source='', freq=''): sel = select([tbl]) if exchange <> '': sel = sel.where(tbl.c.exchange == exchange) if symbol <> '': sel = sel.where(tbl.c.symbol == symbol) if start <> '': if isinstance(start, str): start = tools.dateconv(start) sel = sel.where(tbl.c.timestamp >= start) if end <> '': if isinstance(end, str): end = tools.dateconv(end) sel = sel.where(tbl.c.timestamp <= end) if source <> '': sel = sel.where(tbl.c.source == source) if freq <> '': sel = sel.where(tbl.c.freq == freq) return sel	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 3166, 6317, 1272, 1053, 8492, 13, 13, 3166, 4576, 284, 305, 6764, 29889, 2850, 1053, 1831, 13, 13, 1753, 4817, 7295, 13, 1678, 13872, 353, 1722, 877, 5150, 29918, 2850, 742, 525, 29878, 1495, 13, 1678, 2393, 353, 938, 29898, 3945, 29889, 949, 1220, 2141, 29878, 17010, 28909, 29876, 8785, 13, 1678, 565, 2393, 1275, 29871, 29896, 29901, 13, 4706, 934, 29918, 2084, 353, 13872, 29889, 949, 1220, 2141, 29878, 17010, 28909, 29876, 1495, 13, 4706, 6012, 29918, 710, 353, 525, 22793, 29974, 29886, 952, 1519, 568, 597, 22584, 29879, 29915, 1273, 934, 29918, 2084, 13, 1678, 565, 2393, 1275, 29871, 29906, 29901, 13, 4706, 3495, 353, 13872, 29889, 949, 1220, 2141, 29878, 17010, 28909, 29876, 1495, 13, 4706, 1404, 353, 13872, 29889, 949, 1220, 2141, 29878, 17010, 28909, 29876, 1495, 13, 4706, 4800, 353, 13872, 29889, 949, 1220, 2141, 29878, 17010, 28909, 29876, 1495, 13, 4706, 1024, 353, 13872, 29889, 949, 1220, 2141, 29878, 17010, 28909, 29876, 1495, 13, 4706, 6012, 29918, 710, 353, 525, 7938, 29974, 29886, 962, 952, 1519, 597, 29995, 29879, 16664, 29879, 29992, 29995, 29879, 22584, 29879, 29915, 1273, 313, 1792, 29892, 4800, 29892, 3495, 29892, 1024, 29897, 13, 1678, 736, 6012, 29918, 710, 13, 13, 1753, 4635, 29898, 16400, 29892, 4576, 29918, 1853, 1125, 13, 1678, 565, 4576, 29918, 1853, 1275, 525, 7938, 2396, 13, 4706, 380, 4378, 353, 19018, 29889, 7851, 2141, 13506, 29918, 2541, 877, 6259, 6632, 1525, 1495, 13, 1678, 565, 4576, 29918, 1853, 1275, 525, 22793, 2396, 13, 4706, 380, 4378, 353, 19018, 29889, 7851, 2141, 13506, 29918, 2541, 877, 1955, 306, 29954, 6632, 1525, 1495, 13, 1678, 736, 380, 4378, 13, 13, 29937, 29989, 7583, 368, 9391, 13, 1753, 1831, 29898, 16400, 29892, 14523, 2433, 742, 29871, 5829, 2433, 742, 1369, 2433, 742, 13, 9651, 1095, 2433, 742, 2752, 2433, 742, 3005, 29939, 2433, 29374, 13, 1678, 5535, 353, 1831, 4197, 16400, 2314, 13, 1678, 565, 14523, 15271, 525, 2396, 13, 4706, 5535, 353, 5535, 29889, 3062, 29898, 16400, 29889, 29883, 29889, 6543, 1275, 14523, 29897, 13, 1678, 565, 5829, 15271, 525, 2396, 13, 4706, 5535, 353, 5535, 29889, 3062, 29898, 16400, 29889, 29883, 29889, 18098, 1275, 5829, 29897, 13, 1678, 565, 1369, 15271, 525, 2396, 13, 4706, 565, 338, 8758, 29898, 2962, 29892, 851, 1125, 13, 9651, 1369, 353, 8492, 29889, 1256, 20580, 29898, 2962, 29897, 13, 4706, 5535, 353, 5535, 29889, 3062, 29898, 16400, 29889, 29883, 29889, 16394, 6736, 1369, 29897, 13, 1678, 565, 1095, 15271, 525, 2396, 13, 4706, 565, 338, 8758, 29898, 355, 29892, 851, 1125, 13, 9651, 1095, 353, 8492, 29889, 1256, 20580, 29898, 355, 29897, 13, 9651, 5535, 353, 5535, 29889, 3062, 29898, 16400, 29889, 29883, 29889, 16394, 5277, 1095, 29897, 13, 4706, 565, 2752, 15271, 525, 2396, 13, 9651, 5535, 353, 5535, 29889, 3062, 29898, 16400, 29889, 29883, 29889, 4993, 1275, 2752, 29897, 13, 4706, 565, 3005, 29939, 15271, 525, 2396, 13, 9651, 5535, 353, 5535, 29889, 3062, 29898, 16400, 29889, 29883, 29889, 29888, 7971, 1275, 3005, 29939, 29897, 13, 1678, 736, 5535, 13, 13, 2 ]
layers/functions/sph_prior_box.py	BoChenUIUC/realtime-action-detection	0	185839	<gh_stars>0 """ Generates prior boxes for SSD netowrk Original author: <NAME>, <NAME> for VOC dataset https://github.com/amdegroot/ssd.pytorch """ import torch from math import sqrt as sqrt from itertools import product as product class SphPriorBox(object): """Compute priorbox coordinates in center-offset form for each source feature map. Note: This 'layer' has changed between versions of the original SSD paper, so we include both versions, but note v2 is the most tested and most recent version of the paper. """ def __init__(self, cfg): super(SphPriorBox, self).__init__() # self.type = cfg.name self.image_size = cfg['min_dim'] # number of priors for feature map location (either 4 or 6) self.num_priors = len(cfg['aspect_ratios']) self.variance = cfg['variance'] or [0.1] self.feature_maps = cfg['feature_maps'] self.min_sizes = cfg['min_sizes'] self.max_sizes = cfg['max_sizes'] self.steps = cfg['steps'] self.aspect_ratios = cfg['aspect_ratios'] self.clip = cfg['clip'] self.version = cfg['name'] for v in self.variance: if v <= 0: raise ValueError('Variances must be greater than 0') def forward(self): mean = [] # TODO merge these for k, f in enumerate(self.feature_maps): h,w = f for i in range(h): for j in range(w): boxes = [] f_kx = self.image_size[1] * 1. / self.steps[k][1] f_ky = self.image_size[0] * 1. / self.steps[k][0] # unit center x,y cx = (j + 0.5) / f_kx cy = (i + 0.5) / f_ky # aspect_ratio: 1 # rel size: min_size s_kx = self.min_sizes[k] * 1. /self.image_size[1] s_ky = self.min_sizes[k] * 1. /self.image_size[0] boxes.append([cx, cy, s_kx, s_ky]) # aspect_ratio: 1 # rel size: sqrt(s_k * s_(k+1)) s_kx_prime = sqrt(s_kx * (self.max_sizes[k] * 1. /self.image_size[1])) s_ky_prime = sqrt(s_ky * (self.max_sizes[k] * 1. /self.image_size[0])) boxes.append([cx, cy, s_kx_prime, s_ky_prime]) # rest of aspect ratios for ar in self.aspect_ratios[k]: boxes.append([cx, cy, s_kxsqrt(ar), s_ky/sqrt(ar)]) boxes.append([cx, cy, s_kx/sqrt(ar), s_kysqrt(ar)]) for box in boxes: mean += box # back to torch land output = torch.Tensor(mean).view(-1, 4) if self.clip: output.clamp_(max=1, min=0) return output	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 15945, 29908, 3251, 1078, 7536, 16273, 363, 5886, 29928, 7787, 340, 29878, 29895, 13, 13, 26036, 4148, 29901, 529, 5813, 10202, 529, 5813, 29958, 363, 478, 20166, 8783, 13, 991, 597, 3292, 29889, 510, 29914, 314, 12163, 4632, 29914, 893, 29881, 29889, 2272, 7345, 305, 13, 13, 15945, 29908, 13, 13, 5215, 4842, 305, 13, 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env/lib/python3.6/site-packages/dipy/denoise/tests/test_non_local_means.py	Raniac/NEURO-LEARN	8	1612803	<filename>env/lib/python3.6/site-packages/dipy/denoise/tests/test_non_local_means.py import numpy as np from numpy.testing import (run_module_suite, assert_, assert_equal, assert_array_almost_equal, assert_raises) from dipy.denoise.non_local_means import non_local_means def test_nlmeans_static(): S0 = 100 * np.ones((20, 20, 20), dtype='f8') S0nb = non_local_means(S0, sigma=1.0, rician=False) assert_array_almost_equal(S0, S0nb) def test_nlmeans_random_noise(): S0 = 100 + 2 * np.random.standard_normal((22, 23, 30)) masker = np.zeros(S0.shape[:3]).astype(bool) masker[8:15, 8:15, 8:15] = 1 for mask in [None, masker]: S0nb = non_local_means(S0, sigma=np.std(S0), rician=False, mask=mask) assert_(S0nb[mask].min() > S0[mask].min()) assert_(S0nb[mask].max() < S0[mask].max()) assert_equal(np.round(S0nb[mask].mean()), 100) S0nb = non_local_means(S0, sigma=np.std(S0), rician=False, mask=mask) assert_(S0nb[mask].min() > S0[mask].min()) assert_(S0nb[mask].max() < S0[mask].max()) assert_equal(np.round(S0nb[mask].mean()), 100) def test_scalar_sigma(): S0 = 100 + np.zeros((20, 20, 20)) noise = 2 * np.random.standard_normal((20, 20, 20)) S0 += noise S0[:10, :10, :10] = 300 + noise[:10, :10, :10] assert_raises( ValueError, non_local_means, S0, sigma=noise, rician=False) def test_nlmeans_boundary(): # nlmeans preserves boundaries S0 = 100 + np.zeros((20, 20, 20)) noise = 2 * np.random.standard_normal((20, 20, 20)) S0 += noise S0[:10, :10, :10] = 300 + noise[:10, :10, :10] non_local_means(S0, sigma=np.std(noise), rician=False) assert_(S0[9, 9, 9] > 290) assert_(S0[10, 10, 10] < 110) def test_nlmeans_wrong(): S0 = 100 + np.zeros((10, 10, 10, 10, 10)) assert_raises(ValueError, non_local_means, S0, 1.0) S0 = 100 + np.zeros((20, 20, 20)) mask = np.ones((10, 10)) assert_raises(ValueError, non_local_means, S0, 1.0, mask) def test_nlmeans_4D_and_mask(): S0 = 200 * np.ones((20, 20, 20, 3), dtype='f8') mask = np.zeros((20, 20, 20)) mask[10, 10, 10] = 1 S0n = non_local_means(S0, sigma=1, mask=mask, rician=True) assert_equal(S0.shape, S0n.shape) assert_equal(np.round(S0n[10, 10, 10]), 200) assert_equal(S0n[8, 8, 8], 0) def test_nlmeans_dtype(): S0 = 200 * np.ones((20, 20, 20, 3), dtype='f4') mask = np.zeros((20, 20, 20)) mask[10:14, 10:14, 10:14] = 1 S0n = non_local_means(S0, sigma=1, mask=mask, rician=True) assert_equal(S0.dtype, S0n.dtype) S0 = 200 * np.ones((20, 20, 20), dtype=np.uint16) mask = np.zeros((20, 20, 20)) mask[10:14, 10:14, 10:14] = 1 S0n = non_local_means(S0, sigma=1, mask=mask, rician=True) assert_equal(S0.dtype, S0n.dtype) if __name__ == '__main__': run_module_suite()	[ 1, 529, 9507, 29958, 6272, 29914, 1982, 29914, 4691, 29941, 29889, 29953, 29914, 2746, 29899, 8318, 29914, 29881, 666, 29891, 29914, 1145, 29877, 895, 29914, 21150, 29914, 1688, 29918, 5464, 29918, 2997, 29918, 1004, 550, 29889, 2272, 13, 5215, 12655, 408, 7442, 13, 3166, 12655, 29889, 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preprocess_data/data_preparation_gan.py	tamlhp/dfd_benchmark	7	91486	# Copyright (c) 2018, <NAME>. All rights reserved. # # This work is licensed under the Creative Commons Attribution-NonCommercial # 4.0 International License. To view a copy of this license, visit # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. import os import glob import argparse import numpy as np import tensorflow as tf import PIL.Image import scipy.ndimage from PIL import ImageEnhance,Image from PIL import ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True #---------------------------------------------------------------------------- def error(msg): print('Error: ' + msg) exit(1) #---------------------------------------------------------------------------- class TFRecordExporter: def __init__(self, tfrecord_dir, expected_images, print_progress=True, progress_interval=10, Gaussian_down=0): self.tfrecord_dir = tfrecord_dir self.tfr_prefix = os.path.join(self.tfrecord_dir, os.path.basename(self.tfrecord_dir)) self.expected_images = expected_images self.cur_images = 0 self.shape = None self.resolution_log2 = None self.tfr_writers = [] self.print_progress = print_progress self.progress_interval = progress_interval self.Gaussian_down = Gaussian_down if self.print_progress: print('Creating dataset "%s"' % tfrecord_dir) if not os.path.isdir(self.tfrecord_dir): os.makedirs(self.tfrecord_dir) assert(os.path.isdir(self.tfrecord_dir)) def close(self): if self.print_progress: print('%-40s\r' % 'Flushing data...', end='', flush=True) for tfr_writer in self.tfr_writers: tfr_writer.close() self.tfr_writers = [] if self.print_progress: print('%-40s\r' % '', end='', flush=True) print('Added %d images.' % self.cur_images) def choose_shuffled_order(self): # Note: Images and labels must be added in shuffled order. order = np.arange(self.expected_images) np.random.RandomState(123).shuffle(order) return order def add_image(self, img): if self.print_progress and self.cur_images % self.progress_interval == 0: print('%d / %d\r' % (self.cur_images, self.expected_images), end='', flush=True) if self.shape is None: self.shape = img.shape self.resolution_log2 = int(np.log2(self.shape[1])) assert self.shape[0] in [1, 3] assert self.shape[1] == self.shape[2] assert self.shape[1] == 2*self.resolution_log2 tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE) #for lod in range(self.resolution_log2 - 1): for lod in [0]: tfr_file = self.tfr_prefix + '-r%02d.tfrecords' % (self.resolution_log2 - lod) self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt)) assert img.shape == self.shape for lod, tfr_writer in enumerate(self.tfr_writers): if lod: img = img.astype(np.float32) if self.Gaussian_down: img = scipy.ndimage.convolve(img, gaussian_filter[np.newaxis, :, :], mode='mirror')[:, ::2, ::2] else: img = (img[:, fd00:c2b6:b24b:be67:2827:688d:e6a1:6a3b, 0::2] + img[:, fd00:c2b6:b24b:be67:2827:688d:e6a1:6a3b, 1::2] + img[:, fd00:c2b6:b24b:be67:2827:688d:e6a1:6a3b, 0::2] + img[:, fd00:c2b6:b24b:be67:2827:688d:e6a1:6a3b, fd00:c2b6:b24b:be67:2827:688d:e6a1:6a3b]) 0.25 quant = np.rint(img).clip(0, 255).astype(np.uint8) ex = tf.train.Example(features=tf.train.Features(feature={ 'shape': tf.train.Feature(int64_list=tf.train.Int64List(value=quant.shape)), 'data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[quant.tostring()]))})) tfr_writer.write(ex.SerializeToString()) self.cur_images += 1 def add_labels(self, labels): if self.print_progress: print('%-40s\r' % 'Saving labels...', end='', flush=True) assert labels.shape[0] == self.cur_images with open(self.tfr_prefix + '-rxx.labels', 'wb') as f: np.save(f, labels.astype(np.float32)) def __enter__(self): return self def __exit__(self, args): self.close() #---------------------------------------------------------------------------- def data_preparation(image_dir, tfrecord_dir, resolution=128, shuffle=1, export_labels=1, percentage_samples=100): print('Loading images from "%s"' % image_dir) sources_dir = os.listdir(image_dir) image_filenames = [] for source_dir in sources_dir: image_filenames_temp = sorted(glob.glob(os.path.join(image_dir, source_dir, '.png'))\ +glob.glob(os.path.join(image_dir, source_dir, '.jpeg')) \ + glob.glob(os.path.join(image_dir, source_dir, '.jpg'))\ ) image_filenames += image_filenames_temp[:int(float(len(image_filenames_temp))*float(percentage_samples)/100.0)] if len(image_filenames) == 0: error('No input images found') img = np.asarray(PIL.Image.open(image_filenames[0])) size = img.shape[0] channels = img.shape[2] if img.ndim == 3 else 1 if channels not in [1, 3]: error('Input images must be stored as RGB or grayscale') with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr: order = tfr.choose_shuffled_order() if shuffle else np.arange(len(image_filenames)) for idx in range(order.size): img = np.asarray(PIL.Image.open(image_filenames[order[idx]])) if channels == 1: img = img[:, :, np.newaxis] # HW => HWC img = PIL.Image.fromarray(img, 'RGB') img = img.resize((resolution, resolution), PIL.Image.ANTIALIAS) contrast = ImageEnhance.Contrast(img) img = contrast.enhance(1.0) brightness = ImageEnhance.Brightness(img) img = brightness.enhance(1.0) img = np.asarray(img) img = img.transpose(2, 0, 1) # HWC => CHW tfr.add_image(img) if export_labels: labels = [] for file in image_filenames: name_list = file.split('/') file_source = name_list[-2] for label, source in enumerate(sources_dir): if source == file_source: labels.append(np.uint32(label)) break labels = np.array(labels) onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32) onehot[np.arange(labels.size), labels] = 1.0 tfr.add_labels(onehot[order]) #---------------------------------------------------------------------------- if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--in_dir', type=str, default=' ') # The input directory containing subdirectories of images. Each subdirectory represents a data source, either from the real dataset or generated by a GAN parser.add_argument('--out_dir', type=str, default=' ') # The output directory containing the prepared data format that enables efficient streaming parser.add_argument('--resolution', type=int, default=128) # The resolution to which images are resized parser.add_argument('--shuffle', type=int, default=1) # Shuffle the order of images when streaming? parser.add_argument('--export_labels', type=int, default=1) # Export image source labels? parser.add_argument('--percentage_samples', type=int, default=100) # The percentage of images used for data preparation args = parser.parse_args() import time begin = time.time() data_preparation(args.in_dir, args.out_dir, args.resolution, args.shuffle, args.export_labels, args.percentage_samples) print("Time : ", time.time()-begin)	[ 1, 396, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29947, 29892, 529, 5813, 15513, 2178, 10462, 21676, 29889, 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sort/mysteryAlgorithm.py	udohsolomon/LearnAlgorithms	0	125583	#!/usr/bin/env python3 def mysteryAlgorithm(lst): for i in range(1,len(lst)): while i > 0 and lst[i-1] > lst[i]: lst[i], lst[i-1] = lst[i-1], lst[i] i -= 1 return lst print(mysteryAlgorithm([6, 4, 3, 8, 5]))	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 13, 1753, 29236, 22461, 4540, 29898, 20155, 1125, 268, 13, 1678, 363, 474, 297, 3464, 29898, 29896, 29892, 2435, 29898, 20155, 22164, 308, 13, 4706, 1550, 474, 1405, 29871, 29900, 322, 24471, 29961, 29875, 29899, 29896, 29962, 1405, 24471, 29961, 29875, 5387, 13, 9651, 24471, 29961, 29875, 1402, 24471, 29961, 29875, 29899, 29896, 29962, 353, 24471, 29961, 29875, 29899, 29896, 1402, 24471, 29961, 29875, 29962, 13, 9651, 474, 22361, 29871, 29896, 13, 1678, 736, 24471, 13, 2158, 29898, 29885, 858, 708, 22461, 4540, 4197, 29953, 29892, 29871, 29946, 29892, 29871, 29941, 29892, 29871, 29947, 29892, 29871, 29945, 12622, 2 ]
opensfm/rig.py	cojosef96/OpenSfM	0	68277	"""Tool for handling rigs""" import logging import re from collections import defaultdict from itertools import combinations import networkx as nx import numpy as np from opensfm import actions, pygeometry, pymap from opensfm.dataset import DataSet, DataSetBase logger = logging.getLogger(__name__) def find_image_rig(image, rig_patterns): """Given an image and candidates rig model patterns, return the RigID/RigCameraID/Instance Member ID this image belongs to. """ for rig_id, patterns in rig_patterns.items(): for rig_camera_id, pattern in patterns.items(): instance_member_id = re.sub(pattern, "", image) if instance_member_id == "": continue if instance_member_id != image: return (rig_id, rig_camera_id, instance_member_id) return None, None, None def create_instances_with_patterns(images, rig_patterns): """Using the provided patterns, group images that should belong to the same rig instances. Incomplete rig instances wrt. the expected size are not considered. Returns : A dict of list of list of images, each list being an instances being aggregated by Rig ID """ per_pattern = defaultdict(dict) for image in images: rig_id, rig_camera_id, instance_member_id = find_image_rig(image, rig_patterns) if not rig_id: continue if instance_member_id not in per_pattern[rig_id]: per_pattern[rig_id][instance_member_id] = [] per_pattern[rig_id][instance_member_id].append((image, rig_camera_id)) complete_instances = defaultdict(list) problematic_images = [] for rig_id, patterns in per_pattern.items(): for pattern_images in patterns.values(): expected_size = len(rig_patterns[rig_id]) if len(pattern_images) != expected_size: problematic_images += [im[0] for im in pattern_images] else: complete_instances[rig_id].append(pattern_images) if problematic_images: logger.warning( ( "The following images are part of an incomplete rig, thus" f"won't be considered of being part of a rig\n {problematic_images}" ) ) return complete_instances def create_subset_dataset_from_instances(data: DataSet, instances_per_rig, name): """Given a list of images grouped by rigs instances, pick a subset of images and create a dataset subset with the provided name from them. Returns : A DataSet containing a subset of images containing enough rig instances """ subset_images = [] for instances in instances_per_rig.values(): instances_sorted = sorted( instances, key=lambda x: data.load_exif(x[0][0])["capture_time"] ) subset_size = data.config["rig_calibration_subset_size"] middle = len(instances_sorted) / 2 instances_calibrate = instances_sorted[ max([0, middle - int(subset_size / 2)]) : min( [middle + int(subset_size / 2), len(instances_sorted) - 1] ) ] for instance in instances_calibrate: subset_images += [x[0] for x in instance] return data.subset(name, subset_images) def compute_relative_pose(rig_id, pose_instances): """ Compute a rig model relatives poses given poses grouped by rig instance. """ # Put all poses instances into some canonical frame taken as the mean of their R\|t centered_pose_instances = [] for instance in pose_instances: origin_center = np.zeros(3) rotation_center = np.zeros(3) for shot, _ in instance: rotation_center += shot.pose.rotation origin_center += shot.pose.get_origin() origin_center /= len(instance) rotation_center /= len(instance) centered_pose_instance = [] for shot, rig_camera_id in instance: instance_pose = pygeometry.Pose(rotation_center) instance_pose.set_origin(origin_center) instance_pose_camera = shot.pose.relative_to(instance_pose) centered_pose_instance.append( ( instance_pose_camera, rig_camera_id, shot.camera.id, ) ) centered_pose_instances.append(centered_pose_instance) # Average canonical poses per RigCamera ID average_origin, average_rotation, count_poses, camera_ids = {}, {}, {}, {} for centered_pose_instance in centered_pose_instances: for pose, rig_camera_id, camera_id in centered_pose_instance: if rig_camera_id not in average_origin: average_origin[rig_camera_id] = np.zeros(3) average_rotation[rig_camera_id] = np.zeros(3) count_poses[rig_camera_id] = 0 average_origin[rig_camera_id] += pose.get_origin() average_rotation[rig_camera_id] += pose.rotation camera_ids[rig_camera_id] = camera_id count_poses[rig_camera_id] += 1 # Construct final rig_model results rig_model = pymap.RigModel(rig_id) for rig_camera_id, count in count_poses.items(): o = average_origin[rig_camera_id] / count r = average_rotation[rig_camera_id] / count pose = pygeometry.Pose(r) pose.set_origin(o) rig_model.add_rig_camera(pymap.RigCamera(pose, rig_camera_id)) return rig_model def create_rig_models_from_reconstruction(reconstruction, instances_per_rig): """ Computed rig model's, given a reconstruction and rig instances's shots. """ rig_models = {} reconstructions_shots = set(reconstruction.shots) for rig_id, instances in instances_per_rig.items(): pose_groups = [] for instance in instances: if any( True if shot_id not in reconstructions_shots else False for shot_id, _ in instance ): continue pose_groups.append( [ (reconstruction.shots[shot_id], rig_camera_id) for shot_id, rig_camera_id in instance ] ) rig_models[rig_id] = compute_relative_pose(rig_id, pose_groups) return rig_models def create_rigs_with_pattern(data: DataSet, patterns): """Create rig data (`rig_models.json` and `rig_assignments.json`) by performing pattern matching to group images belonging to the same instances, followed by a bit of ad-hoc SfM to find some initial relative poses. """ # Construct instances assignments for each rig instances_per_rig = create_instances_with_patterns(data.images(), patterns) for rig_id, instances in instances_per_rig.items(): logger.info( f"Found {len(instances)} rig instances for rig {rig_id} using pattern matching." ) # Create some subset DataSet with enough images from each rig subset_data = create_subset_dataset_from_instances( data, instances_per_rig, "rig_calibration" ) # # Run a bit of SfM without any rig logger.info(f"Running SfM on a subset of {len(subset_data.images())} images.") actions.extract_metadata.run_dataset(subset_data) actions.detect_features.run_dataset(subset_data) actions.match_features.run_dataset(subset_data) actions.create_tracks.run_dataset(subset_data) actions.reconstruct.run_dataset(subset_data) # Compute some relative poses rig_models = create_rig_models_from_reconstruction( subset_data.load_reconstruction()[0], instances_per_rig ) data.save_rig_models(rig_models) data.save_rig_assignments(instances_per_rig) def same_rig_shot(meta1, meta2): """True if shots taken at the same time on a rig.""" have_gps = ( "gps" in meta1 and "gps" in meta2 and "latitude" in meta1["gps"] and "latitude" in meta2["gps"] ) same_gps = ( have_gps and meta1["gps"]["latitude"] == meta2["gps"]["latitude"] and meta1["gps"]["longitude"] == meta2["gps"]["longitude"] ) same_time = meta1["capture_time"] == meta2["capture_time"] return same_gps and same_time def detect_rigs(images, data: DataSetBase): """Search for rigs in a set of images. For each image on a rig, returns rig, rig_camera and rig_pose ids. """ # Build graph of connected images and sequences image_graph = nx.Graph() sequence_graph = nx.Graph() for im1, im2 in combinations(images, 2): meta1 = data.load_exif(im1) meta2 = data.load_exif(im2) if same_rig_shot(meta1, meta2): image_graph.add_edge(im1, im2) sequence_graph.add_edge(meta1["skey"], meta2["skey"]) # Build rigs # pyre-fixme[16]: Module `nx` has no attribute `connected_components`. sequence_cc = nx.connected_components(sequence_graph) sequence_rig_info = {} for i, cc in enumerate(sequence_cc): for j, sequence in enumerate(cc): sequence_rig_info[sequence] = {"rig": i, "rig_camera": j} # Build rig poses # pyre-fixme[16]: Module `nx` has no attribute `connected_components`. image_cc = nx.connected_components(image_graph) rig_info = {} for i, cc in enumerate(image_cc): for image in cc: meta = data.load_exif(image) sr = sequence_rig_info[meta["skey"]] rig_info[image] = { "rig": sr["rig"], "rig_camera": sr["rig_camera"], "rig_pose": i, } return rig_info def pose_kernel(x, y, rotation_std, translation_std): """Gaussian kernel on the diff between two poses.""" diff = x.relative_to(y) dr = sum(diff.rotation 2) dt = sum(diff.translation 2) return np.exp(-dr / rotation_std 2 - dt / translation_std 2) def pose_mode(poses, rotation_std, translation_std): """Find the most popular pose. Popular is defined by a Parzen estimatior with the given Gaussian kernel standard deviations. """ best_score = 0 best_pose = None for pose in poses: score = 0 for other in poses: score += pose_kernel(pose, other, rotation_std, translation_std) if score > best_score: best_score = score best_pose = pose return best_pose	[ 1, 9995, 12229, 363, 11415, 12912, 29879, 15945, 29908, 13, 13, 5215, 12183, 13, 5215, 337, 13, 3166, 16250, 1053, 2322, 8977, 13, 3166, 4256, 8504, 1053, 18240, 13, 13, 5215, 3564, 29916, 408, 302, 29916, 13, 5215, 12655, 408, 7442, 13, 3166, 13246, 24826, 1053, 8820, 29892, 11451, 19156, 29892, 282, 962, 481, 13, 3166, 13246, 24826, 29889, 24713, 1053, 3630, 2697, 29892, 3630, 2697, 5160, 13, 13, 13, 21707, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 13, 13, 1753, 1284, 29918, 3027, 29918, 8966, 29898, 3027, 29892, 12912, 29918, 11037, 29879, 1125, 13, 1678, 9995, 29954, 5428, 385, 1967, 322, 21669, 12912, 1904, 15038, 29892, 736, 278, 13, 1678, 390, 335, 1367, 29914, 29934, 335, 20717, 1367, 29914, 4998, 19495, 3553, 445, 1967, 14393, 304, 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action_controller/scripts/ActionControllerNode.py	FablabHome/The_Essense_of_the_Grey_Region	1	20737	<gh_stars>1-10 #!/usr/bin/env python3 """ MIT License Copyright (c) 2020 rootadminWalker 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. """ import json import rospy from core.Nodes import ActionEvaluator class ActionControllerNode(ActionEvaluator): def __init__(self): # Initialize the intent to callback map, must have NotRecognized situation self.intent2callback = { 'Introduce': self.__introduce, 'GiveMenu': self.__show_menu, 'OrderFood': self.__order_food, 'OrderFoodTakeOut': self.__order_food, 'NotRecognized': self.__not_recognized } super(ActionControllerNode, self).__init__() def __introduce(self, intent, slots, raw_text, session): introduce_dialog = ''' Ah, Forgive me for not introducing myself, masters. I'm snippy, your virtual assistant in this restaurant, I'm still under development, so you could only see me talking right now. ''' self.speaker.say_until_end(introduce_dialog) @staticmethod def __show_menu(intent, slots, raw_text, session): menu = ''' Menu Price ------------------------------------- French Fries $7 meat salad $20 spaghetti $23 hot chocolate $14 cappucino $19 tea $0 water $0 Hamburger $19 Ketchup $0 Tacos $15 Marshmellos $10 Steak $27 hot dog $10 ''' print(f"Sorry for your inconvenience, here's the menu\n\n{menu}") def __order_food(self, intent, slots, raw_text, session): order_what = False orders = {} i = 0 if session is not None: rospy.loginfo(json.loads(session.custom_data)) while i < len(slots): if slots[i]['slotName'] == 'amount': amount = int(slots[i]['value']['value']) try: next_slot = slots[i + 1] if next_slot['slotName'] == 'food': orders[next_slot['value']['value']] = amount i += 2 elif next_slot['slotName'] == 'amount': orders[f'Unknown{i}'] = amount i += 1 order_what = True except IndexError: order_what = True orders[f'Unknown{i}'] = amount i += 1 elif slots[i]['slotName'] == 'food': orders[slots[i]['value']['value']] = 1 i += 1 if order_what or len(slots) == 0: self.speaker.say_until_end("I'm sorry, but could you repeat it again?") self.start_session(next_intents=['OrderFood', 'NotRecognized'], custom_data=orders) return if self.on_session(): if set(session.possible_next_intents) == {'OrderFood'}: if not order_what: self.stop_session() self.speaker.say_until_end('Ok, Gotcha') print(orders) def __not_recognized(self, intent, slots, raw_text, session): if len(session) == 0: rospy.loginfo(f"Currently there isn't an action for '{raw_text}'") elif session[0] == 'OrderFood': rospy.loginfo('Sorry, I could not understand what do you want to order, please say it again') self.stop_session() def reset(self): pass if __name__ == '__main__': node = ActionControllerNode()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 15945, 29908, 13, 26349, 19245, 13, 13, 11882, 1266, 313, 29883, 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weapon/__init__.py	dannl/hunter-sim-classic	0	146605	<gh_stars>0 import random import config class Weapon: def __init__(self, min_d, max_d, speed): self.min_d = min_d self.max_d = max_d self.speed = speed def rand_dmg(self): if config.USE_AVERAGE: return self.avr_dmg() return random.randint(self.min_d, self.max_d) def avr_dmg(self): return (self.min_d + self.max_d) / 2 CJ = Weapon(124, 186, 3.4) KL = Weapon(128, 238, 3.2) SS = Weapon(89, 223, 2.7)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 4036, 13, 13, 5215, 2295, 13, 13, 13, 1990, 1334, 481, 265, 29901, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 1375, 29918, 29881, 29892, 4236, 29918, 29881, 29892, 6210, 1125, 13, 4706, 1583, 29889, 1195, 29918, 29881, 353, 1375, 29918, 29881, 13, 4706, 1583, 29889, 3317, 29918, 29881, 353, 4236, 29918, 29881, 13, 4706, 1583, 29889, 19322, 353, 6210, 13, 13, 1678, 822, 20088, 29918, 18933, 29887, 29898, 1311, 1125, 13, 4706, 565, 2295, 29889, 17171, 29918, 29909, 5348, 10461, 29901, 13, 9651, 736, 1583, 29889, 485, 29878, 29918, 18933, 29887, 580, 13, 4706, 736, 4036, 29889, 9502, 524, 29898, 1311, 29889, 1195, 29918, 29881, 29892, 1583, 29889, 3317, 29918, 29881, 29897, 13, 13, 1678, 822, 1029, 29878, 29918, 18933, 29887, 29898, 1311, 1125, 13, 4706, 736, 313, 1311, 29889, 1195, 29918, 29881, 718, 1583, 29889, 3317, 29918, 29881, 29897, 847, 29871, 29906, 13, 13, 13, 29907, 29967, 353, 1334, 481, 265, 29898, 29896, 29906, 29946, 29892, 29871, 29896, 29947, 29953, 29892, 29871, 29941, 29889, 29946, 29897, 13, 29968, 29931, 353, 1334, 481, 265, 29898, 29896, 29906, 29947, 29892, 29871, 29906, 29941, 29947, 29892, 29871, 29941, 29889, 29906, 29897, 13, 1799, 353, 1334, 481, 265, 29898, 29947, 29929, 29892, 29871, 29906, 29906, 29941, 29892, 29871, 29906, 29889, 29955, 29897, 13, 2 ]
segmentation_models_pytorch/encoders/dpn.py	coolcat647/segmentation_models.pytorch	0	193787	<filename>segmentation_models_pytorch/encoders/dpn.py """ Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin` Attributes: _out_channels (list of int): specify number of channels for each encoder feature tensor _depth (int): specify number of stages in decoder (in other words number of downsampling operations) _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3) Methods: forward(self, x: torch.Tensor) produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of shape NCHW (features should be sorted in descending order according to spatial resolution, starting with resolution same as input `x` tensor). Input: `x` with shape (1, 3, 64, 64) Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8), (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ) also should support number of features according to specified depth, e.g. if depth = 5, number of feature tensors = 6 (one with same resolution as input and 5 downsampled), depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled). """ import torch import torch.nn as nn import torch.nn.functional as F from pretrainedmodels.models.dpn import DPN from pretrainedmodels.models.dpn import pretrained_settings from ._base import EncoderMixin class DPNEncorder(DPN, EncoderMixin): def __init__(self, stage_idxs, out_channels, depth=5, kwargs): super(DPNEncorder, self).__init__(kwargs) self._stage_idxs = stage_idxs self._depth = depth self._out_channels = out_channels self._in_channels = 3 del self.last_linear def get_stages(self): return [ nn.Identity(), nn.Sequential(self.features[0].conv, self.features[0].bn, self.features[0].act), nn.Sequential(self.features[0].pool, self.features[1 : self._stage_idxs[0]]), self.features[self._stage_idxs[0] : self._stage_idxs[1]], self.features[self._stage_idxs[1] : self._stage_idxs[2]], self.features[self._stage_idxs[2] : self._stage_idxs[3]], ] def forward(self, x): stages = self.get_stages() features = [] for i in range(self._depth + 1): x = stages[i](x) if isinstance(x, (list, tuple)): features.append(F.relu(torch.cat(x, dim=1), inplace=True)) else: features.append(x) return features def load_state_dict(self, state_dict, kwargs): state_dict.pop("last_linear.bias") state_dict.pop("last_linear.weight") super(DPNEncorder, self).load_state_dict(state_dict, kwargs) dpn_encoders = { "dpn68": { "encoder": DPNEncorder, "pretrained_settings": pretrained_settings["dpn68"], "params": { "stage_idxs": (4, 8, 20, 24), "out_channels": (3, 10, 144, 320, 704, 832), "groups": 32, "inc_sec": (16, 32, 32, 64), "k_r": 128, "k_sec": (3, 4, 12, 3), "num_classes": 1000, "num_init_features": 10, "small": True, "test_time_pool": True, }, }, "dpn68b": { "encoder": DPNEncorder, "pretrained_settings": pretrained_settings["dpn68b"], "params": { "stage_idxs": (4, 8, 20, 24), "out_channels": (3, 10, 144, 320, 704, 832), "b": True, "groups": 32, "inc_sec": (16, 32, 32, 64), "k_r": 128, "k_sec": (3, 4, 12, 3), "num_classes": 1000, "num_init_features": 10, "small": True, "test_time_pool": True, }, }, "dpn92": { "encoder": DPNEncorder, "pretrained_settings": pretrained_settings["dpn92"], "params": { "stage_idxs": (4, 8, 28, 32), "out_channels": (3, 64, 336, 704, 1552, 2688), "groups": 32, "inc_sec": (16, 32, 24, 128), "k_r": 96, "k_sec": (3, 4, 20, 3), "num_classes": 1000, "num_init_features": 64, "test_time_pool": True, }, }, "dpn98": { "encoder": DPNEncorder, "pretrained_settings": pretrained_settings["dpn98"], "params": { "stage_idxs": (4, 10, 30, 34), "out_channels": (3, 96, 336, 768, 1728, 2688), "groups": 40, "inc_sec": (16, 32, 32, 128), "k_r": 160, "k_sec": (3, 6, 20, 3), "num_classes": 1000, "num_init_features": 96, "test_time_pool": True, }, }, "dpn107": { "encoder": DPNEncorder, "pretrained_settings": pretrained_settings["dpn107"], "params": { "stage_idxs": (5, 13, 33, 37), "out_channels": (3, 128, 376, 1152, 2432, 2688), "groups": 50, "inc_sec": (20, 64, 64, 128), "k_r": 200, "k_sec": (4, 8, 20, 3), "num_classes": 1000, "num_init_features": 128, 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pype/hosts/fusion/plugins/publish/increment_current_file_deadline.py	simonebarbieri/pype	0	14196	<filename>pype/hosts/fusion/plugins/publish/increment_current_file_deadline.py import pyblish.api class FusionIncrementCurrentFile(pyblish.api.ContextPlugin): """Increment the current file. Saves the current file with an increased version number. """ label = "Increment current file" order = pyblish.api.IntegratorOrder + 9.0 hosts = ["fusion"] families = ["render.farm"] optional = True def process(self, context): from pype.lib import version_up from pype.action import get_errored_plugins_from_data errored_plugins = get_errored_plugins_from_data(context) if any(plugin.__name__ == "FusionSubmitDeadline" for plugin in errored_plugins): raise RuntimeError("Skipping incrementing current file because " "submission to render farm failed.") comp = context.data.get("currentComp") assert comp, "Must have comp" current_filepath = context.data["currentFile"] new_filepath = version_up(current_filepath) comp.Save(new_filepath)	[ 1, 529, 9507, 29958, 29886, 668, 29914, 23525, 29914, 29888, 3958, 29914, 12800, 29914, 23679, 29914, 25629, 29918, 3784, 29918, 1445, 29918, 311, 328, 1220, 29889, 2272, 13, 5215, 11451, 29890, 1674, 29889, 2754, 13, 13, 13, 1990, 383, 3958, 797, 17053, 7583, 2283, 29898, 2272, 29890, 1674, 29889, 2754, 29889, 2677, 16288, 1125, 13, 1678, 9995, 797, 17053, 278, 1857, 934, 29889, 13, 13, 1678, 317, 5989, 278, 1857, 934, 411, 385, 11664, 1873, 1353, 29889, 13, 13, 1678, 9995, 13, 13, 1678, 3858, 353, 376, 797, 17053, 1857, 934, 29908, 13, 1678, 1797, 353, 11451, 29890, 1674, 29889, 2754, 29889, 23573, 1061, 7514, 718, 29871, 29929, 29889, 29900, 13, 1678, 18982, 353, 6796, 29888, 3958, 3108, 13, 1678, 13175, 353, 6796, 9482, 29889, 29888, 2817, 3108, 13, 1678, 13136, 353, 5852, 13, 13, 1678, 822, 1889, 29898, 1311, 29892, 3030, 1125, 13, 13, 4706, 515, 282, 668, 29889, 1982, 1053, 1873, 29918, 786, 13, 4706, 515, 282, 668, 29889, 2467, 1053, 679, 29918, 2704, 287, 29918, 12800, 29918, 3166, 29918, 1272, 13, 13, 4706, 1059, 287, 29918, 12800, 353, 679, 29918, 2704, 287, 29918, 12800, 29918, 3166, 29918, 1272, 29898, 4703, 29897, 13, 4706, 565, 738, 29898, 8582, 17255, 978, 1649, 1275, 376, 29943, 3958, 16228, 29928, 1479, 1220, 29908, 13, 18884, 363, 7079, 297, 1059, 287, 29918, 12800, 1125, 13, 9651, 12020, 24875, 2392, 703, 29903, 1984, 3262, 11924, 292, 1857, 934, 1363, 376, 13, 462, 1669, 376, 1491, 6737, 304, 4050, 17888, 5229, 23157, 13, 13, 4706, 752, 353, 3030, 29889, 1272, 29889, 657, 703, 3784, 6843, 1159, 13, 4706, 4974, 752, 29892, 376, 29924, 504, 505, 752, 29908, 13, 13, 4706, 1857, 29918, 1445, 2084, 353, 3030, 29889, 1272, 3366, 3784, 2283, 3108, 13, 4706, 716, 29918, 1445, 2084, 353, 1873, 29918, 786, 29898, 3784, 29918, 1445, 2084, 29897, 13, 13, 4706, 752, 29889, 11371, 29898, 1482, 29918, 1445, 2084, 29897, 13, 2 ]
test.py	Tweetsched/tweetsched-publisher	1	16707	<reponame>Tweetsched/tweetsched-publisher<filename>test.py from base64 import b64encode from app import app import unittest from mock import patch import os import json from twython import Twython class TestApp(unittest.TestCase): def setUp(self): self.app = app.test_client() os.environ['SERVICE_KEY'] = 'test-key' os.environ['SERVICE_PASS'] = '<PASSWORD>' os.environ['APP_KEY'] = 'test-key' os.environ['APP_SECRET'] = 'test-secret' os.environ['OAUTH_TOKEN'] = 'test-oauth-token' os.environ['OAUTH_TOKEN_SECRET'] = 'test-oauth-token-secret' @patch('app.Twython.update_status') def test_publish_tweet(self, update_status_mock): update_status_mock.return_value = True auth = (os.environ['SERVICE_KEY'] + ':' + os.environ['SERVICE_PASS']).encode('utf-8') headers = { 'Authorization': 'Basic ' + b64encode(auth).decode() } rv = self.app.post('/api/v1/tweets', data = json.dumps(dict(id = 3, message = 'test tweet', profileId = '1')), content_type = 'application/json', headers = headers) self.assertEqual(rv.status_code, 200) self.assertEqual(update_status_mock.call_count, 1) update_status_mock.assert_called_once() def test_404(self): auth = (os.environ['SERVICE_KEY'] + ':' + os.environ['SERVICE_PASS']).encode('utf-8') headers = { 'Authorization': 'Basic ' + b64encode(auth).decode() } rv = self.app.get('/i-am-not-found', headers=headers) self.assertEqual(rv.status_code, 404) if __name__ == '__main__': unittest.main()	[ 1, 529, 276, 1112, 420, 29958, 29911, 16668, 816, 287, 29914, 29873, 16668, 816, 287, 29899, 23679, 261, 29966, 9507, 29958, 1688, 29889, 2272, 13, 3166, 2967, 29953, 29946, 1053, 289, 29953, 29946, 12508, 13, 3166, 623, 1053, 623, 13, 5215, 443, 27958, 13, 3166, 11187, 1053, 13261, 13, 5215, 2897, 13, 5215, 4390, 13, 3166, 3252, 1656, 1053, 8168, 1656, 13, 13, 1990, 4321, 2052, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 13, 1678, 822, 731, 3373, 29898, 1311, 1125, 13, 4706, 1583, 29889, 932, 353, 623, 29889, 1688, 29918, 4645, 580, 13, 4706, 2897, 29889, 21813, 1839, 6304, 19059, 29918, 10818, 2033, 353, 525, 1688, 29899, 1989, 29915, 13, 4706, 2897, 29889, 21813, 1839, 6304, 19059, 29918, 25711, 2033, 353, 12801, 25711, 17013, 16299, 13, 4706, 2897, 29889, 21813, 1839, 20576, 29918, 10818, 2033, 353, 525, 1688, 29899, 1989, 29915, 13, 4706, 2897, 29889, 21813, 1839, 20576, 29918, 1660, 22245, 29911, 2033, 353, 525, 1688, 29899, 19024, 29915, 13, 4706, 2897, 29889, 21813, 1839, 29949, 20656, 29950, 29918, 4986, 29968, 1430, 2033, 353, 525, 1688, 29899, 23106, 29899, 6979, 29915, 13, 4706, 2897, 29889, 21813, 1839, 29949, 20656, 29950, 29918, 4986, 29968, 1430, 29918, 1660, 22245, 29911, 2033, 353, 525, 1688, 29899, 23106, 29899, 6979, 29899, 19024, 29915, 13, 13, 1678, 732, 5041, 877, 932, 29889, 27418, 1656, 29889, 5504, 29918, 4882, 1495, 13, 1678, 822, 1243, 29918, 23679, 29918, 29873, 16668, 29898, 1311, 29892, 2767, 29918, 4882, 29918, 17640, 1125, 13, 4706, 2767, 29918, 4882, 29918, 17640, 29889, 2457, 29918, 1767, 353, 5852, 13, 13, 4706, 4817, 353, 313, 359, 29889, 21813, 1839, 6304, 19059, 29918, 10818, 2033, 718, 525, 11283, 718, 2897, 29889, 21813, 1839, 6304, 19059, 29918, 25711, 2033, 467, 12508, 877, 9420, 29899, 29947, 1495, 13, 4706, 9066, 353, 426, 13, 9651, 525, 25471, 2396, 525, 16616, 525, 718, 289, 29953, 29946, 12508, 29898, 5150, 467, 13808, 580, 13, 4706, 500, 13, 4706, 364, 29894, 353, 1583, 29889, 932, 29889, 2490, 11219, 2754, 29914, 29894, 29896, 29914, 29873, 705, 1691, 742, 13, 462, 965, 848, 353, 4390, 29889, 29881, 17204, 29898, 8977, 29898, 333, 353, 29871, 29941, 29892, 2643, 353, 525, 1688, 7780, 300, 742, 8722, 1204, 353, 525, 29896, 1495, 511, 13, 462, 965, 2793, 29918, 1853, 353, 525, 6214, 29914, 3126, 742, 13, 462, 965, 9066, 353, 9066, 29897, 13, 13, 4706, 1583, 29889, 9294, 9843, 29898, 15291, 29889, 4882, 29918, 401, 29892, 29871, 29906, 29900, 29900, 29897, 13, 4706, 1583, 29889, 9294, 9843, 29898, 5504, 29918, 4882, 29918, 17640, 29889, 4804, 29918, 2798, 29892, 29871, 29896, 29897, 13, 4706, 2767, 29918, 4882, 29918, 17640, 29889, 9294, 29918, 13998, 29918, 10646, 580, 13, 13, 1678, 822, 1243, 29918, 29946, 29900, 29946, 29898, 1311, 1125, 13, 4706, 4817, 353, 313, 359, 29889, 21813, 1839, 6304, 19059, 29918, 10818, 2033, 718, 525, 11283, 718, 2897, 29889, 21813, 1839, 6304, 19059, 29918, 25711, 2033, 467, 12508, 877, 9420, 29899, 29947, 1495, 13, 4706, 9066, 353, 426, 13, 9651, 525, 25471, 2396, 525, 16616, 525, 718, 289, 29953, 29946, 12508, 29898, 5150, 467, 13808, 580, 13, 4706, 500, 13, 4706, 364, 29894, 353, 1583, 29889, 932, 29889, 657, 11219, 29875, 29899, 314, 29899, 1333, 29899, 11940, 742, 9066, 29922, 13662, 29897, 13, 4706, 1583, 29889, 9294, 9843, 29898, 15291, 29889, 4882, 29918, 401, 29892, 29871, 29946, 29900, 29946, 29897, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 443, 27958, 29889, 3396, 580, 13, 2 ]
opennem/spiders/nem/dispatch_is.py	tourdownunder/opennem	0	58337	<filename>opennem/spiders/nem/dispatch_is.py from opennem.pipelines.nem.opennem import NemwebUnitScadaOpenNEMStorePipeline from opennem.spiders.nemweb import NemwebSpider class NemwebCurrentDispatchIS(NemwebSpider): name = "au.nem.current.dispatch_is" start_url = "http://nemweb.com.au/Reports/Current/DispatchIS_Reports/" limit = 0 pipelines_extra = set([NemwebUnitScadaOpenNEMStorePipeline,]) class NemwebArchiveDispatchIS(NemwebSpider): name = "au.nem.archive.dispatch_is" start_url = "http://nemweb.com.au/Reports/Archive/DispatchIS_Reports/" limit = 0 pipelines_extra = set([NemwebUnitScadaOpenNEMStorePipeline,]) # Archives tend to contain large zips of embedded zips so throttle # to limit memory use custom_settings = { "CONCURRENT_REQUESTS": 1, "CONCURRENT_ITEMS": 1, }	[ 1, 529, 9507, 29958, 459, 2108, 331, 29914, 1028, 11376, 29914, 15344, 29914, 13369, 29918, 275, 29889, 2272, 13, 3166, 1722, 15344, 29889, 13096, 24210, 29889, 15344, 29889, 459, 2108, 331, 1053, 18268, 2676, 8325, 4421, 1114, 6585, 8186, 29924, 9044, 29925, 23828, 13, 3166, 1722, 15344, 29889, 1028, 11376, 29889, 15344, 2676, 1053, 18268, 2676, 5592, 1241, 13, 13, 13, 1990, 18268, 2676, 7583, 14777, 3235, 29898, 29940, 331, 2676, 5592, 1241, 1125, 13, 1678, 1024, 353, 376, 585, 29889, 15344, 29889, 3784, 29889, 13369, 29918, 275, 29908, 13, 1678, 1369, 29918, 2271, 353, 376, 1124, 597, 15344, 2676, 29889, 510, 29889, 585, 29914, 1123, 4011, 29914, 7583, 29914, 14777, 3235, 29918, 1123, 4011, 12975, 13, 1678, 4046, 353, 29871, 29900, 13, 13, 1678, 8450, 24210, 29918, 17833, 353, 731, 4197, 29940, 331, 2676, 8325, 4421, 1114, 6585, 8186, 29924, 9044, 29925, 23828, 29892, 2314, 13, 13, 13, 1990, 18268, 2676, 13197, 573, 14777, 3235, 29898, 29940, 331, 2676, 5592, 1241, 1125, 13, 1678, 1024, 353, 376, 585, 29889, 15344, 29889, 10867, 29889, 13369, 29918, 275, 29908, 13, 1678, 1369, 29918, 2271, 353, 376, 1124, 597, 15344, 2676, 29889, 510, 29889, 585, 29914, 1123, 4011, 29914, 13197, 573, 29914, 14777, 3235, 29918, 1123, 4011, 12975, 13, 1678, 4046, 353, 29871, 29900, 13, 13, 1678, 8450, 24210, 29918, 17833, 353, 731, 4197, 29940, 331, 2676, 8325, 4421, 1114, 6585, 8186, 29924, 9044, 29925, 23828, 29892, 2314, 13, 13, 1678, 396, 28320, 10331, 304, 1712, 2919, 503, 4512, 310, 15685, 503, 4512, 577, 20961, 698, 280, 13, 1678, 396, 304, 4046, 3370, 671, 13, 1678, 2888, 29918, 11027, 353, 426, 13, 4706, 376, 6007, 22484, 29450, 29918, 16244, 29903, 1115, 29871, 29896, 29892, 13, 4706, 376, 6007, 22484, 29450, 29918, 9094, 4345, 1115, 29871, 29896, 29892, 13, 1678, 500, 13, 2 ]
pyunsplash/examples/example_iteration.py	dantuluri/pyunsplash	0	170037	############################################################################### # Copyright (c) 2016 <NAME> <<EMAIL>> # # File: example_iteration.py # # Author: <NAME> <<EMAIL>> # Date: 14 Dec 2016 # Purpose: How to get to every photo in every collection # # Revision: 2 # Comment: What's new in revision 2 # Add application level logging and interaction with # library logging example # # Comment: What's new in revision 1 # Show the use of the different objects # To get every photo, you could just use Photos # # ############################################################################### import logging import os from pyunsplash import PyUnsplash api_key = os.environ.get('APPLICATION_ID', None) or 'DUMMY_APPLICATION_ID' # Initialize app logging logger = logging.getLogger() logging.basicConfig(filename='app.log', level=logging.DEBUG) # pyunsplash logger defaults to level logging.ERROR # If you need to change that, use getLogger/setLevel # on the module logger, like this: logging.getLogger(PyUnsplash.logger_name).setLevel(logging.DEBUG) # instantiate PyUnsplash object py_un = PyUnsplash(api_key=api_key) # Start with the generic collection, maximize number of items # note: this will run until all photos of all collections have # been visited, unless a connection error occurs. # Typically the API hourly limit gets hit during this # collections = py_un.collections(per_page=30) while collections.has_next: for collection in collections.entries: photos = collection.photos() for photo in photos.entries: print(collection.title, photo.link_download) # no need to specify per_page: will take from original object collections = collections.get_next_page()	[ 1, 835, 13383, 13383, 13383, 13383, 7346, 4136, 13, 29937, 1678, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29953, 529, 5813, 29958, 3532, 26862, 6227, 6778, 13, 29937, 13, 29937, 418, 3497, 29901, 1342, 29918, 1524, 362, 29889, 2272, 13, 29937, 13, 29937, 1678, 13361, 29901, 529, 5813, 29958, 3532, 26862, 6227, 6778, 13, 29937, 418, 4712, 29901, 29871, 29896, 29946, 3826, 29871, 29906, 29900, 29896, 29953, 13, 29937, 259, 15247, 4220, 29901, 1128, 304, 679, 304, 1432, 15373, 297, 1432, 4333, 13, 29937, 13, 29937, 29871, 830, 4924, 29901, 29871, 29906, 13, 29937, 259, 461, 29901, 1724, 29915, 29879, 716, 297, 26554, 29871, 29906, 13, 29937, 9651, 3462, 2280, 3233, 12183, 322, 14881, 411, 13, 29937, 9651, 3489, 12183, 1342, 13, 29937, 13, 29937, 259, 461, 29901, 1724, 29915, 29879, 716, 297, 26554, 29871, 29896, 13, 29937, 9651, 7704, 278, 671, 310, 278, 1422, 3618, 13, 29937, 9651, 1763, 679, 1432, 15373, 29892, 366, 1033, 925, 671, 1963, 15788, 13, 29937, 13, 29937, 13, 13383, 13383, 13383, 13383, 7346, 4136, 2277, 29937, 13, 5215, 12183, 13, 5215, 2897, 13, 3166, 11451, 6948, 572, 1161, 1053, 10772, 25807, 572, 1161, 13, 2754, 29918, 1989, 353, 2897, 29889, 21813, 29889, 657, 877, 3301, 7390, 28541, 29918, 1367, 742, 6213, 29897, 470, 525, 29928, 5005, 17870, 29918, 3301, 7390, 28541, 29918, 1367, 29915, 13, 13, 13, 29937, 25455, 623, 12183, 13, 21707, 353, 12183, 29889, 657, 16363, 580, 13, 21027, 29889, 16121, 3991, 29898, 9507, 2433, 932, 29889, 1188, 742, 3233, 29922, 21027, 29889, 18525, 29897, 13, 13, 29937, 11451, 6948, 572, 1161, 17927, 21274, 304, 3233, 12183, 29889, 11432, 13, 29937, 960, 366, 817, 304, 1735, 393, 29892, 671, 679, 16363, 29914, 842, 10108, 13, 29937, 373, 278, 3883, 17927, 29892, 763, 445, 29901, 13, 21027, 29889, 657, 16363, 29898, 19737, 25807, 572, 1161, 29889, 21707, 29918, 978, 467, 842, 10108, 29898, 21027, 29889, 18525, 29897, 13, 13, 13, 29937, 25112, 10772, 25807, 572, 1161, 1203, 13, 2272, 29918, 348, 353, 10772, 25807, 572, 1161, 29898, 2754, 29918, 1989, 29922, 2754, 29918, 1989, 29897, 13, 13, 29937, 7370, 411, 278, 10035, 4333, 29892, 5256, 675, 1353, 310, 4452, 13, 29937, 4443, 29901, 445, 674, 1065, 2745, 599, 20612, 310, 599, 16250, 505, 13, 29937, 539, 1063, 16669, 29892, 6521, 263, 3957, 1059, 10008, 29889, 13, 29937, 539, 14213, 1711, 278, 3450, 7234, 368, 4046, 4947, 7124, 2645, 445, 13, 29937, 13, 29027, 353, 11451, 29918, 348, 29889, 29027, 29898, 546, 29918, 3488, 29922, 29941, 29900, 29897, 13, 8000, 16250, 29889, 5349, 29918, 4622, 29901, 13, 1678, 363, 4333, 297, 16250, 29889, 26586, 29901, 13, 4706, 20612, 353, 4333, 29889, 561, 15788, 580, 13, 4706, 363, 15373, 297, 20612, 29889, 26586, 29901, 13, 9651, 1596, 29898, 10855, 29889, 3257, 29892, 15373, 29889, 2324, 29918, 10382, 29897, 13, 13, 1678, 396, 694, 817, 304, 6084, 639, 29918, 3488, 29901, 674, 2125, 515, 2441, 1203, 13, 1678, 16250, 353, 16250, 29889, 657, 29918, 4622, 29918, 3488, 580, 13, 2 ]
delta/utils/metrics/py_metrics_test.py	didichuxing/delta	1,442	195330	<gh_stars>1000+ # Copyright (C) 2017 Beijing Didi Infinity Technology and Development Co.,Ltd. # All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== ''' python metric unittest ''' import os from pathlib import Path import tempfile import numpy as np import delta.compat as tf from delta import utils from delta.utils import metrics from delta import PACKAGE_ROOT_DIR #pylint: disable=too-many-instance-attributes class MetricTest(tf.test.TestCase): ''' python metrix unittest ''' def setUp(self): super().setUp() ''' setup ''' package_root = Path(PACKAGE_ROOT_DIR) self.config_file_crf = \ package_root.joinpath('../egs/mock_text_seq_label_data/seq-label/v1/config/seq-label-mock.yml') self.conf_str = ''' solver: metrics: pos_label: 1 # int, same to sklearn cals: - name: AccuracyCal arguments: null - name: ConfusionMatrixCal arguments: null - name: PrecisionCal arguments: average: 'micro' - name: RecallCal arguments: average: 'micro' - name: F1ScoreCal arguments: average: 'micro' ''' self.conf_file = tempfile.mktemp(suffix='metric.yaml') with open(self.conf_file, 'w', encoding='utf-8') as f: #pylint: disable=invalid-name f.write(self.conf_str) self.true_label = np.array([1, 1, 2, 3, 4, 6, 5]) self.pred1 = np.array([1, 1, 2, 3, 4, 6, 5]) self.pred2 = np.array([2, 2, 1, 1, 1, 1, 1]) # config for test token error metircs self.token_conf_str = ''' solver: metrics: pos_label: 1 # int, same to sklearn cals: - name: TokenErrCal arguments: eos_id: 0 ''' self.token_conf_file = tempfile.mktemp(suffix='token.yaml') with open(self.token_conf_file, 'w', encoding='utf-8') as f: #pylint: disable=invalid-name f.write(self.token_conf_str) self.token_true_label = [[1, 1, 1, 1], [1, 3, 4, 5]] self.token_pred1 = [[1, 1, 1, 1], [1, 3, 4, 5]] self.token_pred2 = [[1, 2, 2, 2], [1, 0, 0, 0]] def tearDown(self): ''' tear down ''' if os.path.exists(self.conf_file): os.unlink(self.conf_file) def test_metric(self): ''' test get_metrics function ''' config = utils.load_config(self.conf_file) metrics1 = metrics.get_metrics( config, y_true=self.true_label, y_pred=self.pred1) self.assertEqual(1.0, metrics1['AccuracyCal']) self.assertEqual(1.0, metrics1['PrecisionCal']) self.assertEqual(1.0, metrics1['RecallCal']) self.assertEqual(1.0, metrics1['F1ScoreCal']) metrics2 = metrics.get_metrics( config, y_true=self.true_label, y_pred=self.pred2) self.assertEqual(0.0, metrics2['AccuracyCal']) self.assertEqual(0.0, metrics2['PrecisionCal']) self.assertEqual(0.0, metrics2['RecallCal']) self.assertEqual(0.0, metrics2['F1ScoreCal']) def test_token_err(self): ''' test tooken error rate ''' config = utils.load_config(self.token_conf_file) metrics1 = metrics.get_metrics( config, y_true=self.token_true_label, y_pred=self.token_pred1) self.assertEqual(0.0, metrics1['TokenErrCal']) metrics2 = metrics.get_metrics( config, y_true=self.token_true_label, y_pred=self.token_pred2) self.assertEqual(0.75, metrics2['TokenErrCal']) def test_crf_metrics(self): ''' test crf metrics ''' config = utils.load_config(self.config_file_crf) metrics3 = metrics.get_metrics( config, y_true=[self.true_label], y_pred=[self.pred1]) # metrics3: one string. Text summary of the precision, recall, F1 score for each class. # res3 = metrics3['CrfCal'] # print(res3) # for i, s in enumerate(res3): # print(i, s) self.assertEqual('1.0000', metrics3['CrfCal'][67:73]) metrics4 = metrics.get_metrics( config, y_true=[self.true_label], y_pred=[self.pred2]) self.assertEqual('0.0000', metrics4['CrfCal'][67:73]) if __name__ == "__main__": tf.test.main()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29900, 29900, 29974, 13, 29937, 14187, 1266, 313, 29907, 29897, 29871, 29906, 29900, 29896, 29955, 1522, 823, 292, 7440, 29875, 512, 4951, 537, 17968, 322, 14650, 3189, 1696, 29931, 1594, 29889, 13, 29937, 2178, 10462, 21676, 29889, 13, 29937, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 13, 29937, 366, 1122, 451, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 13, 29937, 887, 1122, 4017, 263, 3509, 310, 278, 19245, 472, 13, 29937, 13, 29937, 268, 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tests/data_sources/test_topographic_data_source.py	lenassero/nowcasting_dataset	0	159557	<filename>tests/data_sources/test_topographic_data_source.py<gh_stars>0 import numpy as np import pandas as pd import pytest from nowcasting_dataset.data_sources import TopographicDataSource @pytest.mark.parametrize( "x, y, left, right, top, bottom", [ (0, 0, -128_000, 126_000, 128_000, -126_000), (10, 0, -126_000, 128_000, 128_000, -126_000), (30, 0, -126_000, 128_000, 128_000, -126_000), (1000, 0, -126_000, 128_000, 128_000, -126_000), (0, 1000, -128_000, 126_000, 128_000, -126_000), (1000, 1000, -126_000, 128_000, 128_000, -126_000), (2000, 2000, -126_000, 128_000, 130_000, -124_000), (2000, 1000, -126_000, 128_000, 128_000, -126_000), (2001, 2001, -124_000, 130_000, 130_000, -124_000), ], ) def test_get_example_2km(x, y, left, right, top, bottom): size = 2000 # meters topo_source = TopographicDataSource( filename="tests/data/europe_dem_2km_osgb.tif", image_size_pixels=128, meters_per_pixel=size, forecast_minutes=300, history_minutes=10, ) t0_dt = pd.Timestamp("2019-01-01T13:00") topo_data = topo_source.get_example(t0_dt=t0_dt, x_meters_center=x, y_meters_center=y) assert topo_data.data.shape == (128, 128) assert len(topo_data.x) == 128 assert len(topo_data.y) == 128 assert not np.isnan(topo_data.data).any() # Topo x and y coords are not exactly set on the edges, but the center of the pixels assert np.isclose(left, topo_data.x.values[0], atol=size) assert np.isclose(right, topo_data.x.values[-1], atol=size) assert np.isclose(top, topo_data.y.values[0], atol=size) assert np.isclose(bottom, topo_data.y.values[-1], atol=size) @pytest.mark.skip("CD does not have access to GCS") def test_get_example_gcs(): """Note this test takes ~5 seconds as the topo data has to be downloaded locally""" filename = "gs://solar-pv-nowcasting-data/Topographic/europe_dem_1km_osgb.tif" size = 2000 # meters topo_source = TopographicDataSource( filename=filename, 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macrosynergy/management/shape_dfs.py	macrosynergy/macrosynergy	3	190088	import numpy as np import pandas as pd from typing import List, Union, Tuple import random from macrosynergy.management.simulate_quantamental_data import make_qdf def reduce_df(df: pd.DataFrame, xcats: List[str] = None, cids: List[str] = None, start: str = None, end: str = None, blacklist: dict = None, out_all: bool = False, intersect: bool = False): """ Filter dataframe by xcats and cids and notify about missing xcats and cids. :param <pd.Dataframe> df: standardized dataframe with the following necessary columns: 'cid', 'xcats', 'real_date'. :param <List[str]> xcats: extended categories to be checked on. Default is all in the dataframe. :param <List[str]> cids: cross sections to be checked on. Default is all in the dataframe. :param <str> start: string representing earliest date. Default is None. :param <str> end: string representing the latest date. Default is None. :param <dict> blacklist: cross sections with date ranges that should be excluded from the data frame. If one cross section has several blacklist periods append numbers to the cross-section code. :param <bool> out_all: if True the function returns reduced dataframe and selected/ available xcats and cids. Default is False, i.e. only the dataframe is returned :param <bool> intersect: if True only retains cids that are available for all xcats. Default is False. :return <pd.Dataframe>: reduced dataframe that also removes duplicates or (for out_all True) dataframe and available and selected xcats and cids. """ dfx = df[df['real_date'] >= pd.to_datetime(start)] if start is not None else df dfx = dfx[dfx['real_date'] <= pd.to_datetime(end)] if end is not None else dfx if blacklist is not None: for key, value in blacklist.items(): filt1 = dfx['cid'] == key[:3] filt2 = dfx['real_date'] >= pd.to_datetime(value[0]) filt3 = dfx['real_date'] <= pd.to_datetime(value[1]) dfx = dfx[~(filt1 & filt2 & filt3)] xcats_in_df = dfx['xcat'].unique() if xcats is None: xcats = sorted(xcats_in_df) else: missing = sorted(set(xcats) - set(xcats_in_df)) if len(missing) > 0: print(f"Missing categories: {missing}.") xcats.remove(missing) dfx = dfx[dfx['xcat'].isin(xcats)] if intersect: df_uns = dfx.groupby('xcat')['cid'].unique() cids_in_df = list(df_uns[0]) for i in range(1, len(df_uns)): cids_in_df = [cid for cid in df_uns[i] if cid in cids_in_df] else: cids_in_df = dfx['cid'].unique() if cids is None: cids = sorted(cids_in_df) else: if not isinstance(cids, list): cids = [cids] missing = sorted(set(cids) - set(cids_in_df)) if len(missing) > 0: print(f'Missing cross sections: {missing}') cids = sorted(list(set(cids).intersection(set(cids_in_df)))) dfx = dfx[dfx['cid'].isin(cids)] if out_all: return dfx.drop_duplicates(), xcats, cids else: return dfx.drop_duplicates() def reduce_df_by_ticker(df: pd.DataFrame, ticks: List[str] = None, start: str = None, end: str = None, blacklist: dict = None): """ Filter dataframe by xcats and cids and notify about missing xcats and cids :param <pd.Dataframe> df: standardized dataframe with the following columns: 'cid', 'xcats', 'real_date'. :param <List[str]> ticks: tickers (cross sections + base categories) :param <str> start: string in ISO 8601 representing earliest date. Default is None. :param <str> end: string ISO 8601 representing the latest date. Default is None. :param <dict> blacklist: cross sections with date ranges that should be excluded from the dataframe. If one cross section has several blacklist periods append numbers to the cross section code. :return <pd.Dataframe>: reduced dataframe that also removes duplicates """ dfx = df[df["real_date"] >= pd.to_datetime(start)] if start is not None else df dfx = dfx[dfx["real_date"] <= pd.to_datetime(end)] if end is not None else dfx if blacklist is not None: # blacklisting by cross-section for key, value in blacklist.items(): filt1 = dfx["cid"] == key[:3] filt2 = dfx["real_date"] >= pd.to_datetime(value[0]) filt3 = dfx["real_date"] <= pd.to_datetime(value[1]) dfx = dfx[~(filt1 & filt2 & filt3)] dfx["ticker"] = df["cid"] + '_' + df["xcat"] ticks_in_df = dfx["ticker"].unique() if ticks is None: ticks = sorted(ticks_in_df) else: missing = sorted(set(ticks) - set(ticks_in_df)) if len(missing) > 0: print(f'Missing tickers: {missing}') ticks.remove(missing) dfx = dfx[dfx["ticker"].isin(ticks)] return dfx.drop_duplicates() def categories_df(df: pd.DataFrame, xcats: List[str], cids: List[str] = None, val: str = 'value', start: str = None, end: str = None, blacklist: dict = None, years: int = None, freq: str = 'M', lag: int = 0, fwin: int = 1, xcat_aggs: List[str] = ('mean', 'mean')): """Create custom two-categories dataframe with appropriate frequency and lags suitable for analysis. :param <pd.Dataframe> df: standardized dataframe with the following necessary columns: 'cid', 'xcats', 'real_date' and at least one column with values of interest. :param <List[str]> xcats: exactly two extended categories whose relationship is to be analyzed. :param <List[str]> cids: cross sections to be included. Default is all in the dataframe. :param <str> start: earliest date in ISO 8601 format. Default is None, i.e. earliest date in data frame is used. :param <str> end: latest date in ISO 8601 format. Default is None, i.e. latest date in data frame is used. :param <dict> blacklist: cross sections with date ranges that should be excluded from the data frame. If one cross section has several blacklist periods append numbers to the cross section code. :param <int> years: Number of years over which data are aggregated. Supersedes freq and does not allow lags, Default is None, i.e. no multi-year aggregation. :param <str> val: name of column that contains the values of interest. Default is 'value'. :param <str> freq: letter denoting frequency at which the series are to be sampled. This must be one of 'D', 'W', 'M', 'Q', 'A'. Default is 'M'. :param <int> lag: Lag (delay of arrival) of second category in periods as set by freq. Default is 0. Note: for analyses with dependent and explanatory categories, the second takes the role of the explanatory. :param <int> fwin: Forward moving average window of first category. Default is 1, i.e no average. Note: This parameter is used mainly for target returns as dependent variables. :param <List[str]> xcat_aggs: Exactly two aggregation methods. Default is 'mean' for both. :return <pd.Dataframe>: custom data frame with two category columns """ assert freq in ['D', 'W', 'M', 'Q', 'A'] assert not (years is not None) & (lag != 0), 'Lags cannot be applied to year groups.' if years is not None: assert isinstance(start, str), 'Year aggregation requires a start date.' df, xcats, cids = reduce_df(df, xcats, cids, start, end, blacklist, out_all=True) col_names = ['cid', 'xcat', 'real_date', val] dfc = pd.DataFrame(columns=col_names) if years is None: for i in range(2): dfw = df[df['xcat'] == xcats[i]].pivot(index='real_date', columns='cid', values=val) dfw = dfw.resample(freq).agg(xcat_aggs[i]) if (i == 0) & (fwin > 1): dfw = dfw.rolling(window=fwin).mean().shift(1 - fwin) if (i == 1) & (lag > 0): dfw = dfw.shift(lag) dfx = pd.melt(dfw.reset_index(), id_vars=['real_date'], value_vars=cids, value_name=val) dfx['xcat'] = xcats[i] dfc = dfc.append(dfx[col_names]) else: s_year = pd.to_datetime(start).year e_year = df['real_date'].max().year + 1 grouping = int((e_year - s_year) / years) remainder = (e_year - s_year) % years year_groups = {} for group in range(grouping): value = [i for i in range(s_year, s_year + years)] key = f"{s_year} - {s_year + (years - 1)}" year_groups[key] = value s_year += years v = [i for i in range(s_year, s_year + (remainder + 1))] year_groups[f"{s_year} - now"] = v list_y_groups = list(year_groups.keys()) translate_ = lambda year: list_y_groups[int((year % 2000) / years)] df['custom_date'] = df['real_date'].dt.year.apply(translate_) for i in range(2): dfx = df[df['xcat'] == xcats[i]] dfx = dfx.groupby(['xcat', 'cid', 'custom_date']).agg(xcat_aggs[i]).reset_index() dfx = dfx.rename(columns={"custom_date": "real_date"}) dfc = dfc.append(dfx[col_names]) return dfc.pivot(index=('cid', 'real_date'), columns='xcat', values=val).dropna()[xcats] if __name__ == "__main__": cids = ['NZD', 'AUD', 'GBP', 'CAD'] xcats = ['XR', 'CRY', 'GROWTH', 'INFL'] df_cids = pd.DataFrame(index=cids, columns=['earliest', 'latest', 'mean_add', 'sd_mult']) df_cids.loc['AUD'] = ['2000-01-01', '2020-12-31', 0.1, 1] df_cids.loc['CAD'] = ['2001-01-01', '2020-11-30', 0, 1] df_cids.loc['GBP'] = ['2002-01-01', '2020-11-30', 0, 2] df_cids.loc['NZD'] = ['2002-01-01', '2020-09-30', -0.1, 2] df_xcats = pd.DataFrame(index=xcats, columns=['earliest', 'latest', 'mean_add', 'sd_mult', 'ar_coef', 'back_coef']) df_xcats.loc['XR'] = ['2000-01-01', '2020-12-31', 0.1, 1, 0, 0.3] df_xcats.loc['CRY'] = ['2000-01-01', '2020-10-30', 1, 2, 0.95, 1] df_xcats.loc['GROWTH'] = ['2001-01-01', '2020-10-30', 1, 2, 0.9, 1] df_xcats.loc['INFL'] = ['2001-01-01', '2020-10-30', 1, 2, 0.8, 0.5] black = {'AUD': ['2000-01-01', '2003-12-31'], 'GBP': ['2018-01-01', '2100-01-01']} random.seed(2) dfd = make_qdf(df_cids, df_xcats, back_ar=0.75) dfd_x1 = reduce_df(dfd, xcats=xcats[:-1], cids=cids[0], start='2012-01-01', end='2018-01-31') print(dfd_x1['xcat'].unique()) dfd_x2 = reduce_df(dfd, xcats=xcats, cids=cids, start='2012-01-01', end='2018-01-31') dfd_x3 = reduce_df(dfd, xcats=xcats, cids=cids, blacklist=black) tickers = [cid + "_XR" for cid in cids] dfd_xt = reduce_df_by_ticker(dfd, ticks=tickers, blacklist=black) # Testing categories_df(). dfc1 = categories_df(dfd, xcats=['GROWTH', 'CRY'], cids=cids, freq='M', lag=0, xcat_aggs=['mean', 'mean'], start='2000-01-01', blacklist=black) dfc2 = categories_df(dfd, xcats=['GROWTH', 'CRY'], cids=cids, freq='M', lag=0, fwin=3, xcat_aggs=['mean', 'mean'], start='2000-01-01', blacklist=black) dfc3 = categories_df(dfd, xcats=['GROWTH', 'CRY'], cids=cids, freq='M', lag=0, xcat_aggs=['mean', 'mean'], start='2000-01-01', blacklist=black, years=10) # Testing reduce_df() filt1 = ~((dfd['cid'] == 'AUD') & (dfd['xcat'] == 'XR')) filt2 = ~((dfd['cid'] == 'NZD') & (dfd['xcat'] == 'INFL')) dfdx = dfd[filt1 & filt2] # simulate missing cross sections dfd_x1, xctx, cidx = reduce_df(dfdx, xcats=['XR', 'CRY', 'INFL'], cids=cids, intersect=True, 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lofti_gaia/lofti.py	logan-pearce/lofti_gaia	2	23857	import astropy.units as u import numpy as np from lofti_gaia.loftitools import * from lofti_gaia.cFunctions import calcOFTI_C #from loftitools import * import pickle import time import matplotlib.pyplot as plt # Astroquery throws some warnings we can ignore: import warnings warnings.filterwarnings("ignore") '''This module obtaines measurements from Gaia EDR3 (Gaia DR2 is also available as a secondary option) and runs through the LOFTI Gaia/OFTI wide stellar binary orbit fitting technique. ''' class Fitter(object): '''Initialize the Fitter object for the binary system, and compute observational constraints to be used in the orbit fit. User must provide Gaia source ids, tuples of mass estimates for both objects, specify the number of desired orbits in posterior sample. Fit will be for object 2 relative to object 1. Attributes are tuples of (value,uncertainty) unless otherwise indicated. Attributes with astropy units are retrieved from Gaia archive, attributes without units are computed from Gaia values. All relative values are for object 2 relative to object 1. Args: sourceid1, sourceid2 (int): Gaia source ids for the two objects, fit will be for motion of \ object 2 relative to object 1 mass1, mass2 (tuple, flt): tuple os mass estimate for object 1 and 2, of the form (value, uncertainty) Norbits (int): Number of desired orbits in posterior sample. Default = 100000 results_filename (str): Filename for fit results files. If none, results will be written to files \ named FitResults.yr.mo.day.hr.min.s astrometry (dict): User-supplied astrometric measurements. Must be dictionary or table or pandas dataframe with\ column names "sep,seperr,pa,paerr,dates" or "ra,raerr,dec,decerr,dates". May be same as the rv table. \ Sep, deltaRA, and deltaDEC must be in arcseconds, PA in degrees, dates in decimal years. \ Default = None user_rv (dict): User-supplied radial velocity measurements. Must be dictionary or table or pandas dataframe with\ column names "rv,rverr,rv_dates". May be same as the astrometry table. Default = None. catalog (str): name of Gaia catalog to query. Default = 'gaiaedr3.gaia_source' ruwe1, ruwe2 (flt): RUWE value from Gaia archive ref_epoch (flt): reference epoch in decimal years. For Gaia DR2 this is 2015.5, for Gaia EDR3 it is 2016.0 plx1, plx2 (flt): parallax from Gaia in mas RA1, RA2 (flt): right ascension from Gaia; RA in deg, uncertainty in mas Dec1, Dec2 (flt): declination from Gaia; Dec in deg, uncertainty in mas pmRA1, pmRA2 (flt): proper motion in RA in mas yr^-1 from Gaia pmDec1, pmDec2 (flt): proper motion in DEC in mas yr^-1 from Gaia rv1, rv2 (flt, optional): radial velocity in km s^-1 from Gaia rv (flt, optional): relative RV of 2 relative to 1, if both are present in Gaia plx (flt): weighted mean parallax for the binary system in mas distance (flt): distance of system in pc, computed from Gaia parallax using method \ of Bailer-Jones et. al 2018. deltaRA, deltaDec (flt): relative separation in RA and Dec directions, in mas pmRA, pmDec (flt): relative proper motion in RA/Dec directions in km s^-1 sep (flt): total separation vector in mas pa (flt): postion angle of separation vector in degrees from North sep_au (flt): separation in AU sep_km (flt): separation in km total_vel (flt): total velocity vector in km s^-1. If RV is available for both, \ this is the 3d velocity vector; if not it is just the plane of sky velocity. total_planeofsky_vel (flt): total velocity in the plane of sky in km s^-1. \ In the absence of RV this is equivalent to the total velocity vector. deltaGmag (flt): relative contrast in Gaia G magnitude. Does not include uncertainty. inflateProperMOtionError (flt): an optional factor to mulitply default gaia proper motion error by. Written by <NAME>, 2020 ''' def __init__(self, sourceid1, sourceid2, mass1, mass2, Norbits = 100000, \ results_filename = None, astrometry = None, user_rv = None, catalog = 'gaiaedr3.gaia_source', inflateProperMotionError=1 ): self.sourceid1 = sourceid1 self.sourceid2 = sourceid2 try: self.mass1 = mass1[0] self.mass1err = mass1[1] self.mass2 = mass2[0] self.mass2err = mass2[1] self.mtot = [self.mass1 + self.mass2, np.sqrt((self.mass1err2) + (self.mass2err2))] except: raise ValueError('Masses must be tuples of (value,error), ex: mass1 = (1.0,0.05)') self.Norbits = Norbits if not results_filename: self.results_filename = 'FitResults.'+time.strftime("%Y.%m.%d.%H.%M.%S")+'.txt' self.stats_filename = 'FitResults.Stats.'+time.strftime("%Y.%m.%d.%H.%M.%S")+'.txt' else: self.results_filename = results_filename self.stats_filename = results_filename+'.Stats.txt' self.astrometry = False # check if user supplied astrometry: if astrometry is not None: # if so, set astrometric flag to True: self.astrometry = True # store observation dates: self.astrometric_dates = astrometry['dates'] # if in sep/pa, convert to ra/dec: if 'sep' in astrometry: try: astr_ra = [MonteCarloIt([astrometry['sep'][i],astrometry['seperr'][i]]) * \ np.sin(np.radians(MonteCarloIt([astrometry['pa'][i],astrometry['paerr'][i]]))) \ for i in range(len(astrometry['sep']))] astr_dec = [MonteCarloIt([astrometry['sep'][i],astrometry['seperr'][i]]) * \ np.cos(np.radians(MonteCarloIt([astrometry['pa'][i],astrometry['paerr'][i]]))) \ for i in range(len(astrometry['sep']))] self.astrometric_ra = np.array([ [np.mean(astr_ra[i]) for i in range(len(astrometry['sep']))], [np.std(astr_ra[i]) for i in range(len(astrometry['sep']))] ]) self.astrometric_dec = np.array([ [np.mean(astr_dec[i]) for i in range(len(astrometry['sep']))], [np.std(astr_dec[i]) for i in range(len(astrometry['sep']))] ]) except: raise ValueError('Astrometry keys not recognized. Please provide dictionary or table or pandas dataframe with\ column names "sep,seperr,pa,paerr,dates" or "ra,raerr,dec,decerr,dates"') elif 'ra' in astrometry: # else store the ra/dec as attributes: try: self.astrometric_ra = np.array([astrometry['ra'], astrometry['raerr']]) self.astrometric_dec = np.array([astrometry['dec'], astrometry['decerr']]) except: raise ValueError('Astrometry keys not recognized. Please provide dictionary or table or pandas dataframe with\ column names "sep,seperr,pa,paerr,dates" or "ra,raerr,dec,decerr,dates"') else: raise ValueError('Astrometry keys not recognized. Please provide dictionary or table or pandas dataframe with\ column names "sep,seperr,pa,paerr,dates" or "ra,raerr,dec,decerr,dates"') # Check if user supplied rv: self.use_user_rv = False if user_rv is not None: # set user rv flag to true: self.use_user_rv = True try: # set attributes; multiply rv by -1 due to difference in coordinate systems: self.user_rv = np.array([user_rv['rv']-1,user_rv['rverr']]) self.user_rv_dates = np.array(user_rv['rv_dates']) except: raise ValueError('RV keys not recognized. Please use column names "rv,rverr,rv_dates"') self.catalog = catalog # Get Gaia measurements, compute needed constraints, and add to object: self.PrepareConstraints(catalog=self.catalog,inflateFactor=inflateProperMotionError) def edr3ToICRF(self,pmra,pmdec,ra,dec,G): ''' Corrects for biases in proper motion. The function is from https://arxiv.org/pdf/2103.07432.pdf Args: pmra,pmdec (float): proper motion ra, dec (float): right ascension and declination G (float): G magnitude Written by <NAME>, 2021 ''' if G>=13: return pmra , pmdec import numpy as np def sind(x): return np.sin(np.radians(x)) def cosd(x): return np.cos(np.radians(x)) table1=""" 0.0 9.0 9.0 9.5 9.5 10.0 10.0 10.5 10.5 11.0 11.0 11.5 11.5 11.75 11.75 12.0 12.0 12.25 12.25 12.5 12.5 12.75 12.75 13.0 18.4 33.8 -11.3 14.0 30.7 -19.4 12.8 31.4 -11.8 13.6 35.7 -10.5 16.2 50.0 2.1 19.4 59.9 0.2 21.8 64.2 1.0 17.7 65.6 -1.9 21.3 74.8 2.1 25.7 73.6 1.0 27.3 76.6 0.5 34.9 68.9 -2.9 """ table1 = np.fromstring(table1,sep=" ").reshape((12,5)).T Gmin = table1[0] Gmax = table1[1] #pick the appropriate omegaXYZ for the source’s magnitude: omegaX = table1[2][(Gmin<=G)&(Gmax>G)][0] omegaY = table1[3][(Gmin<=G)&(Gmax>G)][0] omegaZ = table1[4][(Gmin<=G)&(Gmax>G)][0] pmraCorr = -1sind(dec)cosd(ra)omegaX -sind(dec)sind(ra)omegaY + cosd(dec)omegaZ pmdecCorr = sind(ra)omegaX -cosd(ra)omegaY return pmra-pmraCorr/1000., pmdec-pmdecCorr/1000. def PrepareConstraints(self, rv=False, catalog='gaiaedr3.gaia_source', inflateFactor=1.): '''Retrieves parameters for both objects from Gaia EDR3 archive and computes system attriubtes, and assigns them to the Fitter object class. Args: rv (bool): flag for handling the presence or absence of RV measurements for both objects \ in EDR3. Gets set to True if both objects have Gaia RV measurements. Default = False catalog (str): name of Gaia catalog to query. Default = 'gaiaedr3.gaia_source' inflateFactor (flt): Factor by which to inflate the errors on Gaia proper motions to \ account for improper uncertainty estimates. Default = 1.0 Written by <NAME>, 2020 ''' from astroquery.gaia import Gaia deg_to_mas = 3600000. mas_to_deg = 1./3600000. # Retrieve astrometric solution from Gaia EDR3 job = Gaia.launch_job("SELECT FROM "+catalog+" WHERE source_id = "+str(self.sourceid1)) j = job.get_results() job = Gaia.launch_job("SELECT * FROM "+catalog+" WHERE source_id = "+str(self.sourceid2)) k = job.get_results() if catalog == 'gaiadr2.gaia_source': # Retrieve RUWE from RUWE catalog for both sources and add to object state: job = Gaia.launch_job("SELECT * FROM gaiadr2.ruwe WHERE source_id = "+str(self.sourceid1)) jruwe = job.get_results() job = Gaia.launch_job("SELECT * FROM gaiadr2.ruwe WHERE source_id = "+str(self.sourceid2)) kruwe = job.get_results() self.ruwe1 = jruwe['ruwe'][0] self.ruwe2 = kruwe['ruwe'][0] else: # EDR3 contains ruwe in the main catalog: self.ruwe1 = j['ruwe'][0] self.ruwe2 = k['ruwe'][0] # Check RUWE for both objects and warn if too high: if self.ruwe1>1.2 or self.ruwe2>1.2: print('''WARNING: RUWE for one or more of your solutions is greater than 1.2. This indicates that the source might be an unresolved binary or experiencing acceleration during the observation. Orbit fit results may not be trustworthy.''') # reference epoch: self.ref_epoch = j['ref_epoch'][0] # parallax: self.plx1 = [j[0]['parallax']u.mas, j[0]['parallax_error']u.mas] self.plx2 = [k[0]['parallax']u.mas, k[0]['parallax_error']u.mas] # RA/DEC self.RA1 = [j[0]['ra']u.deg, j[0]['ra_error']mas_to_degu.deg] self.RA2 = [k[0]['ra']u.deg, k[0]['ra_error']mas_to_degu.deg] self.Dec1 = [j[0]['dec']u.deg, j[0]['dec_error']mas_to_degu.deg] self.Dec2 = [k[0]['dec']u.deg, k[0]['dec_error']mas_to_degu.deg] # Proper motions pmRACorrected1,pmDecCorrected1 = self.edr3ToICRF(j[0]['pmra'],j[0]['pmdec'],j[0]['ra'],j[0]['dec'],j[0]["phot_g_mean_mag"]) pmRACorrected2,pmDecCorrected2 = self.edr3ToICRF(k[0]['pmra'],k[0]['pmdec'],k[0]['ra'],k[0]['dec'],k[0]["phot_g_mean_mag"]) self.pmRA1 = [pmRACorrected1u.mas/u.yr, j[0]['pmra_error']u.mas/u.yrinflateFactor] self.pmRA2 = [pmRACorrected2u.mas/u.yr, k[0]['pmra_error']u.mas/u.yrinflateFactor] self.pmDec1 = [pmDecCorrected1u.mas/u.yr, j[0]['pmdec_error']u.mas/u.yrinflateFactor] self.pmDec2 = [pmDecCorrected2u.mas/u.yr, k[0]['pmdec_error']u.mas/u.yrinflateFactor] # See if both objects have RV's in DR2: if catalog == 'gaiaedr3.gaia_source': key = 'dr2_radial_velocity' error_key = 'dr2_radial_velocity_error' elif catalog == 'gaiadr2.gaia_source': key = 'radial_velocity' error_key = 'radial_velocity_error' if type(k[0][key]) == np.float64 and type(j[0][key]) == np.float64 or type(k[0][key]) == np.float32 and type(j[0][key]) == np.float32: rv = True self.rv1 = [j[0][key]u.km/u.s,j[0][error_key]u.km/u.s] self.rv2 = [k[0][key]u.km/u.s,k[0][error_key]u.km/u.s] rv1 = MonteCarloIt(self.rv1) rv2 = MonteCarloIt(self.rv2) self.rv = [ -np.mean(rv2-rv1) , np.std(rv2-rv1) ] # km/s # negative to relfect change in coordinate system from RV measurements to lofti # pos RV = towards observer in this coord system else: self.rv = [0,0] # weighted mean of parallax values: plx = np.average([self.plx1[0].value,self.plx2[0].value], weights = [self.plx1[1].value,self.plx2[1].value]) plxerr = np.max([self.plx1[1].value,self.plx2[1].value]) self.plx = [plx,plxerr] # mas self.distance = distance(self.plx) # pc # Compute separations of component 2 relative to 1: r1 = MonteCarloIt(self.RA1) r2 = MonteCarloIt(self.RA2) d1 = MonteCarloIt(self.Dec1) d2 = MonteCarloIt(self.Dec2) ra = (r2deg_to_mas - r1deg_to_mas) np.cos(np.radians(np.mean([self.Dec1[0].value,self.Dec2[0].value]))) dec = ((d2 - d1)u.deg).to(u.mas).value self.deltaRA = [np.mean(ra),np.std(ra)] # mas self.deltaDec = [np.mean(dec),np.std(dec)] # mas # compute relative proper motion: pr1 = MonteCarloIt(self.pmRA1) pr2 = MonteCarloIt(self.pmRA2) pd1 = MonteCarloIt(self.pmDec1) pd2 = MonteCarloIt(self.pmDec2) pmRA = [np.mean(pr2 - pr1), np.std(pr2-pr1)] # mas/yr pmDec = [np.mean(pd2 - pd1), np.std(pd2 - pd1)] # mas/yr self.pmRA = masyr_to_kms(pmRA,self.plx) # km/s self.pmDec = masyr_to_kms(pmDec,self.plx) # km/s # Compute separation/position angle: r, p = to_polar(r1,r2,d1,d2) self.sep = tuple([np.mean(r).value, np.std(r).value]) # mas self.pa = tuple([np.mean(p).value, np.std(p).value]) # deg self.sep_au = tuple([((self.sep[0]/1000)self.distance[0]), ((self.sep[1]/1000)self.distance[0])]) self.sep_km = tuple([ self.sep_au[0]u.au.to(u.km) , self.sep_au[1]u.au.to(u.km)]) # compute total velocities: if rv: self.total_vel = [ add_in_quad([self.pmRA[0],self.pmDec[0],self.rv[0]]) , add_in_quad([self.pmRA[1],self.pmDec[1],self.rv[1]]) ] # km/s self.total_planeofsky_vel = [ add_in_quad([self.pmRA[0],self.pmDec[0]]) , add_in_quad([self.pmRA[1],self.pmDec[1]]) ] # km/s else: self.total_vel = [ add_in_quad([self.pmRA[0],self.pmDec[0]]) , add_in_quad([self.pmRA[1],self.pmDec[1]]) ] # km/s self.total_planeofsky_vel = self.total_vel.copy() # km/s # compute deltamag: self.deltaGmag = j[0]['phot_g_mean_mag'] - k[0]['phot_g_mean_mag'] class FitOrbit(object): ''' Object for performing an orbit fit. Takes attributes from Fitter class. ex: orbits = FitOrbit(fitterobject) Args: fitterobject (Fitter object): Fitter object initialized from the Fitter class write_stats (bool): If True, write out summary statistics of orbit sample at \ conclusion of fit. Default = True. write_results (bool): If True, write out the fit results to a pickle file \ in addition to the text file created during the fit. Default = True. deltaRA, deltaDec (flt): relative separation in RA and Dec directions, in mas pmRA, pmDec (flt): relative proper motion in RA/Dec directions in km s^-1 rv (flt, optional): relative RV of 2 relative to 1, if both are present in Gaia EDR3 mtot_init (flt): initial total system mass in Msun from user input distance (flt): distance of system in pc, computed from Gaia parallax using method of Bailer-Jones et. al 2018. sep (flt): separation vector in mas pa (flt): postion angle of separation vector in degrees from North ref_epoch (flt): epoch of the measurement, 2016.0 for Gaia EDR3 and 2015.5 for Gaia DR2. Norbits (int): number of desired orbit samples write_stats (bool): if True, write summary of sample statistics to human-readable file at end of run. Default = True write_results (bool): if True, write out current state of sample orbits in pickle file in periodic intervals during \ run, and again at the end of the run. RECOMMENDED. Default = True results_filename (str): name of file for saving pickled results to disk. If not supplied, \ defaul name is FitResults.y.mo.d.h.m.s.pkl, saved in same directory as fit was run. stats_filename (str): name of file for saving human-readable file of stats of sample results. If not supplied, \ defaul name is FitResults.Stats.y.mo.d.h.m.s.pkl, saved in same directory as fit was run. run_time (flt): run time for the last fit. astropy units object Written by <NAME>, 2020 ''' def __init__(self, fitterobject, write_stats = True, write_results = True, python_version=False, \ use_pm_cross_term = False, corr_coeff = None): # establish fit parameters: self.deltaRA = fitterobject.deltaRA self.deltaDec = fitterobject.deltaDec self.pmRA = fitterobject.pmRA self.pmDec = fitterobject.pmDec self.rv = fitterobject.rv self.mtot_init = fitterobject.mtot self.distance = fitterobject.distance self.sep = fitterobject.sep self.pa = fitterobject.pa self.ref_epoch = fitterobject.ref_epoch self.Norbits = fitterobject.Norbits self.write_results = write_results self.write_stats = write_stats self.results_filename = fitterobject.results_filename self.stats_filename = fitterobject.stats_filename self.astrometry = fitterobject.astrometry if self.astrometry: self.astrometric_ra = fitterobject.astrometric_ra self.astrometric_dec = fitterobject.astrometric_dec self.astrometric_dates = fitterobject.astrometric_dates self.use_user_rv = fitterobject.use_user_rv if self.use_user_rv: self.user_rv = fitterobject.user_rv self.user_rv_dates = fitterobject.user_rv_dates # run orbit fitter: self.fitorbit(python_fitOFTI=python_version, use_pm_cross_term = use_pm_cross_term, corr_coeff = corr_coeff) def fitorbit(self, save_results_every_X_loops = 100, python_fitOFTI=False, use_pm_cross_term = False, corr_coeff = None): '''Run the OFTI fitting run on the Fitter object. Called when FitOrbit object is created. Args: save_results_every_X_loops (int): on every Xth loop, save status of the \ orbit sample arrays to a pickle file, if write_results = True (Default) python_fitOFTI (bool): If True, fit using python only without using C Kepler's equation solver. Default = False use_pm_cross_term (bool): If True, include the proper motion correlation cross term in the Chi^2 computation \ Default = False Written by <NAME>, 2020 ''' # write header: print('Saving orbits in',self.results_filename) k = open(self.results_filename, 'w') output_file_header = '# sma [arcsec] period [yrs] orbit phase t_0 [yr] ecc incl [deg]\ argp [deg] lan [deg] m_tot [Msun] dist [pc] chi^2 ln(prob) ln(randn)' k.write(output_file_header + "\n") k.close() import time as tm ########### Perform initial run to get initial chi-squared: ############# # Draw random orbits: #parameters = a,T,const,to,e,i,w,O,m1,dist numSamples = 10000 parameters_init = draw_samples(numSamples, self.mtot_init, self.distance, self.ref_epoch) # Compute positions and velocities: if(python_fitOFTI): X,Y,Z,Xdot,Ydot,Zdot,Xddot,Yddot,Zddot,parameters=calc_OFTI(parameters_init,self.ref_epoch,self.sep,self.pa) else: returnArray = np.zeros((19,numSamples)) returnArray = calcOFTI_C(parameters_init,self.ref_epoch,self.sep,self.pa,returnArray.copy()) X,Y,Z,Xdot,Ydot,Zdot,Xddot,Yddot,Zddot = returnArray[0:9] parameters = returnArray[9:] # Compute chi squared: if self.rv[0] != 0: model = np.array([Y,X,Ydot,Xdot,Zdot]) data = np.array([self.deltaRA, self.deltaDec, self.pmRA, self.pmDec, self.rv]) else: model = np.array([Y,X,Ydot,Xdot]) data = np.array([self.deltaRA, self.deltaDec, self.pmRA, self.pmDec]) chi2 = ComputeChi2(data,model) if use_pm_cross_term: chi2 -= ( 2 corr_coeff * (data[2][0] - model[2]) * (data[3][0] - model[3]) ) / (data[2][1] * data[3][1]) if self.astrometry: p = parameters.copy() a,T,const,to,e,i,w,O,m1,dist = p[0],p[1],p[2],p[3],p[4],p[5],p[6],p[7],p[8],p[9] chi2_astr = np.zeros(10000) # Calculate predicted positions at astr observation dates for each orbit: for j in range(self.astrometric_ra.shape[1]): # for each date, compute XYZ for each 10000 trial orbit. We can # skip scale and rotate because that was accomplished in the calc_OFTI call above. X1,Y1,Z1,E1 = calc_XYZ(a,T,to,e,i,w,O,self.astrometric_dates[j]) # Place astrometry into data array where: data[0][0]=ra obs, data[0][1]=ra err, etc: data = np.array([self.astrometric_ra[:,j], self.astrometric_dec[:,j]]) # place corresponding predicited positions at that date for each trial orbit in arcsec: model = np.array([Y11000,X11000]) # compute chi2 for trial orbits at that date and add to the total chi2 sum: chi2_astr += ComputeChi2(data,model) chi2 = chi2 + chi2_astr if self.use_user_rv: p = parameters.copy() a,T,const,to,e,i,w,O,m1,dist = p[0],p[1],p[2],p[3],p[4],p[5],p[6],p[7],p[8],p[9] chi2_rv = np.zeros(10000) for j in range(self.user_rv.shape[1]): # compute ecc anomaly at that date: X1,Y1,Z1,E1 = calc_XYZ(a,T,to,e,i,w,O,self.user_rv_dates[j]) # compute velocities at that ecc anom: Xdot,Ydot,Zdot = calc_velocities(a,T,to,e,i,w,O,dist,E1) # compute chi2: chi2_rv += ComputeChi2(np.array([self.user_rv[:,j]]),np.array([Zdot])) chi2 = chi2 + chi2_rv print('inital chi min',np.nanmin(chi2)) self.chi_min = np.nanmin(chi2) # Accept/reject: accepted, lnprob, lnrand = AcceptOrReject(chi2,self.chi_min) # count number accepted: number_orbits_accepted = np.size(accepted) # tack on chi2, log probability, log random unif number to parameters array: parameters = np.concatenate((parameters,chi2[None,:],lnprob[None,:],lnrand[None,:]), axis = 0) # transpose: parameters=np.transpose(parameters) # write results to file: k = open(self.results_filename, 'a') for params in parameters[accepted]: string = ' '.join([str(p) for p in params]) k.write(string + "\n") k.close() ###### start loop ######## # initialize: loop_count = 0 start=tm.time() while number_orbits_accepted < self.Norbits: # Draw random orbits: numSamples = 10000 parameters_init = draw_samples(numSamples, self.mtot_init, self.distance, self.ref_epoch) # Compute positions and velocities and new parameters array with scaled and rotated values: if(python_fitOFTI): X,Y,Z,Xdot,Ydot,Zdot,Xddot,Yddot,Zddot,parameters=calc_OFTI(parameters_init,self.ref_epoch,self.sep,self.pa) else: returnArray = np.zeros((19,numSamples)) returnArray = calcOFTI_C(parameters_init,self.ref_epoch,self.sep,self.pa,returnArray.copy()) X,Y,Z,Xdot,Ydot,Zdot,Xddot,Yddot,Zddot = returnArray[0:9] parameters = returnArray[9:] returnArray = None # compute chi2 for orbits using Gaia observations: if self.rv[0] != 0: model = np.array([Y,X,Ydot,Xdot,Zdot]) data = np.array([self.deltaRA, self.deltaDec, self.pmRA, self.pmDec, self.rv]) else: model = np.array([Y,X,Ydot,Xdot]) data = np.array([self.deltaRA, self.deltaDec, self.pmRA, self.pmDec]) chi2 = ComputeChi2(data,model) if use_pm_cross_term: chi2 -= ( 2 * (data[2][0] - model[2]) * (data[3][0] - model[3]) ) / (data[2][1] * data[3][1]) # add user astrometry if given: if self.astrometry: p = parameters.copy() a,T,const,to,e,i,w,O,m1,dist = p[0],p[1],p[2],p[3],p[4],p[5],p[6],p[7],p[8],p[9] chi2_astr = np.zeros(10000) # Calculate predicted positions at astr observation dates for each orbit: for j in range(self.astrometric_ra.shape[1]): # for each date, compute XYZ for each 10000 trial orbit. We can # skip scale and rotate because that was accomplished in the calc_OFTI call above. X1,Y1,Z1,E1 = calc_XYZ(a,T,to,e,i,w,O,self.astrometric_dates[j]) # Place astrometry into data array where: data[0][0]=ra obs, data[0][1]=ra err, etc: data = np.array([self.astrometric_ra[:,j], self.astrometric_dec[:,j]]) # place corresponding predicited positions at that date for each trial orbit: model = np.array([Y11000,X11000]) # compute chi2 for trial orbits at that date and add to the total chi2 sum: chi2_astr += ComputeChi2(data,model) chi2 = chi2 + chi2_astr # add user rv if given: if self.use_user_rv: p = parameters.copy() a,T,const,to,e,i,w,O,m1,dist = p[0],p[1],p[2],p[3],p[4],p[5],p[6],p[7],p[8],p[9] chi2_rv = np.zeros(10000) for j in range(self.user_rv.shape[1]): # compute ecc anomaly at that date: X1,Y1,Z1,E1 = calc_XYZ(a,T,to,e,i,w,O,self.user_rv_dates[j]) # compute velocities at that ecc anom: Xdot,Ydot,Zdot = calc_velocities(a,T,to,e,i,w,O,dist,E1) # compute chi2: chi2_rv += ComputeChi2(np.array([self.user_rv[:,j]]),np.array([Zdot])) chi2 = chi2 + chi2_rv # Accept/reject: accepted, lnprob, lnrand = AcceptOrReject(chi2,self.chi_min) if np.size(accepted) == 0: pass else: # count num accepted p = parameters.copy() a,T,const,to,e,i,w,O,m1,dist = p[0],p[1],p[2],p[3],p[4],p[5],p[6],p[7],p[8],p[9] sampleResults = calc_XYZ(a,T,to,e,i/180np.pi,w/180np.pi,O/180np.pi,2016.0) number_orbits_accepted += np.size(accepted) parameters = np.concatenate((parameters,chi2[None,:],lnprob[None,:],lnrand[None,:]), axis = 0) parameters=np.transpose(parameters) k = open(self.results_filename, 'a') for params in parameters[accepted]: string = ' '.join([str(p) for p in params]) k.write(string + "\n") k.close() if np.nanmin(chi2) < self.chi_min: # If there is a new min chi2: self.chi_min = np.nanmin(chi2) #print('found new chi min:',self.chi_min) # re-evaluate to accept/reject with new chi_min: if number_orbits_accepted != 0: dat = np.loadtxt(open(self.results_filename,"r"),delimiter=' ',ndmin=2) lnprob = -(dat[:,10]-self.chi_min)/2.0 dat[:,11] = lnprob accepted_retest = np.where(lnprob > dat[:,12]) q = open(self.results_filename, 'w') q.write(output_file_header + "\n") for data in dat[accepted_retest]: string = ' '.join([str(d) for d in data]) q.write(string + "\n") q.close() dat2 = np.loadtxt(open(self.results_filename,"r"),delimiter=' ',ndmin=2) number_orbits_accepted=dat2.shape[0] loop_count += 1 #print('loop count',loop_count) update_progress(number_orbits_accepted,self.Norbits) # one last accept/reject with final chi_min value: dat = np.loadtxt(open(self.results_filename,"r"),delimiter=' ',ndmin=2) lnprob = -(dat[:,10]-self.chi_min)/2.0 dat[:,11] = lnprob accepted_retest = np.where(lnprob > dat[:,12]) q = open(self.results_filename, 'w') q.write(output_file_header + "\n") for data in dat[accepted_retest]: string = ' '.join([str(d) for d in data]) q.write(string + "\n") q.close() # when finished, upload results and store in object: dat = np.loadtxt(open(self.results_filename,"r"),delimiter=' ',ndmin=2) number_orbits_accepted=dat.shape[0] print('Final Norbits:', number_orbits_accepted) # intialise results object and store accepted orbits: if self.rv[0] != 0: self.results = Results(orbits = dat, limit_lan = False, limit_aop = False) else: self.results = Results(orbits = dat, limit_lan = True, limit_aop = False) self.results.Update(self.results.orbits) # pickle dump the results attribute: if self.write_results: self.results.SaveResults(self.results_filename.replace(".txt", ".pkl"), write_text_file = False) stop = tm.time() self.results.run_time = (stop - start)u.s # compute stats and write to file: self.results.stats = Stats(orbits = self.results.orbits, write_to_file = self.write_stats, filename = self.stats_filename) class Results(object): '''A class for storing and manipulating the results of the orbit fit. Args: orbits (Norbits x 13 array): array of accepted orbits from \ OFTI fit in the same order as the following attributes sma (1 x Norbits array): semi-major axis in arcsec period (1 x Norbits array): period in years orbit_fraction (1 x Norbits array): fraction of orbit past periastron \ passage the observation (2016) occured on. Values: [0,1) t0 (1 x Norbits array): date of periastron passage in decimal years ecc (1 x Norbits array): eccentricity inc (1 x Norbits array): inclination relative to plane of the sky in deg aop (1 x Norbits array): arguement of periastron in deg lan (1 x Norbits array): longitude of ascending node in deg mtot (1 x Norbits array): total system mass in Msun distance (1 x Norbits array): distance to system in parsecs chi2 (1 x Norbits array): chi^2 value for the orbit lnprob (1 x Norbits array): log probability of orbit lnrand (1 x Norbits array): log of random "dice roll" for \ orbit acceptance limit_aop, limit_lan (bool): In the absence of radial velocity info, \ there is a degeneracy between arg of periastron and long of ascending \ node. Common practice is to limit one to the interval [0,180] deg. \ By default, lofti limits lan to this interval if rv = False. The user can \ choose to limit aop instead by setting limit_aop = True, limit_lan = False. \ The orbits[:,6] (aop) and orbits[:,7] (lan) arrays preserve the original values. \ Written by <NAME>, 2020 ''' def __init__(self, orbits = [], limit_aop = False, limit_lan = True): self.orbits = orbits self.limit_lan = limit_lan self.limit_aop = limit_aop def Update(self, orbits): '''Take elements of the "orbits" attribute and populate the orbital element attributes Args: orbits (arr): orbits array from Results class Written by <NAME>, 2020 ''' self.sma = orbits[:,0] self.period = orbits[:,1] self.orbit_fraction = orbits[:,2] self.t0 = orbits[:,3] self.ecc = orbits[:,4] self.inc = orbits[:,5] self.aop = orbits[:,6] if self.limit_aop: self.aop = limit_to_180deg(self.aop) self.lan = orbits[:,7] % 360 if self.limit_lan: self.lan = limit_to_180deg(self.lan) self.mtot = orbits[:,8] self.distance = orbits[:,9] self.chi2 = orbits[:,10] self.lnprob = orbits[:,11] self.lnrand = orbits[:,12] def SaveResults(self, filename, write_text_file = False, text_filename = None): '''Save the orbits and orbital parameters attributes in a pickle file Args: filename (str): filename for pickle file write_text_file (bool): if True, also write out the accepted orbits to a \ human readable text file text_filename (bool): if write_to_text = True, specifify filename for text file Written by <NAME>, 2020 ''' pickle.dump(self, open( filename, "wb" ) ) # write results to file: if write_text_file: k = open(text_filename, 'a') for params in self.orbits: string = ' '.join([str(p) for p in params]) k.write(string + "\n") k.close() def LoadResults(self, filename, append = False): '''Read in the orbits and orbital parameters attributes from a pickle file Args: filename (str): filename of pickle file to load append (bool): if True, append read in orbit samples to another Results \ object. Default = False. Written by <NAME>, 2020 ''' results_in = pickle.load( open( filename, "rb" ) ) if append == False: self.orbits = results_in.orbits self.Update(self.orbits) else: self.orbits = np.vstack((self.orbits,results_in.orbits)) self.Update(self.orbits) # plotting results: def PlotHists(self): '''Plot 1-d histograms of orbital elements 'sma','ecc','inc','aop','lan','t0' from fit results. Written by <NAME>, 2020 ''' if len(self.sma < 50): bins = 50 else: bins = 'fd' fig = plt.figure(figsize=(30, 5.5)) params = np.array([self.sma,self.ecc,self.inc,self.aop,self.lan,self.t0]) names = np.array(['sma','ecc','inc','aop','lan','t0']) for i in range(len(params)): ax = plt.subplot2grid((1,len(params)), (0,i)) plt.hist(params[i],bins=bins,edgecolor='none',alpha=0.8) plt.tick_params(axis='both', left=False, top=False, right=False, bottom=True, \ labelleft=False, labeltop=False, labelright=False, labelbottom=True) plt.xticks(rotation=45, fontsize = 20) plt.xlabel(names[i], fontsize = 25) plt.tight_layout() return fig def PlotOrbits(self, color = True, colorbar = True, ref_epoch = 2016.0, size = 100, plot3d = False, cmap = 'viridis',xlim=False,ylim=False): '''Plot a random selection of orbits from the sample in the plane of the sky. Args: color (bool): if True, plot orbit tracks using a colormap scale to orbit fraction (phase) \ past observation date (2015.5). If False, orbit tracks will be black. Default = True colorbar (bool): if True and color = True, plot colorbar for orbit phase ref_epoch (flt): reference epoch for drawing orbits. Default = 2015.5 size (int): Number of orbits to plot. Default = True plot3d (bool): If True, return a plot of orbits in 3D space. Default = False cmap (str): colormap for orbit phase plot Written by <NAME>, 2020 ''' # Random selection of orbits to plot: if len(self.sma) > size: # if there are more orbits than desired size, randomly select orbits from # the posterior sample: ind = np.random.choice(range(0,len(self.sma)),replace=False,size=size) else: # if there are fewer orbits than desired size, take all of them: ind = np.random.choice(range(0,len(self.sma)),replace=False,size=len(self.sma)) from numpy import tan, arctan, sqrt, cos, sin, arccos # label for colormap axis: colorlabel = 'Phase' # create figure: fig = plt.figure(figsize = (7.5, 6.)) plt.grid(ls=':') # invert X axis for RA: plt.gca().invert_xaxis() if plot3d: # Make 3d axis object: ax = fig.add_subplot(111, projection='3d') # plot central star: ax.scatter(0,0,0,color='orange',marker='',s=300,zorder=10) ax.set_zlabel('Z (")',fontsize=20) else: # plot central star: plt.scatter(0,0,color='orange',marker='',s=300,zorder=10) # For each orbit in the random selection from the posterior samples: for a,T,to,e,i,w,O in zip(self.sma[ind],self.period[ind],self.t0[ind],self.ecc[ind],np.radians(self.inc[ind]),\ np.radians(self.aop[ind]),np.radians(self.lan[ind])): # define an array of times along orbit: times = np.linspace(ref_epoch,ref_epoch+T,5000) X,Y,Z = np.array([]),np.array([]),np.array([]) E = np.array([]) # Compute X,Y,Z positions for each time: for t in times: n = (2np.pi)/T M = n(t-to) nextE = [danby_solve(eccentricity_anomaly, varM,vare, 0.001) for varM,vare in zip([M],[e])] E = np.append(E,nextE) r1 = a(1.-ecos(E)) f1 = sqrt(1.+e)sin(E/2.) f2 = sqrt(1.-e)cos(E/2.) f = 2.np.arctan2(f1,f2) r = (a(1.-e*2))/(1.+(ecos(f))) X1 = r * ( cos(O)cos(w+f) - sin(O)sin(w+f)cos(i) ) Y1 = r ( sin(O)cos(w+f) + cos(O)sin(w+f)cos(i) ) Z1 = r sin(w+f) * sin(i) X,Y,Z = np.append(X,X1),np.append(Y,Y1),np.append(Z,Z1) # Plot the X,Y(Z) positions: if not plot3d: if color: plt.scatter(Y,X,c=((times-ref_epoch)/T),cmap=cmap,s=3,lw=0) plt.gca().set_aspect('equal', adjustable='datalim') else: plt.plot(Y,X, color='black',alpha=0.3) plt.gca().set_aspect('equal', adjustable='datalim') if plot3d: from mpl_toolkits.mplot3d import Axes3D if color: ax.scatter(Y,X,Z,c=((times-ref_epoch)/T),cmap=cmap,s=3,lw=0) else: ax.plot(Y,X,Z, color='black',alpha=0.3) # plot colorbar: if not plot3d: if color: if colorbar == True: cb = plt.colorbar().set_label(colorlabel, fontsize=20) plt.gca().tick_params(labelsize=14) plt.ylabel('Dec (")',fontsize=20) plt.xlabel('RA (")',fontsize=20) plt.gca().tick_params(labelsize=14) if(xlim): plt.xlim(xlim) if(ylim): plt.ylim(ylim) return fig def PlotSepPA(self, ref_epoch = 2016.0, size = 100, timespan = [20,20], orbitcolor = 'skyblue'): '''Plot a random selection of orbits from the sample in separation and position angle as a function of time. Args: ref_epoch (flt): reference epoch for drawing orbits. Default = 2015.5 size (int): Number of orbits to plot. Default = True timespan (tuple, int): number of years before [0] and after [1] the ref epoch to \ plot sep and pa orbitcolor (str): color to use to plot the orbits Written by <NAME>, 2020 ''' # Random selection of orbits to plot: if len(self.sma) > size: # if there are more orbits than desired size, randomly select orbits from # the posterior sample: ind = np.random.choice(range(0,len(self.sma)),replace=False,size=size) else: # if there are fewer orbits than desired size, take all of them: ind = np.random.choice(range(0,len(self.sma)),replace=False,size=len(self.sma)) from numpy import tan, arctan, sqrt, cos, sin, arccos # make figure fig = plt.figure(figsize = (8, 10)) # define subplots: plt.subplot(2,1,1) plt.gca().tick_params(labelsize=14) plt.grid(ls=':') # define times to compute sep/pa: tmin,tmax = ref_epoch - timespan[0],ref_epoch + timespan[1] t = np.linspace(tmin,tmax,2000) date_ticks = np.arange(tmin,tmax,10) # for each selected orbit from the sample: for a,T,to,e,i,w,O in zip(self.sma[ind],self.period[ind],self.t0[ind],self.ecc[ind],np.radians(self.inc[ind]),\ np.radians(self.aop[ind]),np.radians(self.lan[ind])): X = np.array([]) Y = np.array([]) # compute X,Y at each time point: X1,Y1 = orbits_for_plotting(a,T,to,e,i,w,O,t) X = np.append(X, X1) Y = np.append(Y,Y1) # compute sep: r=np.sqrt((X2)+(Y2)) # plot sep in mas: plt.plot(t,r*1000,color=orbitcolor,alpha=0.5) plt.ylabel(r'$\rho$ (mas)',fontsize=20) # next suplot: plt.subplot(2,1,2) plt.grid(ls=':') # for each selected orbit from the sample: for a,T,to,e,i,w,O in zip(self.sma[ind],self.period[ind],self.t0[ind],self.ecc[ind],np.radians(self.inc[ind]),\ np.radians(self.aop[ind]),np.radians(self.lan[ind])): X = np.array([]) Y = np.array([]) X1,Y1 = orbits_for_plotting(a,T,to,e,i,w,O,t) X = np.append(X, X1) Y = np.append(Y,Y1) # compute pa: theta=np.arctan2(X,-Y) theta=(np.degrees(theta)+270.)%360 # plot it: plt.plot(t,theta,color=orbitcolor,alpha=0.5) plt.ylabel(r'P.A. (deg)',fontsize=19) plt.xlabel('Years',fontsize=19) plt.gca().tick_params(labelsize=14) plt.tight_layout() return fig class Stats(object): '''A class for storing and manipulating the statistics of the results of the orbit fit. For every parameter, there is a series of stats computed and saved as stats.param.stat Examples: stats.sma.mean = mean of semimajor axis stats.ecc.ci68 = 68% confidence interval for eccentricity stats.aop.std = standard deviation of arg of periastron Args: orbits (Norbits x 13 array): array of accepted orbits from \ OFTI fit in the same order as the following attributes param.mean (flt): mean of parameter computed using np.mean param.median (flt): np.median of parameter param.mode (flt): mode of parameter param.std (flt): standard deviation from np.std param.ci68 (tuple,flt): 68% minimum credible interval of form (lower bound, upper bound) param.ci95 (tuple,flt): 95% minimum credible interval write_to_file (bool): If True, write stats to a human-readbale text file. filename (str): filename for saving stats file. If not supplied, default \ name is FitResults.Stats.y.mo.d.h.m.s.pkl, saved in same directory as fit was run. Written by <NAME>, 2020 ''' def __init__(self, orbits = [], write_to_file = False, filename = None): self.orbits = orbits # Compute stats on parameter arrays and save as attributes: self.sma = StatsSubclass(self.orbits[:,0]) self.period = StatsSubclass(self.orbits[:,1]) self.orbit_fraction = StatsSubclass(self.orbits[:,2]) self.t0 = StatsSubclass(self.orbits[:,3]) self.ecc = StatsSubclass(self.orbits[:,4]) self.inc = StatsSubclass(self.orbits[:,5]) self.aop = StatsSubclass(self.orbits[:,6]) self.lan = StatsSubclass(self.orbits[:,7]) self.mtot = StatsSubclass(self.orbits[:,8]) self.distance = StatsSubclass(self.orbits[:,9]) if write_to_file: params = np.array([self.sma,self.period,self.orbit_fraction,self.t0,self.ecc,self.inc,\ self.aop,self.lan,self.mtot,self.distance]) names = np.array(['sma','period','orbit fraction','t0','ecc','inc','aop','lan','mtot','distance']) if not filename: filename = 'FitResults.Stats.'+time.strftime("%Y.%m.%d.%H.%M.%S")+'.txt' k = open(filename, 'w') string = 'Parameter Mean Median Mode Std 68% Min Cred Int 95% Min Cred Int' k.write(string + "\n") for i in range(len(params)): string = make_parameter_string(params[i],names[i]) k.write(string + "\n") k.close() class StatsSubclass(Stats): '''Subclass for computing and storing statistics Args: array (arr): array for which to compute statistics ''' def __init__(self, array): self.mean,self.median,self.mode,self.std,self.ci68,self.ci95 = compute_statistics(array)	[ 1, 1053, 8717, 14441, 29889, 348, 1169, 408, 318, 13, 5215, 12655, 408, 7442, 13, 3166, 658, 615, 29875, 29918, 3249, 423, 29889, 417, 615, 277, 8789, 1053, 334, 13, 3166, 658, 615, 29875, 29918, 3249, 423, 29889, 29883, 6678, 29879, 1053, 22235, 29949, 7818, 29902, 29918, 29907, 13, 29937, 3166, 658, 615, 277, 8789, 1053, 334, 13, 5215, 5839, 280, 13, 5215, 931, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 29937, 10186, 307, 1972, 8026, 777, 18116, 591, 508, 11455, 29901, 13, 5215, 18116, 13, 25442, 886, 29889, 4572, 25442, 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support/compute-powers.py	ldanko/fmt	13,148	137832	<gh_stars>1000+ #!/usr/bin/env python # Compute 10 exp with exp in the range [min_exponent, max_exponent] and print # normalized (with most-significant bit equal to 1) significands in hexadecimal. from __future__ import print_function min_exponent = -348 max_exponent = 340 step = 8 significand_size = 64 exp_offset = 2000 class fp: pass powers = [] for i, exp in enumerate(range(min_exponent, max_exponent + 1, step)): result = fp() n = 10 exp if exp >= 0 else 2 exp_offset / 10 -exp k = significand_size + 1 # Convert to binary and round. binary = '{:b}'.format(n) result.f = (int('{:0<{}}'.format(binary[:k], k), 2) + 1) / 2 result.e = len(binary) - (exp_offset if exp < 0 else 0) - significand_size powers.append(result) # Sanity check. exp_offset10 = 400 actual = result.f * 10 ** exp_offset10 if result.e > 0: actual = 2 * result.e else: for j in range(-result.e): actual /= 2 expected = 10 ** (exp_offset10 + exp) precision = len('{}'.format(expected)) - len('{}'.format(actual - expected)) if precision < 19: print('low precision:', precision) exit(1) print('Significands:', end='') for i, fp in enumerate(powers): if i % 3 == 0: print(end='\n ') print(' {:0<#16x}'.format(fp.f, ), end=',') print('\n\nExponents:', end='') for i, fp in enumerate(powers): if i % 11 == 0: print(end='\n ') print(' {:5}'.format(fp.e), end=',') print('\n\nMax exponent difference:', max([x.e - powers[i - 1].e for i, x in enumerate(powers)][1:]))	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29900, 29900, 29974, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 11796, 29872, 29871, 29896, 29900, 3579, 1518, 411, 1518, 297, 278, 3464, 518, 1195, 29918, 735, 3296, 29892, 4236, 29918, 735, 3296, 29962, 322, 1596, 13, 29937, 4226, 1891, 313, 2541, 1556, 29899, 4530, 928, 424, 2586, 5186, 304, 29871, 29896, 29897, 4991, 4167, 297, 15090, 1943, 29883, 3039, 29889, 13, 13, 3166, 4770, 29888, 9130, 1649, 1053, 1596, 29918, 2220, 13, 13, 1195, 29918, 735, 3296, 353, 448, 29941, 29946, 29947, 13, 3317, 29918, 735, 3296, 353, 29871, 29941, 29946, 29900, 13, 10568, 353, 29871, 29947, 13, 4530, 928, 392, 29918, 2311, 353, 29871, 29953, 29946, 13, 4548, 29918, 10289, 353, 29871, 29906, 29900, 29900, 29900, 13, 13, 1990, 285, 29886, 29901, 13, 1678, 1209, 13, 13, 12248, 414, 353, 5159, 13, 1454, 474, 29892, 1518, 297, 26985, 29898, 3881, 29898, 1195, 29918, 735, 3296, 29892, 4236, 29918, 735, 3296, 718, 29871, 29896, 29892, 4331, 22164, 13, 1678, 1121, 353, 285, 29886, 580, 13, 1678, 302, 353, 29871, 29896, 29900, 3579, 1518, 565, 1518, 6736, 29871, 29900, 1683, 29871, 29906, 3579, 1518, 29918, 10289, 847, 29871, 29896, 29900, 3579, 448, 4548, 13, 1678, 413, 353, 4991, 392, 29918, 2311, 718, 29871, 29896, 13, 1678, 396, 14806, 304, 7581, 322, 4513, 29889, 13, 1678, 7581, 353, 22372, 29901, 29890, 29913, 4286, 4830, 29898, 29876, 29897, 13, 1678, 1121, 29889, 29888, 353, 313, 524, 877, 25641, 29900, 29966, 29912, 930, 4286, 4830, 29898, 19541, 7503, 29895, 1402, 413, 511, 29871, 29906, 29897, 718, 29871, 29896, 29897, 847, 29871, 29906, 13, 1678, 1121, 29889, 29872, 353, 7431, 29898, 19541, 29897, 448, 313, 4548, 29918, 10289, 565, 1518, 529, 29871, 29900, 1683, 29871, 29900, 29897, 448, 4991, 392, 29918, 2311, 13, 1678, 10801, 29889, 4397, 29898, 2914, 29897, 13, 1678, 396, 3087, 537, 1423, 29889, 13, 1678, 1518, 29918, 10289, 29896, 29900, 353, 29871, 29946, 29900, 29900, 13, 1678, 3935, 353, 1121, 29889, 29888, 334, 29871, 29896, 29900, 3579, 1518, 29918, 10289, 29896, 29900, 13, 1678, 565, 1121, 29889, 29872, 1405, 29871, 29900, 29901, 13, 4706, 3935, 334, 29922, 29871, 29906, 3579, 1121, 29889, 29872, 13, 1678, 1683, 29901, 13, 4706, 363, 432, 297, 3464, 6278, 2914, 29889, 29872, 1125, 13, 9651, 3935, 847, 29922, 29871, 29906, 13, 1678, 3806, 353, 29871, 29896, 29900, 3579, 313, 4548, 29918, 10289, 29896, 29900, 718, 1518, 29897, 13, 1678, 16716, 353, 7431, 877, 8875, 4286, 4830, 29898, 9684, 876, 448, 7431, 877, 8875, 4286, 4830, 29898, 19304, 448, 3806, 876, 13, 1678, 565, 16716, 529, 29871, 29896, 29929, 29901, 13, 4706, 1596, 877, 677, 16716, 29901, 742, 16716, 29897, 13, 4706, 6876, 29898, 29896, 29897, 13, 13, 2158, 877, 10140, 928, 4167, 29901, 742, 1095, 2433, 1495, 13, 1454, 474, 29892, 285, 29886, 297, 26985, 29898, 12248, 414, 1125, 13, 1678, 565, 474, 1273, 29871, 29941, 1275, 29871, 29900, 29901, 13, 4706, 1596, 29898, 355, 2433, 29905, 29876, 25710, 13, 1678, 1596, 877, 12365, 29900, 29966, 29937, 29896, 29953, 29916, 29913, 4286, 4830, 29898, 18091, 29889, 29888, 29892, 10353, 1095, 29922, 742, 1495, 13, 13, 2158, 28909, 29876, 29905, 29876, 1252, 9340, 29901, 742, 1095, 2433, 1495, 13, 1454, 474, 29892, 285, 29886, 297, 26985, 29898, 12248, 414, 1125, 13, 1678, 565, 474, 1273, 29871, 29896, 29896, 1275, 29871, 29900, 29901, 13, 4706, 1596, 29898, 355, 2433, 29905, 29876, 25710, 13, 1678, 1596, 877, 12365, 29945, 29913, 4286, 4830, 29898, 18091, 29889, 29872, 511, 1095, 29922, 742, 1495, 13, 13, 2158, 28909, 29876, 29905, 29876, 7976, 28869, 4328, 29901, 742, 13, 418, 4236, 4197, 29916, 29889, 29872, 448, 10801, 29961, 29875, 448, 29871, 29896, 1822, 29872, 363, 474, 29892, 921, 297, 26985, 29898, 12248, 414, 29897, 3816, 29896, 29901, 12622, 13, 2 ]
bluebottle/assignments/migrations/0002_auto_20190529_0858.py	terrameijar/bluebottle	10	192060	# -- coding: utf-8 -- # Generated by Django 1.11.15 on 2019-05-29 06:58 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('assignments', '0001_initial'), ] operations = [ migrations.RenameField( model_name='assignment', old_name='end', new_name='deadline', ), ]	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 3251, 630, 491, 15337, 29871, 29896, 29889, 29896, 29896, 29889, 29896, 29945, 373, 29871, 29906, 29900, 29896, 29929, 29899, 29900, 29945, 29899, 29906, 29929, 29871, 29900, 29953, 29901, 29945, 29947, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 13, 3166, 9557, 29889, 2585, 1053, 9725, 800, 13, 13, 13, 1990, 341, 16783, 29898, 26983, 800, 29889, 29924, 16783, 1125, 13, 13, 1678, 9962, 353, 518, 13, 4706, 6702, 16645, 1860, 742, 525, 29900, 29900, 29900, 29896, 29918, 11228, 5477, 13, 1678, 4514, 13, 13, 1678, 6931, 353, 518, 13, 4706, 9725, 800, 29889, 29934, 3871, 3073, 29898, 13, 9651, 1904, 29918, 978, 2433, 465, 10194, 742, 13, 9651, 2030, 29918, 978, 2433, 355, 742, 13, 9651, 716, 29918, 978, 2433, 311, 328, 1220, 742, 13, 4706, 10353, 13, 1678, 4514, 13, 2 ]
LC/263.py	szhu3210/LeetCode_Solutions	2	18959	class Solution(object): def isUgly(self, num): """ :type num: int :rtype: bool """ if num <= 0: return False for x in [2,3,5]: while(num % x ==0): num /= x return num==1	[ 1, 770, 24380, 29898, 3318, 1125, 13, 1678, 822, 338, 29965, 16808, 29898, 1311, 29892, 954, 1125, 13, 4706, 9995, 13, 4706, 584, 1853, 954, 29901, 938, 13, 4706, 584, 29878, 1853, 29901, 6120, 13, 4706, 9995, 13, 4706, 565, 954, 5277, 29871, 29900, 29901, 13, 9651, 736, 7700, 13, 4706, 363, 921, 297, 518, 29906, 29892, 29941, 29892, 29945, 5387, 13, 9651, 1550, 29898, 1949, 1273, 921, 1275, 29900, 1125, 13, 18884, 954, 847, 29922, 921, 13, 4706, 736, 954, 1360, 29896, 2 ]
zoondb/analysis/__init__.py	gord02/Zooniverse-DB	1	187129	#!/usr/bin/env python """ Analysis consists of general purpose functions. Note ---- Function from either alpha or beta are not imported here, since they are not required by default. """ from . import seed # noqa: F401	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 15945, 29908, 13, 21067, 4848, 11624, 310, 2498, 6437, 3168, 29889, 13, 13, 9842, 13, 807, 13, 6678, 515, 2845, 15595, 470, 21762, 526, 451, 19673, 1244, 29892, 1951, 896, 526, 451, 13, 12403, 491, 2322, 29889, 13, 15945, 29908, 13, 13, 3166, 869, 1053, 16717, 29871, 396, 694, 25621, 29901, 383, 29946, 29900, 29896, 13, 2 ]
package/meross_iot/cloud/devices/subdevices/thermostats.py	armandofox/alexa-marantz-py	4	194619	from enum import Enum from typing import Union from meross_iot.cloud.timeouts import LONG_TIMEOUT from meross_iot.cloud.abilities import HUB_MTS100_ALL, HUB_MTS100_TEMPERATURE, HUB_MTS100_MODE, HUB_TOGGLEX, HUB_ONLINE from meross_iot.cloud.devices.subdevices.generic import GenericSubDevice from meross_iot.logger import VALVES_LOGGER as l from meross_iot.meross_event import DeviceSwitchStatusEvent, ThermostatTemperatureChange, ThermostatModeChange, \ DeviceOnlineStatusEvent class ThermostatMode(Enum): COMFORT = 1 CUSTOM = 0 ECONOMY = 2 SCHEDULE = 3 class ThermostatV3Mode(Enum): AUTO = 3 COOL = 2 CUSTOM = 0 ECONOMY = 4 HEAT = 1 class ValveSubDevice(GenericSubDevice): def __init__(self, cloud_client, subdevice_id, parent_hub, kwords): super().__init__(cloud_client, subdevice_id, parent_hub, kwords) def _handle_push_notification(self, namespace, payload, from_myself=False): # Let the Generic handler to handle the common events. handled = super()._handle_push_notification(namespace=namespace, payload=payload, from_myself=from_myself) if handled: return True # If the parent handler was unable to parse it, we do it here. evt = None if namespace == HUB_MTS100_ALL: self._raw_state.update(payload) return True elif namespace == HUB_TOGGLEX: togglex = self._raw_state.get('togglex') if togglex is None: togglex = {} self._raw_state['togglex'] = togglex togglex.update(payload) evt = DeviceSwitchStatusEvent(self, 0, self.onoff, from_myself) self.fire_event(evt) return True elif namespace == HUB_MTS100_MODE: mode = self._raw_state.get('mode') if mode is None: mode = {} self._raw_state['mode'] = mode mode.update(payload) evt = ThermostatModeChange(device=self, mode=self.mode, generated_by_myself=from_myself) self.fire_event(evt) return True elif namespace == HUB_MTS100_TEMPERATURE: temp = self._raw_state.get('temperature') if temp is None: temp = {} self._raw_state['temperature'] = temp temp.update(payload) evt = ThermostatTemperatureChange(device=self, temperature_state=self._raw_state.get('temperature'), generated_by_myself=from_myself) self.fire_event(evt) return True # TODO: handle TIME SYNC event? # elif namespace == HUB_TIME_SYNC: # self._state.get('??').update(payload) else: l.warn("Unsupported/unhandled event: %s" % namespace) l.debug("Namespace: %s, Data: %s" % (namespace, payload)) return False @property def onoff(self): onoff = self._get_property('togglex', 'onoff') return onoff @property def heating(self): heating = self._get_property('temperature', 'heating') if heating is None: return None else: return heating == 1 @property def room_temperature(self): temp = self._get_property('temperature', 'room') if temp is None: return None else: return temp / 10 @property def target_temperature(self): """ Returns the current target temperature :return: """ # The API returns the temperature in decimals. # For this reason, we convert it to integers. temp = self._get_property('temperature', 'currentSet') if temp is None: return None else: return temp / 10 def set_target_temperature(self, target_temp: float = None, timeout : float = LONG_TIMEOUT, callback: callable = None): """ Sets the target temperature of the thermostat :param target_temp: temperature to be set :param callback: :return: """ # The API expects the target temperature in DECIMALS, so we need to multiply the user's input by 10 value = target_temp * 10 payload = {'temperature': [{'id': self.subdevice_id, 'custom': value}]} return self.execute_command(command='SET', namespace=HUB_MTS100_TEMPERATURE, payload=payload, timeout=timeout, callback=callback) def set_preset_temperature(self, away: float = None, comfort: float = None, economy: float = None, timeout: float = LONG_TIMEOUT, callback: callable = None): """ Configures the preset temperature values. The temperature values should be expressed in Celsius degrees. :param away: Target temperature for away preset :param comfort: Target temperature for comfort preset :param economy: Target temperature for economy preset :param callback: :return: """ # The API expects the celsius degrees in DECIMALS, so we need to multiply the user's input by 10 temperature_conf = {'id': self.subdevice_id} if away is not None: temperature_conf['away'] = away * 10 if comfort is not None: temperature_conf['comfort'] = comfort * 10 if economy is not None: temperature_conf['economy'] = economy * 10 payload = {'temperature': [temperature_conf]} return self.execute_command(command='SET', namespace=HUB_MTS100_TEMPERATURE, payload=payload, timeout=timeout, callback=callback) @property def mode(self): state = self._get_property('mode', 'state') if state is None: return None else: # Parse the mode according to the current device type if self.type == 'mts100v3': return ThermostatV3Mode(state) elif self.type == 'mts100': return ThermostatMode(state) else: l.error("The current thermostat mode is not supported.") return None def set_mode(self, mode: Union[ThermostatV3Mode, ThermostatMode, int], timeout=LONG_TIMEOUT, callback=None ): """ Sets the temperature mode for the thermostat :param mode: :return: """ # Make sure we are passing correct values if self.type == 'mts100v3' and not isinstance(mode, ThermostatV3Mode): raise ValueError("This thermostat only supports ThermostatV3Mode modes") elif self.type == 'mts100' and not isinstance(mode, ThermostatMode): raise ValueError("This thermostat only supports ThermostatMode modes") elif isinstance(mode, int): l.warning("Setting a raw integer value as mode. This is not recommended. " "Please use ThermostatMode or ThermostatV3Mode") self.execute_command('SET', HUB_MTS100_MODE, {'mode': [{'id': self.subdevice_id, 'state': mode.value}]}, timeout=timeout, callback=callback) def _togglex(self, onoff, channel=0, timeout=LONG_TIMEOUT, callback=None): payload = {'togglex': [{'channel': channel, 'id': self.subdevice_id, 'onoff': onoff}]} return self.execute_command('SET', HUB_TOGGLEX, payload, timeout=timeout, callback=callback) def turn_on(self, timeout=LONG_TIMEOUT, callback=None): return self._togglex(onoff=1, timeout=timeout, callback=callback) def turn_off(self, timeout=LONG_TIMEOUT, callback=None): return self._togglex(onoff=0, timeout=timeout, callback=callback)	[ 1, 515, 14115, 1053, 1174, 398, 13, 3166, 19229, 1053, 7761, 13, 13, 3166, 2778, 2209, 29918, 24414, 29889, 9274, 29889, 15619, 29879, 1053, 365, 20614, 29918, 15307, 12015, 13, 13, 3166, 2778, 2209, 29918, 24414, 29889, 9274, 29889, 11614, 1053, 379, 7466, 29918, 29924, 9375, 29896, 29900, 29900, 29918, 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29918, 5910, 29918, 24671, 29898, 22377, 29922, 22377, 29892, 20092, 29922, 23813, 29892, 515, 29918, 5781, 761, 29922, 3166, 29918, 5781, 761, 29897, 13, 4706, 565, 16459, 29901, 13, 9651, 736, 5852, 13, 13, 4706, 396, 960, 278, 3847, 7834, 471, 9368, 304, 6088, 372, 29892, 591, 437, 372, 1244, 29889, 13, 4706, 3415, 29873, 353, 6213, 13, 4706, 565, 7397, 1275, 379, 7466, 29918, 29924, 9375, 29896, 29900, 29900, 29918, 9818, 29901, 13, 9651, 1583, 3032, 1610, 29918, 3859, 29889, 5504, 29898, 23813, 29897, 13, 9651, 736, 5852, 13, 13, 4706, 25342, 7397, 1275, 379, 7466, 29918, 4986, 26788, 1307, 29990, 29901, 13, 9651, 17304, 2506, 353, 1583, 3032, 1610, 29918, 3859, 29889, 657, 877, 29873, 468, 29887, 2506, 1495, 13, 9651, 565, 17304, 2506, 338, 6213, 29901, 13, 18884, 17304, 2506, 353, 6571, 13, 18884, 1583, 3032, 1610, 29918, 3859, 1839, 29873, 468, 29887, 2506, 2033, 353, 17304, 2506, 13, 9651, 17304, 2506, 29889, 5504, 29898, 23813, 29897, 13, 9651, 3415, 29873, 353, 21830, 24995, 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1669, 3448, 29901, 5785, 353, 6213, 29892, 13, 462, 1669, 13016, 29901, 5785, 353, 6213, 29892, 13, 462, 1669, 26504, 29901, 5785, 353, 6213, 29892, 13, 462, 1669, 11815, 29901, 5785, 353, 365, 20614, 29918, 15307, 12015, 29892, 13, 462, 1669, 6939, 29901, 1246, 519, 353, 6213, 1125, 13, 4706, 9995, 13, 4706, 12782, 1973, 278, 2225, 300, 10430, 1819, 29889, 450, 10430, 1819, 881, 367, 13384, 297, 13, 4706, 14227, 1039, 375, 14496, 29889, 13, 4706, 584, 3207, 3448, 29901, 17157, 10430, 363, 3448, 2225, 300, 13, 4706, 584, 3207, 13016, 29901, 17157, 10430, 363, 13016, 2225, 300, 13, 4706, 584, 3207, 26504, 29901, 17157, 10430, 363, 26504, 2225, 300, 13, 4706, 584, 3207, 6939, 29901, 13, 4706, 584, 2457, 29901, 13, 4706, 9995, 13, 4706, 396, 450, 3450, 23347, 278, 6432, 1039, 375, 14496, 297, 5012, 29907, 2260, 8547, 29892, 577, 591, 817, 304, 22932, 278, 1404, 29915, 29879, 1881, 491, 29871, 29896, 29900, 13, 4706, 10430, 29918, 5527, 353, 11117, 333, 2396, 1583, 29889, 1491, 10141, 29918, 333, 29913, 13, 4706, 565, 3448, 338, 451, 6213, 29901, 13, 9651, 10430, 29918, 5527, 1839, 21694, 2033, 353, 3448, 334, 29871, 29896, 29900, 13, 4706, 565, 13016, 338, 451, 6213, 29901, 13, 9651, 10430, 29918, 5527, 1839, 510, 3921, 2033, 353, 13016, 334, 29871, 29896, 29900, 13, 4706, 565, 26504, 338, 451, 6213, 29901, 13, 9651, 10430, 29918, 5527, 1839, 29872, 4599, 29891, 2033, 353, 26504, 334, 29871, 29896, 29900, 13, 13, 4706, 20092, 353, 11117, 12863, 1535, 2396, 518, 12863, 1535, 29918, 5527, 12258, 13, 4706, 736, 1583, 29889, 7978, 29918, 6519, 29898, 6519, 2433, 10490, 742, 13, 462, 462, 1678, 7397, 29922, 29950, 7466, 29918, 29924, 9375, 29896, 29900, 29900, 29918, 4330, 3580, 1001, 1299, 11499, 29892, 13, 462, 462, 1678, 20092, 29922, 23813, 29892, 13, 462, 462, 1678, 11815, 29922, 15619, 29892, 13, 462, 462, 1678, 6939, 29922, 14035, 29897, 13, 13, 1678, 732, 6799, 13, 1678, 822, 4464, 29898, 1311, 1125, 13, 4706, 2106, 353, 1583, 3032, 657, 29918, 6799, 877, 8513, 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swim_backend/events.py	eurocontrol-swim/swim-backend	0	24800	<reponame>eurocontrol-swim/swim-backend<filename>swim_backend/events.py """ Copyright 2019 EUROCONTROL ========================================== Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ========================================== Editorial note: this license is an instance of the BSD license template as provided by the Open Source Initiative: http://opensource.org/licenses/BSD-3-Clause Details on EUROCONTROL: http://www.eurocontrol.int """ import abc __author__ = "EUROCONTROL (SWIM)" class Event(list): """ Simplistic implementation of event handling. A list of callable objects. Calling an instance of this will cause a call to each item in the list in ascending order by index. """ _type = 'Generic' def __call__(self, args, kwargs): for handler in self: handler(args, *kwargs) def __repr__(self): return f"{self._type} Event({list.__repr__(self)})" class EventSafe(list): def __call__(self, args, *kwargs): handlers = [handler_class(args, *kwargs) for handler_class in self] processed_handlers = [] for handler in handlers: try: handler.do() processed_handlers.append(handler) except: handler.undo() processed_handlers.reverse() for processed_handler in processed_handlers: processed_handler.undo() raise class EventHandler(abc.ABC): @abc.abstractmethod def do(self, args, *kwargs): pass @abc.abstractmethod def undo(self, args, **kwargs): pass	[ 1, 529, 276, 1112, 420, 29958, 29872, 2192, 6451, 29899, 2774, 326, 29914, 2774, 326, 29899, 27852, 29966, 9507, 29958, 2774, 326, 29918, 27852, 29914, 13604, 29889, 2272, 13, 15945, 29908, 13, 11882, 1266, 29871, 29906, 29900, 29896, 29929, 19007, 1672, 22412, 1672, 29931, 13, 9166, 9166, 4936, 1360, 13, 13, 9039, 391, 3224, 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legacy/extract_feature.py	Tommy-Liu/MovieQA_Contest	0	140619	<filename>legacy/extract_feature.py import argparse import os from functools import partial from math import ceil from multiprocessing import Pool from os.path import join, exists import numpy as np import tensorflow as tf import tensorflow.contrib.slim as slim from numpy.lib.format import read_array_header_1_0, read_magic from tqdm import tqdm from config import MovieQAPath from legacy.inception_preprocessing import preprocess_image from legacy.inception_resnet_v2 import inception_resnet_v2_arg_scope, inception_resnet_v2 from utils import data_utils as du from utils import func_utils as fu _mp = MovieQAPath() def make_parallel(model, num_gpus, imgs): out_split = [] for i in range(num_gpus): with tf.device(tf.DeviceSpec(device_type="GPU", device_index=i)): with tf.variable_scope(tf.get_variable_scope(), reuse=i > 0): out_split.append(model(images=imgs[i])) # return tf.concat(out_split, axis=0) return out_split def models(images): with slim.arg_scope(inception_resnet_v2_arg_scope()): logits, end_points = inception_resnet_v2(images, num_classes=1001, is_training=False) # return tf.nn.max_pool(end_points['Conv2d_7b_1x1'], [1, 2, 2, 1], [1, 2, 2, 1], 'VALID') return end_points['Conv2d_7b_1x1'] def load_shape(n): with open(n, 'rb') as f: major, minor = read_magic(f) shape, fortran, dtype = read_array_header_1_0(f) if len(shape) != 4: raise TypeError('Errr! Single image... %s' % n) return shape def collect(video_data, k): imgs = [join(_mp.image_dir, k, '%s_%05d.jpg' % (k, i + 1)) for i in range(0, video_data[k]['real_frames'], 10)] npy_name = join(_mp.feature_dir, k + '.npy') if not exists(npy_name) or load_shape(npy_name)[0] != len(imgs): return npy_name, len(imgs), imgs else: return None def get_images_path(): file_names, capacity, npy_names = [], [], [] video_data = dict(item for v in du.json_load(_mp.video_data_file).values() for item in v.items()) func = partial(collect, video_data) with Pool(16) as pool, tqdm(total=len(video_data), desc='Collect images') as pbar: for ins in pool.imap_unordered(func, list(video_data.keys())): if ins: npy_names.append(ins[0]) capacity.append(ins[1]) file_names.extend(ins[2]) pbar.update() return file_names, capacity, npy_names def get_images_path_v2(): file_names, capacity, npy_names = [], [], [] video_data = du.json_load(_mp.video_data_file) for imdb_key in tqdm(video_data, desc='Collect Images'): npy_names.append(join(_mp.feature_dir, imdb_key + '.npy')) videos = list(video_data[imdb_key].keys()) videos.sort() num = 0 for v in tqdm(videos): images = [join(_mp.image_dir, v, '%s_%05d.jpg' % (v, i + 1)) for i in range(0, video_data[imdb_key][v]['real_frames'], 15)] file_names.extend(images) num += len(images) capacity.append(num) print(capacity, npy_names) return file_names, capacity, npy_names def get_images_path_v3(): file_names, capacity, npy_names = [], [], [] sample = du.json_load(_mp.sample_frame_file) for imdb_key in tqdm(sample, desc='Collect Images'): # if not exists(join(_mp.feature_dir, imdb_key + '.npy')): npy_names.append(join(_mp.feature_dir, imdb_key + '.npy')) videos = list(sample[imdb_key].keys()) videos.sort() num = 0 for v in tqdm(videos): images = [join(_mp.image_dir, v, '%s_%05d.jpg' % (v, i + 1)) for i in sample[imdb_key][v]] file_names.extend(images) num += len(images) capacity.append(num) # print(capacity, npy_names) return file_names, capacity, npy_names def count_num(features_list): num = 0 for features in features_list: num += features.shape[0] return num def writer_worker(queue, capacity, npy_names): video_idx = 0 local_feature = [] local_filename = [] with tqdm(total=len(npy_names)) as pbar: while len(capacity) > video_idx: item = queue.get() if item: f, n = item local_feature.append(f) local_filename.extend(n) local_size = len(local_filename) while len(capacity) > video_idx and local_size >= capacity[video_idx]: concat_feature = np.concatenate(local_feature, axis=0) final_features = concat_feature[:capacity[video_idx]] final_filename = local_filename[:capacity[video_idx]] assert final_features.shape[0] == capacity[video_idx], \ "%s Both frames are not same!" % npy_names[video_idx] for i in range(len(final_features)): assert fu.basename_wo_ext(npy_names[video_idx]) == \ fu.basename_wo_ext(final_filename[i]).split('.')[0], \ "Wrong images! %s\n%s" % (npy_names[video_idx], final_filename[i]) try: np.save(npy_names[video_idx], final_features) except Exception as e: np.save(npy_names[video_idx], final_features) raise e pbar.set_description(' '.join([fu.basename_wo_ext(npy_names[video_idx]), str(len(final_features))])) del local_feature[:] local_feature.append(concat_feature[capacity[video_idx]:]) local_filename = local_filename[capacity[video_idx]:] local_size = len(local_filename) video_idx += 1 pbar.update() else: break def parse_func(filename): raw_image = tf.read_file(filename) image = tf.image.decode_jpeg(raw_image, channels=3) return image, filename def preprocess_func(image, filename): image = preprocess_image(image, 299, 299, is_training=False) return image, filename def input_pipeline(filename_placeholder, batch_size=32, num_worker=4): dataset = tf.data.Dataset.from_tensor_slices(filename_placeholder) dataset = dataset.repeat(1) images, names_, iterator = [], [], [] if args.num_gpu > 1: for i in range(args.num_gpu): dd = dataset.shard(args.num_gpu, i) dd = dd.map(parse_func, num_parallel_calls=num_worker) dd = dd.map(preprocess_func, num_parallel_calls=num_worker) dd = dd.batch(batch_size) dd = dd.prefetch(16) itit = dd.make_initializable_iterator() im, n_ = itit.get_next() images.append(im) names_.append(n_) iterator.append(itit) else: dataset = dataset.map(parse_func, num_parallel_calls=num_worker) dataset = dataset.batch(batch_size) dataset = dataset.prefetch(1000) iterator = dataset.make_initializable_iterator() images, names_ = iterator.get_next() return images, names_, iterator def args_parse(): parser = argparse.ArgumentParser() parser.add_argument('--num_gpu', default=1, type=int, help='Number of GPU going to be used') parser.add_argument('--batch_size', default=8, type=int, help='Batch size of images') parser.add_argument('--num_worker', default=2, type=int, help='Number of worker reading data.') parser.add_argument('--reset', action='store_true', help='Reset all the extracted features.') return parser.parse_args() if __name__ == '__main__': args = args_parse() bsize = args.batch_size num_w = args.num_worker reset = args.reset if reset: os.system('rm -rf %s' % _mp.feature_dir) fu.make_dirs(_mp.feature_dir) fn, cap, npy_n = get_images_path_v3() num_step = int(ceil(len(fn) / bsize)) fp = tf.placeholder(tf.string, shape=[None]) img, names, it = input_pipeline(fp, bsize, num_w) if args.num_gpu > 1: feature_tensor = make_parallel(models, args.num_gpu, img) else: feature_tensor = models(img) print('Pipeline setup done !!') saver = tf.train.Saver(tf.global_variables()) print('Start extract !!') config = tf.ConfigProto(allow_soft_placement=True) config.gpu_options.allow_growth = True # config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1 with tf.Session(config=config) as sess: # q = Queue() # p = Process(target=writer_worker, args=(q, cap, npy_n)) # p.start() tf.global_variables_initializer().run() tf.local_variables_initializer().run() saver.restore(sess, './inception_resnet_v2_2016_08_30.ckpt') 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R function.py	haideraheem/Simple-Python-Programs	0	26322	# -- coding: utf-8 -- """ Created on Sat Oct 6 17:11:11 2018 @author: <NAME> """ a= float(input("Type a : ")) b= float(input("Type b : ")) c= float(input("Type c : ")) x= (ab)+(bc)+(c*a) y= (a+b+c) r= x/y print( ) print("The result of the calculation is {0:.2f}".format(r))	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 30004, 13, 15945, 19451, 13, 20399, 373, 12178, 4756, 259, 29953, 29871, 29896, 29955, 29901, 29896, 29896, 29901, 29896, 29896, 29871, 29906, 29900, 29896, 29947, 30004, 13, 30004, 13, 29992, 8921, 29901, 529, 5813, 3238, 13, 15945, 19451, 13, 30004, 13, 29874, 29922, 5785, 29898, 2080, 703, 1542, 263, 584, 376, 876, 30004, 13, 29890, 29922, 5785, 29898, 2080, 703, 1542, 289, 584, 376, 876, 30004, 13, 29883, 29922, 5785, 29898, 2080, 703, 1542, 274, 584, 376, 876, 30004, 13, 29916, 29922, 313, 29874, 29930, 29890, 7240, 29898, 29890, 29930, 29883, 7240, 29898, 29883, 29930, 29874, 8443, 13, 29891, 29922, 313, 29874, 29974, 29890, 29974, 29883, 8443, 13, 29878, 29922, 921, 29914, 29891, 6756, 13, 2158, 29898, 1723, 30004, 13, 2158, 703, 1576, 1121, 310, 278, 13944, 338, 426, 29900, 29901, 29889, 29906, 29888, 29913, 1642, 4830, 29898, 29878, 876, 2 ]
configs/semantic_segmentation/common/optims/ep30.py	voldemortX/DeeplabV3_PyTorch1.3_Codebase	1	100109	<gh_stars>1-10 lr_scheduler = dict( name='poly_scheduler', epochs=30, power=0.9 )	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29212, 29918, 816, 14952, 353, 9657, 29898, 13, 1678, 1024, 2433, 22678, 29918, 816, 14952, 742, 13, 1678, 21502, 12168, 29922, 29941, 29900, 29892, 13, 1678, 3081, 29922, 29900, 29889, 29929, 13, 29897, 13, 2 ]
Gds/src/fprime_gds/wxgui/tools/PexpectRunnerConsolGUI.py	hunterpaulson/fprime	0	32742	<reponame>hunterpaulson/fprime # -- coding: utf-8 -- ########################################################################### ## Python code generated with wxFormBuilder (version May 29 2018) ## http://www.wxformbuilder.org/ ## ## PLEASE DO NOT EDIT THIS FILE! ########################################################################### import wx import wx.xrc ########################################################################### ## Class PexpectRunnerGUI ########################################################################### class PexpectRunnerGUI(wx.Frame): def __init__(self, parent): wx.Frame.__init__( self, parent, id=wx.ID_ANY, title=u"Pexpect Output", pos=wx.DefaultPosition, size=wx.Size(500, 300), style=wx.DEFAULT_FRAME_STYLE \| wx.TAB_TRAVERSAL, ) self.SetSizeHints(wx.DefaultSize, wx.DefaultSize) bSizer3 = wx.BoxSizer(wx.VERTICAL) self.TextCtrlConsol = wx.TextCtrl( self, wx.ID_ANY, wx.EmptyString, wx.DefaultPosition, wx.DefaultSize, wx.TE_MULTILINE \| wx.TE_READONLY \| wx.TE_WORDWRAP, ) bSizer3.Add(self.TextCtrlConsol, 1, wx.ALL \| wx.EXPAND, 5) self.SetSizer(bSizer3) self.Layout() self.Centre(wx.BOTH) # Connect Events self.Bind(wx.EVT_CLOSE, self.onWindowClose) self.TextCtrlConsol.Bind(wx.EVT_MOUSEWHEEL, self.onMouseWheel) def __del__(self): pass # Virtual event handlers, overide them in your derived class def onWindowClose(self, event): event.Skip() def onMouseWheel(self, event): event.Skip()	[ 1, 529, 276, 1112, 420, 29958, 29882, 8428, 3274, 352, 1100, 29914, 29888, 10080, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 13383, 13383, 13383, 13383, 7346, 2277, 29937, 13, 2277, 5132, 775, 5759, 411, 26437, 2500, 5627, 313, 3259, 2610, 29871, 29906, 29929, 29871, 29906, 29900, 29896, 29947, 29897, 13, 2277, 1732, 597, 1636, 29889, 23310, 689, 16409, 29889, 990, 29914, 13, 2277, 13, 2277, 349, 14063, 11662, 334, 12256, 29930, 11488, 3446, 3235, 24080, 29991, 13, 13383, 13383, 13383, 13383, 7346, 2277, 29937, 13, 13, 5215, 26437, 13, 5215, 26437, 29889, 29916, 2214, 13, 13, 13383, 13383, 13383, 13383, 7346, 2277, 29937, 13, 2277, 4134, 349, 17854, 16802, 29954, 3120, 13, 13383, 13383, 13383, 13383, 7346, 2277, 29937, 13, 13, 13, 1990, 349, 17854, 16802, 29954, 3120, 29898, 23310, 29889, 4308, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 3847, 1125, 13, 4706, 26437, 29889, 4308, 17255, 2344, 12035, 13, 9651, 1583, 29892, 13, 9651, 3847, 29892, 13, 9651, 1178, 29922, 23310, 29889, 1367, 29918, 2190, 29979, 29892, 13, 9651, 3611, 29922, 29884, 29908, 29925, 17854, 10604, 613, 13, 9651, 926, 29922, 23310, 29889, 4592, 8003, 29892, 13, 9651, 2159, 29922, 23310, 29889, 3505, 29898, 29945, 29900, 29900, 29892, 29871, 29941, 29900, 29900, 511, 13, 9651, 3114, 29922, 23310, 29889, 23397, 29918, 29943, 4717, 2303, 29918, 1254, 29979, 1307, 891, 26437, 29889, 29911, 2882, 29918, 29911, 4717, 5348, 29903, 1964, 29892, 13, 4706, 1723, 13, 13, 4706, 1583, 29889, 2697, 3505, 29950, 9466, 29898, 23310, 29889, 4592, 3505, 29892, 26437, 29889, 4592, 3505, 29897, 13, 13, 4706, 289, 29903, 3950, 29941, 353, 26437, 29889, 3313, 29903, 3950, 29898, 23310, 29889, 5348, 29911, 2965, 1964, 29897, 13, 13, 4706, 1583, 29889, 1626, 18069, 13696, 324, 353, 26437, 29889, 1626, 18069, 29898, 13, 9651, 1583, 29892, 13, 9651, 26437, 29889, 1367, 29918, 2190, 29979, 29892, 13, 9651, 26437, 29889, 8915, 1231, 29892, 13, 9651, 26437, 29889, 4592, 8003, 29892, 13, 9651, 26437, 29889, 4592, 3505, 29892, 13, 9651, 26437, 29889, 4330, 29918, 29924, 8647, 6227, 8895, 891, 26437, 29889, 4330, 29918, 16310, 1164, 16786, 891, 26437, 29889, 4330, 29918, 17013, 9980, 3301, 29892, 13, 4706, 1723, 13, 4706, 289, 29903, 3950, 29941, 29889, 2528, 29898, 1311, 29889, 1626, 18069, 13696, 324, 29892, 29871, 29896, 29892, 26437, 29889, 9818, 891, 26437, 29889, 5746, 29925, 9468, 29892, 29871, 29945, 29897, 13, 13, 4706, 1583, 29889, 2697, 29903, 3950, 29898, 29890, 29903, 3950, 29941, 29897, 13, 4706, 1583, 29889, 3453, 580, 13, 13, 4706, 1583, 29889, 29907, 14056, 29898, 23310, 29889, 29933, 2891, 29950, 29897, 13, 13, 4706, 396, 14971, 28488, 13, 4706, 1583, 29889, 15708, 29898, 23310, 29889, 22240, 29911, 29918, 29907, 3927, 1660, 29892, 1583, 29889, 265, 5907, 11123, 29897, 13, 4706, 1583, 29889, 1626, 18069, 13696, 324, 29889, 15708, 29898, 23310, 29889, 22240, 29911, 29918, 6720, 17171, 29956, 9606, 6670, 29892, 1583, 29889, 265, 14346, 29956, 10552, 29897, 13, 13, 1678, 822, 4770, 6144, 12035, 1311, 1125, 13, 4706, 1209, 13, 13, 1678, 396, 19181, 1741, 25795, 29892, 975, 680, 963, 297, 596, 10723, 770, 13, 1678, 822, 373, 5907, 11123, 29898, 1311, 29892, 1741, 1125, 13, 4706, 1741, 29889, 15797, 666, 580, 13, 13, 1678, 822, 373, 14346, 29956, 10552, 29898, 1311, 29892, 1741, 1125, 13, 4706, 1741, 29889, 15797, 666, 580, 13, 2 ]
AREMApy/v2018/ch_15.py	mwhit74/AREMApy	2	95824	<reponame>mwhit74/AREMApy # -- coding: utf-8 -- """ Created on Tue Jan 14 12:51:05 2020 @author: mwhitten """ import math def eq13141(fax, fa_ax, fb1, fa_b1, fb2, fa_b2, k1, l1, r1, k2, l2, r2, e): """Axial Compression and Bending AREMA 2018 Section 1.3.14.1 Member subject to both axial compression and bending stresses shall be proportioned to satisfy the following requirements: if f_axial/fa_axial <= 0.15: IR = fa/Fa + fb1/Fb1 + fb2/Fb2 else: moment_mag1 = 1-f_axial/(0.514math.pow(math.pi,2)e)math.pow(klr1,2) moment_mag2 = 1-f_axial/(0.514math.pow(math.pi,2)e)math.pow(klr2,2) IR = fa/Fa + fb1/(Fb1moment_mag1) + fb2/(Fb2moment_mag2) Args: fax (float): calculated axial stress [psi] fb1 (float): calculated bending stess about axis 1-1 [psi] fb2 (float): calculated bending stress about axis 2-2 [psi] fa_ax (flaot): allowable axial stress if axial force alone existed [psi] fa_b1 (float): allowable compression bending stress about axis 1-1, that would be permitted if bending alone existed [psi] fa_b2 (float): allowable compression bending stress about axis 2-2, that would be permitted if bending alone existed [psi] k1 (float): effective length factor about 1-1 axis l1 (float): unbraced length about 1-1 axis [inches] r1 (float): radius of gyration about 1-1 axis [inches] k2 (float): effective length factor about 2-2 axis l2 (float): unbraced length about 2-2 axis [inches] r2 (float): radius of gyration about 2-2 axis [inches] e (float): modulus of elasticity [psi] Returns: ir (tuple(float, str)): interaction ratio of the axial stress and bi-axail bending stress per the prescribed equations Notes: 1. Units are in lbs and inches. 2. To ignore moment magnification factors enter k1 = k2 = 0.0. Secondary effects, where they are determined to be significant, should be considered elsewhere in the analysis or design if the moment magnification is ignored here. """ ref_text = "AREMA 2018 Section 1.3.14.1 \n\n" user_input = (f'fax = {fax:.2f}, fa_ax = {fa_ax:.2f},' + f'fb1 = {fb1:.2f}, fa_b1 = {fa_b1:.2f},' + f'fb2 = {fb2:.2f}, fa_b2 = {fa_b2:.2f},' + f'k1 = {k1:.2f}, l1 = {l1:.2f}, r1 = {r1:.2f},' + f'k2 = {k2:.2f}, l2 = {l2:.2f}, r2 = {r2:.2f},' + f'E = {e:.2f} \n\n') fax_ir = fax/fa_ax fb1_ir = fb1/fa_b1 fb2_ir = fb2/fa_b2 text1 = (f'fa/Fa = {fax:.2f}/{fa_ax:.2f} \n' + f'fa/Fa = {fax_ir:.2f} \n\n') text2 = (f'fb1/Fb1 = {fb1:.2f}/{fa_b1:.2f} \n' + f'fb1/Fb1 = {fb1_ir:.3f} \n\n') text3 = (f'fb2/Fb2 = {fb2:.2f}/{fa_b2:.2f} \n' + f'fb2/Fb2 = {fb2_ir:.3f} \n\n') if fax_ir <= 0.15: ir = fax_ir + fb1_ir + fb2_ir text4 = (f'IR = fax/Fax + fb1/Fb1 + fb2/Fb2 \n' f'IR = {fax_ir:.2f} + {fb1_ir:.2f} + {fb2_ir:.2f} \n' f'IR = {ir:.3f}') text = text1 + text2 + text3 + text4 else: klr1 = k1l1/r1 klr2 = k2l2/r2 moment_mag1 = 1-fax/(0.514math.pow(math.pi,2)e)math.pow(klr1,2) moment_mag2 = 1-fax/(0.514math.pow(math.pi,2)e)math.pow(klr2,2) ir = fax_ir + fb1_ir/moment_mag1 + fb2/moment_mag2 text5 = f'k1l1/r1 = {klr1:.2f} \n' text6 = f'k2l2/r2 = {klr2:.2f} \n\n' text7 = (f'Moment Mag. 1 = 1-fax/' + f'(0.514math.pow(math.pi,2)e)math.pow(k1l1/r1,2) \n' + f'Moment Mag. 1 = 1-{fax:.2f}/' + f'(0.514math.pow(math.pi,2){e:.1f})' f'math.pow({klr1:.2f},2) \n' + f'Moment Mag. 1 = {moment_mag1:.3f} \n\n') text8 = (f'Moment Mag. 2 = 1-fax/' + f'(0.514math.pow(math.pi,2)e)math.pow(k2l2/r2,2) \n' + f'Moment Mag. 2 = 1-{fax:.2f}/' + f'(0.514math.pow(math.pi,2){e:.1f})' f'math.pow({klr2:.2f},2) \n' + f'Moment Mag. 2 = {moment_mag2:.3f} \n\n') text9 = (f'IR = fax/Fax + fb1/(Fb1Moment Mag. 1)' f' + fb2/(Fb2Moment Mag. 2) \n' f'IR = {fax_ir:.2f} + {fb1_ir/moment_mag1:.2f}' f' + {fb2_ir/moment_mag1:.2f} \n' f'IR = {ir:.3f} \n') text = text1 + text2 + text3 + text5 + text6 + text7 + text8 + text9 text = ref_text + user_input + text return ir, text def eq141d1(stress_condition, fy, fu): """Axial tension allowable stress subject to direct tensile load AREMA 2018 Table 15-1-11 Row 1 Equations if stress_condition == 1: fa_t = 0.55fy elif stress_condition == 2: fa_tn = 0.47fu elif stress_condition == 3: fa_tnp = 0.45fy Args: stress_condition (int): 1 - Axial tension, structural steel, gross section 2 - Axial tension, structural steel, effective net section (see articles 1.5.8 and 1.6.5) 3 - Axial tension, structural steel, effective net area at cross-section of pin hole of pin-connected members fy (float): yield stress [psi] fu (float): ultimate stress [psi] Returns: fa_t (tuple(float, str)): allowable tension stress on gross section [psi] -OR- fa_tn (tuple(float, str)): allowable tension stress on net section [psi] -OR- fa_tnp (tuple(float, str)): allowable tension stress on net section at cross-section of pin hole of pin-connected members [psi] Notes: 1. Units in lbs and inches. """ ref_text = "AREMA 2018 Section 1.4.1 Table 15-1-11 Row 1 \n\n" user_input = (f'Stress Condition = {stress_condition:d},' + f'Fy = {fy:.2f}, Fu = {fu:.2f} \n') if stress_condition == 1: fa = 0.55fy text = (f'1 - Axial tension, structural steel, gross section \n' + f'fa_tg = 0.55fy \n' + f'fa_tg = 0.55{fy:.1f} \n' + f'fa_tg = {fa:.1f}') elif stress_condition == 2: fa = 0.47fu text = (f'2 - Axial tension, structural steel, effective net section' + f'(see articles 1.5.8 and 1.6.5) \n' f'fa_tn = 0.47fu \n' + f'fa_tn = 0.47{fu:.1f} \n' + f'fa_tn = {fa:.1f}') elif stress_condition == 3: fa = 0.45fy text = (f'3 - Axial tension, structural steel, effective net area' + f'at cross-section of pin hole of pin-connected members \n' f'fa_tnp = 0.45fy \n' + f'fa_tnp = 0.45{fy:.1f} \n' + f'fa_tnp = {fa:.1f}') text = ref_text + user_input + text return fa, text def eq141d2(): pass def eq141d3(fy): """Tension in extreme fibers subject to bending, net section AREMA 2018 Table 15-1-11 Row 3 Equations Tension in extreme fibers of rolled shapes, girders and built-up section, subject to bending, net section fa_bt = 0.55fy Args: fy (float): yield stress [psi] Returns: fa_bt (tuple(float, str)): allowable bending tensile stress [psi] """ ref_text = "AREMA 2018 Section 1.4.1 Table 15-1-11 Row 3 \n\n" user_input = f'Fy = {fy:.2f} \n' fa_bt = 0.55fy text = (f'Tension in extreme fibers of rolled shapes, girder and \n' + f'built-up section, subject to bending, net section \n' + f'fa_bt = 0.55fy \n' + f'fa_bt = 0.55{fy:.1f} \n' + f'fa_bt = {fa_bt:.1f}') text = ref_text + user_input + text return fa_bt, text def eq141d4(): pass def eq141d5(k, l, r, fy, e): """Axial compression allowable stress, gross section AREMA 2018 Table 15-1-11 Row 5 Equations (Except for stiffeners of beams and girders, and splice material) klr = kl/r elastic_limit = 0.629math.sqrt(fy/e) plastic_limit = 5.034math.sqrt(fy/e) if klr <= elastic_limit: fa_ac = 0.55fy elif klr > elastic_limit and klr <= plastic_limit: fa_ac = 0.60fy-math.pow(17500.0fy/e,3/2)klr elif klr > plastic_limit: fa_ac = 0.514math.pow(math.pi, 2)e/math.pow(klr,2) Args: k (float): effective length factor in the axis considered k = 7/8 for members with pin-end connections k = 3/4 for members with bolted or welded end connections l (float): unbraced length in the axis considered [inches] r (float): radius of gyration in the axis considered [inches] fy (float): yield stress [psi] e (float): modulus of elasticity [psi] Returns: fa_ac (tuple(float, str)): allowable axial compression on gross section Notes: 1. Units are in lbs and inches. """ ref_text = "AREMA 2018 Section 1.4.1 Table 15-1-11 Row 5 \n\n" user_input = (f'k = {k:.2f}, l = {l:.2f}, r = {r:.2f}, Fy = {fy:.2f},' + f'E = {e:.2f} \n') klr = kl/r elastic_limit = 0.629/math.sqrt(fy/e) plastic_limit = 5.034/math.sqrt(fy/e) if klr <= elastic_limit: fa_ac = 0.55fy text = (f'kl/r <= 0.629/math.sqrt(fy/e) \n' + f'{klr:.2f} <= {elastic_limit:.2f} \n' + f'fa_ac = 0.55fy \n' + f'fa_ac = 0.55{fy:.1f} \n' + f'fa_ac = {fa_ac:.1f}') elif klr > elastic_limit and klr <= plastic_limit: fa_ac = 0.60fy-math.pow(17500.0fy/e,3/2)klr text = (f'kl/r > 0.629/math.sqrt(fy/e)' f'and kl/r <= 5.034/math.sqrt(fy/e) \n' + f'{klr:.2f} > {elastic_limit:.2f}' f'and {klr:.2f} <= {plastic_limit:.2f} \n' + f'fa_ac = 0.60fy-math.pow(17500.0fy/e,3/2)kl/r \n' + f'fa_ac = 0.60{fy:.1f}-' f'math.pow(17500.0{fy:.1f}/{e:.1f},3/2){klr:.2f} \n' + f'fa_ac = {fa_ac:.1f}') elif klr > plastic_limit: fa_ac = 0.514math.pow(math.pi, 2)e/math.pow(klr,2) text = (f'kl/r > 5.034/math.sqrt(fy/e) \n' + f'{klr:.2f} > {plastic_limit:.2f} \n' + f'fa_ac = 0.514math.pow(math.pi, 2)e/math.pow(kl/r,2) \n' + f'fa_ac = 0.514math.pow(math.pi, 2){e:.2f}' + f'/math.pow({klr:.2f},2) \n' + f'fa_ac = {fa_ac:.1f}') text = ref_text + user_input + text return fa_ac, text def eq141d6(fy): """Compression in extreme fibers of I-type section bending about weak axis AREMA 2018 Section 1.4.1 Table 15-1-11 Row 6 Compression in extreme fibers of I-type members subjected to loading perpendicular to the web. fa_b = 0.55fy Args: fy (float): yield stress [psi] Return: fa_bt (tuple(float, str)): allowable bending tensile stress [psi] """ ref_text = "AREMA 2018 Section 1.4.1 Table 15-1-11 Row 6 \n\n" user_input = (f'Fy = {fy:.2f} \n\n') fa_b = 0.55fy text = (f'fa_b = 0.55fy \n' + f'fa_b = 0.55{fy:.1f} \n' + f'fa_b = {fa_b:.1f}') text = ref_text + user_input + text return fa_b, text def eq141d7(l, ryc, afc, d, fy, e): """Compression in extreme fibers of flexural members allowable stress AREMA 2018 Table 15-1-11 Row 7 Equations Compression in extreme fibers of flexural members symmertical about the principal axis in the plane of the web (other than box-type flexural members) that are rolled beams or welded built-up members with solid rectangular flanges, the larger of the values computer by the following fa_bc1 = (0.55fy - 0.55math.pow(fy,2)/ (6.3math.pow(math.pi,2)e)math.pow(l/ryc,2) fa_bc2 = 0.131math.pie/(ldmath.sqrt(1+mu)/afc) fa_bc3 = 0.55fy fa_bc = min(max(fa1, fa2), fa3) Args: l (float): distance bwteen points of lateral support for the compression flange, unbraced length [inches] ryc (float): minimum radius of gyration of the compression flange and that portion of the web area on the compression side of the axis of bending, about an axis in the plane of the web [inches] afc (float): area of the smaller flange excluding any portion of the web [inches^2] d (float): overall dpeth of the member [inches] fy (float): yield stress [psi] e (float): modulus of elasticity [psi] Returns: fa_bc (float): allowable compression stress in extreme fibers of flexural members [psi] Notes: 1. Units are in lbs and inches. 2. Poisson's ratio, mu, is taken as 0.3. """ ref_text = "AREMA 2018 Section 1.4.1 Table 15-1-11 Row 7 \n\n" user_input = (f'l = {l:.2f}, ryc = {ryc:.2f}, afc = {afc:.2f}, ' + f'd = {d:.2f}, Fy = {fy:.2f}, E = {e:.2f} \n\n') mu = 0.3 fa_bc1 = (0.55fy - 0.55math.pow(fy,2)/ (6.3math.pow(math.pi,2)e)math.pow(l/ryc,2)) fa_bc2 = 0.131math.pie/(ldmath.sqrt(1+mu)/afc) fa_bc3 = 0.55fy fa_bc = min(max(fa_bc1, fa_bc2), fa_bc3) text1 = (f'fa_bc1 = 0.55fy - 0.55math.pow(fy,2)/' + f'(6.3math.pow(math.pi,2)e)math.pow(l/ryc,2) \n' + f'fa_bc1 = 0.55{fy:.1f} - 0.55math.pow({fy:.1f},2)/' + f'(6.3math.pow(math.pi,2){e:.1f})' + f'math.pow({l:.2f}/{ryc:.2f},2) \n' + f"""fa_bc1 = {fa_bc1:.1f} \n\n""") text2 = (f'fa_bc2 = 0.131math.pie/(ldmath.sqrt(1+mu)/afc) \n' + f'fa_bc2 = 0.131math.pi{e:.1f}/' + f'({l:.2f}{d:.2f}math.sqrt(1+{mu:.2f})/{afc:.2f}) \n' + f'fa_bc2 = {fa_bc2:.1f} \n\n') text3 = (f'fa_bc3 = 0.55fy \n' + f'fa_bc3 = 0.55{fy:.2f} \n' + f'fa_bc3 = {fa_bc3:.1f} \n\n') text4 = (f'fa_bc = min(max(fa_bc1, fa_bc2), fa_bc3) \n' + f'fa_bc = min(max({fa_bc1:.2f}, {fa_bc2:.2f}), {fa_bc3:.2f}) \n' + f'fa_bc = min({max(fa_bc1, fa_bc2):.2f}, {fa_bc3:.2f}) \n' + f'fa_bc = {min(max(fa_bc1, fa_bc2), fa_bc3):.1f} \n') text = ref_text + user_input + text1 + text2 + text3 + text4 return fa_bc, text def eq141d10(l, sx, a, sum_st, iy, fy, e): """Compression in extreme fibers of box type flexural members AREMA 2018 Section 1.4.1 Table 15-1-11 Row 10 Compression in the extreme fibers of box type welded or bolted flexural members symmetrical about the principal axis midway between the webs (l/r)e = math.sqrt(1.105math.pi/sxxsqrt(sum(s/t))/ amath.sqrt(i_yy/(1+mu))) fa_bc = 0.55fy - 0.55math.pow(fy,2)/(6.3math.pow(math.pi,2)e) math.pow((l/r)e,2) Args: l (float): distance between points of lateral support for the compression flange, unbraced length [inches] sx (float): section modulus of the box type member about its major axis [inches^3] a (float): total area enclosed within the center lines of the box type member webs and flanges [inches^2] sum_st (float): sum of the ratio width-to-thickness of each flange and ratio of the depth to thickness of each web (neglect any portion of the flange which projects beyond the box section) iy (float): second moment of area of the box type member about its minor axis, [inches^4] Returns: fa_bc (float): allowable compression stress in extreme fibers of box type flexure members Notes: 1. Units in lbs and inches. 2. Poisson's ratio, mu, is taken as 0.3. """ ref_text = "AREMA 2018 Section 1.4.1 Table 15-1-11 Row 10 \n\n" user_input = (f'l = {l:.2f}, Sx = {sx:.2f}, a = {a:.2f}, ' + f'sum_st = {sum_st:.2f}, Iy = {iy:.2f}, Fy = {fy:.1f}, ' + f'E = {e:.1f} \n\n') mu = 0.3 lre = math.sqrt((1.105math.pi/sxmath.sqrt(sum_st))/ (amath.sqrt(iy/(1+mu)))) fa_bc = (0.55fy-0.55math.pow(fy,2)/ (6.3math.pow(math.pi,2)e)math.pow(lre,2)) text1 = (f'(l/r)e = math.sqrt((1.105math.pi/sxmath.sqrt(sum_st))/' + f'(amath.sqrt(iy/(1+mu)))) \n' + f'(l/r)e = math.sqrt((1.105math.pi/{sx:.2f}math.sqrt({sum_st:.2f}))/' + f'({a:.2f}math.sqrt({iy:.2f}/(1+{mu:.2f})))) \n' + f'(l/r)e = {lre:.2f} \n') text2 = (f'fa_bc = (0.55fy-0.55math.pow(fy,2)/' + f'(6.3math.pow(math.pi,2)e)math.pow(lre,2)) \n' + f'fa_bc = (0.55{fy:.1f}-0.55math.pow({fy:.1f},2)/' + f'(6.3math.pow(math.pi,2){e:.1f})math.pow({lre:.2f},2)) \n' + f'fa_bc = {fa_bc:.1f}') text = ref_text + user_input + text1 + text2 return fa_bc, text def eq141d13(fy): """Shear in webs of rolled beams and plate girders, gross section AREMA 2018 Table 15-1-11 Row 13 Equations fa = 0.35fy Args: fy (float): yield stress Returns: fa (tuple(float, str)): Allowable shear stress in webs """ ref_text = "AREMA 2018 Section 1.4.1 Table 15-1-11 Row 13 \n\n" user_input = f'fy = {fy:.1f} psi \n\n' fa_v = 0.35fy text = (f'fa_v = 0.35fy \n' + f'fa_v = 0.35{fy:.2f} \n' + f'fa_v = {fa_v:.2f} \n') text = ref_text + user_input + text return fa_v, text def eq141d17(l, d, fu): """Bearing on F3125 Grade A325 and Grade A490 bolts AREMA 2018 Section 1.4.1 Table 15-1-11 Row 17 Allowable stress of bearing material on F3125 Gr A325 and Gr A490 bolts. fa_bolt_brg = min(lfu/(2d), 1.2fu) Args: l (float): distance measured in the line of the force form the center line of a bolt to the nearest edge of an adjacent bolt or to the end of the connected part toward which the force is directed [inches] d (float): diameter of bolt [inches] fu (float): lowest specified minimum tensile strength of the connected part [psi] Returns: fa_bolt_brg (tuple(float, str)): allowable bearing stress """ ref_text = 'AREMA 2018 Section 1.4.1 Table 15-1-11 Row 17 \n\n' user_input = f'l = {l:.2f}, d = {d:.2f}, fu = {fu:.1f} \n\n' fa_brg_bolt = min(lfu/(2d), 1.2fu) text = (f'fa_brg_bolt = min(lfu/(2d), 1.2fu) \n' + f'fa_brg_bolt = min({l:.2f}{fu:.1f}/(2{d:.2f}),' f'1.2{fu:.1f}) \n'+ f'fa_brg_bolt = min({lfu/(2d):.1f}, {1.2*fu:.1f}) \n' + f'fa_brg_bolt = {fa_brg_bolt:.1f}') text = ref_text + user_input + text return fa_brg_bolt, text def tb15111a(hole_type, surface_type, bolt_grade): """Allowable stress for slip-critical connections Allowable stress for slip-critical connections (Slip Load per Unit Area of Bolt) Args: hole_type (int): 1 - standard and short slotted perpendicular to the direction of load 2 - Oversize and short slotted parallel to the direction of load 3 - Long slotted any direction surface_type (str): A - Class A (slip coefficient 0.30) Uncoated clean mill scale and blast-cleaned surfaces with Class A coatings B - Class B (slip coefficient 0.50) Uncoated blast-cleaned surfaces with Class B coatings and unsealed thermal-sprayed surfaces C - Class C (slip coefficient 0.30) Hot-dip galvanized D - Class D (slip coefficient 0.45) Blast-cleaned surfaces with Class D coatings bolt_grade (int): 1 - F3125 Grade A325 2 - F3125 Grade A490 Returns: fa_bolt (tuple(float, str)): allowable stress for slip critical connection [psi] """ fa_code = str(bolt_grade) + surface_type + str(hole_type) fa_bolt = {'1A1':12900,'1B1':21500,'1C1':12900,'1D1':19300, '2A1':11000,'2B1':18300,'2C1':11000,'2D1':16500, '3A1':9000,'3B1':15000,'3C1':9000,'3D1':13500, '1A2':16200,'1B2':26900,'1C2':16200,'1D2':24200, '2A2':13800,'2B2':23000,'2C2':13800,'2D2':20700, '3A2':11300,'3B2':18900,'3C2':11300,'3D2':17000,} ht_text = {1:('1 - standard and short slotted perpendicular to the '+ 'direction of load'), 2:('2 - Oversize and short slotted parallel to the direction ' + 'of load'), 3:'3 - Long slotted any direction'} def eq162a(element_type, case, t, fy, e): """Outstanding element in compression The width of outstanding elements of members in compression shall not exceed the following, where t, inches, is the thickness of the element: """ pass	[ 1, 529, 276, 1112, 420, 29958, 29885, 1332, 277, 29955, 29946, 29914, 29909, 1525, 1529, 2272, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 15945, 29908, 13, 20399, 373, 323, 434, 2627, 29871, 29896, 29946, 29871, 29896, 29906, 29901, 29945, 29896, 29901, 29900, 29945, 29871, 29906, 29900, 29906, 29900, 13, 13, 29992, 8921, 29901, 286, 1332, 16097, 13, 15945, 29908, 13, 13, 5215, 5844, 13, 13, 1753, 11594, 29896, 29941, 29896, 29946, 29896, 29898, 29888, 1165, 29892, 2258, 29918, 1165, 29892, 285, 29890, 29896, 29892, 2258, 29918, 29890, 29896, 29892, 285, 29890, 29906, 29892, 2258, 29918, 29890, 29906, 29892, 413, 29896, 29892, 301, 29896, 29892, 364, 29896, 29892, 29871, 13, 9651, 413, 29906, 29892, 301, 29906, 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spiketoolkit/validation/quality_metrics.py	teristam/spiketoolk	55	76413	<gh_stars>10-100 from .quality_metric_classes.metric_data import MetricData from .quality_metric_classes.amplitude_cutoff import AmplitudeCutoff from .quality_metric_classes.silhouette_score import SilhouetteScore from .quality_metric_classes.num_spikes import NumSpikes from .quality_metric_classes.firing_rate import FiringRate from .quality_metric_classes.d_prime import DPrime from .quality_metric_classes.l_ratio import LRatio from .quality_metric_classes.presence_ratio import PresenceRatio from .quality_metric_classes.isi_violation import ISIViolation from .quality_metric_classes.snr import SNR from .quality_metric_classes.isolation_distance import IsolationDistance from .quality_metric_classes.noise_overlap import NoiseOverlap from .quality_metric_classes.nearest_neighbor import NearestNeighbor from .quality_metric_classes.drift_metric import DriftMetric from .quality_metric_classes.parameter_dictionaries import update_all_param_dicts_with_kwargs from collections import OrderedDict from copy import deepcopy import pandas all_metrics_list = ["num_spikes", "firing_rate", "presence_ratio", "isi_violation", "amplitude_cutoff", "snr", "max_drift", "cumulative_drift", "silhouette_score", "isolation_distance", "l_ratio", "d_prime", "noise_overlap", "nn_hit_rate", "nn_miss_rate"] def get_quality_metrics_list(): return all_metrics_list def compute_num_spikes( sorting, sampling_frequency=None, unit_ids=None, kwargs ): """ Computes and returns the num spikes for the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated sampling_frequency: float The sampling frequency of the result. If None, will check to see if sampling frequency is in sorting extractor unit_ids: list List of unit ids to compute metric for. If not specified, all units are used kwargs: keyword arguments Keyword arguments among the following: save_property_or_features: bool If True, the metric is saved as sorting property verbose: bool If True, will be verbose in metric computation Returns ---------- num_spikes: np.ndarray The number of spikes of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=sampling_frequency, recording=None, apply_filter=False, freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=None, verbose=params_dict['verbose'], raise_if_empty=False) ns = NumSpikes(metric_data=md) num_spikes = ns.compute_metric(kwargs) return num_spikes def compute_firing_rates( sorting, duration_in_frames, sampling_frequency=None, unit_ids=None, kwargs ): """ Computes and returns the firing rates for the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. duration_in_frames: int Length of recording (in frames). sampling_frequency: float The sampling frequency of the result. If None, will check to see if sampling frequency is in sorting extractor unit_ids: list List of unit ids to compute metric for. If not specified, all units are used kwargs: keyword arguments Keyword arguments among the following: save_property_or_features: bool If True, the metric is saved as sorting property verbose: bool If True, will be verbose in metric computation Returns ---------- firing_rates: np.ndarray The firing rates of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=sampling_frequency, recording=None, apply_filter=False, freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=duration_in_frames, verbose=params_dict['verbose']) fr = FiringRate(metric_data=md) firing_rates = fr.compute_metric(kwargs) return firing_rates def compute_presence_ratios( sorting, duration_in_frames, sampling_frequency=None, unit_ids=None, kwargs ): """ Computes and returns the presence ratios for the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. duration_in_frames: int Length of recording (in frames). sampling_frequency: float The sampling frequency of the result. If None, will check to see if sampling frequency is in sorting extractor unit_ids: list List of unit ids to compute metric for. If not specified, all units are used kwargs: keyword arguments Keyword arguments among the following: save_property_or_features: bool If True, the metric is saved as sorting property verbose: bool If True, will be verbose in metric computation Returns ---------- presence_ratios: np.ndarray The presence ratios of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=sampling_frequency, recording=None, apply_filter=False, freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=duration_in_frames, verbose=params_dict['verbose']) pr = PresenceRatio(metric_data=md) presence_ratios = pr.compute_metric(kwargs) return presence_ratios def compute_isi_violations( sorting, duration_in_frames, isi_threshold=ISIViolation.params['isi_threshold'], min_isi=ISIViolation.params['min_isi'], sampling_frequency=None, unit_ids=None, kwargs ): """ Computes and returns the isi violations for the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. duration_in_frames: int Length of recording (in frames). isi_threshold: float The isi threshold for calculating isi violations min_isi: float The minimum expected isi value sampling_frequency: float The sampling frequency of the result. If None, will check to see if sampling frequency is in sorting extractor unit_ids: list List of unit ids to compute metric for. If not specified, all units are used kwargs: keyword arguments Keyword arguments among the following: save_property_or_features: bool If True, the metric is saved as sorting property verbose: bool If True, will be verbose in metric computation Returns ---------- isi_violations: np.ndarray The isi violations of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=sampling_frequency, recording=None, apply_filter=False, freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=duration_in_frames, verbose=params_dict['verbose']) iv = ISIViolation(metric_data=md) isi_violations = iv.compute_metric(isi_threshold, min_isi, kwargs) return isi_violations def compute_amplitude_cutoffs( sorting, recording, unit_ids=None, kwargs ): """ Computes and returns the amplitude cutoffs for the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. recording: RecordingExtractor The given recording extractor from which to extract amplitudes unit_ids: list List of unit ids to compute metric for. If not specified, all units are used kwargs: keyword arguments Keyword arguments among the following: apply_filter: bool If True, recording is bandpass-filtered. freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) save_property_or_features: bool If true, it will save amplitudes in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes. peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: float Frames after peak to compute amplitude save_property_or_features: bool If True, the metric is saved as sorting property seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- amplitude_cutoffs: np.ndarray The amplitude cutoffs of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=None, verbose=params_dict['verbose']) md.compute_amplitudes(kwargs) ac = AmplitudeCutoff(metric_data=md) amplitude_cutoffs = ac.compute_metric(kwargs) return amplitude_cutoffs def compute_snrs( sorting, recording, snr_mode=SNR.params['snr_mode'], snr_noise_duration=SNR.params['snr_noise_duration'], max_spikes_per_unit_for_snr=SNR.params['max_spikes_per_unit_for_snr'], template_mode=SNR.params['template_mode'], max_channel_peak=SNR.params['max_channel_peak'], unit_ids=None, kwargs ): """ Computes and returns the snrs in the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. recording: RecordingExtractor The given recording extractor from which to extract amplitudes snr_mode: str Mode to compute noise SNR ('mad' \| 'std' - default 'mad') snr_noise_duration: float Number of seconds to compute noise level from (default 10.0) max_spikes_per_unit_for_snr: int Maximum number of spikes to compute templates from (default 1000) template_mode: str Use 'mean' or 'median' to compute templates max_channel_peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) unit_ids: list List of unit ids to compute metric for. If not specified, all units are used kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- snrs: np.ndarray The snrs of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], duration_in_frames=None, freq_max=params_dict["freq_max"], unit_ids=unit_ids, verbose=params_dict['verbose']) snr = SNR(metric_data=md) snrs = snr.compute_metric(snr_mode, snr_noise_duration, max_spikes_per_unit_for_snr, template_mode, max_channel_peak, kwargs) return snrs def compute_noise_overlaps( sorting, recording, num_channels_to_compare=NoiseOverlap.params['num_channels_to_compare'], num_features=NoiseOverlap.params['num_features'], num_knn=NoiseOverlap.params['num_knn'], max_spikes_per_unit_for_noise_overlap=NoiseOverlap.params['max_spikes_per_unit_for_noise_overlap'], unit_ids=None, kwargs ): """ Computes and returns the noise overlaps in the sorted dataset. Noise overlap estimates the fraction of ‘‘noise events’’ in a cluster, i.e., above-threshold events not associated with true firings of this or any of the other clustered units. A large noise overlap implies a high false-positive rate. Implementation from ml_ms4alg. For more information see https://doi.org/10.1016/j.neuron.2017.08.030 Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. recording: RecordingExtractor The given recording extractor from which to extract amplitudes num_features: int Number of features to use for PCA num_knn: int Number of nearest neighbors max_spikes_per_unit_for_noise_overlap: int Number of waveforms to use for noise overlaps estimation unit_ids: list List of unit ids to compute metric for. If not specified, all units are used kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- noise_overlaps: np.ndarray The noise_overlaps of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], duration_in_frames=None, freq_max=params_dict["freq_max"], unit_ids=unit_ids, verbose=params_dict['verbose']) noise_overlap = NoiseOverlap(metric_data=md) noise_overlaps = noise_overlap.compute_metric(num_channels_to_compare, max_spikes_per_unit_for_noise_overlap, num_features, num_knn, kwargs) return noise_overlaps def compute_silhouette_scores( sorting, recording, max_spikes_for_silhouette=SilhouetteScore.params['max_spikes_for_silhouette'], unit_ids=None, kwargs ): """ Computes and returns the silhouette scores in the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated recording: RecordingExtractor The given recording extractor from which to extract amplitudes max_spikes_for_silhouette: int Max spikes to be used for silhouette metric unit_ids: list List of unit ids to compute metric for. If not specified, all units are used kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- silhouette_scores: np.ndarray The sihouette scores of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], duration_in_frames=None, freq_max=params_dict["freq_max"], unit_ids=unit_ids, verbose=params_dict['verbose']) md.compute_pca_scores(kwargs) silhouette_score = SilhouetteScore(metric_data=md) silhouette_scores = silhouette_score.compute_metric(max_spikes_for_silhouette, kwargs) return silhouette_scores def compute_d_primes( sorting, recording, num_channels_to_compare=DPrime.params['num_channels_to_compare'], max_spikes_per_cluster=DPrime.params['max_spikes_per_cluster'], unit_ids=None, kwargs ): """ Computes and returns the d primes in the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated recording: RecordingExtractor The given recording extractor from which to extract amplitudes num_channels_to_compare: int The number of channels to be used for the PC extraction and comparison max_spikes_per_cluster: int Max spikes to be used from each unit unit_ids: list List of unit ids to compute metric for. If not specified, all units are used kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- d_primes: np.ndarray The d primes of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=None, verbose=params_dict['verbose']) md.compute_pca_scores(kwargs) d_prime = DPrime(metric_data=md) d_primes = d_prime.compute_metric(num_channels_to_compare, max_spikes_per_cluster, kwargs) return d_primes def compute_l_ratios( sorting, recording, num_channels_to_compare=LRatio.params['num_channels_to_compare'], max_spikes_per_cluster=LRatio.params['max_spikes_per_cluster'], unit_ids=None, kwargs ): """ Computes and returns the l ratios in the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated recording: RecordingExtractor The given recording extractor from which to extract amplitudes num_channels_to_compare: int The number of channels to be used for the PC extraction and comparison max_spikes_per_cluster: int Max spikes to be used from each unit unit_ids: list List of unit ids to compute metric for. If not specified, all units are used kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- l_ratios: np.ndarray The l ratios of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=None, verbose=params_dict['verbose']) md.compute_pca_scores(kwargs) l_ratio = LRatio(metric_data=md) l_ratios = l_ratio.compute_metric(num_channels_to_compare, max_spikes_per_cluster, kwargs) return l_ratios def compute_isolation_distances( sorting, recording, num_channels_to_compare=IsolationDistance.params['num_channels_to_compare'], max_spikes_per_cluster=IsolationDistance.params['max_spikes_per_cluster'], unit_ids=None, kwargs ): """ Computes and returns the isolation distances in the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. recording: RecordingExtractor The given recording extractor from which to extract amplitudes num_channels_to_compare: int The number of channels to be used for the PC extraction and comparison max_spikes_per_cluster: int Max spikes to be used from each unit unit_ids: list List of unit ids to compute metric for. If not specified, all units are used kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- isolation_distances: np.ndarray The isolation distances of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=None, verbose=params_dict['verbose']) md.compute_pca_scores(kwargs) isolation_distance = IsolationDistance(metric_data=md) isolation_distances = isolation_distance.compute_metric(num_channels_to_compare, max_spikes_per_cluster, kwargs) return isolation_distances def compute_nn_metrics( sorting, recording, num_channels_to_compare=NearestNeighbor.params['num_channels_to_compare'], max_spikes_per_cluster=NearestNeighbor.params['max_spikes_per_cluster'], max_spikes_for_nn=NearestNeighbor.params['max_spikes_for_nn'], n_neighbors=NearestNeighbor.params['n_neighbors'], unit_ids=None, kwargs ): """ Computes and returns the nearest neighbor metrics in the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. recording: RecordingExtractor The given recording extractor from which to extract amplitudes num_channels_to_compare: int The number of channels to be used for the PC extraction and comparison max_spikes_per_cluster: int Max spikes to be used from each unit max_spikes_for_nn: int Max spikes to be used for nearest-neighbors calculation n_neighbors: int Number of neighbors to compare unit_ids: list List of unit ids to compute metric for. If not specified, all units are used kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- nn_metrics: np.ndarray The nearest neighbor metrics of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=None, verbose=params_dict['verbose']) md.compute_pca_scores(kwargs) nn = NearestNeighbor(metric_data=md) nn_metrics = nn.compute_metric(num_channels_to_compare, max_spikes_per_cluster, max_spikes_for_nn, n_neighbors, kwargs) return nn_metrics def compute_drift_metrics( sorting, recording, drift_metrics_interval_s=DriftMetric.params['drift_metrics_interval_s'], drift_metrics_min_spikes_per_interval=DriftMetric.params['drift_metrics_min_spikes_per_interval'], unit_ids=None, kwargs ): """ Computes and returns the drift metrics in the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. recording: RecordingExtractor The given recording extractor from which to extract amplitudes drift_metrics_interval_s: float Time period for evaluating drift. drift_metrics_min_spikes_per_interval: int Minimum number of spikes for evaluating drift metrics per interval. unit_ids: list List of unit ids to compute metric for. If not specified, all units are used kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- dm_metrics: np.ndarray The drift metrics of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=None, verbose=params_dict['verbose']) md.compute_pca_scores(kwargs) dm = DriftMetric(metric_data=md) dm_metrics = dm.compute_metric(drift_metrics_interval_s, drift_metrics_min_spikes_per_interval, kwargs) return dm_metrics def compute_quality_metrics( sorting, recording=None, duration_in_frames=None, sampling_frequency=None, metric_names=None, unit_ids=None, as_dataframe=False, isi_threshold=ISIViolation.params['isi_threshold'], min_isi=ISIViolation.params['min_isi'], snr_mode=SNR.params['snr_mode'], snr_noise_duration=SNR.params['snr_noise_duration'], max_spikes_per_unit_for_snr=SNR.params['max_spikes_per_unit_for_snr'], template_mode=SNR.params['template_mode'], max_channel_peak=SNR.params['max_channel_peak'], max_spikes_per_unit_for_noise_overlap=NoiseOverlap.params['max_spikes_per_unit_for_noise_overlap'], noise_overlap_num_features=NoiseOverlap.params['num_features'], noise_overlap_num_knn=NoiseOverlap.params['num_knn'], drift_metrics_interval_s=DriftMetric.params['drift_metrics_interval_s'], drift_metrics_min_spikes_per_interval=DriftMetric.params['drift_metrics_min_spikes_per_interval'], max_spikes_for_silhouette=SilhouetteScore.params['max_spikes_for_silhouette'], num_channels_to_compare=13, max_spikes_per_cluster=500, max_spikes_for_nn=NearestNeighbor.params['max_spikes_for_nn'], n_neighbors=NearestNeighbor.params['n_neighbors'], kwargs ): """ Computes and returns all specified metrics for the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. recording: RecordingExtractor The given recording extractor from which to extract amplitudes duration_in_frames: int Length of recording (in frames). sampling_frequency: float The sampling frequency of the result. If None, will check to see if sampling frequency is in sorting extractor metric_names: list List of metric names to be computed unit_ids: list List of unit ids to compute metric for. If not specified, all units are used as_dataframe: bool If True, will return dataframe of metrics. If False, will return dictionary. isi_threshold: float The isi threshold for calculating isi violations min_isi: float The minimum expected isi value snr_mode: str Mode to compute noise SNR ('mad' \| 'std' - default 'mad') snr_noise_duration: float Number of seconds to compute noise level from (default 10.0) max_spikes_per_unit_for_snr: int Maximum number of spikes to compute templates for SNR from (default 1000) template_mode: str Use 'mean' or 'median' to compute templates max_channel_peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) max_spikes_per_unit_for_noise_overlap: int Maximum number of spikes to compute templates for noise overlap from (default 1000) noise_overlap_num_features: int Number of features to use for PCA for noise overlap noise_overlap_num_knn: int Number of nearest neighbors for noise overlap drift_metrics_interval_s: float Time period for evaluating drift. drift_metrics_min_spikes_per_interval: int Minimum number of spikes for evaluating drift metrics per interval max_spikes_for_silhouette: int Max spikes to be used for silhouette metric num_channels_to_compare: int The number of channels to be used for the PC extraction and comparison max_spikes_per_cluster: int Max spikes to be used from each unit max_spikes_for_nn: int Max spikes to be used for nearest-neighbors calculation n_neighbors: int Number of neighbors to compare kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- metrics: dictionary OR pandas.dataframe Dictionary or pandas.dataframe of metrics. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) metrics_dict = OrderedDict() if metric_names is None: metric_names = all_metrics_list else: bad_metrics = [] for m in metric_names: if m not in all_metrics_list: bad_metrics.append(m) if len(bad_metrics) > 0: raise ValueError(f"Improper feature names: {str(bad_metrics)}. The following features names can be " f"calculated: {str(all_metrics_list)}") if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=sampling_frequency, recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=duration_in_frames, verbose=params_dict['verbose']) if "firing_rate" in metric_names or "presence_ratio" in metric_names or "isi_violation" in metric_names: if recording is None and duration_in_frames is None: raise ValueError( "duration_in_frames and recording cannot both be None when computing firing_rate, " "presence_ratio, and isi_violation") if "max_drift" in metric_names or "cumulative_drift" in metric_names or "silhouette_score" in metric_names \ or "isolation_distance" in metric_names or "l_ratio" in metric_names or "d_prime" in metric_names \ or "nn_hit_rate" in metric_names or "nn_miss_rate" in metric_names: if recording is None: raise ValueError("The recording cannot be None when computing max_drift, cumulative_drift, " "silhouette_score isolation_distance, l_ratio, d_prime, nn_hit_rate, or amplitude_cutoff.") else: md.compute_pca_scores(kwargs) if "amplitude_cutoff" in metric_names: if recording is None: raise ValueError("The recording cannot be None when computing amplitude cutoffs.") else: md.compute_amplitudes(kwargs) if "snr" in metric_names: if recording is None: raise ValueError("The recording cannot be None when computing snr.") if "num_spikes" in metric_names: ns = NumSpikes(metric_data=md) num_spikes = ns.compute_metric(kwargs) metrics_dict['num_spikes'] = num_spikes if "firing_rate" in metric_names: fr = FiringRate(metric_data=md) firing_rates = fr.compute_metric(kwargs) metrics_dict['firing_rate'] = firing_rates if "presence_ratio" in metric_names: pr = PresenceRatio(metric_data=md) presence_ratios = pr.compute_metric(kwargs) metrics_dict['presence_ratio'] = presence_ratios if "isi_violation" in metric_names: iv = ISIViolation(metric_data=md) isi_violations = iv.compute_metric(isi_threshold, min_isi, kwargs) metrics_dict['isi_violation'] = isi_violations if "amplitude_cutoff" in metric_names: ac = AmplitudeCutoff(metric_data=md) amplitude_cutoffs = ac.compute_metric(kwargs) metrics_dict['amplitude_cutoff'] = amplitude_cutoffs if "snr" in metric_names: snr = SNR(metric_data=md) snrs = snr.compute_metric(snr_mode, snr_noise_duration, max_spikes_per_unit_for_snr, template_mode, max_channel_peak, kwargs) metrics_dict['snr'] = snrs if "max_drift" in metric_names or "cumulative_drift" in metric_names: dm = DriftMetric(metric_data=md) max_drifts, cumulative_drifts = dm.compute_metric(drift_metrics_interval_s, drift_metrics_min_spikes_per_interval, kwargs) if "max_drift" in metric_names: metrics_dict['max_drift'] = max_drifts if "cumulative_drift" in metric_names: metrics_dict['cumulative_drift'] = cumulative_drifts if "silhouette_score" in metric_names: silhouette_score = SilhouetteScore(metric_data=md) silhouette_scores = silhouette_score.compute_metric(max_spikes_for_silhouette, kwargs) metrics_dict['silhouette_score'] = silhouette_scores if "isolation_distance" in metric_names: isolation_distance = IsolationDistance(metric_data=md) isolation_distances = isolation_distance.compute_metric(num_channels_to_compare, max_spikes_per_cluster, kwargs) metrics_dict['isolation_distance'] = isolation_distances if "noise_overlap" in metric_names: noise_overlap = NoiseOverlap(metric_data=md) noise_overlaps = noise_overlap.compute_metric(num_channels_to_compare, max_spikes_per_unit_for_noise_overlap, noise_overlap_num_features, noise_overlap_num_knn, kwargs) metrics_dict['noise_overlap'] = noise_overlaps if "l_ratio" in metric_names: l_ratio = LRatio(metric_data=md) l_ratios = l_ratio.compute_metric(num_channels_to_compare, max_spikes_per_cluster, kwargs) metrics_dict['l_ratio'] = l_ratios if "d_prime" in metric_names: d_prime = DPrime(metric_data=md) d_primes = d_prime.compute_metric(num_channels_to_compare, max_spikes_per_cluster, kwargs) metrics_dict['d_prime'] = d_primes if "nn_hit_rate" in metric_names or "nn_miss_rate" in metric_names: nn = NearestNeighbor(metric_data=md) nn_hit_rates, nn_miss_rates = nn.compute_metric(num_channels_to_compare, max_spikes_per_cluster, max_spikes_for_nn, n_neighbors, **kwargs) if "nn_hit_rate" in metric_names: metrics_dict['nn_hit_rate'] = nn_hit_rates if "nn_miss_rate" in metric_names: metrics_dict['nn_miss_rate'] = nn_miss_rates if as_dataframe: metrics = pandas.DataFrame.from_dict(metrics_dict) metrics = metrics.rename(index={original_idx: unit_ids[i] for i, original_idx in enumerate(range(len(metrics)))}) else: metrics = metrics_dict return metrics	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29899, 29896, 29900, 29900, 13, 3166, 869, 29567, 29918, 16414, 29918, 13203, 29889, 16414, 29918, 1272, 1053, 4737, 2200, 1469, 13, 3166, 869, 29567, 29918, 16414, 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yardstick/tests/unit/benchmark/scenarios/availability/test_scenario_general.py	mythwm/yardstick	0	102238	############################################################################## # Copyright (c) 2016 <NAME> and others # <EMAIL> # All rights reserved. This program and the accompanying materials # are made available under the terms of the Apache License, Version 2.0 # which accompanies this distribution, and is available at # http://www.apache.org/licenses/LICENSE-2.0 ############################################################################## import mock import unittest from yardstick.benchmark.scenarios.availability import scenario_general class ScenarioGeneralTestCase(unittest.TestCase): def setUp(self): self.scenario_cfg = { 'type': "general_scenario", 'options': { 'attackers': [{ 'fault_type': "general-attacker", 'key': "kill-process"}], 'monitors': [{ 'monitor_type': "general-monitor", 'key': "service-status"}], 'steps': [ { 'actionKey': "kill-process", 'actionType': "attacker", 'index': 1}, { 'actionKey': "service-status", 'actionType': "monitor", 'index': 2}] } } self.instance = scenario_general.ScenarioGeneral(self.scenario_cfg, None) self._mock_director = mock.patch.object(scenario_general, 'Director') self.mock_director = self._mock_director.start() self.addCleanup(self._stop_mock) def _stop_mock(self): self._mock_director.stop() def test_scenario_general_all_successful(self): self.instance.setup() self.instance.run({}) self.instance.teardown() def test_scenario_general_exception(self): mock_obj = mock.Mock() mock_obj.createActionPlayer.side_effect = KeyError('Wrong') self.instance.director = mock_obj self.instance.director.data = {} self.instance.run({}) self.instance.teardown() def test_scenario_general_case_fail(self): mock_obj = mock.Mock() mock_obj.verify.return_value = False self.instance.director = mock_obj self.instance.director.data = {} self.instance.run({}) self.instance.pass_flag = True self.instance.teardown()	[ 1, 835, 13383, 13383, 13383, 13383, 7346, 2277, 29937, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29953, 529, 5813, 29958, 322, 4045, 13, 29937, 529, 26862, 6227, 29958, 13, 29937, 2178, 10462, 21676, 29889, 910, 1824, 322, 278, 10259, 1384, 292, 17279, 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find_symbols_sizes.py	Amjad50/Fyp	1	1607647	<filename>find_symbols_sizes.py # helper script to find the default sizes of all symbols in the default # font size (no power or sub) from os import chdir from string import digits, ascii_letters from tempfile import mkdtemp from PIL import Image from tqdm import tqdm from dataset_generator.generator import generate_single_from_template from segmenter.symbol_segmenter import segment_image_crops from segmenter.utils import box_size def get_symbol_size(expr): filename = "tmp_file" _ = generate_single_from_template(expr, ".", filename) img = Image.open(filename + ".png") crops = segment_image_crops(img) assert len(crops) == 1 return box_size(crops[0]) if __name__ == "__main__": characters = list(digits + ascii_letters + "=-+()[],.") characters.extend(["\\sum", "\\pi", "\\int"]) tmp_dir = mkdtemp() chdir(tmp_dir) symbol_sizes_dict = { ch: get_symbol_size(ch) for ch in tqdm(characters) } print(symbol_sizes_dict)	[ 1, 529, 9507, 29958, 2886, 29918, 18098, 29879, 29918, 29879, 7093, 29889, 2272, 13, 29937, 16876, 2471, 304, 1284, 278, 2322, 15786, 310, 599, 15072, 297, 278, 2322, 13, 29937, 4079, 2159, 313, 1217, 3081, 470, 1014, 29897, 13, 13, 3166, 2897, 1053, 521, 3972, 13, 3166, 1347, 1053, 13340, 29892, 408, 18869, 29918, 1026, 2153, 13, 3166, 5694, 1445, 1053, 14690, 29881, 7382, 13, 13, 3166, 349, 6227, 1053, 7084, 13, 3166, 260, 29939, 18933, 1053, 260, 29939, 18933, 13, 13, 3166, 8783, 29918, 27959, 29889, 27959, 1053, 5706, 29918, 14369, 29918, 3166, 29918, 6886, 13, 3166, 10768, 261, 29889, 18098, 29918, 28192, 261, 1053, 10768, 29918, 3027, 29918, 24077, 567, 13, 3166, 10768, 261, 29889, 13239, 1053, 3800, 29918, 2311, 13, 13, 13, 1753, 679, 29918, 18098, 29918, 2311, 29898, 13338, 1125, 13, 1678, 10422, 353, 376, 7050, 29918, 1445, 29908, 13, 13, 1678, 903, 353, 5706, 29918, 14369, 29918, 3166, 29918, 6886, 29898, 13338, 29892, 11393, 613, 10422, 29897, 13, 13, 1678, 10153, 353, 7084, 29889, 3150, 29898, 9507, 718, 11393, 2732, 1159, 13, 1678, 8182, 567, 353, 10768, 29918, 3027, 29918, 24077, 567, 29898, 2492, 29897, 13, 13, 1678, 4974, 7431, 29898, 24077, 567, 29897, 1275, 29871, 29896, 13, 13, 1678, 736, 3800, 29918, 2311, 29898, 24077, 567, 29961, 29900, 2314, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 4890, 353, 1051, 29898, 7501, 1169, 718, 408, 18869, 29918, 1026, 2153, 718, 376, 10457, 29974, 580, 29961, 1402, 23157, 13, 1678, 4890, 29889, 21843, 29898, 3366, 1966, 2083, 613, 376, 1966, 1631, 613, 376, 1966, 524, 20068, 13, 13, 1678, 13128, 29918, 3972, 353, 14690, 29881, 7382, 580, 13, 1678, 521, 3972, 29898, 7050, 29918, 3972, 29897, 13, 13, 1678, 5829, 29918, 29879, 7093, 29918, 8977, 353, 426, 13, 4706, 521, 29901, 679, 29918, 18098, 29918, 2311, 29898, 305, 29897, 13, 4706, 363, 521, 297, 260, 29939, 18933, 29898, 3090, 21706, 29897, 13, 1678, 500, 13, 13, 1678, 1596, 29898, 18098, 29918, 29879, 7093, 29918, 8977, 29897, 13, 2 ]
booknlp/common/calc_coref_metrics.py	ishine/booknlp	539	20111	import subprocess, re, sys def get_coref_score(metric, path_to_scorer, gold=None, preds=None): output=subprocess.check_output(["perl", path_to_scorer, metric, preds, gold]).decode("utf-8") output=output.split("\n")[-3] matcher=re.search("Coreference: Recall: \(.?\) (.?)% Precision: \(.?\) (.?)% F1: (.*?)%", output) if matcher is not None: recall=float(matcher.group(1)) precision=float(matcher.group(2)) f1=float(matcher.group(3)) return recall, precision, f1 def get_conll(path_to_scorer, gold=None, preds=None): bcub_r, bcub_p, bcub_f=get_coref_score("bcub", path_to_scorer, gold, preds) muc_r, muc_p, muc_f=get_coref_score("muc", path_to_scorer, gold, preds) ceaf_r, ceaf_p, ceaf_f=get_coref_score("ceafe", path_to_scorer, gold, preds) print("bcub:\t%.1f" % bcub_f) print("muc:\t%.1f" % muc_f) print("ceaf:\t%.1f" % ceaf_f) avg=(bcub_f + muc_f + ceaf_f)/3. print("Average F1: %.1f" % (avg)) # Generate Latex table # print("%.1f&%.1f&%.1f&%.1f" % (bcub_f, muc_f, ceaf_f, avg)) return bcub_f, avg if __name__ == "__main__": goldFile=sys.argv[1] predFile=sys.argv[2] scorer=sys.argv[3] bcub_f, avg=get_conll(scorer, gold=goldFile, preds=predFile)	[ 1, 1053, 1014, 5014, 29892, 337, 29892, 10876, 13, 13, 1753, 679, 29918, 3221, 29888, 29918, 13628, 29898, 16414, 29892, 2224, 29918, 517, 29918, 1557, 9386, 29892, 7684, 29922, 8516, 29892, 4450, 29879, 29922, 8516, 1125, 13, 13, 12, 4905, 29922, 1491, 5014, 29889, 3198, 29918, 4905, 29898, 3366, 22032, 613, 2224, 29918, 517, 29918, 1557, 9386, 29892, 12714, 29892, 4450, 29879, 29892, 7684, 14664, 13808, 703, 9420, 29899, 29947, 1159, 13, 12, 4905, 29922, 4905, 29889, 5451, 14182, 29876, 1159, 14352, 29941, 29962, 13, 12, 4352, 261, 29922, 276, 29889, 4478, 703, 9203, 1659, 29901, 3599, 497, 29901, 4269, 5575, 29973, 7244, 313, 5575, 7897, 29995, 12, 29925, 3757, 2459, 29901, 4269, 5575, 29973, 7244, 313, 5575, 7897, 29995, 12, 29943, 29896, 29901, 313, 5575, 7897, 29995, 613, 1962, 29897, 13, 12, 361, 1993, 261, 338, 451, 6213, 29901, 13, 12, 12, 3757, 497, 29922, 7411, 29898, 4352, 261, 29889, 2972, 29898, 29896, 876, 13, 12, 12, 17990, 2459, 29922, 7411, 29898, 4352, 261, 29889, 2972, 29898, 29906, 876, 13, 12, 12, 29888, 29896, 29922, 7411, 29898, 4352, 261, 29889, 2972, 29898, 29941, 876, 13, 12, 2457, 17386, 29892, 16716, 29892, 285, 29896, 13, 13, 1753, 679, 29918, 535, 645, 29898, 2084, 29918, 517, 29918, 1557, 9386, 29892, 7684, 29922, 8516, 29892, 4450, 29879, 29922, 8516, 1125, 13, 12, 12328, 431, 29918, 29878, 29892, 289, 29883, 431, 29918, 29886, 29892, 289, 29883, 431, 29918, 29888, 29922, 657, 29918, 3221, 29888, 29918, 13628, 703, 12328, 431, 613, 2224, 29918, 517, 29918, 1557, 9386, 29892, 7684, 29892, 4450, 29879, 29897, 13, 12, 29885, 1682, 29918, 29878, 29892, 286, 1682, 29918, 29886, 29892, 286, 1682, 29918, 29888, 29922, 657, 29918, 3221, 29888, 29918, 13628, 703, 29885, 1682, 613, 2224, 29918, 517, 29918, 1557, 9386, 29892, 7684, 29892, 4450, 29879, 29897, 13, 12, 346, 2142, 29918, 29878, 29892, 2257, 2142, 29918, 29886, 29892, 2257, 2142, 29918, 29888, 29922, 657, 29918, 3221, 29888, 29918, 13628, 703, 346, 29874, 1725, 613, 2224, 29918, 517, 29918, 1557, 9386, 29892, 7684, 29892, 4450, 29879, 29897, 13, 13, 12, 2158, 703, 12328, 431, 3583, 29873, 15543, 29896, 29888, 29908, 1273, 289, 29883, 431, 29918, 29888, 29897, 13, 12, 2158, 703, 29885, 1682, 3583, 29873, 15543, 29896, 29888, 29908, 1273, 286, 1682, 29918, 29888, 29897, 13, 12, 2158, 703, 346, 2142, 3583, 29873, 15543, 29896, 29888, 29908, 1273, 2257, 2142, 29918, 29888, 29897, 13, 12, 485, 29887, 7607, 12328, 431, 29918, 29888, 718, 286, 1682, 29918, 29888, 718, 2257, 2142, 29918, 29888, 6802, 29941, 29889, 13, 12, 2158, 703, 29909, 19698, 383, 29896, 29901, 18695, 29896, 29888, 29908, 1273, 313, 485, 29887, 876, 13, 13, 12, 29937, 3251, 403, 23089, 29916, 1591, 13, 12, 29937, 1596, 11702, 29889, 29896, 29888, 29987, 15543, 29896, 29888, 29987, 15543, 29896, 29888, 29987, 15543, 29896, 29888, 29908, 1273, 313, 12328, 431, 29918, 29888, 29892, 286, 1682, 29918, 29888, 29892, 2257, 2142, 29918, 29888, 29892, 1029, 29887, 876, 13, 13, 12, 2457, 289, 29883, 431, 29918, 29888, 29892, 1029, 29887, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 13, 12, 29887, 1025, 2283, 29922, 9675, 29889, 19218, 29961, 29896, 29962, 13, 12, 11965, 2283, 29922, 9675, 29889, 19218, 29961, 29906, 29962, 13, 12, 1557, 9386, 29922, 9675, 29889, 19218, 29961, 29941, 29962, 13, 12, 12328, 431, 29918, 29888, 29892, 1029, 29887, 29922, 657, 29918, 535, 645, 29898, 1557, 9386, 29892, 7684, 29922, 29887, 1025, 2283, 29892, 4450, 29879, 29922, 11965, 2283, 29897, 13, 12, 13, 13, 12, 2 ]
basetestcase/base_FormTestCase.py	Spleeding1/django-basetestcase	1	1608927	from .base_UtilityTestCase import UtilityTestCase class FormTestCase(UtilityTestCase): def form_field_test(self, field, error_messages={}, help_text='', initial=None, label='', required=True, widget_attrs={}): form = self.form() try: form.fields[field] except KeyError: raise Exception(f'\n Form does not have field\n\n {field}') completed_attrs = [] for attr, expected in widget_attrs.items(): try: actual = form.fields[field].widget.attrs[attr] except KeyError: raise Exception( f'\n {attr} for form field {field} has not been set.' ) self.assertEquals(expected, actual, msg=f'\n {field}: {attr}') completed_attrs.append(attr) for attr, actual in form.fields[field].widget.attrs.items(): if attr not in completed_attrs: raise Exception(f'\n {field}:{attr} is set and should not be.') if required is True and 'required' not in error_messages: error_messages['required'] = 'This field is required.' elif len(error_messages) is not 0: for error, error_message in error_messages.items(): actual_error_message = form.fields[field].error_messages[error] self.assertEquals( error_message, actual_error_message, msg=f'\n {field}: error_message[{error}]' ) actual_help_text = form.fields[field].help_text self.assertEquals( help_text, actual_help_text, msg=f'\n {field}: help_text' ) actual_initial = form.fields[field].initial self.assertEquals( initial, actual_initial, msg=f'\n {field}: initial value' ) actual_label = form.fields[field].label self.assertEquals( label, actual_label, msg=f'\n{field}: label' ) actual_required = form.fields[field].required self.assertEquals( required, actual_required, msg=f'\n {field}: required' ) def form_required_field_error_test(self, data={}, error_messages={}): required_fields = [] Form = self.form() for field in Form.fields: if Form.fields[field].required is True: required_fields.append(field) for field, value in data.items(): if field in required_fields: _data = {data} _data[field] = '' form = self.form(data={_data}) self.assertFalse( form.is_valid(), msg=f'\n Form should not be valid.\n {field} should be blank.\n data={_data}' ) error = 'This field is required.' if field in error_messages: error = error_messages[field] self.assertEqual( form.errors[field], [error], msg=f'{field}' ) def formset_error_test(self, formset, form_index=None, field=None, message=''): formset.is_valid() if form_index is None and field is None: self.assertEqual(formset.non_form_errors(), [message]) if field is None: self.assertEqual(formset[form_index].non_field_errors(), [message]) else: self.assertEqual(formset[form_index].errors[field], [message]) def formset_test(self, baseformset=None, can_delete=False, extra=1, field_data={}, form=None, formset=None, initial=0, max_num=None, min_num=None, model=None, prefix='form', total=1, validate_max=False, validate_min=False): form_data, test_model_instances = self.formset_filler( field_data=field_data, initial=initial, model=model, prefix=prefix, total=total ) test_formset = formset(form_data, prefix=prefix) self.assertEqual(test_formset.extra, extra) if baseformset is not None: self.assertTrue(issubclass(formset, baseformset)) if form is not None: self.assertTrue(issubclass(test_formset.form, form)) if max_num is not None: self.assertEqual(test_formset.max_num, max_num) if min_num is not None: self.assertEqual(test_formset.min_num, min_num) self.assertEqual(test_formset.can_delete, can_delete) self.assertEqual(test_formset.prefix, prefix) self.assertEqual(test_formset.validate_max, validate_max) self.assertEqual(test_formset.validate_min, validate_min) if test_formset.is_valid(): test_formset.save() if model is not None: self.instances_saved_test(model, test_model_instances, total) else: self.fail( f'formset is not valid.\n{data}\n{test_formset.non_form_errors()}\n{test_formset.errors}' )	[ 1, 515, 869, 3188, 29918, 7270, 537, 3057, 8259, 1053, 22310, 537, 3057, 8259, 13, 13, 13, 1990, 3812, 3057, 8259, 29898, 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Exercicios/desafio10_convertermoeda.py	lbarrosandre/Resolucao-Desafios-Python	0	115790	carteira = float(input('Quantos reais tenho na carteira: ')) x = carteira/3.27 print('Tenho R$ {} na minha carteira e consigo comprar $ {:.2f} dollares'.format(carteira, x))	[ 1, 20206, 3055, 353, 5785, 29898, 2080, 877, 22930, 359, 337, 1759, 3006, 1251, 1055, 20206, 3055, 29901, 525, 876, 13, 29916, 353, 20206, 3055, 29914, 29941, 29889, 29906, 29955, 13, 2158, 877, 29911, 264, 1251, 390, 29938, 6571, 1055, 1375, 2350, 20206, 3055, 321, 1136, 5973, 7199, 279, 395, 12365, 29889, 29906, 29888, 29913, 11232, 5114, 4286, 4830, 29898, 4287, 371, 3055, 29892, 921, 876, 13, 2 ]
wavenetlike/examples/analyzer_example.py	redwrasse/wavenetlike	0	24133	<reponame>redwrasse/wavenetlike from wavenetlike.analyzers import DatasetAnalyzer from wavenetlike.datasetid import TorchAudioDataSetId def analyzer_example(): dataset = TorchAudioDataSetId("SPEECHCOMMANDS") data_analyzer = DatasetAnalyzer(dataset) data_analyzer.analyze_dataset() analysis_res = data_analyzer.get_analysis_result() print(analysis_res) if __name__ == "__main__": analyzer_example()	[ 1, 529, 276, 1112, 420, 29958, 1127, 29893, 3417, 344, 29914, 29893, 3496, 300, 4561, 13, 3166, 281, 3496, 300, 4561, 29889, 7054, 12339, 414, 1053, 13373, 24541, 2744, 14997, 3298, 13, 3166, 281, 3496, 300, 4561, 29889, 24713, 333, 1053, 4794, 305, 17111, 28449, 1204, 13, 13, 13, 1753, 16455, 3298, 29918, 4773, 7295, 13, 1678, 8783, 353, 4794, 305, 17111, 28449, 1204, 703, 29903, 4162, 29923, 3210, 19795, 1529, 2797, 29903, 1159, 13, 1678, 848, 29918, 24209, 3298, 353, 13373, 24541, 2744, 14997, 3298, 29898, 24713, 29897, 13, 1678, 848, 29918, 24209, 3298, 29889, 24209, 911, 29918, 24713, 580, 13, 1678, 7418, 29918, 690, 353, 848, 29918, 24209, 3298, 29889, 657, 29918, 15916, 29918, 2914, 580, 13, 1678, 1596, 29898, 15916, 29918, 690, 29897, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 16455, 3298, 29918, 4773, 580, 13, 2 ]
mmpose/core/optimizer/builder.py	vsatyakumar/mmpose	1	5137	<reponame>vsatyakumar/mmpose from mmcv.runner import build_optimizer def build_optimizers(model, cfgs): """Build multiple optimizers from configs. If `cfgs` contains several dicts for optimizers, then a dict for each constructed optimizers will be returned. If `cfgs` only contains one optimizer config, the constructed optimizer itself will be returned. For example, 1) Multiple optimizer configs: .. code-block:: python optimizer_cfg = dict( model1=dict(type='SGD', lr=lr), model2=dict(type='SGD', lr=lr)) The return dict is ``dict('model1': torch.optim.Optimizer, 'model2': torch.optim.Optimizer)`` 2) Single optimizer config: .. code-block:: python optimizer_cfg = dict(type='SGD', lr=lr) The return is ``torch.optim.Optimizer``. Args: model (:obj:`nn.Module`): The model with parameters to be optimized. cfgs (dict): The config dict of the optimizer. Returns: dict[:obj:`torch.optim.Optimizer`] \| :obj:`torch.optim.Optimizer`: The initialized optimizers. """ optimizers = {} if hasattr(model, 'module'): model = model.module # determine whether 'cfgs' has several dicts for optimizers if all(isinstance(v, dict) for v in cfgs.values()): for key, cfg in cfgs.items(): cfg_ = cfg.copy() module = getattr(model, key) optimizers[key] = build_optimizer(module, cfg_) return optimizers else: return build_optimizer(model, cfgs)	[ 1, 529, 276, 1112, 420, 29958, 4270, 11156, 557, 24540, 29914, 29885, 1526, 852, 13, 3166, 5654, 11023, 29889, 27492, 1053, 2048, 29918, 20640, 3950, 13, 13, 13, 1753, 2048, 29918, 20640, 19427, 29898, 4299, 29892, 274, 29888, 3174, 1125, 13, 1678, 9995, 8893, 2999, 5994, 19427, 515, 2295, 29879, 29889, 13, 13, 1678, 960, 421, 6854, 3174, 29952, 3743, 3196, 9657, 29879, 363, 5994, 19427, 29892, 769, 263, 9657, 363, 1269, 13, 1678, 13319, 5994, 19427, 674, 367, 4133, 29889, 13, 1678, 960, 421, 6854, 3174, 29952, 871, 3743, 697, 5994, 3950, 2295, 29892, 278, 13319, 5994, 3950, 13, 1678, 3528, 674, 367, 4133, 29889, 13, 13, 1678, 1152, 1342, 29892, 13, 13, 268, 29896, 29897, 26905, 5994, 3950, 2295, 29879, 29901, 13, 13, 1678, 6317, 775, 29899, 1271, 1057, 3017, 13, 13, 4706, 5994, 3950, 29918, 16859, 353, 9657, 29898, 13, 9651, 1904, 29896, 29922, 8977, 29898, 1853, 2433, 26016, 29928, 742, 301, 29878, 29922, 29212, 511, 13, 9651, 1904, 29906, 29922, 8977, 29898, 1853, 2433, 26016, 29928, 742, 301, 29878, 29922, 29212, 876, 13, 13, 1678, 450, 736, 9657, 338, 13, 1678, 4954, 8977, 877, 4299, 29896, 2396, 4842, 305, 29889, 20640, 29889, 20624, 326, 3950, 29892, 525, 4299, 29906, 2396, 4842, 305, 29889, 20640, 29889, 20624, 326, 3950, 3569, 29952, 13, 13, 268, 29906, 29897, 16740, 5994, 3950, 2295, 29901, 13, 13, 1678, 6317, 775, 29899, 1271, 1057, 3017, 13, 13, 4706, 5994, 3950, 29918, 16859, 353, 9657, 29898, 1853, 2433, 26016, 29928, 742, 301, 29878, 29922, 29212, 29897, 13, 13, 1678, 450, 736, 338, 4954, 7345, 305, 29889, 20640, 29889, 20624, 326, 3950, 29952, 1412, 13, 13, 1678, 826, 3174, 29901, 13, 4706, 1904, 13940, 5415, 18078, 15755, 29889, 7355, 29952, 1125, 450, 1904, 411, 4128, 304, 367, 27545, 29889, 13, 4706, 274, 29888, 3174, 313, 8977, 1125, 450, 2295, 9657, 310, 278, 5994, 3950, 29889, 13, 13, 1678, 16969, 29901, 13, 4706, 9657, 7503, 5415, 18078, 7345, 305, 29889, 20640, 29889, 20624, 326, 3950, 29952, 29962, 891, 584, 5415, 18078, 7345, 305, 29889, 20640, 29889, 20624, 326, 3950, 6998, 13, 9651, 450, 16601, 5994, 19427, 29889, 13, 1678, 9995, 13, 1678, 5994, 19427, 353, 6571, 13, 1678, 565, 756, 5552, 29898, 4299, 29892, 525, 5453, 29374, 13, 4706, 1904, 353, 1904, 29889, 5453, 13, 1678, 396, 8161, 3692, 525, 6854, 3174, 29915, 756, 3196, 9657, 29879, 363, 5994, 19427, 13, 1678, 565, 599, 29898, 275, 8758, 29898, 29894, 29892, 9657, 29897, 363, 325, 297, 274, 29888, 3174, 29889, 5975, 580, 1125, 13, 4706, 363, 1820, 29892, 274, 16434, 297, 274, 29888, 3174, 29889, 7076, 7295, 13, 9651, 274, 16434, 29918, 353, 274, 16434, 29889, 8552, 580, 13, 9651, 3883, 353, 679, 5552, 29898, 4299, 29892, 1820, 29897, 13, 9651, 5994, 19427, 29961, 1989, 29962, 353, 2048, 29918, 20640, 3950, 29898, 5453, 29892, 274, 16434, 19925, 13, 4706, 736, 5994, 19427, 13, 1678, 1683, 29901, 13, 4706, 736, 2048, 29918, 20640, 3950, 29898, 4299, 29892, 274, 29888, 3174, 29897, 13, 2 ]
__Courses__/Python - Introduction to Python Programming - Udacity/C3. Control Flow/{Q}[Zip] Transpose with Zip.py	JUD210/Study-Note	0	59038	<gh_stars>0 """ Use zip to transpose data from a 4-by-3 matrix to a 3-by-4 matrix. There's actually a cool trick for this! Feel free to look at the solutions if you can't figure it out. """ data = ((0, 1, 2), (3, 4, 5), (6, 7, 8), (9, 10, 11)) # 0 1 2 # 3 4 5 # 6 7 8 # 9 10 11 # # Transpose : [data]^T # # 0 3 6 9 # 1 4 7 10 # 2 5 8 11 data_transpose = tuple(zip(*data)) print(data) print(data_transpose)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 15945, 29908, 13, 11403, 14319, 304, 1301, 4220, 848, 515, 263, 29871, 29946, 29899, 1609, 29899, 29941, 4636, 304, 263, 29871, 29941, 29899, 1609, 29899, 29946, 4636, 29889, 1670, 29915, 29879, 2869, 263, 12528, 8938, 363, 445, 29991, 5169, 295, 3889, 304, 1106, 472, 278, 6851, 565, 366, 508, 29915, 29873, 4377, 372, 714, 29889, 13, 15945, 29908, 13, 13, 1272, 353, 5135, 29900, 29892, 29871, 29896, 29892, 29871, 29906, 511, 313, 29941, 29892, 29871, 29946, 29892, 29871, 29945, 511, 313, 29953, 29892, 29871, 29955, 29892, 29871, 29947, 511, 313, 29929, 29892, 29871, 29896, 29900, 29892, 29871, 29896, 29896, 876, 13, 29937, 29871, 29900, 259, 29896, 259, 29906, 13, 29937, 29871, 29941, 259, 29946, 259, 29945, 13, 29937, 29871, 29953, 259, 29955, 259, 29947, 13, 29937, 29871, 29929, 259, 29896, 29900, 29871, 29896, 29896, 13, 29937, 13, 29937, 4103, 4220, 584, 518, 1272, 29962, 29985, 29911, 13, 29937, 13, 29937, 29871, 29900, 259, 29941, 259, 29953, 259, 29929, 13, 29937, 29871, 29896, 259, 29946, 259, 29955, 259, 29896, 29900, 13, 29937, 29871, 29906, 259, 29945, 259, 29947, 259, 29896, 29896, 13, 13, 1272, 29918, 3286, 4220, 353, 18761, 29898, 7554, 10456, 1272, 876, 13, 2158, 29898, 1272, 29897, 13, 2158, 29898, 1272, 29918, 3286, 4220, 29897, 13, 2 ]
python/dbSchemaReplicationLineage.py	sagopal2019/REST-API-Samples	0	151561	<reponame>sagopal2019/REST-API-Samples ''' Created on Jun 26, 2018 @author: dwrigley ************************************************************************************************ DB schema replication custom lineage generator process:- scenario: the tables in 2 different schemas (perhaps in different databases/resources) are replicated in EDC - we have no way to automatically know that there is lineage between the schema contents (tables & columns) this utility will generate the custom lineage import to create the links given two schemas - leftSchema and rightSchema find the 2 schemas objects in the catalog (GET /2/catalog/data/objects) for each schema execute /2/catalog/data/relationships (2 levels schema->table->column) for each table & column - store the id & name (names converted to lower-case for case-insensitive match) for the stored objects (tables/columns) left side... find the same table/column in the right side if found - write a custom lineage link to csv Note: the custom lineage format used is:- Association,From Connection,To Connection,From Object,To Object where: From Connection and To Connection will be empty Assocition will be either core.DirectionalDataFlow or core.DataSetDataFlow the From and To Object will be the full object id when importing - there is no need for auto connection assignment, since the full id's are provided this happens automatically this is possible using v10.2.0 with a patch, and works native in v10.2.1+ ''' import platform import json import requests from requests.auth import HTTPBasicAuth import csv import edcutils import time #******************************************************************* # change these settings #******************************************************************* leftResource = 'ot_oracle' leftSchema = 'OT' leftSchemaType = 'com.infa.ldm.relational.Schema' rightSchema = 'landing' rightResource = 'landing_hive' rightSchemaType = 'com.infa.ldm.relational.Schema' # rightTablePrefix - for cases where the replicated tables have a prefix added (e.g. employee on left ot_employee on right) rightTablePrefix = '' #Note: for SAP Hana DB - schema type is different: com.infa.ldm.extended.saphanadatabase.Schema catalogServer='http://napslxapp01:9085' uid='Administrator' pwd='<PASSWORD>' ''' # if using base64 encoded credentials - you don't need uid/pwd # you can just use authCredentials- with the correct value # comment lines with uid/pwd and uncomment the same line using only header (references to authCredentials) ''' #authCredentials="<KEY>" # format the csv file name for custom lineage csvPrefix="schemaLineage" csvFolder="out" #******************************************************************* # end of settings that should be changed #********************************************************************* # set the csv fileName csvFileName = csvFolder + "/" + csvPrefix + "_" + leftSchema.lower() + "_" + rightSchema.lower() + ".csv" # function - process a single schema - get columns into memory (dict) def getSchemaContents(schemaName, schemaType, resourceName): """ given a schema name, schema class type (e.g. hanadb is different) and resource name find the schema object then execute a relationships call to get the schema tables & columns (parent/child links) note: some models separate primary key columns from regular columns note: some models have different relationships (e.g. sap hana db) returns a dictionary of all tables & columns for the schema & the id of the schema object key=table val=tableid key=table.column val=columnid """ print('\tgetSchemaContents for:' + schemaName+ " resource=" + resourceName) # schemaDict returned key=TABLE.COLUMN value=column id schemaDict = {} tableNames = {} url = catalogServer + '/access/2/catalog/data/objects' query = "core.resourceName:" + resourceName + " and core.classType:" + schemaType + " and core.name_lc_exact:" + schemaName parameters = {'q': query, 'offset': 0, 'pageSize': 1} # header using uid/pwd (no Authorization) header = {"Accept": "application/json"} # header using Authorization - no need to use uid/pwd in the get call #header = {"Accept": "application/json", "Authorization": authCredentials} print("\tquery=" + query) #print("\theader=" + str(header)) schemaId = None tableCount = 0 columnCount = 0 # make the call to find the schema object response = requests.get(url,params=parameters,headers=header, auth=HTTPBasicAuth(uid,pwd)) #response = requests.get(url,params=parameters,headers=header) rc = response.status_code if rc!=200: print ("error reading object: rc=" + str(rc) + " response:" + str(response.json) ) if rc==401: print("\t401:Possible Missing/bad credentials - or server not found/responding") print(str(response)) return # get the total # of objects returned (first part of the json resultset) totalObjects=response.json()['metadata']['totalCount'] print("\tobjects returned: " + str(totalObjects) ) for item in response.json()['items']: schemaId=item["id"] schemaName = edcutils.getFactValue(item, 'core.name') # get the tables & columns print ("\tfound schema: " + schemaName + " id=" + schemaId) lineageURL=catalogServer + '/access/2/catalog/data/relationships' lineageParms={"seed": schemaId , "association": "core.ParentChild" , "depth": "2" , "direction": "OUT" , "includeAttribute": {'core.name', 'core.classType'} , "includeTerms": "false" , "removeDuplicateAggregateLinks": "false" } print("\tGET child rels for schema: " + lineageURL + " parms=" + str(lineageParms) ) # get using uid/pwd lineageResp = requests.get(lineageURL,params=lineageParms,headers=header, auth=HTTPBasicAuth(uid,pwd)) # credentials are in the header #lineageResp = requests.get(lineageURL,params=lineageParms,headers=header) lineageStatus = lineageResp.status_code print("\tlineage resp=" + str(lineageStatus)) if lineageStatus != 200: print ("error getting schema contents (tables) rc=" + str(rc) + " response:" + str(response.json) ) if rc==401: print("\t401:Possible Missing/bad credentials - or server not found/responding") print(str(response)) return if lineageResp.text.startswith('{items:'): # bug (10.2.0 & 10.2.1) - the items collection should be "items" lineageJson = lineageResp.text.replace('items', '"items"', 1) else: lineageJson = lineageResp.text #relsJson = json.loads(lineageJson.replace('items', '"items"')) relsJson = json.loads(lineageJson) #print(len(relsJson)) for lineageItem in relsJson["items"]: #print('\t\t' + str(lineageItem)) inId = lineageItem.get("inId") outId = lineageItem.get("outId") #print('new inId===' + inId + " outId=" + outId) #print(edcutils.getFactValue(lineageItem["inEmbedded"], "core.name")) assocId = lineageItem.get("associationId") #print("\t\t" + inId + " assoc=" + assocId) #if assocId=='com.infa.ldm.relational.SchemaTable': if assocId.endswith('.SchemaTable'): # note - custom lineage does not need table and column - count the tables & store table names tableCount += 1 #tableName = inId.split('/')[-1] tableName = edcutils.getFactValue(lineageItem["inEmbedded"], "core.name").lower() # store the table name (for lookup when processing the columns) key-id, val=name tableNames[inId] = tableName schemaDict[tableName]=inId #if assocId=='com.infa.ldm.relational.TableColumn': if assocId.endswith('.TableColumn') or assocId.endswith(".TablePrimaryKeyColumn"): #columnName = inId.split('/')[-1] columnCount += 1 columnName = edcutils.getFactValue(lineageItem["inEmbedded"], "core.name").lower() tableName = tableNames[outId].lower() #print("column=" + tableName + "." + columnName) schemaDict[tableName+"."+columnName] = inId print("\tgetSchema: returning " + str(columnCount) + " columns, in " + str(tableCount) + " tables") return schemaDict, schemaId def main(): """ initialise the csv file(s) to write call getSchemaContents for both left and right schema objects match the tables/columns from the left schema to the right when matched write a lineage link - table and column level Note: this script generates the newer lineage format using complete object id's and relationship types connection assignment will not be necessary works with v10.2.1+ """ start_time = time.time() print ("dbSchemaReplicationLineage:start") print ("Catalog=" + catalogServer) print ("left: resource=" + leftResource) print ("left: schema=" + leftSchema) print ("right: resource=" + rightResource) print ("right: schema=" + rightSchema) # initialize csv output file columnHeader=["Association","From Connection","To Connection","From Object","To Object"] # python 3 & 2.7 use different methods print("initializing file: " + csvFileName) if str(platform.python_version()).startswith("2.7"): fCSVFile = open(csvFileName,"w") else: fCSVFile = open(csvFileName,"w", newline='', encoding='utf-8') colWriter=csv.writer(fCSVFile) colWriter.writerow(columnHeader) # get the objects from the left schema into memory print("get left schema: name=" + leftSchema + " resource=" + leftResource + " type=" + leftSchemaType) leftObjects, leftSchemaId = getSchemaContents(leftSchema, leftSchemaType, leftResource) # get the objects from the right schema into memory print("get left schema: name=" + rightSchema + " resource=" + rightResource + " type=" + rightSchemaType) rightObjects, rightSchemaId = getSchemaContents(rightSchema, rightSchemaType, rightResource) matches=0 missing=0 if len(leftObjects) > 0 and len(rightObjects) > 0: # create the lineage file colWriter.writerow(["core.DataSourceDataFlow","","",leftSchemaId,rightSchemaId]) # iterate over all left objects - looking for matching right ones print("\nprocessing: " + str(len(leftObjects)) + " objects (left side)") for leftName, leftVal in leftObjects.items(): # if the target is using a prefix - add it to leftName if len(rightTablePrefix)>0: leftName = rightTablePrefix.lower() + leftName #print("key=" + leftName + " " + leftVal + " " + str(leftName.count('.'))) if (leftName in rightObjects.keys()): # match rightVal = rightObjects.get(leftName) matches += 1 #print("\t" + rightVal) # check if it is formatted as table.column or just table if leftName.count('.') == 1: # column lineage - using DirectionalDataFlow colWriter.writerow(["core.DirectionalDataFlow","","",leftVal,rightVal]) else: # table level - using core.DataSetDataFlow colWriter.writerow(["core.DataSetDataFlow","","",leftVal,rightVal]) # write a line to the custom lineage csv file (connection assignment) #colWriter.writerow([leftResource,rightResource,leftRef,rightRef]) else: missing += 1 print("\t no match on right side for key=" + leftName) else: print("error getting schema info... - no linking/lineage created") print ("dbSchemaLineageGen:finished. " + str(matches) + " links created, " + str(missing) + " missing (found in left, no match on right)") print("run time = %s seconds ---" % (time.time() - start_time)) fCSVFile.close() # call main - if not already called or used by another script if __name__== "__main__": main()	[ 1, 529, 276, 1112, 420, 29958, 29879, 351, 459, 284, 29906, 29900, 29896, 29929, 29914, 1525, 1254, 29899, 8787, 29899, 29903, 9422, 13, 12008, 13, 20399, 373, 8378, 29871, 29906, 29953, 29892, 29871, 29906, 29900, 29896, 29947, 13, 13, 29992, 8921, 29901, 11988, 8966, 2330, 13, 7775, 7775, 7775, 7775, 7775, 7775, 1068, 13, 4051, 10938, 1634, 1414, 2888, 1196, 482, 15299, 13, 13, 5014, 13018, 13, 1678, 10483, 29901, 29871, 278, 6131, 297, 29871, 29906, 1422, 1364, 8609, 313, 546, 4252, 297, 1422, 21218, 29914, 13237, 29897, 526, 1634, 9169, 13, 1678, 297, 9408, 29907, 448, 591, 505, 694, 982, 304, 6336, 1073, 393, 727, 338, 1196, 482, 1546, 278, 10938, 8118, 313, 24051, 669, 4341, 29897, 13, 1678, 445, 19725, 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pretraining/model_ensemble.py	VITA-Group/Adv-SS-Pretraining	32	8415	<filename>pretraining/model_ensemble.py ''' model ensemble for cifar10 // input size(32,32) ''' import torch import torchvision import copy import torch.nn as nn from resnetv2 import ResNet50 as resnet50v2 def split_resnet50(model): return nn.Sequential( model.conv1, model.layer1, model.layer2, model.layer3 ) class PretrainEnsembleModel(nn.Module): def __init__(self): super(PretrainEnsembleModel, self).__init__() self.blocks = split_resnet50(resnet50v2()) self.layer4_rotation = resnet50v2().layer4 self.layer4_jigsaw = resnet50v2().layer4 self.fc_rotation = nn.Linear(2048, 4) self.fc_jigsaw = nn.Linear(2048, 31) self.avgpool1 = nn.AdaptiveAvgPool2d((1,1)) self.avgpool2 = nn.AdaptiveAvgPool2d((1,1)) self.avgpool3 = nn.AdaptiveAvgPool2d((1,1)) def _Normal(self,x): mean=torch.Tensor([0.485, 0.456, 0.406]) mean=mean[None,:,None,None].cuda() std = torch.Tensor([0.229, 0.224, 0.225]) std = std[None,:,None,None].cuda() return x.sub(mean).div(std) def forward(self, x): feature_map = self.blocks(self._Normal(x)) return feature_map	[ 1, 529, 9507, 29958, 1457, 26495, 29914, 4299, 29918, 24031, 29889, 2272, 13, 12008, 30004, 13, 4299, 21285, 363, 274, 361, 279, 29896, 29900, 29871, 849, 1881, 2159, 29898, 29941, 29906, 29892, 29941, 29906, 8443, 13, 12008, 30004, 13, 30004, 13, 30004, 13, 5215, 4842, 305, 30004, 13, 5215, 4842, 305, 4924, 30004, 13, 30004, 13, 5215, 3509, 30004, 13, 5215, 4842, 305, 29889, 15755, 408, 302, 29876, 30004, 13, 3166, 620, 1212, 29894, 29906, 1053, 2538, 6779, 29945, 29900, 408, 620, 1212, 29945, 29900, 29894, 29906, 30004, 13, 30004, 13, 30004, 13, 1753, 6219, 29918, 690, 1212, 29945, 29900, 29898, 4299, 1125, 30004, 13, 1678, 736, 302, 29876, 29889, 16941, 2556, 29898, 30004, 13, 4706, 1904, 29889, 20580, 29896, 11167, 13, 4706, 1904, 29889, 13148, 29896, 11167, 13, 4706, 1904, 29889, 13148, 29906, 11167, 13, 4706, 1904, 29889, 13148, 29941, 30004, 13, 1678, 1723, 30004, 13, 30004, 13, 30004, 13, 1990, 349, 2267, 6038, 29923, 1983, 6967, 3195, 29898, 15755, 29889, 7355, 1125, 30004, 13, 30004, 13, 1678, 822, 4770, 2344, 12035, 1311, 1125, 30004, 13, 30004, 13, 4706, 2428, 29898, 29925, 2267, 6038, 29923, 1983, 6967, 3195, 29892, 1583, 467, 1649, 2344, 1649, 26471, 13, 30004, 13, 4706, 1583, 29889, 1271, 29879, 353, 6219, 29918, 690, 1212, 29945, 29900, 29898, 690, 1212, 29945, 29900, 29894, 29906, 3101, 30004, 13, 4706, 1583, 29889, 13148, 29946, 29918, 5450, 362, 353, 620, 1212, 29945, 29900, 29894, 29906, 2141, 13148, 29946, 6756, 13, 4706, 1583, 29889, 13148, 29946, 29918, 29926, 23379, 1450, 353, 620, 1212, 29945, 29900, 29894, 29906, 2141, 13148, 29946, 30004, 13, 30004, 13, 4706, 1583, 29889, 13801, 29918, 5450, 362, 353, 302, 29876, 29889, 12697, 29898, 29906, 29900, 29946, 29947, 29892, 29871, 29946, 8443, 13, 4706, 1583, 29889, 13801, 29918, 29926, 23379, 1450, 353, 302, 29876, 29889, 12697, 29898, 29906, 29900, 29946, 29947, 29892, 29871, 29941, 29896, 8443, 13, 30004, 13, 4706, 1583, 29889, 485, 29887, 10109, 29896, 353, 302, 29876, 29889, 29909, 1388, 415, 573, 12810, 29887, 11426, 29906, 29881, 3552, 29896, 29892, 29896, 876, 30004, 13, 4706, 1583, 29889, 485, 29887, 10109, 29906, 353, 302, 29876, 29889, 29909, 1388, 415, 573, 12810, 29887, 11426, 29906, 29881, 3552, 29896, 29892, 29896, 876, 30004, 13, 4706, 1583, 29889, 485, 29887, 10109, 29941, 353, 302, 29876, 29889, 29909, 1388, 415, 573, 12810, 29887, 11426, 29906, 29881, 3552, 29896, 29892, 29896, 876, 30004, 13, 30004, 13, 30004, 13, 1678, 822, 903, 19077, 29898, 1311, 29892, 29916, 1125, 30004, 13, 4706, 2099, 29922, 7345, 305, 29889, 29911, 6073, 4197, 29900, 29889, 29946, 29947, 29945, 29892, 29871, 29900, 29889, 29946, 29945, 29953, 29892, 29871, 29900, 29889, 29946, 29900, 29953, 2314, 30004, 13, 4706, 2099, 29922, 12676, 29961, 8516, 29892, 29901, 29892, 8516, 29892, 8516, 1822, 29883, 6191, 26471, 13, 4706, 3659, 353, 4842, 305, 29889, 29911, 6073, 4197, 29900, 29889, 29906, 29906, 29929, 29892, 29871, 29900, 29889, 29906, 29906, 29946, 29892, 29871, 29900, 29889, 29906, 29906, 29945, 2314, 30004, 13, 4706, 3659, 353, 3659, 29961, 8516, 29892, 29901, 29892, 8516, 29892, 8516, 1822, 29883, 6191, 26471, 13, 4706, 736, 921, 29889, 1491, 29898, 12676, 467, 4563, 29898, 4172, 8443, 13, 30004, 13, 1678, 822, 6375, 29898, 1311, 29892, 921, 1125, 30004, 13, 30004, 13, 4706, 4682, 29918, 1958, 353, 1583, 29889, 1271, 29879, 29898, 1311, 3032, 19077, 29898, 29916, 876, 30004, 13, 30004, 13, 4706, 736, 4682, 29918, 1958, 30004, 13, 2 ]
config.example.py	idies/SpecDash	1	111936	PORT = 8050 DEBUG = True DASH_TABLE_PAGE_SIZE = 5 DEFAULT_WAVELENGTH_UNIT = "angstrom" DEFAULT_FLUX_UNIT = "F_lambda" LOGS = { "do_log":True, "base_logs_directory":"/base/logs/directory/" } MAX_NUM_TRACES = 30 CATALOGS = { "sdss": { "base_data_path":"/base/data/path/", "api_url":"http://skyserver.sdss.org/public/SkyServerWS/SearchTools/SqlSearch", "example_specid":"2947691243863304192" } }	[ 1, 349, 8476, 353, 29871, 29947, 29900, 29945, 29900, 13, 18525, 353, 5852, 13, 29928, 24943, 29918, 21009, 29918, 7228, 1692, 29918, 14226, 353, 29871, 29945, 13, 23397, 29918, 29956, 7520, 29923, 19433, 29918, 3904, 1806, 353, 376, 574, 303, 456, 29908, 13, 23397, 29918, 10536, 29965, 29990, 29918, 3904, 1806, 353, 376, 29943, 29918, 2892, 29908, 13, 3927, 10749, 353, 426, 376, 1867, 29918, 1188, 1115, 5574, 29892, 376, 3188, 29918, 20756, 29918, 12322, 4710, 29914, 3188, 29914, 20756, 29914, 12322, 12975, 500, 13, 12648, 29918, 13967, 29918, 5659, 2477, 2890, 353, 29871, 29941, 29900, 13, 23972, 1964, 29949, 10749, 353, 29871, 426, 13, 18884, 376, 4928, 893, 1115, 426, 13, 462, 9651, 376, 3188, 29918, 1272, 29918, 2084, 4710, 29914, 3188, 29914, 1272, 29914, 2084, 29914, 613, 13, 462, 9651, 376, 2754, 29918, 2271, 4710, 1124, 597, 7912, 2974, 29889, 4928, 893, 29889, 990, 29914, 3597, 29914, 29903, 3459, 6004, 7811, 29914, 7974, 24183, 29914, 10520, 7974, 613, 13, 462, 9651, 376, 4773, 29918, 6550, 333, 4710, 29906, 29929, 29946, 29955, 29953, 29929, 29896, 29906, 29946, 29941, 29947, 29953, 29941, 29941, 29900, 29946, 29896, 29929, 29906, 29908, 13, 462, 4706, 500, 13, 9651, 500, 2 ]
urbansim_templates/data/column_from_expression.py	AZMAG/urbansim_templates	19	106714	import orca import pandas as pd from urbansim_templates import modelmanager, shared, utils, __version__ from urbansim_templates.shared import CoreTemplateSettings, OutputColumnSettings class ExpressionSettings(): """ Stores custom parameters used by the :mod:`~urbansim_templates.data.ColumnFromExpression` template. Parameters can be passed to the constructor or set as attributes. Parameters ---------- table : str, optional Name of Orca table the expression will be evaluated on. Required before running then template. expression : str, optional String describing operations on existing columns of the table, for example "a/log(b+c)". Required before running. Supports arithmetic and math functions including sqrt, abs, log, log1p, exp, and expm1 -- see Pandas ``df.eval()`` documentation for further details. """ def __init__(self, table = None, expression = None): self.table = table self.expression = expression @classmethod def from_dict(cls, d): return cls(table=d['table'], expression=d['expression']) def to_dict(self): return {'table': self.table, 'expression': self.expression} @modelmanager.template class ColumnFromExpression(): """ Template to register a column of derived data with Orca, based on an expression. Parameters may be passed to the constructor, but they are easier to set as attributes. The expression can refer to any columns in the same table, and will be evaluated using ``df.eval()``. Values will be calculated lazily, only when the column is needed for a specific operation. Parameters ---------- meta : :mod:`~urbansim_templates.shared.CoreTemplateSettings`, optional Standard parameters. This template sets the default value of ``meta.autorun`` to True. data : :mod:`~urbansim_templates.data.ExpressionSettings`, optional Special parameters for this template. output : :mod:`~urbansim_templates.shared.OutputColumnSettings`, optional Parameters for the column that will be generated. This template uses ``data.table`` as the default value for ``output.table``. """ def __init__(self, meta=None, data=None, output=None): self.meta = CoreTemplateSettings(autorun=True) if meta is None else meta self.meta.template = self.__class__.__name__ self.meta.template_version = __version__ self.data = ExpressionSettings() if data is None else data self.output = OutputColumnSettings() if output is None else output @classmethod def from_dict(cls, d): """ Create a class instance from a saved dictionary. """ if 'meta' not in d: return cls.from_dict_0_2_dev5(d) return cls( meta = CoreTemplateSettings.from_dict(d['meta']), data = ExpressionSettings.from_dict(d['data']), output = OutputColumnSettings.from_dict(d['output'])) @classmethod def from_dict_0_2_dev5(cls, d): """ Converter to read saved data from 0.2.dev5 or earlier. Automatically invoked by ``from_dict()`` as needed. """ return cls( meta = CoreTemplateSettings( name = d['name'], tags = d['tags'], autorun = d['autorun']), data = ExpressionSettings( table = d['table'], expression = d['expression']), output = OutputColumnSettings( column_name = d['column_name'], data_type = d['data_type'], missing_values = d['missing_values'], cache = d['cache'], cache_scope = d['cache_scope'])) def to_dict(self): """ Create a dictionary representation of the object. """ return { 'meta': self.meta.to_dict(), 'data': self.data.to_dict(), 'output': self.output.to_dict()} def run(self): """ Run the template, registering a column of derived data with Orca. Requires values to be set for ``data.table``, ``data.expression``, and ``output.column_name``. 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mutatest/api.py	EvanKepner/m	49	64696	<reponame>EvanKepner/m """ API --- These are high level objects for interacting with ``mutatest``. The primary objects include: 1. The ``Genome`` 2. The ``GenomeGroup`` 3. The ``Mutant`` ``Genomes`` are representations of a Python source code file. This includes a representation of the Abstract Syntax Tree (AST) and the locations within the AST that could be mutated. The locations are accessed by the ``targets`` and ``covered_targets`` properties of the ``Genome``, the latter being available if a coverage file is set for the ``Genome``. Locations are represented as ``LocIndex`` objects from ``mutatest.transformers`` which may be referenced as specific points of mutation. ``Mutants`` are created from ``Genome.mutate()`` for a specific ``LocIndex`` in the ``Genome`` targets. A ``mutant`` is an immutable named-tuple with all of the attributes necessary to mutate the appropriate ``__pycache__`` file with the ``write_cache()`` method. Collections of ``Genomes`` can be managed through a ``GenomeGroup``. The ``GenomeGroup`` provides methods for setting global filters, coverage files, and producing targets of ``LocIndex`` objects across the collection of ``Genomes``. This is a useful representation when dealing with a folder of multiple source files. """ import ast import importlib import itertools import logging from collections.abc import MutableMapping from copy import deepcopy from pathlib import Path from typing import ( Any, Dict, ItemsView, Iterable, Iterator, KeysView, Mapping, NamedTuple, Optional, Set, Union, ValuesView, ) from mutatest import cache from mutatest.filters import CategoryCodeFilter, CoverageFilter from mutatest.transformers import CATEGORIES, LocIndex, MutateAST LOGGER = logging.getLogger(__name__) class MutationException(Exception): """Mutation Exception type specifically for mismatches in mutation operations.""" pass class Mutant(NamedTuple): """Mutant definition. Mutants are created through the Genome at specific targets using the mutate method. Mutants are immutable and can be written to disk in the ``__pycache__``. You can create ``Mutants`` using ``Genome.mutate``, and then ``write_cache`` to apply to the ``__pycache__``. """ mutant_code: Any src_file: Path cfile: Path loader: Any source_stats: Mapping[str, Any] mode: int src_idx: LocIndex mutation: Any def write_cache(self) -> None: """Create the cache file for the mutant on disk in ``__pycache__``. Existing target cache files are removed to ensure clean overwrites. Reference: https://github.com/python/cpython/blob/master/Lib/py_compile.py#L157 Returns: None, creates the cache file on disk. """ cache.check_cache_invalidation_mode() bytecode = importlib._bootstrap_external._code_to_timestamp_pyc( # type: ignore self.mutant_code, self.source_stats["mtime"], self.source_stats["size"] ) cache.remove_existing_cache_files(self.src_file) cache.create_cache_dirs(self.cfile) LOGGER.debug("Writing mutant cache file: %s", self.cfile) importlib._bootstrap_external._write_atomic(self.cfile, bytecode, self.mode) # type: ignore class Genome: """The Genome class describes the source file to be mutated. The class describes a single .py file and has properties for the abstract syntax tree (AST) and the viable mutation targets. You can initialize without any arguments. If the ``source_file`` is changed the ast and targets properties will be recalculated for that file. Locations in the Genome may be mutated and written to the ``__pycache__`` using the mutate method. """ def __init__( self, source_file: Optional[Union[str, Path]] = None, coverage_file: Optional[Union[str, Path]] = Path(".coverage"), filter_codes: Optional[Iterable[str]] = None, ) -> None: """Initialize the Genome. There are internal properties prefixed with an underscore used for the lazy evaluation of the AST and mutation targets. Args: source_file: an optional source file path coverage_file: coverage file for filtering covered lines, default value is set to ".coverage". filter_codes: 2-letter category codes to filter returned targets """ # Properties with an underscore prefix are used for local caching and are not designed # to be modified directly. # Related to source files, AST, targets self._source_file = None self._ast: Optional[ast.Module] = None self._targets: Optional[Set[LocIndex]] = None # Related to coverage filtering self._coverage_file = None self._covered_targets: Optional[Set[LocIndex]] = None # Related to category code filtering, not cached but uses a setter for valid value checks self._filter_codes: Set[str] = set() # Initialize set values using properties # These may be set later and clear the cached values in the setters self.source_file = Path(source_file) if source_file else None self.coverage_file = Path(coverage_file) if coverage_file else None self.filter_codes: Set[str] = set(filter_codes) if filter_codes else set() ################################################################################################ # CATEGORY FILTER CODES PROPERTIES ################################################################################################ @property def filter_codes(self) -> Set[str]: """Filter codes applied to targets and covered targets.""" return self._filter_codes @filter_codes.setter def filter_codes(self, value: Iterable[str]) -> None: """Setter for filter codes. These are always applied when set on the Genome. Set this to an empty set to remove all category code filters from returned targets. Args: value: a set of 2-letter codes, use a set of a single code if needed. Returns: None Raises: ValueError: if the 2-letter codes in value are not supported by the transformer. """ value, valid_codes = set(value), set(CATEGORIES.values()) if not value.issubset(valid_codes): raise ValueError( f"Invalid category codes: {value - valid_codes}.\nValid codes: {CATEGORIES}" ) self._filter_codes = value ################################################################################################ # SOURCE FILE PROPERTIES ################################################################################################ @property def source_file(self) -> Optional[Path]: """The source .py file represented by this Genome. Returns: The ``source_file`` path. """ return self._source_file @source_file.setter def source_file(self, value: Optional[Union[str, Path]]) -> None: """Setter for the source_file that clears the AST and targets for recalculation.""" self._source_file = Path(value) if value else None self._ast = None self._targets = None @property def ast(self) -> ast.Module: # type: ignore """Abstract Syntax Tree (AST) representation of the source_file. This is cached locally and updated if the source_file is changed. Returns: Parsed AST for the source file. Raises: TypeError: if ``source_file`` is not set. """ if self._ast is None: if not self.source_file: raise TypeError("Source_file property is set to NoneType.") with open(self.source_file, "rb") as src_stream: self._ast = ast.parse(src_stream.read()) return self._ast @property def targets(self) -> Set[LocIndex]: """Viable mutation targets within the AST of the ``source_file``. This is cached locally and updated if the source_file is changed. Filtering is not cached and applies any time the ``filter_codes`` are changed. Returns: The set of the location index objects from the transformer that could be potential mutation targets. """ if self._targets is None: ro_mast = MutateAST( target_idx=None, mutation=None, readonly=True, src_file=self.source_file ) ro_mast.visit(self.ast) self._targets = ro_mast.locs return CategoryCodeFilter(codes=self.filter_codes).filter(self._targets) ################################################################################################ # COVERAGE FILTER PROPERTIES ################################################################################################ @property def coverage_file(self) -> Optional[Path]: """The .coverage file to use for filtering targets.""" return self._coverage_file @coverage_file.setter def coverage_file(self, value: Optional[Union[str, Path]]) -> None: """Setter for ``coverage_file``, clears the cached ``covered_targets``.""" self._coverage_file = Path(value) if value else None self._covered_targets = None @property def covered_targets(self) -> Set[LocIndex]: """Targets that are marked as covered based on the ``coverage_file``. This is cached locally and updated if the coverage_file is changed. Filtering is not cached and applies any time the filter_codes are changed. Returns: The targets that are covered. Raises: TypeError: if the ``source_file`` or ``coverage_file`` is not set for the Genome. """ if not self.source_file: raise TypeError("Source_file property is set to NoneType.") if not self.coverage_file: raise TypeError("Coverage_file property is set to NoneType.") if self._covered_targets is None: self._covered_targets = CoverageFilter(coverage_file=self.coverage_file).filter( self.targets, self.source_file ) return CategoryCodeFilter(codes=self.filter_codes).filter(self._covered_targets) ################################################################################################ # MUTATION METHODS ################################################################################################ def mutate(self, target_idx: LocIndex, mutation_op: Any, write_cache: bool = False) -> Mutant: """Create a mutant from a single LocIndex that is in the Genome. Mutation_op must be a valid mutation for the target_idx operation code type. Optionally, use write_cache to write the mutant to ``__pycache__`` based on the detected location at the time of creation. The Genome AST is unmodified by mutate. Args: target_idx: the target location index (member of .targets) mutation_op: the mutation operation to use write_cache: optional flag to write to ``__pycache__`` Returns: The mutant definition Raises: MutationException: if ``mutation_op`` is not a valid mutation for the location index. TypeError: if the source_file property is not set on the Genome. ValueError: if the target_idx is not a member of Genome targets. """ op_code = CATEGORIES[target_idx.ast_class] valid_mutations = CategoryCodeFilter(codes=(op_code,)).valid_mutations if mutation_op not in valid_mutations: raise MutationException( f"{mutation_op} is not a member of mutation category {op_code}.\n" f"Valid mutations for {op_code}: {valid_mutations}." ) if not self.source_file: raise TypeError("Source_file is set to NoneType") if target_idx not in self.targets: raise ValueError(f"{target_idx} is not in the Genome targets.") mutant_ast = MutateAST( target_idx=target_idx, mutation=mutation_op, src_file=self.source_file, readonly=False ).visit( deepcopy(self.ast) # deepcopy to avoid in-place modification of AST ) # generate cache file pyc machinery for writing the __pycache__ file loader = importlib.machinery.SourceFileLoader( # type: ignore "<py_compile>", self.source_file ) # create the cache files with the mutated AST mutant = Mutant( mutant_code=compile(mutant_ast, str(self.source_file), "exec"), src_file=Path(self.source_file), cfile=Path(cache.get_cache_file_loc(self.source_file)), loader=loader, source_stats=loader.path_stats(self.source_file), mode=importlib._bootstrap_external._calc_mode(self.source_file), # type: ignore src_idx=target_idx, mutation=mutation_op, ) if write_cache: mutant.write_cache() return mutant class GenomeGroupTarget(NamedTuple): """Container for targets returned from GenomeGroup to associated source path to LocIdx.""" source_path: Path loc_idx: LocIndex class GenomeGroup(MutableMapping): # type: ignore """The GenomeGroup: a MutableMapping of Genomes for operations on the group. """ def __init__(self, source_location: Optional[Union[str, Path]] = None) -> None: """Initialize the GenomeGroup. GenomeGroup is a MutableMapping collection of Genomes with defined ``source_file`` locations. You can use it to apply standard filters or coverage files across the group and get all mutation targets for the group. Folders and files can be added through methods. Args: source_location: an optional folder for initialization using the default settings of no file exclusions except 'test' files. For more flexibility, initialize the class and then use the ``.add_folder()`` method directly. """ # internal mapping for Genomes, not designed for direct modification, use class properties self._store: Dict[Path, Genome] = dict() if source_location is not None: source_location = Path(source_location) if source_location.is_dir(): self.add_folder(source_location) elif source_location.is_file(): self.add_file(source_location) else: raise TypeError(f"{source_location} is not a folder or file.") def __setitem__(self, key: Path, value: Genome) -> None: """Setter for GenomeGroup, enforces Path keys and Genome values. Args: key: key for the mapping, must be a path value: the genome Returns: None """ if not isinstance(key, Path): raise TypeError("Only Path keys are supported.") if not isinstance(value, Genome): raise TypeError("Only Genome values are supported.") self._store[key] = value def __getitem__(self, key: Path) -> Genome: """Getter for keys from the mapping store.""" return self._store[key] def __delitem__(self, key: Path) -> None: """Delete a key from the mapping store.""" del self._store[key] def __iter__(self) -> Iterator[Path]: """Iterate over the mapping store keys.""" return iter(self._store) def __len__(self) -> int: """Count of keys in the mapping store.""" return len(self._store) def __repr__(self) -> str: """Base mapping store repr.""" return self._store.__repr__() def items(self) -> ItemsView[Path, Genome]: """ItemsView for the mapping store.""" return self._store.items() def keys(self) -> KeysView[Path]: """KeysView of the mapping store.""" return self._store.keys() def values(self) -> ValuesView[Genome]: """ValuesView of the mapping store.""" return self._store.values() def add_genome(self, genome: Genome) -> None: """Add a Genome to the GenomeGroup. Genomes must have a defined ``source_file``. Args: genome: the ``Genome`` to add Returns: None Raises: TypeError: if the ``Genome.source_file`` is not set. """ if genome.source_file is None: raise TypeError("Genome source_file is set to NoneType.") self.__setitem__(genome.source_file, genome) def add_file( self, source_file: Union[str, Path], coverage_file: Optional[Union[str, Path]] = Path(".coverage"), ) -> None: """Add a ``.py`` source file to the group as a new Genome. The Genome is created automatically. Args: source_file: the source file to add with Genome creation coverage_file: an optional coverage file to set on the Genome, defaults to ".coverage". Returns: None """ self.add_genome(Genome(source_file=source_file, coverage_file=coverage_file)) def add_folder( self, source_folder: Union[str, Path], exclude_files: Optional[Iterable[Union[str, Path]]] = None, ignore_test_files: bool = True, ) -> None: """Add a folder (recursively) to the GenomeGroup for all ``.py`` files. Args: source_folder: the folder to recursively search exclude_files: optional iterable of specific files in the source_folder to skip ignore_test_files: optional flag, default to true, to ignore files prefixed with ``test_`` or suffixed with ``_test`` in the stem of the file name. Returns: None, adds all files as Genomes to the group. Raises: TypeError: if ``source_folder`` is not a folder. """ source_folder = Path(source_folder) exclude_files = [Path(e).resolve() for e in exclude_files] if exclude_files else set() if not source_folder.is_dir(): raise TypeError(f"{source_folder} is not a directory.") for fn in source_folder.rglob(".py"): if (fn.stem.startswith("test_") or fn.stem.endswith("_test")) and ignore_test_files: continue else: if fn.resolve() not in exclude_files: self.add_file(fn) def set_filter(self, filter_codes: Iterable[str]) -> None: """Set the filter codes for all Genomes in the group. Args: filter_codes: iterable of 2-letter codes to set on all Genomes in the group. Returns: None """ for k, v in self.items(): v.filter_codes = set(filter_codes) def set_coverage(self, coverage_file: Union[str, Path]) -> None: """Set a common coverage file for all Genomes in the group. Args: coverage_file: the coverage file to set. Returns: None """ for k, v in self.items(): v.coverage_file = Path(coverage_file) @property def targets(self) -> Set[GenomeGroupTarget]: """All mutation targets in the group, returned as tuples of ``source_file`` and location indices in a single set. Returns: Set of tuples of ``source_file`` and location index for all targets in the group. These are ``GenomeGroupTargets`` to make attribute access easier. """ targets = set() for k, v in self.items(): targets.update(set(itertools.product([k], v.targets))) return {GenomeGroupTarget(t) for t in targets} @property def covered_targets(self) -> Set[GenomeGroupTarget]: """All mutation targets in the group that are covered, returned as tuples of ``source_file`` and location indices in a single set. Returns: Set of tuples of ``source_file`` and location index for all covered targets in the group. These are ``GenomeGroupTargets`` to make attribute access easier. 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8667, 5387, 13, 4706, 9995, 3596, 5478, 362, 22525, 297, 278, 2318, 29892, 4133, 408, 5291, 2701, 310, 4954, 4993, 29918, 1445, 16159, 322, 4423, 13, 4706, 16285, 297, 263, 2323, 731, 29889, 13, 13, 4706, 16969, 29901, 13, 9651, 3789, 310, 5291, 2701, 310, 4954, 4993, 29918, 1445, 16159, 322, 4423, 2380, 363, 599, 22525, 297, 278, 2318, 29889, 13, 9651, 4525, 526, 4954, 15462, 608, 4782, 8667, 29879, 16159, 304, 1207, 5352, 2130, 6775, 29889, 13, 4706, 9995, 13, 4706, 22525, 353, 731, 580, 13, 4706, 363, 413, 29892, 325, 297, 1583, 29889, 7076, 7295, 13, 9651, 22525, 29889, 5504, 29898, 842, 29898, 1524, 8504, 29889, 4704, 4197, 29895, 1402, 325, 29889, 5182, 29879, 4961, 13, 4706, 736, 426, 15462, 608, 4782, 8667, 10456, 29873, 29897, 363, 260, 297, 22525, 29913, 13, 13, 1678, 732, 6799, 13, 1678, 822, 10664, 29918, 5182, 29879, 29898, 1311, 29897, 1599, 3789, 29961, 15462, 608, 4782, 8667, 5387, 13, 4706, 9995, 3596, 5478, 362, 22525, 297, 278, 2318, 393, 526, 10664, 29892, 13, 4706, 4133, 408, 5291, 2701, 310, 4954, 4993, 29918, 1445, 16159, 322, 4423, 16285, 297, 263, 2323, 731, 29889, 13, 13, 4706, 16969, 29901, 13, 9651, 3789, 310, 5291, 2701, 310, 4954, 4993, 29918, 1445, 16159, 322, 4423, 2380, 363, 599, 10664, 22525, 297, 278, 13, 9651, 2318, 29889, 4525, 526, 4954, 15462, 608, 4782, 8667, 29879, 16159, 304, 1207, 5352, 2130, 6775, 29889, 13, 4706, 9995, 13, 4706, 10664, 29918, 5182, 29879, 353, 731, 580, 13, 4706, 363, 413, 29892, 325, 297, 1583, 29889, 7076, 7295, 13, 9651, 10664, 29918, 5182, 29879, 29889, 5504, 29898, 842, 29898, 1524, 8504, 29889, 4704, 4197, 29895, 1402, 325, 29889, 11911, 287, 29918, 5182, 29879, 4961, 13, 4706, 736, 426, 15462, 608, 4782, 8667, 10456, 29883, 29897, 363, 274, 297, 10664, 29918, 5182, 29879, 29913, 13, 2 ]
learntools/python/utils.py	sfrias/learntools	1	70589	def backtickify(s): return '`{}`'.format(s) def bind_exercises(g, exercises, start=1): for i, ex in enumerate(exercises): qno = i + start varname = 'q{}'.format(qno) assert varname not in g g[varname] = ex yield varname	[ 1, 822, 1250, 24667, 1598, 29898, 29879, 1125, 13, 1678, 736, 525, 29952, 8875, 29952, 4286, 4830, 29898, 29879, 29897, 13, 13, 1753, 7868, 29918, 735, 261, 3476, 267, 29898, 29887, 29892, 24472, 3476, 267, 29892, 1369, 29922, 29896, 1125, 13, 1678, 363, 474, 29892, 429, 297, 26985, 29898, 735, 261, 3476, 267, 1125, 13, 4706, 3855, 1217, 353, 474, 718, 1369, 13, 4706, 722, 978, 353, 525, 29939, 8875, 4286, 4830, 29898, 29939, 1217, 29897, 13, 4706, 4974, 722, 978, 451, 297, 330, 13, 4706, 330, 29961, 1707, 978, 29962, 353, 429, 13, 4706, 7709, 722, 978, 13, 2 ]
auto_process_ngs/test/commands/test_clone_cmd.py	fls-bioinformatics-core/auto_process_ngs	5	124493	####################################################################### # Tests for clone_cmd.py module ####################################################################### import unittest import tempfile import shutil import os from auto_process_ngs.auto_processor import AutoProcess from auto_process_ngs.mock import MockAnalysisDirFactory from auto_process_ngs.mock import UpdateAnalysisDir from auto_process_ngs.metadata import AnalysisDirParameters from auto_process_ngs.commands.clone_cmd import clone # Set to False to keep test output dirs REMOVE_TEST_OUTPUTS = True class TestAutoProcessClone(unittest.TestCase): """ Tests for AutoProcess.clone """ def setUp(self): # Create a temp working dir self.dirn = tempfile.mkdtemp(suffix='TestAutoProcessClone') # Store original location so we can get back at the end self.pwd = os.getcwd() # Move to working dir os.chdir(self.dirn) # Placeholders for test objects self.ap = None def tearDown(self): # Delete autoprocessor object if self.ap is not None: del(self.ap) # Return to original dir os.chdir(self.pwd) # Remove the temporary test directory if REMOVE_TEST_OUTPUTS: shutil.rmtree(self.dirn) def test_clone_analysis_dir(self): """ clone: copies an analysis directory using symlinks """ # Make a source analysis dir analysis_dir = MockAnalysisDirFactory.bcl2fastq2( "190116_M01234_0002_AXYZ123", platform="miseq", paired_end=True, no_lane_splitting=False, include_stats_files=True, top_dir=self.dirn) analysis_dir.create() ap = AutoProcess(analysis_dir.dirn) UpdateAnalysisDir(ap).add_processing_report() ap.add_directory("primary_data/190116_M01234_0002_AXYZ123") # Make a copy clone_dir = os.path.join(self.dirn,"190116_M01234_0002_AXYZ123_copy") self.assertFalse(os.path.exists(clone_dir)) clone(ap,clone_dir) self.assertTrue(os.path.isdir(clone_dir)) # Check contents for subdir in ('logs','ScriptCode'): d = os.path.join(clone_dir,subdir) self.assertTrue(os.path.isdir(d),"Missing '%s'" % subdir) for filen in ('SampleSheet.orig.csv', 'custom_SampleSheet.csv', 'auto_process.info', 'metadata.info', 'statistics.info', 'statistics_full.info', 'per_lane_statistics.info', 'per_lane_sample_stats.info', 'processing_qc.html',): f = os.path.join(clone_dir,filen) self.assertTrue(os.path.isfile(f),"Missing '%s'" % filen) # Check unaligned unaligned = os.path.join(clone_dir,'bcl2fastq') self.assertTrue(os.path.islink(unaligned)) # Check primary data primary_data = os.path.join(clone_dir, 'primary_data', '190116_M01234_0002_AXYZ123') self.assertTrue(os.path.islink(primary_data)) # Check projects for proj in ('AB','CDE','undetermined'): d = os.path.join(clone_dir,proj) self.assertTrue(os.path.isdir(d),"Missing '%s'" % proj) # Check parameters params = AnalysisDirParameters(filen=os.path.join( clone_dir, 'auto_process.info')) self.assertEqual(params.sample_sheet, os.path.join(clone_dir,"custom_SampleSheet.csv")) self.assertEqual(params.primary_data_dir, os.path.join(clone_dir,"primary_data")) def test_clone_analysis_dir_copy_fastqs(self): """ clone: copies an analysis directory """ # Make a source analysis dir analysis_dir = MockAnalysisDirFactory.bcl2fastq2( "190116_M01234_0002_AXYZ123", platform="miseq", paired_end=True, no_lane_splitting=False, include_stats_files=True, top_dir=self.dirn) analysis_dir.create() ap = AutoProcess(analysis_dir.dirn) UpdateAnalysisDir(ap).add_processing_report() ap.add_directory("primary_data/190116_M01234_0002_AXYZ123") # Make a copy clone_dir = os.path.join(self.dirn,"190116_M01234_0002_AXYZ123_copy") self.assertFalse(os.path.exists(clone_dir)) clone(ap,clone_dir,copy_fastqs=True) self.assertTrue(os.path.isdir(clone_dir)) # Check contents for subdir in ('logs','ScriptCode'): d = os.path.join(clone_dir,subdir) self.assertTrue(os.path.isdir(d),"Missing '%s'" % subdir) for filen in ('SampleSheet.orig.csv', 'custom_SampleSheet.csv', 'auto_process.info', 'metadata.info', 'statistics.info', 'statistics_full.info', 'per_lane_statistics.info', 'per_lane_sample_stats.info', 'processing_qc.html',): f = os.path.join(clone_dir,filen) self.assertTrue(os.path.isfile(f),"Missing '%s'" % filen) # Check unaligned unaligned = os.path.join(clone_dir,'bcl2fastq') self.assertTrue(os.path.isdir(unaligned)) # Check primary data primary_data = os.path.join(clone_dir, 'primary_data', '190116_M01234_0002_AXYZ123') self.assertTrue(os.path.islink(primary_data)) # Check projects for proj in ('AB','CDE','undetermined'): d = os.path.join(clone_dir,proj) self.assertTrue(os.path.isdir(d),"Missing '%s'" % proj) # Check parameters params = AnalysisDirParameters(filen=os.path.join( clone_dir, 'auto_process.info')) self.assertEqual(params.sample_sheet, os.path.join(clone_dir,"custom_SampleSheet.csv")) self.assertEqual(params.primary_data_dir, os.path.join(clone_dir,"primary_data")) def test_clone_analysis_dir_no_projects(self): """ clone: copies an analysis directory excluding projects """ # Make a source analysis dir analysis_dir = MockAnalysisDirFactory.bcl2fastq2( "190116_M01234_0002_AXYZ123", platform="miseq", paired_end=True, no_lane_splitting=False, include_stats_files=True, top_dir=self.dirn) analysis_dir.create() ap = AutoProcess(analysis_dir.dirn) UpdateAnalysisDir(ap).add_processing_report() ap.add_directory("primary_data/190116_M01234_0002_AXYZ123") # Make a copy clone_dir = os.path.join(self.dirn,"190116_M01234_0002_AXYZ123_copy") self.assertFalse(os.path.exists(clone_dir)) clone(ap,clone_dir,exclude_projects=True) self.assertTrue(os.path.isdir(clone_dir)) # Check contents for subdir in ('logs','ScriptCode'): d = os.path.join(clone_dir,subdir) self.assertTrue(os.path.isdir(d),"Missing '%s'" % subdir) for filen in ('SampleSheet.orig.csv', 'custom_SampleSheet.csv', 'auto_process.info', 'metadata.info', 'statistics.info', 'statistics_full.info', 'per_lane_statistics.info', 'per_lane_sample_stats.info', 'processing_qc.html',): f = os.path.join(clone_dir,filen) self.assertTrue(os.path.isfile(f),"Missing '%s'" % filen) # Check unaligned unaligned = os.path.join(clone_dir,'bcl2fastq') self.assertTrue(os.path.islink(unaligned)) # Check primary data primary_data = os.path.join(clone_dir, 'primary_data', '190116_M01234_0002_AXYZ123') self.assertTrue(os.path.islink(primary_data)) # Check projects for proj in ('AB','CDE','undetermined'): d = os.path.join(clone_dir,proj) self.assertFalse(os.path.exists(d),"Found '%s'" % proj) # Check parameters params = AnalysisDirParameters(filen=os.path.join( clone_dir, 'auto_process.info')) self.assertEqual(params.sample_sheet, os.path.join(clone_dir,"custom_SampleSheet.csv")) self.assertEqual(params.primary_data_dir, os.path.join(clone_dir,"primary_data")) def test_clone_analysis_dir_empty_params(self): """ clone: copies an analysis directory when parameter file is empty """ # Make a source analysis dir analysis_dir = MockAnalysisDirFactory.bcl2fastq2( "190116_M01234_0002_AXYZ123", platform="miseq", paired_end=True, no_lane_splitting=False, include_stats_files=True, top_dir=self.dirn) analysis_dir.create() ap = AutoProcess(analysis_dir.dirn) UpdateAnalysisDir(ap).add_processing_report() ap.add_directory("primary_data/190116_M01234_0002_AXYZ123") # Remove data from parameter file parameter_file = ap.parameter_file tmp_parameter_file = os.path.join(self.dirn,'new_params.tmp') del(ap) with open(parameter_file,'r') as fp: with open(tmp_parameter_file,'w') as fpp: for line in fp: line = "%s\t." % line.split('\t')[0] fpp.write(line) os.remove(parameter_file) os.rename(tmp_parameter_file,parameter_file) ap = AutoProcess(analysis_dir.dirn) # Make a copy clone_dir = os.path.join(self.dirn,"190116_M01234_0002_AXYZ123_copy") self.assertFalse(os.path.exists(clone_dir)) clone(ap,clone_dir,exclude_projects=False) self.assertTrue(os.path.isdir(clone_dir)) # Check contents for subdir in ('logs','ScriptCode'): d = os.path.join(clone_dir,subdir) self.assertTrue(os.path.isdir(d),"Missing '%s'" % subdir) for filen in ('SampleSheet.orig.csv', 'custom_SampleSheet.csv', 'auto_process.info', 'metadata.info', 'statistics.info', 'statistics_full.info', 'per_lane_statistics.info', 'per_lane_sample_stats.info', 'processing_qc.html',): f = os.path.join(clone_dir,filen) self.assertTrue(os.path.isfile(f),"Missing '%s'" % filen) # Check unaligned unaligned = os.path.join(clone_dir,'bcl2fastq') self.assertTrue(os.path.islink(unaligned)) # Check primary data primary_data = os.path.join(clone_dir, 'primary_data', '190116_M01234_0002_AXYZ123') self.assertFalse(os.path.exists(primary_data)) # Check projects for proj in ('AB','CDE','undetermined'): d = os.path.join(clone_dir,proj) self.assertTrue(os.path.exists(d),"Missing '%s'" % proj) def test_clone_fails_if_target_dir_exists(self): """ clone: raises an exception if target dir already exists """ # Make a source analysis dir analysis_dir = MockAnalysisDirFactory.bcl2fastq2( "190116_M01234_0002_AXYZ123", platform="miseq", paired_end=True, no_lane_splitting=False, include_stats_files=True, top_dir=self.dirn) analysis_dir.create() ap = AutoProcess(analysis_dir.dirn) UpdateAnalysisDir(ap).add_processing_report() ap.add_directory("primary_data/190116_M01234_0002_AXYZ123") # Make target dir clone_dir = os.path.join(self.dirn,"190116_M01234_0002_AXYZ123_copy") os.mkdir(clone_dir) # Try to copy source dir self.assertRaises(Exception, clone, ap,clone_dir)	[ 1, 835, 13383, 13383, 13383, 13383, 4136, 13, 29937, 4321, 29879, 363, 17432, 29918, 9006, 29889, 2272, 3883, 13, 13383, 13383, 13383, 13383, 4136, 2277, 29937, 13, 13, 5215, 443, 27958, 13, 5215, 5694, 1445, 13, 5215, 528, 4422, 13, 5215, 2897, 13, 3166, 4469, 29918, 5014, 29918, 865, 29879, 29889, 6921, 29918, 26482, 1053, 11133, 7032, 13, 3166, 4469, 29918, 5014, 29918, 865, 29879, 29889, 17640, 1053, 26297, 21067, 4848, 9170, 5126, 13, 3166, 4469, 29918, 5014, 29918, 865, 29879, 29889, 17640, 1053, 10318, 21067, 4848, 9170, 13, 3166, 4469, 29918, 5014, 29918, 865, 29879, 29889, 19635, 1053, 24352, 9170, 11507, 13, 3166, 4469, 29918, 5014, 29918, 865, 29879, 29889, 26381, 29889, 16513, 29918, 9006, 1053, 17432, 13, 13, 29937, 3789, 304, 7700, 304, 3013, 1243, 1962, 4516, 29879, 13, 1525, 6720, 12064, 29918, 18267, 29918, 12015, 12336, 29903, 353, 5852, 13, 13, 1990, 4321, 12300, 7032, 6821, 650, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 1678, 9995, 13, 1678, 4321, 29879, 363, 11133, 7032, 29889, 16513, 13, 1678, 9995, 13, 1678, 822, 731, 3373, 29898, 1311, 1125, 13, 4706, 396, 6204, 263, 5694, 1985, 4516, 13, 4706, 1583, 29889, 3972, 29876, 353, 5694, 1445, 29889, 11256, 29881, 7382, 29898, 2146, 600, 861, 2433, 3057, 12300, 7032, 6821, 650, 1495, 13, 4706, 396, 14491, 2441, 4423, 577, 591, 508, 679, 1250, 472, 278, 1095, 13, 4706, 1583, 29889, 29886, 9970, 353, 2897, 29889, 657, 29883, 9970, 580, 13, 4706, 396, 25249, 304, 1985, 4516, 13, 4706, 2897, 29889, 305, 3972, 29898, 1311, 29889, 3972, 29876, 29897, 13, 4706, 396, 15484, 8948, 414, 363, 1243, 3618, 13, 4706, 1583, 29889, 481, 353, 6213, 13, 13, 1678, 822, 734, 279, 6767, 29898, 1311, 1125, 13, 4706, 396, 21267, 1120, 26555, 985, 272, 1203, 13, 4706, 565, 1583, 29889, 481, 338, 451, 6213, 29901, 13, 9651, 628, 29898, 1311, 29889, 481, 29897, 13, 4706, 396, 7106, 304, 2441, 4516, 13, 4706, 2897, 29889, 305, 3972, 29898, 1311, 29889, 29886, 9970, 29897, 13, 4706, 396, 15154, 278, 13201, 1243, 3884, 13, 4706, 565, 5195, 6720, 12064, 29918, 18267, 29918, 12015, 12336, 29903, 29901, 13, 9651, 528, 4422, 29889, 1758, 8336, 29898, 1311, 29889, 3972, 29876, 29897, 13, 13, 1678, 822, 1243, 29918, 16513, 29918, 15916, 29918, 3972, 29898, 1311, 1125, 13, 4706, 9995, 13, 4706, 17432, 29901, 14591, 385, 7418, 3884, 773, 9878, 828, 19363, 13, 4706, 9995, 13, 4706, 396, 8561, 263, 2752, 7418, 4516, 13, 4706, 7418, 29918, 3972, 353, 26297, 21067, 4848, 9170, 5126, 29889, 29890, 695, 29906, 11255, 29939, 29906, 29898, 13, 9651, 376, 29896, 29929, 29900, 29896, 29896, 29953, 29918, 29924, 29900, 29896, 29906, 29941, 29946, 29918, 29900, 29900, 29900, 29906, 29918, 6604, 29979, 29999, 29896, 29906, 29941, 613, 13, 9651, 7481, 543, 29885, 895, 29939, 613, 13, 9651, 3300, 2859, 29918, 355, 29922, 5574, 29892, 13, 9651, 694, 29918, 25821, 29918, 23579, 5367, 29922, 8824, 29892, 13, 9651, 3160, 29918, 16202, 29918, 5325, 29922, 5574, 29892, 13, 9651, 2246, 29918, 3972, 29922, 1311, 29889, 3972, 29876, 29897, 13, 4706, 7418, 29918, 3972, 29889, 3258, 580, 13, 4706, 3095, 353, 11133, 7032, 29898, 15916, 29918, 3972, 29889, 3972, 29876, 29897, 13, 4706, 10318, 21067, 4848, 9170, 29898, 481, 467, 1202, 29918, 19170, 29918, 12276, 580, 13, 4706, 3095, 29889, 1202, 29918, 12322, 703, 16072, 29918, 1272, 29914, 29896, 29929, 29900, 29896, 29896, 29953, 29918, 29924, 29900, 29896, 29906, 29941, 29946, 29918, 29900, 29900, 29900, 29906, 29918, 6604, 29979, 29999, 29896, 29906, 29941, 1159, 13, 4706, 396, 8561, 263, 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pisat/handler/pigpio_digital_input_handler.py	jjj999/pisat	1	57855	from typing import Optional from pisat.util.platform import is_raspberry_pi from pisat.handler.digital_input_handler_base import DigitalInputHandlerBase if is_raspberry_pi(): import pigpio class PigpioDigitalInputHandler(DigitalInputHandlerBase): def __init__(self, pi, pin: int, pullup: bool = False, pulldown: bool = False, name: Optional[str] = None) -> None: self._pi: pigpio.pi = pi self._pi.set_mode(pin, pigpio.INPUT) super().__init__(pin, pullup=pullup, pulldown=pulldown, name=name) def set_pull_up_down(self, pulldown: bool = False) -> None: if pulldown: self._pi.set_pull_up_down(self._pin, pigpio.PUD_DOWN) else: self._pi.set_pull_up_down(self._pin, pigpio.PUD_UP) def clear_pull_up_down(self) -> None: self._pi.set_pull_up_down(self._pin, pigpio.PUD_DOWN) def observe(self) -> bool: return bool(self._pi.read(self._pin))	[ 1, 29871, 13, 3166, 19229, 1053, 28379, 13, 13, 3166, 20066, 271, 29889, 4422, 29889, 12120, 1053, 338, 29918, 3417, 29886, 16344, 29918, 1631, 13, 3166, 20066, 271, 29889, 13789, 29889, 7501, 2410, 29918, 2080, 29918, 13789, 29918, 3188, 1053, 15918, 4290, 4598, 5160, 13, 13, 361, 338, 29918, 3417, 29886, 16344, 29918, 1631, 7295, 13, 1678, 1053, 282, 335, 16168, 13, 13, 13, 1990, 349, 335, 16168, 27103, 4290, 4598, 29898, 27103, 4290, 4598, 5160, 1125, 13, 268, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 29871, 13, 462, 2930, 29892, 13, 462, 12534, 29901, 938, 29892, 13, 462, 8206, 786, 29901, 6120, 353, 7700, 29892, 13, 462, 9505, 430, 776, 29901, 6120, 353, 7700, 29892, 13, 462, 1024, 29901, 28379, 29961, 710, 29962, 353, 6213, 29897, 1599, 6213, 29901, 13, 308, 13, 4706, 1583, 3032, 1631, 29901, 282, 335, 16168, 29889, 1631, 353, 2930, 13, 4706, 1583, 3032, 1631, 29889, 842, 29918, 8513, 29898, 12687, 29892, 282, 335, 16168, 29889, 1177, 12336, 29897, 13, 308, 13, 4706, 2428, 2141, 1649, 2344, 12035, 12687, 29892, 8206, 786, 29922, 26746, 786, 29892, 9505, 430, 776, 29922, 29886, 352, 430, 776, 29892, 1024, 29922, 978, 29897, 13, 268, 13, 1678, 822, 731, 29918, 26746, 29918, 786, 29918, 3204, 29898, 1311, 29892, 9505, 430, 776, 29901, 6120, 353, 7700, 29897, 1599, 6213, 29901, 13, 4706, 565, 9505, 430, 776, 29901, 13, 9651, 1583, 3032, 1631, 29889, 842, 29918, 26746, 29918, 786, 29918, 3204, 29898, 1311, 3032, 12687, 29892, 282, 335, 16168, 29889, 7056, 29928, 29918, 3970, 16048, 29897, 13, 4706, 1683, 29901, 13, 9651, 1583, 3032, 1631, 29889, 842, 29918, 26746, 29918, 786, 29918, 3204, 29898, 1311, 3032, 12687, 29892, 282, 335, 16168, 29889, 7056, 29928, 29918, 4897, 29897, 13, 268, 13, 1678, 822, 2821, 29918, 26746, 29918, 786, 29918, 3204, 29898, 1311, 29897, 1599, 6213, 29901, 13, 4706, 1583, 3032, 1631, 29889, 842, 29918, 26746, 29918, 786, 29918, 3204, 29898, 1311, 3032, 12687, 29892, 282, 335, 16168, 29889, 7056, 29928, 29918, 3970, 16048, 29897, 13, 268, 13, 1678, 822, 14111, 29898, 1311, 29897, 1599, 6120, 29901, 13, 4706, 736, 6120, 29898, 1311, 3032, 1631, 29889, 949, 29898, 1311, 3032, 12687, 876, 13, 308, 2 ]
app/util/draw_img.py	Test8888888888/Myblog	0	196411	from typing import Dict from PIL import Image, ImageDraw, ImageFont from io import BytesIO import requests import re class H3blogDrow: '''自定义图片样式''' def __init__(self) -> None: self.width = 800 self.heigth = 400 self.background_img = '' self.background_color = '#424155' self.layers = [] self.convas = None def parse_config(self, config: Dict) -> None: c = config self.convas = None self.width = c.get('width', 800) self.heigth = c.get('height', 400) self.background_color = c.get('background_color', '#424155') self.background_img = c.get('background_img') layers = c.get('layers', None) if not layers: return self.layers.extend(layers) def _create_canvas(self) -> None: self.convas = Image.new('RGB', (self.width, self.heigth), self.background_color) def draw(self) -> Image: '''画图''' # 创建背景设置画布 self._create_canvas() if self.background_img and len(self.background_img) > 0: regex = re.compile( r'^(?:http\|ftp)s?://' # http:// or https:// r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?\|[A-Z0-9-]{2,}\.?)\|' # domain... r'localhost\|' # localhost... r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})' # ...or ip r'(?::\d+)?' # optional port r'(?:/?\|[/?]\S+)$', re.IGNORECASE) m = re.match(regex, self.background_img) bg_img = None if m: resp = requests.get(self.background_img) _img_bytes = BytesIO() _img_bytes.write(resp.content) bg_img = Image.open(_img_bytes) else: # 创建背景图片 bg_img = Image.open(self.background_img) # 将背景图片写入画布 self.convas.paste(bg_img, (0, 0)) for layer in self.layers: if layer.get('layer_type') == 'text': self._draw_text(layer) if layer.get('layer_type') == 'image': self._draw_image() return self.convas def _darw_image(self, layer: dict) -> None: pass def _draw_text(self, layer: dict) -> None: draw = ImageDraw.Draw(self.convas) _font = layer.get('font') font = ImageFont.truetype(_font.get('font'), _font.get('size', 36)) align = layer.get('align') p = tuple() if align and align == 'center': f_w, f_h = 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pyhermes/apps/django/management/commands/hermes_test.py	romcheg/pyhermes	5	129419	<reponame>romcheg/pyhermes # -- coding: utf-8 -- from django.core.management.base import BaseCommand from pyhermes.exceptions import HermesPublishException from pyhermes.publisher import publish from pyhermes.settings import HERMES_SETTINGS TOPICS_ALL = 'all' class Command(BaseCommand): help = "Testing integration with Hermes" def add_arguments(self, parser): parser.add_argument( '-m', '--message', help='test message', default='From pyhermes With Love' ) parser.add_argument( '-t', '--topic', help='topic', default=TOPICS_ALL, ) def handle(self, args, *options): if not HERMES_SETTINGS.ENABLED: self.stderr.write( 'Hermes integration is disabled. ' 'Check HERMES.ENABLED variable ' 'in your settings or environment.' ) return topic = options.get('topic') message = options.get('message') if topic == TOPICS_ALL: topics = HERMES_SETTINGS.PUBLISHING_TOPICS.keys() else: topics = [topic] for topic in topics: try: self.stdout.write('Sending message to {}'.format(topic)) publish(topic, {'result': message}) except HermesPublishException as e: self.stderr.write(str(e)) else: self.stdout.write( 'Message was sent successfully to {}!'.format(topic) )	[ 1, 529, 276, 1112, 420, 29958, 456, 305, 387, 29914, 2272, 29882, 837, 267, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 3166, 9557, 29889, 3221, 29889, 21895, 29889, 3188, 1053, 7399, 6255, 13, 13, 3166, 11451, 29882, 837, 267, 29889, 11739, 29879, 1053, 10515, 267, 21076, 1674, 2451, 13, 3166, 11451, 29882, 837, 267, 29889, 23679, 261, 1053, 9805, 13, 3166, 11451, 29882, 837, 267, 29889, 11027, 1053, 379, 1001, 2303, 29903, 29918, 10490, 29911, 4214, 29903, 13, 13, 4986, 2227, 9295, 29918, 9818, 353, 525, 497, 29915, 13, 13, 13, 1990, 10516, 29898, 5160, 6255, 1125, 13, 13, 1678, 1371, 353, 376, 3057, 292, 13465, 411, 10515, 267, 29908, 13, 13, 1678, 822, 788, 29918, 25699, 29898, 1311, 29892, 13812, 1125, 13, 4706, 13812, 29889, 1202, 29918, 23516, 29898, 13, 9651, 17411, 29885, 742, 525, 489, 4906, 742, 13, 9651, 1371, 2433, 1688, 2643, 742, 13, 9651, 2322, 2433, 4591, 11451, 29882, 837, 267, 2973, 8155, 29915, 13, 4706, 1723, 13, 4706, 13812, 29889, 1202, 29918, 23516, 29898, 13, 9651, 17411, 29873, 742, 525, 489, 13010, 742, 13, 9651, 1371, 2433, 13010, 742, 13, 9651, 2322, 29922, 4986, 2227, 9295, 29918, 9818, 29892, 13, 4706, 1723, 13, 13, 1678, 822, 4386, 29898, 1311, 29892, 334, 5085, 29892, 3579, 6768, 1125, 13, 4706, 565, 451, 379, 1001, 2303, 29903, 29918, 10490, 29911, 4214, 29903, 29889, 1430, 6181, 29928, 29901, 13, 9651, 1583, 29889, 303, 20405, 29889, 3539, 29898, 13, 18884, 525, 29950, 837, 267, 13465, 338, 12708, 29889, 525, 13, 18884, 525, 5596, 379, 1001, 2303, 29903, 29889, 1430, 6181, 29928, 2286, 525, 13, 18884, 525, 262, 596, 6055, 470, 5177, 6169, 13, 9651, 1723, 13, 9651, 736, 13, 13, 4706, 11261, 353, 3987, 29889, 657, 877, 13010, 1495, 13, 4706, 2643, 353, 3987, 29889, 657, 877, 4906, 1495, 13, 4706, 565, 11261, 1275, 7495, 2227, 9295, 29918, 9818, 29901, 13, 9651, 23820, 353, 379, 1001, 2303, 29903, 29918, 10490, 29911, 4214, 29903, 29889, 7056, 13367, 3235, 29950, 4214, 29918, 4986, 2227, 9295, 29889, 8149, 580, 13, 4706, 1683, 29901, 13, 9651, 23820, 353, 518, 13010, 29962, 13, 13, 4706, 363, 11261, 297, 23820, 29901, 13, 9651, 1018, 29901, 13, 18884, 1583, 29889, 25393, 29889, 3539, 877, 29903, 2548, 2643, 304, 6571, 4286, 4830, 29898, 13010, 876, 13, 18884, 9805, 29898, 13010, 29892, 11117, 2914, 2396, 2643, 1800, 13, 9651, 5174, 10515, 267, 21076, 1674, 2451, 408, 321, 29901, 13, 18884, 1583, 29889, 303, 20405, 29889, 3539, 29898, 710, 29898, 29872, 876, 13, 9651, 1683, 29901, 13, 18884, 1583, 29889, 25393, 29889, 3539, 29898, 13, 462, 1678, 525, 3728, 471, 2665, 8472, 304, 6571, 29991, 4286, 4830, 29898, 13010, 29897, 13, 18884, 1723, 13, 2 ]
tests/extra_lib/test.py	burner/pyd	168	1609267	import os.path, sys import distutils.util # Append the directory in which the binaries were placed to Python's sys.path, # then import the D DLL. libDir = os.path.join('build', 'lib.%s-%s' % ( distutils.util.get_platform(), '.'.join(str(v) for v in sys.version_info[:2]) )) sys.path.append(os.path.abspath(libDir)) import hello_w_libs hello_w_libs.hello()	[ 1, 1053, 2897, 29889, 2084, 29892, 10876, 13, 5215, 1320, 13239, 29889, 4422, 13, 13, 29937, 22871, 278, 3884, 297, 607, 278, 9016, 4314, 892, 7180, 304, 5132, 29915, 29879, 10876, 29889, 2084, 29892, 13, 29937, 769, 1053, 278, 360, 22474, 29889, 13, 1982, 9170, 353, 2897, 29889, 2084, 29889, 7122, 877, 4282, 742, 525, 1982, 29889, 29995, 29879, 19222, 29879, 29915, 1273, 313, 13, 1678, 1320, 13239, 29889, 4422, 29889, 657, 29918, 12120, 3285, 13, 1678, 15300, 4286, 7122, 29898, 710, 29898, 29894, 29897, 363, 325, 297, 10876, 29889, 3259, 29918, 3888, 7503, 29906, 2314, 13, 876, 13, 9675, 29889, 2084, 29889, 4397, 29898, 359, 29889, 2084, 29889, 370, 1028, 493, 29898, 1982, 9170, 876, 13, 13, 5215, 22172, 29918, 29893, 29918, 10254, 13, 13, 12199, 29918, 29893, 29918, 10254, 29889, 12199, 580, 13, 2 ]
mptpy/mpt.py	CognitiveComputationLab/mptpy	0	86656	<reponame>CognitiveComputationLab/mptpy """ Data structure for Multinomial Processing Trees (MPTs). """ from mptpy.mpt_word import MPTWord import mptpy.tools.transformations as trans # pylint: disable=import-error from mptpy.visualization.visualize_mpt import cmd_draw # pylint: disable=import-error from mptpy.tools import misc class MPT(object): """ Multinomial Processing Tree (MPT) data structure. """ def __init__(self, mpt, sep=" ", leaf_test=None): """ Constructs the MPT object. Parameters ---------- mpt : [str, Node] either tree in bmpt or as root object. """ self.subtrees = [] self.word = None self.root = None # mpt given as word if isinstance(mpt, str): self.word = MPTWord(mpt, sep=sep, leaf_test=leaf_test) self.root = trans.word_to_nodes(self.word) # mpt given as root node else: self.root = mpt self.word = MPTWord(str(self)) @property def params(self): return self.word.parameters @property def categories(self): return self.word.answers def formulae(self): """ Calculate the branch formulae for the categories in the tree Returns ------- dict {category : formulae} """ return trans.get_formulae(self) def max_parameters(self): """ The maximal number of free parameters in the model Returns ------- int max number of free parameters """ return sum([len(subtree) - 1 for subtree in self.subtrees]) def get_levels(self, node, level=0): """ Generate a dict with all nodes and their respective level 0 is the root Parameters ---------- node : Node starting node level : int, optional level from which to start counting """ levels = {level: [node]} if not node.leaf: left_dict = self.get_levels(node.pos, level=level + 1) right_dict = self.get_levels(node.neg, level=level + 1) temp = misc.merge_dicts(left_dict, right_dict) levels.update(temp) return levels def save(self, path, form="easy"): """ Saves the tree to a file Parameters ---------- path : str where to save the tree """ to_print = trans.to_easy(self) if form == "easy" else self.word.str_ misc.write_iterable_to_file(path, to_print, newline=False) def draw(self): """ Draw MPT to the command line """ cmd_draw(self) def __eq__(self, other): return self.word.abstract() == other.word.abstract() def __ne__(self, other): return not self.__eq__(other) def __str__(self): if self.word: return self.word.str_ sep = " " def dfs(node): """ depth first search """ if node.leaf: return str(node.content) pos = dfs(node.pos) neg = dfs(node.neg) return node.content + sep + pos + sep + neg return dfs(self.root)	[ 1, 529, 276, 1112, 420, 29958, 29907, 3811, 3321, 1523, 14584, 28632, 29914, 29885, 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simapi/onlinesim/number.py	nm17/simapi	0	68652	<reponame>nm17/simapi import simapi.api_handler as baseapi class OnlineSMSNumber(baseapi.Number): def __init__(self, api_handler, args, kwargs): self.__api_handler = api_handler super().__init__(args, **kwargs) async def set_activation_status(self, status: int): pass @property async def activation_code(self): pass	[ 1, 529, 276, 1112, 420, 29958, 22882, 29896, 29955, 29914, 3601, 2754, 13, 5215, 1027, 2754, 29889, 2754, 29918, 13789, 408, 2967, 2754, 30004, 13, 30004, 13, 30004, 13, 1990, 13542, 29903, 4345, 4557, 29898, 3188, 2754, 29889, 4557, 1125, 30004, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 7882, 29918, 13789, 29892, 334, 5085, 29892, 3579, 19290, 1125, 30004, 13, 4706, 1583, 17255, 2754, 29918, 13789, 353, 7882, 29918, 13789, 30004, 13, 4706, 2428, 2141, 1649, 2344, 1649, 10456, 5085, 29892, 3579, 19290, 8443, 13, 30004, 13, 1678, 7465, 822, 731, 29918, 11236, 362, 29918, 4882, 29898, 1311, 29892, 4660, 29901, 938, 1125, 30004, 13, 4706, 1209, 30004, 13, 30004, 13, 1678, 732, 6799, 30004, 13, 1678, 7465, 822, 26229, 29918, 401, 29898, 1311, 1125, 30004, 13, 4706, 1209, 2 ]
visualization/matplotlib/barwitherror.py	Licas/datascienceexamples	0	5757	<filename>visualization/matplotlib/barwitherror.py<gh_stars>0 from matplotlib import pyplot as plt drinks = ["cappuccino", "latte", "chai", "americano", "mocha", "espresso"] ounces_of_milk = [6, 9, 4, 0, 9, 0] error = [0.6, 0.9, 0.4, 0, 0.9, 0] #Yerr -> element at i position represents +/- error[i] variance on bar[i] value plt.bar( range(len(drinks)),ounces_of_milk, yerr=error, capsize=15) plt.show()	[ 1, 529, 9507, 29958, 20119, 2133, 29914, 2922, 17357, 29914, 1646, 29893, 2121, 729, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 22889, 1053, 11451, 5317, 408, 14770, 13, 13, 7707, 19363, 353, 6796, 29883, 932, 29884, 617, 1789, 613, 376, 5066, 371, 613, 376, 305, 1794, 613, 376, 4183, 13231, 613, 376, 29885, 2878, 29874, 613, 376, 267, 2139, 29877, 3108, 13, 1309, 778, 29918, 974, 29918, 23853, 29895, 353, 518, 29953, 29892, 29871, 29929, 29892, 29871, 29946, 29892, 29871, 29900, 29892, 29871, 29929, 29892, 29871, 29900, 29962, 13, 2704, 353, 518, 29900, 29889, 29953, 29892, 29871, 29900, 29889, 29929, 29892, 29871, 29900, 29889, 29946, 29892, 29871, 29900, 29892, 29871, 29900, 29889, 29929, 29892, 29871, 29900, 29962, 13, 13, 29937, 29979, 3127, 1599, 1543, 472, 474, 2602, 11524, 718, 24028, 1059, 29961, 29875, 29962, 20162, 373, 2594, 29961, 29875, 29962, 995, 13, 572, 29873, 29889, 1646, 29898, 3464, 29898, 2435, 29898, 7707, 19363, 8243, 1309, 778, 29918, 974, 29918, 23853, 29895, 29892, 343, 3127, 29922, 2704, 29892, 2117, 2311, 29922, 29896, 29945, 29897, 13, 572, 29873, 29889, 4294, 580, 2 ]
snippets/show-image.py	district10/snippet-manager	7	186171	# python show image from IPython.display import Image Image("http://scikit-learn.org/dev/_static/ml_map.png", width=800)	[ 1, 396, 3017, 1510, 1967, 13, 3166, 5641, 1656, 29889, 4990, 1053, 7084, 13, 2940, 703, 1124, 597, 26167, 7354, 29899, 19668, 29889, 990, 29914, 3359, 19891, 7959, 29914, 828, 29918, 1958, 29889, 2732, 613, 2920, 29922, 29947, 29900, 29900, 29897, 13, 2 ]
topo-50-50-0-0.py	SoulOH/MPQUIC-different-scheduler-evaluation	0	137393	<gh_stars>0 #!/usr/bin/env python from mininet.net import Mininet from mininet.cli import CLI from mininet.link import Link, TCLink,Intf from subprocess import Popen, PIPE from mininet.log import setLogLevel if '__main__' == __name__: setLogLevel('info') net = Mininet(link=TCLink) # key = "net.mptcp.mptcp_enabled" # value = 1 # p = Popen("sysctl -w %s=%s" % (key, value), shell=True, stdout=PIPE, stderr=PIPE) # stdout, stderr = p.communicate() # print "stdout=",stdout,"stderr=", stderr h1 = net.addHost('h1') h2 = net.addHost('h2') r1 = net.addHost('r1') linkopt_wifi={'bw':10, 'delay':'50ms', "loss":0} linkopt_4g={'bw':10, 'delay':'50ms', "loss":0} linkopt2={'bw':100} net.addLink(r1,h1,cls=TCLink, linkopt_wifi) net.addLink(r1,h1,cls=TCLink, linkopt_4g) net.addLink(r1,h2,cls=TCLink, **linkopt2) net.build() r1.cmd("ifconfig r1-eth0 0") r1.cmd("ifconfig r1-eth1 0") r1.cmd("ifconfig r1-eth2 0") h1.cmd("ifconfig h1-eth0 0") h1.cmd("ifconfig h1-eth1 0") h2.cmd("ifconfig h2-eth0 0") r1.cmd("echo 1 > /proc/sys/net/ipv4/ip_forward") r1.cmd("ifconfig r1-eth0 10.0.0.1 netmask 255.255.255.0") r1.cmd("ifconfig r1-eth1 10.0.1.1 netmask 255.255.255.0") r1.cmd("ifconfig r1-eth2 10.0.2.1 netmask 255.255.255.0") h1.cmd("ifconfig h1-eth0 10.0.0.2 netmask 255.255.255.0") h1.cmd("ifconfig h1-eth1 10.0.1.2 netmask 255.255.255.0") h2.cmd("ifconfig h2-eth0 10.0.2.2 netmask 255.255.255.0") h1.cmd("ip rule add from 10.0.0.2 table 1") h1.cmd("ip rule add from 10.0.1.2 table 2") h1.cmd("ip route add 10.0.0.0/24 dev h1-eth0 scope link table 1") h1.cmd("ip route add default via 10.0.0.1 dev h1-eth0 table 1") h1.cmd("ip route add 10.0.1.0/24 dev h1-eth1 scope link table 2") h1.cmd("ip route add default via 10.0.1.1 dev h1-eth1 table 2") h1.cmd("ip route add default scope global nexthop via 10.0.0.1 dev h1-eth0") h2.cmd("ip rule add from 10.0.2.2 table 1") h2.cmd("ip route add 10.0.2.0/24 dev h2-eth0 scope link table 1") h2.cmd("ip route add default via 10.0.2.1 dev h2-eth0 table 1") h2.cmd("ip route add default scope global nexthop via 10.0.2.1 dev h2-eth0") CLI(net) net.stop()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 3166, 1375, 10157, 29889, 1212, 1053, 3080, 10157, 13, 3166, 1375, 10157, 29889, 11303, 1053, 24492, 13, 3166, 1375, 10157, 29889, 2324, 1053, 6645, 29892, 323, 6154, 682, 29892, 2928, 29888, 13, 3166, 1014, 5014, 1053, 349, 3150, 29892, 349, 29902, 4162, 13, 3166, 1375, 10157, 29889, 1188, 1053, 731, 3403, 10108, 13, 13, 361, 525, 1649, 3396, 1649, 29915, 1275, 4770, 978, 1649, 29901, 13, 1678, 731, 3403, 10108, 877, 3888, 1495, 13, 1678, 7787, 353, 3080, 10157, 29898, 2324, 29922, 29911, 6154, 682, 29897, 13, 1678, 396, 1820, 353, 376, 1212, 29889, 29885, 415, 6814, 29889, 29885, 415, 6814, 29918, 17590, 29908, 13, 1678, 396, 995, 353, 29871, 29896, 13, 1678, 396, 282, 353, 349, 3150, 703, 9675, 16948, 448, 29893, 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1212, 29897, 13, 1678, 7787, 29889, 9847, 580, 13, 13, 2 ]
aptl3/scripts/load_coco.py	matteoterruzzi/aptl3	0	168432	import logging import os from datetime import datetime import random from threading import Thread, Event from pycocotools.coco import COCO from ..db import Database logger = logging.getLogger('load_coco') def load_coco(db: Database, , coco_dir: str, data_type: str = 'train2017', max_samples: int, raise_ki: bool = True, only_one_caption: bool = False, ): try: metadata = dict( source='MSCOCO', creation=str(datetime.now()), data_type=data_type, ) coco = COCO(f'{coco_dir}{os.path.sep}captions_{data_type}.json') total_len = min(len(coco.imgs), max_samples) db.begin_exclusive_transaction() relation_id: int = db.create_relation(metadata, commit=False) logger.info(f'New relation #{relation_id:d}: {metadata}') except KeyboardInterrupt: logger.info('Interrupted before starting.') if raise_ki: raise return stopped = Event() def _download(shard, n): for i, id_ in zip(range(max_samples), coco.getImgIds()): if i % n != shard: continue img_url = coco.imgs[id_]['coco_url'] db.fetch(img_url) if stopped.is_set(): break shards = 4 download_ths = [ Thread(target=_download, args=(_s, shards), daemon=True, name=f'COCO-{_s:d}-of-{shards:d}') for _s in range(shards) ] for th in download_ths: th.start() def _gen_pairs(): for i, id_ in zip(range(max_samples), coco.getImgIds()): img_url = str(coco.imgs[id_]['coco_url']).strip() img_media_id, _flags = db.ingest_url(img_url, commit=False, update_last_access=False) _ann_ids = coco.getAnnIds(id_) if only_one_caption: _ann_ids = [random.choice(list(_ann_ids))] for ann_id in _ann_ids: ann_url = 'data:,' + str(coco.anns[ann_id]['caption']).strip() ann_media_id, _flags = db.ingest_url(ann_url, commit=False, update_last_access=False) yield img_media_id, ann_media_id print(f"Loading MS COCO ({i}/{total_len} images)...", end="\r", flush=True) try: db.add_media_relations(relation_id, media_id_pairs=_gen_pairs(), commit=False, batch_size=64) except KeyboardInterrupt: logger.info(f'Interrupted.') if raise_ki: raise db.commit() c = db.execute('SELECT COUNT() FROM MediaRelations WHERE relation_id = ?', (relation_id,)) inserted: int = c.fetchone()[0] logger.info(f'Added {inserted:d} COCO annotations to relation #{relation_id}.') logger.debug(f'{[th.is_alive() for th in download_ths]=}') stopped.set() for th in download_ths: th.join() return relation_id def main(): import argparse parser = argparse.ArgumentParser(description='Load MS COCO dataset and equivalence') parser.add_argument('db', type=str, help='database data directory') parser.add_argument('coco', type=str, help='COCO dataset directory') parser.add_argument('data', type=str, help='dataset subset (train2017 or val2017)') parser.add_argument('--limit', default=999999, type=int, help='limit the number of samples (defaults to 999999)') parser.add_argument('--log', default='INFO', type=str, help='logging level (defaults to INFO)') args = parser.parse_args() logging.basicConfig(level=args.log.upper(), format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s', datefmt='%m-%d %H:%M') db = Database(args.db) db.execute('PRAGMA synchronous = OFF') load_coco(db=db, coco_dir=args.coco, data_type=args.data, max_samples=args.limit, raise_ki=False) if __name__ == '__main__': main()	[ 1, 1053, 12183, 13, 5215, 2897, 13, 3166, 12865, 1053, 12865, 13, 5215, 4036, 13, 3166, 3244, 292, 1053, 10480, 29892, 6864, 13, 13, 3166, 282, 11078, 542, 327, 8789, 29889, 29883, 6235, 1053, 4810, 3217, 13, 13, 3166, 6317, 2585, 1053, 5470, 13, 13, 21707, 353, 12183, 29889, 657, 16363, 877, 1359, 29918, 29883, 6235, 1495, 13, 13, 13, 1753, 2254, 29918, 29883, 6235, 29898, 2585, 29901, 5470, 29892, 334, 29892, 13, 795, 274, 6235, 29918, 3972, 29901, 851, 29892, 13, 795, 848, 29918, 1853, 29901, 851, 353, 525, 14968, 29906, 29900, 29896, 29955, 742, 13, 795, 4236, 29918, 27736, 29901, 938, 29892, 13, 795, 12020, 29918, 1984, 29901, 6120, 353, 5852, 29892, 13, 795, 871, 29918, 650, 29918, 6671, 29901, 6120, 353, 7700, 29892, 13, 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tests/distributed/test_simple_hub_pods/test_integration.py	hirakjyoti08/jina	1	103705	import os import pytest from jina import __default_host__, __docker_host__ from ..helpers import create_workspace, wait_for_workspace, create_flow, assert_request cur_dir = os.path.dirname(os.path.abspath(__file__)) compose_yml = os.path.join(cur_dir, 'docker-compose.yml') flow_yaml = os.path.join(cur_dir, 'flow.yml') JINAD_HOST = __default_host__ GATEWAY_HOST = __default_host__ JINAD_PORT = 8000 GATEWAY_PORT = 45630 @pytest.mark.skip('jinad with docker-compose not supported for now') @pytest.mark.parametrize('docker_compose', [compose_yml], indirect=['docker_compose']) def test_simple_hub_deployments(docker_compose): workspace_id = create_workspace(filepaths=[flow_yaml]) assert wait_for_workspace(workspace_id) flow_id = create_flow(workspace_id=workspace_id, filename='flow.yml') expected_text = 'text:hey, dude' response = assert_request( method='post', url=f'http://{GATEWAY_HOST}:{GATEWAY_PORT}/search', payload={'top_k': 10, 'data': [expected_text]}, ) print(f'Response is: {response}') assert expected_text + ' hurray' * 2 == response['data']['docs'][0]['text'] assert_request( method='get', url=f'http://{JINAD_HOST}:{JINAD_PORT}/flows/{flow_id}' ) assert_request( method='delete', url=f'http://{JINAD_HOST}:{JINAD_PORT}/flows/{flow_id}', )	[ 1, 1053, 2897, 13, 13, 5215, 11451, 1688, 13, 13, 3166, 432, 1099, 1053, 4770, 4381, 29918, 3069, 1649, 29892, 4770, 14695, 29918, 3069, 1649, 13, 3166, 6317, 3952, 6774, 1053, 1653, 29918, 1287, 3493, 29892, 4480, 29918, 1454, 29918, 1287, 3493, 29892, 1653, 29918, 1731, 29892, 4974, 29918, 3827, 13, 13, 2764, 29918, 3972, 353, 2897, 29889, 2084, 29889, 25721, 29898, 359, 29889, 2084, 29889, 370, 1028, 493, 22168, 1445, 1649, 876, 13, 19438, 29918, 21053, 353, 2897, 29889, 2084, 29889, 7122, 29898, 2764, 29918, 3972, 29892, 525, 14695, 29899, 19438, 29889, 21053, 1495, 13, 1731, 29918, 25162, 353, 2897, 29889, 2084, 29889, 7122, 29898, 2764, 29918, 3972, 29892, 525, 1731, 29889, 21053, 1495, 13, 13, 29967, 1177, 3035, 29918, 20832, 353, 4770, 4381, 29918, 3069, 1649, 13, 29954, 3040, 12982, 29979, 29918, 20832, 353, 4770, 4381, 29918, 3069, 1649, 13, 29967, 1177, 3035, 29918, 15082, 353, 29871, 29947, 29900, 29900, 29900, 13, 29954, 3040, 12982, 29979, 29918, 15082, 353, 29871, 29946, 29945, 29953, 29941, 29900, 13, 13, 13, 29992, 2272, 1688, 29889, 3502, 29889, 11014, 877, 28789, 328, 411, 10346, 29899, 19438, 451, 6969, 363, 1286, 1495, 13, 29992, 2272, 1688, 29889, 3502, 29889, 3207, 300, 374, 911, 877, 14695, 29918, 19438, 742, 518, 19438, 29918, 21053, 1402, 26377, 29922, 1839, 14695, 29918, 19438, 11287, 13, 1753, 1243, 29918, 12857, 29918, 29882, 431, 29918, 16519, 1860, 29898, 14695, 29918, 19438, 1125, 13, 1678, 664, 3493, 29918, 333, 353, 1653, 29918, 1287, 3493, 29898, 1445, 24772, 11759, 1731, 29918, 25162, 2314, 13, 1678, 4974, 4480, 29918, 1454, 29918, 1287, 3493, 29898, 1287, 3493, 29918, 333, 29897, 13, 1678, 4972, 29918, 333, 353, 1653, 29918, 1731, 29898, 1287, 3493, 29918, 333, 29922, 1287, 3493, 29918, 333, 29892, 10422, 2433, 1731, 29889, 21053, 1495, 13, 1678, 3806, 29918, 726, 353, 525, 726, 29901, 354, 29891, 29892, 868, 311, 29915, 13, 1678, 2933, 353, 4974, 29918, 3827, 29898, 13, 4706, 1158, 2433, 2490, 742, 13, 4706, 3142, 29922, 29888, 29915, 1124, 597, 29912, 29954, 3040, 12982, 29979, 29918, 20832, 6177, 29912, 29954, 3040, 12982, 29979, 29918, 15082, 6822, 4478, 742, 13, 4706, 20092, 3790, 29915, 3332, 29918, 29895, 2396, 29871, 29896, 29900, 29892, 525, 1272, 2396, 518, 9684, 29918, 726, 29962, 1118, 13, 1678, 1723, 13, 1678, 1596, 29898, 29888, 29915, 5103, 338, 29901, 426, 5327, 29913, 1495, 13, 13, 1678, 4974, 3806, 29918, 726, 718, 525, 12166, 764, 29915, 334, 29871, 29906, 1275, 2933, 1839, 1272, 16215, 2640, 2033, 29961, 29900, 22322, 726, 2033, 13, 13, 1678, 4974, 29918, 3827, 29898, 13, 4706, 1158, 2433, 657, 742, 3142, 29922, 29888, 29915, 1124, 597, 29912, 29967, 1177, 3035, 29918, 20832, 6177, 29912, 29967, 1177, 3035, 29918, 15082, 6822, 1731, 29879, 19248, 1731, 29918, 333, 10162, 13, 1678, 1723, 13, 1678, 4974, 29918, 3827, 29898, 13, 4706, 1158, 2433, 8143, 742, 13, 4706, 3142, 29922, 29888, 29915, 1124, 597, 29912, 29967, 1177, 3035, 29918, 20832, 6177, 29912, 29967, 1177, 3035, 29918, 15082, 6822, 1731, 29879, 19248, 1731, 29918, 333, 29913, 742, 13, 1678, 1723, 13, 2 ]
beryllia/util.py	examknow/beryllia	0	45089	import re, traceback from datetime import timedelta from ipaddress import ip_address, IPv4Address, IPv6Address from ipaddress import ip_network, IPv4Network, IPv6Network from typing import List, Optional, Set, Tuple, Union from ircrobots import Server from irctokens import build from ircchallenge import Challenge from ircrobots.matching import ANY, Folded, Response, SELF from ircstates.numerics import * # not in ircstates.numerics RPL_STATS = "249" RPL_ENDOFSTATS = "219" RE_OPERNAME = re.compile(r"^is opered as (\S+)(?:,\|$)") SECONDS_MINUTES = 60 SECONDS_HOURS = SECONDS_MINUTES60 SECONDS_DAYS = SECONDS_HOURS24 SECONDS_WEEKS = SECONDS_DAYS7 TIME_UNITS_SHORT = list(zip( "wdhms", [SECONDS_WEEKS, SECONDS_DAYS, SECONDS_HOURS, SECONDS_MINUTES, 1] )) TIME_UNITS_LONG = list(zip( ["weeks", "days", "hours", "minutes", "seconds"], [SECONDS_WEEKS, SECONDS_DAYS, SECONDS_HOURS, SECONDS_MINUTES, 1] )) def pretty_delta( delta: timedelta, max_units: int=2, long: bool=False ) -> str: denominator = int(delta.total_seconds()) outs: List[str] = [] time_units = TIME_UNITS_LONG if long else TIME_UNITS_SHORT for unit, div in time_units: numerator, denominator = divmod(denominator, div) if numerator > 0: sep = " " if long else "" outs.append(f"{numerator}{sep}{unit}") if len(outs) == max_units: break return (" " if long else "").join(outs) async def oper_up( server: Server, oper_name: str, oper_file: str, oper_pass: str): try: challenge = Challenge(keyfile=oper_file, password=<PASSWORD>) except Exception: traceback.print_exc() else: await server.send(build("CHALLENGE", [oper_name])) challenge_text = Response(RPL_RSACHALLENGE2, [SELF, ANY]) challenge_stop = Response(RPL_ENDOFRSACHALLENGE2, [SELF]) #:lithium.libera.chat 740 sandcat :foobarbazmeow #:lithium.libera.chat 741 sandcat :End of CHALLENGE while True: challenge_line = await server.wait_for({ challenge_text, challenge_stop }) if challenge_line.command == RPL_RSACHALLENGE2: challenge.push(challenge_line.params[1]) else: retort = challenge.finalise() await server.send(build("CHALLENGE", [f"+{retort}"])) break async def get_whois( server: Server, nickname: str ) -> Optional[Tuple[str, Optional[str]]]: await server.send(build("WHOIS", [nickname])) whois_mask = Response(RPL_WHOISUSER, [SELF, Folded(nickname)]) whois_oper = Response(RPL_WHOISOPERATOR, [SELF, Folded(nickname)]) whois_end = Response(RPL_ENDOFWHOIS, [SELF, Folded(nickname)]) whois_line = await server.wait_for({whois_mask, whois_end}) if whois_line.command == RPL_WHOISUSER: nick, user, host = whois_line.params[1:4] mask = f"{nick}!{user}@{host}" whois_line = await server.wait_for({whois_oper, whois_end}) if whois_line.command == RPL_WHOISOPERATOR: match = RE_OPERNAME.search(whois_line.params[2]) if match is not None: return (mask, match.group(1)) return (mask, None) else: return None async def get_statsp(server: Server) -> List[Tuple[str, str]]: await server.send(build("STATS", ["p"])) statsp_texts: List[str] = [] while True: statsp_line = await server.wait_for({ Response(RPL_STATS, [SELF, "p", ANY]), Response(RPL_ENDOFSTATS, [SELF, "p"]) }) if statsp_line.command == RPL_STATS: statsp_texts.append(statsp_line.params[2]) else: break opers: List[Tuple[str, str]] = [] for statsp_text in statsp_texts[:-1]: nick, _ = statsp_text.split(" ", 1) whois = await get_whois(server, nick) if whois is not None: mask, oper = whois if oper is not None: opers.append((oper, mask)) return opers def try_parse_ip( ip: str ) -> Optional[Union[IPv4Address, IPv6Address]]: try: return ip_address(ip) except ValueError: return None def try_parse_cidr( cidr: str ) -> Optional[Union[IPv4Network, IPv6Network]]: try: return ip_network(cidr, strict=False) except ValueError: return None def find_unescaped(s: str, c: Set[str]) -> List[int]: indexes: List[int] = [] i = 0 while i < len(s): c2 = s[i] if c2 == "\\": i += 1 elif c2 in c: indexes.append(i) i += 1 return indexes WILDCARDS_GLOB = {"?": "_", "": "%"} WILDCARDS_SQL = set("_%") def glob_to_sql(glob: str) -> str: to_find = WILDCARDS_SQL \| set(WILDCARDS_GLOB.keys()) to_escape = find_unescaped(glob, to_find) glob_l = list(glob) for index in to_escape: char = glob_l[index] # escape sql wildcard characters if char in WILDCARDS_SQL: glob_l[index] = f"\\{char}" # switch glob wildcards to sql wildcards elif char in WILDCARDS_GLOB: glob_l[index] = WILDCARDS_GLOB[char] return "".join(glob_l) def looks_like_glob(s: str) -> bool: return bool(set(s) & set("?"))	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boa3_test/test_sc/built_in_methods_test/StrSplit.py	hal0x2328/neo3-boa	25	119908	from typing import List from boa3.builtin import public @public def main(string: str, sep: str, maxsplit: int) -> List[str]: return string.split(sep, maxsplit)	[ 1, 515, 19229, 1053, 2391, 13, 13, 3166, 1045, 29874, 29941, 29889, 16145, 262, 1053, 970, 13, 13, 13, 29992, 3597, 13, 1753, 1667, 29898, 1807, 29901, 851, 29892, 16345, 29901, 851, 29892, 4236, 5451, 29901, 938, 29897, 1599, 2391, 29961, 710, 5387, 13, 1678, 736, 1347, 29889, 5451, 29898, 19570, 29892, 4236, 5451, 29897, 13, 2 ]
app/views.py	bzg/ApiViz	4	107257	<gh_stars>1-10 #!/usr/bin/env python # -- encoding: utf-8 -- ### good encoding of flash messages ### cf : https://stackoverflow.com/questions/8924014/how-to-handle-my-unicodedecodeerror import sys reload(sys) sys.setdefaultencoding('utf-8') ### import all from app.__init__ from . import * from flask import jsonify, flash, render_template, \ url_for, make_response, request, redirect, \ send_file # from werkzeug.security import generate_password_hash, check_password_hash from werkzeug.exceptions import BadRequest ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### ### AUTH - TOKEN ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### ######################################################################################## # Access token ### from prettyprinted youtube channel """ def token_required(f): @wraps(f) def decorated( args, kwargs ): token = None if 'x-access-token' in request.headers: token = request.headers['x-access-token'] if not token: return jsonify({'message' : 'Token is missing!'}), 401 try: data = jwt.decode(token, app.config['SECRET_KEY']) current_user = User.query.filter_by(public_id=data['public_id']).first() except: return jsonify({'message' : 'Token is invalid!'}), 401 return f(current_user, args, **kwargs) return decorated """ ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### ### ERRORS HANDLERS ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### @app.errorhandler(403) @app.errorhandler(404) @app.errorhandler(500) @app.route("/error/<int:err_code>", defaults={ "error": BadRequest }) def errorHandler(error, err_code=400): if err_code == 404 : # \| error.code == 404 : error_code = 404 template = "errors/404.html" elif err_code == 403 : # \| error.code == 403 : error_code = 403 template = "errors/403.html" elif err_code == 500 : # \| error.code == 500 : error_code = 500 template = "errors/500.html" else : error_code = 400 template = "errors/400.html" app_config = getDocuments(mongo_config_global) return render_template( template, config_name = config_name, # prod or default... site_section = "error", error_code = str(error_code), app_metas = app_metas, app_config = app_config, language = "fr" , languages_dict = app_languages_dict , # error_msg = u"accès interdit", # user_infos = current_user.get_public_infos ), error_code ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### ### CONFIG ROUTES - BACKEND API ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### def DocOidToString(data): # log_app.debug("data : %s", data) obj = {} for key in data: if isinstance(data[key], ObjectId): obj[key] = str(data[key]) else: obj[key] = data[key] # log_app.debug("obj : %s", obj) return obj def getDocuments(collection, query={}, oid_to_id=True, as_list=False, field="field") : ### query collection and transform as list results = list(collection.find(query) ) # log_app.debug("config app route / results - 1 : \n%s", pformat(results) ) ### ObjectId to string if oid_to_id : results = [ DocOidToString(i) for i in results ] # log_app.debug("config app route / results - 2 : \n%s", pformat(results) ) ### list to dict if as_list == False : results = { i[field] : i for i in results } # log_app.debug("config app route / results - 3 : \n%s", pformat(results) ) return results def checkJWT(token, url_check="http://localhost:4100/api/auth/tokens/confirm_access"): ### TO DO return True @app.route('/backend/api/config/<string:collection>/<string:doc_id>', methods=['GET','POST','DELETE']) @app.route('/backend/api/config/<string:collection>', methods=['GET'], defaults={"doc_id" : None}) @app.route('/backend/api/config', methods=['GET'], defaults={'collection': 'global', "doc_id" : None}) def config_global(collection, doc_id=None): """ Main route to GET and POST/PUT/DELETE choices : global \| endpoints \| styles \| routes \| socials variables : <collection> and <doc_id> arguments : as_list (bool), field (str) example : http://localhost:8100/backend/api/config?as_list=true """ log_app.debug("config app route") log_app.debug("config app route / collection : %s", collection ) log_app.debug("config app route / doc_id : %s", doc_id ) ### target right config collection if collection in ["global" , "footer", "navbar", "endpoints" , "styles" , "routes", "socials" ] : mongoColl = mongoConfigColls[collection] ### imported from . (and from there from .api.__init__ ) else : log_app.warning("error : -%s- is not a valid config collection (redirect)", collection) return redirect( "/error/400" ) ### get request args if any as_list = request.args.get('as_list', default=False, type=bool) field = request.args.get('field', default="field", type=str) token = request.args.get('token', default=None, type=str) log_app.debug("config app route / as_list : %s", as_list ) ### filter out field arg to unique identifiers fields in documents if field not in ['_id', 'field'] : field = 'field' ### build query if any query = {} if doc_id : query = {"_id" : ObjectId(doc_id)} ### check if token allows user to POST if token : is_authorized = checkJWT(token) ### TO DO if request.method == 'POST': if is_authorized : return "hello config master / POST ... praise be" else : return "noooope" elif request.method == 'DELETE': if is_authorized : return "hello config master / DELETE ... praise be" else : return "noooope" elif request.method == 'GET': app_config_dict = getDocuments(mongoColl, query=query, as_list=as_list, field=field) return jsonify( { "msg" : "this is the results from your query on the '%s' config collection" % collection, "query" : query, "request" : { "url" : request.url, "args" : request.args, "method" : request.method, "collection" : collection, "doc_id" : doc_id, }, "app_config" : app_config_dict } ) ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### ### CLIENT ROUTES ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### # @app.route('/', methods=['GET']) # @app.route('/home/<string:lang>', methods=['GET']) # @app.route('/home', methods=['GET'], defaults={"lang":"en"}) # def home(lang="fr"): # log_app.debug("entering new home page") # app_config = getDocuments(mongo_config_global) # log_app.debug("app_config :/n%s", pformat(app_config)) # if lang == "fr" : # template = "new-home.html" # else : # template = "new-home-english.html" # return render_template( # template, # config_name = config_name, # prod, testing, default... # app_config = app_config, # app_metas = app_metas, # language = lang, # ) # @app.route('/tools/<string:lang>', methods=['GET']) # @app.route('/tools', methods=['GET'], defaults={"lang":"en"}) # def Tools(lang="en"): # log_app.debug("entering tools page") # app_config = getDocuments(mongo_config_global) # if lang == "fr" : # template = "les-outils.html" # else : # template = "les-outils-english.html" # return render_template( # template, # config_name = config_name, # prod, testing, default... # app_metas = app_metas, # app_config = app_config, # language = lang # ) @app.route('/sonum-banner-carto/<string:lang>', methods=['GET']) @app.route('/sonum-banner-carto', methods=['GET'], defaults={"lang":"fr"}) def Banner(lang="fr"): log_app.debug("entering test banner-carto page") app_config = getDocuments(mongo_config_global) if lang == "fr" : template = "sonum-banner-carto.html" else : template = "sonum-banner-carto.html" return render_template( template, config_name = config_name, # prod, testing, default... app_metas = app_metas, app_config = app_config, language = lang ) @app.route('/', methods=['GET','POST'],defaults={'path': ''}) @app.route('/<path:path>', methods=['GET','POST']) def spa(path): log_app.debug("entering SPA page") app_config = getDocuments(mongo_config_global) return render_template( "spa.html", config_name = config_name, # prod, testing, default... app_config = app_config, app_metas = app_metas, language = "fr" ) ANTI_SPAM_FIELD_NAME = "userMiddlename" ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### ### FILES ROUTES ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### @app.route('/download/<file_ext>/<file_name>', methods=['GET'] ) # this is a job for GET, not POST def download_file(file_ext, file_name): """ send file from server """ log_app.debug("file_name : %s ", file_name) log_app.debug("file_ext : %s ", file_ext) log_app.info("file_ext in AUTHORIZED_FILETYPES_LIST: %s", (file_ext in AUTHORIZED_FILETYPES_LIST) ) if file_ext in AUTHORIZED_FILETYPES_LIST : file_mimetype = AUTHORIZED_FILETYPES_DICT[file_ext]["mimetype"] file_foldername = AUTHORIZED_FILETYPES_DICT[file_ext]["folder"] file_folder = "static/{}/".format(file_foldername) file_name_ext = "{}.{}".format(file_name, file_ext) full_filepath = file_folder + file_name_ext try : return send_file( full_filepath, mimetype = file_mimetype, attachment_filename = file_name_ext, as_attachment = True ) except : log_app.error("downloading this file is not working: %s.%s ", file_name, file_ext ) return redirect(url_for('home')) else : log_app.error("downloading this file is not authorized: %s.%s ", file_name, file_ext ) return redirect(url_for('home'))	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 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lib/utils/blob.py	TheRevanchist/DeepWatershedDetection	0	13327	<reponame>TheRevanchist/DeepWatershedDetection # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by <NAME> - extended by <NAME> # -------------------------------------------------------- """Blob helper functions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import cv2 import random def im_list_to_blob(ims): """Convert a list of images into a network input. Assumes images are already prepared (means subtracted, BGR order, ...). """ max_shape = np.array([im.shape for im in ims]).max(axis=0) num_images = len(ims) blob = np.zeros((num_images, max_shape[0], max_shape[1], 3), dtype=np.float32) for i in range(num_images): im = ims[i] blob[i, 0:im.shape[0], 0:im.shape[1], :] = im return blob def prep_im_for_blob(im, pixel_means, global_scale, args): """Mean subtract and scale an image for use in a blob.""" im = im.astype(np.float32, copy=False) # substract mean if args.substract_mean == "True": im -= pixel_means # do global scaling im = cv2.resize(im, None, None, fx=global_scale, fy=global_scale, interpolation=cv2.INTER_LINEAR) im_size_max = np.max(im.shape[0:2]) # Prevent the biggest axis from being more than MAX_SIZE if im_size_max > args.max_edge: if not args.crop == "True": # scale down if bigger than max size re_scale = (float(args.max_edge) / float(im_size_max)) im = cv2.resize(im, None, None, fx=re_scale, fy=re_scale, interpolation=cv2.INTER_LINEAR) global_scale = global_scalere_scale crop_box = [0,0,im.shape[0],im.shape[1]] else: # Crop image topleft = random.uniform(0,1)<args.crop_top_left_bias # crop to max size if necessary if im.shape[0] <= args.max_edge or topleft: crop_0 = 0 else: crop_0 = random.randint(0,im.shape[0]-args.max_edge) if im.shape[1] <= args.max_edge or topleft: crop_1 = 0 else: crop_1 = random.randint(0,im.shape[1]-args.max_edge) crop_box = [crop_0, crop_1, min(crop_0+args.max_edge,im.shape[0]), min(crop_1+args.max_edge,im.shape[1])] im = im[crop_box[0]:crop_box[2],crop_box[1]:crop_box[3]] else: crop_box = [0, 0, im.shape[0], im.shape[1]] if not args.pad_to == 0: # pad to fit RefineNet #TODO fix refinenet padding problem y_mulity = int(np.ceil(im.shape[0] / float(args.pad_to))) x_mulity = int(np.ceil(im.shape[1] / float(args.pad_to))) canv = np.ones([y_mulity args.pad_to, x_mulity * args.pad_to,3], dtype=np.uint8) * 255 canv[0:im.shape[0], 0:im.shape[1]] = im im = canv return im, global_scale, crop_box	[ 1, 529, 276, 1112, 420, 29958, 1576, 1123, 3703, 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29896, 29962, 847, 5785, 29898, 5085, 29889, 8305, 29918, 517, 4961, 13, 1678, 508, 29894, 353, 7442, 29889, 2873, 4197, 29891, 29918, 16109, 537, 334, 6389, 29889, 8305, 29918, 517, 29892, 921, 29918, 16109, 537, 334, 6389, 29889, 8305, 29918, 517, 29892, 29941, 1402, 26688, 29922, 9302, 29889, 13470, 29947, 29897, 334, 29871, 29906, 29945, 29945, 13, 1678, 508, 29894, 29961, 29900, 29901, 326, 29889, 12181, 29961, 29900, 1402, 29871, 29900, 29901, 326, 29889, 12181, 29961, 29896, 5262, 353, 527, 13, 1678, 527, 353, 508, 29894, 13, 13, 29871, 736, 527, 29892, 5534, 29918, 7052, 29892, 274, 1336, 29918, 1884, 13, 13, 2 ]
src/utils/torch_common.py	quochungto/SIIM-COVID19-Detection	0	7687	import os import gc import random import numpy as np import torch def seed_everything(seed): os.environ['PYTHONHASHSEED'] = str(seed) random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True def memory_cleanup(): """ Cleans up GPU memory https://github.com/huggingface/transformers/issues/1742 """ for obj in gc.get_objects(): if torch.is_tensor(obj): del obj gc.collect() torch.cuda.empty_cache()	[ 1, 1053, 2897, 13, 5215, 330, 29883, 13, 5215, 4036, 13, 5215, 12655, 408, 7442, 13, 5215, 4842, 305, 13, 13, 1753, 16717, 29918, 17991, 1918, 29898, 26776, 1125, 13, 1678, 2897, 29889, 21813, 1839, 20055, 4690, 1164, 29950, 24943, 1660, 3352, 2033, 353, 851, 29898, 26776, 29897, 13, 1678, 4036, 29889, 26776, 29898, 26776, 29897, 13, 1678, 7442, 29889, 8172, 29889, 26776, 29898, 26776, 29897, 13, 1678, 4842, 305, 29889, 11288, 29918, 26776, 29898, 26776, 29897, 13, 1678, 4842, 305, 29889, 29883, 6191, 29889, 11288, 29918, 26776, 29898, 26776, 29897, 13, 1678, 4842, 305, 29889, 1627, 1975, 29889, 29883, 566, 15755, 29889, 4801, 837, 262, 4695, 353, 5852, 13, 1678, 4842, 305, 29889, 1627, 1975, 29889, 29883, 566, 15755, 29889, 1785, 16580, 353, 5852, 13, 268, 13, 13, 1753, 3370, 29918, 14941, 786, 7295, 13, 1678, 9995, 13, 1678, 21386, 550, 701, 22796, 3370, 13, 1678, 2045, 597, 3292, 29889, 510, 29914, 29882, 688, 3460, 2161, 29914, 9067, 414, 29914, 12175, 29914, 29896, 29955, 29946, 29906, 13, 1678, 9995, 13, 1678, 363, 5446, 297, 330, 29883, 29889, 657, 29918, 12650, 7295, 13, 4706, 565, 4842, 305, 29889, 275, 29918, 20158, 29898, 5415, 1125, 13, 9651, 628, 5446, 13, 13, 1678, 330, 29883, 29889, 15914, 580, 13, 1678, 4842, 305, 29889, 29883, 6191, 29889, 6310, 29918, 8173, 580, 13, 2 ]
settings.py	rshorey/ms-match	0	172530	<filename>settings.py<gh_stars>0 from collections import OrderedDict #pull this off your google sheet sheet_id = '125NM05dqHKy3-97GkieVwn4V5Iy0Vj-w7erh9tAJxlA' #do new entries appear immediately on the site #or do they need to be approved first? require_approval = False #what do you want the fields to be called? #the cannonical names are keys, the names you want to display are values #don't remove any keys, things will crash #if you don't want a field to appear, keep the key but make the value None fieldnames = OrderedDict([('Name','Name'), ('Description','Description'), ('Category','Category'), ('Address1',None), ('Address2',None), ('City','City'), ('State','State'), ('Zip','Zipcode'), ('Phone','Phone number'), ('Website','Website'), ('Email','Email Address') ]) #what categories are allowed? Put in as many as you want but #they'll display in a menu so if you do tons it'll look bad #the first item will be the default categories = ['Other', 'Stuff', 'Things']	[ 1, 529, 9507, 29958, 11027, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 16250, 1053, 8170, 287, 21533, 13, 13, 29937, 26746, 445, 1283, 596, 5386, 9869, 13, 9855, 29918, 333, 353, 525, 29896, 29906, 29945, 29940, 29924, 29900, 29945, 29881, 29939, 29950, 29968, 29891, 29941, 29899, 29929, 29955, 29954, 19501, 29963, 1233, 29946, 29963, 29945, 29902, 29891, 29900, 29963, 29926, 29899, 29893, 29955, 261, 29882, 29929, 29873, 29909, 29967, 15524, 29909, 29915, 13, 13, 29937, 1867, 716, 9976, 2615, 7389, 373, 278, 3268, 13, 29937, 272, 437, 896, 817, 304, 367, 23454, 937, 29973, 13, 12277, 29918, 9961, 791, 353, 7700, 13, 13, 13, 29937, 5816, 437, 366, 864, 278, 4235, 304, 367, 2000, 29973, 13, 29937, 1552, 508, 5464, 936, 2983, 526, 6611, 29892, 278, 2983, 366, 864, 304, 2479, 526, 1819, 13, 29937, 9176, 29915, 29873, 3349, 738, 6611, 29892, 2712, 674, 8095, 13, 29937, 361, 366, 1016, 29915, 29873, 864, 263, 1746, 304, 2615, 29892, 3013, 278, 1820, 541, 1207, 278, 995, 6213, 13, 2671, 7039, 353, 8170, 287, 21533, 4197, 877, 1170, 3788, 1170, 5477, 13, 4706, 6702, 9868, 3788, 9868, 5477, 13, 4706, 6702, 10900, 3788, 10900, 5477, 13, 4706, 6702, 7061, 29896, 742, 8516, 511, 13, 4706, 6702, 7061, 29906, 742, 8516, 511, 13, 4706, 6702, 16885, 3788, 16885, 5477, 13, 4706, 6702, 2792, 3788, 2792, 5477, 13, 4706, 6702, 26264, 3788, 26264, 401, 5477, 13, 4706, 6702, 9861, 3788, 9861, 1353, 5477, 13, 4706, 6702, 3609, 2746, 3788, 3609, 2746, 5477, 13, 4706, 6702, 9823, 3788, 9823, 16428, 1495, 13, 308, 2314, 13, 13, 29937, 5816, 13997, 526, 6068, 29973, 12065, 297, 408, 1784, 408, 366, 864, 541, 13, 29937, 19562, 29915, 645, 2479, 297, 263, 6143, 577, 565, 366, 437, 23864, 372, 29915, 645, 1106, 4319, 13, 29937, 1552, 937, 2944, 674, 367, 278, 2322, 13, 20683, 353, 6024, 16107, 742, 525, 855, 3096, 742, 525, 1349, 886, 2033, 2 ]
dbtest.py	AnykeyNL/AutonomousDB_AutonomousLinux_Python	0	101984	import cx_Oracle DB = "xxx_high" DB_USER = "admin" DB_PASSWORD = "password" connection = cx_Oracle.connect(DB_USER, DB_PASSWORD, DB) print ("Connected")	[ 1, 1053, 28232, 29918, 29949, 10792, 13, 13, 4051, 353, 376, 12353, 29918, 9812, 29908, 13, 4051, 29918, 11889, 353, 376, 6406, 29908, 13, 4051, 29918, 25711, 17013, 353, 376, 5630, 29908, 13, 13, 9965, 353, 28232, 29918, 29949, 10792, 29889, 6915, 29898, 4051, 29918, 11889, 29892, 6535, 29918, 25711, 17013, 29892, 6535, 29897, 13, 13, 2158, 4852, 20971, 2954, 1159, 13, 13, 13, 2 ]
code_examples/popart/block_sparse/examples/test_block_sparse.py	payoto/graphcore_examples	260	105344	<reponame>payoto/graphcore_examples # Copyright (c) 2020 Graphcore Ltd. All rights reserved. import os import numpy as np from functools import reduce from operator import mul import popart import pytest # range for filling blocks MATRIX_LOW_VALUE = -10 MATRIX_HIGH_VALUE = 10 # library provides support for 3 kinds of sparse MM's # from set of 2 inputs and one output, 2 of them will be dense # and the third will be sparse g_sparseMatMulTypeLookup = { 'DENSE_LHS_SPARSE_RHS_DENSE_OUT': 0, 'DENSE_LHS_DENSE_RHS_SPARSE_OUT': 1, 'SPARSE_LHS_SPARSE_RHS_SPARSE_OUT': 2 } # g_ -> global # g_input_data_type = "float32" g_output_data_type = "float32" g_pp_data_type = "float32" np.set_printoptions(linewidth=500) g_random_sparse_mask = np.random.RandomState() g_random_data = np.random.RandomState() g_random_labels = np.random.RandomState() """ Set seeds of random generatorts. """ g_random_sparse_mask.seed(1) g_random_data.seed(1) g_random_labels.seed(1) def create_sparse_list(dims, block_size, sparsity, initial_value=0): """ dims: dimensions of the sparse matrix block_size: size of a block (8x8, 16x16 etc) sparsity: sparsity level (0.4 means 40% of blocks are empty) returns-- block_sparse_matrix: np.array of num_blocks * block_sz dense_matrix: np.array if size dim with a dense representation of the matrix. i.e. explcit zeros for a zero block. Used to perform dense MM's for a reference output mask: list with sparsity pattern. size = num_blocks (both dense and sparse) e.g for a sparse matrix of size (6, 6) with block size 2x2 Matrix contains 9 blocks of size 2x2, some sparse and some dense If the 6x6 matrices has 2 non zero blocks, then .. Inputs: dims = [6, 6] block_size = [2,2] sparsity = 0.4 (say) Outputs: block_sparse_matrix = 2 x 4 array dense_matrix = 6x6 array sparsity = 9x1 list """ block_size_row = block_size[0] block_size_col = block_size[1] num_block_rows = dims[0] // block_size_row num_block_cols = dims[1] // block_size_col assert(sparsity < 1.0) proportion = [sparsity, 1 - sparsity] mask = g_random_sparse_mask.choice([0, 1], size=(num_block_rows, num_block_cols), p=proportion) # dont want mask to be all zeros while np.all(mask == 0): mask = g_random_sparse_mask.choice([0, 1], size=(num_block_rows, num_block_cols), p=proportion) if initial_value == 0: dense_matrix = np.zeros((num_block_rows * block_size_row, num_block_cols * block_size_col)) else: dense_matrix = np.empty((num_block_rows * block_size_row, num_block_cols * block_size_col)) dense_matrix.fill(initial_value) block_sparse_matrix = [] for block_row in range(num_block_rows): for block_col in range(num_block_cols): if mask[block_row][block_col]: block_data = g_random_data.randint(low=MATRIX_LOW_VALUE, high=MATRIX_HIGH_VALUE, size=block_size_row * block_size_col).astype("float32") block_sparse_matrix.append(block_data) dense_matrix[block_row * block_size_row: (block_row+1) * block_size_row, block_col * block_size_col: (block_col+1) * block_size_col] = block_data.reshape(block_size_row, block_size_col) # At this point mask is a 2D array, flatten it into 1D list and return, bsr_rhs is already a list (so convert to array) return np.array(block_sparse_matrix), dense_matrix, mask.flatten().tolist() def create_dense_matrix(dims): return g_random_data.randint(low=MATRIX_LOW_VALUE, high=MATRIX_HIGH_VALUE, size=dims).astype(g_input_data_type) def create_sparse_matrix(nominal_shape, block_size, sparsity, initial_value=0): """ Create a sparse_matrix. Inputs: nominal_shape: List of dimensions of the sparse tensor e.g (2, 3, 4, 4) block_size : size of each block (e.g. [8, 8, 8]) sparsity : block sparsity level (0.4 means, 40% of blocks are zeros) Outputs: bsr : sparse representation of matrix (nnz_blocks * block size) lengths_per_2d_plane: List with num-non-zero blocks per group dim. i.e. for a (2, 3, 4, 4) tensor with 2 nnz_blocks in each 4x4 matrix, this will have shape of 6x1 and each row storing 2 dense_matrix: dense representation of the matrix (for ref calc) mask : list of num_blocks (1 for non-zero blocks and 0 for others) """ # skip last two dimensions # last 2 dims enter the MM, others form the group num_grouped_dims = reduce(mul, nominal_shape[:-2], 1) rows = nominal_shape[-2] cols = nominal_shape[-1] # Create dense matrix of nominal dims if initial_value == 0: dense_matrix = np.zeros(nominal_shape).astype(g_input_data_type) else: dense_matrix = np.empty(nominal_shape).astype(g_input_data_type) dense_matrix.fill(initial_value) dense_matrix = dense_matrix.reshape((num_grouped_dims, rows, cols)) dims = [nominal_shape[-2], nominal_shape[-1]] bsr = [] bsr_lengths_per_2d_plane = [] mask = [] for dim in range(num_grouped_dims): _bsr, dense_matrix[dim], _mask = create_sparse_list(dims, block_size, sparsity, initial_value) # _bsr comes as array # _mask comes as list bsr.extend(_bsr) mask.extend(_mask) bsr_lengths_per_2d_plane.append(_bsr.shape[0]) dense_matrix = dense_matrix.reshape(nominal_shape) mask = np.array(mask) block_size_row = block_size[0] block_size_col = block_size[1] num_block_rows = dims[0] // block_size_row num_block_cols = dims[1] // block_size_col # all parameters are returned as numpy arrays return np.array(bsr), np.array(bsr_lengths_per_2d_plane), dense_matrix, mask def mm(lhs, rhs): return np.matmul(lhs, rhs) # Stable softmax numpy implementation def softmax(x): x_max = np.max(x, axis = -1) x = x - np.expand_dims(x_max, axis=-1) x = np.exp(x) x_sum = np.sum(x, axis=-1) x = x / np.expand_dims(x_sum, axis=-1) return x def sparse_mm_infer(sparse_mm_type, lhs_dims, vanilla_rhs_dims, block_size, sparsity_level, transpose_rhs, memory_cycle_ratio, inner_group_size): """ """ if transpose_rhs: matmul_dims = [lhs_dims[-2], vanilla_rhs_dims[-1], vanilla_rhs_dims[-2]] else: matmul_dims = [lhs_dims[-2], vanilla_rhs_dims[-2], vanilla_rhs_dims[-1]] lhs = create_dense_matrix(lhs_dims) if sparse_mm_type == g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT']: bsr_rhs, lengths_per_2d_plane, vanilla_rhs, sparsity_mask = create_sparse_matrix(vanilla_rhs_dims, block_size[1:], sparsity_level) rhs = bsr_rhs rhs_dims = bsr_rhs.shape elif sparse_mm_type == g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT']: output_dims = lhs_dims[:-1] output_dims.append(vanilla_rhs_dims[-1]) output_block_size = [block_size[0], block_size[2]] bsr_output, lengths_per_2d_plane, _, sparsity_mask = create_sparse_matrix(output_dims, output_block_size, sparsity_level) rhs_dims = vanilla_rhs_dims rhs = create_dense_matrix(rhs_dims) # Create a builder and construct a graph builder = popart.Builder() lhs_tensorInfo = popart.TensorInfo("FLOAT", lhs_dims) rhs_tensorInfo = popart.TensorInfo("FLOAT", rhs_dims) lhsTensor = builder.addInputTensor(lhs_tensorInfo) rhsTensor = builder.addInputTensor(rhs_tensorInfo) outTensor = builder.customOp(opName = "BSMatMul", opVersion=1, domain = "ai.graphcore", inputs = [lhsTensor, rhsTensor], attributes = { "bsr_rhs_lengths_per_2d_plane": lengths_per_2d_plane.tolist(), "matrix_dims": matmul_dims, "block_size": block_size, "sparsity_mask": sparsity_mask.tolist(), "bsmatmul_type": sparse_mm_type, "transpose_rhs": transpose_rhs, "memory_cycle_ratio": memory_cycle_ratio, "inner_group_size": inner_group_size, "in_type": g_input_data_type, "out_type": g_output_data_type, "pp_type": g_pp_data_type })[0] builder.addOutputTensor(outTensor) proto = builder.getModelProto() # Describe how to run the model dataFlow = popart.DataFlow(1, {outTensor: popart.AnchorReturnType("ALL")}) # Create a session to compile and execute the graph session = popart.InferenceSession( fnModel=proto, dataFlow=dataFlow, deviceInfo=popart.DeviceManager().acquireAvailableDevice(1)) # Compile graph session.prepareDevice() # Create buffers to receive results from the execution anchors = session.initAnchorArrays() rhs = np.array(rhs, dtype=g_input_data_type) stepio = popart.PyStepIO({lhsTensor: lhs, rhsTensor: rhs}, anchors) session.run(stepio) ipuOutput = anchors[outTensor] if sparse_mm_type == g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT']: if transpose_rhs: transpose_indices = list(range(len(vanilla_rhs_dims))) transpose_indices[-2], transpose_indices[-1] = transpose_indices[-1], transpose_indices[-2] vanilla_rhs = vanilla_rhs.transpose(tuple(transpose_indices)) goldOutput = mm(lhs, vanilla_rhs) else: goldOutput = mm(lhs, vanilla_rhs) else: assert len(lhs.shape) == len(rhs.shape) if(len(lhs.shape) == 2): lhs = np.expand_dims(lhs, 0) rhs = np.expand_dims(rhs, 0) mmOutput = mm(lhs, rhs) totalGroupDims = int(np.prod(lhs_dims[:-2])) num_rows_sparsity_mask_2d = output_dims[-2] // block_size[0] num_cols_sparsity_mask_2d = output_dims[-1] // block_size[2] assert sparsity_mask.shape == (totalGroupDims * num_rows_sparsity_mask_2d * num_cols_sparsity_mask_2d,) mmOutput = mmOutput.reshape((totalGroupDims, lhs_dims[-2], rhs_dims[-1])) goldOutput = [] for dim in range(totalGroupDims): offset = num_rows_sparsity_mask_2d * num_cols_sparsity_mask_2d mmOutput_2d = mmOutput[dim] sliced_sparsity_mask = sparsity_mask[dim * offset: dim * offset + offset] for sparsity_mask_idx in range(len(sliced_sparsity_mask)): if sliced_sparsity_mask[sparsity_mask_idx]: mmOutput_2d_row_start = (sparsity_mask_idx // num_cols_sparsity_mask_2d) * block_size[0] mmOutput_2d_row_end = mmOutput_2d_row_start + block_size[0] mmOutput_2d_col_start = (sparsity_mask_idx % num_cols_sparsity_mask_2d) * block_size[2] mmOutput_2d_col_end = mmOutput_2d_col_start + block_size[2] mmOutput_2d_sliced = mmOutput_2d[mmOutput_2d_row_start: mmOutput_2d_row_end, mmOutput_2d_col_start: mmOutput_2d_col_end] goldOutput.append(mmOutput_2d_sliced.reshape(block_size[0] * block_size[2])) goldOutput = np.array(goldOutput) return ipuOutput, goldOutput def sparse_mm_train(sparse_mm_type, lhs_dims, vanilla_rhs_dims, block_size, sparsity_level, transpose_rhs, memory_cycle_ratio, inner_group_size): if transpose_rhs: matmul_dims = [lhs_dims[-2], vanilla_rhs_dims[-1], vanilla_rhs_dims[-2]] else: matmul_dims = [lhs_dims[-2], vanilla_rhs_dims[-2], vanilla_rhs_dims[-1]] lhs = create_dense_matrix(lhs_dims) if sparse_mm_type == g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT']: bsr_rhs, lengths_per_2d_plane, vanilla_rhs, sparsity_mask = create_sparse_matrix(vanilla_rhs_dims, block_size[1:], sparsity_level) rhs = bsr_rhs rhs_dims = bsr_rhs.shape elif sparse_mm_type == g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT']: output_dims = lhs_dims[:-1] output_dims.append(vanilla_rhs_dims[-1]) output_block_size = [block_size[0], block_size[2]] bsr_output, lengths_per_2d_plane, vanilla_output, sparsity_mask = create_sparse_matrix(output_dims, output_block_size, sparsity_level) lhs_inv = np.linalg.inv(lhs) rhs = np.matmul(lhs_inv, vanilla_output) rhs_dims = vanilla_rhs_dims # MODEL CREATION builder = popart.Builder() lhs_tensorInfo = popart.TensorInfo("FLOAT", lhs_dims) lhsTensor = builder.addInputTensor(lhs_tensorInfo) rhsTensor = builder.addInitializedInputTensor(rhs) outTensor = builder.customOp(opName = "BSMatMul", opVersion=1, domain = "ai.graphcore", inputs = [lhsTensor, rhsTensor], attributes = { "bsr_rhs_lengths_per_2d_plane": lengths_per_2d_plane.tolist(), "matrix_dims": matmul_dims, "block_size": block_size, "sparsity_mask": sparsity_mask.tolist(), "bsmatmul_type": sparse_mm_type, "transpose_rhs": transpose_rhs, "memory_cycle_ratio": memory_cycle_ratio, "inner_group_size": inner_group_size, "in_type": g_input_data_type, "out_type": g_output_data_type, "pp_type": g_pp_data_type })[0] builder.addOutputTensor(outTensor) probs = builder.aiOnnx.softmax([outTensor], axis=1) if sparse_mm_type == g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT']: labels_shape = lhs_dims[:-1] elif sparse_mm_type == g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT']: labels_shape = [np.sum(sparsity_mask)] label_tensorInfo = popart.TensorInfo("INT32", labels_shape) labelTensor = builder.addInputTensor(label_tensorInfo) loss = builder.aiGraphcore.nllloss([probs, labelTensor], debugContext = "nllLossVal") proto = builder.getModelProto() ####################### # Describe how to run the model anchor_desc = { outTensor: popart.AnchorReturnType("ALL"), loss: popart.AnchorReturnType("ALL") } dataFlow = popart.DataFlow(1, anchor_desc) label_data = g_random_labels.choice(9, labels_shape) session = popart.TrainingSession(fnModel=proto, loss=loss, deviceInfo=popart.DeviceManager().acquireAvailableDevice(1), optimizer=popart.ConstSGD(0.01), dataFlow=dataFlow) # Compile graph session.prepareDevice() # Create buffers to receive results from the execution anchors = session.initAnchorArrays() # TRAINING session.weightsFromHost() stepio = popart.PyStepIO({ lhsTensor: lhs, labelTensor: label_data}, anchors) session.run(stepio) def sparse_softmax(dims, block_size, sparsity_level, inner_group_size): """ """ sparse_input, lengths_per_2d_plane, dense_input, sparsity_mask = create_sparse_matrix(dims, block_size, sparsity_level, -1000) # Create a builder and construct a graph builder = popart.Builder() tensor_info = popart.TensorInfo("FLOAT", sparse_input.shape) input_tensor = builder.addInputTensor(tensor_info) output_tensor = builder.customOp(opName = "BsSoftmax", opVersion = 1, domain = "ai.graphcore", inputs = [input_tensor], attributes = { "matrixDims": dims, "blockSize": block_size, "sparsity": sparsity_mask.tolist(), "groupSizes": lengths_per_2d_plane.tolist(), "innerGroupSize": inner_group_size, "subBlockMaskPerGroup": "None" * len(lengths_per_2d_plane) })[0] builder.addOutputTensor(output_tensor) proto = builder.getModelProto() # Describe how to run the model dataFlow = popart.DataFlow(1, {output_tensor: popart.AnchorReturnType("ALL")}) # Create a session to compile and execute the graph session = popart.InferenceSession( fnModel=proto, dataFlow=dataFlow, deviceInfo=popart.DeviceManager().acquireAvailableDevice(1)) # Compile graph session.prepareDevice() # Create buffers to receive results from the execution anchors = session.initAnchorArrays() sparse_input = np.array(sparse_input, dtype=g_input_data_type) stepio = popart.PyStepIO({input_tensor: sparse_input}, anchors) session.run(stepio) ipu_output = anchors[output_tensor] group_dims = dims[:-2] mat_dims = dims[-2:] blocks_2d = [mat_dims[0] // block_size[0], mat_dims[1] // block_size[1]] num_blocks_2d = blocks_2d[0] * blocks_2d[1] block_area = block_size[0] * block_size[1] total_group_dims = int(np.prod(group_dims)) assert sparsity_mask.shape == (total_group_dims * num_blocks_2d,) cpu_output = softmax(dense_input) np.set_printoptions(precision=2) np.set_printoptions(suppress=True) cpu_output = cpu_output.reshape([total_group_dims, blocks_2d[0], block_size[0], blocks_2d[1], block_size[1]]) cpu_output = np.transpose(cpu_output, [0, 1, 3, 2, 4]) cpu_output = cpu_output.reshape(total_group_dims, num_blocks_2d, block_area) gold_output = [] offset = 0 for g in range(total_group_dims): cpu_output_2d = cpu_output[g] sliced_sparsity_mask = sparsity_mask[offset: offset + num_blocks_2d] offset = offset + num_blocks_2d for sparsity_mask_idx in range(num_blocks_2d): if sliced_sparsity_mask[sparsity_mask_idx]: gold_output.append(cpu_output_2d[sparsity_mask_idx]) gold_output = np.array(gold_output) assert ipu_output.shape == gold_output.shape return ipu_output, gold_output # # INFERENCE TEST # # test_data_infer tuple --> (matMulType, lhs_dims, rhs_dims, block_size, sparsity, transpose_rhs, inner_group_size) test_data_infer = [ # 2D ("tag_inf_0", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [8, 8], [8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_1", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [16, 16], [16, 16], [8, 8, 8], 0.1, False, 1), ("tag_inf_2", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [32, 32], [32, 32], [16, 8, 8], 0.8, False, 1), ("tag_inf_3", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [64, 64], [64, 256], [64, 8, 64], 0.9, False, 1), ("tag_inf_4", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [128, 128], [128, 128], [32, 8, 16], 0.2, False, 1), # 3D, False ("tag_inf_5", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [2, 8, 8], [2, 8, 8], [8, 8, 8], 0.1, False, 1), ("tag_inf_6", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [5, 16, 16], [5, 16, 16], [8, 8, 8], 0.3, False, 1), ("tag_inf_7", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [7, 32, 32], [7, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_8", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [11, 64, 64], [11, 64, 64], [64, 8, 64], 0.6, False, 1), ("tag_inf_9", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [12, 128, 128], [12, 128, 128], [32, 8, 16], 0.8, False, 1), # 4D, False ("tag_inf_10", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 8, 8], [1, 1, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_11", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 16, 16], [1, 1, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_inf_12", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 32, 32], [1, 1, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_13", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 64, 128], [1, 1, 128, 256], [64, 8, 64], 0.5, False, 1), ("tag_inf_14", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 128, 64], [1, 1, 64, 128], [32, 8, 16], 0.5, False, 1), ("tag_inf_14", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 2, 8, 8], [1, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_16", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 5, 16, 16], [1, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_inf_17", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 7, 32, 32], [1, 7, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_18", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 5, 64, 128], [1, 5, 128, 256], [64, 8, 64], 0.5, False, 1), ("tag_inf_19", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 12, 128, 64], [1, 12, 64, 128], [32, 8, 16], 0.5, False, 1), ("tag_inf_20", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [2, 2, 8, 8], [2, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_21", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [5, 5, 16, 16], [5, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_inf_22", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [13, 7, 32, 32], [13, 7, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_24", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 12, 128, 64], [1, 12, 64, 128], [32, 8, 16], 0.5, False, 1), # 2D, lhs has to be square to take inverse, False ("tag_inf_25", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [8, 8], [8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_26", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [16, 16], [16, 16], [8, 8, 8], 0.1, False, 1), ("tag_inf_27", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [32, 32], [32, 32], [16, 8, 8], 0.8, False, 1), ("tag_inf_28", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [64, 64], [64, 64], [64, 8, 64], 0.9, False, 1), ("tag_inf_29", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [128, 128], [128, 128], [32, 8, 16], 0.7, False, 1), # 3D, lhs has to be square to take, False ("tag_inf_30", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [2, 8, 8], [2, 8, 8], [8, 8, 8], 0.1, False, 1), ("tag_inf_31", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [5, 16, 16], [5, 16, 16], [8, 8, 8], 0.3, False, 1), ("tag_inf_32", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [7, 32, 32], [7, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_33", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [11, 64, 64], [11, 64, 64], [64, 8, 64], 0.6, False, 1), ("tag_inf_34", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [12, 128, 128], [12, 128, 128], [32, 8, 16], 0.1, False, 1), # 4D, lhs has to be square to take, False ("tag_inf_36", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 8, 8], [1, 1, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_36", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 16, 16], [1, 1, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_inf_37", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 32, 32], [1, 1, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_38", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 64, 64], [1, 1, 64, 256], [64, 8, 64], 0.5, False, 1), ("tag_inf_39", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 128, 128], [1, 1, 128, 128], [32, 8, 16], 0.5, False, 1), ("tag_inf_40", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 2, 8, 8], [1, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_41", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 5, 16, 16], [1, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_inf_42", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 7, 32, 32], [1, 7, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_43", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 11, 64, 64], [1, 11, 64, 256], [64, 8, 64], 0.5, False, 1), ("tag_inf_44", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 12, 128, 128], [1, 12, 128, 128], [32, 8, 16], 0.5, False, 1), ("tag_inf_45", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [2, 2, 8, 8], [2, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_46", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [5, 5, 16, 16], [5, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_inf_47", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [13, 7, 32, 32], [13, 7, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_49", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 12, 128, 128], [1, 12, 128, 1024], [32, 8, 16], 0.5, False, 1), # For transpose_rhs True case, last 2 dimensions of block_size must be 8 ("tag_inf_50", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [8, 8], [8, 8], [8, 8, 8], 0.5, True, 1), ("tag_inf_51", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [16, 16], [16, 16], [8, 8, 8], 0.1, True, 1), ("tag_inf_52", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [32, 32], [32, 32], [16, 8, 8], 0.8, True, 1), ("tag_inf_53", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [64, 64], [256, 64], [64, 8, 8], 0.9, True, 1), ("tag_inf_54", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [128, 128], [128, 128], [32, 8, 8], 0.2, True, 1), ("tag_inf_55", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [2, 8, 8], [2, 8, 8], [8, 8, 8], 0.5, True, 1), ("tag_inf_56", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [3, 16, 16], [3, 16, 16], [8, 8, 8], 0.1, True, 1), ("tag_inf_57", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [7, 128, 128], [7, 128, 128], [32, 8, 8], 0.2, True, 1), ("tag_inf_58", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [3, 5, 8, 8], [3, 5, 8, 8], [8, 8, 8], 0.5, True, 1), ("tag_inf_59", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 6, 16, 16], [1, 6, 16, 16], [8, 8, 8], 0.1, True, 1), ("tag_inf_60", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [4, 4, 32, 32], [4, 4, 32, 32], [16, 8, 8], 0.8, True, 1), # 3D, inner group size > 1 ("tag_inf_61", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [12, 128, 128], [12, 128, 128], [32, 8, 16], 0.8, False, 3), ("tag_inf_62", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [12, 128, 128], [12, 128, 128], [32, 8, 16], 0.1, False, 4), # 4D, inner group size > 1 ("tag_inf_23", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [4, 8, 64, 128], [4, 8, 128, 256], [64, 8, 64], 0.5, False, 4), ("tag_inf_48", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [3, 11, 64, 64], [3, 11, 64, 256], [64, 8, 64], 0.5, False, 3), ] @pytest.mark.parametrize("tag, matmul_type, lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, inner_group_size", test_data_infer) def test_bsmatmul_infer(custom_ops, tag, matmul_type, lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, inner_group_size): print("Running test_bsmatmul_infer() with tag: {}, matmul_type:{}, lhs_dims:{}, rhs_dims:{}, block_size:{}, sparsity_level:{}, transpose_rhs:{}, inner_group_size {}" .format(tag, "DENSE_LHS_SPARSE_RHS_DENSE_OUT" if matmul_type == g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'] else "DENSE_LHS_DENSE_RHS_SPARSE_OUT", lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, inner_group_size)) memory_cycle_ratio = 1.0 ipuOutput, goldOutput = sparse_mm_infer(matmul_type, lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, memory_cycle_ratio, inner_group_size) rtol = 1e-3 atol = 1e-3 np.testing.assert_allclose(ipuOutput, goldOutput, rtol=rtol, atol=atol) # # TRAINING TEST # # test_data_train tuple --> (matMulType, lhs_dims, rhs_dims, block_size, sparsity, transpose_rhs, inner_group_size) test_data_train = [ # 2D ("tag_tr_0", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [8, 8], [8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_1", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [16, 16], [16, 16], [8, 8, 8], 0.1, False, 1), ("tag_tr_2", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [32, 32], [32, 32], [8, 8, 8], 0.8, False, 1), ("tag_tr_3", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [64, 64], [64, 256], [8, 8, 8], 0.9, False, 1), ("tag_tr_4", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [128, 128], [128, 128], [8, 8, 8], 0.2, False, 1), # 3D, ("tag_tr_5", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [2, 16, 16], [2, 16, 16], [8, 8, 8], 0.1, False, 1), ("tag_tr_6", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [5, 16, 16], [5, 16, 16], [8, 8, 8], 0.3, False, 1), ("tag_tr_7", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [7, 32, 32], [7, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_8", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [11, 64, 64], [11, 64, 64], [8, 8, 8], 0.6, False, 1), ("tag_tr_9", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [12, 128, 128], [12, 128, 128], [8, 8, 8], 0.8, False, 1), # 4D, ("tag_tr_10", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 8, 8], [1, 1, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_11", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 16, 16], [1, 1, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_tr_12", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 32, 32], [1, 1, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_13", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 64, 128], [1, 1, 128, 256], [8, 8, 8], 0.5, False, 1), ("tag_tr_14", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 128, 64], [1, 1, 64, 128], [8, 8, 8], 0.5, False, 1), ("tag_tr_15", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 2, 8, 8], [1, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_16", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 5, 16, 16], [1, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_tr_17", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 7, 32, 32], [1, 7, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_18", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 11, 64, 128], [1, 11, 128, 256], [8, 8, 8], 0.5, False, 1), ("tag_tr_19", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 12, 128, 64], [1, 12, 64, 128], [8, 8, 8], 0.5, False, 1), ("tag_tr_20", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [2, 2, 8, 8], [2, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_21", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [5, 5, 16, 16], [5, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_tr_22", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [13, 7, 32, 32], [13, 7, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_23", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [3, 11, 64, 128], [3, 11, 128, 256], [8, 8, 8], 0.5, False, 1), ("tag_tr_24", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 12, 128, 64], [1, 12, 64, 128], [8, 8, 8], 0.5, False, 1), # 2D, lhs has to be square to take inverse ("tag_tr_25", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [8, 8], [8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_26", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [16, 16], [16, 16], [8, 8, 8], 0.1, False, 1), ("tag_tr_27", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [32, 32], [32, 32], [8, 8, 8], 0.8, False, 1), ("tag_tr_28", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [64, 64], [64, 256], [8, 8, 8], 0.9, False, 1), ("tag_tr_29", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [128, 128], [128, 128], [8, 8, 8], 0.2, False, 1), # 3D, lhs has to be square to take ("tag_tr_30", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [2, 8, 8], [2, 8, 8], [8, 8, 8], 0.1, False, 1), ("tag_tr_31", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [5, 16, 16], [5, 16, 16], [8, 8, 8], 0.3, False, 1), ("tag_tr_32", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [7, 32, 32], [7, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_33", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [11, 64, 64], [11, 64, 64], [8, 8, 8], 0.6, False, 1), ("tag_tr_34", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [12, 128, 128], [12, 128, 128], [8, 8, 8], 0.3, False, 1), # 4D, lhs has to be square to take ("tag_tr_35", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 8, 8], [1, 1, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_36", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 16, 16], [1, 1, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_tr_37", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 32, 32], [1, 1, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_38", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 64, 64], [1, 1, 64, 256], [8, 8, 8], 0.5, False, 1), ("tag_tr_39", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 128, 128], [1, 1, 128, 128], [8, 8, 8], 0.5, False, 1), ("tag_tr_40", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 2, 8, 8], [1, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_41", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 5, 16, 16], [1, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_tr_42", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 7, 32, 32], [1, 7, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_43", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 11, 64, 64], [1, 11, 64, 256], [8, 8, 8], 0.5, False, 1), ("tag_tr_44", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 12, 128, 128], [1, 12, 128, 128], [8, 8, 8], 0.5, False, 1), ("tag_tr_45", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [2, 2, 8, 8], [2, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_46", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [5, 5, 16, 16], [5, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_tr_47", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [13, 7, 32, 32], [13, 7, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_48", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [3, 11, 64, 64], [3, 11, 64, 256], [8, 8, 8], 0.5, False, 1), ("tag_tr_49", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 12, 128, 128], [1, 12, 128, 1024], [8, 8, 8], 0.5, False, 1), # For transpose_rhs True case, last 2 dimensions of block_size must be 8 ("tag_tr_50", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [8, 8], [8, 8], [8, 8, 8], 0.5, True, 1), ("tag_tr_51", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [16, 16], [16, 16], [8, 8, 8], 0.1, True, 1), ("tag_tr_52", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [32, 32], [32, 32], [8, 8, 8], 0.8, True, 1), ("tag_tr_53", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [64, 64], [256, 64], [8, 8, 8], 0.9, True, 1), ("tag_tr_54", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [128, 128], [128, 128], [8, 8, 8], 0.2, True, 1), ("tag_tr_55", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [2, 8, 8], [2, 8, 8], [8, 8, 8], 0.5, True, 1), ("tag_tr_56", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [3, 16, 16], [3, 16, 16], [8, 8, 8], 0.1, True, 1), ("tag_tr_57", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [7, 128, 128], [7, 128, 128], [8, 8, 8], 0.2, True, 1), ("tag_tr_58", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [3, 5, 8, 8], [3, 5, 8, 8], [8, 8, 8], 0.5, True, 1), ("tag_tr_59", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 6, 16, 16], [1, 6, 16, 16], [8, 8, 8], 0.1, True, 1), ("tag_tr_60", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [4, 4, 32, 32], [4, 4, 32, 32], [8, 8, 8], 0.8, True, 1), # 3D, inner group size > 1 ("tag_tr_61", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [12, 128, 128], [12, 128, 128], [8, 8, 8], 0.8, False, 3), ("tag_tr_62", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [12, 128, 128], [12, 128, 128], [8, 8, 8], 0.1, False, 4), ] @pytest.mark.parametrize("tag, matmul_type, lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, inner_group_size", test_data_train) def test_bsmatmul_train(custom_ops, tag, matmul_type, lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, inner_group_size): print("Running test_bsmatmul_train() with tag: {}, matmul_type:{}, lhs_dims:{}, rhs_dims:{}, block_size:{}, sparsity_level:{}, transpose_rhs:{}, inner_group_size {}" .format(tag, "DENSE_LHS_SPARSE_RHS_DENSE_OUT" if matmul_type == g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'] else "DENSE_LHS_DENSE_RHS_SPARSE_OUT", lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, inner_group_size)) memory_cycle_ratio = 1.0 sparse_mm_train(matmul_type, lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, memory_cycle_ratio, inner_group_size) # test_data_softmax tuple --> (dims, block_size, sparsity, inner_group_size) test_data_softmax = [ # 2D ("tag_sm_0", [8, 8], [8, 8], 0.0, 1), # 3D ("tag_sm_1", [16, 16], [8, 8], 0.4, 1), # 4D ("tag_sm_2", [2, 2, 16, 16], [8, 8], 0.3, 1), # 5D, inner group size = 1 ("tag_sm_3", [2, 3, 2, 16, 16], [8, 8], 0.1, 1), # 5D, inner group size > 1 ("tag_sm_4", [2, 3, 2, 16, 16], [8, 8], 0.1, 0), ("tag_sm_5", [2, 3, 2, 16, 16], [8, 8], 0.1, 6), ] @pytest.mark.parametrize("tag, dims, block_size, sparsity_level, inner_group_size", test_data_softmax) def test_bs_softmax(custom_ops, tag, dims, block_size, sparsity_level, inner_group_size): print("Running test_bs_softmax() with tag: {}, dims:{}, block_size:{}, sparsity_level:{}, inner_group_size {}" .format(tag, dims, block_size, sparsity_level, inner_group_size)) ipu_output, gold_output = sparse_softmax(dims, block_size, sparsity_level, inner_group_size) np.testing.assert_allclose(ipu_output, gold_output, rtol=1e-2, atol=1e-2) if __name__ == "__main__": ipu_output, gold_output = sparse_softmax([2, 2, 16, 16], [8, 8], 0.3, 1) np.testing.assert_allclose(ipu_output, gold_output, rtol=1e-2, atol=1e-2)	[ 1, 529, 276, 1112, 420, 29958, 10472, 3747, 29914, 4262, 3221, 29918, 19057, 13, 29937, 14187, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29906, 29900, 12367, 3221, 19806, 29889, 2178, 10462, 21676, 29889, 13, 5215, 2897, 13, 5215, 12655, 408, 7442, 13, 3166, 2090, 312, 8789, 1053, 10032, 13, 3166, 5455, 1053, 15065, 13, 5215, 1835, 442, 13, 5215, 11451, 1688, 13, 13, 29937, 3464, 363, 27523, 10930, 13, 29924, 1299, 3960, 29990, 29918, 27998, 29918, 19143, 353, 448, 29896, 29900, 13, 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node_modules/protagonist/drafter/drafter.gyp	bootstraponline/swagger_poc	0	112456	{ "includes": [ "ext/snowcrash/common.gypi" ], "targets" : [ # LIBSOS { 'target_name': 'libsos', 'type': 'static_library', 'direct_dependent_settings' : { 'include_dirs': [ 'ext/sos/src' ], }, 'sources': [ 'ext/sos/src/sos.cc', 'ext/sos/src/sos.h', 'ext/sos/src/sosJSON.h', 'ext/sos/src/sosYAML.h' ] }, # LIBDRAFTER { "target_name": "libdrafter", 'type': '<(libdrafter_type)', "conditions" : [ [ 'libdrafter_type=="shared_library"', { 'defines' : [ 'DRAFTER_BUILD_SHARED' ] }, { 'defines' : [ 'DRAFTER_BUILD_STATIC' ] }], ], 'direct_dependent_settings' : { 'include_dirs': [ 'src', ], }, 'export_dependent_settings': [ 'libsos', 'ext/snowcrash/snowcrash.gyp:libsnowcrash', ], "sources": [ "src/drafter.h", "src/drafter.cc", "src/drafter_private.h", "src/drafter_private.cc", "src/stream.h", "src/Version.h", "src/NodeInfo.h", "src/Serialize.h", "src/Serialize.cc", "src/SerializeAST.h", "src/SerializeAST.cc", "src/SerializeSourcemap.h", "src/SerializeSourcemap.cc", "src/SerializeResult.h", "src/SerializeResult.cc", "src/RefractAPI.h", "src/RefractAPI.cc", "src/RefractDataStructure.h", "src/RefractDataStructure.cc", "src/RefractSourceMap.h", "src/RefractSourceMap.cc", "src/Render.h", "src/Render.cc", "src/NamedTypesRegistry.cc", "src/NamedTypesRegistry.h", "src/RefractElementFactory.h", "src/RefractElementFactory.cc", "src/ConversionContext.cc", "src/ConversionContext.h", # librefract parts - will be separated into other project "src/refract/Element.h", "src/refract/Element.cc", "src/refract/ElementFwd.h", "src/refract/Visitor.h", "src/refract/VisitorUtils.h", "src/refract/VisitorUtils.cc", "src/refract/SerializeCompactVisitor.h", "src/refract/SerializeCompactVisitor.cc", "src/refract/SerializeVisitor.h", "src/refract/SerializeVisitor.cc", "src/refract/ComparableVisitor.h", "src/refract/ComparableVisitor.cc", "src/refract/TypeQueryVisitor.h", "src/refract/TypeQueryVisitor.cc", "src/refract/IsExpandableVisitor.h", "src/refract/IsExpandableVisitor.cc", "src/refract/ExpandVisitor.h", "src/refract/ExpandVisitor.cc", "src/refract/RenderJSONVisitor.h", "src/refract/RenderJSONVisitor.cc", "src/refract/PrintVisitor.h", "src/refract/PrintVisitor.cc", "src/refract/JSONSchemaVisitor.h", "src/refract/JSONSchemaVisitor.cc", "src/refract/FilterVisitor.h", "src/refract/Registry.h", "src/refract/Registry.cc", "src/refract/Build.h", "src/refract/AppendDecorator.h", "src/refract/ElementInserter.h", "src/refract/Query.h", "src/refract/Query.cc", "src/refract/Iterate.h", ], "dependencies": [ "libsos", "ext/snowcrash/snowcrash.gyp:libsnowcrash", ], }, # TESTLIBDRAFTER { 'target_name': 'test-libdrafter', 'type': 'executable', 'include_dirs': [ 'test/vendor/Catch/include', 'test/vendor/dtl/dtl', 'src/refract', ], 'sources': [ "test/test-drafter.cc", "test/test-SerializeResultTest.cc", "test/test-SerializeSourceMapTest.cc", "test/test-RefractDataStructureTest.cc", "test/test-RefractAPITest.cc", "test/test-RefractParseResultTest.cc", "test/test-RenderTest.cc", "test/test-RefractSourceMapTest.cc", "test/test-SchemaTest.cc", "test/test-CircularReferenceTest.cc", "test/test-ApplyVisitorTest.cc", "test/test-ExtendElementTest.cc", "test/test-ElementFactoryTest.cc", "test/test-OneOfTest.cc", "test/test-SyntaxIssuesTest.cc", ], 'dependencies': [ "libdrafter", ], 'conditions': [ [ 'OS=="win"', { 'defines' : [ 'WIN' ] } ] ], }, # DRAFTER { "target_name": "drafter", "type": "executable", "conditions" : [ [ 'libdrafter_type=="static_library"', { 'defines' : [ 'DRAFTER_BUILD_STATIC' ] }], ], "sources": [ "src/main.cc", "src/config.cc", "src/config.h", "src/reporting.cc", "src/reporting.h", ], "include_dirs": [ "ext/cmdline", ], "dependencies": [ "libdrafter", ], }, # DRAFTER C-API TEST { "target_name": "test-capi", "type": "executable", "conditions" : [ [ 'libdrafter_type=="static_library"', { 'defines' : [ 'DRAFTER_BUILD_STATIC' ] }], ], "sources": [ 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tests/samples/test_sample.py	FoxoTech/methylcheck	1	39998	<gh_stars>1-10 import methylcheck import pandas as pd import methylprep # for manifest support from pathlib import Path PATH = Path('docs/example_data/mouse') class TestProcessedSample(): manifest = methylprep.Manifest(methylprep.ArrayType('mouse')) manifest_mouse_design_types = dict(manifest.mouse_data_frame['design'].value_counts()) manifest_control_probe_types = dict(manifest.control_data_frame['Control_Type'].value_counts()) def test_mouse_probes(self): pd_mu = pd.read_pickle(Path(PATH,'mouse_probes.pkl')) mu = methylcheck.load(Path(PATH,'mouse_probes.pkl'), verbose=False, silent=True) if not (isinstance(mu, dict) and isinstance(list(mu.values())[0], pd.DataFrame)): raise AssertionError() mouse_probe_countA = len(list(mu.values())[0]) mouse_probe_countB = len(list(pd_mu.values())[0]) if len(mu) != len(pd_mu): raise AssertionError(f"Got {len(mu)} items in mouse_probes.pkl; expected {len(pd_mu)}.") if mouse_probe_countA != mouse_probe_countB: raise AssertionError(f"Got {mouse_probe_countA} probes in mouse_probes.pkl vs {mouse_probe_countB}.") # mouse_probes_v146 = 10067 mouse_probes_v155 = 32753 if mu['204879580038_R06C02'].shape[0] != mouse_probes_v155: raise AssertionError(f"Got {mu['204879580038_R06C02'].shape[0]} probes in mouse_probes.pkl; expected {mouse_probes_v155}.") print(f"sample and probe count OK") df0 = list(mu.values())[0] #probes_by_type = dict(df0.index.str[:2].value_counts()) #if probes_by_type['cg'] != 5881 and probes_by_type['uk'] != 4186: # raise AssertionError(f"Mismatch in number of cg and uk(nown) probes in mouse_probes.pkl; expected 5881 cg and 4186 uk.") probes_by_type = dict(df0.design.value_counts()) if probes_by_type['Random'] != 27305 or probes_by_type['Multi'] != 5448: raise AssertionError(f"Mismatch in number of Random/Multi CpG probes in mouse_probes.pkl; expected 27305 Random and 5448 Multi.") print('mouse probe counts OK') def __test_mouse_probes_v146(self): pd_mu = pd.read_pickle(Path(PATH,'mouse_probes.pkl')) mu = methylcheck.load(Path(PATH,'mouse_probes.pkl'), verbose=False, silent=True) if not (isinstance(mu, dict) and isinstance(list(mu.values())[0], pd.DataFrame)): raise AssertionError() if len(mu) != len(pd_mu): raise AssertionError(f"Got a {len(mu)} items in mouse_probes.pkl; expected {len(pd_mu)}.") print(f"sample count OK") df0 = list(mu.values())[0] probes_by_type = dict(df0.index.str[:2].value_counts()) if probes_by_type['cg'] != 5881 and probes_by_type['uk'] != 4186: raise AssertionError(f"Mismatch in number of cg and uk(nown) probes in mouse_probes.pkl; expected 5881 cg and 4186 uk.") print('mouse cg,uk count OK') actual_mouse_probes = dict(df0['Probe_Type'].value_counts()) probe_counts = {'mu': 6332, 'rp': 4514, 'ch': 2851, 'rs': 291} # based on C20 manifest probe_count_errors = {} for probe_type, probe_count in probe_counts.items(): if self.manifest_mouse_probe_types[probe_type] != probe_count: probe_count_errors[probe_type] = {'actual': self.manifest_mouse_probe_types[probe_type], 'expected': probe_count} if probe_count_errors: raise AssertionError(f"mouse probe count errors: {probe_count_errors}") print('mouse mu,rp,ch,rs count OK') # compare with manifest diffs = [] for probe_type, probe_count in self.manifest_mouse_probe_types.items(): if probe_count != actual_mouse_probes[probe_type]: diffs.append(f"{probe_type}: {actual_mouse_probes[probe_type]} / {probe_count}") if diffs: print("Probes in manifest NOT in control probes saved:") print('\n'.join(diffs)) # not part of newer (>v1.4.6) mouse_probes.pkl #actual_mouse_probes = dict(df0['Probe_Type'].value_counts()) #probe_counts_C20 = {'mu': 6332, 'rp': 4514, 'ch': 2851, 'rs': 291} # based on C20 manifest, v1.4.6 #probe_counts_mm285_v2 = {'mu': 4821, 'rp': 3048, 'ch': 2085, 'rs': 113} # v1.5.5 #probe_count_errors = {} #for probe_type, probe_count in probe_counts_mm285_v2.items(): # if self.manifest_mouse_probe_types[probe_type] != probe_count: # probe_count_errors[probe_type] = {'actual': self.manifest_mouse_probe_types[probe_type], 'expected': probe_count} #if probe_count_errors: # raise AssertionError(f"mouse probe count errors: {probe_count_errors}") #print('mouse mu,rp,ch,rs count OK') # compare with manifest -- done in test_Array_processed.py already #diffs = [] #for probe_type, probe_count in self.manifest_mouse_probe_types.items(): # if probe_count != actual_mouse_probes[probe_type]: # diffs.append(f"{probe_type}: {actual_mouse_probes[probe_type]} / {probe_count}") #if diffs: # print("Probes in manifest NOT in control probes saved:") # print('\n'.join(diffs)) def test_control_probes(self): con = methylcheck.load(Path(PATH,'control_probes.pkl'), verbose=False, silent=True) con0 = list(con.values())[0] con_types = dict(con0['Control_Type'].value_counts()) #probe_counts_v146 = {'NEGATIVE': 179, 'NORM_T': 24, 'NORM_C': 22, 'NORM_A': 10, 'NORM_G': 9, 'NON-POLYMORPHIC': 3, 'SPECIFICITY I': 3, 'BISULFITE CONVERSION I': 3, 'BISULFITE CONVERSION II': 2, 'SPECIFICITY II': 2, 'HYBRIDIZATION': 2, 'RESTORATION': 1} probe_counts = {'NEGATIVE': 411, 'NORM_T': 58, 'NORM_C': 58, 'NORM_A': 27, 'NORM_G': 27, 'NON-POLYMORPHIC': 9, 'SPECIFICITY I': 12, 'BISULFITE CONVERSION I': 10, 'BISULFITE CONVERSION II': 4, 'SPECIFICITY II': 3, 'HYBRIDIZATION': 3, 'RESTORATION': 1} probe_count_errors = {} for probe_type, probe_count in probe_counts.items(): if con_types[probe_type] != probe_count: probe_count_errors[probe_type] = {'actual': con_types[probe_type], 'expected': probe_count} if probe_count_errors: raise AssertionError(f"control probe count differed: {probe_count_errors}") print('mouse control_probes count OK') diffs = [] for probe_type, probe_count in self.manifest_control_probe_types.items(): if not con_types.get(probe_type): print(f"ERROR: control output data is missing {probe_type} found in manifest.") continue if probe_count != con_types[probe_type]: diffs.append(f"{probe_type}: {con_types[probe_type]} / {probe_count}") if diffs: print("Probes in manifest NOT in control probes saved:") print('\n'.join(diffs)) def test_plot_mouse_betas_from_pickle(self): """ tests SAVE too """ mu = methylcheck.load(Path(PATH,'mouse_probes.pkl'), verbose=False, silent=True) df0 = list(mu.values())[0] df = df0[['beta_value']] methylcheck.sample_plot(df, silent=True, save=True) methylcheck.beta_density_plot(df, silent=True) methylcheck.sample_plot(df, silent=True) methylcheck.mean_beta_plot(df, silent=True) methylcheck.cumulative_sum_beta_distribution(df, silent=True) methylcheck.beta_mds_plot(df, silent=True, save=False) methylcheck.mean_beta_compare(df,df,silent=True) df = df0[['cm_value']] methylcheck.beta_density_plot(df, silent=True) df = df0[['m_value']] methylcheck.beta_density_plot(df, silent=True) Path('./beta.png').unlink() # Path('./beta_mds_n=.png').unlink() -- causes error if this function doesn't create the file to remove methylcheck.beta_mds_plot(df, silent=True, save=True) for saved_png in Path('.').rglob('./beta_mds_n=.png'): print(saved_png) saved_png.unlink() def ignore_test(): with np.errstate(all='raise'): df = methylcheck.load(Path(PATH,'beta_values.pkl'), verbose=False, silent=True) methylcheck.sample_plot(df, silent=True, save=True) methylcheck.beta_density_plot(df, silent=True) methylcheck.sample_plot(df, silent=True) methylcheck.mean_beta_plot(df, silent=True) methylcheck.cumulative_sum_beta_distribution(df, silent=True) methylcheck.beta_mds_plot(df, silent=True, save=False) methylcheck.mean_beta_compare(df,df,silent=True) #df = df0[['cm_value']] #methylcheck.beta_density_plot(df, silent=True) #df = df0[['m_value']] #methylcheck.beta_density_plot(df, silent=True) Path('./beta.png').unlink() # Path('./beta_mds_n=.png').unlink() -- causes error if this function doesn't create the file to remove methylcheck.beta_mds_plot(df, silent=True, save=True) for saved_png in Path('.').rglob('./beta_mds_n=.png'): print(saved_png) saved_png.unlink()	[ 1, 529, 12443, 29918, 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utils/predictions.py	jaingaurav3/ML_sample	19	13468	import os import scipy import numpy as np import pandas as pd import torch from torch.autograd import Variable def predict_batch(net, inputs): v = Variable(inputs.cuda(), volatile=True) return net(v).data.cpu().numpy() def get_probabilities(model, loader): model.eval() return np.vstack(predict_batch(model, data[0]) for data in loader) def get_predictions(probs, thresholds): preds = np.copy(probs) preds[preds >= thresholds] = 1 preds[preds < thresholds] = 0 return preds.astype('uint8') def get_argmax(output): val,idx = torch.max(output, dim=1) return idx.data.cpu().view(-1).numpy() def get_targets(loader): targets = None for data in loader: if targets is None: shape = list(data[1].size()) shape[0] = 0 targets = np.empty(shape) target = data[1] if len(target.size()) == 1: target = target.view(-1,1) target = target.numpy() targets = np.vstack([targets, target]) return targets def ensemble_with_method(arr, method): if method == c.MEAN: return np.mean(arr, axis=0) elif method == c.GMEAN: return scipy.stats.mstats.gmean(arr, axis=0) elif method == c.VOTE: return scipy.stats.mode(arr, axis=0)[0][0] raise Exception("Operation not found")	[ 1, 1053, 2897, 13, 5215, 4560, 2272, 13, 5215, 12655, 408, 7442, 13, 5215, 11701, 408, 10518, 13, 5215, 4842, 305, 13, 3166, 4842, 305, 29889, 1300, 468, 3665, 1053, 28736, 13, 13, 13, 1753, 8500, 29918, 16175, 29898, 1212, 29892, 10970, 1125, 13, 1678, 325, 353, 28736, 29898, 2080, 29879, 29889, 29883, 6191, 3285, 1700, 24285, 29922, 5574, 29897, 13, 1678, 736, 7787, 29898, 29894, 467, 1272, 29889, 21970, 2141, 23749, 580, 13, 13, 13, 1753, 679, 29918, 22795, 11614, 29898, 4299, 29892, 23466, 1125, 13, 1678, 1904, 29889, 14513, 580, 13, 1678, 736, 7442, 29889, 29894, 1429, 29898, 27711, 29918, 16175, 29898, 4299, 29892, 848, 29961, 29900, 2314, 363, 848, 297, 23466, 29897, 13, 13, 13, 1753, 679, 29918, 27711, 1080, 29898, 771, 5824, 29892, 266, 3781, 3361, 1125, 13, 1678, 4450, 29879, 353, 7442, 29889, 8552, 29898, 771, 5824, 29897, 13, 1678, 4450, 29879, 29961, 11965, 29879, 6736, 266, 3781, 3361, 29962, 353, 29871, 29896, 13, 1678, 4450, 29879, 29961, 11965, 29879, 529, 266, 3781, 3361, 29962, 353, 29871, 29900, 13, 1678, 736, 4450, 29879, 29889, 579, 668, 877, 13470, 29947, 1495, 13, 13, 13, 1753, 679, 29918, 1191, 3317, 29898, 4905, 1125, 13, 1678, 659, 29892, 13140, 353, 4842, 305, 29889, 3317, 29898, 4905, 29892, 3964, 29922, 29896, 29897, 13, 1678, 736, 22645, 29889, 1272, 29889, 21970, 2141, 1493, 6278, 29896, 467, 23749, 580, 13, 13, 13, 1753, 679, 29918, 5182, 29879, 29898, 12657, 1125, 13, 1678, 22525, 353, 6213, 13, 1678, 363, 848, 297, 23466, 29901, 13, 4706, 565, 22525, 338, 6213, 29901, 13, 9651, 8267, 353, 1051, 29898, 1272, 29961, 29896, 1822, 2311, 3101, 13, 9651, 8267, 29961, 29900, 29962, 353, 29871, 29900, 13, 9651, 22525, 353, 7442, 29889, 6310, 29898, 12181, 29897, 13, 4706, 3646, 353, 848, 29961, 29896, 29962, 13, 4706, 565, 7431, 29898, 5182, 29889, 2311, 3101, 1275, 29871, 29896, 29901, 13, 9651, 3646, 353, 3646, 29889, 1493, 6278, 29896, 29892, 29896, 29897, 13, 4706, 3646, 353, 3646, 29889, 23749, 580, 13, 4706, 22525, 353, 7442, 29889, 29894, 1429, 4197, 5182, 29879, 29892, 3646, 2314, 13, 1678, 736, 22525, 13, 13, 13, 1753, 21285, 29918, 2541, 29918, 5696, 29898, 2749, 29892, 1158, 1125, 13, 1678, 565, 1158, 1275, 274, 29889, 2303, 2190, 29901, 13, 4706, 736, 7442, 29889, 12676, 29898, 2749, 29892, 9685, 29922, 29900, 29897, 13, 1678, 25342, 1158, 1275, 274, 29889, 29954, 2303, 2190, 29901, 13, 4706, 736, 4560, 2272, 29889, 16202, 29889, 29885, 16202, 29889, 29887, 12676, 29898, 2749, 29892, 9685, 29922, 29900, 29897, 13, 1678, 25342, 1158, 1275, 274, 29889, 29963, 2891, 29923, 29901, 13, 4706, 736, 4560, 2272, 29889, 16202, 29889, 8513, 29898, 2749, 29892, 9685, 29922, 29900, 9601, 29900, 3816, 29900, 29962, 13, 1678, 12020, 8960, 703, 10925, 451, 1476, 1159, 2 ]
salt/modules/kernelpkg_linux_apt.py	markgras/salt	9,425	8077	<filename>salt/modules/kernelpkg_linux_apt.py """ Manage Linux kernel packages on APT-based systems """ import functools import logging import re try: from salt.utils.versions import LooseVersion as _LooseVersion from salt.exceptions import CommandExecutionError HAS_REQUIRED_LIBS = True except ImportError: HAS_REQUIRED_LIBS = False log = logging.getLogger(__name__) __virtualname__ = "kernelpkg" def __virtual__(): """ Load this module on Debian-based systems only """ if not HAS_REQUIRED_LIBS: return (False, "Required library could not be imported") if __grains__.get("os_family", "") in ("Kali", "Debian"): return __virtualname__ elif __grains__.get("os_family", "") == "Cumulus": return __virtualname__ return (False, "Module kernelpkg_linux_apt: no APT based system detected") def active(): """ Return the version of the running kernel. CLI Example: .. code-block:: bash salt '' kernelpkg.active """ if "pkg.normalize_name" in __salt__: return __salt__["pkg.normalize_name"](__grains__["kernelrelease"]) return __grains__["kernelrelease"] def list_installed(): """ Return a list of all installed kernels. CLI Example: .. code-block:: bash salt '' kernelpkg.list_installed """ pkg_re = re.compile(r"^{}-[\d.-]+-{}$".format(_package_prefix(), _kernel_type())) pkgs = __salt__["pkg.list_pkgs"](versions_as_list=True) if pkgs is None: pkgs = [] result = list(filter(pkg_re.match, pkgs)) if result is None: return [] prefix_len = len(_package_prefix()) + 1 return sorted( [pkg[prefix_len:] for pkg in result], key=functools.cmp_to_key(_cmp_version) ) def latest_available(): """ Return the version of the latest kernel from the package repositories. CLI Example: .. code-block:: bash salt '' kernelpkg.latest_available """ result = __salt__["pkg.latest_version"]( "{}-{}".format(_package_prefix(), _kernel_type()) ) if result == "": return latest_installed() version = re.match(r"^(\d+\.\d+\.\d+)\.(\d+)", result) return "{}-{}-{}".format(version.group(1), version.group(2), _kernel_type()) def latest_installed(): """ Return the version of the latest installed kernel. CLI Example: .. code-block:: bash salt '' kernelpkg.latest_installed .. note:: This function may not return the same value as :py:func:`~salt.modules.kernelpkg_linux_apt.active` if a new kernel has been installed and the system has not yet been rebooted. The :py:func:`~salt.modules.kernelpkg_linux_apt.needs_reboot` function exists to detect this condition. """ pkgs = list_installed() if pkgs: return pkgs[-1] return None def needs_reboot(): """ Detect if a new kernel version has been installed but is not running. Returns True if a new kernel is installed, False otherwise. CLI Example: .. code-block:: bash salt '' kernelpkg.needs_reboot """ return _LooseVersion(active()) < _LooseVersion(latest_installed()) def upgrade(reboot=False, at_time=None): """ Upgrade the kernel and optionally reboot the system. reboot : False Request a reboot if a new kernel is available. at_time : immediate Schedule the reboot at some point in the future. This argument is ignored if ``reboot=False``. See :py:func:`~salt.modules.system.reboot` for more details on this argument. CLI Example: .. code-block:: bash salt '' kernelpkg.upgrade salt '' kernelpkg.upgrade reboot=True at_time=1 .. note:: An immediate reboot often shuts down the system before the minion has a chance to return, resulting in errors. A minimal delay (1 minute) is useful to ensure the result is delivered to the master. """ result = __salt__["pkg.install"]( name="{}-{}".format(_package_prefix(), latest_available()) ) _needs_reboot = needs_reboot() ret = { "upgrades": result, "active": active(), "latest_installed": latest_installed(), "reboot_requested": reboot, "reboot_required": _needs_reboot, } if reboot and _needs_reboot: log.warning("Rebooting system due to kernel upgrade") __salt__["system.reboot"](at_time=at_time) return ret def upgrade_available(): """ Detect if a new kernel version is available in the repositories. Returns True if a new kernel is available, False otherwise. CLI Example: .. code-block:: bash salt '' kernelpkg.upgrade_available """ return _LooseVersion(latest_available()) > _LooseVersion(latest_installed()) def remove(release): """ Remove a specific version of the kernel. release The release number of an installed kernel. This must be the entire release number as returned by :py:func:`~salt.modules.kernelpkg_linux_apt.list_installed`, not the package name. CLI Example: .. code-block:: bash salt '' kernelpkg.remove 4.4.0-70-generic """ if release not in list_installed(): raise CommandExecutionError( "Kernel release '{}' is not installed".format(release) ) if release == active(): raise CommandExecutionError("Active kernel cannot be removed") target = "{}-{}".format(_package_prefix(), release) log.info("Removing kernel package %s", target) __salt__["pkg.purge"](target) return {"removed": [target]} def cleanup(keep_latest=True): """ Remove all unused kernel packages from the system. keep_latest : True In the event that the active kernel is not the latest one installed, setting this to True will retain the latest kernel package, in addition to the active one. If False, all kernel packages other than the active one will be removed. CLI Example: .. code-block:: bash salt '' kernelpkg.cleanup """ removed = [] # Loop over all installed kernel packages for kernel in list_installed(): # Keep the active kernel package if kernel == active(): continue # Optionally keep the latest kernel package if keep_latest and kernel == latest_installed(): continue # Remove the kernel package removed.extend(remove(kernel)["removed"]) return {"removed": removed} def _package_prefix(): """ Return static string for the package prefix """ return "linux-image" def _kernel_type(): """ Parse the kernel name and return its type """ return re.match(r"^[\d.-]+-(.+)$", active()).group(1) def _cmp_version(item1, item2): """ Compare function for package version sorting """ vers1 = _LooseVersion(item1) vers2 = _LooseVersion(item2) if vers1 < vers2: return -1 if vers1 > vers2: return 1 return 0	[ 1, 529, 9507, 29958, 29879, 1997, 29914, 7576, 29914, 17460, 15865, 29918, 9389, 29918, 2156, 29889, 2272, 13, 15945, 29908, 13, 2517, 482, 8074, 8466, 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setup.py	norcams/himlar-dp-prep	0	177774	<gh_stars>0 import os from setuptools import setup, find_packages here = os.path.abspath(os.path.dirname(__file__)) with open(os.path.join(here, 'README.md')) as f: README = f.read() with open(os.path.join(here, 'CHANGES.txt')) as f: CHANGES = f.read() requires = [ 'funcsigs==1.0.0', 'pyramid', 'pyramid_mako', # 'pyramid_debugtoolbar', 'authomatic', 'python-keystoneclient==3.10.0', 'waitress', 'grampg', 'pika==0.11.2', ] setup(name='himlar_dp_prep', version='0.0', description='himlar_dp_prep', long_description=README + '\n\n' + CHANGES, classifiers=[ "Programming Language :: Python", "Framework :: Pyramid", "Topic :: Internet :: WWW/HTTP", "Topic :: Internet :: WWW/HTTP :: WSGI :: Application", ], author='<NAME>', author_email='<EMAIL>', url='https://github.com/norcams/himlar-dp-prep', keywords='web pyramid pylons', packages=find_packages(), include_package_data=True, dependency_links = [ 'http://github.com/jhellan/authomatic/tarball/master#egg=authomatic-0.1.0.uninett2' ], zip_safe=False, install_requires=requires, tests_require=requires, test_suite="", entry_points="""\ [paste.app_factory] main = himlar_dp_prep:main """, )	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 2897, 13, 13, 3166, 731, 21245, 8789, 1053, 6230, 29892, 1284, 29918, 8318, 13, 13, 4150, 353, 2897, 29889, 2084, 29889, 370, 1028, 493, 29898, 359, 29889, 2084, 29889, 25721, 22168, 1445, 1649, 876, 13, 2541, 1722, 29898, 359, 29889, 2084, 29889, 7122, 29898, 4150, 29892, 525, 16310, 2303, 29889, 3487, 8785, 408, 285, 29901, 13, 1678, 5195, 3035, 2303, 353, 285, 29889, 949, 580, 13, 2541, 1722, 29898, 359, 29889, 2084, 29889, 7122, 29898, 4150, 29892, 525, 3210, 24336, 29903, 29889, 3945, 8785, 408, 285, 29901, 13, 1678, 5868, 24336, 29903, 353, 285, 29889, 949, 580, 13, 13, 276, 339, 2658, 353, 518, 13, 1678, 525, 7692, 2395, 23379, 1360, 29896, 29889, 29900, 29889, 29900, 742, 13, 1678, 525, 2272, 2572, 333, 742, 13, 1678, 525, 2272, 2572, 333, 29918, 29885, 4614, 742, 13, 29937, 1678, 525, 2272, 2572, 333, 29918, 8382, 10154, 1646, 742, 13, 1678, 525, 5150, 290, 2454, 742, 13, 1678, 525, 4691, 29899, 446, 858, 650, 4645, 1360, 29941, 29889, 29896, 29900, 29889, 29900, 742, 13, 1678, 525, 10685, 1253, 742, 13, 1678, 525, 629, 1160, 29887, 742, 13, 1678, 525, 29886, 4106, 1360, 29900, 29889, 29896, 29896, 29889, 29906, 742, 13, 1678, 4514, 13, 13, 14669, 29898, 978, 2433, 26994, 4675, 29918, 6099, 29918, 15287, 742, 13, 418, 1873, 2433, 29900, 29889, 29900, 742, 13, 418, 6139, 2433, 26994, 4675, 29918, 6099, 29918, 15287, 742, 13, 418, 1472, 29918, 8216, 29922, 16310, 2303, 718, 11297, 29876, 29905, 29876, 29915, 718, 5868, 24336, 29903, 29892, 13, 418, 770, 14903, 11759, 13, 4706, 376, 9283, 4056, 17088, 4761, 5132, 613, 13, 4706, 376, 16660, 4761, 10772, 2572, 333, 613, 13, 4706, 376, 7031, 293, 4761, 4685, 4761, 399, 29956, 29956, 29914, 10493, 613, 13, 4706, 376, 7031, 293, 4761, 4685, 4761, 399, 29956, 29956, 29914, 10493, 4761, 399, 26016, 29902, 4761, 8427, 613, 13, 4706, 21251, 13, 418, 4148, 2433, 29966, 5813, 29958, 742, 13, 418, 4148, 29918, 5269, 2433, 29966, 26862, 6227, 29958, 742, 13, 418, 3142, 2433, 991, 597, 3292, 29889, 510, 29914, 15459, 29883, 2232, 29914, 26994, 4675, 29899, 6099, 29899, 15287, 742, 13, 418, 29361, 2433, 2676, 11451, 2572, 333, 282, 2904, 787, 742, 13, 418, 9741, 29922, 2886, 29918, 8318, 3285, 13, 418, 3160, 29918, 5113, 29918, 1272, 29922, 5574, 29892, 13, 418, 10609, 29918, 4965, 353, 518, 13, 3986, 525, 1124, 597, 3292, 29889, 510, 29914, 29926, 14181, 273, 29914, 5150, 290, 2454, 29914, 12637, 2135, 29914, 6207, 29937, 387, 29887, 29922, 5150, 290, 2454, 29899, 29900, 29889, 29896, 29889, 29900, 29889, 348, 262, 1803, 29906, 29915, 13, 418, 21251, 13, 418, 14319, 29918, 11177, 29922, 8824, 29892, 13, 418, 2601, 29918, 276, 339, 2658, 29922, 276, 339, 2658, 29892, 13, 418, 6987, 29918, 12277, 29922, 276, 339, 2658, 29892, 13, 418, 1243, 29918, 13495, 543, 613, 13, 418, 6251, 29918, 9748, 13776, 26732, 13, 418, 518, 16179, 29889, 932, 29918, 14399, 29962, 13, 418, 1667, 353, 1075, 4675, 29918, 6099, 29918, 15287, 29901, 3396, 13, 418, 5124, 613, 13, 418, 1723, 13, 2 ]
bot/database.py	zd4y/discordbot	0	1614367	from .config import Settings from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker, scoped_session from sqlalchemy.ext.declarative import declarative_base engine = create_engine(Settings.DATABASE_URL, echo=False) session_factory = sessionmaker(bind=engine) session = scoped_session(session_factory) Base = declarative_base() Base.query = session.query_property()	[ 1, 515, 869, 2917, 1053, 19215, 13, 3166, 4576, 284, 305, 6764, 1053, 1653, 29918, 10599, 13, 3166, 4576, 284, 305, 6764, 29889, 555, 1053, 4867, 28107, 29892, 16505, 287, 29918, 7924, 13, 3166, 4576, 284, 305, 6764, 29889, 1062, 29889, 311, 16544, 1230, 1053, 7669, 1230, 29918, 3188, 13, 13, 13, 10599, 353, 1653, 29918, 10599, 29898, 9585, 29889, 25832, 27982, 29918, 4219, 29892, 2916, 29922, 8824, 29897, 13, 7924, 29918, 14399, 353, 4867, 28107, 29898, 5355, 29922, 10599, 29897, 13, 7924, 353, 16505, 287, 29918, 7924, 29898, 7924, 29918, 14399, 29897, 13, 5160, 353, 7669, 1230, 29918, 3188, 580, 13, 5160, 29889, 1972, 353, 4867, 29889, 1972, 29918, 6799, 580, 13, 2 ]
dfvfs/vfs/apfs_container_file_entry.py	jaegeral/dfvfs	0	192719	# -- coding: utf-8 -- """The APFS container file entry implementation.""" from dfvfs.lib import apfs_helper from dfvfs.lib import definitions from dfvfs.lib import errors from dfvfs.resolver import resolver from dfvfs.vfs import apfs_container_directory from dfvfs.vfs import file_entry class APFSContainerFileEntry(file_entry.FileEntry): """File system file entry that uses pyfsapfs.""" TYPE_INDICATOR = definitions.TYPE_INDICATOR_APFS_CONTAINER def __init__( self, resolver_context, file_system, path_spec, is_root=False, is_virtual=False): """Initializes a file entry. Args: resolver_context (Context): resolver context. file_system (FileSystem): file system. path_spec (PathSpec): path specification. is_root (Optional[bool]): True if the file entry is the root file entry of the corresponding file system. is_virtual (Optional[bool]): True if the file entry is a virtual file entry emulated by the corresponding file system. Raises: BackEndError: when the fsapfs volume is missing in a non-virtual file entry. """ fsapfs_volume = file_system.GetAPFSVolumeByPathSpec(path_spec) if not is_virtual and fsapfs_volume is None: raise errors.BackEndError( 'Missing fsapfs volume in non-virtual file entry.') super(APFSContainerFileEntry, self).__init__( resolver_context, file_system, path_spec, is_root=is_root, is_virtual=is_virtual) self._name = None self._fsapfs_volume = fsapfs_volume if self._is_virtual: self.entry_type = definitions.FILE_ENTRY_TYPE_DIRECTORY else: self.entry_type = definitions.FILE_ENTRY_TYPE_FILE def _GetDirectory(self): """Retrieves a directory. Returns: APFSContainerDirectory: a directory. """ if self.entry_type != definitions.FILE_ENTRY_TYPE_DIRECTORY: return None return apfs_container_directory.APFSContainerDirectory( self._file_system, self.path_spec) def _GetSubFileEntries(self): """Retrieves a sub file entries generator. Yields: APFSContainerFileEntry: a sub file entry. """ if self._directory is None: self._directory = self._GetDirectory() if self._directory: for path_spec in self._directory.entries: yield APFSContainerFileEntry( self._resolver_context, self._file_system, path_spec) # TODO: expose date and time values. @property def name(self): """str: name of the file entry, which does not include the full path.""" if self._name is None: location = getattr(self.path_spec, 'location', None) if location is not None: self._name = self._file_system.BasenamePath(location) else: volume_index = apfs_helper.APFSContainerPathSpecGetVolumeIndex( self.path_spec) if volume_index is not None: self._name = 'apfs{0:d}'.format(volume_index + 1) else: self._name = '' return self._name @property def size(self): """int: size of the file entry in bytes or None if not available.""" if self._fsapfs_volume is None: return None # TODO: change libfsapfs so self._fsapfs_volume.size works return 0 @property def sub_file_entries(self): """generator[APFSContainerFileEntry]: sub file entries.""" return self._GetSubFileEntries() def GetAPFSVolume(self): """Retrieves an APFS volume. Returns: pyfsapfs.volume: an APFS volume or None if not available. """ return self._fsapfs_volume def GetParentFileEntry(self): """Retrieves the parent file entry. Returns: APFSContainerFileEntry: parent file entry or None if not available. """ volume_index = apfs_helper.APFSContainerPathSpecGetVolumeIndex( self.path_spec) if volume_index is None: return None return self._file_system.GetRootFileEntry() def IsLocked(self): """Determines if the file entry is locked. Returns: bool: True if the file entry is locked. """ if self._fsapfs_volume is None: return False return self._fsapfs_volume.is_locked() def Unlock(self): """Unlocks the file entry. Returns: bool: True if the file entry was unlocked. 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test/neg shift.py	jbr-ai-labs/PU-OC	0	73100	<reponame>jbr-ai-labs/PU-OC from imports import * def test_negshift(dataset_name, models_block, dim=128, alpha=0.5, n=1, pos_label=0, norm_flag=False, cntrs=4, params=None, svm=False, name='blank'): results = {} for model_type in models_block.values(): results[model_type.__name__] = [] train_holder = get_data(dataset_name, True, pos_label=None, neg_label=None, norm_flag=norm_flag) test_holder = get_data(dataset_name, False, pos_label=None, neg_label=None, norm_flag=norm_flag) encoder = get_encoder(f'nbae_{pos_label}.pcl', dataset_name) for cnt in tqdm(range(cntrs)): # print(f"pos={pos_label}") res = {} for model_type in models_block.values(): res[model_type.__name__] = [] for _ in tqdm(range(n)): negs = np.random.choice(np.arange(1, 10), 2 * cnt + 2, replace=False) train_bin = train_holder.pos_neg_split([pos_label], negs[:cnt + 1]) test_bin = train_holder.pos_neg_split([pos_label], negs[cnt + 1:]) if svm: train_bin.encode_data(encoder) test_bin.encode_data(encoder) train_data, pi = train_bin.get_dataset(alpha, svm_labels=True) test_data, pi_test = test_bin.get_dataset(alpha, c=0, svm_labels=True) ntc_model = models_block["ntc"](dim=dim, encoder=f'nbae_{pos_label}') ntc_model.run_train(train_data) # ntc_model.eval() if not svm: encoded_train = encode_dataset(encoder, train_data) cest, _ = tice(encoded_train.data, encoded_train.s.numpy(), 1, np.random.randint(5, size=len(encoded_train.data)), delta=0.3) aest = (encoded_train.s == 1).sum() * (1 - cest) / cest / (encoded_train.s != 1).sum() else: cest, _ = tice(train_data.data, train_data.s.numpy(), 3, np.random.randint(5, size=len(train_data.data)), delta=0.2) aest = (train_data.s == 1).sum() * (1 - cest) / cest / (train_data.s != 1).sum() pi_est = min(1, aest) cur_models = [models_block["oc"](dim=dim, encoder=f'nbae_{pos_label}', params[pos_label]['oc']['init']), models_block["pu"](dim=dim, pi=pi_est, encoder=f'nbae_{pos_label}', params[pos_label]['pu']['init'])] # cur_models = [models_block["oc"](dim=dim), # models_block["pu"](dim=dim, pi=pi_est)] cur_models[0].run_train(train_data, params[pos_label]['oc']['train']) cur_models[1].run_train(train_data, params[pos_label]['pu']['train']) # cur_models[0].run_train(train_data) # cur_models[1].run_train(train_data) test_models = [ntc_model, cur_models[0], cur_models[1]] # test_models = [cur_models[0]] # print(f"done training {i}") test = test_data, test_data result = run_test(test_models, test, None) for model in result: res[model].append(result[model]["auc"]) for model in result: results[model].append(res[model]) return results	[ 1, 529, 276, 1112, 420, 29958, 29926, 1182, 29899, 1794, 29899, 29880, 6897, 29914, 7056, 29899, 20166, 13, 3166, 24802, 1053, 334, 13, 13, 13, 1753, 1243, 29918, 10052, 10889, 29898, 24713, 29918, 978, 29892, 13, 462, 29871, 4733, 29918, 1271, 29892, 13, 462, 29871, 3964, 29922, 29896, 29906, 29947, 29892, 13, 462, 29871, 15595, 29922, 29900, 29889, 29945, 29892, 13, 462, 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09_Recurrent_Neural_Networks/05_Creating_A_Sequence_To_Sequence_Model/05_seq2seq_translation.py	maxim5/tensorflow_cookbook	2	31383	<reponame>maxim5/tensorflow_cookbook #!/usr/bin/env python # -- coding: utf-8 -- # # Creating Sequence to Sequence Models # ------------------------------------- # Here we show how to implement sequence to sequence models. # Specifically, we will build an English to German translation model. # import io import os import string import sys from collections import Counter from zipfile import ZipFile import matplotlib.pyplot as plt import numpy as np import requests import tensorflow as tf if tf.__version__[0] < '1': from tensorflow.models.rnn.translate import seq2seq_model else: # models can be retrieved from github: https://github.com/tensorflow/models.git # put the models dir under python search lib path. local_repository = 'temp' if not os.path.exists(local_repository): # pip install gitpython from git import Repo tf_model_repository = 'https://github.com/tensorflow/models' Repo.clone_from(tf_model_repository, local_repository) sys.path.insert(0, 'temp/tutorials/rnn/translate/') import seq2seq_model as seq2seq_model # Model Parameters learning_rate = 0.1 lr_decay_rate = 0.99 lr_decay_every = 100 max_gradient = 5.0 batch_size = 50 num_layers = 3 rnn_size = 500 layer_size = 512 generations = 10000 vocab_size = 10000 save_every = 1000 eval_every = 500 output_every = 50 punct = string.punctuation # Data Parameters data_dir = 'temp' data_file = 'eng_ger.txt' model_path = 'seq2seq_model' full_model_dir = os.path.join(data_dir, model_path) # Test Translation from English (lowercase, no punct) test_english = ['hello where is my computer', 'the quick brown fox jumped over the lazy dog', 'is it going to rain tomorrow'] ######################################################################################################################## # Data ######################################################################################################################## # Make Model Directory if not os.path.exists(full_model_dir): os.makedirs(full_model_dir) # Make data directory if not os.path.exists(data_dir): os.makedirs(data_dir) print('Loading English-German Data') # Check for data, if it doesn't exist, download it and save it if not os.path.isfile(os.path.join(data_dir, data_file)): print('Data not found, downloading Eng-Ger sentences from www.manythings.org') sentence_url = 'http://www.manythings.org/anki/deu-eng.zip' r = requests.get(sentence_url) z = ZipFile(io.BytesIO(r.content)) file = z.read('deu.txt') # Format Data eng_ger_data = file.decode(errors='ignore') eng_ger_data = eng_ger_data.encode('ascii', errors='ignore') eng_ger_data = eng_ger_data.decode().split('\n') # Write to file with open(os.path.join(data_dir, data_file), 'w') as out_conn: for sentence in eng_ger_data: out_conn.write(sentence + '\n') else: eng_ger_data = [] with open(os.path.join(data_dir, data_file), 'r') as in_conn: for row in in_conn: eng_ger_data.append(row[:-1]) print('Done!') # Remove punctuation eng_ger_data = [''.join(char for char in sent if char not in punct) for sent in eng_ger_data] # Split each sentence by tabs eng_ger_data = [x.split('\t') for x in eng_ger_data if len(x) >= 1] [english_sentence, german_sentence] = [list(x) for x in zip(*eng_ger_data)] english_sentence = [x.lower().split() for x in english_sentence] german_sentence = [x.lower().split() for x in german_sentence] print('Processing the vocabularies.') # Process the English Vocabulary all_english_words = [word for sentence in english_sentence for word in sentence] all_english_counts = Counter(all_english_words) eng_word_keys = [x[0] for x in all_english_counts.most_common(vocab_size - 1)] # -1 because 0=unknown is also in there eng_vocab2ix = dict(zip(eng_word_keys, range(1, vocab_size))) eng_ix2vocab = {val: key for key, val in eng_vocab2ix.items()} english_processed = [] for sent in english_sentence: temp_sentence = [] for word in sent: try: temp_sentence.append(eng_vocab2ix[word]) except: temp_sentence.append(0) english_processed.append(temp_sentence) # Process the German Vocabulary all_german_words = [word for sentence in german_sentence for word in sentence] all_german_counts = Counter(all_german_words) ger_word_keys = [x[0] for x in all_german_counts.most_common(vocab_size - 1)] ger_vocab2ix = dict(zip(ger_word_keys, range(1, vocab_size))) ger_ix2vocab = {val: key for key, val in ger_vocab2ix.items()} german_processed = [] for sent in german_sentence: temp_sentence = [] for word in sent: try: temp_sentence.append(ger_vocab2ix[word]) except: temp_sentence.append(0) german_processed.append(temp_sentence) # Process the test english sentences, use '0' if word not in our vocab test_data = [] for sentence in test_english: temp_sentence = [] for word in sentence.split(' '): try: temp_sentence.append(eng_vocab2ix[word]) except: # Use '0' if the word isn't in our vocabulary temp_sentence.append(0) test_data.append(temp_sentence) # Define Buckets for sequence lengths # We will split data into the corresponding buckets: # (x1, y1), (x2, y2), ... # Where all entries in bucket 1: len(x)<x1 and len(y)<y1 and so on. x_maxs = [5, 7, 11, 50] y_maxs = [10, 12, 17, 60] buckets = [x for x in zip(x_maxs, y_maxs)] bucketed_data = [[] for _ in range(len(x_maxs))] for eng, ger in zip(english_processed, german_processed): for ix, (x_max, y_max) in enumerate(zip(x_maxs, y_maxs)): if (len(eng) <= x_max) and (len(ger) <= y_max): bucketed_data[ix].append([eng, ger]) break # Print summaries of buckets train_bucket_sizes = [len(bucketed_data[b]) for b in range(len(buckets))] train_total_size = float(sum(train_bucket_sizes)) for ix, bucket in enumerate(bucketed_data): print('Data pts in bucket {}: {}'.format(ix, len(bucket))) ######################################################################################################################## # Create sequence to sequence model ######################################################################################################################## print('Creating Translation Model') # https://stackoverflow.com/questions/44855603/typeerror-cant-pickle-thread-lock-objects-in-seq2seq/47952913#47952913 setattr(tf.contrib.rnn.GRUCell, '__deepcopy__', lambda self, _: self) setattr(tf.contrib.rnn.BasicLSTMCell, '__deepcopy__', lambda self, _: self) setattr(tf.contrib.rnn.MultiRNNCell, '__deepcopy__', lambda self, _: self) def translation_model(input_vocab_size, output_vocab_size, buckets, rnn_size, num_layers, max_gradient, learning_rate, lr_decay_rate, forward_only): model = seq2seq_model.Seq2SeqModel(source_vocab_size=input_vocab_size, target_vocab_size=output_vocab_size, buckets=buckets, size=rnn_size,num_layers=num_layers, max_gradient_norm=max_gradient, batch_size=batch_size, learning_rate=learning_rate, learning_rate_decay_factor=lr_decay_rate, forward_only=forward_only, dtype=tf.float32) return model translate_model = translation_model(input_vocab_size=vocab_size, output_vocab_size=vocab_size, buckets=buckets, rnn_size=rnn_size, num_layers=num_layers, max_gradient=max_gradient, learning_rate=learning_rate, lr_decay_rate=lr_decay_rate, forward_only=False) # Tell TensorFlow to reuse the variables for the test model with tf.variable_scope(tf.get_variable_scope(), reuse=True): # Reuse the variables for the test model test_model = translation_model(input_vocab_size=vocab_size, output_vocab_size=vocab_size, buckets=buckets, rnn_size=rnn_size, num_layers=num_layers, max_gradient=max_gradient, learning_rate=learning_rate, lr_decay_rate=lr_decay_rate, forward_only=True) test_model.batch_size = 1 ######################################################################################################################## # Training session ######################################################################################################################## with tf.Session() as sess: sess.run(tf.global_variables_initializer()) train_loss = [] for i in range(generations): rand_bucket_ix = np.random.choice(len(bucketed_data)) model_outputs = translate_model.get_batch(bucketed_data, rand_bucket_ix) encoder_inputs, decoder_inputs, target_weights = model_outputs # Get the (gradient norm, loss, and outputs) _, step_loss, _ = translate_model.step(sess, encoder_inputs, decoder_inputs, target_weights, rand_bucket_ix, False) # Output status if (i + 1) % output_every == 0: train_loss.append(step_loss) print('Gen #{} out of {}. Loss: {:.4}'.format(i + 1, generations, step_loss)) # Check if we should decay the learning rate if (i + 1) % lr_decay_every == 0: sess.run(translate_model.learning_rate_decay_op) # Save model if (i + 1) % save_every == 0: print('Saving model to {}.'.format(full_model_dir)) model_save_path = os.path.join(full_model_dir, "eng_ger_translation.ckpt") translate_model.saver.save(sess, model_save_path, global_step=i) # Eval on test set if (i + 1) % eval_every == 0: for ix, sentence in enumerate(test_data): # Find which bucket sentence goes in bucket_id = next(index for index, val in enumerate(x_maxs) if val >= len(sentence)) # Get RNN model outputs encoder_inputs, decoder_inputs, target_weights = test_model.get_batch( {bucket_id: [(sentence, [])]}, bucket_id) # Get logits _, test_loss, output_logits = test_model.step(sess, encoder_inputs, decoder_inputs, target_weights, bucket_id, True) ix_output = [int(np.argmax(logit, axis=1)) for logit in output_logits] # If there is a 0 symbol in outputs end the output there. ix_output = ix_output[0:[i for i, x in enumerate(ix_output + [0]) if x == 0][0]] # Get german words from indices test_german = [ger_ix2vocab[x] for x in ix_output] print('English: {}'.format(test_english[ix])) print('German: {}'.format(test_german)) ######################################################################################################################## # Visualization ######################################################################################################################## # Plot train loss loss_generations = [i for i in range(generations) if i % output_every == 0] plt.plot(loss_generations, train_loss, 'k-') plt.title('Sequence to Sequence Loss') plt.xlabel('Generation') plt.ylabel('Loss') plt.show()	[ 1, 529, 276, 1112, 420, 29958, 27525, 29945, 29914, 29056, 29918, 15108, 2909, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 29937, 13, 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start_client.py	xi102/bert_ner	0	97041	<gh_stars>0 import time from tqdm import tqdm print('start') try: from bert_base.client import BertClient except ImportError: raise ImportError('BertClient module is not available, it is required for serving HTTP requests.' 'Please use "pip install -U bert-serving-client" to install it.' 'If you do not want to use it as an HTTP server, ' 'then remove "-http_port" from the command line.') # 指定服务器的IP 127.0.0.1:49164 5555 # BertClient(ip='xxx', ner_model_dir='D:\Projects\Wunianyue\BERT-BiLSTM-CRF-NER\output', show_server_config=False, check_version=False, check_length=False, mode='NER') with BertClient(mode='NER') as bc: start_t = time.perf_counter() # text = text.replace(' ', '-') data句子间不能有空格。 df_path = r'data/add_data/yizhu_301_1000.txt' # data数据最后一行要为空 df = open(df_path, 'r+', encoding='utf-8') list = [] l=[] # 要把每个字用空格分隔，放入训练? for line in df: if line!='\n': l.append(' '.join(line)) list.append(line[:len(line) - 1]) print(len(list)) print('start') rst = bc.encode(l) # 测试同时输入两个句子，多个输入同理 k = 0 with open("annotationdata/301_1000_BIO.txt", "w", encoding='utf-8') as f: for index in tqdm(range(0,len(rst))): try: f.writelines(" ".join(rst[index])) except: k = k+1 pass with open("annotationdata/bert_301_1000.txt", "w", encoding='utf-8') as f: m = 0 j = 0 count = 0 for index in tqdm(range(0, len(list))): if(len(list[index])!=len(rst[index])): print("error in " + str(index)) count = count + 1 try: for i in range(0, len(list[index])): f.writelines(list[index][i] + ' ' + rst[index][i] + '\n') f.writelines('\n') m = m +1 except: j = j + 1 pass print(k) print(j) print(m) print(count) print(time.perf_counter() - start_t) # 2.5W	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 931, 13, 3166, 260, 29939, 18933, 1053, 260, 29939, 18933, 13, 2158, 877, 2962, 1495, 13, 2202, 29901, 13, 1678, 515, 289, 814, 29918, 3188, 29889, 4645, 1053, 16662, 4032, 13, 19499, 16032, 2392, 29901, 13, 1678, 12020, 16032, 2392, 877, 29933, 814, 4032, 3883, 338, 451, 3625, 29892, 372, 338, 3734, 363, 16330, 7331, 7274, 6169, 13, 462, 418, 525, 12148, 671, 376, 13096, 2601, 448, 29965, 289, 814, 29899, 643, 1747, 29899, 4645, 29908, 304, 2601, 372, 6169, 13, 462, 418, 525, 3644, 366, 437, 451, 864, 304, 671, 372, 408, 385, 7331, 1923, 29892, 525, 13, 462, 418, 525, 6098, 3349, 11663, 1124, 29918, 637, 29908, 515, 278, 1899, 1196, 29889, 1495, 13, 13, 29937, 29871, 31084, 30495, 31520, 31358, 30943, 30210, 5690, 29871, 29896, 29906, 29955, 29889, 29900, 29889, 29900, 29889, 29896, 29901, 29946, 29929, 29896, 29953, 29946, 29871, 29945, 29945, 29945, 29945, 13, 29937, 16662, 4032, 29898, 666, 2433, 12353, 742, 26033, 29918, 4299, 29918, 3972, 2433, 29928, 3583, 25119, 29905, 29956, 348, 713, 29891, 434, 29905, 13635, 29911, 29899, 20517, 29931, 1254, 29924, 29899, 11341, 29943, 29899, 13865, 29905, 4905, 742, 1510, 29918, 2974, 29918, 2917, 29922, 8824, 29892, 1423, 29918, 3259, 29922, 8824, 29892, 1423, 29918, 2848, 29922, 8824, 29892, 4464, 2433, 13865, 1495, 13, 2541, 16662, 4032, 29898, 8513, 2433, 13865, 1495, 408, 289, 29883, 29901, 13, 1678, 1369, 29918, 29873, 353, 931, 29889, 546, 29888, 29918, 11808, 580, 13, 1678, 396, 1426, 353, 1426, 29889, 6506, 877, 13420, 17411, 1495, 848, 232, 146, 168, 30319, 31016, 30413, 30815, 30417, 30816, 31168, 30267, 13, 1678, 4489, 29918, 2084, 353, 364, 29915, 1272, 29914, 1202, 29918, 1272, 29914, 29891, 466, 6905, 29918, 29941, 29900, 29896, 29918, 29896, 29900, 29900, 29900, 29889, 3945, 29915, 29871, 396, 848, 30354, 30763, 30878, 30822, 30287, 30448, 30698, 30573, 30816, 13, 1678, 4489, 353, 1722, 29898, 2176, 29918, 2084, 29892, 525, 29878, 29974, 742, 8025, 2433, 9420, 29899, 29947, 1495, 13, 1678, 1051, 353, 5159, 13, 1678, 301, 29922, 2636, 396, 29871, 30698, 233, 141, 141, 31951, 30502, 30578, 30406, 30816, 31168, 30748, 236, 157, 151, 30214, 31182, 30752, 235, 177, 176, 234, 190, 134, 29973, 13, 1678, 363, 1196, 297, 4489, 29901, 13, 4706, 565, 1196, 29991, 2433, 29905, 29876, 2396, 13, 9651, 301, 29889, 4397, 877, 15300, 7122, 29898, 1220, 876, 13, 9651, 1051, 29889, 4397, 29898, 1220, 7503, 2435, 29898, 1220, 29897, 448, 29871, 29896, 2314, 13, 1678, 1596, 29898, 2435, 29898, 1761, 876, 13, 13, 1678, 1596, 877, 2962, 1495, 13, 1678, 364, 303, 353, 289, 29883, 29889, 12508, 29898, 29880, 29897, 29871, 396, 29871, 31851, 31787, 30980, 30594, 31573, 30752, 31977, 30502, 232, 146, 168, 30319, 30214, 30923, 30502, 31573, 30752, 30980, 30687, 13, 1678, 413, 353, 29871, 29900, 13, 1678, 411, 1722, 703, 18317, 1272, 29914, 29941, 29900, 29896, 29918, 29896, 29900, 29900, 29900, 29918, 29933, 5971, 29889, 3945, 613, 376, 29893, 613, 8025, 2433, 9420, 29899, 29947, 1495, 408, 285, 29901, 13, 4706, 363, 2380, 297, 260, 29939, 18933, 29898, 3881, 29898, 29900, 29892, 2435, 29898, 29878, 303, 876, 1125, 13, 9651, 1018, 29901, 13, 18884, 285, 29889, 8231, 24210, 703, 11393, 7122, 29898, 29878, 303, 29961, 2248, 12622, 13, 9651, 5174, 29901, 13, 18884, 413, 353, 413, 29974, 29896, 13, 18884, 1209, 13, 13, 1678, 411, 1722, 703, 18317, 1272, 29914, 2151, 29918, 29941, 29900, 29896, 29918, 29896, 29900, 29900, 29900, 29889, 3945, 613, 376, 29893, 613, 8025, 2433, 9420, 29899, 29947, 1495, 408, 285, 29901, 13, 4706, 286, 353, 29871, 29900, 13, 4706, 432, 353, 29871, 29900, 13, 4706, 2302, 353, 29871, 29900, 13, 4706, 363, 2380, 297, 260, 29939, 18933, 29898, 3881, 29898, 29900, 29892, 7431, 29898, 1761, 876, 1125, 13, 9651, 565, 29898, 2435, 29898, 1761, 29961, 2248, 2314, 19216, 2435, 29898, 29878, 303, 29961, 2248, 12622, 29901, 13, 462, 1678, 1596, 703, 2704, 297, 376, 718, 851, 29898, 2248, 876, 13, 462, 1678, 2302, 353, 2302, 718, 29871, 29896, 13, 9651, 1018, 29901, 13, 18884, 363, 474, 297, 3464, 29898, 29900, 29892, 7431, 29898, 1761, 29961, 2248, 12622, 29901, 13, 462, 1678, 285, 29889, 8231, 24210, 29898, 1761, 29961, 2248, 3816, 29875, 29962, 718, 525, 525, 718, 364, 303, 29961, 2248, 3816, 29875, 29962, 718, 11297, 29876, 1495, 13, 18884, 285, 29889, 8231, 24210, 28909, 29876, 1495, 13, 18884, 286, 353, 286, 718, 29896, 13, 9651, 5174, 29901, 13, 18884, 432, 353, 432, 718, 29871, 29896, 13, 18884, 1209, 13, 1678, 1596, 29898, 29895, 29897, 13, 1678, 1596, 29898, 29926, 29897, 13, 1678, 1596, 29898, 29885, 29897, 13, 1678, 1596, 29898, 2798, 29897, 13, 1678, 1596, 29898, 2230, 29889, 546, 29888, 29918, 11808, 580, 448, 1369, 29918, 29873, 29897, 13, 29937, 418, 29906, 29889, 29945, 29956, 13, 2 ]
src/data/dictionary.py	klimzaporojets/e2e-knowledge-ie	5	122314	<reponame>klimzaporojets/e2e-knowledge-ie import json def load_dictionary(config, path): type = config['type'] filename = config['filename'] filename = filename if filename.startswith("/") else "{}/{}".format(path, filename) if type == 'json': dictionary = Dictionary() dictionary.load_json(filename) else: raise BaseException("no such type", type) return dictionary def create_dictionaries(config, training): path = config['path'] print("Loading dictionaries (training={})".format(training)) if 'dictionaries' in config: dictionaries = {} for name, dict_config in config['dictionaries'].items(): if training: if "init" in dict_config: dictionary = load_dictionary(dict_config['init'], path) # print('init {}: size={}'.format(name, dictionary.size)) else: # print("init {} (blank)".format(name)) dictionary = Dictionary() else: dictionary = load_dictionary(dict_config, path) print('load {}: size={}'.format(name, dictionary.size)) dictionary.prefix = dict_config['prefix'] if 'prefix' in dict_config else '' if 'rewriter' in dict_config: if dict_config['rewriter'] == 'lowercase': dictionary.rewriter = lambda t: t.lower() elif dict_config['rewriter'] == 'none': print("rewriter: none") else: raise BaseException("no such rewriter", dict_config['rewriter']) if 'append' in dict_config: for x in dict_config['append']: idx = dictionary.add(x) print(" add token", x, "->", idx) if 'unknown' in dict_config: dictionary.set_unknown_token(dict_config['unknown']) if 'debug' in dict_config: dictionary.debug = dict_config['debug'] if 'update' in dict_config: dictionary.update = dict_config['update'] if not training: dictionary.update = False dictionaries[name] = dictionary return dictionaries else: print("WARNING: using wikipedia dictionary") words = Dictionary() entities = Dictionary() words.set_unknown_token("UNKNOWN") words.load_spirit_dictionary('data/tokens.dict', 5) entities.set_unknown_token("UNKNOWN") entities.load_spirit_dictionary('data/entities.dict', 5) return { 'words': words, 'entities': entities } class Dictionary: def __init__(self): self.rewriter = lambda t: t self.debug = False self.token_unknown = -1 self.update = True self.prefix = '' self.tmp_unknown = None self.clear() def clear(self): self.word2idx = {} self.matrix = False self.size = 0 self.out_of_voc = 0 self.oov = set() if self.tmp_unknown is not None: self.token_unknown = self.lookup(self.tmp_unknown) def load_json(self, filename): with open(filename) as file: data = json.load(file) if isinstance(data, (list,)): for idx, word in enumerate(data): if self.lookup(word) != idx: print("WARNING: invalid dictionary") else: for word, idx in data.items(): if self.lookup(word) != idx: print("WARNING: invalid dictionary") def lookup(self, token): token = self.prefix + self.rewriter(token) if not token in self.word2idx: if self.update: self.word2idx[token] = self.size self.size += 1 else: if self.debug: print("oov: '{}' -> {}".format(token, self.token_unknown)) self.out_of_voc += 1 return self.token_unknown return self.word2idx[token] def add(self, token): if not token in self.word2idx: self.word2idx[token] = self.size self.size += 1 return self.word2idx[token] def set_unknown_token(self, unknown_token): self.tmp_unknown = unknown_token self.token_unknown = self.word2idx[self.prefix + unknown_token] print(self.get(self.token_unknown), "->", self.token_unknown) def write(self, filename): import json with open(filename, 'w') as file: json.dump(self.word2idx, file) def get(self, index): for word, idx in self.word2idx.items(): if idx == index: return word return None def tolist(self): list = [None] * self.size for word, idx in self.word2idx.items(): list[idx] = word return list	[ 1, 529, 276, 1112, 420, 29958, 29895, 2576, 29920, 26191, 3848, 1691, 29914, 29872, 29906, 29872, 29899, 28385, 5485, 29899, 347, 13, 5215, 4390, 13, 13, 13, 1753, 2254, 29918, 27126, 29898, 2917, 29892, 2224, 1125, 13, 1678, 1134, 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cobrinha trabalhadeira/lab10/lab10.py	ansattz/solving_sto	0	72268	<filename>cobrinha trabalhadeira/lab10/lab10.py # <NAME> # @ansattz #1 - Não delete nem modifique esta linha import readline from random import randint def roll(t): rounds=[] while len(rounds) < t: round0=randint(1,6) rounds.append(round0) return rounds def facerep(t): mydic={} l=roll(t) l1=l[1:] + [':)',] ls=[] z=0 for i in l: if i == l1[z]: ls.append(i) z+=1 for j in ls: mydic[j] = mydic.get(j, 0) + 1 v = list(mydic.values()) return l , len(v) def main(): # Escolha da quantidade de lances pelo usuário print('') print("""⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⢀⣀⣀⠤⠤⢴⣶⣖⣈⣉⣻⣿⣷⣦⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⣾⣿⣿⣿⣷⣶⡶⠚⠛⢻⣿⣯⣤⣤⣽⣿⣿⣷⣦⣀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⣿⣿⣿⣿⣿⣿⣿⣿⣟⠛⠛⢛⣿⣷⣤⣤⣴⣿⣿⣿⣿⣦⠀⠀⠀⠀ ⠀⠀⠀⠀⡟⠻⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠀⠀⠀⠀ ⠀⠀⠀⠀⣷⠀⣿⠿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠟⠛⢻⣿⣿⠀⠀⠀⠀ ⠀⠀⠀⠀⢹⣿⣿⡀⢹⣿⣿⣿⣿⡟⠁⠀⣿⣿⣿⣿⣿⣀⣀⣼⣿⣿⠀⠀⠀⠀ ⠀⠀⠀⠀⢸⠙⣿⣷⣼⣿⣿⣿⣿⣧⣤⣴⣿⣿⠿⠿⣿⣿⣿⣿⣿⡇⠀⠀⠀⠀ ⠀⠀⠀⠀⢸⣆⣸⡟⢿⣿⣿⣿⣿⣿⣿⣿⣿⡃⠀⢀⣿⣿⣿⣿⣿⡇⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠻⣿⡇⠘⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠋⠀⢹⣿⡇⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠘⢿⣶⣿⣿⣿⣿⡿⠁⠀⣹⣿⣿⣿⣿⣤⣴⣿⣿⡇⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠙⣿⣿⣿⣿⣿⣶⣾⣿⣿⣿⡿⠿⠟⠛⠉⠉⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠻⣿⡿⠿⠟⠛⠉⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀""") t = int(input('Quantidade de lances: ')) print('') print('===== Resultados =====') # print('Números sorteados e número de séries de faces repetidas:\n', facerep(t)) print('Número de séries de faces repetidas:', facerep(t)[1]) # main() #Casos de teste da questão 1 - Não delete nem modifique esta linha # input: 0 ====> output: 0 # input: 1 ====> output: 0 # input: 2 ====> output: 1 or 0 # ... #2 - Não delete nem modifique esta linha def trap_area(a,b,c): return (((a+b))c)/2 def quad(a,b,c): return [a2, b2, c*2] def marit(a,b,c): return (a+b+c)/3 def seq(a,b,c): l=[] i=a if a < b: while a < b: a+=c l.append(a) del l[-1] return i + sum(l) else: return "Não foi possível realizar a operação, pois (a > b)" def mymenu(): print('========= Opções válidas =========') print('') print('1 - Área do trapézio de bases a e b e altura c') print('2 - Quadrados de a, b e c') print('3 - Média aritmética entre a, b e c') print('4 - Sequência aritmética de a com um limite superior sendo b e razão c') print('0 - Encerrar programa') print('') print('==================================') while True: mymenu() opcao = input('Escolha uma opção: ') if opcao == '1': a = float(input('Base a: ')) b = float(input('Base b: ')) c = float(input('Altura c: ')) print('') print('A área do trapézio é: ', trap_area(a,b,c)) print('') elif opcao == '2': a = float(input('Valor de a: ')) b = float(input('Valor de b: ')) c = float(input('Valor de c: ')) print('') print(str.format('Os quadrados são:\n {}: {} \n {}: {} \n {}: {} ', a, quad(a,b,c)[0], b, quad(a,b,c)[1], c, quad(a,b,c)[2])) print('') elif opcao == '3': a = float(input('Primeiro valor (a): ')) b = float(input('Segundo valor (b): ')) c = float(input('Terceiro valor (c): ')) print('') print(str.format('A média aritmética entre {}, {} e {} é: ', a, b, c, marit(a,b,c))) print('') elif opcao == '4': a = int(input('Primeiro termo (a): ')) b = int(input('Limite superior (b): ')) c = int(input('Razão da sequência (c): ')) print('') print(str.format('A sequência aritmética de {} com limite superior {} e com razão {} é: ', a, b, c), seq(a,b,c)) print('') elif opcao == '0': print('') print('=====> Programa encerrado :)') break else: print('Opção inválida') print('')	[ 1, 529, 9507, 29958, 29883, 711, 29878, 16479, 19739, 29882, 1943, 3055, 29914, 8205, 29896, 29900, 29914, 8205, 29896, 29900, 29889, 2272, 13, 29937, 529, 5813, 29958, 13, 29937, 732, 550, 1131, 29920, 13, 13, 13, 29937, 29896, 448, 405, 1368, 5217, 6583, 878, 22781, 7444, 6276, 2350, 13, 5215, 1303, 1220, 13, 3166, 4036, 1053, 20088, 524, 13, 1753, 9679, 29898, 29873, 1125, 1678, 13, 1678, 364, 3885, 29922, 2636, 13, 1678, 1550, 7431, 29898, 29878, 3885, 29897, 529, 260, 29901, 13, 4706, 4513, 29900, 29922, 9502, 524, 29898, 29896, 29892, 29953, 29897, 13, 4706, 364, 3885, 29889, 4397, 29898, 14486, 29900, 29897, 13, 1678, 736, 364, 3885, 13, 13, 1753, 4024, 406, 29886, 29898, 29873, 1125, 13, 1678, 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p2db/p2db/p2tools.py	ne75/p2llvm-lib	0	118989	import subprocess from colorama import Fore import tempfile import os from pathlib import Path import re load_cmd = '/opt/p2llvm/bin/loadp2' load_args = ['-ZERO', '-l', '0', '-v', '-FIFO', '4096'] def load(port, app, baud, verbose=False, retries=3): result = False while retries: args = load_args.copy() args.append('-p') args.append(port) args[2] = str(baud) args.append('{}'.format(app)) p = subprocess.Popen([load_cmd] + args, stdout=subprocess.PIPE) if verbose: print(" ".join([load_cmd] + args)) output = p.communicate()[0].decode('ascii') if p.returncode != 0: print(Fore.RED + output + Fore.RESET) print(Fore.YELLOW + "Load failed, retrying...\r" + Fore.RESET) retries -= 1; result = False; else: result = True; break if not result: print(Fore.RED + "Failed to load app\r" + Fore.RESET) if verbose: print("Load command was: " + str([load_cmd] + args)) return result def gen_bin(elf_file, outdir=None): ''' generate a binary file for loading from the given elf file. if outdir is given, save the binary there. otherwise it will be placed in a temporary directory returns the path to the generated binary ''' if not outdir: outdir = tempfile.gettempdir() outfile = os.path.join(outdir, Path(elf_file).stem) p = subprocess.Popen([ '/opt/p2llvm/bin/llvm-objcopy', '-O', 'binary', elf_file, outfile ], stdout=subprocess.PIPE) p.wait() if (p.returncode != 0): return None return outfile def get_objdump_data(elf_file): ''' return a dict of sections in the elf file. each section is keyed with the section header (as given by objdump), and the value is a dict containing a cleaned up version of the address, instruction encoding, and parsed instruction example: { ".cog<__start>": { "section_addr": 0 0: ["f6 03 a1 f8", "mov r0, ptra"] } } ''' p = subprocess.Popen([ '/opt/p2llvm/bin/llvm-objdump', '-d', elf_file, ], stdout=subprocess.PIPE) out, _ = p.communicate() lines = out.decode('ascii').splitlines() current_section = '' current_subsection = '' current_section_key = '' current_section_addr = 0 SECTION_PATERN = r"^Disassembly of section (.?):$" SUBSECTION_PATTERN = r"^([a-f0-9]+) <(.?)>:$" INST_PATTERN = r"^\s+([a-f0-9]+):\s+(([a-f0-9]+\s)+)\s+(.*?)$" output_dict = {} for l in lines: if re.search(SECTION_PATERN, l): # check for the start of a code section current_section = re.search(SECTION_PATERN, l).group(1) elif re.search(SUBSECTION_PATTERN, l): # check for the start of a function section current_section_addr = int(re.search(SUBSECTION_PATTERN, l).group(1), 16) current_subsection = re.search(SUBSECTION_PATTERN, l).group(2) elif re.search(INST_PATTERN, l): # check for an individual instruction inst_match = re.search(INST_PATTERN, l) current_section_key = "{}<{}>".format(current_section, current_subsection) if current_section_key not in output_dict: output_dict[current_section_key] = {} output_dict[current_section_key]["section_addr"] = current_section_addr inst_encoding = inst_match.group(2).split(' ') inst_encoding = [s for s in inst_encoding if s] inst_encoding = inst_encoding[::-1] i_str_fragments = [s.strip() for s in inst_match.group(4).split('\t') if s] if len(i_str_fragments) == 1: i_str_fragments = ['', i_str_fragments[0], ''] elif len(i_str_fragments) == 2: if i_str_fragments[-1] == 'wc' or i_str_fragments[-1] == 'wz' or i_str_fragments[-1] == 'wcz': # we don't have a condition, add a blank to the front i_str_fragments.insert(0, '') else: # we don't have a effect, add a blank to the back i_str_fragments.append('') elif len(i_str_fragments) == 3: pass # don't need to do anything else: print("Unknown instruction format: " + str(i_str_fragments)) i_str = '{: >14} {: <18}{}'.format(i_str_fragments[0], i_str_fragments[1], i_str_fragments[2]) output_dict[current_section_key][int(inst_match.group(1), 16)] = ( ' '.join(inst_encoding), i_str ) return output_dict def get_inst(data, pc): ''' get the instruction at the given pc in data if no such instruction exists, return None ''' for s in data: for i in 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robotcode/language_server/robotframework/parts/goto.py	d-biehl/robotcode	21	84470	from __future__ import annotations import ast from typing import ( TYPE_CHECKING, Any, Awaitable, Callable, List, Optional, Type, Union, cast, ) from ....utils.logging import LoggingDescriptor from ....utils.uri import Uri from ...common.language import language_id from ...common.text_document import TextDocument from ...common.types import Location, LocationLink, Position from ..utils.ast import ( HasTokens, Token, get_nodes_at_position, get_tokens_at_position, range_from_token, range_from_token_or_node, tokenize_variables, ) if TYPE_CHECKING: from ..protocol import RobotLanguageServerProtocol from .model_helper import ModelHelperMixin from .protocol_part import RobotLanguageServerProtocolPart _DefinitionMethod = Callable[ [ast.AST, TextDocument, Position], Awaitable[Union[Location, List[Location], List[LocationLink], None]], ] class RobotGotoProtocolPart(RobotLanguageServerProtocolPart, ModelHelperMixin): _logger = LoggingDescriptor() def __init__(self, parent: RobotLanguageServerProtocol) -> None: super().__init__(parent) parent.definition.collect.add(self.collect) parent.implementation.collect.add(self.collect) def _find_method(self, cls: Type[Any]) -> Optional[_DefinitionMethod]: if cls is ast.AST: return None method_name = "definition_" + cls.__name__ if hasattr(self, method_name): method = getattr(self, method_name) if callable(method): return cast(_DefinitionMethod, method) for base in cls.__bases__: method = self._find_method(base) if method: return cast(_DefinitionMethod, method) return None @language_id("robotframework") async def collect( self, sender: Any, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: result_nodes = await get_nodes_at_position(await self.parent.documents_cache.get_model(document), position) if not result_nodes: return None result_node = result_nodes[-1] if result_node is None: return None method = self._find_method(type(result_node)) if method is not None: result = await method(result_node, document, position) if result is not None: return result return await self._definition_default(result_nodes, document, position) async def _definition_default( self, nodes: List[ast.AST], document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: from robot.api.parsing import Token as RobotToken namespace = await self.parent.documents_cache.get_namespace(document) if namespace is None: return None if not nodes: return None node = nodes[-1] if not isinstance(node, HasTokens): return None tokens = get_tokens_at_position(node, position) for token in tokens: try: for sub_token in filter( lambda s: s.type == RobotToken.VARIABLE, tokenize_variables(token, ignore_errors=True) ): range = range_from_token(sub_token) if position.is_in_range(range): variable = await namespace.find_variable(sub_token.value, nodes, position) if variable is not None and variable.source: return [ LocationLink( origin_selection_range=range_from_token_or_node(node, sub_token), target_uri=str(Uri.from_path(variable.source)), target_range=variable.range(), target_selection_range=range_from_token(variable.name_token) if variable.name_token else variable.range(), ) ] except BaseException: pass return None async def definition_KeywordCall( # noqa: N802 self, node: ast.AST, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: from robot.parsing.lexer.tokens import Token as RobotToken from robot.parsing.model.statements import KeywordCall namespace = await self.parent.documents_cache.get_namespace(document) if namespace is None: return None kw_node = cast(KeywordCall, node) result = await self.get_keyworddoc_and_token_from_position( kw_node.keyword, cast(Token, kw_node.get_token(RobotToken.KEYWORD)), [cast(Token, t) for t in kw_node.get_tokens(RobotToken.ARGUMENT)], namespace, position, ) if result is not None and result[0] is not None: source = result[0].source if source is not None: return [ LocationLink( origin_selection_range=range_from_token_or_node(node, result[1]), target_uri=str(Uri.from_path(source)), target_range=result[0].range, target_selection_range=result[0].range, ) ] return None async def definition_Fixture( # noqa: N802 self, node: ast.AST, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: from robot.parsing.lexer.tokens import Token as RobotToken from robot.parsing.model.statements import Fixture namespace = await self.parent.documents_cache.get_namespace(document) if namespace is None: return None fixture_node = cast(Fixture, node) result = await self.get_keyworddoc_and_token_from_position( fixture_node.name, cast(Token, fixture_node.get_token(RobotToken.NAME)), [cast(Token, t) for t in fixture_node.get_tokens(RobotToken.ARGUMENT)], namespace, position, ) if result is not None and result[0] is not None: source = result[0].source if source is not None: return [ LocationLink( origin_selection_range=range_from_token_or_node(node, result[1]), target_uri=str(Uri.from_path(source)), target_range=result[0].range, target_selection_range=result[0].range, ) ] return None async def _definition_Template_or_TestTemplate( # noqa: N802 self, node: ast.AST, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: from robot.parsing.lexer.tokens import Token as RobotToken from robot.parsing.model.statements import Template, TestTemplate node = cast(Union[Template, TestTemplate], node) if node.value: keyword_token = cast(RobotToken, node.get_token(RobotToken.NAME)) if keyword_token is None: return None if position.is_in_range(range_from_token(keyword_token)): namespace = await self.parent.documents_cache.get_namespace(document) if namespace is None: return None result = await namespace.find_keyword(node.value) if result is not None and result.source is not None: return [ LocationLink( origin_selection_range=range_from_token_or_node(node, keyword_token), target_uri=str(Uri.from_path(result.source)), target_range=result.range, target_selection_range=result.range, ) ] return None async def definition_TestTemplate( # noqa: N802 self, node: ast.AST, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: return await self._definition_Template_or_TestTemplate(node, document, position) async def definition_Template( # noqa: N802 self, node: ast.AST, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: return await self._definition_Template_or_TestTemplate(node, document, position) async def definition_LibraryImport( # noqa: N802 self, node: ast.AST, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: from robot.parsing.lexer.tokens import Token as RobotToken from robot.parsing.model.statements import LibraryImport library_node = cast(LibraryImport, node) if library_node.name: name_token = cast(RobotToken, library_node.get_token(RobotToken.NAME)) if name_token is None: return None if position.is_in_range(range_from_token(name_token)): namespace = await self.parent.documents_cache.get_namespace(document) if namespace is None: return None try: libdoc = await namespace.imports_manager.get_libdoc_for_library_import( library_node.name, library_node.args, str(document.uri.to_path().parent) ) python_source = libdoc.source_or_origin if python_source is not None: return [ LocationLink( origin_selection_range=range_from_token_or_node(library_node, name_token), target_uri=str(Uri.from_path(python_source)), target_range=libdoc.range, target_selection_range=libdoc.range, ) ] except BaseException: pass return None async def definition_ResourceImport( # noqa: N802 self, node: ast.AST, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: from robot.parsing.lexer.tokens import Token as RobotToken from robot.parsing.model.statements import ResourceImport resource_node = cast(ResourceImport, node) if resource_node.name: name_token = cast(RobotToken, resource_node.get_token(RobotToken.NAME)) if name_token is None: return None if position.is_in_range(range_from_token(name_token)): namespace = await self.parent.documents_cache.get_namespace(document) if namespace is None: return None try: libdoc = await namespace.imports_manager.get_libdoc_for_resource_import( resource_node.name, str(document.uri.to_path().parent) ) python_source = libdoc.source_or_origin if python_source is not None: return [ LocationLink( origin_selection_range=range_from_token_or_node(resource_node, name_token), target_uri=str(Uri.from_path(python_source)), target_range=libdoc.range, target_selection_range=libdoc.range, ) ] except BaseException: pass return None	[ 1, 515, 4770, 29888, 9130, 1649, 1053, 25495, 13, 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setup.py	molML/MoleculeACE	9	76186	from setuptools import setup setup( name='MoleculeACE', version='1.0.9', packages=['MoleculeACE', 'MoleculeACE.ML', 'MoleculeACE.CNN', 'MoleculeACE.MLP', 'MoleculeACE.GNN', 'MoleculeACE.GNN.data', 'MoleculeACE.GNN.models', 'MoleculeACE.GNN.models.optimization', 'MoleculeACE.LSTM', 'MoleculeACE.benchmark', 'MoleculeACE.benchmark.utils', 'MoleculeACE.benchmark.models', 'MoleculeACE.benchmark.evaluation', 'MoleculeACE.benchmark.data_processing', 'MoleculeACE.benchmark.data_processing.preprocessing', 'MoleculeACE.Data', 'MoleculeACE.Data.benchmark_data', 'MoleculeACE.Data.benchmark_data.train', 'MoleculeACE.Data.benchmark_data.test', 'MoleculeACE.Data.configures.benchmark', 'MoleculeACE.Data.configures.default', 'MoleculeACE.Data.configures', 'MoleculeACE.Data.configures.benchmark.CHEMBL4203_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL2034_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL233_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL4616_EC50', 'MoleculeACE.Data.configures.benchmark.CHEMBL287_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL218_EC50', 'MoleculeACE.Data.configures.benchmark.CHEMBL264_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL219_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL2835_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL2147_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL231_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL3979_EC50', 'MoleculeACE.Data.configures.benchmark.CHEMBL237_EC50', 'MoleculeACE.Data.configures.benchmark.CHEMBL244_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL4792_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL1871_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL237_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL262_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL2047_EC50', 'MoleculeACE.Data.configures.benchmark.CHEMBL239_EC50', 'MoleculeACE.Data.configures.benchmark.CHEMBL2971_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL204_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL214_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL1862_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL234_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL238_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL235_EC50', 'MoleculeACE.Data.configures.benchmark.CHEMBL4005_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL236_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL228_Ki' ], url='https://github.com/derekvantilborg/MoleculeACE', license='MIT', author='<NAME>', author_email='<EMAIL>', description='MoleculeACE', install_requires=[ 'tqdm', 'requests', 'twine', 'importlib-metadata', 'pandas', 'numpy', 'chembl_webresource_client', 'scikit-learn', 'matplotlib', 'python-Levenshtein', 'progress', 'rdkit-pypi' ], include_package_data=True, package_data={'': ['Data/', 'Data/benchmark_data/', 'Data/benchmark_data/test/', 'Data/benchmark_data/train/', 'Data/configures/', 'Data/configures/default/', 'Data/configures/benchmark/', 'Data/configures/benchmark/CHEMBL4203_Ki/', 'Data/configures/benchmark/CHEMBL2034_Ki/', 'Data/configures/benchmark/CHEMBL233_Ki/', 'Data/configures/benchmark/CHEMBL4616_EC50/', 'Data/configures/benchmark/CHEMBL287_Ki/', 'Data/configures/benchmark/CHEMBL218_EC50/', 'Data/configures/benchmark/CHEMBL264_Ki/', 'Data/configures/benchmark/CHEMBL219_Ki/', 'Data/configures/benchmark/CHEMBL2835_Ki/', 'Data/configures/benchmark/CHEMBL2147_Ki/', 'Data/configures/benchmark/CHEMBL231_Ki/', 'Data/configures/benchmark/CHEMBL3979_EC50/', 'Data/configures/benchmark/CHEMBL237_EC50/', 'Data/configures/benchmark/CHEMBL244_Ki/', 'Data/configures/benchmark/CHEMBL4792_Ki/', 'Data/configures/benchmark/CHEMBL1871_Ki/', 'Data/configures/benchmark/CHEMBL237_Ki/', 'Data/configures/benchmark/CHEMBL262_Ki/', 'Data/configures/benchmark/CHEMBL2047_EC50/', 'Data/configures/benchmark/CHEMBL239_EC50/', 'Data/configures/benchmark/CHEMBL2971_Ki/', 'Data/configures/benchmark/CHEMBL204_Ki/', 'Data/configures/benchmark/CHEMBL214_Ki/', 'Data/configures/benchmark/CHEMBL1862_Ki/', 'Data/configures/benchmark/CHEMBL234_Ki/', 'Data/configures/benchmark/CHEMBL238_Ki/', 'Data/configures/benchmark/CHEMBL235_EC50/', 'Data/configures/benchmark/CHEMBL4005_Ki/', 'Data/configures/benchmark/CHEMBL236_Ki/', 'Data/configures/benchmark/CHEMBL228_Ki/*' ]} )	[ 1, 515, 731, 21245, 8789, 1053, 6230, 13, 13, 14669, 29898, 13, 1678, 1024, 2433, 29924, 1772, 29883, 1297, 11538, 742, 13, 1678, 1873, 2433, 29896, 29889, 29900, 29889, 29929, 742, 13, 1678, 9741, 29922, 1839, 29924, 1772, 29883, 1297, 11538, 742, 525, 29924, 1772, 29883, 1297, 11538, 29889, 1988, 742, 525, 29924, 1772, 29883, 1297, 11538, 29889, 29907, 10262, 742, 525, 29924, 1772, 29883, 1297, 11538, 29889, 1988, 29925, 742, 525, 29924, 1772, 29883, 1297, 11538, 29889, 29954, 10262, 742, 13, 795, 525, 29924, 1772, 29883, 1297, 11538, 29889, 29954, 10262, 29889, 1272, 742, 525, 29924, 1772, 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setup.py	darrylcousins/nzrealme	1	64473	<filename>setup.py #!/usr/bin/env python # -- coding: utf-8 -- try: from setuptools import setup except ImportError: from distutils.core import setup readme = open('README.rst').read() history = open('HISTORY.rst').read().replace('.. :changelog:', '') requirements = [ 'M2Crypto==0.22.3', 'dm.xmlsec.binding==1.3.1', 'isodate==0.5.0', 'defusedxml==0.4.1', 'python-saml', ] test_requirements = [ 'pytest', 'flake8', 'sphinx' ] setup( name='nzrealme', version='0.1.0', description='NZ RealMe python package', long_description=readme + '\n\n' + history, author='<NAME>', author_email='<EMAIL>', url='https://github.com/darrylcousins/nzrealme', packages=[ 'nzrealme', ], package_dir={'nzrealme': 'nzrealme'}, include_package_data=True, install_requires=requirements, license="BSD", zip_safe=False, classifiers=[ 'Development Status :: 2 - Pre-Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Natural Language :: English', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', ], test_suite='tests', tests_require=test_requirements, keywords='saml saml2 xmlsec realme nzrealme', )	[ 1, 529, 9507, 29958, 14669, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 13, 2202, 29901, 13, 1678, 515, 731, 21245, 8789, 1053, 6230, 13, 19499, 16032, 2392, 29901, 13, 1678, 515, 1320, 13239, 29889, 3221, 1053, 6230, 13, 13, 13, 949, 1004, 353, 1722, 877, 16310, 2303, 29889, 29878, 303, 2824, 949, 580, 13, 18434, 353, 1722, 877, 29950, 9047, 18929, 29889, 29878, 303, 2824, 949, 2141, 6506, 877, 636, 584, 305, 9477, 468, 29901, 742, 27255, 13, 13, 12277, 1860, 353, 518, 13, 1678, 525, 29924, 29906, 29907, 17929, 1360, 29900, 29889, 29906, 29906, 29889, 29941, 742, 13, 1678, 525, 18933, 29889, 3134, 3471, 29889, 19672, 1360, 29896, 29889, 29941, 29889, 29896, 742, 13, 1678, 525, 275, 397, 403, 1360, 29900, 29889, 29945, 29889, 29900, 742, 13, 1678, 525, 1753, 3880, 3134, 1360, 29900, 29889, 29946, 29889, 29896, 742, 13, 1678, 525, 4691, 29899, 29879, 8807, 742, 13, 29962, 13, 13, 1688, 29918, 12277, 1860, 353, 518, 13, 1678, 525, 2272, 1688, 742, 13, 1678, 525, 29888, 433, 446, 29947, 742, 13, 1678, 525, 29879, 561, 14668, 29915, 13, 29962, 13, 13, 14669, 29898, 13, 1678, 1024, 2433, 29876, 29920, 6370, 1004, 742, 13, 1678, 1873, 2433, 29900, 29889, 29896, 29889, 29900, 742, 13, 1678, 6139, 2433, 29940, 29999, 8195, 6816, 3017, 3577, 742, 13, 1678, 1472, 29918, 8216, 29922, 949, 1004, 718, 11297, 29876, 29905, 29876, 29915, 718, 4955, 29892, 13, 1678, 4148, 2433, 29966, 5813, 29958, 742, 13, 1678, 4148, 29918, 5269, 2433, 29966, 26862, 6227, 29958, 742, 13, 1678, 3142, 2433, 991, 597, 3292, 29889, 510, 29914, 16702, 719, 29880, 29883, 681, 1144, 29914, 29876, 29920, 6370, 1004, 742, 13, 1678, 9741, 11759, 13, 4706, 525, 29876, 29920, 6370, 1004, 742, 13, 1678, 21251, 13, 1678, 3577, 29918, 3972, 3790, 29915, 29876, 29920, 6370, 1004, 2396, 13, 462, 525, 29876, 29920, 6370, 1004, 16675, 13, 1678, 3160, 29918, 5113, 29918, 1272, 29922, 5574, 29892, 13, 1678, 2601, 29918, 276, 339, 2658, 29922, 12277, 1860, 29892, 13, 1678, 19405, 543, 29933, 7230, 613, 13, 1678, 14319, 29918, 11177, 29922, 8824, 29892, 13, 1678, 770, 14903, 11759, 13, 4706, 525, 21956, 358, 16034, 4761, 29871, 29906, 448, 4721, 29899, 28630, 742, 13, 4706, 525, 2928, 2760, 319, 4749, 663, 4761, 10682, 414, 742, 13, 4706, 525, 29931, 293, 1947, 4761, 438, 5425, 28268, 1490, 4761, 350, 7230, 19245, 742, 13, 4706, 525, 29940, 18771, 17088, 4761, 4223, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29906, 29889, 29955, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 29889, 29941, 742, 13, 4706, 525, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 29889, 29946, 742, 13, 1678, 21251, 13, 1678, 1243, 29918, 13495, 2433, 21150, 742, 13, 1678, 6987, 29918, 12277, 29922, 1688, 29918, 12277, 1860, 29892, 13, 1678, 29361, 2433, 29879, 8807, 3514, 29880, 29906, 4903, 3471, 1855, 1004, 302, 29920, 6370, 1004, 742, 13, 29897, 13, 2 ]
access/__init__.py	dmcgrath/starmade-blueprint-library	6	156742	<gh_stars>1-10 from user import *	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 3166, 1404, 1053, 334, 13, 2 ]
django_postgres_drop_index/utils.py	andrewp-as-is/django-postgres-drop-index.py	1	172281	<gh_stars>1-10 from django.db import connection from .classes import Index def get_indexes(): sql = """ SELECT n.nspname AS schemaname,t.relname AS tablename,c.relname AS indexname from pg_catalog.pg_class c JOIN pg_catalog.pg_namespace n on n.oid = c.relnamespace JOIN pg_catalog.pg_index i on i.indexrelid = c.oid JOIN pg_catalog.pg_class t on i.indrelid = t.oid WHERE c.relkind = 'i' AND NOT i.indisprimary AND NOT i.indisunique AND n.nspname not in ('pg_catalog', 'pg_toast') AND pg_catalog.pg_table_is_visible(c.oid) ORDER BY n.nspname,t.relname,c.relname; """ cursor = connection.cursor() cursor.execute(sql.strip()) indexes = [] for r in cursor.fetchall(): schemaname, tablename, indexname = r index = Index(schemaname, tablename, indexname) indexes.append(index) return indexes def drop_index(indexname, schemaname=None): if not schemaname: schemaname = 'public' sql = """DROP INDEX IF EXISTS "%s"."%s" CASCADE;""" % ( schemaname, indexname) cursor = connection.cursor() print(sql.strip()) cursor.execute(sql.strip()) def drop_table_indexes(tablename, schemaname=None): if not schemaname: schemaname = 'public' for index in get_indexes(): if index.schemaname == schemaname and index.tablename == tablename: drop_index(indexname=index.indexname, schemaname=index.schemaname) def drop_schema_indexes(schemaname): for index in get_indexes(): if index.schemaname == schemaname: drop_index(indexname=index.indexname, schemaname=index.schemaname) def drop_all_indexes(): for index in get_indexes(): drop_index(indexname=index.indexname, schemaname=index.schemaname)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 3166, 9557, 29889, 2585, 1053, 3957, 13, 13, 3166, 869, 13203, 1053, 11374, 13, 13, 13, 1753, 679, 29918, 2248, 267, 7295, 13, 1678, 4576, 353, 9995, 13, 6404, 302, 29889, 29876, 1028, 978, 3339, 1364, 11422, 420, 29892, 29873, 29889, 2674, 978, 3339, 4434, 2435, 420, 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joplin/base/signals/janis_build_triggers.py	cityofaustin/joplin	15	1601521	import requests, json from datetime import datetime from pytz import timezone from django.conf import settings from django.dispatch import receiver from django.db.models.signals import post_save, post_delete from wagtail.core.signals import page_published, page_unpublished from graphene import Node from wagtail.core.models import Page from wagtail.admin.models import get_object_usage from rest_framework_api_key.models import APIKey from snippets.contact.models import Contact from pages.guide_page.models import GuidePage from wagtail.documents.models import Document from pages.home_page.models import HomePage import logging logger = logging.getLogger('joplin') ''' Gets data from a page that is required to send to the Publisher. returns { id: Int, the page_id used by wagtail. global_id: String, a hashed id used to query against graphql api triggered_build: Boolean, was this the page that triggered the publish request? action: "published" - publish action for itself and other pages "unpublished" - this page triggered the unpublish action for itself and other pages "updated_by_snippet" - this page was updated by a snippet being saved or deleted "saved" - a snippet is being saved, has no impact on Janis itself, but it could result in pages getting "secondary_publish_by_snippet" "deleted" - a snippet is being deleted, has no impact on Janis itself, but it could result in pages getting "secondary_publish_by_snippet" is_page: Boolean, is it a page as opposed to a snippet? content_type: String, which specific content_type (this is more for logging) author: Int, the id of the author of the latest revision } TODO: Figure out what format global_id should be in order to run queries with it. The current global_id is basically a placeholder. ''' def get_page_data(page, triggered_build, action): latest_revision = page.get_latest_revision() # imported pages may not have a latest_revision yet. if latest_revision and latest_revision.user: author = latest_revision.user.id else: author = None return { "id": page.id, "global_id": Node.to_global_id(page.content_type.name, page.id), "triggered_build": triggered_build, "action": action, "is_page": True, "content_type": page.specific_class.get_verbose_name(), "author": author, } def collect_pages(primary_page, action): # does this work on page deletion? pages arent deleted right, just unpublished? """ :param primary_page: the page that triggered the publish/unpublish :return: pages: an array of the data from all pages impacted by the publish/unpublish event """ pages = [ get_page_data(primary_page, True, action) ] primary_id = primary_page.id primary_content_type = primary_page.specific_class.get_verbose_name() page_set = get_object_usage(primary_page) # https://github.com/wagtail/wagtail/blob/master/wagtail/admin/models.py#L15 # get_object_usage will also return the wagtail_page itself for page in page_set: # Primary page is also included in page_set. Don't re-add primary_page data. if not (page.id == primary_id): pages.append(get_page_data(page, False, action)) if primary_content_type == 'Service Page' or primary_content_type == 'Information Page': guide_page_data = get_page_data_from_guides(primary_id, action) pages.extend(guide_page_data) return pages def collect_pages_from_snippet(instance, action): """ :param instance: the snippet that has been altered :return: an array of global page ids """ snippet_data = { "id": instance.id, "global_id": None, "triggered_build": True, "action": action, "is_page": False, "content_type": instance.__class__.__name__, "author": None, # TODO: is there a way to get the last editor of a snippet? } pages = [snippet_data] page_set = instance.get_usage() for page in page_set: pages.append(get_page_data(page, False, "updated_by_snippet")) return pages def get_page_data_from_guides(changed_id, action): """ Service Pages and information Pages don't know they are on Guides. So what happens if one is updated? Until we know better, this will go through all our guide pages and check if the page that is changed is in one of the guide's sections :param changed_id: id of the page that was published / unpublished :return: page_data for Guide Pages that include that page """ pages = [] all_guides = GuidePage.objects.all() for g in all_guides: for s in g.sections: # s.value.items() is an ordered dict list_of_values = list(s.value.items()) # the pages are the 5th tuple section_pages = list_of_values[4][1] for page in section_pages: if changed_id == page.id: pages.append(get_page_data(g, False, action)) return pages @receiver(page_published) def page_published_signal(sender, kwargs): # for the future, setting up a way to check if the title changed # https://stackoverflow.com/questions/1355150/when-saving-how-can-you-check-if-a-field-has-changed # because if the title didnt change, pages that contain links to the published page don't need to be updated action = "published" primary_page = Page.objects.get(id=kwargs['instance'].id) pages = collect_pages(primary_page, action) publish(pages, primary_page) @receiver(page_unpublished) def page_unpublished_signal(sender, kwargs): action = "unpublished" primary_page = Page.objects.get(id=kwargs['instance'].id) pages = collect_pages(primary_page, action) publish(pages, primary_page) # TODO: we can probably feed a list of models to attach the hook to # more ideas here # we might want to log but not trigger a build? need some sort of queue @receiver(post_save, sender=Document) @receiver(post_save, sender=Contact) def handle_post_save_signal(sender, kwargs): action = "saved" pages = collect_pages_from_snippet(kwargs['instance'], action) publish(pages) @receiver(post_delete, sender=Document) @receiver(post_delete, sender=Contact) def handle_post_delete_signal(sender, kwargs): action = "deleted" pages = collect_pages_from_snippet(kwargs['instance'], action) publish(pages) def publish(pages, primary_page=None): if settings.MOCK_PUBLISH and primary_page: update_primary_page(primary_page, "mock_pk", "mock_sk") return if not settings.PUBLISH_ENABLED: return # TODO: we want to extract the publish_janis_branch() for each page_id that we're publishing (for example, if we start publishing to different sites). # That logic must happen earlier in the collect_pages logic. # Even though this will work for now, it should not be hardcoded to be the first HomePage object. publish_janis_branch = HomePage.objects.first().publish_janis_branch() if not publish_janis_branch: logger.info("publish_janis_branch must be set in order to publish.") return None api_key = APIKey.objects.create_key( name=f"publisher-{datetime.now(timezone('US/Central')).isoformat()}" )[1] headers = { "x-api-key": settings.PUBLISHER_V2_API_KEY, "content-type": "application/json", } url = settings.PUBLISHER_V2_URL data = { "janis_branch": publish_janis_branch, "pages": pages, "joplin_appname": settings.APPNAME, "api_key": api_key, "env_vars": { "REACT_STATIC_PREFETCH_RATE": "0", }, "build_type": "rebuild", } res = requests.post(url, data=json.dumps(data), headers=headers) if res.status_code != 200: logger.error(f"publish_request failed with status {res.status_code}") logger.error(f"message: {res.json()['message']}") elif primary_page: res_data = res.json() publish_request_pk = res_data['pk'] publish_request_sk = res_data['sk'] update_primary_page(primary_page, publish_request_pk, publish_request_sk) def update_primary_page(primary_page, publish_request_pk, publish_request_sk): primary_page = primary_page.specific primary_page.publish_request_pk = publish_request_pk primary_page.publish_request_sk = publish_request_sk primary_page.publish_request_enqueued = True logger.info(f"published() pk={publish_request_pk}, sk={publish_request_sk}") primary_page.save()	[ 1, 1053, 7274, 29892, 4390, 13, 3166, 12865, 1053, 12865, 13, 3166, 282, 3637, 29920, 1053, 29431, 13, 3166, 9557, 29889, 5527, 1053, 6055, 13, 3166, 9557, 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resources/tasks.py	axonepro/sdk-ooti	1	66511	<gh_stars>1-10 import requests import json from .helper import Helper class Tasks(Helper): def __init__(self, base_url, org_pk, teams_pk, access_token, _csrf_token, headers, pagination): super().__init__(base_url, org_pk, teams_pk, access_token, _csrf_token, headers, pagination) def empty_tasks_trash(self, project_id): """ Set delete all not-completed archived tasks in project """ route = 'v1/tasks/empty-trash/{0}/'.format(project_id) response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, None) return self.process_response(response) def get_task_labels_list(self, page=1): """ Get the list of tasks labels """ route = 'v1/tasks/label/list/{0}/?page_size={1}&page={2}'.format(self.org_pk, self.pagination, page) response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None) return self.process_response(response, True) def create_task_label(self, data): """ Create a new task label Keywords arguments: data -- data of the new label to be created: { "creator": orguser_pk, "team": team_pk, "title": "label title", "description": "new task label" } """ route = 'v1/tasks/label/list/{0}/'.format(self.org_pk) response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, json.dumps(data)) return self.process_response(response) def get_task_label_details(self, label_pk): """ Get the task label details Keywords arguments: label_pk -- pk of the task label """ route = 'v1/tasks/label/{0}/'.format(label_pk) response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None) return self.process_response(response) def update_task_label_details(self, label_pk, data): """ Update the task label details Keywords arguments: label_pk -- pk of the task label data -- content of the update: { "creator": orguser_pk, "team": team_pk, "title": "new title", "description": "description updated" } """ route = 'v1/tasks/label/{0}/'.format(label_pk) response = self.process_request(requests, 'PATCH', self.base_url, route, self.headers, None, json.dumps(data)) return self.process_response(response) def delete_task_label(self, label_pk): """ Delete the task label details Keywords arguments: label_pk -- pk of the task label """ route = 'v1/tasks/label/{0}/'.format(label_pk) response = self.process_request(requests, 'DELETE', self.base_url, route, self.headers, None, None) return self.process_response(response) def get_tasks_list(self, page=1): """ Get the tasks list """ route = 'v1/tasks/list/{0}/?page_size={1}&page={2}'.format(self.org_pk, self.pagination, page) response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None) return self.process_response(response, True) def create_task(self, data): """ Create a new task Keywords arguments: data -- data of the new task to be created: { "creator": orguser_pk, "created_at": "string", "labels": [ label_pk, ... ], "title": "string", "due_date": "string", "description": "string" } """ route = 'v1/tasks/list/{0}/'.format(self.org_pk) response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, json.dumps(data)) return self.process_response(response) def get_tasks_lists_list(self, page=1): """ Get the list of tasks list """ route = 'v1/tasks/lists/list/{0}/?page_size={1}&page={2}'.format(self.org_pk, self.pagination, page) response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None) return self.process_response(response, True) def create_tasks_list(self, data): """ Create a new list of tasks Keywords arguments: data -- data of the new list of tasks to be created: { "author": orguser_pk, "title": "new list", "tasks": [ task_pk, ... ], "followers": [ orguser_pk, ... ] } """ route = 'v1/tasks/lists/list/{0}/'.format(self.org_pk) response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, json.dumps(data)) return self.process_response(response) def get_tasks_list_details(self, list_pk): """ Get the list of tasks details Keywords arguments: list_pk -- the pk of list of tasks """ route = 'v1/tasks/lists/{0}/'.format(list_pk) response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None) return self.process_response(response) def update_tasks_list_details(self, list_pk, data): """ Update the list of tasks details Keywords arguments: list_pk -- the pk of list of tasks data -- content of the update: { "author": orguser_pk, "title": "new list", "tasks": [ task_pk, ... ], "followers": [ orguser_pk, ... ] } """ route = 'v1/tasks/lists/{0}/'.format(list_pk) response = self.process_request(requests, 'PATCH', self.base_url, route, self.headers, None, json.dumps(data)) return self.process_response(response) def delete_tasks_list(self, list_pk): """ Delete the list of tasks Keywords arguments: list_pk -- the pk of list of tasks """ route = 'v1/tasks/lists/{0}/'.format(list_pk) response = self.process_request(requests, 'DELETE', self.base_url, route, self.headers, None, None) return self.process_response(response) def log_tasks(self): """ Set all tasks to is_logged True """ route = 'v1/tasks/log-tasks/{0}/'.format(self.org_pk) response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, None) return self.process_response(response) def get_tasks_timeline(self): route = 'v1/tasks/timeline/{0}/'.format(self.org_pk) response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None) return self.process_response(response) def get_task_details(self, pk): """ Get task details Keywords arguments: pk -- the pk of the task """ route = 'v1/tasks/{0}/'.format(pk) response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None) return self.process_response(response) def update_task_details(self, pk, data): """ Update task details Keywords 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webscan/webscan/log.py	gwvsol/WebScanQRcode-JSON-RPC	0	52875	<reponame>gwvsol/WebScanQRcode-JSON-RPC # import logging from loguru import logger as logging # log_format = '%(asctime)s.%(msecs)d\|\ # %(levelname)s\|%(module)s.%(funcName)s:%(lineno)d %(message)s' # logging.basicConfig(level=logging.INFO, # format=log_format, # datefmt='%Y-%m-%d %H:%M:%S') logging.add(str, format="<green>{time:YYYY-MM-DD HH:mm:ss}</green>\|\ {level}\|<cyan>{name}</cyan>:<cyan>{function}\ </cyan>:<cyan>{line}</cyan> {message}")	[ 1, 529, 276, 1112, 420, 29958, 29887, 29893, 29894, 2929, 29914, 3609, 29083, 29984, 29934, 401, 29899, 7249, 29899, 29934, 9026, 13, 29937, 1053, 12183, 13, 3166, 1480, 20144, 1053, 17927, 408, 12183, 13, 13, 29937, 1480, 29918, 4830, 353, 14210, 29898, 294, 312, 603, 29897, 29879, 29889, 29995, 29898, 29885, 344, 2395, 29897, 29881, 4295, 13, 29937, 1273, 29898, 5563, 978, 29897, 29879, 29989, 29995, 29898, 5453, 29897, 29879, 29889, 29995, 29898, 9891, 1170, 29897, 29879, 16664, 29898, 1915, 8154, 29897, 29881, 1273, 29898, 4906, 29897, 29879, 29915, 13, 29937, 12183, 29889, 16121, 3991, 29898, 5563, 29922, 21027, 29889, 11690, 29892, 13, 29937, 462, 268, 3402, 29922, 1188, 29918, 4830, 29892, 13, 29937, 462, 268, 2635, 23479, 2433, 29995, 29979, 19222, 29885, 19222, 29881, 1273, 29950, 16664, 29924, 16664, 29903, 1495, 13, 13, 21027, 29889, 1202, 29898, 710, 29892, 3402, 543, 29966, 12692, 26208, 2230, 29901, 14995, 14995, 29899, 7428, 29899, 7858, 379, 29950, 29901, 4317, 29901, 893, 16040, 12692, 29958, 4295, 13, 29912, 5563, 11079, 29966, 1270, 273, 26208, 978, 16040, 1270, 273, 23917, 29966, 1270, 273, 26208, 2220, 1012, 13, 829, 1270, 273, 23917, 29966, 1270, 273, 26208, 1220, 16040, 1270, 273, 29958, 426, 4906, 27195, 13, 2 ]

FGR/utils/visual.py

weiyithu/FGR

27

119242

<filename>FGR/utils/visual.py<gh_stars>10-100 from .kitti_utils_official import * import traceback def draw_bbox_3d_to_2d_gt(img, corner, AverageValue_x, AverageValue_y): gt_box_3d = corner.copy() for point in gt_box_3d: point[0] = point[0] * 100 + 250 - AverageValue_x point[2] = point[2] * 100 + 250 - AverageValue_y cv2.line(img, (int(gt_box_3d[0][0]), int(gt_box_3d[0][2])), (int(gt_box_3d[1][0]), int(gt_box_3d[1][2])), (0, 255, 255), 1, 4) cv2.line(img, (int(gt_box_3d[1][0]), int(gt_box_3d[1][2])), (int(gt_box_3d[2][0]), int(gt_box_3d[2][2])), (0, 255, 255), 1, 4) cv2.line(img, (int(gt_box_3d[2][0]), int(gt_box_3d[2][2])), (int(gt_box_3d[3][0]), int(gt_box_3d[3][2])), (0, 255, 255), 1, 4) cv2.line(img, (int(gt_box_3d[3][0]), int(gt_box_3d[3][2])), (int(gt_box_3d[0][0]), int(gt_box_3d[0][2])), (0, 255, 255), 1, 4) return img def draw_frustum_lr_line(img, left_point, right_point, AverageValue_x, AverageValue_y): left_point_draw = np.array([0., 0.]) left_point_draw[0] = (left_point[0] * 20000 + 250 - AverageValue_x) left_point_draw[1] = (left_point[1] * 20000 + 250 - AverageValue_y) right_point_draw = np.array([0., 0.]) right_point_draw[0] = (right_point[0] * 20000 + 250 - AverageValue_x) right_point_draw[1] = (right_point[1] * 20000 + 250 - AverageValue_y) initial_point_draw = np.array([0., 0.]) initial_point_draw[0] = 250 - AverageValue_x initial_point_draw[1] = 250 - AverageValue_y cv2.line(img, tuple(initial_point_draw.astype(np.float32)), tuple(left_point_draw.astype(np.float32)), (255, 255, 0), 1, 4) cv2.line(img, tuple(initial_point_draw.astype(np.float32)), tuple(right_point_draw.astype(np.float32)), (255, 255, 0), 1, 4) return img def draw_bbox_3d_to_2d_psuedo_no_intersection(img, box_no_intersection, AverageValue_x, AverageValue_y): box_draw = box_no_intersection.copy() for var in box_draw: var[0] = var[0] * 100 + 250 - AverageValue_x var[1] = var[1] * 100 + 250 - AverageValue_y cv2.line(img, tuple(box_draw[0]), tuple(box_draw[1]), (255, 0, 255), 1, 4) cv2.line(img, tuple(box_draw[1]), tuple(box_draw[2]), (255, 0, 255), 1, 4) cv2.line(img, tuple(box_draw[2]), tuple(box_draw[3]), (255, 0, 255), 1, 4) cv2.line(img, tuple(box_draw[3]), tuple(box_draw[0]), (255, 0, 255), 1, 4) return img def draw_bbox_3d_to_2d_psuedo_with_key_vertex(img, FinalPoint, loc1, loc2, loc3, AverageValue_x, AverageValue_y): loc1_draw = np.array([0., 0.]) loc2_draw = np.array([0., 0.]) loc3_draw = np.array([0., 0.]) loc1_draw[0] = loc1[0] * 100 + 250 - AverageValue_x loc1_draw[1] = loc1[1] * 100 + 250 - AverageValue_y loc2_draw[0] = loc2[0] * 100 + 250 - AverageValue_x loc2_draw[1] = loc2[1] * 100 + 250 - AverageValue_y loc3_draw[0] = loc3[0] * 100 + 250 - AverageValue_x loc3_draw[1] = loc3[1] * 100 + 250 - AverageValue_y # draw key vertex with larger point than normal point cloud FinalPoint_draw = np.array([0., 0.]) FinalPoint_draw[0] = FinalPoint[0] * 100 + 250 - AverageValue_x FinalPoint_draw[1] = FinalPoint[1] * 100 + 250 - AverageValue_y cv2.circle(img, tuple(FinalPoint_draw.astype(np.float32)), 3, (0, 255, 0), 4) cv2.line(img, tuple(loc1_draw.astype(np.float32)), tuple(FinalPoint_draw.astype(np.float32)), (0, 0, 255), 1, 4) cv2.line(img, tuple(loc1_draw.astype(np.float32)), tuple(loc3_draw.astype(np.float32)), (0, 0, 255), 1, 4) cv2.line(img, tuple(loc3_draw.astype(np.float32)), tuple(loc2_draw.astype(np.float32)), (0, 0, 255), 1, 4) cv2.line(img, tuple(loc2_draw.astype(np.float32)), tuple(FinalPoint_draw.astype(np.float32)), (0, 0, 255), 1, 4) return img def draw_point_clouds(img, KeyPoint_for_draw, AverageValue_x, AverageValue_y): for point in KeyPoint_for_draw: a = point[0] * 100 + 250 - AverageValue_x b = point[1] * 100 + 250 - AverageValue_y cv2.circle(img, (int(a), int(b)), 1, (255, 255, 255), 2) return img def draw_2d_box_in_2d_image(KeyPoint_3d, box_2d, p2): img = np.zeros((1000, 1000, 3), 'f4') KeyPoint_for_draw = np.copy(KeyPoint_3d[:, [0, 2]]) KeyPoint = KeyPoint_3d[:, [0, 2]] AverageValue_x = np.mean(KeyPoint[:, 0]) * 100 AverageValue_y = np.mean(KeyPoint[:, 1]) * 100 for point in KeyPoint_for_draw: a = point[0] * 50 + 500 - AverageValue_x b = point[1] * 50 + 100 - AverageValue_y cv2.circle(img, (int(a), int(b)), 1, (255, 255, 255), 0) left_point = np.array([box_2d[0], 0, 1]) right_point = np.array([box_2d[2], 0, 1]) left_point = np.linalg.inv(p2[:, [0, 1, 2]]).dot(left_point.T) right_point = np.linalg.inv(p2[:, [0, 1, 2]]).dot(right_point.T) left_point = left_point[[0, 2]] right_point = right_point[[0, 2]] left_point_draw = np.array([0., 0.]) right_point_draw = np.array([0., 0.]) while True: zoom_factor = 60000 try: left_point_draw[0] = (left_point[0] * zoom_factor + 100 - AverageValue_x) left_point_draw[1] = (left_point[1] * zoom_factor + 100 - AverageValue_y) right_point_draw[0] = (right_point[0] * zoom_factor + 100 - AverageValue_x) right_point_draw[1] = (right_point[1] * zoom_factor + 100 - AverageValue_y) except OverflowError: zoom_factor /= 6 continue else: break initial_point_draw = np.array([0., 0.]) initial_point_draw[0] = 500 - AverageValue_x initial_point_draw[1] = 100 - AverageValue_y cv2.line(img, tuple(initial_point_draw.astype(np.float32)), tuple(left_point_draw.astype(np.float32)), (255, 255, 0), 1, 4) cv2.line(img, tuple(initial_point_draw.astype(np.float32)), tuple(right_point_draw.astype(np.float32)), (255, 255, 0), 1, 4) return img def draw_3d_box_in_2d_image(img, box): # draw 3D box in 2D RGB image, if needed cv2.line(img, (int(box[0][0]), int(box[0][1])), (int(box[1][0]), int(box[1][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[1][0]), int(box[1][1])), (int(box[2][0]), int(box[2][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[2][0]), int(box[2][1])), (int(box[3][0]), int(box[3][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[3][0]), int(box[3][1])), (int(box[0][0]), int(box[0][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[0][0]), int(box[0][1])), (int(box[4][0]), int(box[4][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[1][0]), int(box[1][1])), (int(box[5][0]), int(box[5][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[2][0]), int(box[2][1])), (int(box[6][0]), int(box[6][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[3][0]), int(box[3][1])), (int(box[7][0]), int(box[7][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[4][0]), int(box[4][1])), (int(box[5][0]), int(box[5][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[5][0]), int(box[5][1])), (int(box[6][0]), int(box[6][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[6][0]), int(box[6][1])), (int(box[7][0]), int(box[7][1])), (0, 0, 255), 1, 4) cv2.line(img, (int(box[7][0]), int(box[7][1])), (int(box[4][0]), int(box[4][1])), (0, 0, 255), 1, 4) return img def show_velodyne_in_camera(loc0, loc1, loc2, loc3, y_min, y_max): # use mayavi to draw 3D bbox corners = np.array([[[loc0[0], loc1[0], loc2[0], loc3[0], loc0[0], loc1[0], loc2[0], loc3[0]], [loc0[1], loc1[1], loc2[1], loc3[1], loc0[1], loc1[1], loc2[1], loc3[1]], [y_min, y_min, y_min, y_min, y_max, y_max, y_max, y_max]]], dtype=np.float32) for i in range(corners.shape[0]): corner = corners[i] idx = np.array([0, 1, 2, 3, 0, 4, 5, 6, 7, 4, 5, 1, 2, 6, 7, 3]) x = corner[0, idx] y = corner[1, idx] z = corner[2, idx] mayavi.mlab.plot3d(x, y, z, color=(0.23, 0.6, 1), colormap='Spectral', representation='wireframe', line_width=5)

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tests/factorys.py

2h4dl/pymilvus

0

17967

# STL imports import random import logging import string import time import datetime import random import struct import sys from functools import wraps # Third party imports import numpy as np import faker from faker.providers import BaseProvider logging.getLogger('faker').setLevel(logging.ERROR) sys.path.append('.') # grpc from milvus.grpc_gen import milvus_pb2 def gen_vectors(num, dim): return [[random.random() for _ in range(dim)] for _ in range(num)] def gen_single_vector(dim): return [[random.random() for _ in range(dim)]] def gen_vector(nb, d, seed=np.random.RandomState(1234)): xb = seed.rand(nb, d).astype("float32") return xb.tolist() def gen_unique_str(str=None): prefix = "".join(random.choice(string.ascii_letters + string.digits) for _ in range(8)) return prefix if str is None else str + "_" + prefix def get_current_day(): return time.strftime('%Y-%m-%d', time.localtime()) def get_last_day(day): tmp = datetime.datetime.now() - datetime.timedelta(days=day) return tmp.strftime('%Y-%m-%d') def get_next_day(day): tmp = datetime.datetime.now() + datetime.timedelta(days=day) return tmp.strftime('%Y-%m-%d') def gen_long_str(num): string = '' for _ in range(num): char = random.choice('tomorrow') string += char def gen_one_binary(topk): ids = [random.randrange(10000000, 99999999) for _ in range(topk)] distances = [random.random() for _ in range(topk)] return milvus_pb2.TopKQueryResult(struct.pack(str(topk) + 'l', *ids), struct.pack(str(topk) + 'd', *distances)) def gen_nq_binaries(nq, topk): return [gen_one_binary(topk) for _ in range(nq)] def fake_query_bin_result(nq, topk): return gen_nq_binaries(nq, topk) class FakerProvider(BaseProvider): def collection_name(self): return 'collection_names' + str(random.randint(1000, 9999)) def name(self): return 'name' + str(random.randint(1000, 9999)) def dim(self): return random.randint(0, 999) fake = faker.Faker() fake.add_provider(FakerProvider) def collection_name_factory(): return fake.collection_name() def records_factory(dimension, nq): return [[random.random() for _ in range(dimension)] for _ in range(nq)] def binary_records_factory(dimension, nq): def binary_record(bsize): s_m = "abcdefghijklmnopqrstuvwxyz" s_list = [s_m[random.randint(0, 25)] for _ in range(bsize)] s = "".join(s_list) return bytes(s, encoding="ASCII") bs = dimension // 8 return [binary_record(bs) for _ in range(nq)] def integer_factory(nq): return [random.randint(0, 128) for _ in range(nq)] def time_it(func): @wraps(func) def inner(*args, **kwrgs): pref = time.perf_counter() result = func(*args, **kwrgs) delt = time.perf_counter() - pref print(f"[{func.__name__}][{delt:.4}s]") return result return inner

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app/__init__.py

oOo0oOo/FoodWorld

0

42142

from flask import Flask, Blueprint from flask_ask import Ask from flask_sqlalchemy import SQLAlchemy from config import config # Extensions db = SQLAlchemy() alexa = Ask(route = '/') # Main blueprint main = Blueprint('main', __name__) from . import models, views def create_app(config_name = 'development'): app = Flask(__name__) app.config.from_object(config[config_name]) config[config_name].init_app(app) db.init_app(app) alexa.init_app(app) from . import main as main_blueprint app.register_blueprint(main_blueprint) return app

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Books/PythonCookBook/P02_String_Text.py

Tim232/Python-Things

2

89247

# Chapter 2. 문자열과 텍스트 # 2.1 여러 구분자로 문자열 나누기 # ▣ 문제 : 문자열을 필드로 나누고 싶지만 구분자가 문자열에 일관적이지 않다. # ▣ 해결 : 문자열 객체의 split() 메소드는 아주 간단한 상황에 사용하도록 설계되었고 여러 개의 구분자나 구분자 주변의 # 공백까지 고려하지는 않는다. 좀 더 유연해져야 할 필요가 있다면 re.split() 메소드를 사용한다. line = 'asdf fjdk; afed, fjek,asdf, foo' import re print(re.split(r'[;,\s]\s*', line)) # ▣ 토론 : re.split() 함수는 분리 구문마다 여러 패턴을 명시할 수 있다는 점이 유리하다. # - re.split() 을 사용할 때는 괄호 안에 묶인 정규 표현식 패턴이 캡처 그룹이 된다. fields = re.split(r'(;|,|\s)\s*', line) print(fields) # - 구분 문자만 출력하는 경우. values = fields[::2] delimiters = fields[1::2] + [''] print(values, delimiters) # - 동일한 구분자로 라인을 구성한다. print(''.join(v+d for v, d in zip(values, delimiters))) # - 분리 구문을 결과에 포함시키고 싶지 않지만 정규 표현식에 괄호를 사용해야 할 필요가 있다면 논캡처 그룹을 사용한다. print(re.split('(?:,|;|\s)\s*', line)) # 2.2 문자열 처음이나 마지막에 텍스트 매칭 # ▣ 문제 : 문자열의 처음이나 마지막에 파일 확장자, URL scheme 등 특정 텍스트 패턴이 포함되었는지 검사하고 싶다. # ▣ 해결 : 문자열의 처음이나 마지막에 패턴이 포함되었는지 확인하는 간단한 방법으로 str.startswith() 나 str.endswith() 메소드가 있다. filename = 'spam.txt' print(filename.endswith('.txt')) print(filename.startswith('file:')) url = 'http://www.python.org' print(url.startswith('http:')) # - 여러 개의 선택지를 검사해야 한다면 검사하고 싶은 값을 튜플에 담아 startswith() 나 endswith() 에 전달한다. import os filenames = os.listdir('.') print(filenames) print([name for name in filenames if name.endswith(('.c', '.h'))]) from urllib.request import urlopen def read_data(name): if name.startswith(('http:', 'https:', 'ftp:')): return urlopen(name).read() else: with open(name) as f: return f.read() # - startswith() 메소드는 튜플만을 입력으로 받는다. choices = ['http:', 'ftp:'] url = 'http://www.python.org' url.startswith(choices) url.startswith(tuple(choices)) # ▣ 토론 : startswith() 와 endswith() 메소드는 접두어와 접미어를 검사할 때 매우 편리하다. # 슬라이스를 사용하면 비슷한 동작을 할 수 있지만 코드의 가독성이 많이 떨어진다. filename = 'spam.txt' print(filename[-4:] == '.txt') url = 'http://www.python.org' print(url[:5] == 'http:' or url[:6] == 'https:' or url[:4] == 'ftp:') # - 정규 표현식을 사용해도 된다. import re url = 'http://www.python.org' print(re.match('http:|https:|ftp:', url)) # - startswith() 와 endswith() 메소드는 일반적인 데이터 감소와 같은 다른 동작에 함께 사용하기에도 좋다. if any(name.endswith(('.c', '.h')) for name in os.listdir('.')): pass # 2.3 쉘 와일드카드 패턴으로 문자열 매칭 # ▣ 문제 : Unix 쉘에 사용하는 것과 동일한 와일드카드 패턴을 텍스트 매칭에 사용하고 싶다.(예: *.py, Dat[0-9]*.csv 등) # ▣ 해결 : fnmatch 모듈에 두 함수 fnmatch() 와 fnmatchcase() 를 사용하면 된다. from fnmatch import fnmatch, fnmatchcase print(fnmatch('foo.txt', '*.txt')) print(fnmatch('foo.txt', '?oo.txt')) print(fnmatch('Dat45.csv', 'Dat[0-9]*')) names = ['Dat1.csv', 'Dat2.csv', 'config.ini', 'foo.py'] print([name for name in names if fnmatch(name, 'Dat*.csv')]) # - 일반적으로 fnmatch() 는 시스템의 파일 시스템과 동일한 대소문자 구문 규칙을 따른다. print(fnmatch('foo.txt', '*.TXT')) # Mac : True print(fnmatch('foo.txt', '*.TXT')) # window : True # - 이런 차이점이 없는 것을 사용하려면 fnmatchcase() 를 사용한다. print(fnmatchcase('foo.txt', '*.TXT')) # - 파일 이름이 아닌 데이터 프로세싱에도 사용할 수 있다. addresses = ['5412 N CLARK ST', '1060 W ADDISON ST', '1039 W GRANVILLE AVE', '2122 N CLARK ST', '4802 N BROADWAY'] from fnmatch import fnmatchcase print([addr for addr in addresses if fnmatchcase(addr, '* ST')]) print([addr for addr in addresses if fnmatchcase(addr, '54[0-9][0-9] *CLARK*')]) # ▣ 토론 : fnmatch 가 수행하는 매칭은 간단한 문자열 메소드의 기능과 정규 표현식의 중간쯤 위치하고 있다. # 데이터 프로세싱을 할 때 간단한 와일드카드를 사용할 생각이라면 이 함수를 사용하는 것이 괜찮은 선택이다. # 2.4 텍스트 패턴 매칭과 검색 # ▣ 문제 : 특정 패턴에 대한 텍스트 매칭이나 검색을 하고 싶다. # ▣ 해결 : 매칭하려는 텍스트가 간단하다면 str.find(), str.endswith(), str.startswith() 와 같은 기본적인 문자열 메소드만으로도 # 충분하다. text = 'yeah, but no, but yeah, but no, but yeah' # - 정확한 매칭 print(text == 'yeah') # - 처음이나 끝에 매칭 print(text.startswith('yeah')) print(text.endswith('no')) # - 처음 나타난 곳 검색 print(text.find('no')) # 해당 값이 처음 나온 인덱스를 리턴. # - 좀 더 복잡한 매칭을 위해 정규 표현식과 re 모듈을 사용한다. # match() : 항상 문자열 처음에서 찾기를 시도. # findall() : 텍스트 전체에 걸쳐 찾기를 시도. text1 = '11/27/2012' text2 = 'Nov 27, 2012' import re # - 간단한 매칭: \d+는 하나 이상의 숫자를 의미 if re.match(r'\d+/\d+/\d+', text1): print('yes') else: print('no') if re.match(r'\d+/\d+/\d+', text2): print('yes') else: print('no') # - 동일한 패턴으로 매칭을 많이 수행할 예정이라면 정규 표현식을 미리 컴파일해서 패턴 객체로 만들어 놓는다. datepat = re.compile(r'\d+/\d+/\d+') if datepat.match(text1): print('yes') else: print('no') if datepat.match(text2): print('yes') else: print('no') # - 텍스트 전체에 걸쳐 패턴을 찾으려면 findall() 메소드를 사용한다. text = 'Today is 11/27/2012. PyCon starts 3/13/2013.' print(datepat.findall(text)) # - 정규 표현식을 정의할 때 괄호를 사용해 캡처 그룹을 만드는 것이 일반적이다. datepat = re.compile(r'(\d+)/(\d+)/(\d+)') m = datepat.match('11/27/2012') print(m.group(0), m.group(1), m.group(2), m.group(3), m.groups()) # 그룹별로 출력 가능 (0 은 전체 출력) month, day, year = m.groups() for month, day, year in datepat.findall(text): print('{}-{}-{}'.format(year, month, day)) # - 한 번에 결과를 얻지 않고 텍스트를 순환하며 찾으려면 finditer() 를 사용한다. for m in datepat.finditer(text): print(m.groups()) # ▣ 토론 : 핵심이 되는 기능은 re.compile() 을 사용해 패턴을 컴파일하고 그것을 match(), findall(), finditer() 등에 사용한다. # 패턴을 명시할 때 r'(\d+)/(\d+)/(\d+)'와 같이 raw string 을 그대로 쓰는것이 일반적이다. # 이 형식은 백슬래시 문자를 해석하지 않고 남겨 두기 때문에 정규 표현식과 같은 곳에 유용하다. # - match() 메소드는 문자열의 처음만 확인하므로, 예상치 못한 것에 매칭할 확률도 있다. m = datepat.match('11/27/2012abcdef') print(m) print(m.group()) datepat = re.compile(r'(\d+)/(\d+)/(\d+)$') print(datepat.match('11/27/2012abcdef')) print(datepat.match('11/27/2012')) # 2.5 텍스트 검색과 치환 # ▣ 문제 : 문자열에서 텍스트 패턴을 검색하고 치환하고 싶다. # ▣ 해결 : 간단한 패턴이라면 str.replace() 메소드를 사용한다. # 조금 더 복잡한 패턴을 사용하려면 re 모듈의 sub() 함수/메소드를 사용한다. text = 'yeah, but no, but yeah, but no, but yeah' print(text.replace('yeah', 'yep')) text = 'Today is 11/27/2012. PyCon starts 3/13/2013.' import re print(re.sub(r'(\d+)/(\d+)/(\d+)', r'\3-\1-\2', text)) # - 동일한 패턴을 사용한 치환을 계속해야 한다면 성능 향상을 위해 컴파일링을 고려해 보는 것이 좋다. import re datepat = re.compile(r'(\d+)/(\d+)/(\d+)') print(datepat.sub(r'\3-\1-\2', text)) # - 더 복잡한 치환을 위한 콜백 함수 명시. from calendar import month_abbr def change_date(m): mon_name = month_abbr[int(m.group(1))] return '{} {} {}'.format(m.group(2), mon_name, m.group(3)) print(datepat.sub(change_date, text)) # - 치환이 몇 번 발생했는지 알고 싶다면 re.subn() 을 사용한다. newtext, n = datepat.subn(r'\3-\1-\2', text) print(newtext, n) # ▣ 토론 : 앞서 살펴본 sub() 메소드에 정규 표현식 검색과 치환 이외에 어려운 것은 없다. # 2.6 대소문자를 구별하지 않는 검색과 치환 # ▣ 문제 : 텍스트를 검색하고 치환할 때 대소문자를 구별하지 않고 싶다. # ▣ 해결 : 텍스트 관련 작업을 할 때 대소문자를 구별하지 않기 위해서는 re 모듈을 사용해야 하고 re.IGNORECASE 플래그를 지정해야 한다. text = 'UPPER PYTHON, lower python, Mixed Python' print(re.findall('python', text, flags=re.IGNORECASE)) print(re.sub('python', 'snake', text, flags=re.IGNORECASE)) def matchcase(word): def replace(m): text = m.group() if text.isupper(): return word.upper() elif text.islower(): return word.lower() elif text[0].isupper(): return word.capitalize() else: return word return replace print(re.sub('python', matchcase('snake'), text, flags=re.IGNORECASE)) # ▣ 토론 : 대개의 경우 re.IGNORECASE 를 사용하는 것만으로 대소문자를 무시한 텍스트 작업에 무리가 없다. # 하지만 유니코드가 포함된 작업을 하기에는 부족할 수도 있다. # 2.7 가장 짧은 매칭을 위한 정규 표현식 # ▣ 문제 : 정규 표현식을 사용한 텍스트 매칭을 하고 싶지만 텍스트에서 가장 긴 부분을 찾아낸다. # 만약 가장 짧은 부분을 찾아내고 싶다면 어떻게 해야 할까? # ▣ 해결 : * 뒤에 ? 를 붙이면 된다. import re str_pat = re.compile(r'\"(.*)\"') text1 = 'Computer says "no."' print(str_pat.findall(text1)) text2 = 'Computer says "no." Phone says "yes."' print(str_pat.findall(text2)) str_pat = re.compile(r'\"(.*?)\"') print(str_pat.findall(text2)) # ▣ 토론 : 패턴에서 점은 개행문을 제외한 모든 문제에 매칭하므로, ? 를 * 나 + 뒤에 붙여준다. # 2.8 여러 줄에 걸친 정규 표현식 사용 # ▣ 문제 : 여러 줄에 걸친 정규 표현식 매칭을 사용하고 싶다. # ▣ 해결 : 패턴에서 (?:.|\n)인 논캡처 그룹을 명시한다. comment = re.compile(r'/\*(.*?)\*/') text1 = '/* this is a comment */' text2 = '''/* this is a multiline comment */ ''' print(comment.findall(text1)) print(comment.findall(text2)) comment = re.compile(r'/\*((?:.|\n)*?)\*/') print(comment.findall(text2)) # ▣ 토론 : re.compile() 함수에 re.DOTALL 이라는 유용한 플래그를 사용할 수 있다. comment = re.compile(r'/\*(.*?)\*/', re.DOTALL) print(comment.findall(text2)) # 2.9 유니코드 텍스트 노멀화 # ▣ 문제 : 유니코드 모든 문자열에 동일한 표현식을 갖도록 보장해주자. # ▣ 해결 : unicodedata 모듈로 텍스트를 노멀화해서 표준 표현식으로 바꿔야 한다. s1 = 'Spicy Jalape\u00f1o' s2 = 'Spicy Jalapen\u0303o' print(s1, s2) print(s1 == s2, len(s1), len(s2)) # - 위의 경우 같은 문자이지만 표현 방식이 달라 다른 문자열로 인식하였다. # 따라서. 텍스트 노멀화를 통해 표준 표현식으로 변경해주어야 한다. import unicodedata t1 = unicodedata.normalize('NFC', s1) t2 = unicodedata.normalize('NFC', s2) print(t1 == t2) print(ascii(t1)) t3 = unicodedata.normalize('NFD', s1) t4 = unicodedata.normalize('NFD', s2) print(t3 == t4) print(ascii(t3)) # - 파이썬은 특정 문자를 다룰 수 있도록 추가적인 호환성을 부여하는 NFKC 와 NFKD 노멀화도 지원한다. s = '\ufb01' print(s) print(unicodedata.normalize('NFD', s)) print(unicodedata.normalize('NFKD', s)) # 개별 문자로 분리 print(unicodedata.normalize('NFKC', s)) # 개별 문자로 분리 # ▣ 토론 : 일관적이고 안전한 유니코드 텍스트 작업을 위해서 노멀화는 아주 중요하다. # 특히 인코딩을 조절할 수 없는 상황에서 사용자에게 문자열 입력을 받는 경우에는 특히 조심해야 한다. t1 = unicodedata.normalize('NFD', s1) print(''.join(c for c in t1 if not unicodedata.combining(c))) # combining() 함수는 문자가 결합 문자인지 확인한다. # 2.10 정규 표현식에 유니코드 사용 # ▣ 문제 : 텍스트 프로세싱에 정규 표현식을 사용 중이다. 하지만 유니코드 문자 처리가 걱정된다. # ▣ 해결 : 기본적인 유니코드 처리를 위한 대부분의 기능을 re 모듈이 제공한다. import re num = re.compile('\d+') print(num.match('123')) # 아스키 숫자 print(num.match('\u0661\u0662\u0663')) # 아라비아 숫자 # - 특정 유니코드 문자를 패턴에 포함하고 싶으면, 유니코드 문자에 이스케이프 시퀀스를 사용한다. arabic = re.compile('[\u0600-\u06ff\u0750-\u077f\u08a0-\u08ff]+') # - 대소문자를 무시하는 매칭에 대소문자 변환을 합친 코드는 다음과 같다. pat = re.compile('stra\u00dfe', re.IGNORECASE) s = 'straße' print(pat.match(s)) print(pat.match(s.upper()), s.upper()) # ▣ 토론 : 유니코드와 정규 표현식을 같이 사용하려면 서드파티 regex 라이브러리를 설치하고 유니코드 대소문자 변환 등을 기본으로 # 제공하는 많은 기능을 이용하는 것이 좋다. # 2.11 문자열에서 문자 잘라내기 # ▣ 문제 : 텍스트의 처음, 끝, 중간에서 원하지 않는 공백문 등을 잘라내고 싶다. # ▣ 해결 : strip() 메소드를 사용하면 문자열의 처음과 끝에서 문자를 잘라낼 수 있다. # 기본적으로 공백이나 \n 을 잘라내지만 원하는 문자를 지정할 수도 있다. s = ' hello world \n' print(s.strip()) print(s.lstrip()) print(s.rstrip()) t = '-----hello=====' print(t.strip('-')) print(t.strip('=')) print(t.strip('-=')) # ▣ 토론 : 데이터를 보기 좋게 만들기 위한 용도로 여러 strip() 메소드를 일반적으로 사용한다. # 하지만 텍스트의 중간에서 잘라내기를 할 수는 없다. s = ' hello world \n' print(s.strip()) # - 중간의 공백을 없애기 위해서는 replace() 메소드나 정규 표현식의 치환과 같은 다른 기술을 사용해야 한다. print(s.replace(' ', '')) import re print(re.sub('\s+', ' ', s).strip()) with open('files\\somefile.txt') as f: # 파일 전체 compile 해야 경로 인식. lines = (line.strip() for line in f) for line in lines: print(re.sub('\s+', ' ', line).strip()) # 2.12 텍스트 정리 # ▣ 문제 : 당신의 웹 페이지에 어떤 사람이 장난스럽게 "python" 이라는 특수문자를 입력했다. 이를 정리하고 싶다. # ▣ 해결 : 특정 범위의 문자나 발음 구별 구호를 없애려고 할 때는 str.translate() 메소드를 사용해야 한다. s = 'pýtĥöñ\fis\tawesome\r\n' print(s) # - 우선 문자열에서 공백문을 잘라내기 위해 작은 변환 테이블을 만들어 놓고 translate() 를 사용한다. remap = { ord('\t'): ' ', # ord() : 하나의 문자열에 대해 유니코드를 나타내는 의미로 변환. ord('\f'): ' ', ord('\r'): None # 삭제됨 } a = s.translate(remap) print(a) # - 결합 문자를 없애는 방법. import unicodedata import sys cmb_chrs = dict.fromkeys(c for c in range(sys.maxunicode) if unicodedata.combining(chr(c))) # combining() : 유니코드 중에 조합된 것을 추출. print(cmb_chrs) b = unicodedata.normalize('NFD', a) print(b) print(b.translate(cmb_chrs)) # - 유니코드 숫자 문자를 이와 관련 있는 아스키 숫자에 매핑하도록 변환 테이블을 작성한다. digitmap = { c: ord('0') + unicodedata.digit(chr(c)) for c in range(sys.maxunicode) if unicodedata.category(chr(c)) == 'Nd' } print(len(digitmap), digitmap) x = '\u0661\u0662\u0663' print(x.translate(digitmap)) # - 또 다른 텍스트 정리 기술로 I/O 인코딩, 디코딩 함수가 있다. 이 방식은 텍스트를 우선 정리해 놓고 encode() 나 decode() 를 # 실행해서 잘라내거나 변경한다. print(a) b = unicodedata.normalize('NFD', a) print(b.encode('ascii', 'ignore').decode('ascii')) # ascii 형태로 인코딩 및 디코딩 # ▣ 토론 : 텍스트 정리를 하다 보면 실행 성능 문제에 직면하기도 한다. # 간단한 치환을 위해서는 str.replace() 함수를 사용하는 것이 빠르고 복잡한 치환을 하는 경우에는 str.translate() 함수를 # 사용하는 것이 좋다. # 2.13 텍스트 정렬 # ▣ 문제 : 텍스트를 특정 형식에 맞추어 정렬하고 싶다. # ▣ 해결 : ljust(), rjust(), center() 등이 있다. text = 'Hello World' print(text.ljust(20)) print(text.rjust(20)) print(text.center(20)) # - 세 개의 메소드는 별도의 채워 넣기 문자를 사용할 수 있다. print(text.rjust(20, '=')) print(text.center(20, '*')) # - format() 함수를 사용하면 인자로 <, >, ^ 를 적절하게 사용해 주면 된다. print(format(text, '>20')) # rjust 와 동일 print(format(text, '<20')) # ljust 와 동일 print(format(text, '^20')) # center 와 동일 # - 공백 대신 특정 문자를 채워 넣고 싶다면 정렬 문자 앞에 그 문자를 지정한다. print(format(text, '=>20')) print(format(text, '*^20')) # - 포맷 코드는 format() 메소드에 사용해 여러 값을 서식화할 수도 있다. print('{:>10} {:>10}'.format('Hello', 'World')) # - format() 을 사용하면 문자열뿐만 아니라 숫자 값 등 모든 값에 동작한다. x = 1.2345 print(format(x, '>10')) # str 타입으로 변환 print(format(x, '^10.2f')) # 소수점 자리수 지정 가능 # ▣ 토론 : 오래된 코드를 보면 % 연산자를 사용해 텍스트를 서식화하기도 했다. print('%-20s ' % text) print('%20s ' % text) # 2.14 문자열 합치기 # ▣ 문제 : 작은 문자열 여러 개를 합쳐 하나의 긴 문자열을 만들고 싶다. # ▣ 해결 : 합치고자 하는 문자열이 시퀀스나 순환 객체 안에 있다면 join() 메소드를 사용하는 것이 가장 빠르다. parts = ['Is', 'Chicago', 'Not', 'Chicago?'] print(' '.join(parts)) print(','.join(parts)) print(''.join(parts)) # - 합치려고 하는 문자열의 수가 아주 적다면 + 를 사용하면 된다. a = 'Is Chicago' b = 'Not Chicago?' print(a + ' ' + b) # - + 연산자는 조금 더 복잡한 문자열 서식 연산에 사용해도 잘 동작한다. print('{} {}'.format(a, b)) print(a + ' ' + b) a = 'Hello' 'World' print(a) # ▣ 토론 : 명심해야 할 부분은, + 연산자로 많은 문자열을 합치려고 하면 메모리 복사와 가비지 컬렉션으로 인해 매우 비효율적이라는 점이다. s = '' for p in parts: s += p # - 생성자 표현식으로 합치는 방법이 있다. data = ['ACME', 50, 91.1] print(' '.join(str(v) for v in data)) # - 불필요한 문자열 합치기를 하고 있지 않은지도 주의하자. a = 'qwer' b = 'asdf' c = 'zxcv' print(a + ':' + b + ':' + c) # 좋지 않은 방식 print(':'.join([a, b, c])) # 개선된 방식 print(a, b, c, sep=':') # 좋은 방식 # - 수많은 짧은 문자열을 하나로 합쳐 문자열을 만드는 코드를 작성한다면, yeild 를 사용한 생성자 함수를 고려하자. def sample(): yield 'Is' yield 'Chicago' yield 'Not' yield 'Chicago?' text = ''.join(sample()) print(text) # - 문자열을 입출력(I/O)으로 리다이렉트 할 수 있다. for part in sample(): f.write(part) # - 입출력을 조합한 하이브리드 방식 구현도 가능하다. f = open('D:\\KYH\\02.PYTHON\\data\\combine.txt', 'a') def combine(source, maxsize): parts = [] size = 0 for part in source: parts.append(part) size += len(part) if size >= maxsize: yield ''.join(parts) parts = [] size = 0 yield ''.join(parts) for part in combine(sample(), 32768): f.write(part) # 2.15 문자열에 변수 사용 # ▣ 문제 : 문자열에 변수를 사용하고 이 변수에 맞는 값을 채우고 싶다. # ▣ 해결 : 파이썬 문자열에 변수 값을 치환하는 간단한 방법은 존재하지 않는다. # 하지만 format() 메소드를 사용하면 비슷하게 흉내 낼 수 있다. s = '{name} hash {n} messages.' print(s.format(name='Guido', n=37)) # - 치환할 값이 변수에 들어 있다면 format_map() 과 vars() 를 함께 사용하면 된다. name = 'Guido' n = 37 print(s.format_map(vars())) # - vars() 에는 인스턴스를 사용할 수도 있다. class Info: def __init__(self, name, n): self.name = name self.n = n a = Info('Guido', 37) print(s.format_map(vars(a))) # - format() 또는 format_map() 사용시 빠진 값은 __missing__() 메소드가 있는 딕셔너리 클래스를 정의해서 피할 수 있다. class safesub(dict): def __missing__(self, key): return '{' + key + '}' del n print(s.format_map(safesub(vars()))) # - 코드에서 변수 치환을 빈번히 사용할 것 같다면 치환하는 작업을 유틸리티 함수에 모아 놓고 소위 "프레임 핵(frame hack)"으로 사용할 수 있다. import sys def sub(text): return text.format_map(safesub(sys._getframe(1).f_locals)) name = 'Guido' n = 37 print(sub('Hello {name}')) print(sub('You have {n} messages.')) print(sub('Your favorite color is {color}')) # ▣ 토론 : 파이썬 자체에서 변수 보간법이 존재하지 않아서 다양한 대안이 생겼다. name = 'Guido' n = 37 print('%(name) has %(n) messages.' % vars()) import string s = string.Template('$name has $n messages.') print(s.substitute(vars())) # 2.16 텍스트 열의 개수 고정 # ▣ 문제 : 긴 문자열의 서식을 바꿔 열의 개수를 조절하고 싶다. # ▣ 해결 : textwrap 모듈을 사용해서 텍스트를 재서식화 한다. s = 'Look into my eyes, look into my eyes, the eyes, the eyes,' \ "the eyes, not around the eyes, don't look around the eyes," \ "look into my eyes, you're under." import textwrap print(textwrap.fill(s, 70)) print(textwrap.fill(s, 40)) print(textwrap.fill(s, 40, initial_indent=' ')) print(textwrap.fill(s, 40, subsequent_indent=' ')) # ▣ 토론 : 텍스트를 출력하기 전에 textwrap 모듈을 사용하면 깔끔하게 서식을 맞출 수 있다. # 특히 터미널에 사용할 텍스트에 적합하다. # 터미널의 크기를 얻으려면 os.get_terminal_size() 를 사용한다. import os print(os.get_terminal_size().columns) # 2.17 HTML 과 XML 엔티티 처리 # ▣ 문제 : &entity; 나 &#code; 와 같은 HTML, XML 엔터티를 이에 일치하는 문자로 치환하고 싶다. # 혹은 텍스트를 생성할 때 특정 문자(<, >, & 등)를 피하고 싶다. # ▣ 해결 : 텍스트를 생성할 때 <나>와 같은 특수 문자를 치환하는 것은 html.escape() 함수를 사용하면 상대적으로 간단히 처리할 수 있다. s = 'Elements are written as "<tag>text</tag>".' import html print(s) print(html.escape(s)) # - 따옴표는 남겨 두도록 지정 print(html.escape(s, quote=False)) # - 텍스트를 아스키로 만들고 캐릭터 코드를 아스키가 아닌 문자에 끼워 넣고 싶으면 errors='xmlcharrefreplace' 인자를 입출력 관련 함수에 사용한다. s = 'Spicy Jalapeño' print(s.encode('ascii', errors='xmlcharrefreplace')) # - 수동으로 치환을 해야 한다면 HTML, XML 파서에 내장되어 있는 여러 유틸리티 함수나 메소드를 사용한다. s = 'Spicy "Jalapeño&quot.' from html.parser import HTMLParser p = HTMLParser() print(p.unescape(s)) # 파이썬 3.5 버전부터 deprecated 됨. print(html.unescape(s)) t = 'The prompt is >>>' from xml.sax.saxutils import unescape print(unescape(t)) # ▣ 토론 : HTML, XML 을 생성할 때 특수 문자를 제대로 이스케이핑하는 과정을 간과하기 쉽다. # print() 로 결과물을 생성하거나 기본적인 문자열 서식 기능을 사용할 때 특히 더 그렇다. # 가장 쉬운 해결책은 html.escape() 와 같은 유틸리티 함수를 사용하는 것이다. # 2.18 텍스트 토큰화 # ▣ 문제 : 문자열을 파싱해서 토큰화하고 싶다. # ▣ 해결 : 정규 표현식과 scanner() 메소드를 사용한다. # - 정규 표현식을 사용.(이름 있는 캡처 그룹) text = 'foo = 23 + 42 * 10' import re NAME = r'(?P<NAME>[a-zA-Z_][a-zA-Z_0-9]*)' NUM = r'(?P<NUM>\d+)' PLUS = r'(?P<PLUS>\+)' TIMES = r'(?P<TIMES>\*)' EQ = r'(?P<EQ>=)' WS = r'(?P<WS>\s+)' master_pat = re.compile('|'.join([NAME, NUM, PLUS, TIMES, EQ, WS])) # - scanner() 메소드를 사용. scanner = master_pat.scanner('foo = 42') scanner.match() print(_.lastgroup, _.group()) # _ 는 파이썬 2.x 버전에서 사용 가능함. scanner.match() print(_.lastgroup, _.group()) scanner.match() print(_.lastgroup, _.group()) scanner.match() print(_.lastgroup, _.group()) scanner.match() print(_.lastgroup, _.group()) # - 이 기술을 사용해 간결한 생성자를 만들 수 있다. from collections import namedtuple Token = namedtuple('Token', ['type', 'value']) def generate_tokens(pat, text): scanner = pat.scanner(text) for m in iter(scanner.match, None): yield Token(m.lastgroup, m.group()) for tok in generate_tokens(master_pat, 'foo = 42'): print(tok) tokens = (tok for tok in generate_tokens(master_pat, text) if tok.type != 'WS') for tok in tokens: print(tok) # ▣ 토론 : 보통 더 복잡한 텍스트 파싱이나 처리를 하기 전에 토큰화를 한다. # 매칭할 때 re 는 명시한 순서대로 패턴을 매칭한다. 따라서 한 패턴이 다른 패턴의 부분이 되는 경우가 있다면 # 항상 더 긴 패턴을 먼저 넣어야 한다. LT = r'(?P<LT><)' LE = r'(?P<LE><=)' EQ = r'(?P<EQ>=)' master_pat = re.compile('|'.join([LE, LT, EQ])) # 올바름 master_pat = re.compile('|'.join([LT, LE, EQ])) # 틀림 for tok in generate_tokens(master_pat, '<='): print(tok) # - 패턴이 부분 문자열을 형성하는 경우도 조심해야 한다. PRINT = r'(?P<PRINT>print)' NAME = r'(?P<NAME>[a-zA-Z_][a-zA-Z_0-9]*)' master_pat = re.compile('|'.join([PRINT, NAME])) for tok in generate_tokens(master_pat, 'printer'): print(tok) # 2.19 간단한 재귀 파서 작성 # ▣ 문제 : 주어진 문법 규칙에 따라 텍스트를 파싱하고 동작을 수행하거나 입력된 텍스트를 추상 신택스 트리로 나타내야 한다. # 문법은 간단하지만 프레임워크를 사용하지 않고 파서를 직접 작성하고 싶다. # ▣ 해결 : 이 문제는 특정 문법에 따라 텍스트를 파싱하는 데 집중한다. # 우선 문법의 정규 스펙을 BNF 나 EBNF 로 하는 데서 시작한다. import re import collections # - 토큰 스펙화. NUM = r'(?P<NUM>\d+)' PLUS = r'(?P<PLUS>\+)' MINUS = r'(?P<MINUS>-)' TIMES = r'(?P<TIMES>\*)' DIVIDE = r'(?P<DIVIDE>/)' LPAREN = r'(?P<LPAREN>$)' RPAREN = r'(?P<RPAREN>$)' WS = r'(?P<WS>\s+)' master_pat = re.compile('|'.join([NUM, PLUS, MINUS, TIMES, DIVIDE, LPAREN, RPAREN, WS])) # - 토큰화. Token = collections.namedtuple('Token', ['type', 'value']) def generate_tokens(text): scanner = master_pat.scanner(text) for m in iter(scanner.match, None): tok = Token(m.lastgroup, m.group()) if tok.type != 'WS': yield tok # - 파서. class ExpressionEvaluator: ''' 재귀 파서 구현, 모든 메소드는 하나의 문법 규칙을 구현한다. 현재 룩어헤드 토큰을 받고 테스트하는 용도로 ._accept()를 사용한다. 입력 받은 내역에 완벽히 매칭하고 다음 토큰을 무시할 때는 ._expect()를 사용한다. (혹시 매칭하지 않는 경우에는 SyntaxError 를 발생한다.) ''' def parse(self, text): self.tokens = generate_tokens(text) self.tok = None # 마지막 심볼 소비 self.nexttok = None # 다음 심볼 토큰화 self._advance() # 처음 룩어헤드 토큰 불러오기 return self.expr() # generate_tokens() 메서드에서 가져온 토큰을 순차적으로 설정하는 함수 def _advance(self): 'Advance one token ahead' self.tok, self.nexttok = self.nexttok, next(self.tokens, None) # next 함수는 iterator 를 순차적으로 리턴시켜주는 함수 # 다음 토큰이 원하는 토큰인 경우 self.tok 에 다음 토큰을 담고 True 를 리턴하는 함수 def _accept(self, toktype): 'Test and consume the next token if it matches toktype' if self.nexttok and self.nexttok.type == toktype: self._advance() return True else: return False def _expect(self, toktype): 'Consume next token if it matches toktype or raise SyntaxError' if not self._accept(toktype): raise SyntaxError('Expected ' + toktype) # - 문법 규칙. def expr(self): "expression ::= term { ('+'|'-') term }*" exprval = self.term() while self._accept('PLUS') or self._accept('MINUS'): op = self.tok.type right = self.term() if op == 'PLUS': exprval += right elif op == 'MINUS': exprval -= right return exprval def term(self): "term ::= factor { ('*'|'/') factor }*" termval = self.factor() while self._accept('TIMES') or self._accept('DIVIDE'): op = self.tok.type right = self.factor() if op == 'TIMES': termval *= right elif op == 'DIVIDE': termval /= right return termval def factor(self): "factor ::= NUM | ( expr )" if self._accept('NUM'): return int(self.tok.value) elif self._accept('LPAREN'): exprval = self.expr() self._expect('RPAREN') return exprval else: raise SyntaxError('Expected NUMBER or LPAREN') e = ExpressionEvaluator() print(e.parse('2')) print(e.parse('2 + 3')) print(e.parse('2 + 3 * 4')) print(e.parse('2 + (3 + 4) * 5')) # print(e.parse('2 + (3 + * 4)')) class ExpressionTreeBuilder(ExpressionEvaluator): def expr(self): "expression ::= term { ('+'|'-') term }" exprval = self.term() while self._accept('PLUS') or self._accept('MINUS'): op = self.tok.type right = self.term() if op == 'PLUS': exprval = ('+', exprval, right) elif op == 'MINUS': exprval = ('-', exprval, right) return exprval def term(self): "term ::= factor { ('*'|'/') factor }" termval = self.factor() while self._accept('TIMES') or self._accept('DIVIDE'): op = self.tok.type right = self.factor() if op == 'TIMES': termval = ('*', termval, right) elif op == 'DIVIDE': termval = ('/', termval, right) return termval def factor(self): 'factor ::= NUM | ( expr )' if self._accept('NUM'): return int(self.tok.value) elif self._accept('LPAREN'): exprval = self.expr() self._expect('RPAREN') return exprval else: raise SyntaxError('Expected NUMBER or LPAREN') e = ExpressionTreeBuilder() print(e.parse('2 + 3')) print(e.parse('2 + 3 * 4')) print(e.parse('2 + (3 + 4) * 5')) print(e.parse('2 + 3 + 4')) # ▣ 토론 : 파싱은 컴파일러 과목에서 3주 이상을 할애해서 배우는 쉽지 않은 주제이다. # 파싱 알고리즘이나 문법과 같은 기본적인 지식을 좀 더 알고 싶다면 우선 컴파일러 책을 한 권 읽어야 한다. # 재귀 파서의 한 가지 제약으로 좌측 재귀가 포함된 어떠한 문법 규칙에 대해서도 사용할 수 없다. # - 정말 복잡한 문법이 있다면 PyParsing 이나 PLY 와 같은 파싱 도구를 사용하는 것이 더 좋다. from ply.lex import lex from ply.yacc import yacc # 토큰 리스트 tokens = ['NUM', 'PLUS', 'MINUS', 'TIMES', 'DIVIDE', 'LPAREN', 'RPAREN'] # 무시 문자 t_ignore = '\t\n' # 토큰 스펙 (정규 표현식으로) t_PLUS = r'\+' t_MINUS = r'-' t_TIMES = r'\*' t_DIVIDE = r'/' t_LPAREN = r'$' t_RPAREN = r'$' # 토큰화 함수 def t_NUM(t): r'\d+' t.value = int(t.value) return t # 에러 핸들러 def t_error(t): print('Bad character: {!r}'.format(t.value[0])) t.skip(1) # 렉서(lexer) 만들기 lexer = lex() # 문법 규칙과 핸들러 함수 def p_expr(p): ''' expr : expr PLUS term | expr MINUS term ''' if p[2] == '+': p[0] = p[1] + p[3] elif p[2] == '-': p[0] = p[1] - p[3] def p_expr_term(p): ''' expr : term ''' p[0] = p[1] def p_term(p): ''' term : term TIMES factor | term DIVIDE factor ''' if p[2] == '*': p[0] = p[1] * p[3] elif p[2] == '/': p[0] = p[1] / p[3] def p_term_factor(p): ''' term : factor ''' p[0] = p[1] def p_factor(p): ''' factor : NUM ''' p[0] = p[1] def p_factor_group(p): ''' factor : LPAREN expr RPAREN ''' p[0] = p[2] def p_error(p): print('Syntax error') parser = yacc() print(parser.parse('2')) # 2.20 바이트 문자열에 텍스트 연산 수행 # ▣ 문제 : 바이트 문자열(byte string)에 일반적인 텍스트 연산(잘라내기, 검색, 치환 등)을 수행하고 싶다. # ▣ 해결 : 바이트 문자열도 텍스트 문자열과 마찬가지로 대부분의 연산을 내장하고 있다. data = b'Hello World' print(data[0:5]) print(data.startswith(b'Hello')) print(data.split()) print(data.replace(b'Hello', b'Hello Cruel')) # - 바이트 배열에도 사용 가능하다. data = bytearray(b'Hello World') print(data[0:5]) print(data.startswith(b'Hello')) print(data.split()) print(data.replace(b'Hello', b'Hello Cruel')) # - 바이트 배열에서도 정규 표현식이 가능하다. data = b'FOO:BAR,SPAM' import re print(re.split(b'[:,]', data)) # 패턴도 바이트로 나타내야 한다. # ▣ 토론 : 대개의 경우 텍스트 문자열에 있는 연산 기능은 바이트 문자열에도 내장되어 있다. # 하지만 주의해야 할 차이점이 몇 가지 있다. # - 첫째. 바이트 문자열에 인덱스를 사용하면 개별 문자가 아니라 정수를 가리킨다. a = 'Hello World' print(a[0], a[1]) b = b'Hello World' print(b[0], b[1]) # - 둘째. 바이트 문자열은 보기 좋은 표현식을 지원하지 않으며, 텍스트 문자열로 변환하지 않으면 깔끔하게 출력할 수도 없다. s = b'Hello World' print(s) print(s.decode('ascii')) # - 셋째. 바이트 문자열은 서식화를 지원하지 않는다. # print(b'%10s %10d %10.2f' %(b'ACME', 100, 490.1)) # print(b'{} {} {}'.format(b'ACME', 100, 490.1)) print('{:10s} {:10d} {:10.2f}'.format('ACME', 100, 490.1).encode('ascii')) # - 넷째. 바이트 문자열을 사용하면 특정 연산의 문법에 영향을 주기도 한다. with open('files/somefile.txt', 'w') as f: f.write('spicy') import os print(os.listdir('.')) print(os.listdir(b'.')) # ※ 바이트 데이터가 성능상 더 빠르더라도, 코드가 매우 지저분하고 이해하기 어려워지므로 텍스트 데이터를 사용하는 것이 좋다.

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backend/app/services.py

lfvilella/b2blue-challenge

2

59842

""" Services This module is reponsible to handle all interactions to the database and bussiness rules """ import typing import environs import dotenv import requests import sqlalchemy.orm from . import models, schemas, cache env = environs.Env() dotenv.load_dotenv() SECRET_KEY_RECAPTCHA = env("RECAPTCHA_SECRET_KEY", "") VALIDATE_RECAPTCHA = env.bool("VALIDATE_RECAPTCHA", True) class ServiceException(Exception): """ Service Exception This error is raised when data passed to the function is not valid """ class ValidationError(ServiceException): pass _cache = cache.get_cache() class CityService: """ City Service Service class designed to provide reusable functionalities relate to city """ _db: sqlalchemy.orm.Session def __init__(self, db: sqlalchemy.orm.Session): """ This constructor set database to others methods """ self._db = db def __getstate__(self): """ Prevent database connection to be cached """ state = self.__dict__.copy() state.pop("_db") # do not pickle _db session return state def get_city_by_id(self, name: str, state: str) -> models.City: """ Get City By ID This method is used to get City Args: name (str): City's name state (str): City's state Returns: Instance of models.City """ db_city = ( self._db.query(models.City) .filter_by(id=models.City.generate_id(name=name, state=state)) .first() ) return db_city def create_city(self, city: schemas.CityBase) -> models.City: """ Create City This method is used to create a City Args: city (schemas.CityInput): City's fields Returns: models.City """ if None in city.dict().values(): raise ValidationError("Invalid Post") db_city = self.get_city_by_id(name=city.name, state=city.state) if db_city: raise ValidationError("City already exist") city = models.City(**city.dict()) city.id = models.City.generate_id(name=city.name, state=city.state) self._db.add(city) self._db.commit() self._db.flush() self.cached_filter_city.invalidate_all() return city def filter_city(self, name: str) -> typing.List[models.City]: """ Filter City This method is used to filter a Cities Args: name (str): City's name Returns: list of cities """ query = self._db.query(models.City).filter( models.City.name.contains(name) ) return query.all() @_cache.cache(ttl=60) def cached_filter_city(self, name: str): """ Cached Filter City Cached version of filter_city it prevents from hitting database for alredy cached queries Args: name (str): City's name Returns: list of cities """ return self.filter_city(name) class GoogleService: _RECAPTCHA_SITEVERIFY_URL = ( "https://www.google.com/recaptcha/api/siteverify" ) def validate_recaptcha(self, response_token: str) -> bool: if not VALIDATE_RECAPTCHA: return True data = { "response": response_token, "secret": SECRET_KEY_RECAPTCHA, } response = requests.post(self._RECAPTCHA_SITEVERIFY_URL, data=data) if response.json().get("success") is not True: return False return True

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pylighter/annotation.py

infinex/PyLighter

0

147837

<reponame>infinex/PyLighter import os import threading import pandas as pd import spacy from pylighter import config from pylighter import display as display_helper from pylighter import utils from pylighter.chunk_models import Chunk, Chunks from pylighter.shortcut_helper import shortcut_helper from pylighter.tokenizer import SpacyTokenizer class Annotation: """ Class used to annotate datasets. """ # -------------------------------------------- # Initialization # -------------------------------------------- def __init__( self, corpus, labels=None, start_index=0, save_path=config.ANNOTATION_SAVE_PATH, labels_names=config.LABELS_NAMES, labels_colors=config.DEFAULT_COLORS, additional_infos=None, additional_outputs_values=None, additional_outputs_elements=None, standard_shortcuts=config.SHORTCUTS, labels_shortcuts=None, char_params=config.CHAR_PARAMS, ): """ Class that starts the user interface to annotate the given corpus. Parameters ---------- corpus : List[str] The corpus to annotate. labels : List[List[str]], optional The labels of the documents. It must have the same length as documents. Moreover, the i-th label must have the same length as the number of characters of the i-th document. By default, none of the documents are annotated. start_index : int, optional The index of the document to start on. Default value is 0. save_path : str, optional Path to store the annotated corpus into a csv when clicking the save button. By default, the (document, labels) are stored in config.ANNOTATION_SAVE_PATH. labels_names : List[str], optional The names of your labels. Default value is config.LABELS_NAMES. labels_colors : List[str], optional The colors for your labels in hex format (ex: "#2DA9D5"). The first label in labels_names will have the first color in labels_colors. By default, the labels colors are defined in config.DEFAULT_COLORS. labels_shortcuts : List[Shortcut], optional Keyboard shortcuts to use to select a given label. A shortcut is linked to one of the label button only if they share the same name. By default, none of the labels buttons have keyboard shortcuts. additional_infos : pd.DataFrame, optional Dataframe of size (size of the corpus, n) containing additional infos to display for each document. By default, no additional infos are displayed. additional_outputs_elements : List[AdditionalOutputElement], optional List containing additional elements to display. 5 elements type are supported: checkbox, int_text, float_text, text, text_area. By default, there won't be any additional outputs. additional_outputs_values : pandas.DataFrame, optional DataFrame of size (size of the corpus, n) containing the values of additional outputs for each document. If additional_outputs_elements are given, then the columns of this DataFrame must match the names of the elements given. By default, it will be initialized with default values given by the additional_outputs_elements if it exists. shortcuts : List[Shortcut], optional Keyboard shortcuts for the different buttons (Next, Previous, Skip and Save). By default, it uses the keyboard shortcuts defined in config.SHORTCUTS char_params : Dict[str, str], optional Parameters to modify the display of the characters in the document. The correct values are: min_width_between_chars -- Min distance between two chars. Expects a css value as string (ex: "4px"). width_white_space -- Size of a white space. Expects a css value as string (ex: "10px"). font_size -- Size of the font. Expects a css value as string (ex:"large"). """ # Check input consistency utils.assert_input_consistency(corpus, labels, start_index) # Init "global" variables self.start_index = start_index self.current_index = start_index self.corpus = corpus self.tk = SpacyTokenizer() self.toggle_click_annotation = False self.labels = self._init_labels(labels) self.save_path = save_path self.char_params = char_params self.additional_infos = additional_infos self._init_additional_outputs( additional_outputs_values, additional_outputs_elements ) self.labels_names = labels_names self.labels_colors = self._init_labels_colors(labels_colors) self.threads = [] display_helper.start_display() all_shortcuts = standard_shortcuts[:] if labels_shortcuts: all_shortcuts += labels_shortcuts self.shortcuts_displays_helpers = ( shortcut_helper.create_shortcuts_displays_helpers(all_shortcuts) ) display_helper.define_keyboard_shortcuts( all_shortcuts, self.shortcuts_displays_helpers ) self.preloaded_displays = utils.PreloadedDisplays() # Start annotating self._annotate() def _init_labels(self, labels): """ Init labels as "empty" if not labels are given """ if not labels: labels = [] for document in self.corpus: labels.append(["O"] * len(document)) return labels def _init_additional_outputs( self, additional_outputs_values, additional_outputs_elements ): self.additional_outputs_elements = additional_outputs_elements if ( additional_outputs_elements is not None and additional_outputs_values is None ): # Create empty dataframe if none is given columns = [element.name for element in additional_outputs_elements] self.additional_outputs_values = pd.DataFrame( columns=columns, index=range(len(self.corpus)) ) else: # Use given data self.additional_outputs_values = additional_outputs_values def _init_labels_colors(self, labels_colors_hex): labels_colors = [] for index, label_name in enumerate(self.labels_names): labels_colors.append( utils.LabelColor( name=label_name, text_color="white", background_color=labels_colors_hex[index % len(labels_colors_hex)], ) ) return labels_colors # -------------------------------------------- # Main # -------------------------------------------- def _annotate(self): """ Method to annotate the current document. It is responsible for setting up the user interface and the variables needed on buttons clicked. """ # Init variables specific to the current document self.document = self.corpus[self.current_index] _, token_spans_lookup = self.tk.word_tokenizer(self.document) self.token_spans_lookup = token_spans_lookup self.chunks = Chunks(labels=self.labels[self.current_index]) self.selected_labeliser = self.labels_names[0] self.label_start_index = None self.additional_outputs_elements_displays = None # Display loader until the display is finished loader = display_helper.display_loader() # Load current core if not preloaded self._async_load(self.preloaded_displays.current, 0) # Define custom styles display_helper.define_custom_styles(self.labels_colors, self.char_params) # Display header display_helper.display_header( self.current_index, len(self.corpus), self._change_document, self.additional_infos, ) # Display moving buttons display_helper.display_top_buttons( self._change_document, self._clear_current, self.shortcuts_displays_helpers, ) # Display document, toolbox and chunk area utils.wait_for_threads(self.threads) display_helper.display_core(self.preloaded_displays.current["core_display"]) self.char_buttons = self.preloaded_displays.current["char_buttons"] self.labels_buttons = self.preloaded_displays.current["labels_buttons"] self.toggle_button = self.preloaded_displays.current["toggle_button"] # Display current chunks for chunk in self.chunks.chunks: self._sync_chunks( chunk, self.document[chunk.start_index : chunk.end_index + 1] # noqa ) if self.additional_outputs_elements: self.additional_outputs_elements_displays = ( display_helper.display_additional_outputs( self.additional_outputs_elements, self.additional_outputs_values.iloc[self.current_index], ) ) # Display footer display_helper.display_footer( self._save, self._quit, self.shortcuts_displays_helpers, ) # Remove loader display_helper.remove_loader(loader) # Prepare toast for on_save display_helper.prepare_toast() # Preload missing displays self._async_load(self.preloaded_displays.next, 1) self._async_load(self.preloaded_displays.previous, -1) # sync ui with data self._sync_toggle_labeliser() def _async_load(self, preloaded, direction): """ Compute the core display (ie toolbox, document and chunk area) and stores it in the preloaded object if not already precomputed. The displays are computed in other threads, so make sure to join the threads before accessing/updating any value in the preloaded object. Parameters ---------- preloaded : Dict[str, ...] Object for storing the values returned by the display if the dict is empty. Values are: - core_display {ipywidgets.Box}: The widget that represents the core display. - char_buttons {List[ipywidgets.Button]}: The buttons for every char. - labels_buttons {List[ipywidgets.Button]}: The buttons for every label. direction : int Represents the direction (and the distance) to the next document. For instance, if the direction equals 2 then it preloads the document at current index + 2. """ if ( not preloaded and self.current_index + direction < len(self.corpus) and self.current_index + direction >= 0 ): thread = threading.Thread( target=display_helper.preload_core, kwargs={ "obj": preloaded, "document": self.corpus[self.current_index + direction], "char_params": self.char_params, "char_on_click": self._labelise, "labels_names": self.labels_names, "selected_labeliser": self.labels_names[0], "shortcuts_displays_helpers": self.shortcuts_displays_helpers, "label_on_click": self._select_new_labeliser, "label_on_toggle": self._toggle_labeliser, }, ) thread.start() self.threads.append(thread) # -------------------------------------------- # On click functions # -------------------------------------------- def _sync_toggle_labeliser(self): description = (self.toggle_button.description or self.toggle_button.icon) if not self.toggle_click_annotation: self.toggle_button.add_class(f"{description}_color_selected") else: self.toggle_button.remove_class(f"{description}_color_selected") def _toggle_labeliser(self, button, button_index): description = (self.toggle_button.description or self.toggle_button.icon) self.toggle_button.remove_class(f"{description}_color_selected") self.toggle_click_annotation = not self.toggle_click_annotation self._sync_toggle_labeliser() def _select_new_labeliser(self, button, button_index): # Remove selected class from all buttons for labeliser_button in self.labels_buttons: labeliser_button.remove_class( f"{labeliser_button.description or labeliser_button.icon}_color_selected" ) labeliser_button.add_class( f"{labeliser_button.description or labeliser_button.icon}_color" ) # Add selected class to the current selected labeliser description = ( self.labels_buttons[button_index].description or self.labels_buttons[button_index].icon ) self.labels_buttons[button_index].add_class(f"{description}_color_selected") self.selected_labeliser = self.labels_buttons[button_index].description if button_index == len(self.labels_buttons) - 1: # Selects the eraser self.selected_labeliser = None # Restart the index to none self.label_start_index = None def _labelise(self, button, char_index): # Selecting the first part and create chunks accordingly if not self.toggle_click_annotation: if self.label_start_index is None: chunk = Chunk( start_index=char_index, end_index=char_index, label=self.selected_labeliser, ) self.label_start_index = char_index # Selecting the second part and create chunks accordingly else: start_index = self.label_start_index end_index = char_index if end_index < start_index: start_index, end_index = end_index, start_index chunk = Chunk( start_index=start_index, end_index=end_index, label=self.selected_labeliser, ) self.label_start_index = None else: start_end_index = self.token_spans_lookup.get(char_index) if start_end_index: chunk = Chunk( start_index=start_end_index[0], end_index=start_end_index[1] - 1, label=self.selected_labeliser, ) self.label_start_index = None else: return # Update chunks with the new chunk chunk_text = self.document[chunk.start_index : chunk.end_index + 1] # noqa updated_chunks, removed_chunks = self.chunks.add_new_chunk_and_update(chunk) # Remove the freshly created chunk if the eraser is selected display_new_chunk = True if self.selected_labeliser is None: self.chunks.remove_chunk_by_id(chunk.id) display_new_chunk = False chunk.display_id = None # Sync the display with the chunks self._sync_chunks( chunk, chunk_text, updated_chunks, removed_chunks, display_new_chunk ) def _sync_chunks( self, chunk, chunk_text, updated_chunks=[], removed_chunks=[], display_chunk=True, ): """ Sync the given chunk display with the current state of the chunks. """ # Display new char highlight display_helper.highlight_chars( char_buttons=self.char_buttons[ chunk.start_index : chunk.end_index + 1 # noqa ], selected_labeliser=chunk.label, labels_names=self.labels_names, undo=False, ) # Remove chunks from display for chunk_removed in removed_chunks: display_helper.remove_chunk(chunk_removed) # Update chunks text for chunk_updated in updated_chunks: display_helper.update_chunk_text( self.document[ chunk_updated.start_index : chunk_updated.end_index + 1 # noqa ], chunk_updated, delete_chunk_on_click=self._delete_chunk, ) # Display new chunk if display_chunk: display_helper.display_chunk( chunk, chunk_text, self._delete_chunk, ) def _delete_chunk(self, button, chunk): # Remove chunk from chunks self.chunks.remove_chunk_by_id(chunk.id) # Remove chunk display_helper.remove_chunk(chunk) # Remove char chunk highlight display_helper.highlight_chars( char_buttons=self.char_buttons[ chunk.start_index : chunk.end_index + 1 # noqa ], selected_labeliser=chunk.label, labels_names=self.labels_names, undo=True, ) def _change_document(self, button, direction, skip=False): """ Go from the current document at index i to the document at index i + direction. If skip is True, the current annotation is not added to the labels. """ # Add current annotation to the labels if skip is False if not skip: self.labels[self.current_index] = self.chunks.to_labels() # Update additional outputs values if self.additional_outputs_elements: for index, element in enumerate(self.additional_outputs_elements): self.additional_outputs_values.iloc[self.current_index][ element.name ] = self.additional_outputs_elements_displays[index].value if self.current_index + direction < 0: return # Move to the next document self.current_index += direction # Clear the current display display_helper.clear_display() if self.current_index >= len(self.corpus): # All done self._save() self._quit() else: # Continue annotation utils.wait_for_threads(self.threads) self.preloaded_displays.update(direction) self._annotate() def _clear_current(self, button): # Clearing the current document <=> Using the eraser on the whole document. current_labeliser = self.selected_labeliser self.selected_labeliser = None self.label_start_index = 0 self._labelise(None, len(self.document) - 1) self.selected_labeliser = current_labeliser def _save(self, button=None, file_path=None): """ Saves the current state of the corpus and the labels into a csv. """ try: # Save file in path if not file_path: file_path = self.save_path file_path = os.path.abspath(file_path) utils.annotation_to_csv( self.corpus, self.labels, self.additional_outputs_values, file_path ) # Display success toast display_helper.show_toast( msg=f"File successfully saved in <b>{file_path}</b> !", success=True ) except Exception as err: # Display error toast with error message display_helper.show_toast(msg=str(err), success=False) def _quit(self, button=None): # Display end screen display_helper.clear_display() display_helper.display_quit_text( self.current_index, len(self.corpus), self.start_index )

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beanstalktop.py

OldhamMade/beanstalktop

4

1600019

<reponame>OldhamMade/beanstalktop #!/bin/python import curses import optparse import select import sys import errno import beanstalkc class BeanstalkTopUI(object): def __init__(self, win, options): self.win = win self.options = options self.resize() try: curses.use_default_colors() curses.start_color() curses.curs_set(0) except curses.error: pass self._connection = None self.default_overview = dict( (i, '-') for i in ( 'pid', 'total-jobs', 'current-connections', 'current-producers', 'current-workers', 'current-tubes', 'current-jobs-ready', 'current-jobs-urgent', 'current-jobs-buried', 'current-jobs-reserved' )) self.default_row = dict( (i, '-') for i in ( 'current-jobs-buried', 'current-jobs-delayed', 'current-jobs-ready', 'current-jobs-reserved', 'current-jobs-urgent' )) self.default_row.update({'name': 'default'}) def _get_connection(self): return beanstalkc.Connection(host=self.options.host, port=int(self.options.port)) if not self._connection: try: self._connection = beanstalkc.Connection(host=self.options.host, port=int(self.options.port)) except beanstalkc.SocketError: self.win.erase() raise SystemExit('Host {0} not contactable on port {1}'.format( self.options.host, self.options.port )) return self._connection connection = property(_get_connection) def _format_uptime(self, seconds): hours, remainder = divmod(seconds, 3600) minutes, seconds = divmod(remainder, 60) return '{0}h {1}m {2}s'.format(hours, minutes, seconds) def run(self): poll = select.poll() poll.register(sys.stdin.fileno(), select.POLLIN | select.POLLPRI) while 1: self.resize() self.refresh_display() try: events = poll.poll(self.options.delay_seconds * 1000.0) except select.error as e: if e.args and e.args[0] == errno.EINTR: events = 0 else: raise except KeyboardInterrupt: break if events: key = self.win.getch() self.handle_key(key) def handle_key(self, key): key_bindings = { ord('q'): lambda: sys.exit(0), ord('Q'): lambda: sys.exit(0), } action = key_bindings.get(key, lambda: None) action() def resize(self): self.height, self.width = self.win.getmaxyx() def refresh_display(self): self.win.erase() titles = ( 'TUBE', 'READY', 'URGENT', 'RESRVD', 'DELAYD', 'BURIED', ) overview, lines = self.get_data() try: overview['uptime'] = self._format_uptime(overview.get('uptime', 0)) except: overview['uptime'] = self._format_uptime(0) summary_items = [item.format(**overview) for item in ( 'PID: {pid}', 'Uptime: {uptime}', 'Total Jobs: {total-jobs}', 'Connections: {current-connections} ({current-producers}:{current-workers})', 'Cur. Tubes: {current-tubes}', 'Cur. Ready: {current-jobs-ready}', 'Cur. Urgent: {current-jobs-urgent}', 'Cur. Buried: {current-jobs-buried}', 'Cur. Reserved: {current-jobs-reserved}', )] summary_lines = [ summary_items[0:3], summary_items[3:6], summary_items[6:], ] summarywidth = self.width // max(len(i) for i in summary_lines) for item in summary_lines: line = ''.join(s.ljust(summarywidth) for s in item) self.win.addstr(line.ljust(self.width)) self.win.addstr(' ' * self.width) colwidth = self.width // len(titles) + 1 titlelen = 0 for i in range(len(titles)): attr = curses.A_REVERSE if i is 0: title = (' ' + titles[i]).ljust(colwidth - 1) else: title = (titles[i] + ' ').rjust(colwidth - 1) titlelen += len(title) self.win.addstr(title, attr) self.win.addstr(' ' * (self.width - titlelen), curses.A_REVERSE) columns = ( 'name', 'current-jobs-ready', 'current-jobs-urgent', 'current-jobs-reserved', 'current-jobs-delayed', 'current-jobs-buried', ) max_lines = self.height - (len(summary_lines) + 1) sortedlines = sorted(lines, key=lambda x: x['current-jobs-ready'])[::-1] for i in range(max_lines): try: line = sortedlines[i] except IndexError: break row = '' for c, column in enumerate(columns): try: if c is 0: row += (' ' + str(line[column])).ljust(colwidth - 1) else: row += (str(line[column]) + ' ').rjust(colwidth - 1) except curses.error: pass try: self.win.addstr(row.ljust(self.width)) except curses.error: pass self.win.refresh() def get_data(self): """ Main statistics { 'binlog-current-index': 0, 'binlog-max-size': 10485760, 'binlog-oldest-index': 0, 'binlog-records-migrated': 0, 'binlog-records-written': 0, 'cmd-bury': 580, 'cmd-delete': 15291, 'cmd-ignore': 3, 'cmd-kick': 0, 'cmd-list-tube-used': 0, 'cmd-list-tubes': 1, 'cmd-list-tubes-watched': 0, 'cmd-pause-tube': 0, 'cmd-peek': 0, 'cmd-peek-buried': 0, 'cmd-peek-delayed': 0, 'cmd-peek-ready': 0, 'cmd-put': 19623, 'cmd-release': 0, 'cmd-reserve': 0, 'cmd-reserve-with-timeout': 15873, 'cmd-stats': 1, 'cmd-stats-job': 22719, 'cmd-stats-tube': 0, 'cmd-touch': 0, 'cmd-use': 10603, 'cmd-watch': 5, 'current-connections': 8, 'current-jobs-buried': 580, 'current-jobs-delayed': 0, 'current-jobs-ready': 3750, 'current-jobs-reserved': 2, 'current-jobs-urgent': 0, 'current-producers': 3, 'current-tubes': 8, 'current-waiting': 0, 'current-workers': 3, 'job-timeouts': 0, 'max-job-size': 65535, 'pid': 78938, 'rusage-stime': 2.585616, 'rusage-utime': 1.005601, 'total-connections': 8, 'total-jobs': 19623, 'uptime': 156, 'version': 1.6, } Tube-specific statistics { 'cmd-delete': 892, 'cmd-pause-tube': 0, 'current-jobs-buried': 685, 'current-jobs-delayed': 0, 'current-jobs-ready': 1001, 'current-jobs-reserved': 1, 'current-jobs-urgent': 0 'current-using': 0, 'current-waiting': 0, 'current-watching': 1, 'name': 'default', 'pause': 0, 'pause-time-left': 0, 'total-jobs': 2579, } """ try: return self.connection.stats(), [self.connection.stats_tube(tube) for tube in self.connection.tubes()] except (TypeError, beanstalkc.SocketError, beanstalkc.CommandFailed): return self.default_overview, [self.default_row] def run_beanstalktop_window(win, options): ui = BeanstalkTopUI(win, options) ui.run() def run_beanstalktop(options): return curses.wrapper(run_beanstalktop_window, options) def main(): parser = optparse.OptionParser() parser.add_option('--host', dest="host", default="0.0.0.0", help="beanstalkd host [0.0.0.0]" ) parser.add_option('-p', '--port', dest="port", default=11300, help="beanstalkd port [11300]" ) parser.add_option('-d', '--delay', dest="delay_seconds", default=1, metavar='NUM', help="delay between refreshes [1s]" ) options, args = parser.parse_args() if args: parser.error('Unexpected arguments: ' + ' '.join(args)) main_loop = lambda: run_beanstalktop(options) main_loop() if __name__ == '__main__': main()

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tinyq/__init__.py

mozillazg/tinyq

14

19808

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mic/hej.py

safl/offload

0

1611093

#!/usr/bin/env python import numpy as np import time if "flush" in dir(np): np.flush() begin = time.time() #a = np.sum(((np.ones(100)+1.0)*2.0)/2.0) a = np.sum(np.random.random(50000000)) #a = np.multiply.accumulate(np.ones((8,8), dtype=np.float32)) print(a) if "flush" in dir(np): np.flush() end = time.time() - begin print(end)

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catalog/bindings/gmd/vertical_cs_2.py

NIVANorge/s-enda-playground

0

101559

from dataclasses import dataclass from bindings.gmd.vertical_csproperty_type import VerticalCspropertyType __NAMESPACE__ = "http://www.opengis.net/gml" @dataclass class VerticalCs2(VerticalCspropertyType): """ gml:verticalCS is an association role to the vertical coordinate system used by this CRS. """ class Meta: name = "verticalCS" namespace = "http://www.opengis.net/gml"

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nwswx/exceptions.py

stacybrock/nws-wx-client

1

95565

""" WxAPI Exceptions """ class WxAPIException(Exception): """Base class for exceptions in this module""" def __init__(self, message): self.message = message class InvalidFormat(WxAPIException): """The format provided is invalid""" class FormatNotAllowed(WxAPIException): """The format provided is not allowed by this endpoint""" class APIError(WxAPIException): """The API returned an error"""

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cryptoPortfolioManager.py

srivathsanvenkateswaran/CryptoPortfolioManager

5

189664

import csv from termcolor import colored from pycoingecko import CoinGeckoAPI import sys # Handling system args if(len(sys.argv) > 1): if(sys.argv[1] in ['-h', '--help']): print( '''Usage: python3 cryptoPortfolioManager.py [OPTION]... Crypto Portfolio Manager - A CLI Tool to keep track of your crypto Portfolio. List of supported long options and short options. -h, --help Display the Help command. -w, --weightage Show the crypto Portfolio along with Coin Weightage. ''' ) sys.exit() cg = CoinGeckoAPI() sheetPath = '/home/srivathsan/Documents/Important Documents/crypto_portfolio.csv' fiatCurrency = 'inr' def getUSTaxBacket(totalProfits): if totalProfits <= 40400: return 1 elif totalProfits >= 40401 and totalProfits <= 445850: return 0.85 elif totalProfits >= 445851: return 0.8 def getTaxMultiplier(totalProfits): if fiatCurrency == 'inr': taxMultiplier = 0.4 elif fiatCurrency == 'usd': taxMultiplier = getUSTaxBacket(totalProfits=totalProfits) def getPrice(cryptoId): return cg.get_price(ids=cryptoId, vs_currencies=fiatCurrency) fields = [] rows = [] with open(sheetPath, 'r') as file: csvreader = csv.reader(file) fields = next(csvreader) fields[0] = 'Coin' for row in csvreader: rows.append(row) colors = ['yellow', 'green', 'red', 'white', 'cyan'] spaces = [] fields.append('Invested') fields.append('Current Value') fields.append('Gains (%)') for field in fields: if len(field) < 5: spaces.append(5) elif len(field) < 10: spaces.append(10) else: spaces.append(15) for field in fields: if field == 'Coingecko ID': continue print(colored(" "*(spaces[fields.index(field)]-len(field)) + field + " |", colors[0]), end='') print('\n') ticker = [] coins = [] quantity = [] buyingAverage = [] for row in rows: ticker.append(row[0]) coins.append(row[1]) quantity.append(float(row[2])) buyingAverage.append(float(row[3])) current = [round(quantity[i]*getPrice(coins[i])[coins[i]][fiatCurrency], 2) for i in range(len(coins))] invested = [round(quantity[i]*buyingAverage[i], 2) for i in range(len(coins))] profits = [current[i]-invested[i] for i in range(len(coins))] gains = [round(profits[i]/invested[i] * 100, 2) for i in range(len(coins))] for i in range(len(coins)): if profits[i]>0: color = colors[1] elif profits[i] == 0: color = colors[3] else: color = colors[2] print(colored(ticker[i] + " "*(spaces[0]-len(ticker[i])) + ' |' + " "*(spaces[2]-len(str(quantity[i]))) + str(quantity[i]) + ' |' + " "*(spaces[3]-len(str(buyingAverage[i]))) + str(buyingAverage[i]) + ' |' + " "*(spaces[4]-len(str(invested[i]))) + str(invested[i]) + ' |' + " "*(spaces[5]-len(str(current[i]))) + str(current[i]) + ' |' + " "*(spaces[6]-len(str(gains[i]) + '%')) + str(gains[i]) + '%' + ' |', color=color)) print('') totalInvested = round(sum(invested), 2) totalCurrent = round(sum(current), 2) totalProfits = round(totalCurrent - totalInvested, 2) print(colored(' '*20 + f'Total Invested: {totalInvested}', colors[0])) print(colored(' '*20 + f'Total Current: {totalCurrent}', colors[0])) if totalProfits > 0: clr = colors[1] upOrDown = 'up' else: clr = colors[2] upOrDown = 'down' print(colored(' '*20 + f'Total Profits: {totalProfits}', clr)) print(colored(' '*20 + f'Portfolio {upOrDown} by ', colors[0]), end="") print(colored(f'{round(totalProfits/totalInvested * 100, 2)} %', clr)) print() netProfit = totalProfits if upOrDown == 'up': netProfit *= getTaxMultiplier(totalProfits=totalProfits) print(colored(' '*20 + f'Net Profits: {netProfit}', clr)) print(colored(' '*20 + f'Net Portfolio {upOrDown} by ', colors[0]), end="") print(colored(f'{round(netProfit/totalInvested * 100, 2)} %', clr)) print() if(len(sys.argv) > 1): if(sys.argv[1] in ['-w', '--weightage']): investedDict = {} for i in range(0, len(ticker)): investedDict[ticker[i]] = invested[i] investedDict = dict(sorted(investedDict.items(), key=lambda item: item[1])) investedDict = dict(reversed(list(investedDict.items()))) print(colored(' '*10 + 'Portfolio Weightage: ', colors[1])) print() print(' '*10 + 'Coin | Invested | Share ') for key, value in investedDict.items(): coinShare = str(round(value/totalInvested * 100, 2)) print(colored(' '*10 + key + ' '*(6-len(key)), colors[0]) + '|' + colored(' '*(10-len(str(value))) + str(value), colors[0]), end = " ") print('|' + colored(' '*(7-len(coinShare)) + coinShare + ' %', colors[4]))

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src/waldur_mastermind/marketplace_openstack/migrations/0006_change_billing_type_for_volumes.py

geant-multicloud/MCMS-mastermind

26

69264

from django.db import migrations INSTANCE_TYPE = 'OpenStackTenant.Instance' VOLUME_TYPE = 'OpenStackTenant.Volume' def change_billing_types(apps, schema_editor): Offering = apps.get_model('marketplace', 'Offering') OfferingComponent = apps.get_model('marketplace', 'OfferingComponent') for offering in Offering.objects.filter( type__in=(INSTANCE_TYPE, VOLUME_TYPE) ).all(): components = offering.components.filter(type__startswith='gigabytes_') if components: for component in components: component.type = OfferingComponent.BillingTypes.FIXED component.save(update_fields=['type']) class Migration(migrations.Migration): dependencies = [ ('marketplace_openstack', '0005_change_private_offerings_customers') ] operations = [migrations.RunPython(change_billing_types)]

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Desafio 70.py

MoomenEltelbany/PythonDesafios

0

191999

<reponame>MoomenEltelbany/PythonDesafios print('-' * 30) print(f'{"Lojas do Moomen":^30}') print('-' * 30) soma = totmais = menor = cont = 0 while True: produto = str(input('Nome do produto: ')) preco = float(input('Preço: R$')) soma += preco cont += 1 if cont == 1 or preco < menor: menor = preco prodmenor = produto if preco > 1000: totmais += 1 resp = ' ' while resp not in 'SN': resp = str(input('Quer Continuar [S/N]: ')).upper().strip()[0] print('-' * 40) if resp == 'N': break print(f'{"Fim do Programa":-^40}') print(f'O total de compra foi R${soma:.2f}') print(f'Temos {totmais} produtos que custam mais de R$1000.00') print(f'O produto mais barato foi a/o {prodmenor} que custa R${menor:.2f}')

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solutions/Interview-04.12-Paths-with-Sum-LCCI/0412.py

Wonz5130/LeetCode-Solutions

12

91941

<gh_stars>10-100 # Definition for a binary tree node. # class TreeNode: # def __init__(self, x): # self.val = x # self.left = None # self.right = None class Solution: def pathSum(self, root: TreeNode, sum: int) -> int: if not root: return 0 return self.dfs(root, sum) + self.pathSum(root.left, sum) + self.pathSum(root.right, sum) def dfs(self, root, sum): # 要特判为空，否则下面 sum == root.val 会报错 if not root: return 0 res = 0 if sum == root.val: res += 1 res += self.dfs(root.left, sum - root.val) res += self.dfs(root.right, sum - root.val) return res

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examples/vault-consul-ami/auth/sign-request.py

crowd-ai/terraform-aws-vault

651

157093

#!/usr/bin/env python # -What------------------------------------------------------------------------- # This script creates a request to the AWS Security Token Service API # with the action "GetCallerIdentity" and then signs the request using the # AWS credentials. It was modified from the python 2.x example published by # <NAME>, the author of the Vault IAM auth method, at the vault support # mailing list. https://groups.google.com/forum/#!topic/vault-tool/Mfi3O-lW60I # -Why-------------------------------------------------------------------------- # We are using python here instead of bash to take advantage of the boto3 library # which facilitates this work by an order of magnitude # -What-for--------------------------------------------------------------------- # This is useful for authenticating to Vault, because a client can use # this script to generate this request and this request is sent with the # login attempt to the Vault server. Vault then executes this request and gets # the response from GetCallerIdentity, which tells who is trying to authenticate # ------------------------------------------------------------------------------ import botocore.session from botocore.awsrequest import create_request_object import json import base64 import sys def headers_to_go_style(headers): retval = {} for k, v in headers.iteritems(): retval[k] = [v] return retval def generate_vault_request(awsIamServerId): session = botocore.session.get_session() client = session.create_client('sts') endpoint = client._endpoint operation_model = client._service_model.operation_model('GetCallerIdentity') request_dict = client._convert_to_request_dict({}, operation_model) request_dict['headers']['X-Vault-AWS-IAM-Server-ID'] = awsIamServerId request = endpoint.create_request(request_dict, operation_model) return { 'iam_http_request_method': request.method, 'iam_request_url': base64.b64encode(request.url), 'iam_request_body': base64.b64encode(request.body), 'iam_request_headers': base64.b64encode(json.dumps(headers_to_go_style(dict(request.headers)))), # It's a CaseInsensitiveDict, which is not JSON-serializable } if __name__ == "__main__": awsIamServerId = sys.argv[1] print json.dumps(generate_vault_request(awsIamServerId))

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bitquant/sql/stmt.py

rosspalmer/bitQuant

27

67123

<gh_stars>10-100 from ..data import tools from sqlalchemy.sql import select def auth(): txt = open('auth_sql', 'r') typ = int(txt.readline().rstrip('\n')) if typ == 1: file_path = txt.readline().rstrip('\n') engine_str = 'sqlite+pysqlite:///%s' % file_path if typ == 2: host = txt.readline().rstrip('\n') user = txt.readline().rstrip('\n') password = txt.readline().rstrip('\n') name = txt.readline().rstrip('\n') engine_str = 'mysql+pymysql://%s:%s@%s/%s' % (user, password, host, name) return engine_str def insert(tbl, sql_type): if sql_type == 'mysql': stmt = tbl.insert().prefix_with('IGNORE') if sql_type == 'sqlite': stmt = tbl.insert().prefix_with('OR IGNORE') return stmt #|Currently broken def select(tbl, exchange='', symbol='', start='', end='', source='', freq=''): sel = select([tbl]) if exchange <> '': sel = sel.where(tbl.c.exchange == exchange) if symbol <> '': sel = sel.where(tbl.c.symbol == symbol) if start <> '': if isinstance(start, str): start = tools.dateconv(start) sel = sel.where(tbl.c.timestamp >= start) if end <> '': if isinstance(end, str): end = tools.dateconv(end) sel = sel.where(tbl.c.timestamp <= end) if source <> '': sel = sel.where(tbl.c.source == source) if freq <> '': sel = sel.where(tbl.c.freq == freq) return sel

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layers/functions/sph_prior_box.py

BoChenUIUC/realtime-action-detection

0

185839

<gh_stars>0 """ Generates prior boxes for SSD netowrk Original author: <NAME>, <NAME> for VOC dataset https://github.com/amdegroot/ssd.pytorch """ import torch from math import sqrt as sqrt from itertools import product as product class SphPriorBox(object): """Compute priorbox coordinates in center-offset form for each source feature map. Note: This 'layer' has changed between versions of the original SSD paper, so we include both versions, but note v2 is the most tested and most recent version of the paper. """ def __init__(self, cfg): super(SphPriorBox, self).__init__() # self.type = cfg.name self.image_size = cfg['min_dim'] # number of priors for feature map location (either 4 or 6) self.num_priors = len(cfg['aspect_ratios']) self.variance = cfg['variance'] or [0.1] self.feature_maps = cfg['feature_maps'] self.min_sizes = cfg['min_sizes'] self.max_sizes = cfg['max_sizes'] self.steps = cfg['steps'] self.aspect_ratios = cfg['aspect_ratios'] self.clip = cfg['clip'] self.version = cfg['name'] for v in self.variance: if v <= 0: raise ValueError('Variances must be greater than 0') def forward(self): mean = [] # TODO merge these for k, f in enumerate(self.feature_maps): h,w = f for i in range(h): for j in range(w): boxes = [] f_kx = self.image_size[1] * 1. / self.steps[k][1] f_ky = self.image_size[0] * 1. / self.steps[k][0] # unit center x,y cx = (j + 0.5) / f_kx cy = (i + 0.5) / f_ky # aspect_ratio: 1 # rel size: min_size s_kx = self.min_sizes[k] * 1. /self.image_size[1] s_ky = self.min_sizes[k] * 1. /self.image_size[0] boxes.append([cx, cy, s_kx, s_ky]) # aspect_ratio: 1 # rel size: sqrt(s_k * s_(k+1)) s_kx_prime = sqrt(s_kx * (self.max_sizes[k] * 1. /self.image_size[1])) s_ky_prime = sqrt(s_ky * (self.max_sizes[k] * 1. /self.image_size[0])) boxes.append([cx, cy, s_kx_prime, s_ky_prime]) # rest of aspect ratios for ar in self.aspect_ratios[k]: boxes.append([cx, cy, s_kx*sqrt(ar), s_ky/sqrt(ar)]) boxes.append([cx, cy, s_kx/sqrt(ar), s_ky*sqrt(ar)]) for box in boxes: mean += box # back to torch land output = torch.Tensor(mean).view(-1, 4) if self.clip: output.clamp_(max=1, min=0) return output

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env/lib/python3.6/site-packages/dipy/denoise/tests/test_non_local_means.py

Raniac/NEURO-LEARN

8

1612803

<filename>env/lib/python3.6/site-packages/dipy/denoise/tests/test_non_local_means.py import numpy as np from numpy.testing import (run_module_suite, assert_, assert_equal, assert_array_almost_equal, assert_raises) from dipy.denoise.non_local_means import non_local_means def test_nlmeans_static(): S0 = 100 * np.ones((20, 20, 20), dtype='f8') S0nb = non_local_means(S0, sigma=1.0, rician=False) assert_array_almost_equal(S0, S0nb) def test_nlmeans_random_noise(): S0 = 100 + 2 * np.random.standard_normal((22, 23, 30)) masker = np.zeros(S0.shape[:3]).astype(bool) masker[8:15, 8:15, 8:15] = 1 for mask in [None, masker]: S0nb = non_local_means(S0, sigma=np.std(S0), rician=False, mask=mask) assert_(S0nb[mask].min() > S0[mask].min()) assert_(S0nb[mask].max() < S0[mask].max()) assert_equal(np.round(S0nb[mask].mean()), 100) S0nb = non_local_means(S0, sigma=np.std(S0), rician=False, mask=mask) assert_(S0nb[mask].min() > S0[mask].min()) assert_(S0nb[mask].max() < S0[mask].max()) assert_equal(np.round(S0nb[mask].mean()), 100) def test_scalar_sigma(): S0 = 100 + np.zeros((20, 20, 20)) noise = 2 * np.random.standard_normal((20, 20, 20)) S0 += noise S0[:10, :10, :10] = 300 + noise[:10, :10, :10] assert_raises( ValueError, non_local_means, S0, sigma=noise, rician=False) def test_nlmeans_boundary(): # nlmeans preserves boundaries S0 = 100 + np.zeros((20, 20, 20)) noise = 2 * np.random.standard_normal((20, 20, 20)) S0 += noise S0[:10, :10, :10] = 300 + noise[:10, :10, :10] non_local_means(S0, sigma=np.std(noise), rician=False) assert_(S0[9, 9, 9] > 290) assert_(S0[10, 10, 10] < 110) def test_nlmeans_wrong(): S0 = 100 + np.zeros((10, 10, 10, 10, 10)) assert_raises(ValueError, non_local_means, S0, 1.0) S0 = 100 + np.zeros((20, 20, 20)) mask = np.ones((10, 10)) assert_raises(ValueError, non_local_means, S0, 1.0, mask) def test_nlmeans_4D_and_mask(): S0 = 200 * np.ones((20, 20, 20, 3), dtype='f8') mask = np.zeros((20, 20, 20)) mask[10, 10, 10] = 1 S0n = non_local_means(S0, sigma=1, mask=mask, rician=True) assert_equal(S0.shape, S0n.shape) assert_equal(np.round(S0n[10, 10, 10]), 200) assert_equal(S0n[8, 8, 8], 0) def test_nlmeans_dtype(): S0 = 200 * np.ones((20, 20, 20, 3), dtype='f4') mask = np.zeros((20, 20, 20)) mask[10:14, 10:14, 10:14] = 1 S0n = non_local_means(S0, sigma=1, mask=mask, rician=True) assert_equal(S0.dtype, S0n.dtype) S0 = 200 * np.ones((20, 20, 20), dtype=np.uint16) mask = np.zeros((20, 20, 20)) mask[10:14, 10:14, 10:14] = 1 S0n = non_local_means(S0, sigma=1, mask=mask, rician=True) assert_equal(S0.dtype, S0n.dtype) if __name__ == '__main__': run_module_suite()

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preprocess_data/data_preparation_gan.py

tamlhp/dfd_benchmark

7

91486

# Copyright (c) 2018, <NAME>. All rights reserved. # # This work is licensed under the Creative Commons Attribution-NonCommercial # 4.0 International License. To view a copy of this license, visit # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. import os import glob import argparse import numpy as np import tensorflow as tf import PIL.Image import scipy.ndimage from PIL import ImageEnhance,Image from PIL import ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True #---------------------------------------------------------------------------- def error(msg): print('Error: ' + msg) exit(1) #---------------------------------------------------------------------------- class TFRecordExporter: def __init__(self, tfrecord_dir, expected_images, print_progress=True, progress_interval=10, Gaussian_down=0): self.tfrecord_dir = tfrecord_dir self.tfr_prefix = os.path.join(self.tfrecord_dir, os.path.basename(self.tfrecord_dir)) self.expected_images = expected_images self.cur_images = 0 self.shape = None self.resolution_log2 = None self.tfr_writers = [] self.print_progress = print_progress self.progress_interval = progress_interval self.Gaussian_down = Gaussian_down if self.print_progress: print('Creating dataset "%s"' % tfrecord_dir) if not os.path.isdir(self.tfrecord_dir): os.makedirs(self.tfrecord_dir) assert(os.path.isdir(self.tfrecord_dir)) def close(self): if self.print_progress: print('%-40s\r' % 'Flushing data...', end='', flush=True) for tfr_writer in self.tfr_writers: tfr_writer.close() self.tfr_writers = [] if self.print_progress: print('%-40s\r' % '', end='', flush=True) print('Added %d images.' % self.cur_images) def choose_shuffled_order(self): # Note: Images and labels must be added in shuffled order. order = np.arange(self.expected_images) np.random.RandomState(123).shuffle(order) return order def add_image(self, img): if self.print_progress and self.cur_images % self.progress_interval == 0: print('%d / %d\r' % (self.cur_images, self.expected_images), end='', flush=True) if self.shape is None: self.shape = img.shape self.resolution_log2 = int(np.log2(self.shape[1])) assert self.shape[0] in [1, 3] assert self.shape[1] == self.shape[2] assert self.shape[1] == 2**self.resolution_log2 tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE) #for lod in range(self.resolution_log2 - 1): for lod in [0]: tfr_file = self.tfr_prefix + '-r%02d.tfrecords' % (self.resolution_log2 - lod) self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt)) assert img.shape == self.shape for lod, tfr_writer in enumerate(self.tfr_writers): if lod: img = img.astype(np.float32) if self.Gaussian_down: img = scipy.ndimage.convolve(img, gaussian_filter[np.newaxis, :, :], mode='mirror')[:, ::2, ::2] else: img = (img[:, fd00:c2b6:b24b:be67:2827:688d:e6a1:6a3b, 0::2] + img[:, fd00:c2b6:b24b:be67:2827:688d:e6a1:6a3b, 1::2] + img[:, fd00:c2b6:b24b:be67:2827:688d:e6a1:6a3b, 0::2] + img[:, fd00:c2b6:b24b:be67:2827:688d:e6a1:6a3b, fd00:c2b6:b24b:be67:2827:688d:e6a1:6a3b]) * 0.25 quant = np.rint(img).clip(0, 255).astype(np.uint8) ex = tf.train.Example(features=tf.train.Features(feature={ 'shape': tf.train.Feature(int64_list=tf.train.Int64List(value=quant.shape)), 'data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[quant.tostring()]))})) tfr_writer.write(ex.SerializeToString()) self.cur_images += 1 def add_labels(self, labels): if self.print_progress: print('%-40s\r' % 'Saving labels...', end='', flush=True) assert labels.shape[0] == self.cur_images with open(self.tfr_prefix + '-rxx.labels', 'wb') as f: np.save(f, labels.astype(np.float32)) def __enter__(self): return self def __exit__(self, *args): self.close() #---------------------------------------------------------------------------- def data_preparation(image_dir, tfrecord_dir, resolution=128, shuffle=1, export_labels=1, percentage_samples=100): print('Loading images from "%s"' % image_dir) sources_dir = os.listdir(image_dir) image_filenames = [] for source_dir in sources_dir: image_filenames_temp = sorted(glob.glob(os.path.join(image_dir, source_dir, '*.png'))\ +glob.glob(os.path.join(image_dir, source_dir, '*.jpeg')) \ + glob.glob(os.path.join(image_dir, source_dir, '*.jpg'))\ ) image_filenames += image_filenames_temp[:int(float(len(image_filenames_temp))*float(percentage_samples)/100.0)] if len(image_filenames) == 0: error('No input images found') img = np.asarray(PIL.Image.open(image_filenames[0])) size = img.shape[0] channels = img.shape[2] if img.ndim == 3 else 1 if channels not in [1, 3]: error('Input images must be stored as RGB or grayscale') with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr: order = tfr.choose_shuffled_order() if shuffle else np.arange(len(image_filenames)) for idx in range(order.size): img = np.asarray(PIL.Image.open(image_filenames[order[idx]])) if channels == 1: img = img[:, :, np.newaxis] # HW => HWC img = PIL.Image.fromarray(img, 'RGB') img = img.resize((resolution, resolution), PIL.Image.ANTIALIAS) contrast = ImageEnhance.Contrast(img) img = contrast.enhance(1.0) brightness = ImageEnhance.Brightness(img) img = brightness.enhance(1.0) img = np.asarray(img) img = img.transpose(2, 0, 1) # HWC => CHW tfr.add_image(img) if export_labels: labels = [] for file in image_filenames: name_list = file.split('/') file_source = name_list[-2] for label, source in enumerate(sources_dir): if source == file_source: labels.append(np.uint32(label)) break labels = np.array(labels) onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32) onehot[np.arange(labels.size), labels] = 1.0 tfr.add_labels(onehot[order]) #---------------------------------------------------------------------------- if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--in_dir', type=str, default=' ') # The input directory containing subdirectories of images. Each subdirectory represents a data source, either from the real dataset or generated by a GAN parser.add_argument('--out_dir', type=str, default=' ') # The output directory containing the prepared data format that enables efficient streaming parser.add_argument('--resolution', type=int, default=128) # The resolution to which images are resized parser.add_argument('--shuffle', type=int, default=1) # Shuffle the order of images when streaming? parser.add_argument('--export_labels', type=int, default=1) # Export image source labels? parser.add_argument('--percentage_samples', type=int, default=100) # The percentage of images used for data preparation args = parser.parse_args() import time begin = time.time() data_preparation(args.in_dir, args.out_dir, args.resolution, args.shuffle, args.export_labels, args.percentage_samples) print("Time : ", time.time()-begin)

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sort/mysteryAlgorithm.py

udohsolomon/LearnAlgorithms

0

125583

#!/usr/bin/env python3 def mysteryAlgorithm(lst): for i in range(1,len(lst)): while i > 0 and lst[i-1] > lst[i]: lst[i], lst[i-1] = lst[i-1], lst[i] i -= 1 return lst print(mysteryAlgorithm([6, 4, 3, 8, 5]))

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opensfm/rig.py

cojosef96/OpenSfM

0

68277

"""Tool for handling rigs""" import logging import re from collections import defaultdict from itertools import combinations import networkx as nx import numpy as np from opensfm import actions, pygeometry, pymap from opensfm.dataset import DataSet, DataSetBase logger = logging.getLogger(__name__) def find_image_rig(image, rig_patterns): """Given an image and candidates rig model patterns, return the RigID/RigCameraID/Instance Member ID this image belongs to. """ for rig_id, patterns in rig_patterns.items(): for rig_camera_id, pattern in patterns.items(): instance_member_id = re.sub(pattern, "", image) if instance_member_id == "": continue if instance_member_id != image: return (rig_id, rig_camera_id, instance_member_id) return None, None, None def create_instances_with_patterns(images, rig_patterns): """Using the provided patterns, group images that should belong to the same rig instances. Incomplete rig instances wrt. the expected size are not considered. Returns : A dict of list of list of images, each list being an instances being aggregated by Rig ID """ per_pattern = defaultdict(dict) for image in images: rig_id, rig_camera_id, instance_member_id = find_image_rig(image, rig_patterns) if not rig_id: continue if instance_member_id not in per_pattern[rig_id]: per_pattern[rig_id][instance_member_id] = [] per_pattern[rig_id][instance_member_id].append((image, rig_camera_id)) complete_instances = defaultdict(list) problematic_images = [] for rig_id, patterns in per_pattern.items(): for pattern_images in patterns.values(): expected_size = len(rig_patterns[rig_id]) if len(pattern_images) != expected_size: problematic_images += [im[0] for im in pattern_images] else: complete_instances[rig_id].append(pattern_images) if problematic_images: logger.warning( ( "The following images are part of an incomplete rig, thus" f"won't be considered of being part of a rig\n {problematic_images}" ) ) return complete_instances def create_subset_dataset_from_instances(data: DataSet, instances_per_rig, name): """Given a list of images grouped by rigs instances, pick a subset of images and create a dataset subset with the provided name from them. Returns : A DataSet containing a subset of images containing enough rig instances """ subset_images = [] for instances in instances_per_rig.values(): instances_sorted = sorted( instances, key=lambda x: data.load_exif(x[0][0])["capture_time"] ) subset_size = data.config["rig_calibration_subset_size"] middle = len(instances_sorted) / 2 instances_calibrate = instances_sorted[ max([0, middle - int(subset_size / 2)]) : min( [middle + int(subset_size / 2), len(instances_sorted) - 1] ) ] for instance in instances_calibrate: subset_images += [x[0] for x in instance] return data.subset(name, subset_images) def compute_relative_pose(rig_id, pose_instances): """ Compute a rig model relatives poses given poses grouped by rig instance. """ # Put all poses instances into some canonical frame taken as the mean of their R|t centered_pose_instances = [] for instance in pose_instances: origin_center = np.zeros(3) rotation_center = np.zeros(3) for shot, _ in instance: rotation_center += shot.pose.rotation origin_center += shot.pose.get_origin() origin_center /= len(instance) rotation_center /= len(instance) centered_pose_instance = [] for shot, rig_camera_id in instance: instance_pose = pygeometry.Pose(rotation_center) instance_pose.set_origin(origin_center) instance_pose_camera = shot.pose.relative_to(instance_pose) centered_pose_instance.append( ( instance_pose_camera, rig_camera_id, shot.camera.id, ) ) centered_pose_instances.append(centered_pose_instance) # Average canonical poses per RigCamera ID average_origin, average_rotation, count_poses, camera_ids = {}, {}, {}, {} for centered_pose_instance in centered_pose_instances: for pose, rig_camera_id, camera_id in centered_pose_instance: if rig_camera_id not in average_origin: average_origin[rig_camera_id] = np.zeros(3) average_rotation[rig_camera_id] = np.zeros(3) count_poses[rig_camera_id] = 0 average_origin[rig_camera_id] += pose.get_origin() average_rotation[rig_camera_id] += pose.rotation camera_ids[rig_camera_id] = camera_id count_poses[rig_camera_id] += 1 # Construct final rig_model results rig_model = pymap.RigModel(rig_id) for rig_camera_id, count in count_poses.items(): o = average_origin[rig_camera_id] / count r = average_rotation[rig_camera_id] / count pose = pygeometry.Pose(r) pose.set_origin(o) rig_model.add_rig_camera(pymap.RigCamera(pose, rig_camera_id)) return rig_model def create_rig_models_from_reconstruction(reconstruction, instances_per_rig): """ Computed rig model's, given a reconstruction and rig instances's shots. """ rig_models = {} reconstructions_shots = set(reconstruction.shots) for rig_id, instances in instances_per_rig.items(): pose_groups = [] for instance in instances: if any( True if shot_id not in reconstructions_shots else False for shot_id, _ in instance ): continue pose_groups.append( [ (reconstruction.shots[shot_id], rig_camera_id) for shot_id, rig_camera_id in instance ] ) rig_models[rig_id] = compute_relative_pose(rig_id, pose_groups) return rig_models def create_rigs_with_pattern(data: DataSet, patterns): """Create rig data (`rig_models.json` and `rig_assignments.json`) by performing pattern matching to group images belonging to the same instances, followed by a bit of ad-hoc SfM to find some initial relative poses. """ # Construct instances assignments for each rig instances_per_rig = create_instances_with_patterns(data.images(), patterns) for rig_id, instances in instances_per_rig.items(): logger.info( f"Found {len(instances)} rig instances for rig {rig_id} using pattern matching." ) # Create some subset DataSet with enough images from each rig subset_data = create_subset_dataset_from_instances( data, instances_per_rig, "rig_calibration" ) # # Run a bit of SfM without any rig logger.info(f"Running SfM on a subset of {len(subset_data.images())} images.") actions.extract_metadata.run_dataset(subset_data) actions.detect_features.run_dataset(subset_data) actions.match_features.run_dataset(subset_data) actions.create_tracks.run_dataset(subset_data) actions.reconstruct.run_dataset(subset_data) # Compute some relative poses rig_models = create_rig_models_from_reconstruction( subset_data.load_reconstruction()[0], instances_per_rig ) data.save_rig_models(rig_models) data.save_rig_assignments(instances_per_rig) def same_rig_shot(meta1, meta2): """True if shots taken at the same time on a rig.""" have_gps = ( "gps" in meta1 and "gps" in meta2 and "latitude" in meta1["gps"] and "latitude" in meta2["gps"] ) same_gps = ( have_gps and meta1["gps"]["latitude"] == meta2["gps"]["latitude"] and meta1["gps"]["longitude"] == meta2["gps"]["longitude"] ) same_time = meta1["capture_time"] == meta2["capture_time"] return same_gps and same_time def detect_rigs(images, data: DataSetBase): """Search for rigs in a set of images. For each image on a rig, returns rig, rig_camera and rig_pose ids. """ # Build graph of connected images and sequences image_graph = nx.Graph() sequence_graph = nx.Graph() for im1, im2 in combinations(images, 2): meta1 = data.load_exif(im1) meta2 = data.load_exif(im2) if same_rig_shot(meta1, meta2): image_graph.add_edge(im1, im2) sequence_graph.add_edge(meta1["skey"], meta2["skey"]) # Build rigs # pyre-fixme[16]: Module `nx` has no attribute `connected_components`. sequence_cc = nx.connected_components(sequence_graph) sequence_rig_info = {} for i, cc in enumerate(sequence_cc): for j, sequence in enumerate(cc): sequence_rig_info[sequence] = {"rig": i, "rig_camera": j} # Build rig poses # pyre-fixme[16]: Module `nx` has no attribute `connected_components`. image_cc = nx.connected_components(image_graph) rig_info = {} for i, cc in enumerate(image_cc): for image in cc: meta = data.load_exif(image) sr = sequence_rig_info[meta["skey"]] rig_info[image] = { "rig": sr["rig"], "rig_camera": sr["rig_camera"], "rig_pose": i, } return rig_info def pose_kernel(x, y, rotation_std, translation_std): """Gaussian kernel on the diff between two poses.""" diff = x.relative_to(y) dr = sum(diff.rotation ** 2) dt = sum(diff.translation ** 2) return np.exp(-dr / rotation_std ** 2 - dt / translation_std ** 2) def pose_mode(poses, rotation_std, translation_std): """Find the most popular pose. Popular is defined by a Parzen estimatior with the given Gaussian kernel standard deviations. """ best_score = 0 best_pose = None for pose in poses: score = 0 for other in poses: score += pose_kernel(pose, other, rotation_std, translation_std) if score > best_score: best_score = score best_pose = pose return best_pose

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action_controller/scripts/ActionControllerNode.py

FablabHome/The_Essense_of_the_Grey_Region

1

20737

<gh_stars>1-10 #!/usr/bin/env python3 """ MIT License Copyright (c) 2020 rootadminWalker 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. """ import json import rospy from core.Nodes import ActionEvaluator class ActionControllerNode(ActionEvaluator): def __init__(self): # Initialize the intent to callback map, must have NotRecognized situation self.intent2callback = { 'Introduce': self.__introduce, 'GiveMenu': self.__show_menu, 'OrderFood': self.__order_food, 'OrderFoodTakeOut': self.__order_food, 'NotRecognized': self.__not_recognized } super(ActionControllerNode, self).__init__() def __introduce(self, intent, slots, raw_text, session): introduce_dialog = ''' Ah, Forgive me for not introducing myself, masters. I'm snippy, your virtual assistant in this restaurant, I'm still under development, so you could only see me talking right now. ''' self.speaker.say_until_end(introduce_dialog) @staticmethod def __show_menu(intent, slots, raw_text, session): menu = ''' Menu Price ------------------------------------- French Fries $7 meat salad $20 spaghetti $23 hot chocolate $14 cappucino $19 tea $0 water $0 Hamburger $19 Ketchup $0 Tacos $15 Marshmellos $10 Steak $27 hot dog $10 ''' print(f"Sorry for your inconvenience, here's the menu\n\n{menu}") def __order_food(self, intent, slots, raw_text, session): order_what = False orders = {} i = 0 if session is not None: rospy.loginfo(json.loads(session.custom_data)) while i < len(slots): if slots[i]['slotName'] == 'amount': amount = int(slots[i]['value']['value']) try: next_slot = slots[i + 1] if next_slot['slotName'] == 'food': orders[next_slot['value']['value']] = amount i += 2 elif next_slot['slotName'] == 'amount': orders[f'Unknown{i}'] = amount i += 1 order_what = True except IndexError: order_what = True orders[f'Unknown{i}'] = amount i += 1 elif slots[i]['slotName'] == 'food': orders[slots[i]['value']['value']] = 1 i += 1 if order_what or len(slots) == 0: self.speaker.say_until_end("I'm sorry, but could you repeat it again?") self.start_session(next_intents=['OrderFood', 'NotRecognized'], custom_data=orders) return if self.on_session(): if set(session.possible_next_intents) == {'OrderFood'}: if not order_what: self.stop_session() self.speaker.say_until_end('Ok, Gotcha') print(orders) def __not_recognized(self, intent, slots, raw_text, session): if len(session) == 0: rospy.loginfo(f"Currently there isn't an action for '{raw_text}'") elif session[0] == 'OrderFood': rospy.loginfo('Sorry, I could not understand what do you want to order, please say it again') self.stop_session() def reset(self): pass if __name__ == '__main__': node = ActionControllerNode()

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weapon/__init__.py

dannl/hunter-sim-classic

0

146605

<gh_stars>0 import random import config class Weapon: def __init__(self, min_d, max_d, speed): self.min_d = min_d self.max_d = max_d self.speed = speed def rand_dmg(self): if config.USE_AVERAGE: return self.avr_dmg() return random.randint(self.min_d, self.max_d) def avr_dmg(self): return (self.min_d + self.max_d) / 2 CJ = Weapon(124, 186, 3.4) KL = Weapon(128, 238, 3.2) SS = Weapon(89, 223, 2.7)

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segmentation_models_pytorch/encoders/dpn.py

coolcat647/segmentation_models.pytorch

0

193787

<filename>segmentation_models_pytorch/encoders/dpn.py """ Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin` Attributes: _out_channels (list of int): specify number of channels for each encoder feature tensor _depth (int): specify number of stages in decoder (in other words number of downsampling operations) _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3) Methods: forward(self, x: torch.Tensor) produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of shape NCHW (features should be sorted in descending order according to spatial resolution, starting with resolution same as input `x` tensor). Input: `x` with shape (1, 3, 64, 64) Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8), (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ) also should support number of features according to specified depth, e.g. if depth = 5, number of feature tensors = 6 (one with same resolution as input and 5 downsampled), depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled). """ import torch import torch.nn as nn import torch.nn.functional as F from pretrainedmodels.models.dpn import DPN from pretrainedmodels.models.dpn import pretrained_settings from ._base import EncoderMixin class DPNEncorder(DPN, EncoderMixin): def __init__(self, stage_idxs, out_channels, depth=5, **kwargs): super(DPNEncorder, self).__init__(**kwargs) self._stage_idxs = stage_idxs self._depth = depth self._out_channels = out_channels self._in_channels = 3 del self.last_linear def get_stages(self): return [ nn.Identity(), nn.Sequential(self.features[0].conv, self.features[0].bn, self.features[0].act), nn.Sequential(self.features[0].pool, self.features[1 : self._stage_idxs[0]]), self.features[self._stage_idxs[0] : self._stage_idxs[1]], self.features[self._stage_idxs[1] : self._stage_idxs[2]], self.features[self._stage_idxs[2] : self._stage_idxs[3]], ] def forward(self, x): stages = self.get_stages() features = [] for i in range(self._depth + 1): x = stages[i](x) if isinstance(x, (list, tuple)): features.append(F.relu(torch.cat(x, dim=1), inplace=True)) else: features.append(x) return features def load_state_dict(self, state_dict, **kwargs): state_dict.pop("last_linear.bias") state_dict.pop("last_linear.weight") super(DPNEncorder, self).load_state_dict(state_dict, **kwargs) dpn_encoders = { "dpn68": { "encoder": DPNEncorder, "pretrained_settings": pretrained_settings["dpn68"], "params": { "stage_idxs": (4, 8, 20, 24), "out_channels": (3, 10, 144, 320, 704, 832), "groups": 32, "inc_sec": (16, 32, 32, 64), "k_r": 128, "k_sec": (3, 4, 12, 3), "num_classes": 1000, "num_init_features": 10, "small": True, "test_time_pool": True, }, }, "dpn68b": { "encoder": DPNEncorder, "pretrained_settings": pretrained_settings["dpn68b"], "params": { "stage_idxs": (4, 8, 20, 24), "out_channels": (3, 10, 144, 320, 704, 832), "b": True, "groups": 32, "inc_sec": (16, 32, 32, 64), "k_r": 128, "k_sec": (3, 4, 12, 3), "num_classes": 1000, "num_init_features": 10, "small": True, "test_time_pool": True, }, }, "dpn92": { "encoder": DPNEncorder, "pretrained_settings": pretrained_settings["dpn92"], "params": { "stage_idxs": (4, 8, 28, 32), "out_channels": (3, 64, 336, 704, 1552, 2688), "groups": 32, "inc_sec": (16, 32, 24, 128), "k_r": 96, "k_sec": (3, 4, 20, 3), "num_classes": 1000, "num_init_features": 64, "test_time_pool": True, }, }, "dpn98": { "encoder": DPNEncorder, "pretrained_settings": pretrained_settings["dpn98"], "params": { "stage_idxs": (4, 10, 30, 34), "out_channels": (3, 96, 336, 768, 1728, 2688), "groups": 40, "inc_sec": (16, 32, 32, 128), "k_r": 160, "k_sec": (3, 6, 20, 3), "num_classes": 1000, "num_init_features": 96, "test_time_pool": True, }, }, "dpn107": { "encoder": DPNEncorder, "pretrained_settings": pretrained_settings["dpn107"], "params": { "stage_idxs": (5, 13, 33, 37), "out_channels": (3, 128, 376, 1152, 2432, 2688), "groups": 50, "inc_sec": (20, 64, 64, 128), "k_r": 200, "k_sec": (4, 8, 20, 3), "num_classes": 1000, "num_init_features": 128, "test_time_pool": True, }, }, "dpn131": { "encoder": DPNEncorder, "pretrained_settings": pretrained_settings["dpn131"], "params": { "stage_idxs": (5, 13, 41, 45), "out_channels": (3, 128, 352, 832, 1984, 2688), "groups": 40, "inc_sec": (16, 32, 32, 128), "k_r": 160, "k_sec": (4, 8, 28, 3), "num_classes": 1000, "num_init_features": 128, "test_time_pool": True, }, }, }

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pype/hosts/fusion/plugins/publish/increment_current_file_deadline.py

simonebarbieri/pype

0

14196

<filename>pype/hosts/fusion/plugins/publish/increment_current_file_deadline.py import pyblish.api class FusionIncrementCurrentFile(pyblish.api.ContextPlugin): """Increment the current file. Saves the current file with an increased version number. """ label = "Increment current file" order = pyblish.api.IntegratorOrder + 9.0 hosts = ["fusion"] families = ["render.farm"] optional = True def process(self, context): from pype.lib import version_up from pype.action import get_errored_plugins_from_data errored_plugins = get_errored_plugins_from_data(context) if any(plugin.__name__ == "FusionSubmitDeadline" for plugin in errored_plugins): raise RuntimeError("Skipping incrementing current file because " "submission to render farm failed.") comp = context.data.get("currentComp") assert comp, "Must have comp" current_filepath = context.data["currentFile"] new_filepath = version_up(current_filepath) comp.Save(new_filepath)

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test.py

Tweetsched/tweetsched-publisher

1

16707

<reponame>Tweetsched/tweetsched-publisher<filename>test.py from base64 import b64encode from app import app import unittest from mock import patch import os import json from twython import Twython class TestApp(unittest.TestCase): def setUp(self): self.app = app.test_client() os.environ['SERVICE_KEY'] = 'test-key' os.environ['SERVICE_PASS'] = '<PASSWORD>' os.environ['APP_KEY'] = 'test-key' os.environ['APP_SECRET'] = 'test-secret' os.environ['OAUTH_TOKEN'] = 'test-oauth-token' os.environ['OAUTH_TOKEN_SECRET'] = 'test-oauth-token-secret' @patch('app.Twython.update_status') def test_publish_tweet(self, update_status_mock): update_status_mock.return_value = True auth = (os.environ['SERVICE_KEY'] + ':' + os.environ['SERVICE_PASS']).encode('utf-8') headers = { 'Authorization': 'Basic ' + b64encode(auth).decode() } rv = self.app.post('/api/v1/tweets', data = json.dumps(dict(id = 3, message = 'test tweet', profileId = '1')), content_type = 'application/json', headers = headers) self.assertEqual(rv.status_code, 200) self.assertEqual(update_status_mock.call_count, 1) update_status_mock.assert_called_once() def test_404(self): auth = (os.environ['SERVICE_KEY'] + ':' + os.environ['SERVICE_PASS']).encode('utf-8') headers = { 'Authorization': 'Basic ' + b64encode(auth).decode() } rv = self.app.get('/i-am-not-found', headers=headers) self.assertEqual(rv.status_code, 404) if __name__ == '__main__': unittest.main()

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opennem/spiders/nem/dispatch_is.py

tourdownunder/opennem

0

58337

<filename>opennem/spiders/nem/dispatch_is.py from opennem.pipelines.nem.opennem import NemwebUnitScadaOpenNEMStorePipeline from opennem.spiders.nemweb import NemwebSpider class NemwebCurrentDispatchIS(NemwebSpider): name = "au.nem.current.dispatch_is" start_url = "http://nemweb.com.au/Reports/Current/DispatchIS_Reports/" limit = 0 pipelines_extra = set([NemwebUnitScadaOpenNEMStorePipeline,]) class NemwebArchiveDispatchIS(NemwebSpider): name = "au.nem.archive.dispatch_is" start_url = "http://nemweb.com.au/Reports/Archive/DispatchIS_Reports/" limit = 0 pipelines_extra = set([NemwebUnitScadaOpenNEMStorePipeline,]) # Archives tend to contain large zips of embedded zips so throttle # to limit memory use custom_settings = { "CONCURRENT_REQUESTS": 1, "CONCURRENT_ITEMS": 1, }

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pyunsplash/examples/example_iteration.py

dantuluri/pyunsplash

0

170037

############################################################################### # Copyright (c) 2016 <NAME> <<EMAIL>> # # File: example_iteration.py # # Author: <NAME> <<EMAIL>> # Date: 14 Dec 2016 # Purpose: How to get to every photo in every collection # # Revision: 2 # Comment: What's new in revision 2 # Add application level logging and interaction with # library logging example # # Comment: What's new in revision 1 # Show the use of the different objects # To get every photo, you could just use Photos # # ############################################################################### import logging import os from pyunsplash import PyUnsplash api_key = os.environ.get('APPLICATION_ID', None) or 'DUMMY_APPLICATION_ID' # Initialize app logging logger = logging.getLogger() logging.basicConfig(filename='app.log', level=logging.DEBUG) # pyunsplash logger defaults to level logging.ERROR # If you need to change that, use getLogger/setLevel # on the module logger, like this: logging.getLogger(PyUnsplash.logger_name).setLevel(logging.DEBUG) # instantiate PyUnsplash object py_un = PyUnsplash(api_key=api_key) # Start with the generic collection, maximize number of items # note: this will run until all photos of all collections have # been visited, unless a connection error occurs. # Typically the API hourly limit gets hit during this # collections = py_un.collections(per_page=30) while collections.has_next: for collection in collections.entries: photos = collection.photos() for photo in photos.entries: print(collection.title, photo.link_download) # no need to specify per_page: will take from original object collections = collections.get_next_page()

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delta/utils/metrics/py_metrics_test.py

didichuxing/delta

1,442

195330

<gh_stars>1000+ # Copyright (C) 2017 Beijing Didi Infinity Technology and Development Co.,Ltd. # All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== ''' python metric unittest ''' import os from pathlib import Path import tempfile import numpy as np import delta.compat as tf from delta import utils from delta.utils import metrics from delta import PACKAGE_ROOT_DIR #pylint: disable=too-many-instance-attributes class MetricTest(tf.test.TestCase): ''' python metrix unittest ''' def setUp(self): super().setUp() ''' setup ''' package_root = Path(PACKAGE_ROOT_DIR) self.config_file_crf = \ package_root.joinpath('../egs/mock_text_seq_label_data/seq-label/v1/config/seq-label-mock.yml') self.conf_str = ''' solver: metrics: pos_label: 1 # int, same to sklearn cals: - name: AccuracyCal arguments: null - name: ConfusionMatrixCal arguments: null - name: PrecisionCal arguments: average: 'micro' - name: RecallCal arguments: average: 'micro' - name: F1ScoreCal arguments: average: 'micro' ''' self.conf_file = tempfile.mktemp(suffix='metric.yaml') with open(self.conf_file, 'w', encoding='utf-8') as f: #pylint: disable=invalid-name f.write(self.conf_str) self.true_label = np.array([1, 1, 2, 3, 4, 6, 5]) self.pred1 = np.array([1, 1, 2, 3, 4, 6, 5]) self.pred2 = np.array([2, 2, 1, 1, 1, 1, 1]) # config for test token error metircs self.token_conf_str = ''' solver: metrics: pos_label: 1 # int, same to sklearn cals: - name: TokenErrCal arguments: eos_id: 0 ''' self.token_conf_file = tempfile.mktemp(suffix='token.yaml') with open(self.token_conf_file, 'w', encoding='utf-8') as f: #pylint: disable=invalid-name f.write(self.token_conf_str) self.token_true_label = [[1, 1, 1, 1], [1, 3, 4, 5]] self.token_pred1 = [[1, 1, 1, 1], [1, 3, 4, 5]] self.token_pred2 = [[1, 2, 2, 2], [1, 0, 0, 0]] def tearDown(self): ''' tear down ''' if os.path.exists(self.conf_file): os.unlink(self.conf_file) def test_metric(self): ''' test get_metrics function ''' config = utils.load_config(self.conf_file) metrics1 = metrics.get_metrics( config, y_true=self.true_label, y_pred=self.pred1) self.assertEqual(1.0, metrics1['AccuracyCal']) self.assertEqual(1.0, metrics1['PrecisionCal']) self.assertEqual(1.0, metrics1['RecallCal']) self.assertEqual(1.0, metrics1['F1ScoreCal']) metrics2 = metrics.get_metrics( config, y_true=self.true_label, y_pred=self.pred2) self.assertEqual(0.0, metrics2['AccuracyCal']) self.assertEqual(0.0, metrics2['PrecisionCal']) self.assertEqual(0.0, metrics2['RecallCal']) self.assertEqual(0.0, metrics2['F1ScoreCal']) def test_token_err(self): ''' test tooken error rate ''' config = utils.load_config(self.token_conf_file) metrics1 = metrics.get_metrics( config, y_true=self.token_true_label, y_pred=self.token_pred1) self.assertEqual(0.0, metrics1['TokenErrCal']) metrics2 = metrics.get_metrics( config, y_true=self.token_true_label, y_pred=self.token_pred2) self.assertEqual(0.75, metrics2['TokenErrCal']) def test_crf_metrics(self): ''' test crf metrics ''' config = utils.load_config(self.config_file_crf) metrics3 = metrics.get_metrics( config, y_true=[self.true_label], y_pred=[self.pred1]) # metrics3: one string. Text summary of the precision, recall, F1 score for each class. # res3 = metrics3['CrfCal'] # print(res3) # for i, s in enumerate(res3): # print(i, s) self.assertEqual('1.0000', metrics3['CrfCal'][67:73]) metrics4 = metrics.get_metrics( config, y_true=[self.true_label], y_pred=[self.pred2]) self.assertEqual('0.0000', metrics4['CrfCal'][67:73]) if __name__ == "__main__": tf.test.main()

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tests/data_sources/test_topographic_data_source.py

lenassero/nowcasting_dataset

0

159557

<filename>tests/data_sources/test_topographic_data_source.py<gh_stars>0 import numpy as np import pandas as pd import pytest from nowcasting_dataset.data_sources import TopographicDataSource @pytest.mark.parametrize( "x, y, left, right, top, bottom", [ (0, 0, -128_000, 126_000, 128_000, -126_000), (10, 0, -126_000, 128_000, 128_000, -126_000), (30, 0, -126_000, 128_000, 128_000, -126_000), (1000, 0, -126_000, 128_000, 128_000, -126_000), (0, 1000, -128_000, 126_000, 128_000, -126_000), (1000, 1000, -126_000, 128_000, 128_000, -126_000), (2000, 2000, -126_000, 128_000, 130_000, -124_000), (2000, 1000, -126_000, 128_000, 128_000, -126_000), (2001, 2001, -124_000, 130_000, 130_000, -124_000), ], ) def test_get_example_2km(x, y, left, right, top, bottom): size = 2000 # meters topo_source = TopographicDataSource( filename="tests/data/europe_dem_2km_osgb.tif", image_size_pixels=128, meters_per_pixel=size, forecast_minutes=300, history_minutes=10, ) t0_dt = pd.Timestamp("2019-01-01T13:00") topo_data = topo_source.get_example(t0_dt=t0_dt, x_meters_center=x, y_meters_center=y) assert topo_data.data.shape == (128, 128) assert len(topo_data.x) == 128 assert len(topo_data.y) == 128 assert not np.isnan(topo_data.data).any() # Topo x and y coords are not exactly set on the edges, but the center of the pixels assert np.isclose(left, topo_data.x.values[0], atol=size) assert np.isclose(right, topo_data.x.values[-1], atol=size) assert np.isclose(top, topo_data.y.values[0], atol=size) assert np.isclose(bottom, topo_data.y.values[-1], atol=size) @pytest.mark.skip("CD does not have access to GCS") def test_get_example_gcs(): """Note this test takes ~5 seconds as the topo data has to be downloaded locally""" filename = "gs://solar-pv-nowcasting-data/Topographic/europe_dem_1km_osgb.tif" size = 2000 # meters topo_source = TopographicDataSource( filename=filename, image_size_pixels=128, meters_per_pixel=size, forecast_minutes=300, history_minutes=10, ) t0_dt = pd.Timestamp("2019-01-01T13:00") _ = topo_source.get_example(t0_dt=t0_dt, x_meters_center=0, y_meters_center=0)

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macrosynergy/management/shape_dfs.py

macrosynergy/macrosynergy

3

190088

import numpy as np import pandas as pd from typing import List, Union, Tuple import random from macrosynergy.management.simulate_quantamental_data import make_qdf def reduce_df(df: pd.DataFrame, xcats: List[str] = None, cids: List[str] = None, start: str = None, end: str = None, blacklist: dict = None, out_all: bool = False, intersect: bool = False): """ Filter dataframe by xcats and cids and notify about missing xcats and cids. :param <pd.Dataframe> df: standardized dataframe with the following necessary columns: 'cid', 'xcats', 'real_date'. :param <List[str]> xcats: extended categories to be checked on. Default is all in the dataframe. :param <List[str]> cids: cross sections to be checked on. Default is all in the dataframe. :param <str> start: string representing earliest date. Default is None. :param <str> end: string representing the latest date. Default is None. :param <dict> blacklist: cross sections with date ranges that should be excluded from the data frame. If one cross section has several blacklist periods append numbers to the cross-section code. :param <bool> out_all: if True the function returns reduced dataframe and selected/ available xcats and cids. Default is False, i.e. only the dataframe is returned :param <bool> intersect: if True only retains cids that are available for all xcats. Default is False. :return <pd.Dataframe>: reduced dataframe that also removes duplicates or (for out_all True) dataframe and available and selected xcats and cids. """ dfx = df[df['real_date'] >= pd.to_datetime(start)] if start is not None else df dfx = dfx[dfx['real_date'] <= pd.to_datetime(end)] if end is not None else dfx if blacklist is not None: for key, value in blacklist.items(): filt1 = dfx['cid'] == key[:3] filt2 = dfx['real_date'] >= pd.to_datetime(value[0]) filt3 = dfx['real_date'] <= pd.to_datetime(value[1]) dfx = dfx[~(filt1 & filt2 & filt3)] xcats_in_df = dfx['xcat'].unique() if xcats is None: xcats = sorted(xcats_in_df) else: missing = sorted(set(xcats) - set(xcats_in_df)) if len(missing) > 0: print(f"Missing categories: {missing}.") xcats.remove(missing) dfx = dfx[dfx['xcat'].isin(xcats)] if intersect: df_uns = dfx.groupby('xcat')['cid'].unique() cids_in_df = list(df_uns[0]) for i in range(1, len(df_uns)): cids_in_df = [cid for cid in df_uns[i] if cid in cids_in_df] else: cids_in_df = dfx['cid'].unique() if cids is None: cids = sorted(cids_in_df) else: if not isinstance(cids, list): cids = [cids] missing = sorted(set(cids) - set(cids_in_df)) if len(missing) > 0: print(f'Missing cross sections: {missing}') cids = sorted(list(set(cids).intersection(set(cids_in_df)))) dfx = dfx[dfx['cid'].isin(cids)] if out_all: return dfx.drop_duplicates(), xcats, cids else: return dfx.drop_duplicates() def reduce_df_by_ticker(df: pd.DataFrame, ticks: List[str] = None, start: str = None, end: str = None, blacklist: dict = None): """ Filter dataframe by xcats and cids and notify about missing xcats and cids :param <pd.Dataframe> df: standardized dataframe with the following columns: 'cid', 'xcats', 'real_date'. :param <List[str]> ticks: tickers (cross sections + base categories) :param <str> start: string in ISO 8601 representing earliest date. Default is None. :param <str> end: string ISO 8601 representing the latest date. Default is None. :param <dict> blacklist: cross sections with date ranges that should be excluded from the dataframe. If one cross section has several blacklist periods append numbers to the cross section code. :return <pd.Dataframe>: reduced dataframe that also removes duplicates """ dfx = df[df["real_date"] >= pd.to_datetime(start)] if start is not None else df dfx = dfx[dfx["real_date"] <= pd.to_datetime(end)] if end is not None else dfx if blacklist is not None: # blacklisting by cross-section for key, value in blacklist.items(): filt1 = dfx["cid"] == key[:3] filt2 = dfx["real_date"] >= pd.to_datetime(value[0]) filt3 = dfx["real_date"] <= pd.to_datetime(value[1]) dfx = dfx[~(filt1 & filt2 & filt3)] dfx["ticker"] = df["cid"] + '_' + df["xcat"] ticks_in_df = dfx["ticker"].unique() if ticks is None: ticks = sorted(ticks_in_df) else: missing = sorted(set(ticks) - set(ticks_in_df)) if len(missing) > 0: print(f'Missing tickers: {missing}') ticks.remove(missing) dfx = dfx[dfx["ticker"].isin(ticks)] return dfx.drop_duplicates() def categories_df(df: pd.DataFrame, xcats: List[str], cids: List[str] = None, val: str = 'value', start: str = None, end: str = None, blacklist: dict = None, years: int = None, freq: str = 'M', lag: int = 0, fwin: int = 1, xcat_aggs: List[str] = ('mean', 'mean')): """Create custom two-categories dataframe with appropriate frequency and lags suitable for analysis. :param <pd.Dataframe> df: standardized dataframe with the following necessary columns: 'cid', 'xcats', 'real_date' and at least one column with values of interest. :param <List[str]> xcats: exactly two extended categories whose relationship is to be analyzed. :param <List[str]> cids: cross sections to be included. Default is all in the dataframe. :param <str> start: earliest date in ISO 8601 format. Default is None, i.e. earliest date in data frame is used. :param <str> end: latest date in ISO 8601 format. Default is None, i.e. latest date in data frame is used. :param <dict> blacklist: cross sections with date ranges that should be excluded from the data frame. If one cross section has several blacklist periods append numbers to the cross section code. :param <int> years: Number of years over which data are aggregated. Supersedes freq and does not allow lags, Default is None, i.e. no multi-year aggregation. :param <str> val: name of column that contains the values of interest. Default is 'value'. :param <str> freq: letter denoting frequency at which the series are to be sampled. This must be one of 'D', 'W', 'M', 'Q', 'A'. Default is 'M'. :param <int> lag: Lag (delay of arrival) of second category in periods as set by freq. Default is 0. Note: for analyses with dependent and explanatory categories, the second takes the role of the explanatory. :param <int> fwin: Forward moving average window of first category. Default is 1, i.e no average. Note: This parameter is used mainly for target returns as dependent variables. :param <List[str]> xcat_aggs: Exactly two aggregation methods. Default is 'mean' for both. :return <pd.Dataframe>: custom data frame with two category columns """ assert freq in ['D', 'W', 'M', 'Q', 'A'] assert not (years is not None) & (lag != 0), 'Lags cannot be applied to year groups.' if years is not None: assert isinstance(start, str), 'Year aggregation requires a start date.' df, xcats, cids = reduce_df(df, xcats, cids, start, end, blacklist, out_all=True) col_names = ['cid', 'xcat', 'real_date', val] dfc = pd.DataFrame(columns=col_names) if years is None: for i in range(2): dfw = df[df['xcat'] == xcats[i]].pivot(index='real_date', columns='cid', values=val) dfw = dfw.resample(freq).agg(xcat_aggs[i]) if (i == 0) & (fwin > 1): dfw = dfw.rolling(window=fwin).mean().shift(1 - fwin) if (i == 1) & (lag > 0): dfw = dfw.shift(lag) dfx = pd.melt(dfw.reset_index(), id_vars=['real_date'], value_vars=cids, value_name=val) dfx['xcat'] = xcats[i] dfc = dfc.append(dfx[col_names]) else: s_year = pd.to_datetime(start).year e_year = df['real_date'].max().year + 1 grouping = int((e_year - s_year) / years) remainder = (e_year - s_year) % years year_groups = {} for group in range(grouping): value = [i for i in range(s_year, s_year + years)] key = f"{s_year} - {s_year + (years - 1)}" year_groups[key] = value s_year += years v = [i for i in range(s_year, s_year + (remainder + 1))] year_groups[f"{s_year} - now"] = v list_y_groups = list(year_groups.keys()) translate_ = lambda year: list_y_groups[int((year % 2000) / years)] df['custom_date'] = df['real_date'].dt.year.apply(translate_) for i in range(2): dfx = df[df['xcat'] == xcats[i]] dfx = dfx.groupby(['xcat', 'cid', 'custom_date']).agg(xcat_aggs[i]).reset_index() dfx = dfx.rename(columns={"custom_date": "real_date"}) dfc = dfc.append(dfx[col_names]) return dfc.pivot(index=('cid', 'real_date'), columns='xcat', values=val).dropna()[xcats] if __name__ == "__main__": cids = ['NZD', 'AUD', 'GBP', 'CAD'] xcats = ['XR', 'CRY', 'GROWTH', 'INFL'] df_cids = pd.DataFrame(index=cids, columns=['earliest', 'latest', 'mean_add', 'sd_mult']) df_cids.loc['AUD'] = ['2000-01-01', '2020-12-31', 0.1, 1] df_cids.loc['CAD'] = ['2001-01-01', '2020-11-30', 0, 1] df_cids.loc['GBP'] = ['2002-01-01', '2020-11-30', 0, 2] df_cids.loc['NZD'] = ['2002-01-01', '2020-09-30', -0.1, 2] df_xcats = pd.DataFrame(index=xcats, columns=['earliest', 'latest', 'mean_add', 'sd_mult', 'ar_coef', 'back_coef']) df_xcats.loc['XR'] = ['2000-01-01', '2020-12-31', 0.1, 1, 0, 0.3] df_xcats.loc['CRY'] = ['2000-01-01', '2020-10-30', 1, 2, 0.95, 1] df_xcats.loc['GROWTH'] = ['2001-01-01', '2020-10-30', 1, 2, 0.9, 1] df_xcats.loc['INFL'] = ['2001-01-01', '2020-10-30', 1, 2, 0.8, 0.5] black = {'AUD': ['2000-01-01', '2003-12-31'], 'GBP': ['2018-01-01', '2100-01-01']} random.seed(2) dfd = make_qdf(df_cids, df_xcats, back_ar=0.75) dfd_x1 = reduce_df(dfd, xcats=xcats[:-1], cids=cids[0], start='2012-01-01', end='2018-01-31') print(dfd_x1['xcat'].unique()) dfd_x2 = reduce_df(dfd, xcats=xcats, cids=cids, start='2012-01-01', end='2018-01-31') dfd_x3 = reduce_df(dfd, xcats=xcats, cids=cids, blacklist=black) tickers = [cid + "_XR" for cid in cids] dfd_xt = reduce_df_by_ticker(dfd, ticks=tickers, blacklist=black) # Testing categories_df(). dfc1 = categories_df(dfd, xcats=['GROWTH', 'CRY'], cids=cids, freq='M', lag=0, xcat_aggs=['mean', 'mean'], start='2000-01-01', blacklist=black) dfc2 = categories_df(dfd, xcats=['GROWTH', 'CRY'], cids=cids, freq='M', lag=0, fwin=3, xcat_aggs=['mean', 'mean'], start='2000-01-01', blacklist=black) dfc3 = categories_df(dfd, xcats=['GROWTH', 'CRY'], cids=cids, freq='M', lag=0, xcat_aggs=['mean', 'mean'], start='2000-01-01', blacklist=black, years=10) # Testing reduce_df() filt1 = ~((dfd['cid'] == 'AUD') & (dfd['xcat'] == 'XR')) filt2 = ~((dfd['cid'] == 'NZD') & (dfd['xcat'] == 'INFL')) dfdx = dfd[filt1 & filt2] # simulate missing cross sections dfd_x1, xctx, cidx = reduce_df(dfdx, xcats=['XR', 'CRY', 'INFL'], cids=cids, intersect=True, out_all=True)

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lofti_gaia/lofti.py

logan-pearce/lofti_gaia

2

23857

import astropy.units as u import numpy as np from lofti_gaia.loftitools import * from lofti_gaia.cFunctions import calcOFTI_C #from loftitools import * import pickle import time import matplotlib.pyplot as plt # Astroquery throws some warnings we can ignore: import warnings warnings.filterwarnings("ignore") '''This module obtaines measurements from Gaia EDR3 (Gaia DR2 is also available as a secondary option) and runs through the LOFTI Gaia/OFTI wide stellar binary orbit fitting technique. ''' class Fitter(object): '''Initialize the Fitter object for the binary system, and compute observational constraints to be used in the orbit fit. User must provide Gaia source ids, tuples of mass estimates for both objects, specify the number of desired orbits in posterior sample. Fit will be for object 2 relative to object 1. Attributes are tuples of (value,uncertainty) unless otherwise indicated. Attributes with astropy units are retrieved from Gaia archive, attributes without units are computed from Gaia values. All relative values are for object 2 relative to object 1. Args: sourceid1, sourceid2 (int): Gaia source ids for the two objects, fit will be for motion of \ object 2 relative to object 1 mass1, mass2 (tuple, flt): tuple os mass estimate for object 1 and 2, of the form (value, uncertainty) Norbits (int): Number of desired orbits in posterior sample. Default = 100000 results_filename (str): Filename for fit results files. If none, results will be written to files \ named FitResults.yr.mo.day.hr.min.s astrometry (dict): User-supplied astrometric measurements. Must be dictionary or table or pandas dataframe with\ column names "sep,seperr,pa,paerr,dates" or "ra,raerr,dec,decerr,dates". May be same as the rv table. \ Sep, deltaRA, and deltaDEC must be in arcseconds, PA in degrees, dates in decimal years. \ Default = None user_rv (dict): User-supplied radial velocity measurements. Must be dictionary or table or pandas dataframe with\ column names "rv,rverr,rv_dates". May be same as the astrometry table. Default = None. catalog (str): name of Gaia catalog to query. Default = 'gaiaedr3.gaia_source' ruwe1, ruwe2 (flt): RUWE value from Gaia archive ref_epoch (flt): reference epoch in decimal years. For Gaia DR2 this is 2015.5, for Gaia EDR3 it is 2016.0 plx1, plx2 (flt): parallax from Gaia in mas RA1, RA2 (flt): right ascension from Gaia; RA in deg, uncertainty in mas Dec1, Dec2 (flt): declination from Gaia; Dec in deg, uncertainty in mas pmRA1, pmRA2 (flt): proper motion in RA in mas yr^-1 from Gaia pmDec1, pmDec2 (flt): proper motion in DEC in mas yr^-1 from Gaia rv1, rv2 (flt, optional): radial velocity in km s^-1 from Gaia rv (flt, optional): relative RV of 2 relative to 1, if both are present in Gaia plx (flt): weighted mean parallax for the binary system in mas distance (flt): distance of system in pc, computed from Gaia parallax using method \ of Bailer-Jones et. al 2018. deltaRA, deltaDec (flt): relative separation in RA and Dec directions, in mas pmRA, pmDec (flt): relative proper motion in RA/Dec directions in km s^-1 sep (flt): total separation vector in mas pa (flt): postion angle of separation vector in degrees from North sep_au (flt): separation in AU sep_km (flt): separation in km total_vel (flt): total velocity vector in km s^-1. If RV is available for both, \ this is the 3d velocity vector; if not it is just the plane of sky velocity. total_planeofsky_vel (flt): total velocity in the plane of sky in km s^-1. \ In the absence of RV this is equivalent to the total velocity vector. deltaGmag (flt): relative contrast in Gaia G magnitude. Does not include uncertainty. inflateProperMOtionError (flt): an optional factor to mulitply default gaia proper motion error by. Written by <NAME>, 2020 ''' def __init__(self, sourceid1, sourceid2, mass1, mass2, Norbits = 100000, \ results_filename = None, astrometry = None, user_rv = None, catalog = 'gaiaedr3.gaia_source', inflateProperMotionError=1 ): self.sourceid1 = sourceid1 self.sourceid2 = sourceid2 try: self.mass1 = mass1[0] self.mass1err = mass1[1] self.mass2 = mass2[0] self.mass2err = mass2[1] self.mtot = [self.mass1 + self.mass2, np.sqrt((self.mass1err**2) + (self.mass2err**2))] except: raise ValueError('Masses must be tuples of (value,error), ex: mass1 = (1.0,0.05)') self.Norbits = Norbits if not results_filename: self.results_filename = 'FitResults.'+time.strftime("%Y.%m.%d.%H.%M.%S")+'.txt' self.stats_filename = 'FitResults.Stats.'+time.strftime("%Y.%m.%d.%H.%M.%S")+'.txt' else: self.results_filename = results_filename self.stats_filename = results_filename+'.Stats.txt' self.astrometry = False # check if user supplied astrometry: if astrometry is not None: # if so, set astrometric flag to True: self.astrometry = True # store observation dates: self.astrometric_dates = astrometry['dates'] # if in sep/pa, convert to ra/dec: if 'sep' in astrometry: try: astr_ra = [MonteCarloIt([astrometry['sep'][i],astrometry['seperr'][i]]) * \ np.sin(np.radians(MonteCarloIt([astrometry['pa'][i],astrometry['paerr'][i]]))) \ for i in range(len(astrometry['sep']))] astr_dec = [MonteCarloIt([astrometry['sep'][i],astrometry['seperr'][i]]) * \ np.cos(np.radians(MonteCarloIt([astrometry['pa'][i],astrometry['paerr'][i]]))) \ for i in range(len(astrometry['sep']))] self.astrometric_ra = np.array([ [np.mean(astr_ra[i]) for i in range(len(astrometry['sep']))], [np.std(astr_ra[i]) for i in range(len(astrometry['sep']))] ]) self.astrometric_dec = np.array([ [np.mean(astr_dec[i]) for i in range(len(astrometry['sep']))], [np.std(astr_dec[i]) for i in range(len(astrometry['sep']))] ]) except: raise ValueError('Astrometry keys not recognized. Please provide dictionary or table or pandas dataframe with\ column names "sep,seperr,pa,paerr,dates" or "ra,raerr,dec,decerr,dates"') elif 'ra' in astrometry: # else store the ra/dec as attributes: try: self.astrometric_ra = np.array([astrometry['ra'], astrometry['raerr']]) self.astrometric_dec = np.array([astrometry['dec'], astrometry['decerr']]) except: raise ValueError('Astrometry keys not recognized. Please provide dictionary or table or pandas dataframe with\ column names "sep,seperr,pa,paerr,dates" or "ra,raerr,dec,decerr,dates"') else: raise ValueError('Astrometry keys not recognized. Please provide dictionary or table or pandas dataframe with\ column names "sep,seperr,pa,paerr,dates" or "ra,raerr,dec,decerr,dates"') # Check if user supplied rv: self.use_user_rv = False if user_rv is not None: # set user rv flag to true: self.use_user_rv = True try: # set attributes; multiply rv by -1 due to difference in coordinate systems: self.user_rv = np.array([user_rv['rv']*-1,user_rv['rverr']]) self.user_rv_dates = np.array(user_rv['rv_dates']) except: raise ValueError('RV keys not recognized. Please use column names "rv,rverr,rv_dates"') self.catalog = catalog # Get Gaia measurements, compute needed constraints, and add to object: self.PrepareConstraints(catalog=self.catalog,inflateFactor=inflateProperMotionError) def edr3ToICRF(self,pmra,pmdec,ra,dec,G): ''' Corrects for biases in proper motion. The function is from https://arxiv.org/pdf/2103.07432.pdf Args: pmra,pmdec (float): proper motion ra, dec (float): right ascension and declination G (float): G magnitude Written by <NAME>, 2021 ''' if G>=13: return pmra , pmdec import numpy as np def sind(x): return np.sin(np.radians(x)) def cosd(x): return np.cos(np.radians(x)) table1=""" 0.0 9.0 9.0 9.5 9.5 10.0 10.0 10.5 10.5 11.0 11.0 11.5 11.5 11.75 11.75 12.0 12.0 12.25 12.25 12.5 12.5 12.75 12.75 13.0 18.4 33.8 -11.3 14.0 30.7 -19.4 12.8 31.4 -11.8 13.6 35.7 -10.5 16.2 50.0 2.1 19.4 59.9 0.2 21.8 64.2 1.0 17.7 65.6 -1.9 21.3 74.8 2.1 25.7 73.6 1.0 27.3 76.6 0.5 34.9 68.9 -2.9 """ table1 = np.fromstring(table1,sep=" ").reshape((12,5)).T Gmin = table1[0] Gmax = table1[1] #pick the appropriate omegaXYZ for the source’s magnitude: omegaX = table1[2][(Gmin<=G)&(Gmax>G)][0] omegaY = table1[3][(Gmin<=G)&(Gmax>G)][0] omegaZ = table1[4][(Gmin<=G)&(Gmax>G)][0] pmraCorr = -1*sind(dec)*cosd(ra)*omegaX -sind(dec)*sind(ra)*omegaY + cosd(dec)*omegaZ pmdecCorr = sind(ra)*omegaX -cosd(ra)*omegaY return pmra-pmraCorr/1000., pmdec-pmdecCorr/1000. def PrepareConstraints(self, rv=False, catalog='gaiaedr3.gaia_source', inflateFactor=1.): '''Retrieves parameters for both objects from Gaia EDR3 archive and computes system attriubtes, and assigns them to the Fitter object class. Args: rv (bool): flag for handling the presence or absence of RV measurements for both objects \ in EDR3. Gets set to True if both objects have Gaia RV measurements. Default = False catalog (str): name of Gaia catalog to query. Default = 'gaiaedr3.gaia_source' inflateFactor (flt): Factor by which to inflate the errors on Gaia proper motions to \ account for improper uncertainty estimates. Default = 1.0 Written by <NAME>, 2020 ''' from astroquery.gaia import Gaia deg_to_mas = 3600000. mas_to_deg = 1./3600000. # Retrieve astrometric solution from Gaia EDR3 job = Gaia.launch_job("SELECT * FROM "+catalog+" WHERE source_id = "+str(self.sourceid1)) j = job.get_results() job = Gaia.launch_job("SELECT * FROM "+catalog+" WHERE source_id = "+str(self.sourceid2)) k = job.get_results() if catalog == 'gaiadr2.gaia_source': # Retrieve RUWE from RUWE catalog for both sources and add to object state: job = Gaia.launch_job("SELECT * FROM gaiadr2.ruwe WHERE source_id = "+str(self.sourceid1)) jruwe = job.get_results() job = Gaia.launch_job("SELECT * FROM gaiadr2.ruwe WHERE source_id = "+str(self.sourceid2)) kruwe = job.get_results() self.ruwe1 = jruwe['ruwe'][0] self.ruwe2 = kruwe['ruwe'][0] else: # EDR3 contains ruwe in the main catalog: self.ruwe1 = j['ruwe'][0] self.ruwe2 = k['ruwe'][0] # Check RUWE for both objects and warn if too high: if self.ruwe1>1.2 or self.ruwe2>1.2: print('''WARNING: RUWE for one or more of your solutions is greater than 1.2. This indicates that the source might be an unresolved binary or experiencing acceleration during the observation. Orbit fit results may not be trustworthy.''') # reference epoch: self.ref_epoch = j['ref_epoch'][0] # parallax: self.plx1 = [j[0]['parallax']*u.mas, j[0]['parallax_error']*u.mas] self.plx2 = [k[0]['parallax']*u.mas, k[0]['parallax_error']*u.mas] # RA/DEC self.RA1 = [j[0]['ra']*u.deg, j[0]['ra_error']*mas_to_deg*u.deg] self.RA2 = [k[0]['ra']*u.deg, k[0]['ra_error']*mas_to_deg*u.deg] self.Dec1 = [j[0]['dec']*u.deg, j[0]['dec_error']*mas_to_deg*u.deg] self.Dec2 = [k[0]['dec']*u.deg, k[0]['dec_error']*mas_to_deg*u.deg] # Proper motions pmRACorrected1,pmDecCorrected1 = self.edr3ToICRF(j[0]['pmra'],j[0]['pmdec'],j[0]['ra'],j[0]['dec'],j[0]["phot_g_mean_mag"]) pmRACorrected2,pmDecCorrected2 = self.edr3ToICRF(k[0]['pmra'],k[0]['pmdec'],k[0]['ra'],k[0]['dec'],k[0]["phot_g_mean_mag"]) self.pmRA1 = [pmRACorrected1*u.mas/u.yr, j[0]['pmra_error']*u.mas/u.yr*inflateFactor] self.pmRA2 = [pmRACorrected2*u.mas/u.yr, k[0]['pmra_error']*u.mas/u.yr*inflateFactor] self.pmDec1 = [pmDecCorrected1*u.mas/u.yr, j[0]['pmdec_error']*u.mas/u.yr*inflateFactor] self.pmDec2 = [pmDecCorrected2*u.mas/u.yr, k[0]['pmdec_error']*u.mas/u.yr*inflateFactor] # See if both objects have RV's in DR2: if catalog == 'gaiaedr3.gaia_source': key = 'dr2_radial_velocity' error_key = 'dr2_radial_velocity_error' elif catalog == 'gaiadr2.gaia_source': key = 'radial_velocity' error_key = 'radial_velocity_error' if type(k[0][key]) == np.float64 and type(j[0][key]) == np.float64 or type(k[0][key]) == np.float32 and type(j[0][key]) == np.float32: rv = True self.rv1 = [j[0][key]*u.km/u.s,j[0][error_key]*u.km/u.s] self.rv2 = [k[0][key]*u.km/u.s,k[0][error_key]*u.km/u.s] rv1 = MonteCarloIt(self.rv1) rv2 = MonteCarloIt(self.rv2) self.rv = [ -np.mean(rv2-rv1) , np.std(rv2-rv1) ] # km/s # negative to relfect change in coordinate system from RV measurements to lofti # pos RV = towards observer in this coord system else: self.rv = [0,0] # weighted mean of parallax values: plx = np.average([self.plx1[0].value,self.plx2[0].value], weights = [self.plx1[1].value,self.plx2[1].value]) plxerr = np.max([self.plx1[1].value,self.plx2[1].value]) self.plx = [plx,plxerr] # mas self.distance = distance(*self.plx) # pc # Compute separations of component 2 relative to 1: r1 = MonteCarloIt(self.RA1) r2 = MonteCarloIt(self.RA2) d1 = MonteCarloIt(self.Dec1) d2 = MonteCarloIt(self.Dec2) ra = (r2*deg_to_mas - r1*deg_to_mas) * np.cos(np.radians(np.mean([self.Dec1[0].value,self.Dec2[0].value]))) dec = ((d2 - d1)*u.deg).to(u.mas).value self.deltaRA = [np.mean(ra),np.std(ra)] # mas self.deltaDec = [np.mean(dec),np.std(dec)] # mas # compute relative proper motion: pr1 = MonteCarloIt(self.pmRA1) pr2 = MonteCarloIt(self.pmRA2) pd1 = MonteCarloIt(self.pmDec1) pd2 = MonteCarloIt(self.pmDec2) pmRA = [np.mean(pr2 - pr1), np.std(pr2-pr1)] # mas/yr pmDec = [np.mean(pd2 - pd1), np.std(pd2 - pd1)] # mas/yr self.pmRA = masyr_to_kms(pmRA,self.plx) # km/s self.pmDec = masyr_to_kms(pmDec,self.plx) # km/s # Compute separation/position angle: r, p = to_polar(r1,r2,d1,d2) self.sep = tuple([np.mean(r).value, np.std(r).value]) # mas self.pa = tuple([np.mean(p).value, np.std(p).value]) # deg self.sep_au = tuple([((self.sep[0]/1000)*self.distance[0]), ((self.sep[1]/1000)*self.distance[0])]) self.sep_km = tuple([ self.sep_au[0]*u.au.to(u.km) , self.sep_au[1]*u.au.to(u.km)]) # compute total velocities: if rv: self.total_vel = [ add_in_quad([self.pmRA[0],self.pmDec[0],self.rv[0]]) , add_in_quad([self.pmRA[1],self.pmDec[1],self.rv[1]]) ] # km/s self.total_planeofsky_vel = [ add_in_quad([self.pmRA[0],self.pmDec[0]]) , add_in_quad([self.pmRA[1],self.pmDec[1]]) ] # km/s else: self.total_vel = [ add_in_quad([self.pmRA[0],self.pmDec[0]]) , add_in_quad([self.pmRA[1],self.pmDec[1]]) ] # km/s self.total_planeofsky_vel = self.total_vel.copy() # km/s # compute deltamag: self.deltaGmag = j[0]['phot_g_mean_mag'] - k[0]['phot_g_mean_mag'] class FitOrbit(object): ''' Object for performing an orbit fit. Takes attributes from Fitter class. ex: orbits = FitOrbit(fitterobject) Args: fitterobject (Fitter object): Fitter object initialized from the Fitter class write_stats (bool): If True, write out summary statistics of orbit sample at \ conclusion of fit. Default = True. write_results (bool): If True, write out the fit results to a pickle file \ in addition to the text file created during the fit. Default = True. deltaRA, deltaDec (flt): relative separation in RA and Dec directions, in mas pmRA, pmDec (flt): relative proper motion in RA/Dec directions in km s^-1 rv (flt, optional): relative RV of 2 relative to 1, if both are present in Gaia EDR3 mtot_init (flt): initial total system mass in Msun from user input distance (flt): distance of system in pc, computed from Gaia parallax using method of Bailer-Jones et. al 2018. sep (flt): separation vector in mas pa (flt): postion angle of separation vector in degrees from North ref_epoch (flt): epoch of the measurement, 2016.0 for Gaia EDR3 and 2015.5 for Gaia DR2. Norbits (int): number of desired orbit samples write_stats (bool): if True, write summary of sample statistics to human-readable file at end of run. Default = True write_results (bool): if True, write out current state of sample orbits in pickle file in periodic intervals during \ run, and again at the end of the run. RECOMMENDED. Default = True results_filename (str): name of file for saving pickled results to disk. If not supplied, \ defaul name is FitResults.y.mo.d.h.m.s.pkl, saved in same directory as fit was run. stats_filename (str): name of file for saving human-readable file of stats of sample results. If not supplied, \ defaul name is FitResults.Stats.y.mo.d.h.m.s.pkl, saved in same directory as fit was run. run_time (flt): run time for the last fit. astropy units object Written by <NAME>, 2020 ''' def __init__(self, fitterobject, write_stats = True, write_results = True, python_version=False, \ use_pm_cross_term = False, corr_coeff = None): # establish fit parameters: self.deltaRA = fitterobject.deltaRA self.deltaDec = fitterobject.deltaDec self.pmRA = fitterobject.pmRA self.pmDec = fitterobject.pmDec self.rv = fitterobject.rv self.mtot_init = fitterobject.mtot self.distance = fitterobject.distance self.sep = fitterobject.sep self.pa = fitterobject.pa self.ref_epoch = fitterobject.ref_epoch self.Norbits = fitterobject.Norbits self.write_results = write_results self.write_stats = write_stats self.results_filename = fitterobject.results_filename self.stats_filename = fitterobject.stats_filename self.astrometry = fitterobject.astrometry if self.astrometry: self.astrometric_ra = fitterobject.astrometric_ra self.astrometric_dec = fitterobject.astrometric_dec self.astrometric_dates = fitterobject.astrometric_dates self.use_user_rv = fitterobject.use_user_rv if self.use_user_rv: self.user_rv = fitterobject.user_rv self.user_rv_dates = fitterobject.user_rv_dates # run orbit fitter: self.fitorbit(python_fitOFTI=python_version, use_pm_cross_term = use_pm_cross_term, corr_coeff = corr_coeff) def fitorbit(self, save_results_every_X_loops = 100, python_fitOFTI=False, use_pm_cross_term = False, corr_coeff = None): '''Run the OFTI fitting run on the Fitter object. Called when FitOrbit object is created. Args: save_results_every_X_loops (int): on every Xth loop, save status of the \ orbit sample arrays to a pickle file, if write_results = True (Default) python_fitOFTI (bool): If True, fit using python only without using C Kepler's equation solver. Default = False use_pm_cross_term (bool): If True, include the proper motion correlation cross term in the Chi^2 computation \ Default = False Written by <NAME>, 2020 ''' # write header: print('Saving orbits in',self.results_filename) k = open(self.results_filename, 'w') output_file_header = '# sma [arcsec] period [yrs] orbit phase t_0 [yr] ecc incl [deg]\ argp [deg] lan [deg] m_tot [Msun] dist [pc] chi^2 ln(prob) ln(randn)' k.write(output_file_header + "\n") k.close() import time as tm ########### Perform initial run to get initial chi-squared: ############# # Draw random orbits: #parameters = a,T,const,to,e,i,w,O,m1,dist numSamples = 10000 parameters_init = draw_samples(numSamples, self.mtot_init, self.distance, self.ref_epoch) # Compute positions and velocities: if(python_fitOFTI): X,Y,Z,Xdot,Ydot,Zdot,Xddot,Yddot,Zddot,parameters=calc_OFTI(parameters_init,self.ref_epoch,self.sep,self.pa) else: returnArray = np.zeros((19,numSamples)) returnArray = calcOFTI_C(parameters_init,self.ref_epoch,self.sep,self.pa,returnArray.copy()) X,Y,Z,Xdot,Ydot,Zdot,Xddot,Yddot,Zddot = returnArray[0:9] parameters = returnArray[9:] # Compute chi squared: if self.rv[0] != 0: model = np.array([Y,X,Ydot,Xdot,Zdot]) data = np.array([self.deltaRA, self.deltaDec, self.pmRA, self.pmDec, self.rv]) else: model = np.array([Y,X,Ydot,Xdot]) data = np.array([self.deltaRA, self.deltaDec, self.pmRA, self.pmDec]) chi2 = ComputeChi2(data,model) if use_pm_cross_term: chi2 -= ( 2 * corr_coeff * (data[2][0] - model[2]) * (data[3][0] - model[3]) ) / (data[2][1] * data[3][1]) if self.astrometry: p = parameters.copy() a,T,const,to,e,i,w,O,m1,dist = p[0],p[1],p[2],p[3],p[4],p[5],p[6],p[7],p[8],p[9] chi2_astr = np.zeros(10000) # Calculate predicted positions at astr observation dates for each orbit: for j in range(self.astrometric_ra.shape[1]): # for each date, compute XYZ for each 10000 trial orbit. We can # skip scale and rotate because that was accomplished in the calc_OFTI call above. X1,Y1,Z1,E1 = calc_XYZ(a,T,to,e,i,w,O,self.astrometric_dates[j]) # Place astrometry into data array where: data[0][0]=ra obs, data[0][1]=ra err, etc: data = np.array([self.astrometric_ra[:,j], self.astrometric_dec[:,j]]) # place corresponding predicited positions at that date for each trial orbit in arcsec: model = np.array([Y1*1000,X1*1000]) # compute chi2 for trial orbits at that date and add to the total chi2 sum: chi2_astr += ComputeChi2(data,model) chi2 = chi2 + chi2_astr if self.use_user_rv: p = parameters.copy() a,T,const,to,e,i,w,O,m1,dist = p[0],p[1],p[2],p[3],p[4],p[5],p[6],p[7],p[8],p[9] chi2_rv = np.zeros(10000) for j in range(self.user_rv.shape[1]): # compute ecc anomaly at that date: X1,Y1,Z1,E1 = calc_XYZ(a,T,to,e,i,w,O,self.user_rv_dates[j]) # compute velocities at that ecc anom: Xdot,Ydot,Zdot = calc_velocities(a,T,to,e,i,w,O,dist,E1) # compute chi2: chi2_rv += ComputeChi2(np.array([self.user_rv[:,j]]),np.array([Zdot])) chi2 = chi2 + chi2_rv print('inital chi min',np.nanmin(chi2)) self.chi_min = np.nanmin(chi2) # Accept/reject: accepted, lnprob, lnrand = AcceptOrReject(chi2,self.chi_min) # count number accepted: number_orbits_accepted = np.size(accepted) # tack on chi2, log probability, log random unif number to parameters array: parameters = np.concatenate((parameters,chi2[None,:],lnprob[None,:],lnrand[None,:]), axis = 0) # transpose: parameters=np.transpose(parameters) # write results to file: k = open(self.results_filename, 'a') for params in parameters[accepted]: string = ' '.join([str(p) for p in params]) k.write(string + "\n") k.close() ###### start loop ######## # initialize: loop_count = 0 start=tm.time() while number_orbits_accepted < self.Norbits: # Draw random orbits: numSamples = 10000 parameters_init = draw_samples(numSamples, self.mtot_init, self.distance, self.ref_epoch) # Compute positions and velocities and new parameters array with scaled and rotated values: if(python_fitOFTI): X,Y,Z,Xdot,Ydot,Zdot,Xddot,Yddot,Zddot,parameters=calc_OFTI(parameters_init,self.ref_epoch,self.sep,self.pa) else: returnArray = np.zeros((19,numSamples)) returnArray = calcOFTI_C(parameters_init,self.ref_epoch,self.sep,self.pa,returnArray.copy()) X,Y,Z,Xdot,Ydot,Zdot,Xddot,Yddot,Zddot = returnArray[0:9] parameters = returnArray[9:] returnArray = None # compute chi2 for orbits using Gaia observations: if self.rv[0] != 0: model = np.array([Y,X,Ydot,Xdot,Zdot]) data = np.array([self.deltaRA, self.deltaDec, self.pmRA, self.pmDec, self.rv]) else: model = np.array([Y,X,Ydot,Xdot]) data = np.array([self.deltaRA, self.deltaDec, self.pmRA, self.pmDec]) chi2 = ComputeChi2(data,model) if use_pm_cross_term: chi2 -= ( 2 * (data[2][0] - model[2]) * (data[3][0] - model[3]) ) / (data[2][1] * data[3][1]) # add user astrometry if given: if self.astrometry: p = parameters.copy() a,T,const,to,e,i,w,O,m1,dist = p[0],p[1],p[2],p[3],p[4],p[5],p[6],p[7],p[8],p[9] chi2_astr = np.zeros(10000) # Calculate predicted positions at astr observation dates for each orbit: for j in range(self.astrometric_ra.shape[1]): # for each date, compute XYZ for each 10000 trial orbit. We can # skip scale and rotate because that was accomplished in the calc_OFTI call above. X1,Y1,Z1,E1 = calc_XYZ(a,T,to,e,i,w,O,self.astrometric_dates[j]) # Place astrometry into data array where: data[0][0]=ra obs, data[0][1]=ra err, etc: data = np.array([self.astrometric_ra[:,j], self.astrometric_dec[:,j]]) # place corresponding predicited positions at that date for each trial orbit: model = np.array([Y1*1000,X1*1000]) # compute chi2 for trial orbits at that date and add to the total chi2 sum: chi2_astr += ComputeChi2(data,model) chi2 = chi2 + chi2_astr # add user rv if given: if self.use_user_rv: p = parameters.copy() a,T,const,to,e,i,w,O,m1,dist = p[0],p[1],p[2],p[3],p[4],p[5],p[6],p[7],p[8],p[9] chi2_rv = np.zeros(10000) for j in range(self.user_rv.shape[1]): # compute ecc anomaly at that date: X1,Y1,Z1,E1 = calc_XYZ(a,T,to,e,i,w,O,self.user_rv_dates[j]) # compute velocities at that ecc anom: Xdot,Ydot,Zdot = calc_velocities(a,T,to,e,i,w,O,dist,E1) # compute chi2: chi2_rv += ComputeChi2(np.array([self.user_rv[:,j]]),np.array([Zdot])) chi2 = chi2 + chi2_rv # Accept/reject: accepted, lnprob, lnrand = AcceptOrReject(chi2,self.chi_min) if np.size(accepted) == 0: pass else: # count num accepted p = parameters.copy() a,T,const,to,e,i,w,O,m1,dist = p[0],p[1],p[2],p[3],p[4],p[5],p[6],p[7],p[8],p[9] sampleResults = calc_XYZ(a,T,to,e,i/180*np.pi,w/180*np.pi,O/180*np.pi,2016.0) number_orbits_accepted += np.size(accepted) parameters = np.concatenate((parameters,chi2[None,:],lnprob[None,:],lnrand[None,:]), axis = 0) parameters=np.transpose(parameters) k = open(self.results_filename, 'a') for params in parameters[accepted]: string = ' '.join([str(p) for p in params]) k.write(string + "\n") k.close() if np.nanmin(chi2) < self.chi_min: # If there is a new min chi2: self.chi_min = np.nanmin(chi2) #print('found new chi min:',self.chi_min) # re-evaluate to accept/reject with new chi_min: if number_orbits_accepted != 0: dat = np.loadtxt(open(self.results_filename,"r"),delimiter=' ',ndmin=2) lnprob = -(dat[:,10]-self.chi_min)/2.0 dat[:,11] = lnprob accepted_retest = np.where(lnprob > dat[:,12]) q = open(self.results_filename, 'w') q.write(output_file_header + "\n") for data in dat[accepted_retest]: string = ' '.join([str(d) for d in data]) q.write(string + "\n") q.close() dat2 = np.loadtxt(open(self.results_filename,"r"),delimiter=' ',ndmin=2) number_orbits_accepted=dat2.shape[0] loop_count += 1 #print('loop count',loop_count) update_progress(number_orbits_accepted,self.Norbits) # one last accept/reject with final chi_min value: dat = np.loadtxt(open(self.results_filename,"r"),delimiter=' ',ndmin=2) lnprob = -(dat[:,10]-self.chi_min)/2.0 dat[:,11] = lnprob accepted_retest = np.where(lnprob > dat[:,12]) q = open(self.results_filename, 'w') q.write(output_file_header + "\n") for data in dat[accepted_retest]: string = ' '.join([str(d) for d in data]) q.write(string + "\n") q.close() # when finished, upload results and store in object: dat = np.loadtxt(open(self.results_filename,"r"),delimiter=' ',ndmin=2) number_orbits_accepted=dat.shape[0] print('Final Norbits:', number_orbits_accepted) # intialise results object and store accepted orbits: if self.rv[0] != 0: self.results = Results(orbits = dat, limit_lan = False, limit_aop = False) else: self.results = Results(orbits = dat, limit_lan = True, limit_aop = False) self.results.Update(self.results.orbits) # pickle dump the results attribute: if self.write_results: self.results.SaveResults(self.results_filename.replace(".txt", ".pkl"), write_text_file = False) stop = tm.time() self.results.run_time = (stop - start)*u.s # compute stats and write to file: self.results.stats = Stats(orbits = self.results.orbits, write_to_file = self.write_stats, filename = self.stats_filename) class Results(object): '''A class for storing and manipulating the results of the orbit fit. Args: orbits (Norbits x 13 array): array of accepted orbits from \ OFTI fit in the same order as the following attributes sma (1 x Norbits array): semi-major axis in arcsec period (1 x Norbits array): period in years orbit_fraction (1 x Norbits array): fraction of orbit past periastron \ passage the observation (2016) occured on. Values: [0,1) t0 (1 x Norbits array): date of periastron passage in decimal years ecc (1 x Norbits array): eccentricity inc (1 x Norbits array): inclination relative to plane of the sky in deg aop (1 x Norbits array): arguement of periastron in deg lan (1 x Norbits array): longitude of ascending node in deg mtot (1 x Norbits array): total system mass in Msun distance (1 x Norbits array): distance to system in parsecs chi2 (1 x Norbits array): chi^2 value for the orbit lnprob (1 x Norbits array): log probability of orbit lnrand (1 x Norbits array): log of random "dice roll" for \ orbit acceptance limit_aop, limit_lan (bool): In the absence of radial velocity info, \ there is a degeneracy between arg of periastron and long of ascending \ node. Common practice is to limit one to the interval [0,180] deg. \ By default, lofti limits lan to this interval if rv = False. The user can \ choose to limit aop instead by setting limit_aop = True, limit_lan = False. \ The orbits[:,6] (aop) and orbits[:,7] (lan) arrays preserve the original values. \ Written by <NAME>, 2020 ''' def __init__(self, orbits = [], limit_aop = False, limit_lan = True): self.orbits = orbits self.limit_lan = limit_lan self.limit_aop = limit_aop def Update(self, orbits): '''Take elements of the "orbits" attribute and populate the orbital element attributes Args: orbits (arr): orbits array from Results class Written by <NAME>, 2020 ''' self.sma = orbits[:,0] self.period = orbits[:,1] self.orbit_fraction = orbits[:,2] self.t0 = orbits[:,3] self.ecc = orbits[:,4] self.inc = orbits[:,5] self.aop = orbits[:,6] if self.limit_aop: self.aop = limit_to_180deg(self.aop) self.lan = orbits[:,7] % 360 if self.limit_lan: self.lan = limit_to_180deg(self.lan) self.mtot = orbits[:,8] self.distance = orbits[:,9] self.chi2 = orbits[:,10] self.lnprob = orbits[:,11] self.lnrand = orbits[:,12] def SaveResults(self, filename, write_text_file = False, text_filename = None): '''Save the orbits and orbital parameters attributes in a pickle file Args: filename (str): filename for pickle file write_text_file (bool): if True, also write out the accepted orbits to a \ human readable text file text_filename (bool): if write_to_text = True, specifify filename for text file Written by <NAME>, 2020 ''' pickle.dump(self, open( filename, "wb" ) ) # write results to file: if write_text_file: k = open(text_filename, 'a') for params in self.orbits: string = ' '.join([str(p) for p in params]) k.write(string + "\n") k.close() def LoadResults(self, filename, append = False): '''Read in the orbits and orbital parameters attributes from a pickle file Args: filename (str): filename of pickle file to load append (bool): if True, append read in orbit samples to another Results \ object. Default = False. Written by <NAME>, 2020 ''' results_in = pickle.load( open( filename, "rb" ) ) if append == False: self.orbits = results_in.orbits self.Update(self.orbits) else: self.orbits = np.vstack((self.orbits,results_in.orbits)) self.Update(self.orbits) # plotting results: def PlotHists(self): '''Plot 1-d histograms of orbital elements 'sma','ecc','inc','aop','lan','t0' from fit results. Written by <NAME>, 2020 ''' if len(self.sma < 50): bins = 50 else: bins = 'fd' fig = plt.figure(figsize=(30, 5.5)) params = np.array([self.sma,self.ecc,self.inc,self.aop,self.lan,self.t0]) names = np.array(['sma','ecc','inc','aop','lan','t0']) for i in range(len(params)): ax = plt.subplot2grid((1,len(params)), (0,i)) plt.hist(params[i],bins=bins,edgecolor='none',alpha=0.8) plt.tick_params(axis='both', left=False, top=False, right=False, bottom=True, \ labelleft=False, labeltop=False, labelright=False, labelbottom=True) plt.xticks(rotation=45, fontsize = 20) plt.xlabel(names[i], fontsize = 25) plt.tight_layout() return fig def PlotOrbits(self, color = True, colorbar = True, ref_epoch = 2016.0, size = 100, plot3d = False, cmap = 'viridis',xlim=False,ylim=False): '''Plot a random selection of orbits from the sample in the plane of the sky. Args: color (bool): if True, plot orbit tracks using a colormap scale to orbit fraction (phase) \ past observation date (2015.5). If False, orbit tracks will be black. Default = True colorbar (bool): if True and color = True, plot colorbar for orbit phase ref_epoch (flt): reference epoch for drawing orbits. Default = 2015.5 size (int): Number of orbits to plot. Default = True plot3d (bool): If True, return a plot of orbits in 3D space. Default = False cmap (str): colormap for orbit phase plot Written by <NAME>, 2020 ''' # Random selection of orbits to plot: if len(self.sma) > size: # if there are more orbits than desired size, randomly select orbits from # the posterior sample: ind = np.random.choice(range(0,len(self.sma)),replace=False,size=size) else: # if there are fewer orbits than desired size, take all of them: ind = np.random.choice(range(0,len(self.sma)),replace=False,size=len(self.sma)) from numpy import tan, arctan, sqrt, cos, sin, arccos # label for colormap axis: colorlabel = 'Phase' # create figure: fig = plt.figure(figsize = (7.5, 6.)) plt.grid(ls=':') # invert X axis for RA: plt.gca().invert_xaxis() if plot3d: # Make 3d axis object: ax = fig.add_subplot(111, projection='3d') # plot central star: ax.scatter(0,0,0,color='orange',marker='*',s=300,zorder=10) ax.set_zlabel('Z (")',fontsize=20) else: # plot central star: plt.scatter(0,0,color='orange',marker='*',s=300,zorder=10) # For each orbit in the random selection from the posterior samples: for a,T,to,e,i,w,O in zip(self.sma[ind],self.period[ind],self.t0[ind],self.ecc[ind],np.radians(self.inc[ind]),\ np.radians(self.aop[ind]),np.radians(self.lan[ind])): # define an array of times along orbit: times = np.linspace(ref_epoch,ref_epoch+T,5000) X,Y,Z = np.array([]),np.array([]),np.array([]) E = np.array([]) # Compute X,Y,Z positions for each time: for t in times: n = (2*np.pi)/T M = n*(t-to) nextE = [danby_solve(eccentricity_anomaly, varM,vare, 0.001) for varM,vare in zip([M],[e])] E = np.append(E,nextE) r1 = a*(1.-e*cos(E)) f1 = sqrt(1.+e)*sin(E/2.) f2 = sqrt(1.-e)*cos(E/2.) f = 2.*np.arctan2(f1,f2) r = (a*(1.-e**2))/(1.+(e*cos(f))) X1 = r * ( cos(O)*cos(w+f) - sin(O)*sin(w+f)*cos(i) ) Y1 = r * ( sin(O)*cos(w+f) + cos(O)*sin(w+f)*cos(i) ) Z1 = r * sin(w+f) * sin(i) X,Y,Z = np.append(X,X1),np.append(Y,Y1),np.append(Z,Z1) # Plot the X,Y(Z) positions: if not plot3d: if color: plt.scatter(Y,X,c=((times-ref_epoch)/T),cmap=cmap,s=3,lw=0) plt.gca().set_aspect('equal', adjustable='datalim') else: plt.plot(Y,X, color='black',alpha=0.3) plt.gca().set_aspect('equal', adjustable='datalim') if plot3d: from mpl_toolkits.mplot3d import Axes3D if color: ax.scatter(Y,X,Z,c=((times-ref_epoch)/T),cmap=cmap,s=3,lw=0) else: ax.plot(Y,X,Z, color='black',alpha=0.3) # plot colorbar: if not plot3d: if color: if colorbar == True: cb = plt.colorbar().set_label(colorlabel, fontsize=20) plt.gca().tick_params(labelsize=14) plt.ylabel('Dec (")',fontsize=20) plt.xlabel('RA (")',fontsize=20) plt.gca().tick_params(labelsize=14) if(xlim): plt.xlim(xlim) if(ylim): plt.ylim(ylim) return fig def PlotSepPA(self, ref_epoch = 2016.0, size = 100, timespan = [20,20], orbitcolor = 'skyblue'): '''Plot a random selection of orbits from the sample in separation and position angle as a function of time. Args: ref_epoch (flt): reference epoch for drawing orbits. Default = 2015.5 size (int): Number of orbits to plot. Default = True timespan (tuple, int): number of years before [0] and after [1] the ref epoch to \ plot sep and pa orbitcolor (str): color to use to plot the orbits Written by <NAME>, 2020 ''' # Random selection of orbits to plot: if len(self.sma) > size: # if there are more orbits than desired size, randomly select orbits from # the posterior sample: ind = np.random.choice(range(0,len(self.sma)),replace=False,size=size) else: # if there are fewer orbits than desired size, take all of them: ind = np.random.choice(range(0,len(self.sma)),replace=False,size=len(self.sma)) from numpy import tan, arctan, sqrt, cos, sin, arccos # make figure fig = plt.figure(figsize = (8, 10)) # define subplots: plt.subplot(2,1,1) plt.gca().tick_params(labelsize=14) plt.grid(ls=':') # define times to compute sep/pa: tmin,tmax = ref_epoch - timespan[0],ref_epoch + timespan[1] t = np.linspace(tmin,tmax,2000) date_ticks = np.arange(tmin,tmax,10) # for each selected orbit from the sample: for a,T,to,e,i,w,O in zip(self.sma[ind],self.period[ind],self.t0[ind],self.ecc[ind],np.radians(self.inc[ind]),\ np.radians(self.aop[ind]),np.radians(self.lan[ind])): X = np.array([]) Y = np.array([]) # compute X,Y at each time point: X1,Y1 = orbits_for_plotting(a,T,to,e,i,w,O,t) X = np.append(X, X1) Y = np.append(Y,Y1) # compute sep: r=np.sqrt((X**2)+(Y**2)) # plot sep in mas: plt.plot(t,r*1000,color=orbitcolor,alpha=0.5) plt.ylabel(r'$\rho$ (mas)',fontsize=20) # next suplot: plt.subplot(2,1,2) plt.grid(ls=':') # for each selected orbit from the sample: for a,T,to,e,i,w,O in zip(self.sma[ind],self.period[ind],self.t0[ind],self.ecc[ind],np.radians(self.inc[ind]),\ np.radians(self.aop[ind]),np.radians(self.lan[ind])): X = np.array([]) Y = np.array([]) X1,Y1 = orbits_for_plotting(a,T,to,e,i,w,O,t) X = np.append(X, X1) Y = np.append(Y,Y1) # compute pa: theta=np.arctan2(X,-Y) theta=(np.degrees(theta)+270.)%360 # plot it: plt.plot(t,theta,color=orbitcolor,alpha=0.5) plt.ylabel(r'P.A. (deg)',fontsize=19) plt.xlabel('Years',fontsize=19) plt.gca().tick_params(labelsize=14) plt.tight_layout() return fig class Stats(object): '''A class for storing and manipulating the statistics of the results of the orbit fit. For every parameter, there is a series of stats computed and saved as stats.param.stat Examples: stats.sma.mean = mean of semimajor axis stats.ecc.ci68 = 68% confidence interval for eccentricity stats.aop.std = standard deviation of arg of periastron Args: orbits (Norbits x 13 array): array of accepted orbits from \ OFTI fit in the same order as the following attributes param.mean (flt): mean of parameter computed using np.mean param.median (flt): np.median of parameter param.mode (flt): mode of parameter param.std (flt): standard deviation from np.std param.ci68 (tuple,flt): 68% minimum credible interval of form (lower bound, upper bound) param.ci95 (tuple,flt): 95% minimum credible interval write_to_file (bool): If True, write stats to a human-readbale text file. filename (str): filename for saving stats file. If not supplied, default \ name is FitResults.Stats.y.mo.d.h.m.s.pkl, saved in same directory as fit was run. Written by <NAME>, 2020 ''' def __init__(self, orbits = [], write_to_file = False, filename = None): self.orbits = orbits # Compute stats on parameter arrays and save as attributes: self.sma = StatsSubclass(self.orbits[:,0]) self.period = StatsSubclass(self.orbits[:,1]) self.orbit_fraction = StatsSubclass(self.orbits[:,2]) self.t0 = StatsSubclass(self.orbits[:,3]) self.ecc = StatsSubclass(self.orbits[:,4]) self.inc = StatsSubclass(self.orbits[:,5]) self.aop = StatsSubclass(self.orbits[:,6]) self.lan = StatsSubclass(self.orbits[:,7]) self.mtot = StatsSubclass(self.orbits[:,8]) self.distance = StatsSubclass(self.orbits[:,9]) if write_to_file: params = np.array([self.sma,self.period,self.orbit_fraction,self.t0,self.ecc,self.inc,\ self.aop,self.lan,self.mtot,self.distance]) names = np.array(['sma','period','orbit fraction','t0','ecc','inc','aop','lan','mtot','distance']) if not filename: filename = 'FitResults.Stats.'+time.strftime("%Y.%m.%d.%H.%M.%S")+'.txt' k = open(filename, 'w') string = 'Parameter Mean Median Mode Std 68% Min Cred Int 95% Min Cred Int' k.write(string + "\n") for i in range(len(params)): string = make_parameter_string(params[i],names[i]) k.write(string + "\n") k.close() class StatsSubclass(Stats): '''Subclass for computing and storing statistics Args: array (arr): array for which to compute statistics ''' def __init__(self, array): self.mean,self.median,self.mode,self.std,self.ci68,self.ci95 = compute_statistics(array)

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support/compute-powers.py

ldanko/fmt

13,148

137832

<gh_stars>1000+ #!/usr/bin/env python # Compute 10 ** exp with exp in the range [min_exponent, max_exponent] and print # normalized (with most-significant bit equal to 1) significands in hexadecimal. from __future__ import print_function min_exponent = -348 max_exponent = 340 step = 8 significand_size = 64 exp_offset = 2000 class fp: pass powers = [] for i, exp in enumerate(range(min_exponent, max_exponent + 1, step)): result = fp() n = 10 ** exp if exp >= 0 else 2 ** exp_offset / 10 ** -exp k = significand_size + 1 # Convert to binary and round. binary = '{:b}'.format(n) result.f = (int('{:0<{}}'.format(binary[:k], k), 2) + 1) / 2 result.e = len(binary) - (exp_offset if exp < 0 else 0) - significand_size powers.append(result) # Sanity check. exp_offset10 = 400 actual = result.f * 10 ** exp_offset10 if result.e > 0: actual *= 2 ** result.e else: for j in range(-result.e): actual /= 2 expected = 10 ** (exp_offset10 + exp) precision = len('{}'.format(expected)) - len('{}'.format(actual - expected)) if precision < 19: print('low precision:', precision) exit(1) print('Significands:', end='') for i, fp in enumerate(powers): if i % 3 == 0: print(end='\n ') print(' {:0<#16x}'.format(fp.f, ), end=',') print('\n\nExponents:', end='') for i, fp in enumerate(powers): if i % 11 == 0: print(end='\n ') print(' {:5}'.format(fp.e), end=',') print('\n\nMax exponent difference:', max([x.e - powers[i - 1].e for i, x in enumerate(powers)][1:]))

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bluebottle/assignments/migrations/0002_auto_20190529_0858.py

terrameijar/bluebottle

10

192060

# -*- coding: utf-8 -*- # Generated by Django 1.11.15 on 2019-05-29 06:58 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('assignments', '0001_initial'), ] operations = [ migrations.RenameField( model_name='assignment', old_name='end', new_name='deadline', ), ]

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LC/263.py

szhu3210/LeetCode_Solutions

2

18959

class Solution(object): def isUgly(self, num): """ :type num: int :rtype: bool """ if num <= 0: return False for x in [2,3,5]: while(num % x ==0): num /= x return num==1

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zoondb/analysis/__init__.py

gord02/Zooniverse-DB

1

187129

#!/usr/bin/env python """ Analysis consists of general purpose functions. Note ---- Function from either alpha or beta are not imported here, since they are not required by default. """ from . import seed # noqa: F401

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package/meross_iot/cloud/devices/subdevices/thermostats.py

armandofox/alexa-marantz-py

4

194619

from enum import Enum from typing import Union from meross_iot.cloud.timeouts import LONG_TIMEOUT from meross_iot.cloud.abilities import HUB_MTS100_ALL, HUB_MTS100_TEMPERATURE, HUB_MTS100_MODE, HUB_TOGGLEX, HUB_ONLINE from meross_iot.cloud.devices.subdevices.generic import GenericSubDevice from meross_iot.logger import VALVES_LOGGER as l from meross_iot.meross_event import DeviceSwitchStatusEvent, ThermostatTemperatureChange, ThermostatModeChange, \ DeviceOnlineStatusEvent class ThermostatMode(Enum): COMFORT = 1 CUSTOM = 0 ECONOMY = 2 SCHEDULE = 3 class ThermostatV3Mode(Enum): AUTO = 3 COOL = 2 CUSTOM = 0 ECONOMY = 4 HEAT = 1 class ValveSubDevice(GenericSubDevice): def __init__(self, cloud_client, subdevice_id, parent_hub, **kwords): super().__init__(cloud_client, subdevice_id, parent_hub, **kwords) def _handle_push_notification(self, namespace, payload, from_myself=False): # Let the Generic handler to handle the common events. handled = super()._handle_push_notification(namespace=namespace, payload=payload, from_myself=from_myself) if handled: return True # If the parent handler was unable to parse it, we do it here. evt = None if namespace == HUB_MTS100_ALL: self._raw_state.update(payload) return True elif namespace == HUB_TOGGLEX: togglex = self._raw_state.get('togglex') if togglex is None: togglex = {} self._raw_state['togglex'] = togglex togglex.update(payload) evt = DeviceSwitchStatusEvent(self, 0, self.onoff, from_myself) self.fire_event(evt) return True elif namespace == HUB_MTS100_MODE: mode = self._raw_state.get('mode') if mode is None: mode = {} self._raw_state['mode'] = mode mode.update(payload) evt = ThermostatModeChange(device=self, mode=self.mode, generated_by_myself=from_myself) self.fire_event(evt) return True elif namespace == HUB_MTS100_TEMPERATURE: temp = self._raw_state.get('temperature') if temp is None: temp = {} self._raw_state['temperature'] = temp temp.update(payload) evt = ThermostatTemperatureChange(device=self, temperature_state=self._raw_state.get('temperature'), generated_by_myself=from_myself) self.fire_event(evt) return True # TODO: handle TIME SYNC event? # elif namespace == HUB_TIME_SYNC: # self._state.get('??').update(payload) else: l.warn("Unsupported/unhandled event: %s" % namespace) l.debug("Namespace: %s, Data: %s" % (namespace, payload)) return False @property def onoff(self): onoff = self._get_property('togglex', 'onoff') return onoff @property def heating(self): heating = self._get_property('temperature', 'heating') if heating is None: return None else: return heating == 1 @property def room_temperature(self): temp = self._get_property('temperature', 'room') if temp is None: return None else: return temp / 10 @property def target_temperature(self): """ Returns the current target temperature :return: """ # The API returns the temperature in decimals. # For this reason, we convert it to integers. temp = self._get_property('temperature', 'currentSet') if temp is None: return None else: return temp / 10 def set_target_temperature(self, target_temp: float = None, timeout : float = LONG_TIMEOUT, callback: callable = None): """ Sets the target temperature of the thermostat :param target_temp: temperature to be set :param callback: :return: """ # The API expects the target temperature in DECIMALS, so we need to multiply the user's input by 10 value = target_temp * 10 payload = {'temperature': [{'id': self.subdevice_id, 'custom': value}]} return self.execute_command(command='SET', namespace=HUB_MTS100_TEMPERATURE, payload=payload, timeout=timeout, callback=callback) def set_preset_temperature(self, away: float = None, comfort: float = None, economy: float = None, timeout: float = LONG_TIMEOUT, callback: callable = None): """ Configures the preset temperature values. The temperature values should be expressed in Celsius degrees. :param away: Target temperature for away preset :param comfort: Target temperature for comfort preset :param economy: Target temperature for economy preset :param callback: :return: """ # The API expects the celsius degrees in DECIMALS, so we need to multiply the user's input by 10 temperature_conf = {'id': self.subdevice_id} if away is not None: temperature_conf['away'] = away * 10 if comfort is not None: temperature_conf['comfort'] = comfort * 10 if economy is not None: temperature_conf['economy'] = economy * 10 payload = {'temperature': [temperature_conf]} return self.execute_command(command='SET', namespace=HUB_MTS100_TEMPERATURE, payload=payload, timeout=timeout, callback=callback) @property def mode(self): state = self._get_property('mode', 'state') if state is None: return None else: # Parse the mode according to the current device type if self.type == 'mts100v3': return ThermostatV3Mode(state) elif self.type == 'mts100': return ThermostatMode(state) else: l.error("The current thermostat mode is not supported.") return None def set_mode(self, mode: Union[ThermostatV3Mode, ThermostatMode, int], timeout=LONG_TIMEOUT, callback=None ): """ Sets the temperature mode for the thermostat :param mode: :return: """ # Make sure we are passing correct values if self.type == 'mts100v3' and not isinstance(mode, ThermostatV3Mode): raise ValueError("This thermostat only supports ThermostatV3Mode modes") elif self.type == 'mts100' and not isinstance(mode, ThermostatMode): raise ValueError("This thermostat only supports ThermostatMode modes") elif isinstance(mode, int): l.warning("Setting a raw integer value as mode. This is not recommended. " "Please use ThermostatMode or ThermostatV3Mode") self.execute_command('SET', HUB_MTS100_MODE, {'mode': [{'id': self.subdevice_id, 'state': mode.value}]}, timeout=timeout, callback=callback) def _togglex(self, onoff, channel=0, timeout=LONG_TIMEOUT, callback=None): payload = {'togglex': [{'channel': channel, 'id': self.subdevice_id, 'onoff': onoff}]} return self.execute_command('SET', HUB_TOGGLEX, payload, timeout=timeout, callback=callback) def turn_on(self, timeout=LONG_TIMEOUT, callback=None): return self._togglex(onoff=1, timeout=timeout, callback=callback) def turn_off(self, timeout=LONG_TIMEOUT, callback=None): return self._togglex(onoff=0, timeout=timeout, callback=callback)

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swim_backend/events.py

eurocontrol-swim/swim-backend

0

24800

<reponame>eurocontrol-swim/swim-backend<filename>swim_backend/events.py """ Copyright 2019 EUROCONTROL ========================================== Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ========================================== Editorial note: this license is an instance of the BSD license template as provided by the Open Source Initiative: http://opensource.org/licenses/BSD-3-Clause Details on EUROCONTROL: http://www.eurocontrol.int """ import abc __author__ = "EUROCONTROL (SWIM)" class Event(list): """ Simplistic implementation of event handling. A list of callable objects. Calling an instance of this will cause a call to each item in the list in ascending order by index. """ _type = 'Generic' def __call__(self, *args, **kwargs): for handler in self: handler(*args, **kwargs) def __repr__(self): return f"{self._type} Event({list.__repr__(self)})" class EventSafe(list): def __call__(self, *args, **kwargs): handlers = [handler_class(*args, **kwargs) for handler_class in self] processed_handlers = [] for handler in handlers: try: handler.do() processed_handlers.append(handler) except: handler.undo() processed_handlers.reverse() for processed_handler in processed_handlers: processed_handler.undo() raise class EventHandler(abc.ABC): @abc.abstractmethod def do(self, *args, **kwargs): pass @abc.abstractmethod def undo(self, *args, **kwargs): pass

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legacy/extract_feature.py

Tommy-Liu/MovieQA_Contest

0

140619

<filename>legacy/extract_feature.py import argparse import os from functools import partial from math import ceil from multiprocessing import Pool from os.path import join, exists import numpy as np import tensorflow as tf import tensorflow.contrib.slim as slim from numpy.lib.format import read_array_header_1_0, read_magic from tqdm import tqdm from config import MovieQAPath from legacy.inception_preprocessing import preprocess_image from legacy.inception_resnet_v2 import inception_resnet_v2_arg_scope, inception_resnet_v2 from utils import data_utils as du from utils import func_utils as fu _mp = MovieQAPath() def make_parallel(model, num_gpus, imgs): out_split = [] for i in range(num_gpus): with tf.device(tf.DeviceSpec(device_type="GPU", device_index=i)): with tf.variable_scope(tf.get_variable_scope(), reuse=i > 0): out_split.append(model(images=imgs[i])) # return tf.concat(out_split, axis=0) return out_split def models(images): with slim.arg_scope(inception_resnet_v2_arg_scope()): logits, end_points = inception_resnet_v2(images, num_classes=1001, is_training=False) # return tf.nn.max_pool(end_points['Conv2d_7b_1x1'], [1, 2, 2, 1], [1, 2, 2, 1], 'VALID') return end_points['Conv2d_7b_1x1'] def load_shape(n): with open(n, 'rb') as f: major, minor = read_magic(f) shape, fortran, dtype = read_array_header_1_0(f) if len(shape) != 4: raise TypeError('Errr! Single image... %s' % n) return shape def collect(video_data, k): imgs = [join(_mp.image_dir, k, '%s_%05d.jpg' % (k, i + 1)) for i in range(0, video_data[k]['real_frames'], 10)] npy_name = join(_mp.feature_dir, k + '.npy') if not exists(npy_name) or load_shape(npy_name)[0] != len(imgs): return npy_name, len(imgs), imgs else: return None def get_images_path(): file_names, capacity, npy_names = [], [], [] video_data = dict(item for v in du.json_load(_mp.video_data_file).values() for item in v.items()) func = partial(collect, video_data) with Pool(16) as pool, tqdm(total=len(video_data), desc='Collect images') as pbar: for ins in pool.imap_unordered(func, list(video_data.keys())): if ins: npy_names.append(ins[0]) capacity.append(ins[1]) file_names.extend(ins[2]) pbar.update() return file_names, capacity, npy_names def get_images_path_v2(): file_names, capacity, npy_names = [], [], [] video_data = du.json_load(_mp.video_data_file) for imdb_key in tqdm(video_data, desc='Collect Images'): npy_names.append(join(_mp.feature_dir, imdb_key + '.npy')) videos = list(video_data[imdb_key].keys()) videos.sort() num = 0 for v in tqdm(videos): images = [join(_mp.image_dir, v, '%s_%05d.jpg' % (v, i + 1)) for i in range(0, video_data[imdb_key][v]['real_frames'], 15)] file_names.extend(images) num += len(images) capacity.append(num) print(capacity, npy_names) return file_names, capacity, npy_names def get_images_path_v3(): file_names, capacity, npy_names = [], [], [] sample = du.json_load(_mp.sample_frame_file) for imdb_key in tqdm(sample, desc='Collect Images'): # if not exists(join(_mp.feature_dir, imdb_key + '.npy')): npy_names.append(join(_mp.feature_dir, imdb_key + '.npy')) videos = list(sample[imdb_key].keys()) videos.sort() num = 0 for v in tqdm(videos): images = [join(_mp.image_dir, v, '%s_%05d.jpg' % (v, i + 1)) for i in sample[imdb_key][v]] file_names.extend(images) num += len(images) capacity.append(num) # print(capacity, npy_names) return file_names, capacity, npy_names def count_num(features_list): num = 0 for features in features_list: num += features.shape[0] return num def writer_worker(queue, capacity, npy_names): video_idx = 0 local_feature = [] local_filename = [] with tqdm(total=len(npy_names)) as pbar: while len(capacity) > video_idx: item = queue.get() if item: f, n = item local_feature.append(f) local_filename.extend(n) local_size = len(local_filename) while len(capacity) > video_idx and local_size >= capacity[video_idx]: concat_feature = np.concatenate(local_feature, axis=0) final_features = concat_feature[:capacity[video_idx]] final_filename = local_filename[:capacity[video_idx]] assert final_features.shape[0] == capacity[video_idx], \ "%s Both frames are not same!" % npy_names[video_idx] for i in range(len(final_features)): assert fu.basename_wo_ext(npy_names[video_idx]) == \ fu.basename_wo_ext(final_filename[i]).split('.')[0], \ "Wrong images! %s\n%s" % (npy_names[video_idx], final_filename[i]) try: np.save(npy_names[video_idx], final_features) except Exception as e: np.save(npy_names[video_idx], final_features) raise e pbar.set_description(' '.join([fu.basename_wo_ext(npy_names[video_idx]), str(len(final_features))])) del local_feature[:] local_feature.append(concat_feature[capacity[video_idx]:]) local_filename = local_filename[capacity[video_idx]:] local_size = len(local_filename) video_idx += 1 pbar.update() else: break def parse_func(filename): raw_image = tf.read_file(filename) image = tf.image.decode_jpeg(raw_image, channels=3) return image, filename def preprocess_func(image, filename): image = preprocess_image(image, 299, 299, is_training=False) return image, filename def input_pipeline(filename_placeholder, batch_size=32, num_worker=4): dataset = tf.data.Dataset.from_tensor_slices(filename_placeholder) dataset = dataset.repeat(1) images, names_, iterator = [], [], [] if args.num_gpu > 1: for i in range(args.num_gpu): dd = dataset.shard(args.num_gpu, i) dd = dd.map(parse_func, num_parallel_calls=num_worker) dd = dd.map(preprocess_func, num_parallel_calls=num_worker) dd = dd.batch(batch_size) dd = dd.prefetch(16) itit = dd.make_initializable_iterator() im, n_ = itit.get_next() images.append(im) names_.append(n_) iterator.append(itit) else: dataset = dataset.map(parse_func, num_parallel_calls=num_worker) dataset = dataset.batch(batch_size) dataset = dataset.prefetch(1000) iterator = dataset.make_initializable_iterator() images, names_ = iterator.get_next() return images, names_, iterator def args_parse(): parser = argparse.ArgumentParser() parser.add_argument('--num_gpu', default=1, type=int, help='Number of GPU going to be used') parser.add_argument('--batch_size', default=8, type=int, help='Batch size of images') parser.add_argument('--num_worker', default=2, type=int, help='Number of worker reading data.') parser.add_argument('--reset', action='store_true', help='Reset all the extracted features.') return parser.parse_args() if __name__ == '__main__': args = args_parse() bsize = args.batch_size num_w = args.num_worker reset = args.reset if reset: os.system('rm -rf %s' % _mp.feature_dir) fu.make_dirs(_mp.feature_dir) fn, cap, npy_n = get_images_path_v3() num_step = int(ceil(len(fn) / bsize)) fp = tf.placeholder(tf.string, shape=[None]) img, names, it = input_pipeline(fp, bsize, num_w) if args.num_gpu > 1: feature_tensor = make_parallel(models, args.num_gpu, img) else: feature_tensor = models(img) print('Pipeline setup done !!') saver = tf.train.Saver(tf.global_variables()) print('Start extract !!') config = tf.ConfigProto(allow_soft_placement=True) config.gpu_options.allow_growth = True # config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1 with tf.Session(config=config) as sess: # q = Queue() # p = Process(target=writer_worker, args=(q, cap, npy_n)) # p.start() tf.global_variables_initializer().run() tf.local_variables_initializer().run() saver.restore(sess, './inception_resnet_v2_2016_08_30.ckpt') sess.run([iit.initializer for iit in it], feed_dict={fp: fn}) # print(sess.run(feature_tensor).shape) try: for _ in range(num_step): feature, name = sess.run([feature_tensor, names]) # q.put((feature, [i.decode() for i in name])) # q.put(None) except KeyboardInterrupt: print() # p.terminate() finally: pass # p.join() # q.close()

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R function.py

haideraheem/Simple-Python-Programs

0

26322

# -*- coding: utf-8 -*- """ Created on Sat Oct 6 17:11:11 2018 @author: <NAME> """ a= float(input("Type a : ")) b= float(input("Type b : ")) c= float(input("Type c : ")) x= (a*b)+(b*c)+(c*a) y= (a+b+c) r= x/y print( ) print("The result of the calculation is {0:.2f}".format(r))

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configs/semantic_segmentation/common/optims/ep30.py

voldemortX/DeeplabV3_PyTorch1.3_Codebase

1

100109

<gh_stars>1-10 lr_scheduler = dict( name='poly_scheduler', epochs=30, power=0.9 )

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Gds/src/fprime_gds/wxgui/tools/PexpectRunnerConsolGUI.py

hunterpaulson/fprime

0

32742

<reponame>hunterpaulson/fprime # -*- coding: utf-8 -*- ########################################################################### ## Python code generated with wxFormBuilder (version May 29 2018) ## http://www.wxformbuilder.org/ ## ## PLEASE DO *NOT* EDIT THIS FILE! ########################################################################### import wx import wx.xrc ########################################################################### ## Class PexpectRunnerGUI ########################################################################### class PexpectRunnerGUI(wx.Frame): def __init__(self, parent): wx.Frame.__init__( self, parent, id=wx.ID_ANY, title=u"Pexpect Output", pos=wx.DefaultPosition, size=wx.Size(500, 300), style=wx.DEFAULT_FRAME_STYLE | wx.TAB_TRAVERSAL, ) self.SetSizeHints(wx.DefaultSize, wx.DefaultSize) bSizer3 = wx.BoxSizer(wx.VERTICAL) self.TextCtrlConsol = wx.TextCtrl( self, wx.ID_ANY, wx.EmptyString, wx.DefaultPosition, wx.DefaultSize, wx.TE_MULTILINE | wx.TE_READONLY | wx.TE_WORDWRAP, ) bSizer3.Add(self.TextCtrlConsol, 1, wx.ALL | wx.EXPAND, 5) self.SetSizer(bSizer3) self.Layout() self.Centre(wx.BOTH) # Connect Events self.Bind(wx.EVT_CLOSE, self.onWindowClose) self.TextCtrlConsol.Bind(wx.EVT_MOUSEWHEEL, self.onMouseWheel) def __del__(self): pass # Virtual event handlers, overide them in your derived class def onWindowClose(self, event): event.Skip() def onMouseWheel(self, event): event.Skip()

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AREMApy/v2018/ch_15.py

mwhit74/AREMApy

2

95824

<reponame>mwhit74/AREMApy # -*- coding: utf-8 -*- """ Created on Tue Jan 14 12:51:05 2020 @author: mwhitten """ import math def eq13141(fax, fa_ax, fb1, fa_b1, fb2, fa_b2, k1, l1, r1, k2, l2, r2, e): """Axial Compression and Bending AREMA 2018 Section 1.3.14.1 Member subject to both axial compression and bending stresses shall be proportioned to satisfy the following requirements: if f_axial/fa_axial <= 0.15: IR = fa/Fa + fb1/Fb1 + fb2/Fb2 else: moment_mag1 = 1-f_axial/(0.514*math.pow(math.pi,2)*e)*math.pow(klr1,2) moment_mag2 = 1-f_axial/(0.514*math.pow(math.pi,2)*e)*math.pow(klr2,2) IR = fa/Fa + fb1/(Fb1*moment_mag1) + fb2/(Fb2*moment_mag2) Args: fax (float): calculated axial stress [psi] fb1 (float): calculated bending stess about axis 1-1 [psi] fb2 (float): calculated bending stress about axis 2-2 [psi] fa_ax (flaot): allowable axial stress if axial force alone existed [psi] fa_b1 (float): allowable compression bending stress about axis 1-1, that would be permitted if bending alone existed [psi] fa_b2 (float): allowable compression bending stress about axis 2-2, that would be permitted if bending alone existed [psi] k1 (float): effective length factor about 1-1 axis l1 (float): unbraced length about 1-1 axis [inches] r1 (float): radius of gyration about 1-1 axis [inches] k2 (float): effective length factor about 2-2 axis l2 (float): unbraced length about 2-2 axis [inches] r2 (float): radius of gyration about 2-2 axis [inches] e (float): modulus of elasticity [psi] Returns: ir (tuple(float, str)): interaction ratio of the axial stress and bi-axail bending stress per the prescribed equations Notes: 1. Units are in lbs and inches. 2. To ignore moment magnification factors enter k1 = k2 = 0.0. Secondary effects, where they are determined to be significant, should be considered elsewhere in the analysis or design if the moment magnification is ignored here. """ ref_text = "AREMA 2018 Section 1.3.14.1 \n\n" user_input = (f'fax = {fax:.2f}, fa_ax = {fa_ax:.2f},' + f'fb1 = {fb1:.2f}, fa_b1 = {fa_b1:.2f},' + f'fb2 = {fb2:.2f}, fa_b2 = {fa_b2:.2f},' + f'k1 = {k1:.2f}, l1 = {l1:.2f}, r1 = {r1:.2f},' + f'k2 = {k2:.2f}, l2 = {l2:.2f}, r2 = {r2:.2f},' + f'E = {e:.2f} \n\n') fax_ir = fax/fa_ax fb1_ir = fb1/fa_b1 fb2_ir = fb2/fa_b2 text1 = (f'fa/Fa = {fax:.2f}/{fa_ax:.2f} \n' + f'fa/Fa = {fax_ir:.2f} \n\n') text2 = (f'fb1/Fb1 = {fb1:.2f}/{fa_b1:.2f} \n' + f'fb1/Fb1 = {fb1_ir:.3f} \n\n') text3 = (f'fb2/Fb2 = {fb2:.2f}/{fa_b2:.2f} \n' + f'fb2/Fb2 = {fb2_ir:.3f} \n\n') if fax_ir <= 0.15: ir = fax_ir + fb1_ir + fb2_ir text4 = (f'IR = fax/Fax + fb1/Fb1 + fb2/Fb2 \n' f'IR = {fax_ir:.2f} + {fb1_ir:.2f} + {fb2_ir:.2f} \n' f'IR = {ir:.3f}') text = text1 + text2 + text3 + text4 else: klr1 = k1*l1/r1 klr2 = k2*l2/r2 moment_mag1 = 1-fax/(0.514*math.pow(math.pi,2)*e)*math.pow(klr1,2) moment_mag2 = 1-fax/(0.514*math.pow(math.pi,2)*e)*math.pow(klr2,2) ir = fax_ir + fb1_ir/moment_mag1 + fb2/moment_mag2 text5 = f'k1*l1/r1 = {klr1:.2f} \n' text6 = f'k2*l2/r2 = {klr2:.2f} \n\n' text7 = (f'Moment Mag. 1 = 1-fax/' + f'(0.514*math.pow(math.pi,2)*e)*math.pow(k1*l1/r1,2) \n' + f'Moment Mag. 1 = 1-{fax:.2f}/' + f'(0.514*math.pow(math.pi,2)*{e:.1f})' f'*math.pow({klr1:.2f},2) \n' + f'Moment Mag. 1 = {moment_mag1:.3f} \n\n') text8 = (f'Moment Mag. 2 = 1-fax/' + f'(0.514*math.pow(math.pi,2)*e)*math.pow(k2*l2/r2,2) \n' + f'Moment Mag. 2 = 1-{fax:.2f}/' + f'(0.514*math.pow(math.pi,2)*{e:.1f})' f'*math.pow({klr2:.2f},2) \n' + f'Moment Mag. 2 = {moment_mag2:.3f} \n\n') text9 = (f'IR = fax/Fax + fb1/(Fb1*Moment Mag. 1)' f' + fb2/(Fb2*Moment Mag. 2) \n' f'IR = {fax_ir:.2f} + {fb1_ir/moment_mag1:.2f}' f' + {fb2_ir/moment_mag1:.2f} \n' f'IR = {ir:.3f} \n') text = text1 + text2 + text3 + text5 + text6 + text7 + text8 + text9 text = ref_text + user_input + text return ir, text def eq141d1(stress_condition, fy, fu): """Axial tension allowable stress subject to direct tensile load AREMA 2018 Table 15-1-11 Row 1 Equations if stress_condition == 1: fa_t = 0.55*fy elif stress_condition == 2: fa_tn = 0.47*fu elif stress_condition == 3: fa_tnp = 0.45*fy Args: stress_condition (int): 1 - Axial tension, structural steel, gross section 2 - Axial tension, structural steel, effective net section (see articles 1.5.8 and 1.6.5) 3 - Axial tension, structural steel, effective net area at cross-section of pin hole of pin-connected members fy (float): yield stress [psi] fu (float): ultimate stress [psi] Returns: fa_t (tuple(float, str)): allowable tension stress on gross section [psi] -OR- fa_tn (tuple(float, str)): allowable tension stress on net section [psi] -OR- fa_tnp (tuple(float, str)): allowable tension stress on net section at cross-section of pin hole of pin-connected members [psi] Notes: 1. Units in lbs and inches. """ ref_text = "AREMA 2018 Section 1.4.1 Table 15-1-11 Row 1 \n\n" user_input = (f'Stress Condition = {stress_condition:d},' + f'Fy = {fy:.2f}, Fu = {fu:.2f} \n') if stress_condition == 1: fa = 0.55*fy text = (f'1 - Axial tension, structural steel, gross section \n' + f'fa_tg = 0.55*fy \n' + f'fa_tg = 0.55*{fy:.1f} \n' + f'fa_tg = {fa:.1f}') elif stress_condition == 2: fa = 0.47*fu text = (f'2 - Axial tension, structural steel, effective net section' + f'(see articles 1.5.8 and 1.6.5) \n' f'fa_tn = 0.47*fu \n' + f'fa_tn = 0.47*{fu:.1f} \n' + f'fa_tn = {fa:.1f}') elif stress_condition == 3: fa = 0.45*fy text = (f'3 - Axial tension, structural steel, effective net area' + f'at cross-section of pin hole of pin-connected members \n' f'fa_tnp = 0.45*fy \n' + f'fa_tnp = 0.45*{fy:.1f} \n' + f'fa_tnp = {fa:.1f}') text = ref_text + user_input + text return fa, text def eq141d2(): pass def eq141d3(fy): """Tension in extreme fibers subject to bending, net section AREMA 2018 Table 15-1-11 Row 3 Equations Tension in extreme fibers of rolled shapes, girders and built-up section, subject to bending, net section fa_bt = 0.55*fy Args: fy (float): yield stress [psi] Returns: fa_bt (tuple(float, str)): allowable bending tensile stress [psi] """ ref_text = "AREMA 2018 Section 1.4.1 Table 15-1-11 Row 3 \n\n" user_input = f'Fy = {fy:.2f} \n' fa_bt = 0.55*fy text = (f'Tension in extreme fibers of rolled shapes, girder and \n' + f'built-up section, subject to bending, net section \n' + f'fa_bt = 0.55*fy \n' + f'fa_bt = 0.55*{fy:.1f} \n' + f'fa_bt = {fa_bt:.1f}') text = ref_text + user_input + text return fa_bt, text def eq141d4(): pass def eq141d5(k, l, r, fy, e): """Axial compression allowable stress, gross section AREMA 2018 Table 15-1-11 Row 5 Equations (Except for stiffeners of beams and girders, and splice material) klr = k*l/r elastic_limit = 0.629*math.sqrt(fy/e) plastic_limit = 5.034*math.sqrt(fy/e) if klr <= elastic_limit: fa_ac = 0.55*fy elif klr > elastic_limit and klr <= plastic_limit: fa_ac = 0.60*fy-math.pow(17500.0*fy/e,3/2)*klr elif klr > plastic_limit: fa_ac = 0.514*math.pow(math.pi, 2)*e/math.pow(klr,2) Args: k (float): effective length factor in the axis considered k = 7/8 for members with pin-end connections k = 3/4 for members with bolted or welded end connections l (float): unbraced length in the axis considered [inches] r (float): radius of gyration in the axis considered [inches] fy (float): yield stress [psi] e (float): modulus of elasticity [psi] Returns: fa_ac (tuple(float, str)): allowable axial compression on gross section Notes: 1. Units are in lbs and inches. """ ref_text = "AREMA 2018 Section 1.4.1 Table 15-1-11 Row 5 \n\n" user_input = (f'k = {k:.2f}, l = {l:.2f}, r = {r:.2f}, Fy = {fy:.2f},' + f'E = {e:.2f} \n') klr = k*l/r elastic_limit = 0.629/math.sqrt(fy/e) plastic_limit = 5.034/math.sqrt(fy/e) if klr <= elastic_limit: fa_ac = 0.55*fy text = (f'kl/r <= 0.629/math.sqrt(fy/e) \n' + f'{klr:.2f} <= {elastic_limit:.2f} \n' + f'fa_ac = 0.55*fy \n' + f'fa_ac = 0.55*{fy:.1f} \n' + f'fa_ac = {fa_ac:.1f}') elif klr > elastic_limit and klr <= plastic_limit: fa_ac = 0.60*fy-math.pow(17500.0*fy/e,3/2)*klr text = (f'kl/r > 0.629/math.sqrt(fy/e)' f'and kl/r <= 5.034/math.sqrt(fy/e) \n' + f'{klr:.2f} > {elastic_limit:.2f}' f'and {klr:.2f} <= {plastic_limit:.2f} \n' + f'fa_ac = 0.60*fy-math.pow(17500.0*fy/e,3/2)*kl/r \n' + f'fa_ac = 0.60*{fy:.1f}-' f'math.pow(17500.0*{fy:.1f}/{e:.1f},3/2)*{klr:.2f} \n' + f'fa_ac = {fa_ac:.1f}') elif klr > plastic_limit: fa_ac = 0.514*math.pow(math.pi, 2)*e/math.pow(klr,2) text = (f'kl/r > 5.034/math.sqrt(fy/e) \n' + f'{klr:.2f} > {plastic_limit:.2f} \n' + f'fa_ac = 0.514*math.pow(math.pi, 2)*e/math.pow(kl/r,2) \n' + f'fa_ac = 0.514*math.pow(math.pi, 2)*{e:.2f}' + f'/math.pow({klr:.2f},2) \n' + f'fa_ac = {fa_ac:.1f}') text = ref_text + user_input + text return fa_ac, text def eq141d6(fy): """Compression in extreme fibers of I-type section bending about weak axis AREMA 2018 Section 1.4.1 Table 15-1-11 Row 6 Compression in extreme fibers of I-type members subjected to loading perpendicular to the web. fa_b = 0.55*fy Args: fy (float): yield stress [psi] Return: fa_bt (tuple(float, str)): allowable bending tensile stress [psi] """ ref_text = "AREMA 2018 Section 1.4.1 Table 15-1-11 Row 6 \n\n" user_input = (f'Fy = {fy:.2f} \n\n') fa_b = 0.55*fy text = (f'fa_b = 0.55*fy \n' + f'fa_b = 0.55*{fy:.1f} \n' + f'fa_b = {fa_b:.1f}') text = ref_text + user_input + text return fa_b, text def eq141d7(l, ryc, afc, d, fy, e): """Compression in extreme fibers of flexural members allowable stress AREMA 2018 Table 15-1-11 Row 7 Equations Compression in extreme fibers of flexural members symmertical about the principal axis in the plane of the web (other than box-type flexural members) that are rolled beams or welded built-up members with solid rectangular flanges, the larger of the values computer by the following fa_bc1 = (0.55*fy - 0.55*math.pow(fy,2)/ (6.3*math.pow(math.pi,2)*e)*math.pow(l/ryc,2) fa_bc2 = 0.131*math.pi*e/(l*d*math.sqrt(1+mu)/afc) fa_bc3 = 0.55*fy fa_bc = min(max(fa1, fa2), fa3) Args: l (float): distance bwteen points of lateral support for the compression flange, unbraced length [inches] ryc (float): minimum radius of gyration of the compression flange and that portion of the web area on the compression side of the axis of bending, about an axis in the plane of the web [inches] afc (float): area of the smaller flange excluding any portion of the web [inches^2] d (float): overall dpeth of the member [inches] fy (float): yield stress [psi] e (float): modulus of elasticity [psi] Returns: fa_bc (float): allowable compression stress in extreme fibers of flexural members [psi] Notes: 1. Units are in lbs and inches. 2. Poisson's ratio, mu, is taken as 0.3. """ ref_text = "AREMA 2018 Section 1.4.1 Table 15-1-11 Row 7 \n\n" user_input = (f'l = {l:.2f}, ryc = {ryc:.2f}, afc = {afc:.2f}, ' + f'd = {d:.2f}, Fy = {fy:.2f}, E = {e:.2f} \n\n') mu = 0.3 fa_bc1 = (0.55*fy - 0.55*math.pow(fy,2)/ (6.3*math.pow(math.pi,2)*e)*math.pow(l/ryc,2)) fa_bc2 = 0.131*math.pi*e/(l*d*math.sqrt(1+mu)/afc) fa_bc3 = 0.55*fy fa_bc = min(max(fa_bc1, fa_bc2), fa_bc3) text1 = (f'fa_bc1 = 0.55*fy - 0.55*math.pow(fy,2)/' + f'(6.3*math.pow(math.pi,2)*e)*math.pow(l/ryc,2) \n' + f'fa_bc1 = 0.55*{fy:.1f} - 0.55*math.pow({fy:.1f},2)/' + f'(6.3*math.pow(math.pi,2)*{e:.1f})' + f'*math.pow({l:.2f}/{ryc:.2f},2) \n' + f"""fa_bc1 = {fa_bc1:.1f} \n\n""") text2 = (f'fa_bc2 = 0.131*math.pi*e/(l*d*math.sqrt(1+mu)/afc) \n' + f'fa_bc2 = 0.131*math.pi*{e:.1f}/' + f'({l:.2f}*{d:.2f}*math.sqrt(1+{mu:.2f})/{afc:.2f}) \n' + f'fa_bc2 = {fa_bc2:.1f} \n\n') text3 = (f'fa_bc3 = 0.55*fy \n' + f'fa_bc3 = 0.55*{fy:.2f} \n' + f'fa_bc3 = {fa_bc3:.1f} \n\n') text4 = (f'fa_bc = min(max(fa_bc1, fa_bc2), fa_bc3) \n' + f'fa_bc = min(max({fa_bc1:.2f}, {fa_bc2:.2f}), {fa_bc3:.2f}) \n' + f'fa_bc = min({max(fa_bc1, fa_bc2):.2f}, {fa_bc3:.2f}) \n' + f'fa_bc = {min(max(fa_bc1, fa_bc2), fa_bc3):.1f} \n') text = ref_text + user_input + text1 + text2 + text3 + text4 return fa_bc, text def eq141d10(l, sx, a, sum_st, iy, fy, e): """Compression in extreme fibers of box type flexural members AREMA 2018 Section 1.4.1 Table 15-1-11 Row 10 Compression in the extreme fibers of box type welded or bolted flexural members symmetrical about the principal axis midway between the webs (l/r)e = math.sqrt(1.105*math.pi/sxx*sqrt(sum(s/t))/ a*math.sqrt(i_yy/(1+mu))) fa_bc = 0.55*fy - 0.55*math.pow(fy,2)/(6.3*math.pow(math.pi,2)*e)* math.pow((l/r)e,2) Args: l (float): distance between points of lateral support for the compression flange, unbraced length [inches] sx (float): section modulus of the box type member about its major axis [inches^3] a (float): total area enclosed within the center lines of the box type member webs and flanges [inches^2] sum_st (float): sum of the ratio width-to-thickness of each flange and ratio of the depth to thickness of each web (neglect any portion of the flange which projects beyond the box section) iy (float): second moment of area of the box type member about its minor axis, [inches^4] Returns: fa_bc (float): allowable compression stress in extreme fibers of box type flexure members Notes: 1. Units in lbs and inches. 2. Poisson's ratio, mu, is taken as 0.3. """ ref_text = "AREMA 2018 Section 1.4.1 Table 15-1-11 Row 10 \n\n" user_input = (f'l = {l:.2f}, Sx = {sx:.2f}, a = {a:.2f}, ' + f'sum_st = {sum_st:.2f}, Iy = {iy:.2f}, Fy = {fy:.1f}, ' + f'E = {e:.1f} \n\n') mu = 0.3 lre = math.sqrt((1.105*math.pi/sx*math.sqrt(sum_st))/ (a*math.sqrt(iy/(1+mu)))) fa_bc = (0.55*fy-0.55*math.pow(fy,2)/ (6.3*math.pow(math.pi,2)*e)*math.pow(lre,2)) text1 = (f'(l/r)e = math.sqrt((1.105*math.pi/sx*math.sqrt(sum_st))/' + f'(a*math.sqrt(iy/(1+mu)))) \n' + f'(l/r)e = math.sqrt((1.105*math.pi/{sx:.2f}*math.sqrt({sum_st:.2f}))/' + f'({a:.2f}*math.sqrt({iy:.2f}/(1+{mu:.2f})))) \n' + f'(l/r)e = {lre:.2f} \n') text2 = (f'fa_bc = (0.55*fy-0.55*math.pow(fy,2)/' + f'(6.3*math.pow(math.pi,2)*e)*math.pow(lre,2)) \n' + f'fa_bc = (0.55*{fy:.1f}-0.55*math.pow({fy:.1f},2)/' + f'(6.3*math.pow(math.pi,2)*{e:.1f})*math.pow({lre:.2f},2)) \n' + f'fa_bc = {fa_bc:.1f}') text = ref_text + user_input + text1 + text2 return fa_bc, text def eq141d13(fy): """Shear in webs of rolled beams and plate girders, gross section AREMA 2018 Table 15-1-11 Row 13 Equations fa = 0.35*fy Args: fy (float): yield stress Returns: fa (tuple(float, str)): Allowable shear stress in webs """ ref_text = "AREMA 2018 Section 1.4.1 Table 15-1-11 Row 13 \n\n" user_input = f'fy = {fy:.1f} psi \n\n' fa_v = 0.35*fy text = (f'fa_v = 0.35*fy \n' + f'fa_v = 0.35*{fy:.2f} \n' + f'fa_v = {fa_v:.2f} \n') text = ref_text + user_input + text return fa_v, text def eq141d17(l, d, fu): """Bearing on F3125 Grade A325 and Grade A490 bolts AREMA 2018 Section 1.4.1 Table 15-1-11 Row 17 Allowable stress of bearing material on F3125 Gr A325 and Gr A490 bolts. fa_bolt_brg = min(l*fu/(2*d), 1.2*fu) Args: l (float): distance measured in the line of the force form the center line of a bolt to the nearest edge of an adjacent bolt or to the end of the connected part toward which the force is directed [inches] d (float): diameter of bolt [inches] fu (float): lowest specified minimum tensile strength of the connected part [psi] Returns: fa_bolt_brg (tuple(float, str)): allowable bearing stress """ ref_text = 'AREMA 2018 Section 1.4.1 Table 15-1-11 Row 17 \n\n' user_input = f'l = {l:.2f}, d = {d:.2f}, fu = {fu:.1f} \n\n' fa_brg_bolt = min(l*fu/(2*d), 1.2*fu) text = (f'fa_brg_bolt = min(l*fu/(2*d), 1.2*fu) \n' + f'fa_brg_bolt = min({l:.2f}*{fu:.1f}/(2*{d:.2f}),' f'1.2*{fu:.1f}) \n'+ f'fa_brg_bolt = min({l*fu/(2*d):.1f}, {1.2*fu:.1f}) \n' + f'fa_brg_bolt = {fa_brg_bolt:.1f}') text = ref_text + user_input + text return fa_brg_bolt, text def tb15111a(hole_type, surface_type, bolt_grade): """Allowable stress for slip-critical connections Allowable stress for slip-critical connections (Slip Load per Unit Area of Bolt) Args: hole_type (int): 1 - standard and short slotted perpendicular to the direction of load 2 - Oversize and short slotted parallel to the direction of load 3 - Long slotted any direction surface_type (str): A - Class A (slip coefficient 0.30) Uncoated clean mill scale and blast-cleaned surfaces with Class A coatings B - Class B (slip coefficient 0.50) Uncoated blast-cleaned surfaces with Class B coatings and unsealed thermal-sprayed surfaces C - Class C (slip coefficient 0.30) Hot-dip galvanized D - Class D (slip coefficient 0.45) Blast-cleaned surfaces with Class D coatings bolt_grade (int): 1 - F3125 Grade A325 2 - F3125 Grade A490 Returns: fa_bolt (tuple(float, str)): allowable stress for slip critical connection [psi] """ fa_code = str(bolt_grade) + surface_type + str(hole_type) fa_bolt = {'1A1':12900,'1B1':21500,'1C1':12900,'1D1':19300, '2A1':11000,'2B1':18300,'2C1':11000,'2D1':16500, '3A1':9000,'3B1':15000,'3C1':9000,'3D1':13500, '1A2':16200,'1B2':26900,'1C2':16200,'1D2':24200, '2A2':13800,'2B2':23000,'2C2':13800,'2D2':20700, '3A2':11300,'3B2':18900,'3C2':11300,'3D2':17000,} ht_text = {1:('1 - standard and short slotted perpendicular to the '+ 'direction of load'), 2:('2 - Oversize and short slotted parallel to the direction ' + 'of load'), 3:'3 - Long slotted any direction'} def eq162a(element_type, case, t, fy, e): """Outstanding element in compression The width of outstanding elements of members in compression shall not exceed the following, where t, inches, is the thickness of the element: """ pass

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spiketoolkit/validation/quality_metrics.py

teristam/spiketoolk

55

76413

<gh_stars>10-100 from .quality_metric_classes.metric_data import MetricData from .quality_metric_classes.amplitude_cutoff import AmplitudeCutoff from .quality_metric_classes.silhouette_score import SilhouetteScore from .quality_metric_classes.num_spikes import NumSpikes from .quality_metric_classes.firing_rate import FiringRate from .quality_metric_classes.d_prime import DPrime from .quality_metric_classes.l_ratio import LRatio from .quality_metric_classes.presence_ratio import PresenceRatio from .quality_metric_classes.isi_violation import ISIViolation from .quality_metric_classes.snr import SNR from .quality_metric_classes.isolation_distance import IsolationDistance from .quality_metric_classes.noise_overlap import NoiseOverlap from .quality_metric_classes.nearest_neighbor import NearestNeighbor from .quality_metric_classes.drift_metric import DriftMetric from .quality_metric_classes.parameter_dictionaries import update_all_param_dicts_with_kwargs from collections import OrderedDict from copy import deepcopy import pandas all_metrics_list = ["num_spikes", "firing_rate", "presence_ratio", "isi_violation", "amplitude_cutoff", "snr", "max_drift", "cumulative_drift", "silhouette_score", "isolation_distance", "l_ratio", "d_prime", "noise_overlap", "nn_hit_rate", "nn_miss_rate"] def get_quality_metrics_list(): return all_metrics_list def compute_num_spikes( sorting, sampling_frequency=None, unit_ids=None, **kwargs ): """ Computes and returns the num spikes for the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated sampling_frequency: float The sampling frequency of the result. If None, will check to see if sampling frequency is in sorting extractor unit_ids: list List of unit ids to compute metric for. If not specified, all units are used **kwargs: keyword arguments Keyword arguments among the following: save_property_or_features: bool If True, the metric is saved as sorting property verbose: bool If True, will be verbose in metric computation Returns ---------- num_spikes: np.ndarray The number of spikes of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=sampling_frequency, recording=None, apply_filter=False, freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=None, verbose=params_dict['verbose'], raise_if_empty=False) ns = NumSpikes(metric_data=md) num_spikes = ns.compute_metric(**kwargs) return num_spikes def compute_firing_rates( sorting, duration_in_frames, sampling_frequency=None, unit_ids=None, **kwargs ): """ Computes and returns the firing rates for the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. duration_in_frames: int Length of recording (in frames). sampling_frequency: float The sampling frequency of the result. If None, will check to see if sampling frequency is in sorting extractor unit_ids: list List of unit ids to compute metric for. If not specified, all units are used **kwargs: keyword arguments Keyword arguments among the following: save_property_or_features: bool If True, the metric is saved as sorting property verbose: bool If True, will be verbose in metric computation Returns ---------- firing_rates: np.ndarray The firing rates of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=sampling_frequency, recording=None, apply_filter=False, freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=duration_in_frames, verbose=params_dict['verbose']) fr = FiringRate(metric_data=md) firing_rates = fr.compute_metric(**kwargs) return firing_rates def compute_presence_ratios( sorting, duration_in_frames, sampling_frequency=None, unit_ids=None, **kwargs ): """ Computes and returns the presence ratios for the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. duration_in_frames: int Length of recording (in frames). sampling_frequency: float The sampling frequency of the result. If None, will check to see if sampling frequency is in sorting extractor unit_ids: list List of unit ids to compute metric for. If not specified, all units are used **kwargs: keyword arguments Keyword arguments among the following: save_property_or_features: bool If True, the metric is saved as sorting property verbose: bool If True, will be verbose in metric computation Returns ---------- presence_ratios: np.ndarray The presence ratios of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=sampling_frequency, recording=None, apply_filter=False, freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=duration_in_frames, verbose=params_dict['verbose']) pr = PresenceRatio(metric_data=md) presence_ratios = pr.compute_metric(**kwargs) return presence_ratios def compute_isi_violations( sorting, duration_in_frames, isi_threshold=ISIViolation.params['isi_threshold'], min_isi=ISIViolation.params['min_isi'], sampling_frequency=None, unit_ids=None, **kwargs ): """ Computes and returns the isi violations for the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. duration_in_frames: int Length of recording (in frames). isi_threshold: float The isi threshold for calculating isi violations min_isi: float The minimum expected isi value sampling_frequency: float The sampling frequency of the result. If None, will check to see if sampling frequency is in sorting extractor unit_ids: list List of unit ids to compute metric for. If not specified, all units are used **kwargs: keyword arguments Keyword arguments among the following: save_property_or_features: bool If True, the metric is saved as sorting property verbose: bool If True, will be verbose in metric computation Returns ---------- isi_violations: np.ndarray The isi violations of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=sampling_frequency, recording=None, apply_filter=False, freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=duration_in_frames, verbose=params_dict['verbose']) iv = ISIViolation(metric_data=md) isi_violations = iv.compute_metric(isi_threshold, min_isi, **kwargs) return isi_violations def compute_amplitude_cutoffs( sorting, recording, unit_ids=None, **kwargs ): """ Computes and returns the amplitude cutoffs for the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. recording: RecordingExtractor The given recording extractor from which to extract amplitudes unit_ids: list List of unit ids to compute metric for. If not specified, all units are used **kwargs: keyword arguments Keyword arguments among the following: apply_filter: bool If True, recording is bandpass-filtered. freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) save_property_or_features: bool If true, it will save amplitudes in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes. peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: float Frames after peak to compute amplitude save_property_or_features: bool If True, the metric is saved as sorting property seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- amplitude_cutoffs: np.ndarray The amplitude cutoffs of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=None, verbose=params_dict['verbose']) md.compute_amplitudes(**kwargs) ac = AmplitudeCutoff(metric_data=md) amplitude_cutoffs = ac.compute_metric(**kwargs) return amplitude_cutoffs def compute_snrs( sorting, recording, snr_mode=SNR.params['snr_mode'], snr_noise_duration=SNR.params['snr_noise_duration'], max_spikes_per_unit_for_snr=SNR.params['max_spikes_per_unit_for_snr'], template_mode=SNR.params['template_mode'], max_channel_peak=SNR.params['max_channel_peak'], unit_ids=None, **kwargs ): """ Computes and returns the snrs in the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. recording: RecordingExtractor The given recording extractor from which to extract amplitudes snr_mode: str Mode to compute noise SNR ('mad' | 'std' - default 'mad') snr_noise_duration: float Number of seconds to compute noise level from (default 10.0) max_spikes_per_unit_for_snr: int Maximum number of spikes to compute templates from (default 1000) template_mode: str Use 'mean' or 'median' to compute templates max_channel_peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) unit_ids: list List of unit ids to compute metric for. If not specified, all units are used **kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- snrs: np.ndarray The snrs of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], duration_in_frames=None, freq_max=params_dict["freq_max"], unit_ids=unit_ids, verbose=params_dict['verbose']) snr = SNR(metric_data=md) snrs = snr.compute_metric(snr_mode, snr_noise_duration, max_spikes_per_unit_for_snr, template_mode, max_channel_peak, **kwargs) return snrs def compute_noise_overlaps( sorting, recording, num_channels_to_compare=NoiseOverlap.params['num_channels_to_compare'], num_features=NoiseOverlap.params['num_features'], num_knn=NoiseOverlap.params['num_knn'], max_spikes_per_unit_for_noise_overlap=NoiseOverlap.params['max_spikes_per_unit_for_noise_overlap'], unit_ids=None, **kwargs ): """ Computes and returns the noise overlaps in the sorted dataset. Noise overlap estimates the fraction of ‘‘noise events’’ in a cluster, i.e., above-threshold events not associated with true firings of this or any of the other clustered units. A large noise overlap implies a high false-positive rate. Implementation from ml_ms4alg. For more information see https://doi.org/10.1016/j.neuron.2017.08.030 Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. recording: RecordingExtractor The given recording extractor from which to extract amplitudes num_features: int Number of features to use for PCA num_knn: int Number of nearest neighbors max_spikes_per_unit_for_noise_overlap: int Number of waveforms to use for noise overlaps estimation unit_ids: list List of unit ids to compute metric for. If not specified, all units are used **kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- noise_overlaps: np.ndarray The noise_overlaps of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], duration_in_frames=None, freq_max=params_dict["freq_max"], unit_ids=unit_ids, verbose=params_dict['verbose']) noise_overlap = NoiseOverlap(metric_data=md) noise_overlaps = noise_overlap.compute_metric(num_channels_to_compare, max_spikes_per_unit_for_noise_overlap, num_features, num_knn, **kwargs) return noise_overlaps def compute_silhouette_scores( sorting, recording, max_spikes_for_silhouette=SilhouetteScore.params['max_spikes_for_silhouette'], unit_ids=None, **kwargs ): """ Computes and returns the silhouette scores in the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated recording: RecordingExtractor The given recording extractor from which to extract amplitudes max_spikes_for_silhouette: int Max spikes to be used for silhouette metric unit_ids: list List of unit ids to compute metric for. If not specified, all units are used **kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- silhouette_scores: np.ndarray The sihouette scores of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], duration_in_frames=None, freq_max=params_dict["freq_max"], unit_ids=unit_ids, verbose=params_dict['verbose']) md.compute_pca_scores(**kwargs) silhouette_score = SilhouetteScore(metric_data=md) silhouette_scores = silhouette_score.compute_metric(max_spikes_for_silhouette, **kwargs) return silhouette_scores def compute_d_primes( sorting, recording, num_channels_to_compare=DPrime.params['num_channels_to_compare'], max_spikes_per_cluster=DPrime.params['max_spikes_per_cluster'], unit_ids=None, **kwargs ): """ Computes and returns the d primes in the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated recording: RecordingExtractor The given recording extractor from which to extract amplitudes num_channels_to_compare: int The number of channels to be used for the PC extraction and comparison max_spikes_per_cluster: int Max spikes to be used from each unit unit_ids: list List of unit ids to compute metric for. If not specified, all units are used **kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- d_primes: np.ndarray The d primes of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=None, verbose=params_dict['verbose']) md.compute_pca_scores(**kwargs) d_prime = DPrime(metric_data=md) d_primes = d_prime.compute_metric(num_channels_to_compare, max_spikes_per_cluster, **kwargs) return d_primes def compute_l_ratios( sorting, recording, num_channels_to_compare=LRatio.params['num_channels_to_compare'], max_spikes_per_cluster=LRatio.params['max_spikes_per_cluster'], unit_ids=None, **kwargs ): """ Computes and returns the l ratios in the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated recording: RecordingExtractor The given recording extractor from which to extract amplitudes num_channels_to_compare: int The number of channels to be used for the PC extraction and comparison max_spikes_per_cluster: int Max spikes to be used from each unit unit_ids: list List of unit ids to compute metric for. If not specified, all units are used **kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- l_ratios: np.ndarray The l ratios of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=None, verbose=params_dict['verbose']) md.compute_pca_scores(**kwargs) l_ratio = LRatio(metric_data=md) l_ratios = l_ratio.compute_metric(num_channels_to_compare, max_spikes_per_cluster, **kwargs) return l_ratios def compute_isolation_distances( sorting, recording, num_channels_to_compare=IsolationDistance.params['num_channels_to_compare'], max_spikes_per_cluster=IsolationDistance.params['max_spikes_per_cluster'], unit_ids=None, **kwargs ): """ Computes and returns the isolation distances in the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. recording: RecordingExtractor The given recording extractor from which to extract amplitudes num_channels_to_compare: int The number of channels to be used for the PC extraction and comparison max_spikes_per_cluster: int Max spikes to be used from each unit unit_ids: list List of unit ids to compute metric for. If not specified, all units are used **kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- isolation_distances: np.ndarray The isolation distances of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=None, verbose=params_dict['verbose']) md.compute_pca_scores(**kwargs) isolation_distance = IsolationDistance(metric_data=md) isolation_distances = isolation_distance.compute_metric(num_channels_to_compare, max_spikes_per_cluster, **kwargs) return isolation_distances def compute_nn_metrics( sorting, recording, num_channels_to_compare=NearestNeighbor.params['num_channels_to_compare'], max_spikes_per_cluster=NearestNeighbor.params['max_spikes_per_cluster'], max_spikes_for_nn=NearestNeighbor.params['max_spikes_for_nn'], n_neighbors=NearestNeighbor.params['n_neighbors'], unit_ids=None, **kwargs ): """ Computes and returns the nearest neighbor metrics in the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. recording: RecordingExtractor The given recording extractor from which to extract amplitudes num_channels_to_compare: int The number of channels to be used for the PC extraction and comparison max_spikes_per_cluster: int Max spikes to be used from each unit max_spikes_for_nn: int Max spikes to be used for nearest-neighbors calculation n_neighbors: int Number of neighbors to compare unit_ids: list List of unit ids to compute metric for. If not specified, all units are used **kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- nn_metrics: np.ndarray The nearest neighbor metrics of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=None, verbose=params_dict['verbose']) md.compute_pca_scores(**kwargs) nn = NearestNeighbor(metric_data=md) nn_metrics = nn.compute_metric(num_channels_to_compare, max_spikes_per_cluster, max_spikes_for_nn, n_neighbors, **kwargs) return nn_metrics def compute_drift_metrics( sorting, recording, drift_metrics_interval_s=DriftMetric.params['drift_metrics_interval_s'], drift_metrics_min_spikes_per_interval=DriftMetric.params['drift_metrics_min_spikes_per_interval'], unit_ids=None, **kwargs ): """ Computes and returns the drift metrics in the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. recording: RecordingExtractor The given recording extractor from which to extract amplitudes drift_metrics_interval_s: float Time period for evaluating drift. drift_metrics_min_spikes_per_interval: int Minimum number of spikes for evaluating drift metrics per interval. unit_ids: list List of unit ids to compute metric for. If not specified, all units are used **kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- dm_metrics: np.ndarray The drift metrics of the sorted units. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=recording.get_sampling_frequency(), recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=None, verbose=params_dict['verbose']) md.compute_pca_scores(**kwargs) dm = DriftMetric(metric_data=md) dm_metrics = dm.compute_metric(drift_metrics_interval_s, drift_metrics_min_spikes_per_interval, **kwargs) return dm_metrics def compute_quality_metrics( sorting, recording=None, duration_in_frames=None, sampling_frequency=None, metric_names=None, unit_ids=None, as_dataframe=False, isi_threshold=ISIViolation.params['isi_threshold'], min_isi=ISIViolation.params['min_isi'], snr_mode=SNR.params['snr_mode'], snr_noise_duration=SNR.params['snr_noise_duration'], max_spikes_per_unit_for_snr=SNR.params['max_spikes_per_unit_for_snr'], template_mode=SNR.params['template_mode'], max_channel_peak=SNR.params['max_channel_peak'], max_spikes_per_unit_for_noise_overlap=NoiseOverlap.params['max_spikes_per_unit_for_noise_overlap'], noise_overlap_num_features=NoiseOverlap.params['num_features'], noise_overlap_num_knn=NoiseOverlap.params['num_knn'], drift_metrics_interval_s=DriftMetric.params['drift_metrics_interval_s'], drift_metrics_min_spikes_per_interval=DriftMetric.params['drift_metrics_min_spikes_per_interval'], max_spikes_for_silhouette=SilhouetteScore.params['max_spikes_for_silhouette'], num_channels_to_compare=13, max_spikes_per_cluster=500, max_spikes_for_nn=NearestNeighbor.params['max_spikes_for_nn'], n_neighbors=NearestNeighbor.params['n_neighbors'], **kwargs ): """ Computes and returns all specified metrics for the sorted dataset. Parameters ---------- sorting: SortingExtractor The sorting result to be evaluated. recording: RecordingExtractor The given recording extractor from which to extract amplitudes duration_in_frames: int Length of recording (in frames). sampling_frequency: float The sampling frequency of the result. If None, will check to see if sampling frequency is in sorting extractor metric_names: list List of metric names to be computed unit_ids: list List of unit ids to compute metric for. If not specified, all units are used as_dataframe: bool If True, will return dataframe of metrics. If False, will return dictionary. isi_threshold: float The isi threshold for calculating isi violations min_isi: float The minimum expected isi value snr_mode: str Mode to compute noise SNR ('mad' | 'std' - default 'mad') snr_noise_duration: float Number of seconds to compute noise level from (default 10.0) max_spikes_per_unit_for_snr: int Maximum number of spikes to compute templates for SNR from (default 1000) template_mode: str Use 'mean' or 'median' to compute templates max_channel_peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) max_spikes_per_unit_for_noise_overlap: int Maximum number of spikes to compute templates for noise overlap from (default 1000) noise_overlap_num_features: int Number of features to use for PCA for noise overlap noise_overlap_num_knn: int Number of nearest neighbors for noise overlap drift_metrics_interval_s: float Time period for evaluating drift. drift_metrics_min_spikes_per_interval: int Minimum number of spikes for evaluating drift metrics per interval max_spikes_for_silhouette: int Max spikes to be used for silhouette metric num_channels_to_compare: int The number of channels to be used for the PC extraction and comparison max_spikes_per_cluster: int Max spikes to be used from each unit max_spikes_for_nn: int Max spikes to be used for nearest-neighbors calculation n_neighbors: int Number of neighbors to compare **kwargs: keyword arguments Keyword arguments among the following: method: str If 'absolute' (default), amplitudes are absolute amplitudes in uV are returned. If 'relative', amplitudes are returned as ratios between waveform amplitudes and template amplitudes peak: str If maximum channel has to be found among negative peaks ('neg'), positive ('pos') or both ('both' - default) frames_before: int Frames before peak to compute amplitude frames_after: int Frames after peak to compute amplitude apply_filter: bool If True, recording is bandpass-filtered freq_min: float High-pass frequency for optional filter (default 300 Hz) freq_max: float Low-pass frequency for optional filter (default 6000 Hz) grouping_property: str Property to group channels. E.g. if the recording extractor has the 'group' property and 'grouping_property' is 'group', then waveforms are computed group-wise. ms_before: float Time period in ms to cut waveforms before the spike events ms_after: float Time period in ms to cut waveforms after the spike events dtype: dtype The numpy dtype of the waveforms compute_property_from_recording: bool If True and 'grouping_property' is given, the property of each unit is assigned as the corresponding property of the recording extractor channel on which the average waveform is the largest max_channels_per_waveforms: int or None Maximum channels per waveforms to return. If None, all channels are returned n_jobs: int Number of parallel jobs (default 1) memmap: bool If True, waveforms are saved as memmap object (recommended for long recordings with many channels) save_property_or_features: bool If true, it will save features in the sorting extractor recompute_info: bool If True, waveforms are recomputed max_spikes_per_unit: int The maximum number of spikes to extract per unit seed: int Random seed for reproducibility verbose: bool If True, will be verbose in metric computation Returns ---------- metrics: dictionary OR pandas.dataframe Dictionary or pandas.dataframe of metrics. """ params_dict = update_all_param_dicts_with_kwargs(kwargs) metrics_dict = OrderedDict() if metric_names is None: metric_names = all_metrics_list else: bad_metrics = [] for m in metric_names: if m not in all_metrics_list: bad_metrics.append(m) if len(bad_metrics) > 0: raise ValueError(f"Improper feature names: {str(bad_metrics)}. The following features names can be " f"calculated: {str(all_metrics_list)}") if unit_ids is None: unit_ids = sorting.get_unit_ids() md = MetricData(sorting=sorting, sampling_frequency=sampling_frequency, recording=recording, apply_filter=params_dict["apply_filter"], freq_min=params_dict["freq_min"], freq_max=params_dict["freq_max"], unit_ids=unit_ids, duration_in_frames=duration_in_frames, verbose=params_dict['verbose']) if "firing_rate" in metric_names or "presence_ratio" in metric_names or "isi_violation" in metric_names: if recording is None and duration_in_frames is None: raise ValueError( "duration_in_frames and recording cannot both be None when computing firing_rate, " "presence_ratio, and isi_violation") if "max_drift" in metric_names or "cumulative_drift" in metric_names or "silhouette_score" in metric_names \ or "isolation_distance" in metric_names or "l_ratio" in metric_names or "d_prime" in metric_names \ or "nn_hit_rate" in metric_names or "nn_miss_rate" in metric_names: if recording is None: raise ValueError("The recording cannot be None when computing max_drift, cumulative_drift, " "silhouette_score isolation_distance, l_ratio, d_prime, nn_hit_rate, or amplitude_cutoff.") else: md.compute_pca_scores(**kwargs) if "amplitude_cutoff" in metric_names: if recording is None: raise ValueError("The recording cannot be None when computing amplitude cutoffs.") else: md.compute_amplitudes(**kwargs) if "snr" in metric_names: if recording is None: raise ValueError("The recording cannot be None when computing snr.") if "num_spikes" in metric_names: ns = NumSpikes(metric_data=md) num_spikes = ns.compute_metric(**kwargs) metrics_dict['num_spikes'] = num_spikes if "firing_rate" in metric_names: fr = FiringRate(metric_data=md) firing_rates = fr.compute_metric(**kwargs) metrics_dict['firing_rate'] = firing_rates if "presence_ratio" in metric_names: pr = PresenceRatio(metric_data=md) presence_ratios = pr.compute_metric(**kwargs) metrics_dict['presence_ratio'] = presence_ratios if "isi_violation" in metric_names: iv = ISIViolation(metric_data=md) isi_violations = iv.compute_metric(isi_threshold, min_isi, **kwargs) metrics_dict['isi_violation'] = isi_violations if "amplitude_cutoff" in metric_names: ac = AmplitudeCutoff(metric_data=md) amplitude_cutoffs = ac.compute_metric(**kwargs) metrics_dict['amplitude_cutoff'] = amplitude_cutoffs if "snr" in metric_names: snr = SNR(metric_data=md) snrs = snr.compute_metric(snr_mode, snr_noise_duration, max_spikes_per_unit_for_snr, template_mode, max_channel_peak, **kwargs) metrics_dict['snr'] = snrs if "max_drift" in metric_names or "cumulative_drift" in metric_names: dm = DriftMetric(metric_data=md) max_drifts, cumulative_drifts = dm.compute_metric(drift_metrics_interval_s, drift_metrics_min_spikes_per_interval, **kwargs) if "max_drift" in metric_names: metrics_dict['max_drift'] = max_drifts if "cumulative_drift" in metric_names: metrics_dict['cumulative_drift'] = cumulative_drifts if "silhouette_score" in metric_names: silhouette_score = SilhouetteScore(metric_data=md) silhouette_scores = silhouette_score.compute_metric(max_spikes_for_silhouette, **kwargs) metrics_dict['silhouette_score'] = silhouette_scores if "isolation_distance" in metric_names: isolation_distance = IsolationDistance(metric_data=md) isolation_distances = isolation_distance.compute_metric(num_channels_to_compare, max_spikes_per_cluster, **kwargs) metrics_dict['isolation_distance'] = isolation_distances if "noise_overlap" in metric_names: noise_overlap = NoiseOverlap(metric_data=md) noise_overlaps = noise_overlap.compute_metric(num_channels_to_compare, max_spikes_per_unit_for_noise_overlap, noise_overlap_num_features, noise_overlap_num_knn, **kwargs) metrics_dict['noise_overlap'] = noise_overlaps if "l_ratio" in metric_names: l_ratio = LRatio(metric_data=md) l_ratios = l_ratio.compute_metric(num_channels_to_compare, max_spikes_per_cluster, **kwargs) metrics_dict['l_ratio'] = l_ratios if "d_prime" in metric_names: d_prime = DPrime(metric_data=md) d_primes = d_prime.compute_metric(num_channels_to_compare, max_spikes_per_cluster, **kwargs) metrics_dict['d_prime'] = d_primes if "nn_hit_rate" in metric_names or "nn_miss_rate" in metric_names: nn = NearestNeighbor(metric_data=md) nn_hit_rates, nn_miss_rates = nn.compute_metric(num_channels_to_compare, max_spikes_per_cluster, max_spikes_for_nn, n_neighbors, **kwargs) if "nn_hit_rate" in metric_names: metrics_dict['nn_hit_rate'] = nn_hit_rates if "nn_miss_rate" in metric_names: metrics_dict['nn_miss_rate'] = nn_miss_rates if as_dataframe: metrics = pandas.DataFrame.from_dict(metrics_dict) metrics = metrics.rename(index={original_idx: unit_ids[i] for i, original_idx in enumerate(range(len(metrics)))}) else: metrics = metrics_dict return metrics

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yardstick/tests/unit/benchmark/scenarios/availability/test_scenario_general.py

mythwm/yardstick

0

102238

############################################################################## # Copyright (c) 2016 <NAME> and others # <EMAIL> # All rights reserved. This program and the accompanying materials # are made available under the terms of the Apache License, Version 2.0 # which accompanies this distribution, and is available at # http://www.apache.org/licenses/LICENSE-2.0 ############################################################################## import mock import unittest from yardstick.benchmark.scenarios.availability import scenario_general class ScenarioGeneralTestCase(unittest.TestCase): def setUp(self): self.scenario_cfg = { 'type': "general_scenario", 'options': { 'attackers': [{ 'fault_type': "general-attacker", 'key': "kill-process"}], 'monitors': [{ 'monitor_type': "general-monitor", 'key': "service-status"}], 'steps': [ { 'actionKey': "kill-process", 'actionType': "attacker", 'index': 1}, { 'actionKey': "service-status", 'actionType': "monitor", 'index': 2}] } } self.instance = scenario_general.ScenarioGeneral(self.scenario_cfg, None) self._mock_director = mock.patch.object(scenario_general, 'Director') self.mock_director = self._mock_director.start() self.addCleanup(self._stop_mock) def _stop_mock(self): self._mock_director.stop() def test_scenario_general_all_successful(self): self.instance.setup() self.instance.run({}) self.instance.teardown() def test_scenario_general_exception(self): mock_obj = mock.Mock() mock_obj.createActionPlayer.side_effect = KeyError('Wrong') self.instance.director = mock_obj self.instance.director.data = {} self.instance.run({}) self.instance.teardown() def test_scenario_general_case_fail(self): mock_obj = mock.Mock() mock_obj.verify.return_value = False self.instance.director = mock_obj self.instance.director.data = {} self.instance.run({}) self.instance.pass_flag = True self.instance.teardown()

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find_symbols_sizes.py

Amjad50/Fyp

1

1607647

<filename>find_symbols_sizes.py # helper script to find the default sizes of all symbols in the default # font size (no power or sub) from os import chdir from string import digits, ascii_letters from tempfile import mkdtemp from PIL import Image from tqdm import tqdm from dataset_generator.generator import generate_single_from_template from segmenter.symbol_segmenter import segment_image_crops from segmenter.utils import box_size def get_symbol_size(expr): filename = "tmp_file" _ = generate_single_from_template(expr, ".", filename) img = Image.open(filename + ".png") crops = segment_image_crops(img) assert len(crops) == 1 return box_size(crops[0]) if __name__ == "__main__": characters = list(digits + ascii_letters + "=-+()[],.") characters.extend(["\\sum", "\\pi", "\\int"]) tmp_dir = mkdtemp() chdir(tmp_dir) symbol_sizes_dict = { ch: get_symbol_size(ch) for ch in tqdm(characters) } print(symbol_sizes_dict)

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booknlp/common/calc_coref_metrics.py

ishine/booknlp

539

20111

import subprocess, re, sys def get_coref_score(metric, path_to_scorer, gold=None, preds=None): output=subprocess.check_output(["perl", path_to_scorer, metric, preds, gold]).decode("utf-8") output=output.split("\n")[-3] matcher=re.search("Coreference: Recall: $.*?$ (.*?)% Precision: $.*?$ (.*?)% F1: (.*?)%", output) if matcher is not None: recall=float(matcher.group(1)) precision=float(matcher.group(2)) f1=float(matcher.group(3)) return recall, precision, f1 def get_conll(path_to_scorer, gold=None, preds=None): bcub_r, bcub_p, bcub_f=get_coref_score("bcub", path_to_scorer, gold, preds) muc_r, muc_p, muc_f=get_coref_score("muc", path_to_scorer, gold, preds) ceaf_r, ceaf_p, ceaf_f=get_coref_score("ceafe", path_to_scorer, gold, preds) print("bcub:\t%.1f" % bcub_f) print("muc:\t%.1f" % muc_f) print("ceaf:\t%.1f" % ceaf_f) avg=(bcub_f + muc_f + ceaf_f)/3. print("Average F1: %.1f" % (avg)) # Generate Latex table # print("%.1f&%.1f&%.1f&%.1f" % (bcub_f, muc_f, ceaf_f, avg)) return bcub_f, avg if __name__ == "__main__": goldFile=sys.argv[1] predFile=sys.argv[2] scorer=sys.argv[3] bcub_f, avg=get_conll(scorer, gold=goldFile, preds=predFile)

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basetestcase/base_FormTestCase.py

Spleeding1/django-basetestcase

1

1608927

from .base_UtilityTestCase import UtilityTestCase class FormTestCase(UtilityTestCase): def form_field_test(self, field, error_messages={}, help_text='', initial=None, label='', required=True, widget_attrs={}): form = self.form() try: form.fields[field] except KeyError: raise Exception(f'\n Form does not have field\n\n {field}') completed_attrs = [] for attr, expected in widget_attrs.items(): try: actual = form.fields[field].widget.attrs[attr] except KeyError: raise Exception( f'\n {attr} for form field {field} has not been set.' ) self.assertEquals(expected, actual, msg=f'\n {field}: {attr}') completed_attrs.append(attr) for attr, actual in form.fields[field].widget.attrs.items(): if attr not in completed_attrs: raise Exception(f'\n {field}:{attr} is set and should not be.') if required is True and 'required' not in error_messages: error_messages['required'] = 'This field is required.' elif len(error_messages) is not 0: for error, error_message in error_messages.items(): actual_error_message = form.fields[field].error_messages[error] self.assertEquals( error_message, actual_error_message, msg=f'\n {field}: error_message[{error}]' ) actual_help_text = form.fields[field].help_text self.assertEquals( help_text, actual_help_text, msg=f'\n {field}: help_text' ) actual_initial = form.fields[field].initial self.assertEquals( initial, actual_initial, msg=f'\n {field}: initial value' ) actual_label = form.fields[field].label self.assertEquals( label, actual_label, msg=f'\n{field}: label' ) actual_required = form.fields[field].required self.assertEquals( required, actual_required, msg=f'\n {field}: required' ) def form_required_field_error_test(self, data={}, error_messages={}): required_fields = [] Form = self.form() for field in Form.fields: if Form.fields[field].required is True: required_fields.append(field) for field, value in data.items(): if field in required_fields: _data = {**data} _data[field] = '' form = self.form(data={**_data}) self.assertFalse( form.is_valid(), msg=f'\n Form should not be valid.\n {field} should be blank.\n data={_data}' ) error = 'This field is required.' if field in error_messages: error = error_messages[field] self.assertEqual( form.errors[field], [error], msg=f'{field}' ) def formset_error_test(self, formset, form_index=None, field=None, message=''): formset.is_valid() if form_index is None and field is None: self.assertEqual(formset.non_form_errors(), [message]) if field is None: self.assertEqual(formset[form_index].non_field_errors(), [message]) else: self.assertEqual(formset[form_index].errors[field], [message]) def formset_test(self, baseformset=None, can_delete=False, extra=1, field_data={}, form=None, formset=None, initial=0, max_num=None, min_num=None, model=None, prefix='form', total=1, validate_max=False, validate_min=False): form_data, test_model_instances = self.formset_filler( field_data=field_data, initial=initial, model=model, prefix=prefix, total=total ) test_formset = formset(form_data, prefix=prefix) self.assertEqual(test_formset.extra, extra) if baseformset is not None: self.assertTrue(issubclass(formset, baseformset)) if form is not None: self.assertTrue(issubclass(test_formset.form, form)) if max_num is not None: self.assertEqual(test_formset.max_num, max_num) if min_num is not None: self.assertEqual(test_formset.min_num, min_num) self.assertEqual(test_formset.can_delete, can_delete) self.assertEqual(test_formset.prefix, prefix) self.assertEqual(test_formset.validate_max, validate_max) self.assertEqual(test_formset.validate_min, validate_min) if test_formset.is_valid(): test_formset.save() if model is not None: self.instances_saved_test(model, test_model_instances, total) else: self.fail( f'formset is not valid.\n{data}\n{test_formset.non_form_errors()}\n{test_formset.errors}' )

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Exercicios/desafio10_convertermoeda.py

lbarrosandre/Resolucao-Desafios-Python

0

115790

carteira = float(input('Quantos reais tenho na carteira: ')) x = carteira/3.27 print('Tenho R$ {} na minha carteira e consigo comprar $ {:.2f} dollares'.format(carteira, x))

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wavenetlike/examples/analyzer_example.py

redwrasse/wavenetlike

0

24133

<reponame>redwrasse/wavenetlike from wavenetlike.analyzers import DatasetAnalyzer from wavenetlike.datasetid import TorchAudioDataSetId def analyzer_example(): dataset = TorchAudioDataSetId("SPEECHCOMMANDS") data_analyzer = DatasetAnalyzer(dataset) data_analyzer.analyze_dataset() analysis_res = data_analyzer.get_analysis_result() print(analysis_res) if __name__ == "__main__": analyzer_example()

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mmpose/core/optimizer/builder.py

vsatyakumar/mmpose

1

5137

<reponame>vsatyakumar/mmpose from mmcv.runner import build_optimizer def build_optimizers(model, cfgs): """Build multiple optimizers from configs. If `cfgs` contains several dicts for optimizers, then a dict for each constructed optimizers will be returned. If `cfgs` only contains one optimizer config, the constructed optimizer itself will be returned. For example, 1) Multiple optimizer configs: .. code-block:: python optimizer_cfg = dict( model1=dict(type='SGD', lr=lr), model2=dict(type='SGD', lr=lr)) The return dict is ``dict('model1': torch.optim.Optimizer, 'model2': torch.optim.Optimizer)`` 2) Single optimizer config: .. code-block:: python optimizer_cfg = dict(type='SGD', lr=lr) The return is ``torch.optim.Optimizer``. Args: model (:obj:`nn.Module`): The model with parameters to be optimized. cfgs (dict): The config dict of the optimizer. Returns: dict[:obj:`torch.optim.Optimizer`] | :obj:`torch.optim.Optimizer`: The initialized optimizers. """ optimizers = {} if hasattr(model, 'module'): model = model.module # determine whether 'cfgs' has several dicts for optimizers if all(isinstance(v, dict) for v in cfgs.values()): for key, cfg in cfgs.items(): cfg_ = cfg.copy() module = getattr(model, key) optimizers[key] = build_optimizer(module, cfg_) return optimizers else: return build_optimizer(model, cfgs)

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__Courses__/Python - Introduction to Python Programming - Udacity/C3. Control Flow/{Q}[Zip] Transpose with Zip.py

JUD210/Study-Note

0

59038

<gh_stars>0 """ Use zip to transpose data from a 4-by-3 matrix to a 3-by-4 matrix. There's actually a cool trick for this! Feel free to look at the solutions if you can't figure it out. """ data = ((0, 1, 2), (3, 4, 5), (6, 7, 8), (9, 10, 11)) # 0 1 2 # 3 4 5 # 6 7 8 # 9 10 11 # # Transpose : [data]^T # # 0 3 6 9 # 1 4 7 10 # 2 5 8 11 data_transpose = tuple(zip(*data)) print(data) print(data_transpose)

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python/dbSchemaReplicationLineage.py

sagopal2019/REST-API-Samples

0

151561

<reponame>sagopal2019/REST-API-Samples ''' Created on Jun 26, 2018 @author: dwrigley ************************************************************************************************** DB schema replication custom lineage generator process:- scenario: the tables in 2 different schemas (perhaps in different databases/resources) are replicated in EDC - we have no way to automatically know that there is lineage between the schema contents (tables & columns) this utility will generate the custom lineage import to create the links given two schemas - leftSchema and rightSchema find the 2 schemas objects in the catalog (GET /2/catalog/data/objects) for each schema execute /2/catalog/data/relationships (2 levels schema->table->column) for each table & column - store the id & name (names converted to lower-case for case-insensitive match) for the stored objects (tables/columns) left side... find the same table/column in the right side if found - write a custom lineage link to csv Note: the custom lineage format used is:- Association,From Connection,To Connection,From Object,To Object where: From Connection and To Connection will be empty Assocition will be either core.DirectionalDataFlow or core.DataSetDataFlow the From and To Object will be the full object id when importing - there is no need for auto connection assignment, since the full id's are provided this happens automatically this is possible using v10.2.0 with a patch, and works native in v10.2.1+ ''' import platform import json import requests from requests.auth import HTTPBasicAuth import csv import edcutils import time #*********************************************************************** # change these settings #*********************************************************************** leftResource = 'ot_oracle' leftSchema = 'OT' leftSchemaType = 'com.infa.ldm.relational.Schema' rightSchema = 'landing' rightResource = 'landing_hive' rightSchemaType = 'com.infa.ldm.relational.Schema' # rightTablePrefix - for cases where the replicated tables have a prefix added (e.g. employee on left ot_employee on right) rightTablePrefix = '' #Note: for SAP Hana DB - schema type is different: com.infa.ldm.extended.saphanadatabase.Schema catalogServer='http://napslxapp01:9085' uid='Administrator' pwd='<PASSWORD>' ''' # if using base64 encoded credentials - you don't need uid/pwd # you can just use authCredentials- with the correct value # comment lines with uid/pwd and uncomment the same line using only header (references to authCredentials) ''' #authCredentials="<KEY>" # format the csv file name for custom lineage csvPrefix="schemaLineage" csvFolder="out" #*********************************************************************** # end of settings that should be changed #*********************************************************************** # set the csv fileName csvFileName = csvFolder + "/" + csvPrefix + "_" + leftSchema.lower() + "_" + rightSchema.lower() + ".csv" # function - process a single schema - get columns into memory (dict) def getSchemaContents(schemaName, schemaType, resourceName): """ given a schema name, schema class type (e.g. hanadb is different) and resource name find the schema object then execute a relationships call to get the schema tables & columns (parent/child links) note: some models separate primary key columns from regular columns note: some models have different relationships (e.g. sap hana db) returns a dictionary of all tables & columns for the schema & the id of the schema object key=table val=tableid key=table.column val=columnid """ print('\tgetSchemaContents for:' + schemaName+ " resource=" + resourceName) # schemaDict returned key=TABLE.COLUMN value=column id schemaDict = {} tableNames = {} url = catalogServer + '/access/2/catalog/data/objects' query = "core.resourceName:" + resourceName + " and core.classType:" + schemaType + " and core.name_lc_exact:" + schemaName parameters = {'q': query, 'offset': 0, 'pageSize': 1} # header using uid/pwd (no Authorization) header = {"Accept": "application/json"} # header using Authorization - no need to use uid/pwd in the get call #header = {"Accept": "application/json", "Authorization": authCredentials} print("\tquery=" + query) #print("\theader=" + str(header)) schemaId = None tableCount = 0 columnCount = 0 # make the call to find the schema object response = requests.get(url,params=parameters,headers=header, auth=HTTPBasicAuth(uid,pwd)) #response = requests.get(url,params=parameters,headers=header) rc = response.status_code if rc!=200: print ("error reading object: rc=" + str(rc) + " response:" + str(response.json) ) if rc==401: print("\t401:Possible Missing/bad credentials - or server not found/responding") print(str(response)) return # get the total # of objects returned (first part of the json resultset) totalObjects=response.json()['metadata']['totalCount'] print("\tobjects returned: " + str(totalObjects) ) for item in response.json()['items']: schemaId=item["id"] schemaName = edcutils.getFactValue(item, 'core.name') # get the tables & columns print ("\tfound schema: " + schemaName + " id=" + schemaId) lineageURL=catalogServer + '/access/2/catalog/data/relationships' lineageParms={"seed": schemaId , "association": "core.ParentChild" , "depth": "2" , "direction": "OUT" , "includeAttribute": {'core.name', 'core.classType'} , "includeTerms": "false" , "removeDuplicateAggregateLinks": "false" } print("\tGET child rels for schema: " + lineageURL + " parms=" + str(lineageParms) ) # get using uid/pwd lineageResp = requests.get(lineageURL,params=lineageParms,headers=header, auth=HTTPBasicAuth(uid,pwd)) # credentials are in the header #lineageResp = requests.get(lineageURL,params=lineageParms,headers=header) lineageStatus = lineageResp.status_code print("\tlineage resp=" + str(lineageStatus)) if lineageStatus != 200: print ("error getting schema contents (tables) rc=" + str(rc) + " response:" + str(response.json) ) if rc==401: print("\t401:Possible Missing/bad credentials - or server not found/responding") print(str(response)) return if lineageResp.text.startswith('{items:'): # bug (10.2.0 & 10.2.1) - the items collection should be "items" lineageJson = lineageResp.text.replace('items', '"items"', 1) else: lineageJson = lineageResp.text #relsJson = json.loads(lineageJson.replace('items', '"items"')) relsJson = json.loads(lineageJson) #print(len(relsJson)) for lineageItem in relsJson["items"]: #print('\t\t' + str(lineageItem)) inId = lineageItem.get("inId") outId = lineageItem.get("outId") #print('new inId===' + inId + " outId=" + outId) #print(edcutils.getFactValue(lineageItem["inEmbedded"], "core.name")) assocId = lineageItem.get("associationId") #print("\t\t" + inId + " assoc=" + assocId) #if assocId=='com.infa.ldm.relational.SchemaTable': if assocId.endswith('.SchemaTable'): # note - custom lineage does not need table and column - count the tables & store table names tableCount += 1 #tableName = inId.split('/')[-1] tableName = edcutils.getFactValue(lineageItem["inEmbedded"], "core.name").lower() # store the table name (for lookup when processing the columns) key-id, val=name tableNames[inId] = tableName schemaDict[tableName]=inId #if assocId=='com.infa.ldm.relational.TableColumn': if assocId.endswith('.TableColumn') or assocId.endswith(".TablePrimaryKeyColumn"): #columnName = inId.split('/')[-1] columnCount += 1 columnName = edcutils.getFactValue(lineageItem["inEmbedded"], "core.name").lower() tableName = tableNames[outId].lower() #print("column=" + tableName + "." + columnName) schemaDict[tableName+"."+columnName] = inId print("\tgetSchema: returning " + str(columnCount) + " columns, in " + str(tableCount) + " tables") return schemaDict, schemaId def main(): """ initialise the csv file(s) to write call getSchemaContents for both left and right schema objects match the tables/columns from the left schema to the right when matched write a lineage link - table and column level Note: this script generates the newer lineage format using complete object id's and relationship types connection assignment will not be necessary works with v10.2.1+ """ start_time = time.time() print ("dbSchemaReplicationLineage:start") print ("Catalog=" + catalogServer) print ("left: resource=" + leftResource) print ("left: schema=" + leftSchema) print ("right: resource=" + rightResource) print ("right: schema=" + rightSchema) # initialize csv output file columnHeader=["Association","From Connection","To Connection","From Object","To Object"] # python 3 & 2.7 use different methods print("initializing file: " + csvFileName) if str(platform.python_version()).startswith("2.7"): fCSVFile = open(csvFileName,"w") else: fCSVFile = open(csvFileName,"w", newline='', encoding='utf-8') colWriter=csv.writer(fCSVFile) colWriter.writerow(columnHeader) # get the objects from the left schema into memory print("get left schema: name=" + leftSchema + " resource=" + leftResource + " type=" + leftSchemaType) leftObjects, leftSchemaId = getSchemaContents(leftSchema, leftSchemaType, leftResource) # get the objects from the right schema into memory print("get left schema: name=" + rightSchema + " resource=" + rightResource + " type=" + rightSchemaType) rightObjects, rightSchemaId = getSchemaContents(rightSchema, rightSchemaType, rightResource) matches=0 missing=0 if len(leftObjects) > 0 and len(rightObjects) > 0: # create the lineage file colWriter.writerow(["core.DataSourceDataFlow","","",leftSchemaId,rightSchemaId]) # iterate over all left objects - looking for matching right ones print("\nprocessing: " + str(len(leftObjects)) + " objects (left side)") for leftName, leftVal in leftObjects.items(): # if the target is using a prefix - add it to leftName if len(rightTablePrefix)>0: leftName = rightTablePrefix.lower() + leftName #print("key=" + leftName + " " + leftVal + " " + str(leftName.count('.'))) if (leftName in rightObjects.keys()): # match rightVal = rightObjects.get(leftName) matches += 1 #print("\t" + rightVal) # check if it is formatted as table.column or just table if leftName.count('.') == 1: # column lineage - using DirectionalDataFlow colWriter.writerow(["core.DirectionalDataFlow","","",leftVal,rightVal]) else: # table level - using core.DataSetDataFlow colWriter.writerow(["core.DataSetDataFlow","","",leftVal,rightVal]) # write a line to the custom lineage csv file (connection assignment) #colWriter.writerow([leftResource,rightResource,leftRef,rightRef]) else: missing += 1 print("\t no match on right side for key=" + leftName) else: print("error getting schema info... - no linking/lineage created") print ("dbSchemaLineageGen:finished. " + str(matches) + " links created, " + str(missing) + " missing (found in left, no match on right)") print("run time = %s seconds ---" % (time.time() - start_time)) fCSVFile.close() # call main - if not already called or used by another script if __name__== "__main__": main()

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pretraining/model_ensemble.py

VITA-Group/Adv-SS-Pretraining

32

8415

<filename>pretraining/model_ensemble.py ''' model ensemble for cifar10 // input size(32,32) ''' import torch import torchvision import copy import torch.nn as nn from resnetv2 import ResNet50 as resnet50v2 def split_resnet50(model): return nn.Sequential( model.conv1, model.layer1, model.layer2, model.layer3 ) class PretrainEnsembleModel(nn.Module): def __init__(self): super(PretrainEnsembleModel, self).__init__() self.blocks = split_resnet50(resnet50v2()) self.layer4_rotation = resnet50v2().layer4 self.layer4_jigsaw = resnet50v2().layer4 self.fc_rotation = nn.Linear(2048, 4) self.fc_jigsaw = nn.Linear(2048, 31) self.avgpool1 = nn.AdaptiveAvgPool2d((1,1)) self.avgpool2 = nn.AdaptiveAvgPool2d((1,1)) self.avgpool3 = nn.AdaptiveAvgPool2d((1,1)) def _Normal(self,x): mean=torch.Tensor([0.485, 0.456, 0.406]) mean=mean[None,:,None,None].cuda() std = torch.Tensor([0.229, 0.224, 0.225]) std = std[None,:,None,None].cuda() return x.sub(mean).div(std) def forward(self, x): feature_map = self.blocks(self._Normal(x)) return feature_map

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config.example.py

idies/SpecDash

1

111936

PORT = 8050 DEBUG = True DASH_TABLE_PAGE_SIZE = 5 DEFAULT_WAVELENGTH_UNIT = "angstrom" DEFAULT_FLUX_UNIT = "F_lambda" LOGS = { "do_log":True, "base_logs_directory":"/base/logs/directory/" } MAX_NUM_TRACES = 30 CATALOGS = { "sdss": { "base_data_path":"/base/data/path/", "api_url":"http://skyserver.sdss.org/public/SkyServerWS/SearchTools/SqlSearch", "example_specid":"2947691243863304192" } }

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urbansim_templates/data/column_from_expression.py

AZMAG/urbansim_templates

19

106714

import orca import pandas as pd from urbansim_templates import modelmanager, shared, utils, __version__ from urbansim_templates.shared import CoreTemplateSettings, OutputColumnSettings class ExpressionSettings(): """ Stores custom parameters used by the :mod:`~urbansim_templates.data.ColumnFromExpression` template. Parameters can be passed to the constructor or set as attributes. Parameters ---------- table : str, optional Name of Orca table the expression will be evaluated on. Required before running then template. expression : str, optional String describing operations on existing columns of the table, for example "a/log(b+c)". Required before running. Supports arithmetic and math functions including sqrt, abs, log, log1p, exp, and expm1 -- see Pandas ``df.eval()`` documentation for further details. """ def __init__(self, table = None, expression = None): self.table = table self.expression = expression @classmethod def from_dict(cls, d): return cls(table=d['table'], expression=d['expression']) def to_dict(self): return {'table': self.table, 'expression': self.expression} @modelmanager.template class ColumnFromExpression(): """ Template to register a column of derived data with Orca, based on an expression. Parameters may be passed to the constructor, but they are easier to set as attributes. The expression can refer to any columns in the same table, and will be evaluated using ``df.eval()``. Values will be calculated lazily, only when the column is needed for a specific operation. Parameters ---------- meta : :mod:`~urbansim_templates.shared.CoreTemplateSettings`, optional Standard parameters. This template sets the default value of ``meta.autorun`` to True. data : :mod:`~urbansim_templates.data.ExpressionSettings`, optional Special parameters for this template. output : :mod:`~urbansim_templates.shared.OutputColumnSettings`, optional Parameters for the column that will be generated. This template uses ``data.table`` as the default value for ``output.table``. """ def __init__(self, meta=None, data=None, output=None): self.meta = CoreTemplateSettings(autorun=True) if meta is None else meta self.meta.template = self.__class__.__name__ self.meta.template_version = __version__ self.data = ExpressionSettings() if data is None else data self.output = OutputColumnSettings() if output is None else output @classmethod def from_dict(cls, d): """ Create a class instance from a saved dictionary. """ if 'meta' not in d: return cls.from_dict_0_2_dev5(d) return cls( meta = CoreTemplateSettings.from_dict(d['meta']), data = ExpressionSettings.from_dict(d['data']), output = OutputColumnSettings.from_dict(d['output'])) @classmethod def from_dict_0_2_dev5(cls, d): """ Converter to read saved data from 0.2.dev5 or earlier. Automatically invoked by ``from_dict()`` as needed. """ return cls( meta = CoreTemplateSettings( name = d['name'], tags = d['tags'], autorun = d['autorun']), data = ExpressionSettings( table = d['table'], expression = d['expression']), output = OutputColumnSettings( column_name = d['column_name'], data_type = d['data_type'], missing_values = d['missing_values'], cache = d['cache'], cache_scope = d['cache_scope'])) def to_dict(self): """ Create a dictionary representation of the object. """ return { 'meta': self.meta.to_dict(), 'data': self.data.to_dict(), 'output': self.output.to_dict()} def run(self): """ Run the template, registering a column of derived data with Orca. Requires values to be set for ``data.table``, ``data.expression``, and ``output.column_name``. """ if self.data.table is None: raise ValueError("Please provide a table") if self.data.expression is None: raise ValueError("Please provide an expression") if self.output.column_name is None: raise ValueError("Please provide a column name") settings = self.output if settings.table is None: settings.table = self.data.table cols = utils.cols_in_expression(self.data.expression) def build_column(): df = utils.get_df(self.data.table, columns=cols) series = df.eval(self.data.expression) return series shared.register_column(build_column, settings)

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mutatest/api.py

EvanKepner/m

49

64696

<reponame>EvanKepner/m """ API --- These are high level objects for interacting with ``mutatest``. The primary objects include: 1. The ``Genome`` 2. The ``GenomeGroup`` 3. The ``Mutant`` ``Genomes`` are representations of a Python source code file. This includes a representation of the Abstract Syntax Tree (AST) and the locations within the AST that could be mutated. The locations are accessed by the ``targets`` and ``covered_targets`` properties of the ``Genome``, the latter being available if a coverage file is set for the ``Genome``. Locations are represented as ``LocIndex`` objects from ``mutatest.transformers`` which may be referenced as specific points of mutation. ``Mutants`` are created from ``Genome.mutate()`` for a specific ``LocIndex`` in the ``Genome`` targets. A ``mutant`` is an immutable named-tuple with all of the attributes necessary to mutate the appropriate ``__pycache__`` file with the ``write_cache()`` method. Collections of ``Genomes`` can be managed through a ``GenomeGroup``. The ``GenomeGroup`` provides methods for setting global filters, coverage files, and producing targets of ``LocIndex`` objects across the collection of ``Genomes``. This is a useful representation when dealing with a folder of multiple source files. """ import ast import importlib import itertools import logging from collections.abc import MutableMapping from copy import deepcopy from pathlib import Path from typing import ( Any, Dict, ItemsView, Iterable, Iterator, KeysView, Mapping, NamedTuple, Optional, Set, Union, ValuesView, ) from mutatest import cache from mutatest.filters import CategoryCodeFilter, CoverageFilter from mutatest.transformers import CATEGORIES, LocIndex, MutateAST LOGGER = logging.getLogger(__name__) class MutationException(Exception): """Mutation Exception type specifically for mismatches in mutation operations.""" pass class Mutant(NamedTuple): """Mutant definition. Mutants are created through the Genome at specific targets using the mutate method. Mutants are immutable and can be written to disk in the ``__pycache__``. You can create ``Mutants`` using ``Genome.mutate``, and then ``write_cache`` to apply to the ``__pycache__``. """ mutant_code: Any src_file: Path cfile: Path loader: Any source_stats: Mapping[str, Any] mode: int src_idx: LocIndex mutation: Any def write_cache(self) -> None: """Create the cache file for the mutant on disk in ``__pycache__``. Existing target cache files are removed to ensure clean overwrites. Reference: https://github.com/python/cpython/blob/master/Lib/py_compile.py#L157 Returns: None, creates the cache file on disk. """ cache.check_cache_invalidation_mode() bytecode = importlib._bootstrap_external._code_to_timestamp_pyc( # type: ignore self.mutant_code, self.source_stats["mtime"], self.source_stats["size"] ) cache.remove_existing_cache_files(self.src_file) cache.create_cache_dirs(self.cfile) LOGGER.debug("Writing mutant cache file: %s", self.cfile) importlib._bootstrap_external._write_atomic(self.cfile, bytecode, self.mode) # type: ignore class Genome: """The Genome class describes the source file to be mutated. The class describes a single .py file and has properties for the abstract syntax tree (AST) and the viable mutation targets. You can initialize without any arguments. If the ``source_file`` is changed the ast and targets properties will be recalculated for that file. Locations in the Genome may be mutated and written to the ``__pycache__`` using the mutate method. """ def __init__( self, source_file: Optional[Union[str, Path]] = None, coverage_file: Optional[Union[str, Path]] = Path(".coverage"), filter_codes: Optional[Iterable[str]] = None, ) -> None: """Initialize the Genome. There are internal properties prefixed with an underscore used for the lazy evaluation of the AST and mutation targets. Args: source_file: an optional source file path coverage_file: coverage file for filtering covered lines, default value is set to ".coverage". filter_codes: 2-letter category codes to filter returned targets """ # Properties with an underscore prefix are used for local caching and are not designed # to be modified directly. # Related to source files, AST, targets self._source_file = None self._ast: Optional[ast.Module] = None self._targets: Optional[Set[LocIndex]] = None # Related to coverage filtering self._coverage_file = None self._covered_targets: Optional[Set[LocIndex]] = None # Related to category code filtering, not cached but uses a setter for valid value checks self._filter_codes: Set[str] = set() # Initialize set values using properties # These may be set later and clear the cached values in the setters self.source_file = Path(source_file) if source_file else None self.coverage_file = Path(coverage_file) if coverage_file else None self.filter_codes: Set[str] = set(filter_codes) if filter_codes else set() ################################################################################################ # CATEGORY FILTER CODES PROPERTIES ################################################################################################ @property def filter_codes(self) -> Set[str]: """Filter codes applied to targets and covered targets.""" return self._filter_codes @filter_codes.setter def filter_codes(self, value: Iterable[str]) -> None: """Setter for filter codes. These are always applied when set on the Genome. Set this to an empty set to remove all category code filters from returned targets. Args: value: a set of 2-letter codes, use a set of a single code if needed. Returns: None Raises: ValueError: if the 2-letter codes in value are not supported by the transformer. """ value, valid_codes = set(value), set(CATEGORIES.values()) if not value.issubset(valid_codes): raise ValueError( f"Invalid category codes: {value - valid_codes}.\nValid codes: {CATEGORIES}" ) self._filter_codes = value ################################################################################################ # SOURCE FILE PROPERTIES ################################################################################################ @property def source_file(self) -> Optional[Path]: """The source .py file represented by this Genome. Returns: The ``source_file`` path. """ return self._source_file @source_file.setter def source_file(self, value: Optional[Union[str, Path]]) -> None: """Setter for the source_file that clears the AST and targets for recalculation.""" self._source_file = Path(value) if value else None self._ast = None self._targets = None @property def ast(self) -> ast.Module: # type: ignore """Abstract Syntax Tree (AST) representation of the source_file. This is cached locally and updated if the source_file is changed. Returns: Parsed AST for the source file. Raises: TypeError: if ``source_file`` is not set. """ if self._ast is None: if not self.source_file: raise TypeError("Source_file property is set to NoneType.") with open(self.source_file, "rb") as src_stream: self._ast = ast.parse(src_stream.read()) return self._ast @property def targets(self) -> Set[LocIndex]: """Viable mutation targets within the AST of the ``source_file``. This is cached locally and updated if the source_file is changed. Filtering is not cached and applies any time the ``filter_codes`` are changed. Returns: The set of the location index objects from the transformer that could be potential mutation targets. """ if self._targets is None: ro_mast = MutateAST( target_idx=None, mutation=None, readonly=True, src_file=self.source_file ) ro_mast.visit(self.ast) self._targets = ro_mast.locs return CategoryCodeFilter(codes=self.filter_codes).filter(self._targets) ################################################################################################ # COVERAGE FILTER PROPERTIES ################################################################################################ @property def coverage_file(self) -> Optional[Path]: """The .coverage file to use for filtering targets.""" return self._coverage_file @coverage_file.setter def coverage_file(self, value: Optional[Union[str, Path]]) -> None: """Setter for ``coverage_file``, clears the cached ``covered_targets``.""" self._coverage_file = Path(value) if value else None self._covered_targets = None @property def covered_targets(self) -> Set[LocIndex]: """Targets that are marked as covered based on the ``coverage_file``. This is cached locally and updated if the coverage_file is changed. Filtering is not cached and applies any time the filter_codes are changed. Returns: The targets that are covered. Raises: TypeError: if the ``source_file`` or ``coverage_file`` is not set for the Genome. """ if not self.source_file: raise TypeError("Source_file property is set to NoneType.") if not self.coverage_file: raise TypeError("Coverage_file property is set to NoneType.") if self._covered_targets is None: self._covered_targets = CoverageFilter(coverage_file=self.coverage_file).filter( self.targets, self.source_file ) return CategoryCodeFilter(codes=self.filter_codes).filter(self._covered_targets) ################################################################################################ # MUTATION METHODS ################################################################################################ def mutate(self, target_idx: LocIndex, mutation_op: Any, write_cache: bool = False) -> Mutant: """Create a mutant from a single LocIndex that is in the Genome. Mutation_op must be a valid mutation for the target_idx operation code type. Optionally, use write_cache to write the mutant to ``__pycache__`` based on the detected location at the time of creation. The Genome AST is unmodified by mutate. Args: target_idx: the target location index (member of .targets) mutation_op: the mutation operation to use write_cache: optional flag to write to ``__pycache__`` Returns: The mutant definition Raises: MutationException: if ``mutation_op`` is not a valid mutation for the location index. TypeError: if the source_file property is not set on the Genome. ValueError: if the target_idx is not a member of Genome targets. """ op_code = CATEGORIES[target_idx.ast_class] valid_mutations = CategoryCodeFilter(codes=(op_code,)).valid_mutations if mutation_op not in valid_mutations: raise MutationException( f"{mutation_op} is not a member of mutation category {op_code}.\n" f"Valid mutations for {op_code}: {valid_mutations}." ) if not self.source_file: raise TypeError("Source_file is set to NoneType") if target_idx not in self.targets: raise ValueError(f"{target_idx} is not in the Genome targets.") mutant_ast = MutateAST( target_idx=target_idx, mutation=mutation_op, src_file=self.source_file, readonly=False ).visit( deepcopy(self.ast) # deepcopy to avoid in-place modification of AST ) # generate cache file pyc machinery for writing the __pycache__ file loader = importlib.machinery.SourceFileLoader( # type: ignore "<py_compile>", self.source_file ) # create the cache files with the mutated AST mutant = Mutant( mutant_code=compile(mutant_ast, str(self.source_file), "exec"), src_file=Path(self.source_file), cfile=Path(cache.get_cache_file_loc(self.source_file)), loader=loader, source_stats=loader.path_stats(self.source_file), mode=importlib._bootstrap_external._calc_mode(self.source_file), # type: ignore src_idx=target_idx, mutation=mutation_op, ) if write_cache: mutant.write_cache() return mutant class GenomeGroupTarget(NamedTuple): """Container for targets returned from GenomeGroup to associated source path to LocIdx.""" source_path: Path loc_idx: LocIndex class GenomeGroup(MutableMapping): # type: ignore """The GenomeGroup: a MutableMapping of Genomes for operations on the group. """ def __init__(self, source_location: Optional[Union[str, Path]] = None) -> None: """Initialize the GenomeGroup. GenomeGroup is a MutableMapping collection of Genomes with defined ``source_file`` locations. You can use it to apply standard filters or coverage files across the group and get all mutation targets for the group. Folders and files can be added through methods. Args: source_location: an optional folder for initialization using the default settings of no file exclusions except 'test' files. For more flexibility, initialize the class and then use the ``.add_folder()`` method directly. """ # internal mapping for Genomes, not designed for direct modification, use class properties self._store: Dict[Path, Genome] = dict() if source_location is not None: source_location = Path(source_location) if source_location.is_dir(): self.add_folder(source_location) elif source_location.is_file(): self.add_file(source_location) else: raise TypeError(f"{source_location} is not a folder or file.") def __setitem__(self, key: Path, value: Genome) -> None: """Setter for GenomeGroup, enforces Path keys and Genome values. Args: key: key for the mapping, must be a path value: the genome Returns: None """ if not isinstance(key, Path): raise TypeError("Only Path keys are supported.") if not isinstance(value, Genome): raise TypeError("Only Genome values are supported.") self._store[key] = value def __getitem__(self, key: Path) -> Genome: """Getter for keys from the mapping store.""" return self._store[key] def __delitem__(self, key: Path) -> None: """Delete a key from the mapping store.""" del self._store[key] def __iter__(self) -> Iterator[Path]: """Iterate over the mapping store keys.""" return iter(self._store) def __len__(self) -> int: """Count of keys in the mapping store.""" return len(self._store) def __repr__(self) -> str: """Base mapping store repr.""" return self._store.__repr__() def items(self) -> ItemsView[Path, Genome]: """ItemsView for the mapping store.""" return self._store.items() def keys(self) -> KeysView[Path]: """KeysView of the mapping store.""" return self._store.keys() def values(self) -> ValuesView[Genome]: """ValuesView of the mapping store.""" return self._store.values() def add_genome(self, genome: Genome) -> None: """Add a Genome to the GenomeGroup. Genomes must have a defined ``source_file``. Args: genome: the ``Genome`` to add Returns: None Raises: TypeError: if the ``Genome.source_file`` is not set. """ if genome.source_file is None: raise TypeError("Genome source_file is set to NoneType.") self.__setitem__(genome.source_file, genome) def add_file( self, source_file: Union[str, Path], coverage_file: Optional[Union[str, Path]] = Path(".coverage"), ) -> None: """Add a ``.py`` source file to the group as a new Genome. The Genome is created automatically. Args: source_file: the source file to add with Genome creation coverage_file: an optional coverage file to set on the Genome, defaults to ".coverage". Returns: None """ self.add_genome(Genome(source_file=source_file, coverage_file=coverage_file)) def add_folder( self, source_folder: Union[str, Path], exclude_files: Optional[Iterable[Union[str, Path]]] = None, ignore_test_files: bool = True, ) -> None: """Add a folder (recursively) to the GenomeGroup for all ``.py`` files. Args: source_folder: the folder to recursively search exclude_files: optional iterable of specific files in the source_folder to skip ignore_test_files: optional flag, default to true, to ignore files prefixed with ``test_`` or suffixed with ``_test`` in the stem of the file name. Returns: None, adds all files as Genomes to the group. Raises: TypeError: if ``source_folder`` is not a folder. """ source_folder = Path(source_folder) exclude_files = [Path(e).resolve() for e in exclude_files] if exclude_files else set() if not source_folder.is_dir(): raise TypeError(f"{source_folder} is not a directory.") for fn in source_folder.rglob("*.py"): if (fn.stem.startswith("test_") or fn.stem.endswith("_test")) and ignore_test_files: continue else: if fn.resolve() not in exclude_files: self.add_file(fn) def set_filter(self, filter_codes: Iterable[str]) -> None: """Set the filter codes for all Genomes in the group. Args: filter_codes: iterable of 2-letter codes to set on all Genomes in the group. Returns: None """ for k, v in self.items(): v.filter_codes = set(filter_codes) def set_coverage(self, coverage_file: Union[str, Path]) -> None: """Set a common coverage file for all Genomes in the group. Args: coverage_file: the coverage file to set. Returns: None """ for k, v in self.items(): v.coverage_file = Path(coverage_file) @property def targets(self) -> Set[GenomeGroupTarget]: """All mutation targets in the group, returned as tuples of ``source_file`` and location indices in a single set. Returns: Set of tuples of ``source_file`` and location index for all targets in the group. These are ``GenomeGroupTargets`` to make attribute access easier. """ targets = set() for k, v in self.items(): targets.update(set(itertools.product([k], v.targets))) return {GenomeGroupTarget(*t) for t in targets} @property def covered_targets(self) -> Set[GenomeGroupTarget]: """All mutation targets in the group that are covered, returned as tuples of ``source_file`` and location indices in a single set. Returns: Set of tuples of ``source_file`` and location index for all covered targets in the group. These are ``GenomeGroupTargets`` to make attribute access easier. """ covered_targets = set() for k, v in self.items(): covered_targets.update(set(itertools.product([k], v.covered_targets))) return {GenomeGroupTarget(*c) for c in covered_targets}

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learntools/python/utils.py

sfrias/learntools

1

70589

def backtickify(s): return '`{}`'.format(s) def bind_exercises(g, exercises, start=1): for i, ex in enumerate(exercises): qno = i + start varname = 'q{}'.format(qno) assert varname not in g g[varname] = ex yield varname

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auto_process_ngs/test/commands/test_clone_cmd.py

fls-bioinformatics-core/auto_process_ngs

5

124493

####################################################################### # Tests for clone_cmd.py module ####################################################################### import unittest import tempfile import shutil import os from auto_process_ngs.auto_processor import AutoProcess from auto_process_ngs.mock import MockAnalysisDirFactory from auto_process_ngs.mock import UpdateAnalysisDir from auto_process_ngs.metadata import AnalysisDirParameters from auto_process_ngs.commands.clone_cmd import clone # Set to False to keep test output dirs REMOVE_TEST_OUTPUTS = True class TestAutoProcessClone(unittest.TestCase): """ Tests for AutoProcess.clone """ def setUp(self): # Create a temp working dir self.dirn = tempfile.mkdtemp(suffix='TestAutoProcessClone') # Store original location so we can get back at the end self.pwd = os.getcwd() # Move to working dir os.chdir(self.dirn) # Placeholders for test objects self.ap = None def tearDown(self): # Delete autoprocessor object if self.ap is not None: del(self.ap) # Return to original dir os.chdir(self.pwd) # Remove the temporary test directory if REMOVE_TEST_OUTPUTS: shutil.rmtree(self.dirn) def test_clone_analysis_dir(self): """ clone: copies an analysis directory using symlinks """ # Make a source analysis dir analysis_dir = MockAnalysisDirFactory.bcl2fastq2( "190116_M01234_0002_AXYZ123", platform="miseq", paired_end=True, no_lane_splitting=False, include_stats_files=True, top_dir=self.dirn) analysis_dir.create() ap = AutoProcess(analysis_dir.dirn) UpdateAnalysisDir(ap).add_processing_report() ap.add_directory("primary_data/190116_M01234_0002_AXYZ123") # Make a copy clone_dir = os.path.join(self.dirn,"190116_M01234_0002_AXYZ123_copy") self.assertFalse(os.path.exists(clone_dir)) clone(ap,clone_dir) self.assertTrue(os.path.isdir(clone_dir)) # Check contents for subdir in ('logs','ScriptCode'): d = os.path.join(clone_dir,subdir) self.assertTrue(os.path.isdir(d),"Missing '%s'" % subdir) for filen in ('SampleSheet.orig.csv', 'custom_SampleSheet.csv', 'auto_process.info', 'metadata.info', 'statistics.info', 'statistics_full.info', 'per_lane_statistics.info', 'per_lane_sample_stats.info', 'processing_qc.html',): f = os.path.join(clone_dir,filen) self.assertTrue(os.path.isfile(f),"Missing '%s'" % filen) # Check unaligned unaligned = os.path.join(clone_dir,'bcl2fastq') self.assertTrue(os.path.islink(unaligned)) # Check primary data primary_data = os.path.join(clone_dir, 'primary_data', '190116_M01234_0002_AXYZ123') self.assertTrue(os.path.islink(primary_data)) # Check projects for proj in ('AB','CDE','undetermined'): d = os.path.join(clone_dir,proj) self.assertTrue(os.path.isdir(d),"Missing '%s'" % proj) # Check parameters params = AnalysisDirParameters(filen=os.path.join( clone_dir, 'auto_process.info')) self.assertEqual(params.sample_sheet, os.path.join(clone_dir,"custom_SampleSheet.csv")) self.assertEqual(params.primary_data_dir, os.path.join(clone_dir,"primary_data")) def test_clone_analysis_dir_copy_fastqs(self): """ clone: copies an analysis directory """ # Make a source analysis dir analysis_dir = MockAnalysisDirFactory.bcl2fastq2( "190116_M01234_0002_AXYZ123", platform="miseq", paired_end=True, no_lane_splitting=False, include_stats_files=True, top_dir=self.dirn) analysis_dir.create() ap = AutoProcess(analysis_dir.dirn) UpdateAnalysisDir(ap).add_processing_report() ap.add_directory("primary_data/190116_M01234_0002_AXYZ123") # Make a copy clone_dir = os.path.join(self.dirn,"190116_M01234_0002_AXYZ123_copy") self.assertFalse(os.path.exists(clone_dir)) clone(ap,clone_dir,copy_fastqs=True) self.assertTrue(os.path.isdir(clone_dir)) # Check contents for subdir in ('logs','ScriptCode'): d = os.path.join(clone_dir,subdir) self.assertTrue(os.path.isdir(d),"Missing '%s'" % subdir) for filen in ('SampleSheet.orig.csv', 'custom_SampleSheet.csv', 'auto_process.info', 'metadata.info', 'statistics.info', 'statistics_full.info', 'per_lane_statistics.info', 'per_lane_sample_stats.info', 'processing_qc.html',): f = os.path.join(clone_dir,filen) self.assertTrue(os.path.isfile(f),"Missing '%s'" % filen) # Check unaligned unaligned = os.path.join(clone_dir,'bcl2fastq') self.assertTrue(os.path.isdir(unaligned)) # Check primary data primary_data = os.path.join(clone_dir, 'primary_data', '190116_M01234_0002_AXYZ123') self.assertTrue(os.path.islink(primary_data)) # Check projects for proj in ('AB','CDE','undetermined'): d = os.path.join(clone_dir,proj) self.assertTrue(os.path.isdir(d),"Missing '%s'" % proj) # Check parameters params = AnalysisDirParameters(filen=os.path.join( clone_dir, 'auto_process.info')) self.assertEqual(params.sample_sheet, os.path.join(clone_dir,"custom_SampleSheet.csv")) self.assertEqual(params.primary_data_dir, os.path.join(clone_dir,"primary_data")) def test_clone_analysis_dir_no_projects(self): """ clone: copies an analysis directory excluding projects """ # Make a source analysis dir analysis_dir = MockAnalysisDirFactory.bcl2fastq2( "190116_M01234_0002_AXYZ123", platform="miseq", paired_end=True, no_lane_splitting=False, include_stats_files=True, top_dir=self.dirn) analysis_dir.create() ap = AutoProcess(analysis_dir.dirn) UpdateAnalysisDir(ap).add_processing_report() ap.add_directory("primary_data/190116_M01234_0002_AXYZ123") # Make a copy clone_dir = os.path.join(self.dirn,"190116_M01234_0002_AXYZ123_copy") self.assertFalse(os.path.exists(clone_dir)) clone(ap,clone_dir,exclude_projects=True) self.assertTrue(os.path.isdir(clone_dir)) # Check contents for subdir in ('logs','ScriptCode'): d = os.path.join(clone_dir,subdir) self.assertTrue(os.path.isdir(d),"Missing '%s'" % subdir) for filen in ('SampleSheet.orig.csv', 'custom_SampleSheet.csv', 'auto_process.info', 'metadata.info', 'statistics.info', 'statistics_full.info', 'per_lane_statistics.info', 'per_lane_sample_stats.info', 'processing_qc.html',): f = os.path.join(clone_dir,filen) self.assertTrue(os.path.isfile(f),"Missing '%s'" % filen) # Check unaligned unaligned = os.path.join(clone_dir,'bcl2fastq') self.assertTrue(os.path.islink(unaligned)) # Check primary data primary_data = os.path.join(clone_dir, 'primary_data', '190116_M01234_0002_AXYZ123') self.assertTrue(os.path.islink(primary_data)) # Check projects for proj in ('AB','CDE','undetermined'): d = os.path.join(clone_dir,proj) self.assertFalse(os.path.exists(d),"Found '%s'" % proj) # Check parameters params = AnalysisDirParameters(filen=os.path.join( clone_dir, 'auto_process.info')) self.assertEqual(params.sample_sheet, os.path.join(clone_dir,"custom_SampleSheet.csv")) self.assertEqual(params.primary_data_dir, os.path.join(clone_dir,"primary_data")) def test_clone_analysis_dir_empty_params(self): """ clone: copies an analysis directory when parameter file is empty """ # Make a source analysis dir analysis_dir = MockAnalysisDirFactory.bcl2fastq2( "190116_M01234_0002_AXYZ123", platform="miseq", paired_end=True, no_lane_splitting=False, include_stats_files=True, top_dir=self.dirn) analysis_dir.create() ap = AutoProcess(analysis_dir.dirn) UpdateAnalysisDir(ap).add_processing_report() ap.add_directory("primary_data/190116_M01234_0002_AXYZ123") # Remove data from parameter file parameter_file = ap.parameter_file tmp_parameter_file = os.path.join(self.dirn,'new_params.tmp') del(ap) with open(parameter_file,'r') as fp: with open(tmp_parameter_file,'w') as fpp: for line in fp: line = "%s\t." % line.split('\t')[0] fpp.write(line) os.remove(parameter_file) os.rename(tmp_parameter_file,parameter_file) ap = AutoProcess(analysis_dir.dirn) # Make a copy clone_dir = os.path.join(self.dirn,"190116_M01234_0002_AXYZ123_copy") self.assertFalse(os.path.exists(clone_dir)) clone(ap,clone_dir,exclude_projects=False) self.assertTrue(os.path.isdir(clone_dir)) # Check contents for subdir in ('logs','ScriptCode'): d = os.path.join(clone_dir,subdir) self.assertTrue(os.path.isdir(d),"Missing '%s'" % subdir) for filen in ('SampleSheet.orig.csv', 'custom_SampleSheet.csv', 'auto_process.info', 'metadata.info', 'statistics.info', 'statistics_full.info', 'per_lane_statistics.info', 'per_lane_sample_stats.info', 'processing_qc.html',): f = os.path.join(clone_dir,filen) self.assertTrue(os.path.isfile(f),"Missing '%s'" % filen) # Check unaligned unaligned = os.path.join(clone_dir,'bcl2fastq') self.assertTrue(os.path.islink(unaligned)) # Check primary data primary_data = os.path.join(clone_dir, 'primary_data', '190116_M01234_0002_AXYZ123') self.assertFalse(os.path.exists(primary_data)) # Check projects for proj in ('AB','CDE','undetermined'): d = os.path.join(clone_dir,proj) self.assertTrue(os.path.exists(d),"Missing '%s'" % proj) def test_clone_fails_if_target_dir_exists(self): """ clone: raises an exception if target dir already exists """ # Make a source analysis dir analysis_dir = MockAnalysisDirFactory.bcl2fastq2( "190116_M01234_0002_AXYZ123", platform="miseq", paired_end=True, no_lane_splitting=False, include_stats_files=True, top_dir=self.dirn) analysis_dir.create() ap = AutoProcess(analysis_dir.dirn) UpdateAnalysisDir(ap).add_processing_report() ap.add_directory("primary_data/190116_M01234_0002_AXYZ123") # Make target dir clone_dir = os.path.join(self.dirn,"190116_M01234_0002_AXYZ123_copy") os.mkdir(clone_dir) # Try to copy source dir self.assertRaises(Exception, clone, ap,clone_dir)

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pisat/handler/pigpio_digital_input_handler.py

jjj999/pisat

1

57855

from typing import Optional from pisat.util.platform import is_raspberry_pi from pisat.handler.digital_input_handler_base import DigitalInputHandlerBase if is_raspberry_pi(): import pigpio class PigpioDigitalInputHandler(DigitalInputHandlerBase): def __init__(self, pi, pin: int, pullup: bool = False, pulldown: bool = False, name: Optional[str] = None) -> None: self._pi: pigpio.pi = pi self._pi.set_mode(pin, pigpio.INPUT) super().__init__(pin, pullup=pullup, pulldown=pulldown, name=name) def set_pull_up_down(self, pulldown: bool = False) -> None: if pulldown: self._pi.set_pull_up_down(self._pin, pigpio.PUD_DOWN) else: self._pi.set_pull_up_down(self._pin, pigpio.PUD_UP) def clear_pull_up_down(self) -> None: self._pi.set_pull_up_down(self._pin, pigpio.PUD_DOWN) def observe(self) -> bool: return bool(self._pi.read(self._pin))

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app/util/draw_img.py

Test8888888888/Myblog

0

196411

from typing import Dict from PIL import Image, ImageDraw, ImageFont from io import BytesIO import requests import re class H3blogDrow: '''自定义图片样式''' def __init__(self) -> None: self.width = 800 self.heigth = 400 self.background_img = '' self.background_color = '#424155' self.layers = [] self.convas = None def parse_config(self, config: Dict) -> None: c = config self.convas = None self.width = c.get('width', 800) self.heigth = c.get('height', 400) self.background_color = c.get('background_color', '#424155') self.background_img = c.get('background_img') layers = c.get('layers', None) if not layers: return self.layers.extend(layers) def _create_canvas(self) -> None: self.convas = Image.new('RGB', (self.width, self.heigth), self.background_color) def draw(self) -> Image: '''画图''' # 创建背景设置画布 self._create_canvas() if self.background_img and len(self.background_img) > 0: regex = re.compile( r'^(?:http|ftp)s?://' # http:// or https:// r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}\.?)|' # domain... r'localhost|' # localhost... r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})' # ...or ip r'(?::\d+)?' # optional port r'(?:/?|[/?]\S+)$', re.IGNORECASE) m = re.match(regex, self.background_img) bg_img = None if m: resp = requests.get(self.background_img) _img_bytes = BytesIO() _img_bytes.write(resp.content) bg_img = Image.open(_img_bytes) else: # 创建背景图片 bg_img = Image.open(self.background_img) # 将背景图片写入画布 self.convas.paste(bg_img, (0, 0)) for layer in self.layers: if layer.get('layer_type') == 'text': self._draw_text(layer) if layer.get('layer_type') == 'image': self._draw_image() return self.convas def _darw_image(self, layer: dict) -> None: pass def _draw_text(self, layer: dict) -> None: draw = ImageDraw.Draw(self.convas) _font = layer.get('font') font = ImageFont.truetype(_font.get('font'), _font.get('size', 36)) align = layer.get('align') p = tuple() if align and align == 'center': f_w, f_h = font.getsize(layer.get('text')) # 获取字体大小 p = ((self.convas.width - f_w) / 2, (self.convas.height - f_h) / 2) elif align and align == 'top-left': p = (0, 0) elif align and align == 'top-right': f_w, f_h = font.getsize(layer.get('text')) # 获取字体大小 p = (self.convas.width - f_w, 0) elif align and align == 'bottom-left': f_w, f_h = font.getsize(layer.get('text')) # 获取字体大小 p = (0, self.convas.height - f_h) elif align and align == 'bottom-right': f_w, f_h = font.getsize(layer.get('text')) # 获取字体大小 p = (self.convas.width - f_w, self.convas.height - f_w) else: p = tuple([int(i) for i in layer.get('position', '0,0').split(',')]) color = layer.get('color', '0,0,0') draw.text(p, layer.get('text', ''), fill=color, font=font)

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pyhermes/apps/django/management/commands/hermes_test.py

romcheg/pyhermes

5

129419

<reponame>romcheg/pyhermes # -*- coding: utf-8 -*- from django.core.management.base import BaseCommand from pyhermes.exceptions import HermesPublishException from pyhermes.publisher import publish from pyhermes.settings import HERMES_SETTINGS TOPICS_ALL = 'all' class Command(BaseCommand): help = "Testing integration with Hermes" def add_arguments(self, parser): parser.add_argument( '-m', '--message', help='test message', default='From pyhermes With Love' ) parser.add_argument( '-t', '--topic', help='topic', default=TOPICS_ALL, ) def handle(self, *args, **options): if not HERMES_SETTINGS.ENABLED: self.stderr.write( 'Hermes integration is disabled. ' 'Check HERMES.ENABLED variable ' 'in your settings or environment.' ) return topic = options.get('topic') message = options.get('message') if topic == TOPICS_ALL: topics = HERMES_SETTINGS.PUBLISHING_TOPICS.keys() else: topics = [topic] for topic in topics: try: self.stdout.write('Sending message to {}'.format(topic)) publish(topic, {'result': message}) except HermesPublishException as e: self.stderr.write(str(e)) else: self.stdout.write( 'Message was sent successfully to {}!'.format(topic) )

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tests/extra_lib/test.py

burner/pyd

168

1609267

import os.path, sys import distutils.util # Append the directory in which the binaries were placed to Python's sys.path, # then import the D DLL. libDir = os.path.join('build', 'lib.%s-%s' % ( distutils.util.get_platform(), '.'.join(str(v) for v in sys.version_info[:2]) )) sys.path.append(os.path.abspath(libDir)) import hello_w_libs hello_w_libs.hello()

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mptpy/mpt.py

CognitiveComputationLab/mptpy

0

86656

<reponame>CognitiveComputationLab/mptpy """ Data structure for Multinomial Processing Trees (MPTs). """ from mptpy.mpt_word import MPTWord import mptpy.tools.transformations as trans # pylint: disable=import-error from mptpy.visualization.visualize_mpt import cmd_draw # pylint: disable=import-error from mptpy.tools import misc class MPT(object): """ Multinomial Processing Tree (MPT) data structure. """ def __init__(self, mpt, sep=" ", leaf_test=None): """ Constructs the MPT object. Parameters ---------- mpt : [str, Node] either tree in bmpt or as root object. """ self.subtrees = [] self.word = None self.root = None # mpt given as word if isinstance(mpt, str): self.word = MPTWord(mpt, sep=sep, leaf_test=leaf_test) self.root = trans.word_to_nodes(self.word) # mpt given as root node else: self.root = mpt self.word = MPTWord(str(self)) @property def params(self): return self.word.parameters @property def categories(self): return self.word.answers def formulae(self): """ Calculate the branch formulae for the categories in the tree Returns ------- dict {category : formulae} """ return trans.get_formulae(self) def max_parameters(self): """ The maximal number of free parameters in the model Returns ------- int max number of free parameters """ return sum([len(subtree) - 1 for subtree in self.subtrees]) def get_levels(self, node, level=0): """ Generate a dict with all nodes and their respective level 0 is the root Parameters ---------- node : Node starting node level : int, optional level from which to start counting """ levels = {level: [node]} if not node.leaf: left_dict = self.get_levels(node.pos, level=level + 1) right_dict = self.get_levels(node.neg, level=level + 1) temp = misc.merge_dicts(left_dict, right_dict) levels.update(temp) return levels def save(self, path, form="easy"): """ Saves the tree to a file Parameters ---------- path : str where to save the tree """ to_print = trans.to_easy(self) if form == "easy" else self.word.str_ misc.write_iterable_to_file(path, to_print, newline=False) def draw(self): """ Draw MPT to the command line """ cmd_draw(self) def __eq__(self, other): return self.word.abstract() == other.word.abstract() def __ne__(self, other): return not self.__eq__(other) def __str__(self): if self.word: return self.word.str_ sep = " " def dfs(node): """ depth first search """ if node.leaf: return str(node.content) pos = dfs(node.pos) neg = dfs(node.neg) return node.content + sep + pos + sep + neg return dfs(self.root)

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simapi/onlinesim/number.py

nm17/simapi

0

68652

<reponame>nm17/simapi import simapi.api_handler as baseapi class OnlineSMSNumber(baseapi.Number): def __init__(self, api_handler, *args, **kwargs): self.__api_handler = api_handler super().__init__(*args, **kwargs) async def set_activation_status(self, status: int): pass @property async def activation_code(self): pass

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visualization/matplotlib/barwitherror.py

Licas/datascienceexamples

0

5757

<filename>visualization/matplotlib/barwitherror.py<gh_stars>0 from matplotlib import pyplot as plt drinks = ["cappuccino", "latte", "chai", "americano", "mocha", "espresso"] ounces_of_milk = [6, 9, 4, 0, 9, 0] error = [0.6, 0.9, 0.4, 0, 0.9, 0] #Yerr -> element at i position represents +/- error[i] variance on bar[i] value plt.bar( range(len(drinks)),ounces_of_milk, yerr=error, capsize=15) plt.show()

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snippets/show-image.py

district10/snippet-manager

7

186171

# python show image from IPython.display import Image Image("http://scikit-learn.org/dev/_static/ml_map.png", width=800)

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topo-50-50-0-0.py

SoulOH/MPQUIC-different-scheduler-evaluation

0

137393

<gh_stars>0 #!/usr/bin/env python from mininet.net import Mininet from mininet.cli import CLI from mininet.link import Link, TCLink,Intf from subprocess import Popen, PIPE from mininet.log import setLogLevel if '__main__' == __name__: setLogLevel('info') net = Mininet(link=TCLink) # key = "net.mptcp.mptcp_enabled" # value = 1 # p = Popen("sysctl -w %s=%s" % (key, value), shell=True, stdout=PIPE, stderr=PIPE) # stdout, stderr = p.communicate() # print "stdout=",stdout,"stderr=", stderr h1 = net.addHost('h1') h2 = net.addHost('h2') r1 = net.addHost('r1') linkopt_wifi={'bw':10, 'delay':'50ms', "loss":0} linkopt_4g={'bw':10, 'delay':'50ms', "loss":0} linkopt2={'bw':100} net.addLink(r1,h1,cls=TCLink, **linkopt_wifi) net.addLink(r1,h1,cls=TCLink, **linkopt_4g) net.addLink(r1,h2,cls=TCLink, **linkopt2) net.build() r1.cmd("ifconfig r1-eth0 0") r1.cmd("ifconfig r1-eth1 0") r1.cmd("ifconfig r1-eth2 0") h1.cmd("ifconfig h1-eth0 0") h1.cmd("ifconfig h1-eth1 0") h2.cmd("ifconfig h2-eth0 0") r1.cmd("echo 1 > /proc/sys/net/ipv4/ip_forward") r1.cmd("ifconfig r1-eth0 10.0.0.1 netmask 255.255.255.0") r1.cmd("ifconfig r1-eth1 10.0.1.1 netmask 255.255.255.0") r1.cmd("ifconfig r1-eth2 10.0.2.1 netmask 255.255.255.0") h1.cmd("ifconfig h1-eth0 10.0.0.2 netmask 255.255.255.0") h1.cmd("ifconfig h1-eth1 10.0.1.2 netmask 255.255.255.0") h2.cmd("ifconfig h2-eth0 10.0.2.2 netmask 255.255.255.0") h1.cmd("ip rule add from 10.0.0.2 table 1") h1.cmd("ip rule add from 10.0.1.2 table 2") h1.cmd("ip route add 10.0.0.0/24 dev h1-eth0 scope link table 1") h1.cmd("ip route add default via 10.0.0.1 dev h1-eth0 table 1") h1.cmd("ip route add 10.0.1.0/24 dev h1-eth1 scope link table 2") h1.cmd("ip route add default via 10.0.1.1 dev h1-eth1 table 2") h1.cmd("ip route add default scope global nexthop via 10.0.0.1 dev h1-eth0") h2.cmd("ip rule add from 10.0.2.2 table 1") h2.cmd("ip route add 10.0.2.0/24 dev h2-eth0 scope link table 1") h2.cmd("ip route add default via 10.0.2.1 dev h2-eth0 table 1") h2.cmd("ip route add default scope global nexthop via 10.0.2.1 dev h2-eth0") CLI(net) net.stop()

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aptl3/scripts/load_coco.py

matteoterruzzi/aptl3

0

168432

import logging import os from datetime import datetime import random from threading import Thread, Event from pycocotools.coco import COCO from ..db import Database logger = logging.getLogger('load_coco') def load_coco(db: Database, *, coco_dir: str, data_type: str = 'train2017', max_samples: int, raise_ki: bool = True, only_one_caption: bool = False, ): try: metadata = dict( source='MSCOCO', creation=str(datetime.now()), data_type=data_type, ) coco = COCO(f'{coco_dir}{os.path.sep}captions_{data_type}.json') total_len = min(len(coco.imgs), max_samples) db.begin_exclusive_transaction() relation_id: int = db.create_relation(metadata, commit=False) logger.info(f'New relation #{relation_id:d}: {metadata}') except KeyboardInterrupt: logger.info('Interrupted before starting.') if raise_ki: raise return stopped = Event() def _download(shard, n): for i, id_ in zip(range(max_samples), coco.getImgIds()): if i % n != shard: continue img_url = coco.imgs[id_]['coco_url'] db.fetch(img_url) if stopped.is_set(): break shards = 4 download_ths = [ Thread(target=_download, args=(_s, shards), daemon=True, name=f'COCO-{_s:d}-of-{shards:d}') for _s in range(shards) ] for th in download_ths: th.start() def _gen_pairs(): for i, id_ in zip(range(max_samples), coco.getImgIds()): img_url = str(coco.imgs[id_]['coco_url']).strip() img_media_id, _flags = db.ingest_url(img_url, commit=False, update_last_access=False) _ann_ids = coco.getAnnIds(id_) if only_one_caption: _ann_ids = [random.choice(list(_ann_ids))] for ann_id in _ann_ids: ann_url = 'data:,' + str(coco.anns[ann_id]['caption']).strip() ann_media_id, _flags = db.ingest_url(ann_url, commit=False, update_last_access=False) yield img_media_id, ann_media_id print(f"Loading MS COCO ({i}/{total_len} images)...", end="\r", flush=True) try: db.add_media_relations(relation_id, media_id_pairs=_gen_pairs(), commit=False, batch_size=64) except KeyboardInterrupt: logger.info(f'Interrupted.') if raise_ki: raise db.commit() c = db.execute('SELECT COUNT(*) FROM MediaRelations WHERE relation_id = ?', (relation_id,)) inserted: int = c.fetchone()[0] logger.info(f'Added {inserted:d} COCO annotations to relation #{relation_id}.') logger.debug(f'{[th.is_alive() for th in download_ths]=}') stopped.set() for th in download_ths: th.join() return relation_id def main(): import argparse parser = argparse.ArgumentParser(description='Load MS COCO dataset and equivalence') parser.add_argument('db', type=str, help='database data directory') parser.add_argument('coco', type=str, help='COCO dataset directory') parser.add_argument('data', type=str, help='dataset subset (train2017 or val2017)') parser.add_argument('--limit', default=999999, type=int, help='limit the number of samples (defaults to 999999)') parser.add_argument('--log', default='INFO', type=str, help='logging level (defaults to INFO)') args = parser.parse_args() logging.basicConfig(level=args.log.upper(), format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s', datefmt='%m-%d %H:%M') db = Database(args.db) db.execute('PRAGMA synchronous = OFF') load_coco(db=db, coco_dir=args.coco, data_type=args.data, max_samples=args.limit, raise_ki=False) if __name__ == '__main__': main()

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tests/distributed/test_simple_hub_pods/test_integration.py

hirakjyoti08/jina

1

103705

import os import pytest from jina import __default_host__, __docker_host__ from ..helpers import create_workspace, wait_for_workspace, create_flow, assert_request cur_dir = os.path.dirname(os.path.abspath(__file__)) compose_yml = os.path.join(cur_dir, 'docker-compose.yml') flow_yaml = os.path.join(cur_dir, 'flow.yml') JINAD_HOST = __default_host__ GATEWAY_HOST = __default_host__ JINAD_PORT = 8000 GATEWAY_PORT = 45630 @pytest.mark.skip('jinad with docker-compose not supported for now') @pytest.mark.parametrize('docker_compose', [compose_yml], indirect=['docker_compose']) def test_simple_hub_deployments(docker_compose): workspace_id = create_workspace(filepaths=[flow_yaml]) assert wait_for_workspace(workspace_id) flow_id = create_flow(workspace_id=workspace_id, filename='flow.yml') expected_text = 'text:hey, dude' response = assert_request( method='post', url=f'http://{GATEWAY_HOST}:{GATEWAY_PORT}/search', payload={'top_k': 10, 'data': [expected_text]}, ) print(f'Response is: {response}') assert expected_text + ' hurray' * 2 == response['data']['docs'][0]['text'] assert_request( method='get', url=f'http://{JINAD_HOST}:{JINAD_PORT}/flows/{flow_id}' ) assert_request( method='delete', url=f'http://{JINAD_HOST}:{JINAD_PORT}/flows/{flow_id}', )

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beryllia/util.py

examknow/beryllia

0

45089

import re, traceback from datetime import timedelta from ipaddress import ip_address, IPv4Address, IPv6Address from ipaddress import ip_network, IPv4Network, IPv6Network from typing import List, Optional, Set, Tuple, Union from ircrobots import Server from irctokens import build from ircchallenge import Challenge from ircrobots.matching import ANY, Folded, Response, SELF from ircstates.numerics import * # not in ircstates.numerics RPL_STATS = "249" RPL_ENDOFSTATS = "219" RE_OPERNAME = re.compile(r"^is opered as (\S+)(?:,|$)") SECONDS_MINUTES = 60 SECONDS_HOURS = SECONDS_MINUTES*60 SECONDS_DAYS = SECONDS_HOURS*24 SECONDS_WEEKS = SECONDS_DAYS*7 TIME_UNITS_SHORT = list(zip( "wdhms", [SECONDS_WEEKS, SECONDS_DAYS, SECONDS_HOURS, SECONDS_MINUTES, 1] )) TIME_UNITS_LONG = list(zip( ["weeks", "days", "hours", "minutes", "seconds"], [SECONDS_WEEKS, SECONDS_DAYS, SECONDS_HOURS, SECONDS_MINUTES, 1] )) def pretty_delta( delta: timedelta, max_units: int=2, long: bool=False ) -> str: denominator = int(delta.total_seconds()) outs: List[str] = [] time_units = TIME_UNITS_LONG if long else TIME_UNITS_SHORT for unit, div in time_units: numerator, denominator = divmod(denominator, div) if numerator > 0: sep = " " if long else "" outs.append(f"{numerator}{sep}{unit}") if len(outs) == max_units: break return (" " if long else "").join(outs) async def oper_up( server: Server, oper_name: str, oper_file: str, oper_pass: str): try: challenge = Challenge(keyfile=oper_file, password=<PASSWORD>) except Exception: traceback.print_exc() else: await server.send(build("CHALLENGE", [oper_name])) challenge_text = Response(RPL_RSACHALLENGE2, [SELF, ANY]) challenge_stop = Response(RPL_ENDOFRSACHALLENGE2, [SELF]) #:lithium.libera.chat 740 sandcat :foobarbazmeow #:lithium.libera.chat 741 sandcat :End of CHALLENGE while True: challenge_line = await server.wait_for({ challenge_text, challenge_stop }) if challenge_line.command == RPL_RSACHALLENGE2: challenge.push(challenge_line.params[1]) else: retort = challenge.finalise() await server.send(build("CHALLENGE", [f"+{retort}"])) break async def get_whois( server: Server, nickname: str ) -> Optional[Tuple[str, Optional[str]]]: await server.send(build("WHOIS", [nickname])) whois_mask = Response(RPL_WHOISUSER, [SELF, Folded(nickname)]) whois_oper = Response(RPL_WHOISOPERATOR, [SELF, Folded(nickname)]) whois_end = Response(RPL_ENDOFWHOIS, [SELF, Folded(nickname)]) whois_line = await server.wait_for({whois_mask, whois_end}) if whois_line.command == RPL_WHOISUSER: nick, user, host = whois_line.params[1:4] mask = f"{nick}!{user}@{host}" whois_line = await server.wait_for({whois_oper, whois_end}) if whois_line.command == RPL_WHOISOPERATOR: match = RE_OPERNAME.search(whois_line.params[2]) if match is not None: return (mask, match.group(1)) return (mask, None) else: return None async def get_statsp(server: Server) -> List[Tuple[str, str]]: await server.send(build("STATS", ["p"])) statsp_texts: List[str] = [] while True: statsp_line = await server.wait_for({ Response(RPL_STATS, [SELF, "p", ANY]), Response(RPL_ENDOFSTATS, [SELF, "p"]) }) if statsp_line.command == RPL_STATS: statsp_texts.append(statsp_line.params[2]) else: break opers: List[Tuple[str, str]] = [] for statsp_text in statsp_texts[:-1]: nick, *_ = statsp_text.split(" ", 1) whois = await get_whois(server, nick) if whois is not None: mask, oper = whois if oper is not None: opers.append((oper, mask)) return opers def try_parse_ip( ip: str ) -> Optional[Union[IPv4Address, IPv6Address]]: try: return ip_address(ip) except ValueError: return None def try_parse_cidr( cidr: str ) -> Optional[Union[IPv4Network, IPv6Network]]: try: return ip_network(cidr, strict=False) except ValueError: return None def find_unescaped(s: str, c: Set[str]) -> List[int]: indexes: List[int] = [] i = 0 while i < len(s): c2 = s[i] if c2 == "\\": i += 1 elif c2 in c: indexes.append(i) i += 1 return indexes WILDCARDS_GLOB = {"?": "_", "*": "%"} WILDCARDS_SQL = set("_%") def glob_to_sql(glob: str) -> str: to_find = WILDCARDS_SQL | set(WILDCARDS_GLOB.keys()) to_escape = find_unescaped(glob, to_find) glob_l = list(glob) for index in to_escape: char = glob_l[index] # escape sql wildcard characters if char in WILDCARDS_SQL: glob_l[index] = f"\\{char}" # switch glob wildcards to sql wildcards elif char in WILDCARDS_GLOB: glob_l[index] = WILDCARDS_GLOB[char] return "".join(glob_l) def looks_like_glob(s: str) -> bool: return bool(set(s) & set("?*"))

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boa3_test/test_sc/built_in_methods_test/StrSplit.py

hal0x2328/neo3-boa

25

119908

from typing import List from boa3.builtin import public @public def main(string: str, sep: str, maxsplit: int) -> List[str]: return string.split(sep, maxsplit)

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app/views.py

bzg/ApiViz

4

107257

<gh_stars>1-10 #!/usr/bin/env python # -*- encoding: utf-8 -*- ### good encoding of flash messages ### cf : https://stackoverflow.com/questions/8924014/how-to-handle-my-unicodedecodeerror import sys reload(sys) sys.setdefaultencoding('utf-8') ### import all from app.__init__ from . import * from flask import jsonify, flash, render_template, \ url_for, make_response, request, redirect, \ send_file # from werkzeug.security import generate_password_hash, check_password_hash from werkzeug.exceptions import BadRequest ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### ### AUTH - TOKEN ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### ######################################################################################## # Access token ### from prettyprinted youtube channel """ def token_required(f): @wraps(f) def decorated( *args, **kwargs ): token = None if 'x-access-token' in request.headers: token = request.headers['x-access-token'] if not token: return jsonify({'message' : 'Token is missing!'}), 401 try: data = jwt.decode(token, app.config['SECRET_KEY']) current_user = User.query.filter_by(public_id=data['public_id']).first() except: return jsonify({'message' : 'Token is invalid!'}), 401 return f(current_user, *args, **kwargs) return decorated """ ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### ### ERRORS HANDLERS ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### @app.errorhandler(403) @app.errorhandler(404) @app.errorhandler(500) @app.route("/error/<int:err_code>", defaults={ "error": BadRequest }) def errorHandler(error, err_code=400): if err_code == 404 : # | error.code == 404 : error_code = 404 template = "errors/404.html" elif err_code == 403 : # | error.code == 403 : error_code = 403 template = "errors/403.html" elif err_code == 500 : # | error.code == 500 : error_code = 500 template = "errors/500.html" else : error_code = 400 template = "errors/400.html" app_config = getDocuments(mongo_config_global) return render_template( template, config_name = config_name, # prod or default... site_section = "error", error_code = str(error_code), app_metas = app_metas, app_config = app_config, language = "fr" , languages_dict = app_languages_dict , # error_msg = u"accès interdit", # user_infos = current_user.get_public_infos ), error_code ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### ### CONFIG ROUTES - BACKEND API ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### def DocOidToString(data): # log_app.debug("data : %s", data) obj = {} for key in data: if isinstance(data[key], ObjectId): obj[key] = str(data[key]) else: obj[key] = data[key] # log_app.debug("obj : %s", obj) return obj def getDocuments(collection, query={}, oid_to_id=True, as_list=False, field="field") : ### query collection and transform as list results = list(collection.find(query) ) # log_app.debug("config app route / results - 1 : \n%s", pformat(results) ) ### ObjectId to string if oid_to_id : results = [ DocOidToString(i) for i in results ] # log_app.debug("config app route / results - 2 : \n%s", pformat(results) ) ### list to dict if as_list == False : results = { i[field] : i for i in results } # log_app.debug("config app route / results - 3 : \n%s", pformat(results) ) return results def checkJWT(token, url_check="http://localhost:4100/api/auth/tokens/confirm_access"): ### TO DO return True @app.route('/backend/api/config/<string:collection>/<string:doc_id>', methods=['GET','POST','DELETE']) @app.route('/backend/api/config/<string:collection>', methods=['GET'], defaults={"doc_id" : None}) @app.route('/backend/api/config', methods=['GET'], defaults={'collection': 'global', "doc_id" : None}) def config_global(collection, doc_id=None): """ Main route to GET and POST/PUT/DELETE choices : global | endpoints | styles | routes | socials variables : <collection> and <doc_id> arguments : as_list (bool), field (str) example : http://localhost:8100/backend/api/config?as_list=true """ log_app.debug("config app route") log_app.debug("config app route / collection : %s", collection ) log_app.debug("config app route / doc_id : %s", doc_id ) ### target right config collection if collection in ["global" , "footer", "navbar", "endpoints" , "styles" , "routes", "socials" ] : mongoColl = mongoConfigColls[collection] ### imported from . (and from there from .api.__init__ ) else : log_app.warning("error : -%s- is not a valid config collection (redirect)", collection) return redirect( "/error/400" ) ### get request args if any as_list = request.args.get('as_list', default=False, type=bool) field = request.args.get('field', default="field", type=str) token = request.args.get('token', default=None, type=str) log_app.debug("config app route / as_list : %s", as_list ) ### filter out field arg to unique identifiers fields in documents if field not in ['_id', 'field'] : field = 'field' ### build query if any query = {} if doc_id : query = {"_id" : ObjectId(doc_id)} ### check if token allows user to POST if token : is_authorized = checkJWT(token) ### TO DO if request.method == 'POST': if is_authorized : return "hello config master / POST ... praise be" else : return "noooope" elif request.method == 'DELETE': if is_authorized : return "hello config master / DELETE ... praise be" else : return "noooope" elif request.method == 'GET': app_config_dict = getDocuments(mongoColl, query=query, as_list=as_list, field=field) return jsonify( { "msg" : "this is the results from your query on the '%s' config collection" % collection, "query" : query, "request" : { "url" : request.url, "args" : request.args, "method" : request.method, "collection" : collection, "doc_id" : doc_id, }, "app_config" : app_config_dict } ) ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### ### CLIENT ROUTES ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### # @app.route('/', methods=['GET']) # @app.route('/home/<string:lang>', methods=['GET']) # @app.route('/home', methods=['GET'], defaults={"lang":"en"}) # def home(lang="fr"): # log_app.debug("entering new home page") # app_config = getDocuments(mongo_config_global) # log_app.debug("app_config :/n%s", pformat(app_config)) # if lang == "fr" : # template = "new-home.html" # else : # template = "new-home-english.html" # return render_template( # template, # config_name = config_name, # prod, testing, default... # app_config = app_config, # app_metas = app_metas, # language = lang, # ) # @app.route('/tools/<string:lang>', methods=['GET']) # @app.route('/tools', methods=['GET'], defaults={"lang":"en"}) # def Tools(lang="en"): # log_app.debug("entering tools page") # app_config = getDocuments(mongo_config_global) # if lang == "fr" : # template = "les-outils.html" # else : # template = "les-outils-english.html" # return render_template( # template, # config_name = config_name, # prod, testing, default... # app_metas = app_metas, # app_config = app_config, # language = lang # ) @app.route('/sonum-banner-carto/<string:lang>', methods=['GET']) @app.route('/sonum-banner-carto', methods=['GET'], defaults={"lang":"fr"}) def Banner(lang="fr"): log_app.debug("entering test banner-carto page") app_config = getDocuments(mongo_config_global) if lang == "fr" : template = "sonum-banner-carto.html" else : template = "sonum-banner-carto.html" return render_template( template, config_name = config_name, # prod, testing, default... app_metas = app_metas, app_config = app_config, language = lang ) @app.route('/', methods=['GET','POST'],defaults={'path': ''}) @app.route('/<path:path>', methods=['GET','POST']) def spa(path): log_app.debug("entering SPA page") app_config = getDocuments(mongo_config_global) return render_template( "spa.html", config_name = config_name, # prod, testing, default... app_config = app_config, app_metas = app_metas, language = "fr" ) ANTI_SPAM_FIELD_NAME = "userMiddlename" ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### ### FILES ROUTES ### + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ### @app.route('/download/<file_ext>/<file_name>', methods=['GET'] ) # this is a job for GET, not POST def download_file(file_ext, file_name): """ send file from server """ log_app.debug("file_name : %s ", file_name) log_app.debug("file_ext : %s ", file_ext) log_app.info("file_ext in AUTHORIZED_FILETYPES_LIST: %s", (file_ext in AUTHORIZED_FILETYPES_LIST) ) if file_ext in AUTHORIZED_FILETYPES_LIST : file_mimetype = AUTHORIZED_FILETYPES_DICT[file_ext]["mimetype"] file_foldername = AUTHORIZED_FILETYPES_DICT[file_ext]["folder"] file_folder = "static/{}/".format(file_foldername) file_name_ext = "{}.{}".format(file_name, file_ext) full_filepath = file_folder + file_name_ext try : return send_file( full_filepath, mimetype = file_mimetype, attachment_filename = file_name_ext, as_attachment = True ) except : log_app.error("downloading this file is not working: %s.%s ", file_name, file_ext ) return redirect(url_for('home')) else : log_app.error("downloading this file is not authorized: %s.%s ", file_name, file_ext ) return redirect(url_for('home'))

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lib/utils/blob.py

TheRevanchist/DeepWatershedDetection

0

13327

<reponame>TheRevanchist/DeepWatershedDetection # -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by <NAME> - extended by <NAME> # -------------------------------------------------------- """Blob helper functions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import cv2 import random def im_list_to_blob(ims): """Convert a list of images into a network input. Assumes images are already prepared (means subtracted, BGR order, ...). """ max_shape = np.array([im.shape for im in ims]).max(axis=0) num_images = len(ims) blob = np.zeros((num_images, max_shape[0], max_shape[1], 3), dtype=np.float32) for i in range(num_images): im = ims[i] blob[i, 0:im.shape[0], 0:im.shape[1], :] = im return blob def prep_im_for_blob(im, pixel_means, global_scale, args): """Mean subtract and scale an image for use in a blob.""" im = im.astype(np.float32, copy=False) # substract mean if args.substract_mean == "True": im -= pixel_means # do global scaling im = cv2.resize(im, None, None, fx=global_scale, fy=global_scale, interpolation=cv2.INTER_LINEAR) im_size_max = np.max(im.shape[0:2]) # Prevent the biggest axis from being more than MAX_SIZE if im_size_max > args.max_edge: if not args.crop == "True": # scale down if bigger than max size re_scale = (float(args.max_edge) / float(im_size_max)) im = cv2.resize(im, None, None, fx=re_scale, fy=re_scale, interpolation=cv2.INTER_LINEAR) global_scale = global_scale*re_scale crop_box = [0,0,im.shape[0],im.shape[1]] else: # Crop image topleft = random.uniform(0,1)<args.crop_top_left_bias # crop to max size if necessary if im.shape[0] <= args.max_edge or topleft: crop_0 = 0 else: crop_0 = random.randint(0,im.shape[0]-args.max_edge) if im.shape[1] <= args.max_edge or topleft: crop_1 = 0 else: crop_1 = random.randint(0,im.shape[1]-args.max_edge) crop_box = [crop_0, crop_1, min(crop_0+args.max_edge,im.shape[0]), min(crop_1+args.max_edge,im.shape[1])] im = im[crop_box[0]:crop_box[2],crop_box[1]:crop_box[3]] else: crop_box = [0, 0, im.shape[0], im.shape[1]] if not args.pad_to == 0: # pad to fit RefineNet #TODO fix refinenet padding problem y_mulity = int(np.ceil(im.shape[0] / float(args.pad_to))) x_mulity = int(np.ceil(im.shape[1] / float(args.pad_to))) canv = np.ones([y_mulity * args.pad_to, x_mulity * args.pad_to,3], dtype=np.uint8) * 255 canv[0:im.shape[0], 0:im.shape[1]] = im im = canv return im, global_scale, crop_box

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src/utils/torch_common.py

quochungto/SIIM-COVID19-Detection

0

7687

import os import gc import random import numpy as np import torch def seed_everything(seed): os.environ['PYTHONHASHSEED'] = str(seed) random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True def memory_cleanup(): """ Cleans up GPU memory https://github.com/huggingface/transformers/issues/1742 """ for obj in gc.get_objects(): if torch.is_tensor(obj): del obj gc.collect() torch.cuda.empty_cache()

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settings.py

rshorey/ms-match

0

172530

<filename>settings.py<gh_stars>0 from collections import OrderedDict #pull this off your google sheet sheet_id = '125NM05dqHKy3-97GkieVwn4V5Iy0Vj-w7erh9tAJxlA' #do new entries appear immediately on the site #or do they need to be approved first? require_approval = False #what do you want the fields to be called? #the cannonical names are keys, the names you want to display are values #don't remove any keys, things will crash #if you don't want a field to appear, keep the key but make the value None fieldnames = OrderedDict([('Name','Name'), ('Description','Description'), ('Category','Category'), ('Address1',None), ('Address2',None), ('City','City'), ('State','State'), ('Zip','Zipcode'), ('Phone','Phone number'), ('Website','Website'), ('Email','Email Address') ]) #what categories are allowed? Put in as many as you want but #they'll display in a menu so if you do tons it'll look bad #the first item will be the default categories = ['Other', 'Stuff', 'Things']

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dbtest.py

AnykeyNL/AutonomousDB_AutonomousLinux_Python

0

101984

import cx_Oracle DB = "xxx_high" DB_USER = "admin" DB_PASSWORD = "password" connection = cx_Oracle.connect(DB_USER, DB_PASSWORD, DB) print ("Connected")

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code_examples/popart/block_sparse/examples/test_block_sparse.py

payoto/graphcore_examples

260

105344

<reponame>payoto/graphcore_examples # Copyright (c) 2020 Graphcore Ltd. All rights reserved. import os import numpy as np from functools import reduce from operator import mul import popart import pytest # range for filling blocks MATRIX_LOW_VALUE = -10 MATRIX_HIGH_VALUE = 10 # library provides support for 3 kinds of sparse MM's # from set of 2 inputs and one output, 2 of them will be dense # and the third will be sparse g_sparseMatMulTypeLookup = { 'DENSE_LHS_SPARSE_RHS_DENSE_OUT': 0, 'DENSE_LHS_DENSE_RHS_SPARSE_OUT': 1, 'SPARSE_LHS_SPARSE_RHS_SPARSE_OUT': 2 } # g_ -> global # g_input_data_type = "float32" g_output_data_type = "float32" g_pp_data_type = "float32" np.set_printoptions(linewidth=500) g_random_sparse_mask = np.random.RandomState() g_random_data = np.random.RandomState() g_random_labels = np.random.RandomState() """ Set seeds of random generatorts. """ g_random_sparse_mask.seed(1) g_random_data.seed(1) g_random_labels.seed(1) def create_sparse_list(dims, block_size, sparsity, initial_value=0): """ dims: dimensions of the sparse matrix block_size: size of a block (8x8, 16x16 etc) sparsity: sparsity level (0.4 means 40% of blocks are empty) returns-- block_sparse_matrix: np.array of num_blocks * block_sz dense_matrix: np.array if size dim with a dense representation of the matrix. i.e. explcit zeros for a zero block. Used to perform dense MM's for a reference output mask: list with sparsity pattern. size = num_blocks (both dense and sparse) e.g for a sparse matrix of size (6, 6) with block size 2x2 Matrix contains 9 blocks of size 2x2, some sparse and some dense If the 6x6 matrices has 2 non zero blocks, then .. Inputs: dims = [6, 6] block_size = [2,2] sparsity = 0.4 (say) Outputs: block_sparse_matrix = 2 x 4 array dense_matrix = 6x6 array sparsity = 9x1 list """ block_size_row = block_size[0] block_size_col = block_size[1] num_block_rows = dims[0] // block_size_row num_block_cols = dims[1] // block_size_col assert(sparsity < 1.0) proportion = [sparsity, 1 - sparsity] mask = g_random_sparse_mask.choice([0, 1], size=(num_block_rows, num_block_cols), p=proportion) # dont want mask to be all zeros while np.all(mask == 0): mask = g_random_sparse_mask.choice([0, 1], size=(num_block_rows, num_block_cols), p=proportion) if initial_value == 0: dense_matrix = np.zeros((num_block_rows * block_size_row, num_block_cols * block_size_col)) else: dense_matrix = np.empty((num_block_rows * block_size_row, num_block_cols * block_size_col)) dense_matrix.fill(initial_value) block_sparse_matrix = [] for block_row in range(num_block_rows): for block_col in range(num_block_cols): if mask[block_row][block_col]: block_data = g_random_data.randint(low=MATRIX_LOW_VALUE, high=MATRIX_HIGH_VALUE, size=block_size_row * block_size_col).astype("float32") block_sparse_matrix.append(block_data) dense_matrix[block_row * block_size_row: (block_row+1) * block_size_row, block_col * block_size_col: (block_col+1) * block_size_col] = block_data.reshape(block_size_row, block_size_col) # At this point mask is a 2D array, flatten it into 1D list and return, bsr_rhs is already a list (so convert to array) return np.array(block_sparse_matrix), dense_matrix, mask.flatten().tolist() def create_dense_matrix(dims): return g_random_data.randint(low=MATRIX_LOW_VALUE, high=MATRIX_HIGH_VALUE, size=dims).astype(g_input_data_type) def create_sparse_matrix(nominal_shape, block_size, sparsity, initial_value=0): """ Create a sparse_matrix. Inputs: nominal_shape: List of dimensions of the sparse tensor e.g (2, 3, 4, 4) block_size : size of each block (e.g. [8, 8, 8]) sparsity : block sparsity level (0.4 means, 40% of blocks are zeros) Outputs: bsr : sparse representation of matrix (nnz_blocks * block size) lengths_per_2d_plane: List with num-non-zero blocks per group dim. i.e. for a (2, 3, 4, 4) tensor with 2 nnz_blocks in each 4x4 matrix, this will have shape of 6x1 and each row storing 2 dense_matrix: dense representation of the matrix (for ref calc) mask : list of num_blocks (1 for non-zero blocks and 0 for others) """ # skip last two dimensions # last 2 dims enter the MM, others form the group num_grouped_dims = reduce(mul, nominal_shape[:-2], 1) rows = nominal_shape[-2] cols = nominal_shape[-1] # Create dense matrix of nominal dims if initial_value == 0: dense_matrix = np.zeros(nominal_shape).astype(g_input_data_type) else: dense_matrix = np.empty(nominal_shape).astype(g_input_data_type) dense_matrix.fill(initial_value) dense_matrix = dense_matrix.reshape((num_grouped_dims, rows, cols)) dims = [nominal_shape[-2], nominal_shape[-1]] bsr = [] bsr_lengths_per_2d_plane = [] mask = [] for dim in range(num_grouped_dims): _bsr, dense_matrix[dim], _mask = create_sparse_list(dims, block_size, sparsity, initial_value) # _bsr comes as array # _mask comes as list bsr.extend(_bsr) mask.extend(_mask) bsr_lengths_per_2d_plane.append(_bsr.shape[0]) dense_matrix = dense_matrix.reshape(nominal_shape) mask = np.array(mask) block_size_row = block_size[0] block_size_col = block_size[1] num_block_rows = dims[0] // block_size_row num_block_cols = dims[1] // block_size_col # all parameters are returned as numpy arrays return np.array(bsr), np.array(bsr_lengths_per_2d_plane), dense_matrix, mask def mm(lhs, rhs): return np.matmul(lhs, rhs) # Stable softmax numpy implementation def softmax(x): x_max = np.max(x, axis = -1) x = x - np.expand_dims(x_max, axis=-1) x = np.exp(x) x_sum = np.sum(x, axis=-1) x = x / np.expand_dims(x_sum, axis=-1) return x def sparse_mm_infer(sparse_mm_type, lhs_dims, vanilla_rhs_dims, block_size, sparsity_level, transpose_rhs, memory_cycle_ratio, inner_group_size): """ """ if transpose_rhs: matmul_dims = [lhs_dims[-2], vanilla_rhs_dims[-1], vanilla_rhs_dims[-2]] else: matmul_dims = [lhs_dims[-2], vanilla_rhs_dims[-2], vanilla_rhs_dims[-1]] lhs = create_dense_matrix(lhs_dims) if sparse_mm_type == g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT']: bsr_rhs, lengths_per_2d_plane, vanilla_rhs, sparsity_mask = create_sparse_matrix(vanilla_rhs_dims, block_size[1:], sparsity_level) rhs = bsr_rhs rhs_dims = bsr_rhs.shape elif sparse_mm_type == g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT']: output_dims = lhs_dims[:-1] output_dims.append(vanilla_rhs_dims[-1]) output_block_size = [block_size[0], block_size[2]] bsr_output, lengths_per_2d_plane, _, sparsity_mask = create_sparse_matrix(output_dims, output_block_size, sparsity_level) rhs_dims = vanilla_rhs_dims rhs = create_dense_matrix(rhs_dims) # Create a builder and construct a graph builder = popart.Builder() lhs_tensorInfo = popart.TensorInfo("FLOAT", lhs_dims) rhs_tensorInfo = popart.TensorInfo("FLOAT", rhs_dims) lhsTensor = builder.addInputTensor(lhs_tensorInfo) rhsTensor = builder.addInputTensor(rhs_tensorInfo) outTensor = builder.customOp(opName = "BSMatMul", opVersion=1, domain = "ai.graphcore", inputs = [lhsTensor, rhsTensor], attributes = { "bsr_rhs_lengths_per_2d_plane": lengths_per_2d_plane.tolist(), "matrix_dims": matmul_dims, "block_size": block_size, "sparsity_mask": sparsity_mask.tolist(), "bsmatmul_type": sparse_mm_type, "transpose_rhs": transpose_rhs, "memory_cycle_ratio": memory_cycle_ratio, "inner_group_size": inner_group_size, "in_type": g_input_data_type, "out_type": g_output_data_type, "pp_type": g_pp_data_type })[0] builder.addOutputTensor(outTensor) proto = builder.getModelProto() # Describe how to run the model dataFlow = popart.DataFlow(1, {outTensor: popart.AnchorReturnType("ALL")}) # Create a session to compile and execute the graph session = popart.InferenceSession( fnModel=proto, dataFlow=dataFlow, deviceInfo=popart.DeviceManager().acquireAvailableDevice(1)) # Compile graph session.prepareDevice() # Create buffers to receive results from the execution anchors = session.initAnchorArrays() rhs = np.array(rhs, dtype=g_input_data_type) stepio = popart.PyStepIO({lhsTensor: lhs, rhsTensor: rhs}, anchors) session.run(stepio) ipuOutput = anchors[outTensor] if sparse_mm_type == g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT']: if transpose_rhs: transpose_indices = list(range(len(vanilla_rhs_dims))) transpose_indices[-2], transpose_indices[-1] = transpose_indices[-1], transpose_indices[-2] vanilla_rhs = vanilla_rhs.transpose(tuple(transpose_indices)) goldOutput = mm(lhs, vanilla_rhs) else: goldOutput = mm(lhs, vanilla_rhs) else: assert len(lhs.shape) == len(rhs.shape) if(len(lhs.shape) == 2): lhs = np.expand_dims(lhs, 0) rhs = np.expand_dims(rhs, 0) mmOutput = mm(lhs, rhs) totalGroupDims = int(np.prod(lhs_dims[:-2])) num_rows_sparsity_mask_2d = output_dims[-2] // block_size[0] num_cols_sparsity_mask_2d = output_dims[-1] // block_size[2] assert sparsity_mask.shape == (totalGroupDims * num_rows_sparsity_mask_2d * num_cols_sparsity_mask_2d,) mmOutput = mmOutput.reshape((totalGroupDims, lhs_dims[-2], rhs_dims[-1])) goldOutput = [] for dim in range(totalGroupDims): offset = num_rows_sparsity_mask_2d * num_cols_sparsity_mask_2d mmOutput_2d = mmOutput[dim] sliced_sparsity_mask = sparsity_mask[dim * offset: dim * offset + offset] for sparsity_mask_idx in range(len(sliced_sparsity_mask)): if sliced_sparsity_mask[sparsity_mask_idx]: mmOutput_2d_row_start = (sparsity_mask_idx // num_cols_sparsity_mask_2d) * block_size[0] mmOutput_2d_row_end = mmOutput_2d_row_start + block_size[0] mmOutput_2d_col_start = (sparsity_mask_idx % num_cols_sparsity_mask_2d) * block_size[2] mmOutput_2d_col_end = mmOutput_2d_col_start + block_size[2] mmOutput_2d_sliced = mmOutput_2d[mmOutput_2d_row_start: mmOutput_2d_row_end, mmOutput_2d_col_start: mmOutput_2d_col_end] goldOutput.append(mmOutput_2d_sliced.reshape(block_size[0] * block_size[2])) goldOutput = np.array(goldOutput) return ipuOutput, goldOutput def sparse_mm_train(sparse_mm_type, lhs_dims, vanilla_rhs_dims, block_size, sparsity_level, transpose_rhs, memory_cycle_ratio, inner_group_size): if transpose_rhs: matmul_dims = [lhs_dims[-2], vanilla_rhs_dims[-1], vanilla_rhs_dims[-2]] else: matmul_dims = [lhs_dims[-2], vanilla_rhs_dims[-2], vanilla_rhs_dims[-1]] lhs = create_dense_matrix(lhs_dims) if sparse_mm_type == g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT']: bsr_rhs, lengths_per_2d_plane, vanilla_rhs, sparsity_mask = create_sparse_matrix(vanilla_rhs_dims, block_size[1:], sparsity_level) rhs = bsr_rhs rhs_dims = bsr_rhs.shape elif sparse_mm_type == g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT']: output_dims = lhs_dims[:-1] output_dims.append(vanilla_rhs_dims[-1]) output_block_size = [block_size[0], block_size[2]] bsr_output, lengths_per_2d_plane, vanilla_output, sparsity_mask = create_sparse_matrix(output_dims, output_block_size, sparsity_level) lhs_inv = np.linalg.inv(lhs) rhs = np.matmul(lhs_inv, vanilla_output) rhs_dims = vanilla_rhs_dims # MODEL CREATION builder = popart.Builder() lhs_tensorInfo = popart.TensorInfo("FLOAT", lhs_dims) lhsTensor = builder.addInputTensor(lhs_tensorInfo) rhsTensor = builder.addInitializedInputTensor(rhs) outTensor = builder.customOp(opName = "BSMatMul", opVersion=1, domain = "ai.graphcore", inputs = [lhsTensor, rhsTensor], attributes = { "bsr_rhs_lengths_per_2d_plane": lengths_per_2d_plane.tolist(), "matrix_dims": matmul_dims, "block_size": block_size, "sparsity_mask": sparsity_mask.tolist(), "bsmatmul_type": sparse_mm_type, "transpose_rhs": transpose_rhs, "memory_cycle_ratio": memory_cycle_ratio, "inner_group_size": inner_group_size, "in_type": g_input_data_type, "out_type": g_output_data_type, "pp_type": g_pp_data_type })[0] builder.addOutputTensor(outTensor) probs = builder.aiOnnx.softmax([outTensor], axis=1) if sparse_mm_type == g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT']: labels_shape = lhs_dims[:-1] elif sparse_mm_type == g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT']: labels_shape = [np.sum(sparsity_mask)] label_tensorInfo = popart.TensorInfo("INT32", labels_shape) labelTensor = builder.addInputTensor(label_tensorInfo) loss = builder.aiGraphcore.nllloss([probs, labelTensor], debugContext = "nllLossVal") proto = builder.getModelProto() ####################### # Describe how to run the model anchor_desc = { outTensor: popart.AnchorReturnType("ALL"), loss: popart.AnchorReturnType("ALL") } dataFlow = popart.DataFlow(1, anchor_desc) label_data = g_random_labels.choice(9, labels_shape) session = popart.TrainingSession(fnModel=proto, loss=loss, deviceInfo=popart.DeviceManager().acquireAvailableDevice(1), optimizer=popart.ConstSGD(0.01), dataFlow=dataFlow) # Compile graph session.prepareDevice() # Create buffers to receive results from the execution anchors = session.initAnchorArrays() # TRAINING session.weightsFromHost() stepio = popart.PyStepIO({ lhsTensor: lhs, labelTensor: label_data}, anchors) session.run(stepio) def sparse_softmax(dims, block_size, sparsity_level, inner_group_size): """ """ sparse_input, lengths_per_2d_plane, dense_input, sparsity_mask = create_sparse_matrix(dims, block_size, sparsity_level, -1000) # Create a builder and construct a graph builder = popart.Builder() tensor_info = popart.TensorInfo("FLOAT", sparse_input.shape) input_tensor = builder.addInputTensor(tensor_info) output_tensor = builder.customOp(opName = "BsSoftmax", opVersion = 1, domain = "ai.graphcore", inputs = [input_tensor], attributes = { "matrixDims": dims, "blockSize": block_size, "sparsity": sparsity_mask.tolist(), "groupSizes": lengths_per_2d_plane.tolist(), "innerGroupSize": inner_group_size, "subBlockMaskPerGroup": "None" * len(lengths_per_2d_plane) })[0] builder.addOutputTensor(output_tensor) proto = builder.getModelProto() # Describe how to run the model dataFlow = popart.DataFlow(1, {output_tensor: popart.AnchorReturnType("ALL")}) # Create a session to compile and execute the graph session = popart.InferenceSession( fnModel=proto, dataFlow=dataFlow, deviceInfo=popart.DeviceManager().acquireAvailableDevice(1)) # Compile graph session.prepareDevice() # Create buffers to receive results from the execution anchors = session.initAnchorArrays() sparse_input = np.array(sparse_input, dtype=g_input_data_type) stepio = popart.PyStepIO({input_tensor: sparse_input}, anchors) session.run(stepio) ipu_output = anchors[output_tensor] group_dims = dims[:-2] mat_dims = dims[-2:] blocks_2d = [mat_dims[0] // block_size[0], mat_dims[1] // block_size[1]] num_blocks_2d = blocks_2d[0] * blocks_2d[1] block_area = block_size[0] * block_size[1] total_group_dims = int(np.prod(group_dims)) assert sparsity_mask.shape == (total_group_dims * num_blocks_2d,) cpu_output = softmax(dense_input) np.set_printoptions(precision=2) np.set_printoptions(suppress=True) cpu_output = cpu_output.reshape([total_group_dims, blocks_2d[0], block_size[0], blocks_2d[1], block_size[1]]) cpu_output = np.transpose(cpu_output, [0, 1, 3, 2, 4]) cpu_output = cpu_output.reshape(total_group_dims, num_blocks_2d, block_area) gold_output = [] offset = 0 for g in range(total_group_dims): cpu_output_2d = cpu_output[g] sliced_sparsity_mask = sparsity_mask[offset: offset + num_blocks_2d] offset = offset + num_blocks_2d for sparsity_mask_idx in range(num_blocks_2d): if sliced_sparsity_mask[sparsity_mask_idx]: gold_output.append(cpu_output_2d[sparsity_mask_idx]) gold_output = np.array(gold_output) assert ipu_output.shape == gold_output.shape return ipu_output, gold_output # # INFERENCE TEST # # test_data_infer tuple --> (matMulType, lhs_dims, rhs_dims, block_size, sparsity, transpose_rhs, inner_group_size) test_data_infer = [ # 2D ("tag_inf_0", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [8, 8], [8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_1", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [16, 16], [16, 16], [8, 8, 8], 0.1, False, 1), ("tag_inf_2", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [32, 32], [32, 32], [16, 8, 8], 0.8, False, 1), ("tag_inf_3", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [64, 64], [64, 256], [64, 8, 64], 0.9, False, 1), ("tag_inf_4", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [128, 128], [128, 128], [32, 8, 16], 0.2, False, 1), # 3D, False ("tag_inf_5", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [2, 8, 8], [2, 8, 8], [8, 8, 8], 0.1, False, 1), ("tag_inf_6", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [5, 16, 16], [5, 16, 16], [8, 8, 8], 0.3, False, 1), ("tag_inf_7", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [7, 32, 32], [7, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_8", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [11, 64, 64], [11, 64, 64], [64, 8, 64], 0.6, False, 1), ("tag_inf_9", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [12, 128, 128], [12, 128, 128], [32, 8, 16], 0.8, False, 1), # 4D, False ("tag_inf_10", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 8, 8], [1, 1, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_11", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 16, 16], [1, 1, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_inf_12", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 32, 32], [1, 1, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_13", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 64, 128], [1, 1, 128, 256], [64, 8, 64], 0.5, False, 1), ("tag_inf_14", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 128, 64], [1, 1, 64, 128], [32, 8, 16], 0.5, False, 1), ("tag_inf_14", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 2, 8, 8], [1, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_16", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 5, 16, 16], [1, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_inf_17", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 7, 32, 32], [1, 7, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_18", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 5, 64, 128], [1, 5, 128, 256], [64, 8, 64], 0.5, False, 1), ("tag_inf_19", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 12, 128, 64], [1, 12, 64, 128], [32, 8, 16], 0.5, False, 1), ("tag_inf_20", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [2, 2, 8, 8], [2, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_21", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [5, 5, 16, 16], [5, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_inf_22", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [13, 7, 32, 32], [13, 7, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_24", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 12, 128, 64], [1, 12, 64, 128], [32, 8, 16], 0.5, False, 1), # 2D, lhs has to be square to take inverse, False ("tag_inf_25", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [8, 8], [8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_26", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [16, 16], [16, 16], [8, 8, 8], 0.1, False, 1), ("tag_inf_27", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [32, 32], [32, 32], [16, 8, 8], 0.8, False, 1), ("tag_inf_28", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [64, 64], [64, 64], [64, 8, 64], 0.9, False, 1), ("tag_inf_29", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [128, 128], [128, 128], [32, 8, 16], 0.7, False, 1), # 3D, lhs has to be square to take, False ("tag_inf_30", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [2, 8, 8], [2, 8, 8], [8, 8, 8], 0.1, False, 1), ("tag_inf_31", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [5, 16, 16], [5, 16, 16], [8, 8, 8], 0.3, False, 1), ("tag_inf_32", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [7, 32, 32], [7, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_33", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [11, 64, 64], [11, 64, 64], [64, 8, 64], 0.6, False, 1), ("tag_inf_34", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [12, 128, 128], [12, 128, 128], [32, 8, 16], 0.1, False, 1), # 4D, lhs has to be square to take, False ("tag_inf_36", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 8, 8], [1, 1, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_36", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 16, 16], [1, 1, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_inf_37", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 32, 32], [1, 1, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_38", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 64, 64], [1, 1, 64, 256], [64, 8, 64], 0.5, False, 1), ("tag_inf_39", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 128, 128], [1, 1, 128, 128], [32, 8, 16], 0.5, False, 1), ("tag_inf_40", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 2, 8, 8], [1, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_41", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 5, 16, 16], [1, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_inf_42", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 7, 32, 32], [1, 7, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_43", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 11, 64, 64], [1, 11, 64, 256], [64, 8, 64], 0.5, False, 1), ("tag_inf_44", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 12, 128, 128], [1, 12, 128, 128], [32, 8, 16], 0.5, False, 1), ("tag_inf_45", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [2, 2, 8, 8], [2, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_inf_46", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [5, 5, 16, 16], [5, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_inf_47", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [13, 7, 32, 32], [13, 7, 32, 32], [16, 8, 8], 0.5, False, 1), ("tag_inf_49", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 12, 128, 128], [1, 12, 128, 1024], [32, 8, 16], 0.5, False, 1), # For transpose_rhs True case, last 2 dimensions of block_size must be 8 ("tag_inf_50", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [8, 8], [8, 8], [8, 8, 8], 0.5, True, 1), ("tag_inf_51", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [16, 16], [16, 16], [8, 8, 8], 0.1, True, 1), ("tag_inf_52", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [32, 32], [32, 32], [16, 8, 8], 0.8, True, 1), ("tag_inf_53", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [64, 64], [256, 64], [64, 8, 8], 0.9, True, 1), ("tag_inf_54", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [128, 128], [128, 128], [32, 8, 8], 0.2, True, 1), ("tag_inf_55", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [2, 8, 8], [2, 8, 8], [8, 8, 8], 0.5, True, 1), ("tag_inf_56", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [3, 16, 16], [3, 16, 16], [8, 8, 8], 0.1, True, 1), ("tag_inf_57", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [7, 128, 128], [7, 128, 128], [32, 8, 8], 0.2, True, 1), ("tag_inf_58", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [3, 5, 8, 8], [3, 5, 8, 8], [8, 8, 8], 0.5, True, 1), ("tag_inf_59", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 6, 16, 16], [1, 6, 16, 16], [8, 8, 8], 0.1, True, 1), ("tag_inf_60", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [4, 4, 32, 32], [4, 4, 32, 32], [16, 8, 8], 0.8, True, 1), # 3D, inner group size > 1 ("tag_inf_61", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [12, 128, 128], [12, 128, 128], [32, 8, 16], 0.8, False, 3), ("tag_inf_62", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [12, 128, 128], [12, 128, 128], [32, 8, 16], 0.1, False, 4), # 4D, inner group size > 1 ("tag_inf_23", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [4, 8, 64, 128], [4, 8, 128, 256], [64, 8, 64], 0.5, False, 4), ("tag_inf_48", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [3, 11, 64, 64], [3, 11, 64, 256], [64, 8, 64], 0.5, False, 3), ] @pytest.mark.parametrize("tag, matmul_type, lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, inner_group_size", test_data_infer) def test_bsmatmul_infer(custom_ops, tag, matmul_type, lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, inner_group_size): print("Running test_bsmatmul_infer() with tag: {}, matmul_type:{}, lhs_dims:{}, rhs_dims:{}, block_size:{}, sparsity_level:{}, transpose_rhs:{}, inner_group_size {}" .format(tag, "DENSE_LHS_SPARSE_RHS_DENSE_OUT" if matmul_type == g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'] else "DENSE_LHS_DENSE_RHS_SPARSE_OUT", lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, inner_group_size)) memory_cycle_ratio = 1.0 ipuOutput, goldOutput = sparse_mm_infer(matmul_type, lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, memory_cycle_ratio, inner_group_size) rtol = 1e-3 atol = 1e-3 np.testing.assert_allclose(ipuOutput, goldOutput, rtol=rtol, atol=atol) # # TRAINING TEST # # test_data_train tuple --> (matMulType, lhs_dims, rhs_dims, block_size, sparsity, transpose_rhs, inner_group_size) test_data_train = [ # 2D ("tag_tr_0", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [8, 8], [8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_1", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [16, 16], [16, 16], [8, 8, 8], 0.1, False, 1), ("tag_tr_2", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [32, 32], [32, 32], [8, 8, 8], 0.8, False, 1), ("tag_tr_3", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [64, 64], [64, 256], [8, 8, 8], 0.9, False, 1), ("tag_tr_4", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [128, 128], [128, 128], [8, 8, 8], 0.2, False, 1), # 3D, ("tag_tr_5", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [2, 16, 16], [2, 16, 16], [8, 8, 8], 0.1, False, 1), ("tag_tr_6", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [5, 16, 16], [5, 16, 16], [8, 8, 8], 0.3, False, 1), ("tag_tr_7", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [7, 32, 32], [7, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_8", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [11, 64, 64], [11, 64, 64], [8, 8, 8], 0.6, False, 1), ("tag_tr_9", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [12, 128, 128], [12, 128, 128], [8, 8, 8], 0.8, False, 1), # 4D, ("tag_tr_10", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 8, 8], [1, 1, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_11", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 16, 16], [1, 1, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_tr_12", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 32, 32], [1, 1, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_13", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 64, 128], [1, 1, 128, 256], [8, 8, 8], 0.5, False, 1), ("tag_tr_14", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 1, 128, 64], [1, 1, 64, 128], [8, 8, 8], 0.5, False, 1), ("tag_tr_15", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 2, 8, 8], [1, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_16", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 5, 16, 16], [1, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_tr_17", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 7, 32, 32], [1, 7, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_18", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 11, 64, 128], [1, 11, 128, 256], [8, 8, 8], 0.5, False, 1), ("tag_tr_19", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 12, 128, 64], [1, 12, 64, 128], [8, 8, 8], 0.5, False, 1), ("tag_tr_20", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [2, 2, 8, 8], [2, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_21", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [5, 5, 16, 16], [5, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_tr_22", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [13, 7, 32, 32], [13, 7, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_23", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [3, 11, 64, 128], [3, 11, 128, 256], [8, 8, 8], 0.5, False, 1), ("tag_tr_24", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 12, 128, 64], [1, 12, 64, 128], [8, 8, 8], 0.5, False, 1), # 2D, lhs has to be square to take inverse ("tag_tr_25", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [8, 8], [8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_26", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [16, 16], [16, 16], [8, 8, 8], 0.1, False, 1), ("tag_tr_27", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [32, 32], [32, 32], [8, 8, 8], 0.8, False, 1), ("tag_tr_28", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [64, 64], [64, 256], [8, 8, 8], 0.9, False, 1), ("tag_tr_29", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [128, 128], [128, 128], [8, 8, 8], 0.2, False, 1), # 3D, lhs has to be square to take ("tag_tr_30", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [2, 8, 8], [2, 8, 8], [8, 8, 8], 0.1, False, 1), ("tag_tr_31", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [5, 16, 16], [5, 16, 16], [8, 8, 8], 0.3, False, 1), ("tag_tr_32", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [7, 32, 32], [7, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_33", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [11, 64, 64], [11, 64, 64], [8, 8, 8], 0.6, False, 1), ("tag_tr_34", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [12, 128, 128], [12, 128, 128], [8, 8, 8], 0.3, False, 1), # 4D, lhs has to be square to take ("tag_tr_35", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 8, 8], [1, 1, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_36", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 16, 16], [1, 1, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_tr_37", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 32, 32], [1, 1, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_38", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 64, 64], [1, 1, 64, 256], [8, 8, 8], 0.5, False, 1), ("tag_tr_39", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 1, 128, 128], [1, 1, 128, 128], [8, 8, 8], 0.5, False, 1), ("tag_tr_40", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 2, 8, 8], [1, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_41", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 5, 16, 16], [1, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_tr_42", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 7, 32, 32], [1, 7, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_43", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 11, 64, 64], [1, 11, 64, 256], [8, 8, 8], 0.5, False, 1), ("tag_tr_44", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 12, 128, 128], [1, 12, 128, 128], [8, 8, 8], 0.5, False, 1), ("tag_tr_45", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [2, 2, 8, 8], [2, 2, 8, 8], [8, 8, 8], 0.5, False, 1), ("tag_tr_46", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [5, 5, 16, 16], [5, 5, 16, 16], [8, 8, 8], 0.8, False, 1), ("tag_tr_47", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [13, 7, 32, 32], [13, 7, 32, 32], [8, 8, 8], 0.5, False, 1), ("tag_tr_48", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [3, 11, 64, 64], [3, 11, 64, 256], [8, 8, 8], 0.5, False, 1), ("tag_tr_49", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [1, 12, 128, 128], [1, 12, 128, 1024], [8, 8, 8], 0.5, False, 1), # For transpose_rhs True case, last 2 dimensions of block_size must be 8 ("tag_tr_50", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [8, 8], [8, 8], [8, 8, 8], 0.5, True, 1), ("tag_tr_51", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [16, 16], [16, 16], [8, 8, 8], 0.1, True, 1), ("tag_tr_52", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [32, 32], [32, 32], [8, 8, 8], 0.8, True, 1), ("tag_tr_53", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [64, 64], [256, 64], [8, 8, 8], 0.9, True, 1), ("tag_tr_54", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [128, 128], [128, 128], [8, 8, 8], 0.2, True, 1), ("tag_tr_55", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [2, 8, 8], [2, 8, 8], [8, 8, 8], 0.5, True, 1), ("tag_tr_56", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [3, 16, 16], [3, 16, 16], [8, 8, 8], 0.1, True, 1), ("tag_tr_57", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [7, 128, 128], [7, 128, 128], [8, 8, 8], 0.2, True, 1), ("tag_tr_58", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [3, 5, 8, 8], [3, 5, 8, 8], [8, 8, 8], 0.5, True, 1), ("tag_tr_59", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [1, 6, 16, 16], [1, 6, 16, 16], [8, 8, 8], 0.1, True, 1), ("tag_tr_60", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [4, 4, 32, 32], [4, 4, 32, 32], [8, 8, 8], 0.8, True, 1), # 3D, inner group size > 1 ("tag_tr_61", g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'], [12, 128, 128], [12, 128, 128], [8, 8, 8], 0.8, False, 3), ("tag_tr_62", g_sparseMatMulTypeLookup['DENSE_LHS_DENSE_RHS_SPARSE_OUT'], [12, 128, 128], [12, 128, 128], [8, 8, 8], 0.1, False, 4), ] @pytest.mark.parametrize("tag, matmul_type, lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, inner_group_size", test_data_train) def test_bsmatmul_train(custom_ops, tag, matmul_type, lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, inner_group_size): print("Running test_bsmatmul_train() with tag: {}, matmul_type:{}, lhs_dims:{}, rhs_dims:{}, block_size:{}, sparsity_level:{}, transpose_rhs:{}, inner_group_size {}" .format(tag, "DENSE_LHS_SPARSE_RHS_DENSE_OUT" if matmul_type == g_sparseMatMulTypeLookup['DENSE_LHS_SPARSE_RHS_DENSE_OUT'] else "DENSE_LHS_DENSE_RHS_SPARSE_OUT", lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, inner_group_size)) memory_cycle_ratio = 1.0 sparse_mm_train(matmul_type, lhs_dims, rhs_dims, block_size, sparsity_level, transpose_rhs, memory_cycle_ratio, inner_group_size) # test_data_softmax tuple --> (dims, block_size, sparsity, inner_group_size) test_data_softmax = [ # 2D ("tag_sm_0", [8, 8], [8, 8], 0.0, 1), # 3D ("tag_sm_1", [16, 16], [8, 8], 0.4, 1), # 4D ("tag_sm_2", [2, 2, 16, 16], [8, 8], 0.3, 1), # 5D, inner group size = 1 ("tag_sm_3", [2, 3, 2, 16, 16], [8, 8], 0.1, 1), # 5D, inner group size > 1 ("tag_sm_4", [2, 3, 2, 16, 16], [8, 8], 0.1, 0), ("tag_sm_5", [2, 3, 2, 16, 16], [8, 8], 0.1, 6), ] @pytest.mark.parametrize("tag, dims, block_size, sparsity_level, inner_group_size", test_data_softmax) def test_bs_softmax(custom_ops, tag, dims, block_size, sparsity_level, inner_group_size): print("Running test_bs_softmax() with tag: {}, dims:{}, block_size:{}, sparsity_level:{}, inner_group_size {}" .format(tag, dims, block_size, sparsity_level, inner_group_size)) ipu_output, gold_output = sparse_softmax(dims, block_size, sparsity_level, inner_group_size) np.testing.assert_allclose(ipu_output, gold_output, rtol=1e-2, atol=1e-2) if __name__ == "__main__": ipu_output, gold_output = sparse_softmax([2, 2, 16, 16], [8, 8], 0.3, 1) np.testing.assert_allclose(ipu_output, gold_output, rtol=1e-2, atol=1e-2)

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node_modules/protagonist/drafter/drafter.gyp

bootstraponline/swagger_poc

0

112456

{ "includes": [ "ext/snowcrash/common.gypi" ], "targets" : [ # LIBSOS { 'target_name': 'libsos', 'type': 'static_library', 'direct_dependent_settings' : { 'include_dirs': [ 'ext/sos/src' ], }, 'sources': [ 'ext/sos/src/sos.cc', 'ext/sos/src/sos.h', 'ext/sos/src/sosJSON.h', 'ext/sos/src/sosYAML.h' ] }, # LIBDRAFTER { "target_name": "libdrafter", 'type': '<(libdrafter_type)', "conditions" : [ [ 'libdrafter_type=="shared_library"', { 'defines' : [ 'DRAFTER_BUILD_SHARED' ] }, { 'defines' : [ 'DRAFTER_BUILD_STATIC' ] }], ], 'direct_dependent_settings' : { 'include_dirs': [ 'src', ], }, 'export_dependent_settings': [ 'libsos', 'ext/snowcrash/snowcrash.gyp:libsnowcrash', ], "sources": [ "src/drafter.h", "src/drafter.cc", "src/drafter_private.h", "src/drafter_private.cc", "src/stream.h", "src/Version.h", "src/NodeInfo.h", "src/Serialize.h", "src/Serialize.cc", "src/SerializeAST.h", "src/SerializeAST.cc", "src/SerializeSourcemap.h", "src/SerializeSourcemap.cc", "src/SerializeResult.h", "src/SerializeResult.cc", "src/RefractAPI.h", "src/RefractAPI.cc", "src/RefractDataStructure.h", "src/RefractDataStructure.cc", "src/RefractSourceMap.h", "src/RefractSourceMap.cc", "src/Render.h", "src/Render.cc", "src/NamedTypesRegistry.cc", "src/NamedTypesRegistry.h", "src/RefractElementFactory.h", "src/RefractElementFactory.cc", "src/ConversionContext.cc", "src/ConversionContext.h", # librefract parts - will be separated into other project "src/refract/Element.h", "src/refract/Element.cc", "src/refract/ElementFwd.h", "src/refract/Visitor.h", "src/refract/VisitorUtils.h", "src/refract/VisitorUtils.cc", "src/refract/SerializeCompactVisitor.h", "src/refract/SerializeCompactVisitor.cc", "src/refract/SerializeVisitor.h", "src/refract/SerializeVisitor.cc", "src/refract/ComparableVisitor.h", "src/refract/ComparableVisitor.cc", "src/refract/TypeQueryVisitor.h", "src/refract/TypeQueryVisitor.cc", "src/refract/IsExpandableVisitor.h", "src/refract/IsExpandableVisitor.cc", "src/refract/ExpandVisitor.h", "src/refract/ExpandVisitor.cc", "src/refract/RenderJSONVisitor.h", "src/refract/RenderJSONVisitor.cc", "src/refract/PrintVisitor.h", "src/refract/PrintVisitor.cc", "src/refract/JSONSchemaVisitor.h", "src/refract/JSONSchemaVisitor.cc", "src/refract/FilterVisitor.h", "src/refract/Registry.h", "src/refract/Registry.cc", "src/refract/Build.h", "src/refract/AppendDecorator.h", "src/refract/ElementInserter.h", "src/refract/Query.h", "src/refract/Query.cc", "src/refract/Iterate.h", ], "dependencies": [ "libsos", "ext/snowcrash/snowcrash.gyp:libsnowcrash", ], }, # TESTLIBDRAFTER { 'target_name': 'test-libdrafter', 'type': 'executable', 'include_dirs': [ 'test/vendor/Catch/include', 'test/vendor/dtl/dtl', 'src/refract', ], 'sources': [ "test/test-drafter.cc", "test/test-SerializeResultTest.cc", "test/test-SerializeSourceMapTest.cc", "test/test-RefractDataStructureTest.cc", "test/test-RefractAPITest.cc", "test/test-RefractParseResultTest.cc", "test/test-RenderTest.cc", "test/test-RefractSourceMapTest.cc", "test/test-SchemaTest.cc", "test/test-CircularReferenceTest.cc", "test/test-ApplyVisitorTest.cc", "test/test-ExtendElementTest.cc", "test/test-ElementFactoryTest.cc", "test/test-OneOfTest.cc", "test/test-SyntaxIssuesTest.cc", ], 'dependencies': [ "libdrafter", ], 'conditions': [ [ 'OS=="win"', { 'defines' : [ 'WIN' ] } ] ], }, # DRAFTER { "target_name": "drafter", "type": "executable", "conditions" : [ [ 'libdrafter_type=="static_library"', { 'defines' : [ 'DRAFTER_BUILD_STATIC' ] }], ], "sources": [ "src/main.cc", "src/config.cc", "src/config.h", "src/reporting.cc", "src/reporting.h", ], "include_dirs": [ "ext/cmdline", ], "dependencies": [ "libdrafter", ], }, # DRAFTER C-API TEST { "target_name": "test-capi", "type": "executable", "conditions" : [ [ 'libdrafter_type=="static_library"', { 'defines' : [ 'DRAFTER_BUILD_STATIC' ] }], ], "sources": [ "test/test-CAPI.c" ], "dependencies": [ "libdrafter", ], }, ], }

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tests/samples/test_sample.py

FoxoTech/methylcheck

1

39998

<gh_stars>1-10 import methylcheck import pandas as pd import methylprep # for manifest support from pathlib import Path PATH = Path('docs/example_data/mouse') class TestProcessedSample(): manifest = methylprep.Manifest(methylprep.ArrayType('mouse')) manifest_mouse_design_types = dict(manifest.mouse_data_frame['design'].value_counts()) manifest_control_probe_types = dict(manifest.control_data_frame['Control_Type'].value_counts()) def test_mouse_probes(self): pd_mu = pd.read_pickle(Path(PATH,'mouse_probes.pkl')) mu = methylcheck.load(Path(PATH,'mouse_probes.pkl'), verbose=False, silent=True) if not (isinstance(mu, dict) and isinstance(list(mu.values())[0], pd.DataFrame)): raise AssertionError() mouse_probe_countA = len(list(mu.values())[0]) mouse_probe_countB = len(list(pd_mu.values())[0]) if len(mu) != len(pd_mu): raise AssertionError(f"Got {len(mu)} items in mouse_probes.pkl; expected {len(pd_mu)}.") if mouse_probe_countA != mouse_probe_countB: raise AssertionError(f"Got {mouse_probe_countA} probes in mouse_probes.pkl vs {mouse_probe_countB}.") # mouse_probes_v146 = 10067 mouse_probes_v155 = 32753 if mu['204879580038_R06C02'].shape[0] != mouse_probes_v155: raise AssertionError(f"Got {mu['204879580038_R06C02'].shape[0]} probes in mouse_probes.pkl; expected {mouse_probes_v155}.") print(f"sample and probe count OK") df0 = list(mu.values())[0] #probes_by_type = dict(df0.index.str[:2].value_counts()) #if probes_by_type['cg'] != 5881 and probes_by_type['uk'] != 4186: # raise AssertionError(f"Mismatch in number of cg and uk(nown) probes in mouse_probes.pkl; expected 5881 cg and 4186 uk.") probes_by_type = dict(df0.design.value_counts()) if probes_by_type['Random'] != 27305 or probes_by_type['Multi'] != 5448: raise AssertionError(f"Mismatch in number of Random/Multi CpG probes in mouse_probes.pkl; expected 27305 Random and 5448 Multi.") print('mouse probe counts OK') def __test_mouse_probes_v146(self): pd_mu = pd.read_pickle(Path(PATH,'mouse_probes.pkl')) mu = methylcheck.load(Path(PATH,'mouse_probes.pkl'), verbose=False, silent=True) if not (isinstance(mu, dict) and isinstance(list(mu.values())[0], pd.DataFrame)): raise AssertionError() if len(mu) != len(pd_mu): raise AssertionError(f"Got a {len(mu)} items in mouse_probes.pkl; expected {len(pd_mu)}.") print(f"sample count OK") df0 = list(mu.values())[0] probes_by_type = dict(df0.index.str[:2].value_counts()) if probes_by_type['cg'] != 5881 and probes_by_type['uk'] != 4186: raise AssertionError(f"Mismatch in number of cg and uk(nown) probes in mouse_probes.pkl; expected 5881 cg and 4186 uk.") print('mouse cg,uk count OK') actual_mouse_probes = dict(df0['Probe_Type'].value_counts()) probe_counts = {'mu': 6332, 'rp': 4514, 'ch': 2851, 'rs': 291} # based on C20 manifest probe_count_errors = {} for probe_type, probe_count in probe_counts.items(): if self.manifest_mouse_probe_types[probe_type] != probe_count: probe_count_errors[probe_type] = {'actual': self.manifest_mouse_probe_types[probe_type], 'expected': probe_count} if probe_count_errors: raise AssertionError(f"mouse probe count errors: {probe_count_errors}") print('mouse mu,rp,ch,rs count OK') # compare with manifest diffs = [] for probe_type, probe_count in self.manifest_mouse_probe_types.items(): if probe_count != actual_mouse_probes[probe_type]: diffs.append(f"{probe_type}: {actual_mouse_probes[probe_type]} / {probe_count}") if diffs: print("Probes in manifest NOT in control probes saved:") print('\n'.join(diffs)) # not part of newer (>v1.4.6) mouse_probes.pkl #actual_mouse_probes = dict(df0['Probe_Type'].value_counts()) #probe_counts_C20 = {'mu': 6332, 'rp': 4514, 'ch': 2851, 'rs': 291} # based on C20 manifest, v1.4.6 #probe_counts_mm285_v2 = {'mu': 4821, 'rp': 3048, 'ch': 2085, 'rs': 113} # v1.5.5 #probe_count_errors = {} #for probe_type, probe_count in probe_counts_mm285_v2.items(): # if self.manifest_mouse_probe_types[probe_type] != probe_count: # probe_count_errors[probe_type] = {'actual': self.manifest_mouse_probe_types[probe_type], 'expected': probe_count} #if probe_count_errors: # raise AssertionError(f"mouse probe count errors: {probe_count_errors}") #print('mouse mu,rp,ch,rs count OK') # compare with manifest -- done in test_Array_processed.py already #diffs = [] #for probe_type, probe_count in self.manifest_mouse_probe_types.items(): # if probe_count != actual_mouse_probes[probe_type]: # diffs.append(f"{probe_type}: {actual_mouse_probes[probe_type]} / {probe_count}") #if diffs: # print("Probes in manifest NOT in control probes saved:") # print('\n'.join(diffs)) def test_control_probes(self): con = methylcheck.load(Path(PATH,'control_probes.pkl'), verbose=False, silent=True) con0 = list(con.values())[0] con_types = dict(con0['Control_Type'].value_counts()) #probe_counts_v146 = {'NEGATIVE': 179, 'NORM_T': 24, 'NORM_C': 22, 'NORM_A': 10, 'NORM_G': 9, 'NON-POLYMORPHIC': 3, 'SPECIFICITY I': 3, 'BISULFITE CONVERSION I': 3, 'BISULFITE CONVERSION II': 2, 'SPECIFICITY II': 2, 'HYBRIDIZATION': 2, 'RESTORATION': 1} probe_counts = {'NEGATIVE': 411, 'NORM_T': 58, 'NORM_C': 58, 'NORM_A': 27, 'NORM_G': 27, 'NON-POLYMORPHIC': 9, 'SPECIFICITY I': 12, 'BISULFITE CONVERSION I': 10, 'BISULFITE CONVERSION II': 4, 'SPECIFICITY II': 3, 'HYBRIDIZATION': 3, 'RESTORATION': 1} probe_count_errors = {} for probe_type, probe_count in probe_counts.items(): if con_types[probe_type] != probe_count: probe_count_errors[probe_type] = {'actual': con_types[probe_type], 'expected': probe_count} if probe_count_errors: raise AssertionError(f"control probe count differed: {probe_count_errors}") print('mouse control_probes count OK') diffs = [] for probe_type, probe_count in self.manifest_control_probe_types.items(): if not con_types.get(probe_type): print(f"ERROR: control output data is missing {probe_type} found in manifest.") continue if probe_count != con_types[probe_type]: diffs.append(f"{probe_type}: {con_types[probe_type]} / {probe_count}") if diffs: print("Probes in manifest NOT in control probes saved:") print('\n'.join(diffs)) def test_plot_mouse_betas_from_pickle(self): """ tests SAVE too """ mu = methylcheck.load(Path(PATH,'mouse_probes.pkl'), verbose=False, silent=True) df0 = list(mu.values())[0] df = df0[['beta_value']] methylcheck.sample_plot(df, silent=True, save=True) methylcheck.beta_density_plot(df, silent=True) methylcheck.sample_plot(df, silent=True) methylcheck.mean_beta_plot(df, silent=True) methylcheck.cumulative_sum_beta_distribution(df, silent=True) methylcheck.beta_mds_plot(df, silent=True, save=False) methylcheck.mean_beta_compare(df,df,silent=True) df = df0[['cm_value']] methylcheck.beta_density_plot(df, silent=True) df = df0[['m_value']] methylcheck.beta_density_plot(df, silent=True) Path('./beta.png').unlink() # Path('./beta_mds_n=*.png').unlink() -- causes error if this function doesn't create the file to remove methylcheck.beta_mds_plot(df, silent=True, save=True) for saved_png in Path('.').rglob('./beta_mds_n=*.png'): print(saved_png) saved_png.unlink() def ignore_test(): with np.errstate(all='raise'): df = methylcheck.load(Path(PATH,'beta_values.pkl'), verbose=False, silent=True) methylcheck.sample_plot(df, silent=True, save=True) methylcheck.beta_density_plot(df, silent=True) methylcheck.sample_plot(df, silent=True) methylcheck.mean_beta_plot(df, silent=True) methylcheck.cumulative_sum_beta_distribution(df, silent=True) methylcheck.beta_mds_plot(df, silent=True, save=False) methylcheck.mean_beta_compare(df,df,silent=True) #df = df0[['cm_value']] #methylcheck.beta_density_plot(df, silent=True) #df = df0[['m_value']] #methylcheck.beta_density_plot(df, silent=True) Path('./beta.png').unlink() # Path('./beta_mds_n=*.png').unlink() -- causes error if this function doesn't create the file to remove methylcheck.beta_mds_plot(df, silent=True, save=True) for saved_png in Path('.').rglob('./beta_mds_n=*.png'): print(saved_png) saved_png.unlink()

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utils/predictions.py

jaingaurav3/ML_sample

19

13468

import os import scipy import numpy as np import pandas as pd import torch from torch.autograd import Variable def predict_batch(net, inputs): v = Variable(inputs.cuda(), volatile=True) return net(v).data.cpu().numpy() def get_probabilities(model, loader): model.eval() return np.vstack(predict_batch(model, data[0]) for data in loader) def get_predictions(probs, thresholds): preds = np.copy(probs) preds[preds >= thresholds] = 1 preds[preds < thresholds] = 0 return preds.astype('uint8') def get_argmax(output): val,idx = torch.max(output, dim=1) return idx.data.cpu().view(-1).numpy() def get_targets(loader): targets = None for data in loader: if targets is None: shape = list(data[1].size()) shape[0] = 0 targets = np.empty(shape) target = data[1] if len(target.size()) == 1: target = target.view(-1,1) target = target.numpy() targets = np.vstack([targets, target]) return targets def ensemble_with_method(arr, method): if method == c.MEAN: return np.mean(arr, axis=0) elif method == c.GMEAN: return scipy.stats.mstats.gmean(arr, axis=0) elif method == c.VOTE: return scipy.stats.mode(arr, axis=0)[0][0] raise Exception("Operation not found")

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salt/modules/kernelpkg_linux_apt.py

markgras/salt

9,425

8077

<filename>salt/modules/kernelpkg_linux_apt.py """ Manage Linux kernel packages on APT-based systems """ import functools import logging import re try: from salt.utils.versions import LooseVersion as _LooseVersion from salt.exceptions import CommandExecutionError HAS_REQUIRED_LIBS = True except ImportError: HAS_REQUIRED_LIBS = False log = logging.getLogger(__name__) __virtualname__ = "kernelpkg" def __virtual__(): """ Load this module on Debian-based systems only """ if not HAS_REQUIRED_LIBS: return (False, "Required library could not be imported") if __grains__.get("os_family", "") in ("Kali", "Debian"): return __virtualname__ elif __grains__.get("os_family", "") == "Cumulus": return __virtualname__ return (False, "Module kernelpkg_linux_apt: no APT based system detected") def active(): """ Return the version of the running kernel. CLI Example: .. code-block:: bash salt '*' kernelpkg.active """ if "pkg.normalize_name" in __salt__: return __salt__["pkg.normalize_name"](__grains__["kernelrelease"]) return __grains__["kernelrelease"] def list_installed(): """ Return a list of all installed kernels. CLI Example: .. code-block:: bash salt '*' kernelpkg.list_installed """ pkg_re = re.compile(r"^{}-[\d.-]+-{}$".format(_package_prefix(), _kernel_type())) pkgs = __salt__["pkg.list_pkgs"](versions_as_list=True) if pkgs is None: pkgs = [] result = list(filter(pkg_re.match, pkgs)) if result is None: return [] prefix_len = len(_package_prefix()) + 1 return sorted( [pkg[prefix_len:] for pkg in result], key=functools.cmp_to_key(_cmp_version) ) def latest_available(): """ Return the version of the latest kernel from the package repositories. CLI Example: .. code-block:: bash salt '*' kernelpkg.latest_available """ result = __salt__["pkg.latest_version"]( "{}-{}".format(_package_prefix(), _kernel_type()) ) if result == "": return latest_installed() version = re.match(r"^(\d+\.\d+\.\d+)\.(\d+)", result) return "{}-{}-{}".format(version.group(1), version.group(2), _kernel_type()) def latest_installed(): """ Return the version of the latest installed kernel. CLI Example: .. code-block:: bash salt '*' kernelpkg.latest_installed .. note:: This function may not return the same value as :py:func:`~salt.modules.kernelpkg_linux_apt.active` if a new kernel has been installed and the system has not yet been rebooted. The :py:func:`~salt.modules.kernelpkg_linux_apt.needs_reboot` function exists to detect this condition. """ pkgs = list_installed() if pkgs: return pkgs[-1] return None def needs_reboot(): """ Detect if a new kernel version has been installed but is not running. Returns True if a new kernel is installed, False otherwise. CLI Example: .. code-block:: bash salt '*' kernelpkg.needs_reboot """ return _LooseVersion(active()) < _LooseVersion(latest_installed()) def upgrade(reboot=False, at_time=None): """ Upgrade the kernel and optionally reboot the system. reboot : False Request a reboot if a new kernel is available. at_time : immediate Schedule the reboot at some point in the future. This argument is ignored if ``reboot=False``. See :py:func:`~salt.modules.system.reboot` for more details on this argument. CLI Example: .. code-block:: bash salt '*' kernelpkg.upgrade salt '*' kernelpkg.upgrade reboot=True at_time=1 .. note:: An immediate reboot often shuts down the system before the minion has a chance to return, resulting in errors. A minimal delay (1 minute) is useful to ensure the result is delivered to the master. """ result = __salt__["pkg.install"]( name="{}-{}".format(_package_prefix(), latest_available()) ) _needs_reboot = needs_reboot() ret = { "upgrades": result, "active": active(), "latest_installed": latest_installed(), "reboot_requested": reboot, "reboot_required": _needs_reboot, } if reboot and _needs_reboot: log.warning("Rebooting system due to kernel upgrade") __salt__["system.reboot"](at_time=at_time) return ret def upgrade_available(): """ Detect if a new kernel version is available in the repositories. Returns True if a new kernel is available, False otherwise. CLI Example: .. code-block:: bash salt '*' kernelpkg.upgrade_available """ return _LooseVersion(latest_available()) > _LooseVersion(latest_installed()) def remove(release): """ Remove a specific version of the kernel. release The release number of an installed kernel. This must be the entire release number as returned by :py:func:`~salt.modules.kernelpkg_linux_apt.list_installed`, not the package name. CLI Example: .. code-block:: bash salt '*' kernelpkg.remove 4.4.0-70-generic """ if release not in list_installed(): raise CommandExecutionError( "Kernel release '{}' is not installed".format(release) ) if release == active(): raise CommandExecutionError("Active kernel cannot be removed") target = "{}-{}".format(_package_prefix(), release) log.info("Removing kernel package %s", target) __salt__["pkg.purge"](target) return {"removed": [target]} def cleanup(keep_latest=True): """ Remove all unused kernel packages from the system. keep_latest : True In the event that the active kernel is not the latest one installed, setting this to True will retain the latest kernel package, in addition to the active one. If False, all kernel packages other than the active one will be removed. CLI Example: .. code-block:: bash salt '*' kernelpkg.cleanup """ removed = [] # Loop over all installed kernel packages for kernel in list_installed(): # Keep the active kernel package if kernel == active(): continue # Optionally keep the latest kernel package if keep_latest and kernel == latest_installed(): continue # Remove the kernel package removed.extend(remove(kernel)["removed"]) return {"removed": removed} def _package_prefix(): """ Return static string for the package prefix """ return "linux-image" def _kernel_type(): """ Parse the kernel name and return its type """ return re.match(r"^[\d.-]+-(.+)$", active()).group(1) def _cmp_version(item1, item2): """ Compare function for package version sorting """ vers1 = _LooseVersion(item1) vers2 = _LooseVersion(item2) if vers1 < vers2: return -1 if vers1 > vers2: return 1 return 0

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setup.py

norcams/himlar-dp-prep

0

177774

<gh_stars>0 import os from setuptools import setup, find_packages here = os.path.abspath(os.path.dirname(__file__)) with open(os.path.join(here, 'README.md')) as f: README = f.read() with open(os.path.join(here, 'CHANGES.txt')) as f: CHANGES = f.read() requires = [ 'funcsigs==1.0.0', 'pyramid', 'pyramid_mako', # 'pyramid_debugtoolbar', 'authomatic', 'python-keystoneclient==3.10.0', 'waitress', 'grampg', 'pika==0.11.2', ] setup(name='himlar_dp_prep', version='0.0', description='himlar_dp_prep', long_description=README + '\n\n' + CHANGES, classifiers=[ "Programming Language :: Python", "Framework :: Pyramid", "Topic :: Internet :: WWW/HTTP", "Topic :: Internet :: WWW/HTTP :: WSGI :: Application", ], author='<NAME>', author_email='<EMAIL>', url='https://github.com/norcams/himlar-dp-prep', keywords='web pyramid pylons', packages=find_packages(), include_package_data=True, dependency_links = [ 'http://github.com/jhellan/authomatic/tarball/master#egg=authomatic-0.1.0.uninett2' ], zip_safe=False, install_requires=requires, tests_require=requires, test_suite="", entry_points="""\ [paste.app_factory] main = himlar_dp_prep:main """, )

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bot/database.py

zd4y/discordbot

0

1614367

from .config import Settings from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker, scoped_session from sqlalchemy.ext.declarative import declarative_base engine = create_engine(Settings.DATABASE_URL, echo=False) session_factory = sessionmaker(bind=engine) session = scoped_session(session_factory) Base = declarative_base() Base.query = session.query_property()

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dfvfs/vfs/apfs_container_file_entry.py

jaegeral/dfvfs

0

192719

# -*- coding: utf-8 -*- """The APFS container file entry implementation.""" from dfvfs.lib import apfs_helper from dfvfs.lib import definitions from dfvfs.lib import errors from dfvfs.resolver import resolver from dfvfs.vfs import apfs_container_directory from dfvfs.vfs import file_entry class APFSContainerFileEntry(file_entry.FileEntry): """File system file entry that uses pyfsapfs.""" TYPE_INDICATOR = definitions.TYPE_INDICATOR_APFS_CONTAINER def __init__( self, resolver_context, file_system, path_spec, is_root=False, is_virtual=False): """Initializes a file entry. Args: resolver_context (Context): resolver context. file_system (FileSystem): file system. path_spec (PathSpec): path specification. is_root (Optional[bool]): True if the file entry is the root file entry of the corresponding file system. is_virtual (Optional[bool]): True if the file entry is a virtual file entry emulated by the corresponding file system. Raises: BackEndError: when the fsapfs volume is missing in a non-virtual file entry. """ fsapfs_volume = file_system.GetAPFSVolumeByPathSpec(path_spec) if not is_virtual and fsapfs_volume is None: raise errors.BackEndError( 'Missing fsapfs volume in non-virtual file entry.') super(APFSContainerFileEntry, self).__init__( resolver_context, file_system, path_spec, is_root=is_root, is_virtual=is_virtual) self._name = None self._fsapfs_volume = fsapfs_volume if self._is_virtual: self.entry_type = definitions.FILE_ENTRY_TYPE_DIRECTORY else: self.entry_type = definitions.FILE_ENTRY_TYPE_FILE def _GetDirectory(self): """Retrieves a directory. Returns: APFSContainerDirectory: a directory. """ if self.entry_type != definitions.FILE_ENTRY_TYPE_DIRECTORY: return None return apfs_container_directory.APFSContainerDirectory( self._file_system, self.path_spec) def _GetSubFileEntries(self): """Retrieves a sub file entries generator. Yields: APFSContainerFileEntry: a sub file entry. """ if self._directory is None: self._directory = self._GetDirectory() if self._directory: for path_spec in self._directory.entries: yield APFSContainerFileEntry( self._resolver_context, self._file_system, path_spec) # TODO: expose date and time values. @property def name(self): """str: name of the file entry, which does not include the full path.""" if self._name is None: location = getattr(self.path_spec, 'location', None) if location is not None: self._name = self._file_system.BasenamePath(location) else: volume_index = apfs_helper.APFSContainerPathSpecGetVolumeIndex( self.path_spec) if volume_index is not None: self._name = 'apfs{0:d}'.format(volume_index + 1) else: self._name = '' return self._name @property def size(self): """int: size of the file entry in bytes or None if not available.""" if self._fsapfs_volume is None: return None # TODO: change libfsapfs so self._fsapfs_volume.size works return 0 @property def sub_file_entries(self): """generator[APFSContainerFileEntry]: sub file entries.""" return self._GetSubFileEntries() def GetAPFSVolume(self): """Retrieves an APFS volume. Returns: pyfsapfs.volume: an APFS volume or None if not available. """ return self._fsapfs_volume def GetParentFileEntry(self): """Retrieves the parent file entry. Returns: APFSContainerFileEntry: parent file entry or None if not available. """ volume_index = apfs_helper.APFSContainerPathSpecGetVolumeIndex( self.path_spec) if volume_index is None: return None return self._file_system.GetRootFileEntry() def IsLocked(self): """Determines if the file entry is locked. Returns: bool: True if the file entry is locked. """ if self._fsapfs_volume is None: return False return self._fsapfs_volume.is_locked() def Unlock(self): """Unlocks the file entry. Returns: bool: True if the file entry was unlocked. """ if not self._fsapfs_volume or not self._fsapfs_volume.is_locked(): return True return apfs_helper.APFSUnlockVolume( self._fsapfs_volume, self.path_spec, resolver.Resolver.key_chain)

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test/neg shift.py

jbr-ai-labs/PU-OC

0

73100

<reponame>jbr-ai-labs/PU-OC from imports import * def test_negshift(dataset_name, models_block, dim=128, alpha=0.5, n=1, pos_label=0, norm_flag=False, cntrs=4, params=None, svm=False, name='blank'): results = {} for model_type in models_block.values(): results[model_type.__name__] = [] train_holder = get_data(dataset_name, True, pos_label=None, neg_label=None, norm_flag=norm_flag) test_holder = get_data(dataset_name, False, pos_label=None, neg_label=None, norm_flag=norm_flag) encoder = get_encoder(f'nbae_{pos_label}.pcl', dataset_name) for cnt in tqdm(range(cntrs)): # print(f"pos={pos_label}") res = {} for model_type in models_block.values(): res[model_type.__name__] = [] for _ in tqdm(range(n)): negs = np.random.choice(np.arange(1, 10), 2 * cnt + 2, replace=False) train_bin = train_holder.pos_neg_split([pos_label], negs[:cnt + 1]) test_bin = train_holder.pos_neg_split([pos_label], negs[cnt + 1:]) if svm: train_bin.encode_data(encoder) test_bin.encode_data(encoder) train_data, pi = train_bin.get_dataset(alpha, svm_labels=True) test_data, pi_test = test_bin.get_dataset(alpha, c=0, svm_labels=True) ntc_model = models_block["ntc"](dim=dim, encoder=f'nbae_{pos_label}') ntc_model.run_train(train_data) # ntc_model.eval() if not svm: encoded_train = encode_dataset(encoder, train_data) cest, _ = tice(encoded_train.data, encoded_train.s.numpy(), 1, np.random.randint(5, size=len(encoded_train.data)), delta=0.3) aest = (encoded_train.s == 1).sum() * (1 - cest) / cest / (encoded_train.s != 1).sum() else: cest, _ = tice(train_data.data, train_data.s.numpy(), 3, np.random.randint(5, size=len(train_data.data)), delta=0.2) aest = (train_data.s == 1).sum() * (1 - cest) / cest / (train_data.s != 1).sum() pi_est = min(1, aest) cur_models = [models_block["oc"](dim=dim, encoder=f'nbae_{pos_label}', **params[pos_label]['oc']['init']), models_block["pu"](dim=dim, pi=pi_est, encoder=f'nbae_{pos_label}', **params[pos_label]['pu']['init'])] # cur_models = [models_block["oc"](dim=dim), # models_block["pu"](dim=dim, pi=pi_est)] cur_models[0].run_train(train_data, **params[pos_label]['oc']['train']) cur_models[1].run_train(train_data, **params[pos_label]['pu']['train']) # cur_models[0].run_train(train_data) # cur_models[1].run_train(train_data) test_models = [ntc_model, cur_models[0], cur_models[1]] # test_models = [cur_models[0]] # print(f"done training {i}") test = test_data, test_data result = run_test(test_models, test, None) for model in result: res[model].append(result[model]["auc"]) for model in result: results[model].append(res[model]) return results

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09_Recurrent_Neural_Networks/05_Creating_A_Sequence_To_Sequence_Model/05_seq2seq_translation.py

maxim5/tensorflow_cookbook

2

31383

<reponame>maxim5/tensorflow_cookbook #!/usr/bin/env python # -*- coding: utf-8 -*- # # Creating Sequence to Sequence Models # ------------------------------------- # Here we show how to implement sequence to sequence models. # Specifically, we will build an English to German translation model. # import io import os import string import sys from collections import Counter from zipfile import ZipFile import matplotlib.pyplot as plt import numpy as np import requests import tensorflow as tf if tf.__version__[0] < '1': from tensorflow.models.rnn.translate import seq2seq_model else: # models can be retrieved from github: https://github.com/tensorflow/models.git # put the models dir under python search lib path. local_repository = 'temp' if not os.path.exists(local_repository): # pip install gitpython from git import Repo tf_model_repository = 'https://github.com/tensorflow/models' Repo.clone_from(tf_model_repository, local_repository) sys.path.insert(0, 'temp/tutorials/rnn/translate/') import seq2seq_model as seq2seq_model # Model Parameters learning_rate = 0.1 lr_decay_rate = 0.99 lr_decay_every = 100 max_gradient = 5.0 batch_size = 50 num_layers = 3 rnn_size = 500 layer_size = 512 generations = 10000 vocab_size = 10000 save_every = 1000 eval_every = 500 output_every = 50 punct = string.punctuation # Data Parameters data_dir = 'temp' data_file = 'eng_ger.txt' model_path = 'seq2seq_model' full_model_dir = os.path.join(data_dir, model_path) # Test Translation from English (lowercase, no punct) test_english = ['hello where is my computer', 'the quick brown fox jumped over the lazy dog', 'is it going to rain tomorrow'] ######################################################################################################################## # Data ######################################################################################################################## # Make Model Directory if not os.path.exists(full_model_dir): os.makedirs(full_model_dir) # Make data directory if not os.path.exists(data_dir): os.makedirs(data_dir) print('Loading English-German Data') # Check for data, if it doesn't exist, download it and save it if not os.path.isfile(os.path.join(data_dir, data_file)): print('Data not found, downloading Eng-Ger sentences from www.manythings.org') sentence_url = 'http://www.manythings.org/anki/deu-eng.zip' r = requests.get(sentence_url) z = ZipFile(io.BytesIO(r.content)) file = z.read('deu.txt') # Format Data eng_ger_data = file.decode(errors='ignore') eng_ger_data = eng_ger_data.encode('ascii', errors='ignore') eng_ger_data = eng_ger_data.decode().split('\n') # Write to file with open(os.path.join(data_dir, data_file), 'w') as out_conn: for sentence in eng_ger_data: out_conn.write(sentence + '\n') else: eng_ger_data = [] with open(os.path.join(data_dir, data_file), 'r') as in_conn: for row in in_conn: eng_ger_data.append(row[:-1]) print('Done!') # Remove punctuation eng_ger_data = [''.join(char for char in sent if char not in punct) for sent in eng_ger_data] # Split each sentence by tabs eng_ger_data = [x.split('\t') for x in eng_ger_data if len(x) >= 1] [english_sentence, german_sentence] = [list(x) for x in zip(*eng_ger_data)] english_sentence = [x.lower().split() for x in english_sentence] german_sentence = [x.lower().split() for x in german_sentence] print('Processing the vocabularies.') # Process the English Vocabulary all_english_words = [word for sentence in english_sentence for word in sentence] all_english_counts = Counter(all_english_words) eng_word_keys = [x[0] for x in all_english_counts.most_common(vocab_size - 1)] # -1 because 0=unknown is also in there eng_vocab2ix = dict(zip(eng_word_keys, range(1, vocab_size))) eng_ix2vocab = {val: key for key, val in eng_vocab2ix.items()} english_processed = [] for sent in english_sentence: temp_sentence = [] for word in sent: try: temp_sentence.append(eng_vocab2ix[word]) except: temp_sentence.append(0) english_processed.append(temp_sentence) # Process the German Vocabulary all_german_words = [word for sentence in german_sentence for word in sentence] all_german_counts = Counter(all_german_words) ger_word_keys = [x[0] for x in all_german_counts.most_common(vocab_size - 1)] ger_vocab2ix = dict(zip(ger_word_keys, range(1, vocab_size))) ger_ix2vocab = {val: key for key, val in ger_vocab2ix.items()} german_processed = [] for sent in german_sentence: temp_sentence = [] for word in sent: try: temp_sentence.append(ger_vocab2ix[word]) except: temp_sentence.append(0) german_processed.append(temp_sentence) # Process the test english sentences, use '0' if word not in our vocab test_data = [] for sentence in test_english: temp_sentence = [] for word in sentence.split(' '): try: temp_sentence.append(eng_vocab2ix[word]) except: # Use '0' if the word isn't in our vocabulary temp_sentence.append(0) test_data.append(temp_sentence) # Define Buckets for sequence lengths # We will split data into the corresponding buckets: # (x1, y1), (x2, y2), ... # Where all entries in bucket 1: len(x)<x1 and len(y)<y1 and so on. x_maxs = [5, 7, 11, 50] y_maxs = [10, 12, 17, 60] buckets = [x for x in zip(x_maxs, y_maxs)] bucketed_data = [[] for _ in range(len(x_maxs))] for eng, ger in zip(english_processed, german_processed): for ix, (x_max, y_max) in enumerate(zip(x_maxs, y_maxs)): if (len(eng) <= x_max) and (len(ger) <= y_max): bucketed_data[ix].append([eng, ger]) break # Print summaries of buckets train_bucket_sizes = [len(bucketed_data[b]) for b in range(len(buckets))] train_total_size = float(sum(train_bucket_sizes)) for ix, bucket in enumerate(bucketed_data): print('Data pts in bucket {}: {}'.format(ix, len(bucket))) ######################################################################################################################## # Create sequence to sequence model ######################################################################################################################## print('Creating Translation Model') # https://stackoverflow.com/questions/44855603/typeerror-cant-pickle-thread-lock-objects-in-seq2seq/47952913#47952913 setattr(tf.contrib.rnn.GRUCell, '__deepcopy__', lambda self, _: self) setattr(tf.contrib.rnn.BasicLSTMCell, '__deepcopy__', lambda self, _: self) setattr(tf.contrib.rnn.MultiRNNCell, '__deepcopy__', lambda self, _: self) def translation_model(input_vocab_size, output_vocab_size, buckets, rnn_size, num_layers, max_gradient, learning_rate, lr_decay_rate, forward_only): model = seq2seq_model.Seq2SeqModel(source_vocab_size=input_vocab_size, target_vocab_size=output_vocab_size, buckets=buckets, size=rnn_size,num_layers=num_layers, max_gradient_norm=max_gradient, batch_size=batch_size, learning_rate=learning_rate, learning_rate_decay_factor=lr_decay_rate, forward_only=forward_only, dtype=tf.float32) return model translate_model = translation_model(input_vocab_size=vocab_size, output_vocab_size=vocab_size, buckets=buckets, rnn_size=rnn_size, num_layers=num_layers, max_gradient=max_gradient, learning_rate=learning_rate, lr_decay_rate=lr_decay_rate, forward_only=False) # Tell TensorFlow to reuse the variables for the test model with tf.variable_scope(tf.get_variable_scope(), reuse=True): # Reuse the variables for the test model test_model = translation_model(input_vocab_size=vocab_size, output_vocab_size=vocab_size, buckets=buckets, rnn_size=rnn_size, num_layers=num_layers, max_gradient=max_gradient, learning_rate=learning_rate, lr_decay_rate=lr_decay_rate, forward_only=True) test_model.batch_size = 1 ######################################################################################################################## # Training session ######################################################################################################################## with tf.Session() as sess: sess.run(tf.global_variables_initializer()) train_loss = [] for i in range(generations): rand_bucket_ix = np.random.choice(len(bucketed_data)) model_outputs = translate_model.get_batch(bucketed_data, rand_bucket_ix) encoder_inputs, decoder_inputs, target_weights = model_outputs # Get the (gradient norm, loss, and outputs) _, step_loss, _ = translate_model.step(sess, encoder_inputs, decoder_inputs, target_weights, rand_bucket_ix, False) # Output status if (i + 1) % output_every == 0: train_loss.append(step_loss) print('Gen #{} out of {}. Loss: {:.4}'.format(i + 1, generations, step_loss)) # Check if we should decay the learning rate if (i + 1) % lr_decay_every == 0: sess.run(translate_model.learning_rate_decay_op) # Save model if (i + 1) % save_every == 0: print('Saving model to {}.'.format(full_model_dir)) model_save_path = os.path.join(full_model_dir, "eng_ger_translation.ckpt") translate_model.saver.save(sess, model_save_path, global_step=i) # Eval on test set if (i + 1) % eval_every == 0: for ix, sentence in enumerate(test_data): # Find which bucket sentence goes in bucket_id = next(index for index, val in enumerate(x_maxs) if val >= len(sentence)) # Get RNN model outputs encoder_inputs, decoder_inputs, target_weights = test_model.get_batch( {bucket_id: [(sentence, [])]}, bucket_id) # Get logits _, test_loss, output_logits = test_model.step(sess, encoder_inputs, decoder_inputs, target_weights, bucket_id, True) ix_output = [int(np.argmax(logit, axis=1)) for logit in output_logits] # If there is a 0 symbol in outputs end the output there. ix_output = ix_output[0:[i for i, x in enumerate(ix_output + [0]) if x == 0][0]] # Get german words from indices test_german = [ger_ix2vocab[x] for x in ix_output] print('English: {}'.format(test_english[ix])) print('German: {}'.format(test_german)) ######################################################################################################################## # Visualization ######################################################################################################################## # Plot train loss loss_generations = [i for i in range(generations) if i % output_every == 0] plt.plot(loss_generations, train_loss, 'k-') plt.title('Sequence to Sequence Loss') plt.xlabel('Generation') plt.ylabel('Loss') plt.show()

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start_client.py

xi102/bert_ner

0

97041

<gh_stars>0 import time from tqdm import tqdm print('start') try: from bert_base.client import BertClient except ImportError: raise ImportError('BertClient module is not available, it is required for serving HTTP requests.' 'Please use "pip install -U bert-serving-client" to install it.' 'If you do not want to use it as an HTTP server, ' 'then remove "-http_port" from the command line.') # 指定服务器的IP 127.0.0.1:49164 5555 # BertClient(ip='xxx', ner_model_dir='D:\Projects\Wunianyue\BERT-BiLSTM-CRF-NER\output', show_server_config=False, check_version=False, check_length=False, mode='NER') with BertClient(mode='NER') as bc: start_t = time.perf_counter() # text = text.replace(' ', '-') data句子间不能有空格。 df_path = r'data/add_data/yizhu_301_1000.txt' # data数据最后一行要为空 df = open(df_path, 'r+', encoding='utf-8') list = [] l=[] # 要把每个字用空格分隔，放入训练? for line in df: if line!='\n': l.append(' '.join(line)) list.append(line[:len(line) - 1]) print(len(list)) print('start') rst = bc.encode(l) # 测试同时输入两个句子，多个输入同理 k = 0 with open("annotationdata/301_1000_BIO.txt", "w", encoding='utf-8') as f: for index in tqdm(range(0,len(rst))): try: f.writelines(" ".join(rst[index])) except: k = k+1 pass with open("annotationdata/bert_301_1000.txt", "w", encoding='utf-8') as f: m = 0 j = 0 count = 0 for index in tqdm(range(0, len(list))): if(len(list[index])!=len(rst[index])): print("error in " + str(index)) count = count + 1 try: for i in range(0, len(list[index])): f.writelines(list[index][i] + ' ' + rst[index][i] + '\n') f.writelines('\n') m = m +1 except: j = j + 1 pass print(k) print(j) print(m) print(count) print(time.perf_counter() - start_t) # 2.5W

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src/data/dictionary.py

klimzaporojets/e2e-knowledge-ie

5

122314

<reponame>klimzaporojets/e2e-knowledge-ie import json def load_dictionary(config, path): type = config['type'] filename = config['filename'] filename = filename if filename.startswith("/") else "{}/{}".format(path, filename) if type == 'json': dictionary = Dictionary() dictionary.load_json(filename) else: raise BaseException("no such type", type) return dictionary def create_dictionaries(config, training): path = config['path'] print("Loading dictionaries (training={})".format(training)) if 'dictionaries' in config: dictionaries = {} for name, dict_config in config['dictionaries'].items(): if training: if "init" in dict_config: dictionary = load_dictionary(dict_config['init'], path) # print('init {}: size={}'.format(name, dictionary.size)) else: # print("init {} (blank)".format(name)) dictionary = Dictionary() else: dictionary = load_dictionary(dict_config, path) print('load {}: size={}'.format(name, dictionary.size)) dictionary.prefix = dict_config['prefix'] if 'prefix' in dict_config else '' if 'rewriter' in dict_config: if dict_config['rewriter'] == 'lowercase': dictionary.rewriter = lambda t: t.lower() elif dict_config['rewriter'] == 'none': print("rewriter: none") else: raise BaseException("no such rewriter", dict_config['rewriter']) if 'append' in dict_config: for x in dict_config['append']: idx = dictionary.add(x) print(" add token", x, "->", idx) if 'unknown' in dict_config: dictionary.set_unknown_token(dict_config['unknown']) if 'debug' in dict_config: dictionary.debug = dict_config['debug'] if 'update' in dict_config: dictionary.update = dict_config['update'] if not training: dictionary.update = False dictionaries[name] = dictionary return dictionaries else: print("WARNING: using wikipedia dictionary") words = Dictionary() entities = Dictionary() words.set_unknown_token("UNKNOWN") words.load_spirit_dictionary('data/tokens.dict', 5) entities.set_unknown_token("UNKNOWN") entities.load_spirit_dictionary('data/entities.dict', 5) return { 'words': words, 'entities': entities } class Dictionary: def __init__(self): self.rewriter = lambda t: t self.debug = False self.token_unknown = -1 self.update = True self.prefix = '' self.tmp_unknown = None self.clear() def clear(self): self.word2idx = {} self.matrix = False self.size = 0 self.out_of_voc = 0 self.oov = set() if self.tmp_unknown is not None: self.token_unknown = self.lookup(self.tmp_unknown) def load_json(self, filename): with open(filename) as file: data = json.load(file) if isinstance(data, (list,)): for idx, word in enumerate(data): if self.lookup(word) != idx: print("WARNING: invalid dictionary") else: for word, idx in data.items(): if self.lookup(word) != idx: print("WARNING: invalid dictionary") def lookup(self, token): token = self.prefix + self.rewriter(token) if not token in self.word2idx: if self.update: self.word2idx[token] = self.size self.size += 1 else: if self.debug: print("oov: '{}' -> {}".format(token, self.token_unknown)) self.out_of_voc += 1 return self.token_unknown return self.word2idx[token] def add(self, token): if not token in self.word2idx: self.word2idx[token] = self.size self.size += 1 return self.word2idx[token] def set_unknown_token(self, unknown_token): self.tmp_unknown = unknown_token self.token_unknown = self.word2idx[self.prefix + unknown_token] print(self.get(self.token_unknown), "->", self.token_unknown) def write(self, filename): import json with open(filename, 'w') as file: json.dump(self.word2idx, file) def get(self, index): for word, idx in self.word2idx.items(): if idx == index: return word return None def tolist(self): list = [None] * self.size for word, idx in self.word2idx.items(): list[idx] = word return list

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cobrinha trabalhadeira/lab10/lab10.py

ansattz/solving_sto

0

72268

<filename>cobrinha trabalhadeira/lab10/lab10.py # <NAME> # @ansattz #1 - Não delete nem modifique esta linha import readline from random import randint def roll(t): rounds=[] while len(rounds) < t: round0=randint(1,6) rounds.append(round0) return rounds def facerep(t): mydic={} l=roll(t) l1=l[1:] + [':)',] ls=[] z=0 for i in l: if i == l1[z]: ls.append(i) z+=1 for j in ls: mydic[j] = mydic.get(j, 0) + 1 v = list(mydic.values()) return l , len(v) def main(): # Escolha da quantidade de lances pelo usuário print('') print("""⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⢀⣀⣀⠤⠤⢴⣶⣖⣈⣉⣻⣿⣷⣦⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⣾⣿⣿⣿⣷⣶⡶⠚⠛⢻⣿⣯⣤⣤⣽⣿⣿⣷⣦⣀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⣿⣿⣿⣿⣿⣿⣿⣿⣟⠛⠛⢛⣿⣷⣤⣤⣴⣿⣿⣿⣿⣦⠀⠀⠀⠀ ⠀⠀⠀⠀⡟⠻⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠀⠀⠀⠀ ⠀⠀⠀⠀⣷⠀⣿⠿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠟⠛⢻⣿⣿⠀⠀⠀⠀ ⠀⠀⠀⠀⢹⣿⣿⡀⢹⣿⣿⣿⣿⡟⠁⠀⣿⣿⣿⣿⣿⣀⣀⣼⣿⣿⠀⠀⠀⠀ ⠀⠀⠀⠀⢸⠙⣿⣷⣼⣿⣿⣿⣿⣧⣤⣴⣿⣿⠿⠿⣿⣿⣿⣿⣿⡇⠀⠀⠀⠀ ⠀⠀⠀⠀⢸⣆⣸⡟⢿⣿⣿⣿⣿⣿⣿⣿⣿⡃⠀⢀⣿⣿⣿⣿⣿⡇⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠻⣿⡇⠘⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⠋⠀⢹⣿⡇⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠘⢿⣶⣿⣿⣿⣿⡿⠁⠀⣹⣿⣿⣿⣿⣤⣴⣿⣿⡇⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠙⣿⣿⣿⣿⣿⣶⣾⣿⣿⣿⡿⠿⠟⠛⠉⠉⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠻⣿⡿⠿⠟⠛⠉⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀""") t = int(input('Quantidade de lances: ')) print('') print('===== Resultados =====') # print('Números sorteados e número de séries de faces repetidas:\n', facerep(t)) print('Número de séries de faces repetidas:', facerep(t)[1]) # main() #Casos de teste da questão 1 - Não delete nem modifique esta linha # input: 0 ====> output: 0 # input: 1 ====> output: 0 # input: 2 ====> output: 1 or 0 # ... #2 - Não delete nem modifique esta linha def trap_area(a,b,c): return (((a+b))*c)/2 def quad(a,b,c): return [a**2, b**2, c**2] def marit(a,b,c): return (a+b+c)/3 def seq(a,b,c): l=[] i=a if a < b: while a < b: a+=c l.append(a) del l[-1] return i + sum(l) else: return "Não foi possível realizar a operação, pois (a > b)" def mymenu(): print('========= Opções válidas =========') print('') print('1 - Área do trapézio de bases a e b e altura c') print('2 - Quadrados de a, b e c') print('3 - Média aritmética entre a, b e c') print('4 - Sequência aritmética de a com um limite superior sendo b e razão c') print('0 - Encerrar programa') print('') print('==================================') while True: mymenu() opcao = input('Escolha uma opção: ') if opcao == '1': a = float(input('Base a: ')) b = float(input('Base b: ')) c = float(input('Altura c: ')) print('') print('A área do trapézio é: ', trap_area(a,b,c)) print('') elif opcao == '2': a = float(input('Valor de a: ')) b = float(input('Valor de b: ')) c = float(input('Valor de c: ')) print('') print(str.format('Os quadrados são:\n {}: {} \n {}: {} \n {}: {} ', a, quad(a,b,c)[0], b, quad(a,b,c)[1], c, quad(a,b,c)[2])) print('') elif opcao == '3': a = float(input('Primeiro valor (a): ')) b = float(input('Segundo valor (b): ')) c = float(input('Terceiro valor (c): ')) print('') print(str.format('A média aritmética entre {}, {} e {} é: ', a, b, c, marit(a,b,c))) print('') elif opcao == '4': a = int(input('Primeiro termo (a): ')) b = int(input('Limite superior (b): ')) c = int(input('Razão da sequência (c): ')) print('') print(str.format('A sequência aritmética de {} com limite superior {} e com razão {} é: ', a, b, c), seq(a,b,c)) print('') elif opcao == '0': print('') print('=====> Programa encerrado :)') break else: print('Opção inválida') print('')

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p2db/p2db/p2tools.py

ne75/p2llvm-lib

0

118989

import subprocess from colorama import Fore import tempfile import os from pathlib import Path import re load_cmd = '/opt/p2llvm/bin/loadp2' load_args = ['-ZERO', '-l', '0', '-v', '-FIFO', '4096'] def load(port, app, baud, verbose=False, retries=3): result = False while retries: args = load_args.copy() args.append('-p') args.append(port) args[2] = str(baud) args.append('{}'.format(app)) p = subprocess.Popen([load_cmd] + args, stdout=subprocess.PIPE) if verbose: print(" ".join([load_cmd] + args)) output = p.communicate()[0].decode('ascii') if p.returncode != 0: print(Fore.RED + output + Fore.RESET) print(Fore.YELLOW + "Load failed, retrying...\r" + Fore.RESET) retries -= 1; result = False; else: result = True; break if not result: print(Fore.RED + "Failed to load app\r" + Fore.RESET) if verbose: print("Load command was: " + str([load_cmd] + args)) return result def gen_bin(elf_file, outdir=None): ''' generate a binary file for loading from the given elf file. if outdir is given, save the binary there. otherwise it will be placed in a temporary directory returns the path to the generated binary ''' if not outdir: outdir = tempfile.gettempdir() outfile = os.path.join(outdir, Path(elf_file).stem) p = subprocess.Popen([ '/opt/p2llvm/bin/llvm-objcopy', '-O', 'binary', elf_file, outfile ], stdout=subprocess.PIPE) p.wait() if (p.returncode != 0): return None return outfile def get_objdump_data(elf_file): ''' return a dict of sections in the elf file. each section is keyed with the section header (as given by objdump), and the value is a dict containing a cleaned up version of the address, instruction encoding, and parsed instruction example: { ".cog<__start>": { "section_addr": 0 0: ["f6 03 a1 f8", "mov r0, ptra"] } } ''' p = subprocess.Popen([ '/opt/p2llvm/bin/llvm-objdump', '-d', elf_file, ], stdout=subprocess.PIPE) out, _ = p.communicate() lines = out.decode('ascii').splitlines() current_section = '' current_subsection = '' current_section_key = '' current_section_addr = 0 SECTION_PATERN = r"^Disassembly of section (.*?):$" SUBSECTION_PATTERN = r"^([a-f0-9]+) <(.*?)>:$" INST_PATTERN = r"^\s+([a-f0-9]+):\s+(([a-f0-9]+\s)+)\s+(.*?)$" output_dict = {} for l in lines: if re.search(SECTION_PATERN, l): # check for the start of a code section current_section = re.search(SECTION_PATERN, l).group(1) elif re.search(SUBSECTION_PATTERN, l): # check for the start of a function section current_section_addr = int(re.search(SUBSECTION_PATTERN, l).group(1), 16) current_subsection = re.search(SUBSECTION_PATTERN, l).group(2) elif re.search(INST_PATTERN, l): # check for an individual instruction inst_match = re.search(INST_PATTERN, l) current_section_key = "{}<{}>".format(current_section, current_subsection) if current_section_key not in output_dict: output_dict[current_section_key] = {} output_dict[current_section_key]["section_addr"] = current_section_addr inst_encoding = inst_match.group(2).split(' ') inst_encoding = [s for s in inst_encoding if s] inst_encoding = inst_encoding[::-1] i_str_fragments = [s.strip() for s in inst_match.group(4).split('\t') if s] if len(i_str_fragments) == 1: i_str_fragments = ['', i_str_fragments[0], ''] elif len(i_str_fragments) == 2: if i_str_fragments[-1] == 'wc' or i_str_fragments[-1] == 'wz' or i_str_fragments[-1] == 'wcz': # we don't have a condition, add a blank to the front i_str_fragments.insert(0, '') else: # we don't have a effect, add a blank to the back i_str_fragments.append('') elif len(i_str_fragments) == 3: pass # don't need to do anything else: print("Unknown instruction format: " + str(i_str_fragments)) i_str = '{: >14} {: <18}{}'.format(i_str_fragments[0], i_str_fragments[1], i_str_fragments[2]) output_dict[current_section_key][int(inst_match.group(1), 16)] = ( ' '.join(inst_encoding), i_str ) return output_dict def get_inst(data, pc): ''' get the instruction at the given pc in data if no such instruction exists, return None ''' for s in data: for i in data[s]: if isinstance(i, int) and i == pc: return data[s][i] return None def get_section(data, addr) -> str: ''' get the section name for a given address ''' for s in data: if data[s]["section_addr"] == addr: return s return ""

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robotcode/language_server/robotframework/parts/goto.py

d-biehl/robotcode

21

84470

from __future__ import annotations import ast from typing import ( TYPE_CHECKING, Any, Awaitable, Callable, List, Optional, Type, Union, cast, ) from ....utils.logging import LoggingDescriptor from ....utils.uri import Uri from ...common.language import language_id from ...common.text_document import TextDocument from ...common.types import Location, LocationLink, Position from ..utils.ast import ( HasTokens, Token, get_nodes_at_position, get_tokens_at_position, range_from_token, range_from_token_or_node, tokenize_variables, ) if TYPE_CHECKING: from ..protocol import RobotLanguageServerProtocol from .model_helper import ModelHelperMixin from .protocol_part import RobotLanguageServerProtocolPart _DefinitionMethod = Callable[ [ast.AST, TextDocument, Position], Awaitable[Union[Location, List[Location], List[LocationLink], None]], ] class RobotGotoProtocolPart(RobotLanguageServerProtocolPart, ModelHelperMixin): _logger = LoggingDescriptor() def __init__(self, parent: RobotLanguageServerProtocol) -> None: super().__init__(parent) parent.definition.collect.add(self.collect) parent.implementation.collect.add(self.collect) def _find_method(self, cls: Type[Any]) -> Optional[_DefinitionMethod]: if cls is ast.AST: return None method_name = "definition_" + cls.__name__ if hasattr(self, method_name): method = getattr(self, method_name) if callable(method): return cast(_DefinitionMethod, method) for base in cls.__bases__: method = self._find_method(base) if method: return cast(_DefinitionMethod, method) return None @language_id("robotframework") async def collect( self, sender: Any, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: result_nodes = await get_nodes_at_position(await self.parent.documents_cache.get_model(document), position) if not result_nodes: return None result_node = result_nodes[-1] if result_node is None: return None method = self._find_method(type(result_node)) if method is not None: result = await method(result_node, document, position) if result is not None: return result return await self._definition_default(result_nodes, document, position) async def _definition_default( self, nodes: List[ast.AST], document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: from robot.api.parsing import Token as RobotToken namespace = await self.parent.documents_cache.get_namespace(document) if namespace is None: return None if not nodes: return None node = nodes[-1] if not isinstance(node, HasTokens): return None tokens = get_tokens_at_position(node, position) for token in tokens: try: for sub_token in filter( lambda s: s.type == RobotToken.VARIABLE, tokenize_variables(token, ignore_errors=True) ): range = range_from_token(sub_token) if position.is_in_range(range): variable = await namespace.find_variable(sub_token.value, nodes, position) if variable is not None and variable.source: return [ LocationLink( origin_selection_range=range_from_token_or_node(node, sub_token), target_uri=str(Uri.from_path(variable.source)), target_range=variable.range(), target_selection_range=range_from_token(variable.name_token) if variable.name_token else variable.range(), ) ] except BaseException: pass return None async def definition_KeywordCall( # noqa: N802 self, node: ast.AST, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: from robot.parsing.lexer.tokens import Token as RobotToken from robot.parsing.model.statements import KeywordCall namespace = await self.parent.documents_cache.get_namespace(document) if namespace is None: return None kw_node = cast(KeywordCall, node) result = await self.get_keyworddoc_and_token_from_position( kw_node.keyword, cast(Token, kw_node.get_token(RobotToken.KEYWORD)), [cast(Token, t) for t in kw_node.get_tokens(RobotToken.ARGUMENT)], namespace, position, ) if result is not None and result[0] is not None: source = result[0].source if source is not None: return [ LocationLink( origin_selection_range=range_from_token_or_node(node, result[1]), target_uri=str(Uri.from_path(source)), target_range=result[0].range, target_selection_range=result[0].range, ) ] return None async def definition_Fixture( # noqa: N802 self, node: ast.AST, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: from robot.parsing.lexer.tokens import Token as RobotToken from robot.parsing.model.statements import Fixture namespace = await self.parent.documents_cache.get_namespace(document) if namespace is None: return None fixture_node = cast(Fixture, node) result = await self.get_keyworddoc_and_token_from_position( fixture_node.name, cast(Token, fixture_node.get_token(RobotToken.NAME)), [cast(Token, t) for t in fixture_node.get_tokens(RobotToken.ARGUMENT)], namespace, position, ) if result is not None and result[0] is not None: source = result[0].source if source is not None: return [ LocationLink( origin_selection_range=range_from_token_or_node(node, result[1]), target_uri=str(Uri.from_path(source)), target_range=result[0].range, target_selection_range=result[0].range, ) ] return None async def _definition_Template_or_TestTemplate( # noqa: N802 self, node: ast.AST, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: from robot.parsing.lexer.tokens import Token as RobotToken from robot.parsing.model.statements import Template, TestTemplate node = cast(Union[Template, TestTemplate], node) if node.value: keyword_token = cast(RobotToken, node.get_token(RobotToken.NAME)) if keyword_token is None: return None if position.is_in_range(range_from_token(keyword_token)): namespace = await self.parent.documents_cache.get_namespace(document) if namespace is None: return None result = await namespace.find_keyword(node.value) if result is not None and result.source is not None: return [ LocationLink( origin_selection_range=range_from_token_or_node(node, keyword_token), target_uri=str(Uri.from_path(result.source)), target_range=result.range, target_selection_range=result.range, ) ] return None async def definition_TestTemplate( # noqa: N802 self, node: ast.AST, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: return await self._definition_Template_or_TestTemplate(node, document, position) async def definition_Template( # noqa: N802 self, node: ast.AST, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: return await self._definition_Template_or_TestTemplate(node, document, position) async def definition_LibraryImport( # noqa: N802 self, node: ast.AST, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: from robot.parsing.lexer.tokens import Token as RobotToken from robot.parsing.model.statements import LibraryImport library_node = cast(LibraryImport, node) if library_node.name: name_token = cast(RobotToken, library_node.get_token(RobotToken.NAME)) if name_token is None: return None if position.is_in_range(range_from_token(name_token)): namespace = await self.parent.documents_cache.get_namespace(document) if namespace is None: return None try: libdoc = await namespace.imports_manager.get_libdoc_for_library_import( library_node.name, library_node.args, str(document.uri.to_path().parent) ) python_source = libdoc.source_or_origin if python_source is not None: return [ LocationLink( origin_selection_range=range_from_token_or_node(library_node, name_token), target_uri=str(Uri.from_path(python_source)), target_range=libdoc.range, target_selection_range=libdoc.range, ) ] except BaseException: pass return None async def definition_ResourceImport( # noqa: N802 self, node: ast.AST, document: TextDocument, position: Position ) -> Union[Location, List[Location], List[LocationLink], None]: from robot.parsing.lexer.tokens import Token as RobotToken from robot.parsing.model.statements import ResourceImport resource_node = cast(ResourceImport, node) if resource_node.name: name_token = cast(RobotToken, resource_node.get_token(RobotToken.NAME)) if name_token is None: return None if position.is_in_range(range_from_token(name_token)): namespace = await self.parent.documents_cache.get_namespace(document) if namespace is None: return None try: libdoc = await namespace.imports_manager.get_libdoc_for_resource_import( resource_node.name, str(document.uri.to_path().parent) ) python_source = libdoc.source_or_origin if python_source is not None: return [ LocationLink( origin_selection_range=range_from_token_or_node(resource_node, name_token), target_uri=str(Uri.from_path(python_source)), target_range=libdoc.range, target_selection_range=libdoc.range, ) ] except BaseException: pass return None

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setup.py

molML/MoleculeACE

9

76186

from setuptools import setup setup( name='MoleculeACE', version='1.0.9', packages=['MoleculeACE', 'MoleculeACE.ML', 'MoleculeACE.CNN', 'MoleculeACE.MLP', 'MoleculeACE.GNN', 'MoleculeACE.GNN.data', 'MoleculeACE.GNN.models', 'MoleculeACE.GNN.models.optimization', 'MoleculeACE.LSTM', 'MoleculeACE.benchmark', 'MoleculeACE.benchmark.utils', 'MoleculeACE.benchmark.models', 'MoleculeACE.benchmark.evaluation', 'MoleculeACE.benchmark.data_processing', 'MoleculeACE.benchmark.data_processing.preprocessing', 'MoleculeACE.Data', 'MoleculeACE.Data.benchmark_data', 'MoleculeACE.Data.benchmark_data.train', 'MoleculeACE.Data.benchmark_data.test', 'MoleculeACE.Data.configures.benchmark', 'MoleculeACE.Data.configures.default', 'MoleculeACE.Data.configures', 'MoleculeACE.Data.configures.benchmark.CHEMBL4203_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL2034_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL233_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL4616_EC50', 'MoleculeACE.Data.configures.benchmark.CHEMBL287_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL218_EC50', 'MoleculeACE.Data.configures.benchmark.CHEMBL264_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL219_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL2835_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL2147_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL231_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL3979_EC50', 'MoleculeACE.Data.configures.benchmark.CHEMBL237_EC50', 'MoleculeACE.Data.configures.benchmark.CHEMBL244_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL4792_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL1871_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL237_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL262_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL2047_EC50', 'MoleculeACE.Data.configures.benchmark.CHEMBL239_EC50', 'MoleculeACE.Data.configures.benchmark.CHEMBL2971_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL204_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL214_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL1862_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL234_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL238_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL235_EC50', 'MoleculeACE.Data.configures.benchmark.CHEMBL4005_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL236_Ki', 'MoleculeACE.Data.configures.benchmark.CHEMBL228_Ki' ], url='https://github.com/derekvantilborg/MoleculeACE', license='MIT', author='<NAME>', author_email='<EMAIL>', description='MoleculeACE', install_requires=[ 'tqdm', 'requests', 'twine', 'importlib-metadata', 'pandas', 'numpy', 'chembl_webresource_client', 'scikit-learn', 'matplotlib', 'python-Levenshtein', 'progress', 'rdkit-pypi' ], include_package_data=True, package_data={'': ['Data/*', 'Data/benchmark_data/*', 'Data/benchmark_data/test/*', 'Data/benchmark_data/train/*', 'Data/configures/*', 'Data/configures/default/*', 'Data/configures/benchmark/*', 'Data/configures/benchmark/CHEMBL4203_Ki/*', 'Data/configures/benchmark/CHEMBL2034_Ki/*', 'Data/configures/benchmark/CHEMBL233_Ki/*', 'Data/configures/benchmark/CHEMBL4616_EC50/*', 'Data/configures/benchmark/CHEMBL287_Ki/*', 'Data/configures/benchmark/CHEMBL218_EC50/*', 'Data/configures/benchmark/CHEMBL264_Ki/*', 'Data/configures/benchmark/CHEMBL219_Ki/*', 'Data/configures/benchmark/CHEMBL2835_Ki/*', 'Data/configures/benchmark/CHEMBL2147_Ki/*', 'Data/configures/benchmark/CHEMBL231_Ki/*', 'Data/configures/benchmark/CHEMBL3979_EC50/*', 'Data/configures/benchmark/CHEMBL237_EC50/*', 'Data/configures/benchmark/CHEMBL244_Ki/*', 'Data/configures/benchmark/CHEMBL4792_Ki/*', 'Data/configures/benchmark/CHEMBL1871_Ki/*', 'Data/configures/benchmark/CHEMBL237_Ki/*', 'Data/configures/benchmark/CHEMBL262_Ki/*', 'Data/configures/benchmark/CHEMBL2047_EC50/*', 'Data/configures/benchmark/CHEMBL239_EC50/*', 'Data/configures/benchmark/CHEMBL2971_Ki/*', 'Data/configures/benchmark/CHEMBL204_Ki/*', 'Data/configures/benchmark/CHEMBL214_Ki/*', 'Data/configures/benchmark/CHEMBL1862_Ki/*', 'Data/configures/benchmark/CHEMBL234_Ki/*', 'Data/configures/benchmark/CHEMBL238_Ki/*', 'Data/configures/benchmark/CHEMBL235_EC50/*', 'Data/configures/benchmark/CHEMBL4005_Ki/*', 'Data/configures/benchmark/CHEMBL236_Ki/*', 'Data/configures/benchmark/CHEMBL228_Ki/*' ]} )

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setup.py

darrylcousins/nzrealme

1

64473

<filename>setup.py #!/usr/bin/env python # -*- coding: utf-8 -*- try: from setuptools import setup except ImportError: from distutils.core import setup readme = open('README.rst').read() history = open('HISTORY.rst').read().replace('.. :changelog:', '') requirements = [ 'M2Crypto==0.22.3', 'dm.xmlsec.binding==1.3.1', 'isodate==0.5.0', 'defusedxml==0.4.1', 'python-saml', ] test_requirements = [ 'pytest', 'flake8', 'sphinx' ] setup( name='nzrealme', version='0.1.0', description='NZ RealMe python package', long_description=readme + '\n\n' + history, author='<NAME>', author_email='<EMAIL>', url='https://github.com/darrylcousins/nzrealme', packages=[ 'nzrealme', ], package_dir={'nzrealme': 'nzrealme'}, include_package_data=True, install_requires=requirements, license="BSD", zip_safe=False, classifiers=[ 'Development Status :: 2 - Pre-Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Natural Language :: English', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', ], test_suite='tests', tests_require=test_requirements, keywords='saml saml2 xmlsec realme nzrealme', )

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access/__init__.py

dmcgrath/starmade-blueprint-library

6

156742

<gh_stars>1-10 from user import *

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django_postgres_drop_index/utils.py

andrewp-as-is/django-postgres-drop-index.py

1

172281

<gh_stars>1-10 from django.db import connection from .classes import Index def get_indexes(): sql = """ SELECT n.nspname AS schemaname,t.relname AS tablename,c.relname AS indexname from pg_catalog.pg_class c JOIN pg_catalog.pg_namespace n on n.oid = c.relnamespace JOIN pg_catalog.pg_index i on i.indexrelid = c.oid JOIN pg_catalog.pg_class t on i.indrelid = t.oid WHERE c.relkind = 'i' AND NOT i.indisprimary AND NOT i.indisunique AND n.nspname not in ('pg_catalog', 'pg_toast') AND pg_catalog.pg_table_is_visible(c.oid) ORDER BY n.nspname,t.relname,c.relname; """ cursor = connection.cursor() cursor.execute(sql.strip()) indexes = [] for r in cursor.fetchall(): schemaname, tablename, indexname = r index = Index(schemaname, tablename, indexname) indexes.append(index) return indexes def drop_index(indexname, schemaname=None): if not schemaname: schemaname = 'public' sql = """DROP INDEX IF EXISTS "%s"."%s" CASCADE;""" % ( schemaname, indexname) cursor = connection.cursor() print(sql.strip()) cursor.execute(sql.strip()) def drop_table_indexes(tablename, schemaname=None): if not schemaname: schemaname = 'public' for index in get_indexes(): if index.schemaname == schemaname and index.tablename == tablename: drop_index(indexname=index.indexname, schemaname=index.schemaname) def drop_schema_indexes(schemaname): for index in get_indexes(): if index.schemaname == schemaname: drop_index(indexname=index.indexname, schemaname=index.schemaname) def drop_all_indexes(): for index in get_indexes(): drop_index(indexname=index.indexname, schemaname=index.schemaname)

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joplin/base/signals/janis_build_triggers.py

cityofaustin/joplin

15

1601521

import requests, json from datetime import datetime from pytz import timezone from django.conf import settings from django.dispatch import receiver from django.db.models.signals import post_save, post_delete from wagtail.core.signals import page_published, page_unpublished from graphene import Node from wagtail.core.models import Page from wagtail.admin.models import get_object_usage from rest_framework_api_key.models import APIKey from snippets.contact.models import Contact from pages.guide_page.models import GuidePage from wagtail.documents.models import Document from pages.home_page.models import HomePage import logging logger = logging.getLogger('joplin') ''' Gets data from a page that is required to send to the Publisher. returns { id: Int, the page_id used by wagtail. global_id: String, a hashed id used to query against graphql api triggered_build: Boolean, was this the page that triggered the publish request? action: "published" - publish action for itself and other pages "unpublished" - this page triggered the unpublish action for itself and other pages "updated_by_snippet" - this page was updated by a snippet being saved or deleted "saved" - a snippet is being saved, has no impact on Janis itself, but it could result in pages getting "secondary_publish_by_snippet" "deleted" - a snippet is being deleted, has no impact on Janis itself, but it could result in pages getting "secondary_publish_by_snippet" is_page: Boolean, is it a page as opposed to a snippet? content_type: String, which specific content_type (this is more for logging) author: Int, the id of the author of the latest revision } TODO: Figure out what format global_id should be in order to run queries with it. The current global_id is basically a placeholder. ''' def get_page_data(page, triggered_build, action): latest_revision = page.get_latest_revision() # imported pages may not have a latest_revision yet. if latest_revision and latest_revision.user: author = latest_revision.user.id else: author = None return { "id": page.id, "global_id": Node.to_global_id(page.content_type.name, page.id), "triggered_build": triggered_build, "action": action, "is_page": True, "content_type": page.specific_class.get_verbose_name(), "author": author, } def collect_pages(primary_page, action): # does this work on page deletion? pages arent deleted right, just unpublished? """ :param primary_page: the page that triggered the publish/unpublish :return: pages: an array of the data from all pages impacted by the publish/unpublish event """ pages = [ get_page_data(primary_page, True, action) ] primary_id = primary_page.id primary_content_type = primary_page.specific_class.get_verbose_name() page_set = get_object_usage(primary_page) # https://github.com/wagtail/wagtail/blob/master/wagtail/admin/models.py#L15 # get_object_usage will also return the wagtail_page itself for page in page_set: # Primary page is also included in page_set. Don't re-add primary_page data. if not (page.id == primary_id): pages.append(get_page_data(page, False, action)) if primary_content_type == 'Service Page' or primary_content_type == 'Information Page': guide_page_data = get_page_data_from_guides(primary_id, action) pages.extend(guide_page_data) return pages def collect_pages_from_snippet(instance, action): """ :param instance: the snippet that has been altered :return: an array of global page ids """ snippet_data = { "id": instance.id, "global_id": None, "triggered_build": True, "action": action, "is_page": False, "content_type": instance.__class__.__name__, "author": None, # TODO: is there a way to get the last editor of a snippet? } pages = [snippet_data] page_set = instance.get_usage() for page in page_set: pages.append(get_page_data(page, False, "updated_by_snippet")) return pages def get_page_data_from_guides(changed_id, action): """ Service Pages and information Pages don't know they are on Guides. So what happens if one is updated? Until we know better, this will go through all our guide pages and check if the page that is changed is in one of the guide's sections :param changed_id: id of the page that was published / unpublished :return: page_data for Guide Pages that include that page """ pages = [] all_guides = GuidePage.objects.all() for g in all_guides: for s in g.sections: # s.value.items() is an ordered dict list_of_values = list(s.value.items()) # the pages are the 5th tuple section_pages = list_of_values[4][1] for page in section_pages: if changed_id == page.id: pages.append(get_page_data(g, False, action)) return pages @receiver(page_published) def page_published_signal(sender, **kwargs): # for the future, setting up a way to check if the title changed # https://stackoverflow.com/questions/1355150/when-saving-how-can-you-check-if-a-field-has-changed # because if the title didnt change, pages that contain links to the published page don't need to be updated action = "published" primary_page = Page.objects.get(id=kwargs['instance'].id) pages = collect_pages(primary_page, action) publish(pages, primary_page) @receiver(page_unpublished) def page_unpublished_signal(sender, **kwargs): action = "unpublished" primary_page = Page.objects.get(id=kwargs['instance'].id) pages = collect_pages(primary_page, action) publish(pages, primary_page) # TODO: we can probably feed a list of models to attach the hook to # more ideas here # we might want to log but not trigger a build? need some sort of queue @receiver(post_save, sender=Document) @receiver(post_save, sender=Contact) def handle_post_save_signal(sender, **kwargs): action = "saved" pages = collect_pages_from_snippet(kwargs['instance'], action) publish(pages) @receiver(post_delete, sender=Document) @receiver(post_delete, sender=Contact) def handle_post_delete_signal(sender, **kwargs): action = "deleted" pages = collect_pages_from_snippet(kwargs['instance'], action) publish(pages) def publish(pages, primary_page=None): if settings.MOCK_PUBLISH and primary_page: update_primary_page(primary_page, "mock_pk", "mock_sk") return if not settings.PUBLISH_ENABLED: return # TODO: we want to extract the publish_janis_branch() for each page_id that we're publishing (for example, if we start publishing to different sites). # That logic must happen earlier in the collect_pages logic. # Even though this will work for now, it should not be hardcoded to be the first HomePage object. publish_janis_branch = HomePage.objects.first().publish_janis_branch() if not publish_janis_branch: logger.info("publish_janis_branch must be set in order to publish.") return None api_key = APIKey.objects.create_key( name=f"publisher-{datetime.now(timezone('US/Central')).isoformat()}" )[1] headers = { "x-api-key": settings.PUBLISHER_V2_API_KEY, "content-type": "application/json", } url = settings.PUBLISHER_V2_URL data = { "janis_branch": publish_janis_branch, "pages": pages, "joplin_appname": settings.APPNAME, "api_key": api_key, "env_vars": { "REACT_STATIC_PREFETCH_RATE": "0", }, "build_type": "rebuild", } res = requests.post(url, data=json.dumps(data), headers=headers) if res.status_code != 200: logger.error(f"publish_request failed with status {res.status_code}") logger.error(f"message: {res.json()['message']}") elif primary_page: res_data = res.json() publish_request_pk = res_data['pk'] publish_request_sk = res_data['sk'] update_primary_page(primary_page, publish_request_pk, publish_request_sk) def update_primary_page(primary_page, publish_request_pk, publish_request_sk): primary_page = primary_page.specific primary_page.publish_request_pk = publish_request_pk primary_page.publish_request_sk = publish_request_sk primary_page.publish_request_enqueued = True logger.info(f"published() pk={publish_request_pk}, sk={publish_request_sk}") primary_page.save()

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resources/tasks.py

axonepro/sdk-ooti

1

66511

<gh_stars>1-10 import requests import json from .helper import Helper class Tasks(Helper): def __init__(self, base_url, org_pk, teams_pk, access_token, _csrf_token, headers, pagination): super().__init__(base_url, org_pk, teams_pk, access_token, _csrf_token, headers, pagination) def empty_tasks_trash(self, project_id): """ Set delete all not-completed archived tasks in project """ route = 'v1/tasks/empty-trash/{0}/'.format(project_id) response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, None) return self.process_response(response) def get_task_labels_list(self, page=1): """ Get the list of tasks labels """ route = 'v1/tasks/label/list/{0}/?page_size={1}&page={2}'.format(self.org_pk, self.pagination, page) response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None) return self.process_response(response, True) def create_task_label(self, data): """ Create a new task label Keywords arguments: data -- data of the new label to be created: { "creator": orguser_pk, "team": team_pk, "title": "label title", "description": "new task label" } """ route = 'v1/tasks/label/list/{0}/'.format(self.org_pk) response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, json.dumps(data)) return self.process_response(response) def get_task_label_details(self, label_pk): """ Get the task label details Keywords arguments: label_pk -- pk of the task label """ route = 'v1/tasks/label/{0}/'.format(label_pk) response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None) return self.process_response(response) def update_task_label_details(self, label_pk, data): """ Update the task label details Keywords arguments: label_pk -- pk of the task label data -- content of the update: { "creator": orguser_pk, "team": team_pk, "title": "new title", "description": "description updated" } """ route = 'v1/tasks/label/{0}/'.format(label_pk) response = self.process_request(requests, 'PATCH', self.base_url, route, self.headers, None, json.dumps(data)) return self.process_response(response) def delete_task_label(self, label_pk): """ Delete the task label details Keywords arguments: label_pk -- pk of the task label """ route = 'v1/tasks/label/{0}/'.format(label_pk) response = self.process_request(requests, 'DELETE', self.base_url, route, self.headers, None, None) return self.process_response(response) def get_tasks_list(self, page=1): """ Get the tasks list """ route = 'v1/tasks/list/{0}/?page_size={1}&page={2}'.format(self.org_pk, self.pagination, page) response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None) return self.process_response(response, True) def create_task(self, data): """ Create a new task Keywords arguments: data -- data of the new task to be created: { "creator": orguser_pk, "created_at": "string", "labels": [ label_pk, ... ], "title": "string", "due_date": "string", "description": "string" } """ route = 'v1/tasks/list/{0}/'.format(self.org_pk) response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, json.dumps(data)) return self.process_response(response) def get_tasks_lists_list(self, page=1): """ Get the list of tasks list """ route = 'v1/tasks/lists/list/{0}/?page_size={1}&page={2}'.format(self.org_pk, self.pagination, page) response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None) return self.process_response(response, True) def create_tasks_list(self, data): """ Create a new list of tasks Keywords arguments: data -- data of the new list of tasks to be created: { "author": orguser_pk, "title": "new list", "tasks": [ task_pk, ... ], "followers": [ orguser_pk, ... ] } """ route = 'v1/tasks/lists/list/{0}/'.format(self.org_pk) response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, json.dumps(data)) return self.process_response(response) def get_tasks_list_details(self, list_pk): """ Get the list of tasks details Keywords arguments: list_pk -- the pk of list of tasks """ route = 'v1/tasks/lists/{0}/'.format(list_pk) response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None) return self.process_response(response) def update_tasks_list_details(self, list_pk, data): """ Update the list of tasks details Keywords arguments: list_pk -- the pk of list of tasks data -- content of the update: { "author": orguser_pk, "title": "new list", "tasks": [ task_pk, ... ], "followers": [ orguser_pk, ... ] } """ route = 'v1/tasks/lists/{0}/'.format(list_pk) response = self.process_request(requests, 'PATCH', self.base_url, route, self.headers, None, json.dumps(data)) return self.process_response(response) def delete_tasks_list(self, list_pk): """ Delete the list of tasks Keywords arguments: list_pk -- the pk of list of tasks """ route = 'v1/tasks/lists/{0}/'.format(list_pk) response = self.process_request(requests, 'DELETE', self.base_url, route, self.headers, None, None) return self.process_response(response) def log_tasks(self): """ Set all tasks to is_logged True """ route = 'v1/tasks/log-tasks/{0}/'.format(self.org_pk) response = self.process_request(requests, 'POST', self.base_url, route, self.headers, None, None) return self.process_response(response) def get_tasks_timeline(self): route = 'v1/tasks/timeline/{0}/'.format(self.org_pk) response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None) return self.process_response(response) def get_task_details(self, pk): """ Get task details Keywords arguments: pk -- the pk of the task """ route = 'v1/tasks/{0}/'.format(pk) response = self.process_request(requests, 'GET', self.base_url, route, self.headers, None, None) return self.process_response(response) def update_task_details(self, pk, data): """ Update task details Keywords arguments: pk -- the pk of the task data -- content of the update: { "creator": orguser_pk, "created_at": "string", "estimate": 0, "is_logged": true, "labels": [ "string" ], "title": "string", "due_date": "string", "completed_at": "string", "description": "string", "is_completed": true } """ route = 'v1/tasks/{0}/'.format(pk) response = self.process_request(requests, 'PATCH', self.base_url, route, self.headers, None, json.dumps(data)) return self.process_response(response) def delete_task(self, pk): """ Delete task Keywords arguments: pk -- the pk of the task """ route = 'v1/tasks/{0}/'.format(pk) response = self.process_request(requests, 'DELETE', self.base_url, route, self.headers, None, None) return self.process_response(response)

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webscan/webscan/log.py

gwvsol/WebScanQRcode-JSON-RPC

0

52875

<reponame>gwvsol/WebScanQRcode-JSON-RPC # import logging from loguru import logger as logging # log_format = '%(asctime)s.%(msecs)d|\ # %(levelname)s|%(module)s.%(funcName)s:%(lineno)d %(message)s' # logging.basicConfig(level=logging.INFO, # format=log_format, # datefmt='%Y-%m-%d %H:%M:%S') logging.add(str, format="<green>{time:YYYY-MM-DD HH:mm:ss}</green>|\ {level}|<cyan>{name}</cyan>:<cyan>{function}\ </cyan>:<cyan>{line}</cyan> {message}")

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