xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

0446287e343b809baec3c4682452b35d3c243b0d

566

py

Python

tests/test_math.py

JosephMontoya-TRI/monty

facef1776c7d05c941191a32a0b93f986a9761dd

[ "MIT" ]

null

tests/test_math.py

JosephMontoya-TRI/monty

facef1776c7d05c941191a32a0b93f986a9761dd

[ "MIT" ]

null

tests/test_math.py

JosephMontoya-TRI/monty

facef1776c7d05c941191a32a0b93f986a9761dd

[ "MIT" ]

null

# coding: utf-8 #!/usr/bin/env python from __future__ import division, unicode_literals __author__ = 'Shyue Ping Ong' __copyright__ = 'Copyright 2014, The Materials Virtual Lab' __version__ = '0.1' __maintainer__ = 'Shyue Ping Ong' __email__ = 'ongsp@ucsd.edu' __date__ = '1/24/14' import unittest from monty.math import nCr, nPr class FuncTest(unittest.TestCase): def test_nCr(self): self.assertEqual(nCr(4, 2), 6) def test_deprecated_property(self): self.assertEqual(nPr(4, 2), 12) if __name__ == "__main__": unittest.main()

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null

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044734cfe1196714dd666049793f24e1b681b8b3

4,450

py

Python

exercises/fit_gaussian_estimators.py

AlonViz/IML.HUJI

107f7c20b8bd64d41452e4a5b66abe843af7eb18

[ "MIT" ]

null

exercises/fit_gaussian_estimators.py

AlonViz/IML.HUJI

107f7c20b8bd64d41452e4a5b66abe843af7eb18

[ "MIT" ]

null

exercises/fit_gaussian_estimators.py

AlonViz/IML.HUJI

107f7c20b8bd64d41452e4a5b66abe843af7eb18

[ "MIT" ]

null

from IMLearn.learners import UnivariateGaussian, MultivariateGaussian import numpy as np import plotly.io as pio import plotly.express as px import pandas as pd pio.templates.default = "simple_white" def test_univariate_gaussian(): # Question 1 - Draw samples and print fitted model """Create a sample of size 1000 of N(10,1) and print the expectaton and variance estimations of the univariate gaussian estimator""" mu_, stdev_ = 10, 1 var_ = stdev_ * stdev_ samples_ = np.random.normal(loc=mu_, scale=stdev_, size=1000) estimator = UnivariateGaussian() estimator.fit(samples_) print(estimator.mu_, estimator.var_) # Question 2 - Empirically showing sample mean is consistent """using samples of increasing sizes(10,20,...,1000), plot the difference of the estimated and real expectation as a function of sample size.""" sample_estimator = UnivariateGaussian() estimations = list() for size in range(10, 1010, 10): sample_estimator.fit(samples_[:size]) estimations.append(np.abs(mu_ - sample_estimator.mu_)) # Plot: df_sample_size = pd.DataFrame( np.array([list(range(10, 1010, 10)), estimations]).transpose(), columns=["Sample Size", "Estimation Error"]) fig_1 = px.scatter(df_sample_size, x="Sample Size", y="Estimation Error") fig_1.update_layout( title_text='Univariate Gaussian Estimator<br><sup> Error in' ' expectancy as a function of sample size</sup>' , title_x=0.5, title_font_size=25) fig_1.show() # Question 3: Plot the PDF using fitted model """Compute the PDF of the previously drawn samples using the model fitted in question 1. Plot the empirical PDF function under the fitted model""" # Create: samples_.sort() pdfs_ = estimator.pdf(samples_) fig_2 = px.scatter(x=samples_, y=pdfs_) fig_2.update_layout(title_text="Univariate Gaussian Estimator<br><sup>" " sample density plotted on empirical PDF</sup>", xaxis_title="Sample Value", yaxis_title="Empirical PDF", title_x=0.5, title_font_size=25) fig_2.update_traces(marker=dict(size=2)) fig_2.show() def test_multivariate_gaussian(): # Question 4 - Draw samples and print fitted model """Fit a multivariate Gaussian and print the estimated expectation and covariance matrix.""" mu_ = np.array([0, 0, 4, 0]) cov_ = np.array([[1, 0.2, 0, 0.5], [0.2, 2, 0, 0], [0, 0, 1, 0], [0.5, 0, 0, 1]]) samples_ = np.random.multivariate_normal(mu_, cov_, size=1000) estimator = MultivariateGaussian() estimator.fit(samples_) print(estimator.mu_) print(estimator.cov_) # Question 5 - Likelihood evaluation """Using the samples drawn in the question above calculate the log-likelihood for models with expectation µ = [f1,0,f3,0]. Plot a heatmap of f1 values as rows, f3 values as columns and the color being the calculated log likelihood.""" sample_count = 200 # needs to be 200 f1 = np.linspace(-10, 10, sample_count) f3 = np.linspace(-10, 10, sample_count) func = lambda x, y: MultivariateGaussian.log_likelihood( np.array([x, 0, y, 0]), cov_, samples_) func_vec = np.vectorize(func) res = func_vec(f1[:, np.newaxis], f3) labels_dict = {"x": "f3", "y": "f1", "color": "Log-likelihood"} fig = px.imshow(res, x=f1, y=f3, labels=labels_dict) fig.update_layout(title_text="Multivariate Gaussian Estimator<br><sup>" "Log-likelihood of expectation µ = [f1,0,f3,0] and known covariance, " "values drawn with expectation [0,0,4,0]</sup>", title_x=0.5, title_font_size=25, legend_x=0) fig.show() # Question 6 - Maximum likelihood """Of all values tested in question 5, which model (pair of values for feature 1 and 3) achieved the maximum log-likelihood value? Round to 3 decimal places""" argmax_tup = np.unravel_index(res.argmax(), res.shape) print("Max value achieved: {0}".format(res.max())) print("Argmax: f1 = {0}, f3 = {1}".format(f1[argmax_tup[0]], f3[argmax_tup[1]])) if __name__ == '__main__': np.random.seed(0) test_univariate_gaussian() test_multivariate_gaussian()

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0448e36555008095af9e18f603a31c577ffa7f06

22,847

py

Python

matilda/data_pipeline/data_scapers/financial_statements_scraper/xbrl_scraper_sec_edgar.py

AlainDaccache/Quantropy

6cfa06ed2b764471382ebf94d40af867f10433bb

[ "MIT" ]

45

2021-01-28T04:12:21.000Z

2022-02-24T13:15:50.000Z

matilda/data_pipeline/data_scapers/financial_statements_scraper/xbrl_scraper_sec_edgar.py

AlainDaccache/Quantropy

6cfa06ed2b764471382ebf94d40af867f10433bb

[ "MIT" ]

32

2021-03-02T18:45:16.000Z

2022-03-12T00:53:10.000Z

matilda/data_pipeline/data_scapers/financial_statements_scraper/xbrl_scraper_sec_edgar.py

AlainDaccache/Quantropy

6cfa06ed2b764471382ebf94d40af867f10433bb

[ "MIT" ]

10

2020-12-25T15:02:40.000Z

2021-12-30T11:40:15.000Z

import traceback import xml.etree.ElementTree as ET from datetime import datetime, timedelta from pprint import pprint import requests import re from bs4 import BeautifulSoup, NavigableString from zope.interface import implementer import numpy as np from matilda.data_pipeline.data_preparation_helpers import flatten_dict from matilda.data_pipeline.data_scapers.financial_statements_scraper import financial_statements_scraper def get_company_cik(ticker): URL = 'http://www.sec.gov/cgi-bin/browse-edgar?CIK={}&Find=Search&owner=exclude&action=getcompany'.format(ticker) response = requests.get(URL) CIK_RE = re.compile(r'.*CIK=(\d{10}).*') cik = CIK_RE.findall(response.text)[0] print('Company CIK for {} is {}'.format(ticker, cik)) return cik def get_filings_urls_first_layer(cik, filing_type): base_url = "https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK={}&type={}".format(cik, filing_type) edgar_resp = requests.get(base_url).text print(base_url) soup = BeautifulSoup(edgar_resp, 'html.parser') table_tag = soup.find('table', class_='tableFile2') rows = table_tag.find_all('tr') doc_links = [] for row in rows[1:]: cells = row.find_all('td') doc_links.append('https://www.sec.gov' + cells[1].a['href']) return doc_links def get_filings_urls_second_layer(doc_links): dates_and_links = [] for doc_link in doc_links: doc_resp = requests.get(doc_link).text # Obtain HTML for document page soup = BeautifulSoup(doc_resp, 'html.parser') # Find the XBRL link head_divs = soup.find_all('div', class_='infoHead') # first, find period of report cell_index = next((index for (index, item) in enumerate(head_divs) if item.text == 'Period of Report'), -1) period_of_report = '' try: siblings = head_divs[cell_index].next_siblings for sib in siblings: if isinstance(sib, NavigableString): continue else: period_of_report = sib.text break except: traceback.print_exc() # first, try finding a XML document table_tag = soup.find('table', class_='tableFile', summary='Data Files') if table_tag is not None: rows = table_tag.find_all('tr') for row_index, row in enumerate(rows[1:]): cells = row.find_all('td') link = 'https://www.sec.gov' + cells[2].a['href'] if 'XML' in cells[3].text or 'INS' in cells[3].text: dates_and_links.append((period_of_report, link)) return dates_and_links @implementer(financial_statements_scraper.FinancialStatementsParserInterface) class XbrlParser: regex_patterns = { 'Balance Sheet': { 'Assets': { 'Current Assets': { 'Cash and Short Term Investments': { 'Cash and Cash Equivalents': r'^Cash And Cash Equivalents At Carrying Value$', 'Marketable Securities Current': r'(^Available For Sale Securities Current' r'|Available For Sale Securities Debt Securities' r'|Marketable Securities Current$)', 'Cash and Short Term Investments': r'$^', }, 'Accounts Receivable': { 'Allowance for Doubtful Accounts': r'^Allowance For Doubtful Accounts Receivable Current$', 'Net Accounts Receivable': r'^Accounts Receivable Net Current$', 'Vendor Nontrade Receivables, Current': r'^Nontrade Receivables Current$' }, 'Prepaid Expense, Current': r'$^', 'Inventory, Net': r'^Inventory Net$', 'Income Taxes Receivable, Current': r'$^', 'Assets Held-for-sale': r'$^', # taxes that have been already paid despite not yet having been incurred 'Deferred Tax Assets, Current': r'$^', 'Other Current Assets': r'^Other Assets Current$', 'Total Current Assets': r'^Assets Current$' }, 'Non Current Assets': { 'Marketable Securities Non Current': r'^Marketable Securities Noncurrent$', 'Restricted Cash Non Current': r'$^', 'Property, Plant and Equipment': { 'Gross Property, Plant and Equipment': r'^Property Plant And Equipment Gross$', 'Accumulated Depreciation and Amortization': r'^Accumulated Depreciation Depletion And Amortization Property Plant And Equipment$', 'Property, Plant and Equipment, Net': r'^Property Plant And Equipment Net$', }, 'Operating Lease Right-of-use Assets': r'$^', 'Deferred Tax Assets Non Current': r'$^', 'Intangible Assets': { 'Goodwill': r'^Goodwill$', 'Intangible Assets, Net (Excluding Goodwill)': r'^Intangible Assets Net Excluding Goodwill$', 'Total Intangible Assets': r'$^', }, 'Other Non Current Assets': r'^Other Assets Noncurrent$', 'Total Non Current Assets': r'^Assets Noncurrent$' }, 'Total Assets': r'^Assets$' }, "Liabilities and Shareholders\' Equity": { 'Liabilities': { 'Current Liabilities': { # this is the short-term debt, i.e. the amount of a loan that is payable to the lender within one year. 'Long-term Debt, Current Maturities': r'^LongTermDebtCurrent$', 'Accounts Payable': r'^Accounts Payable Current$', # always a current anyways 'Other Accounts Payable': r'^Accounts Payable Other Current$', 'Operating Lease, Liability, Current': r'$^', 'Employee-related Liabilities, Current': r'$^', 'Accrued Income Taxes': r'$^', 'Accrued Liabilities, Current': r'^Accrued Liabilities Current$', 'Deferred Revenue, Current': r'^Contract With Customer Liability Current$', 'Commercial Paper': r'^Commercial Paper$', 'Income Taxes Payable': r'$^', 'Other Current Liabilities': r'^Other Liabilities Current$', 'Total Current Liabilities': r'^Liabilities Current$', }, 'Non Current Liabilities': { 'Deferred Tax Liabilities': r'$^', # this debt is due after one year in contrast to current maturities which are due within this year 'Long-term Debt, Noncurrent Maturities': r'^Long Term Debt Noncurrent$', 'Operating Lease, Liability, Noncurrent': r'$^', 'Liability, Defined Benefit Plan, Noncurrent': r'$^', 'Accrued Income Taxes, Noncurrent': r'$^', 'Deferred Revenue, Noncurrent': r'$^', 'Long-Term Unearned Revenue': r'$^', 'Other Liabilities, Noncurrent': r'^Other Liabilities Noncurrent$', 'Total Non Current Liabilities': r'^Liabilities Noncurrent$' }, 'Total Liabilities': r'^Liabilities$' # sometimes at the bottom there are two tabs, our code can't catch it i.e. Other non-current liabilities then tab Total non-current liabilities then tab Total liabilities }, "Shareholders' Equity": { 'Preferred Stock, Value, Issued': r'$^', 'Common Stock and Additional Paid in Capital': { 'Common Stock, Value, Issued': r'^Common Stock Value$', 'Additional Paid in Capital': r'^Additional Paid In Capital$', 'Common Stocks, Including Additional Paid in Capital': r'^Common Stocks Including Additional Paid In Capital$', 'Weighted Average Number of Shares Outstanding, Basic': r'^Weighted Average Number Of Shares Outstanding Basic$ ', 'Weighted Average Number Diluted Shares Outstanding Adjustment': r'$^', 'Weighted Average Number of Shares Outstanding, Diluted': r'^Weighted Average Number Of Diluted Shares Outstanding$', }, 'Treasury Stock, Value': r'$^', 'Retained Earnings (Accumulated Deficit)': r'^Retained Earnings Accumulated Deficit$', 'Accumulated Other Comprehensive Income (Loss)': r'^Accumulated Other Comprehensive Income Loss Net Of Tax$', 'Deferred Stock Compensation': r'$^', 'Stockholders\' Equity Attributable to Parent': r'$^', 'Minority Interest': r'$^', 'Stockholders\' Equity, Including Portion Attributable to Noncontrolling Interest': '(?!.*Before)(?=.*Noncontrolling interest)(?=.*Equity(?!.*[_]))(?!.*Liabilities(?!.*[_]))' }, 'Total Liabilities and Shareholders\' Equity': r'^Liabilities And Stockholders Equity$' }, }, 'Income Statement': { 'Revenues': { 'Service Sales': r'$^', 'Product Sales': r'$^', 'Net Sales': r'^(Revenue From Contract With Customer Excluding Assessed Tax|Sales Revenue Net)$' }, 'Cost of Goods and Services Sold': { 'Cost of Products': r'^(Cost Of Goods And Services Sold ProductMember|Revenue From Contract With Customer Excluding Assessed Tax ProductMember)$', 'Cost of Services': r'^(Cost Of Goods And Services Sold ServiceMember|Revenue From Contract With Customer Excluding Assessed Tax ServiceMember)$', 'Cost of Goods and Services Sold': r'^(Cost Of Revenue|Cost Of Goods And Services Sold)$', 'Gross Margin': r'^Gross Profit$', }, 'Provision for Loan, Lease, and Other Losses': r'$^', 'Operating Expenses': { 'Research and Development Expense': r'^Research And Development Expense$', 'Selling, General and Administrative': { 'Marketing Expense': r'$^', 'Selling and Marketing Expense': r'^Selling And Marketing Expense$', 'General and Administrative Expense': r'^General And Administrative Expense$', 'Selling, General and Administrative Expense': r'^Selling General And Administrative Expense$' }, 'Other Operating Expenses': r'$^', # TODO 'EBITDA': r'$^', 'Total Operating Expenses': r'^Operating Expenses$' }, 'Costs and Expenses': r'^Costs And Expenses$', 'Operating Income (Loss) / EBIT': r'^Operating Income Loss$', 'Other (Non-Operating) Income (Expense)': { 'Interest Income': r'^Investment Income Interest$', 'Interest and Dividend Income': r'$^', 'Interest Expense': r'^Interest Expense$', 'Interest Income (Expense), Net': r'$^', 'Foreign Currency Transaction Gain (Loss)': r'^Foreign Currency Transaction Gain Loss Before Tax$', 'Other Nonoperating Income (Expense)': '^Other Nonoperating Income Expense$', # below is for 'Interest and other income, net' and 'Total other income/(expense), net' 'Non-Operating Income (Expense)': r'^Nonoperating Income Expense$' }, 'Income (Loss) before Income Taxes, Noncontrolling Interest': r'^Income Loss From Continuing Operations Before Income Taxes Extraordinary Items Noncontrolling Interest$', 'Income Tax Expense (Benefit)': r'^Income Tax Expense Benefit$', 'Net Income (Loss), Including Portion Attributable to Noncontrolling Interest': r'$^', 'Net Income (Loss) Attributable to Noncontrolling (Minority) Interest': r'$^', 'Net Income (Loss) Attributable to Parent': r'^Net Income Loss$', 'Undistributed Earnings (Loss) Allocated to Participating Securities, Basic': r'^Undistributed Earnings Loss Allocated To Participating Securities Basic$', 'Preferred Stock Dividends': r'$^', 'Net Income (Loss) Available to Common Stockholders, Basic': r'^Net Income Loss Available To Common Stockholders Basic$', 'Other Comprehensive Income (Loss)': r'$^', 'Comprehensive Income (Loss), Net of Tax, Attributable to Parent': r'$^', 'Earnings Per Share, Basic': '^Earnings Per Share Basic$', 'Earnings Per Share, Diluted': '^Earnings Per Share Diluted$', }, 'Cash Flow Statement': { 'Cash, Cash Equivalents, Restricted Cash and Restricted Cash Equivalents, Beginning Balance': '$^', 'Operating Activities': { 'Net Income (Loss) Attributable to Parent': r'$^', 'Adjustments to Reconcile Net Income': { 'Depreciation, Depletion and Amortization': r'$^', 'Share-based Payment Arrangement, Noncash Expense': r'$^', 'Deferred Income Tax Expense (Benefit)': r'$^', 'Other Noncash Income (Expense)': r'$^' }, 'Change in Assets and Liabilities': { 'Increase (Decrease) in Accounts Receivable': r'$^', 'Increase (Decrease) in Inventories': r'$^', 'Increase (Decrease) in Other Receivables': r'$^', 'Increase (Decrease) in Prepaid Expense and Other Assets': r'$^', 'Increase (Decrease) in Other Operating Assets': r'$^', 'Increase (Decrease) in Accounts Payable': r'$^', 'Increase (Decrease) in Other Accounts Payable': r'$^', 'Increase (Decrease) in Accrued Liabilities': r'$^', 'Increase (Decrease) in Deferred Revenue, Liability': r'$^', 'Increase (Decrease) in Other Operating Liabilities': r'$^' }, 'Net Cash Provided by (Used in) Operating Activities': r'$^' }, 'Investing Activities': { 'Payments to Acquire Marketable Securities, Available-for-sale': r'$^', 'Proceeds from Maturities, Prepayments and Calls of Debt Securities, Available-for-sale': r'$^', 'Proceeds from Sale of Debt Securities, Available-for-sale': r'$^', 'Payments to Acquire Property, Plant, and Equipment': r'$^', 'Payments to Acquire Businesses, Net of Cash Acquired': r'$^', 'Payments to Acquire Other Investments': r'$^', 'Proceeds from Sale and Maturity of Other Investments': r'$^', 'Payments for (Proceeds from) Other Investing Activities': r'$^', 'Net Cash Provided by (Used in) Investing Activities': r'$^' }, 'Financing Activities': { 'Proceeds from Issuance of Common Stock': r'$^', 'Payment, Tax Withholding, Share-based Payment Arrangement': r'$^', 'Payments of Dividends': r'$^', 'Payments for Repurchase of Common Stock': r'$^', 'Proceeds from Issuance of Long-term Debt': r'$^', 'Repayments of Long-term Debt': r'$^', 'Finance Lease, Principal Payments': r'$^', 'Proceeds from (Repayments of) Bank Overdrafts': r'$^', 'Proceeds from (Repayments of) Commercial Paper': r'$^', 'Proceeds from (Payments for) Other Financing Activities': r'$^', 'Net Cash Provided by (Used in) Financing Activities': r'$^' }, 'Effect of Exchange Rate on Cash, Cash Equivalents, Restricted Cash and Restricted Cash Equivalents': r'$^', 'Cash, Cash Equivalents, Restricted Cash and Restricted Cash Equivalents, Period Increase (Decrease), Including Exchange Rate Effect': r'$^', # we are hardcoding the Ending balance to be Cash, Cash Equivalents, Restricted Cash and Restricted Cash Equivalents in XBRL because we filtered the beginning balance (which can be taken from previous year) 'Cash, Cash Equivalents, Restricted Cash and Restricted Cash Equivalents, Ending Balance': r'$^', 'Supplemental': {} } } def load_data_source(self, ticker: str) -> dict: """Load in the file links""" cik = get_company_cik(ticker) doc_links_yearly = get_filings_urls_first_layer(cik, '10-K') doc_links_quarterly = get_filings_urls_first_layer(cik, '10-Q') filings_dictio_yearly = get_filings_urls_second_layer(doc_links_yearly) filings_dictio_quarterly = get_filings_urls_second_layer(doc_links_quarterly) return {'Yearly': filings_dictio_yearly, 'Quarterly': filings_dictio_quarterly} def scrape_tables(self, url: str, filing_date: datetime, filing_type: str) -> dict: """Extract tables from the currently loaded file.""" current_quarter = '' response = requests.get(url).text elements = ET.fromstring(response) all_in_one_dict = {'Yearly': {filing_date: {'': {}}}, 'Quarterly': {filing_date: {'': {}}}, '6 Months': {filing_date: {'': {}}}, '9 Months': {filing_date: {'': {}}}} # First, get all the us-gaap xbrl tags (that correspond to the current year or quarter) found_current_quarter = False for element in elements.iter(): if 'context' in element.tag and not found_current_quarter: pattern = re.search('(Q\d)', element.attrib['id']) if pattern: current_quarter = pattern.groups()[-1] found_current_quarter = True if 'contextRef' in element.attrib.keys(): tag_name = re.sub(r"(\w)([A-Z])", r"\1 \2", element.tag.split('}')[1]) try: tag_value = int(element.text) except: continue axis_pattern = re.search(r'ProductOrServiceAxis_us-gaap_(.*)', element.attrib['contextRef'], re.IGNORECASE) if axis_pattern: tag_name = tag_name + ' ' + axis_pattern.groups()[-1] if ('Axis' not in element.attrib['contextRef']) or axis_pattern: period = filing_type # first pattern for date and period date_pattern_yyyymmdd = re.search(r'(........)-{}'.format(filing_date.strftime('%Y%m%d')), element.attrib['contextRef']) if date_pattern_yyyymmdd: prior_date = date_pattern_yyyymmdd.groups()[-1] prior_date = datetime.strptime(prior_date, '%Y%m%d') if filing_date > prior_date + timedelta(days=270): period = '9 Months' elif filing_date > prior_date + timedelta(days=180): period = '6 Months' all_in_one_dict[period][filing_date][''][tag_name] = tag_value # second pattern for date and period date_pattern_FDYYYYQd = re.search(r'FD{}{}(...)?'.format(filing_date.year, current_quarter), element.attrib['contextRef']) if date_pattern_FDYYYYQd and found_current_quarter: period = date_pattern_FDYYYYQd.groups()[-1] if period == 'YTD': period = 'Yearly' elif period == 'QTD': period = 'Quarterly' all_in_one_dict[period][filing_date][''][tag_name] = tag_value # third pattern for date and period date_pattern_FIYYYQd = re.search(r'FI{}{}'.format(filing_date.year, current_quarter), element.attrib['contextRef']) if date_pattern_FIYYYQd and found_current_quarter: period = 'Yearly' all_in_one_dict[period][filing_date][''][tag_name] = tag_value # fourth pattern for date and period i.e. 'STD_364_20150926' date_pattern_STD_delta_yyymmdd = re.search(r'STD_(\d+)_{}'.format(filing_date.strftime('%Y%m%d')), element.attrib['contextRef']) if date_pattern_STD_delta_yyymmdd: period_unformatted = date_pattern_STD_delta_yyymmdd.groups()[-1] if period_unformatted == '364' or period_unformatted == '0': period = 'Yearly' else: period = 'Quarterly' all_in_one_dict[period][filing_date][''][tag_name] = tag_value return all_in_one_dict def normalize_tables(self, filing_date, input_dict, visited_data_names) -> (dict, dict): """Standardize tables to match across years and companies""" master_dict = {} for normalized_category, pattern_string in flatten_dict(self.regex_patterns).items(): master_dict[normalized_category] = np.nan for title, table in input_dict.items(): for scraped_name, scraped_value in flatten_dict(table).items(): for normalized_category, pattern_string in flatten_dict(self.regex_patterns).items(): if re.search(pattern_string, scraped_name, re.IGNORECASE): master_dict[normalized_category] = scraped_value break pprint(master_dict) return {}, master_dict # # if __name__ == '__main__': # facebook = DataView('FB', '2019-12-31', '10-K') # facebook.traverse_tree('StatementOfFinancialPositionClassified')

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044acfb56a61857481ffe8d49421650a3a0564af

412

py

Python

py/mbib.py

michaelpalmeruw/mbib

2f9a076cce10fda821f9d2b9f60b79fab84c7d6d

[ "Apache-2.0" ]

null

py/mbib.py

michaelpalmeruw/mbib

2f9a076cce10fda821f9d2b9f60b79fab84c7d6d

[ "Apache-2.0" ]

null

py/mbib.py

michaelpalmeruw/mbib

2f9a076cce10fda821f9d2b9f60b79fab84c7d6d

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import os batch_arg = os.getenv('mbib_batch') # print("batch_arg", batch_arg) if batch_arg is None: from bibapp import BibApp from hub import hub hub.is_batch = False _app = BibApp() hub.register('app', _app) hub.register('tree', _app.tree) hub.register('exit', _app.exit) hub.app() else: from batchmode import BatchMode BatchMode(batch_arg)()

18.727273

35

0.667476

60

412

4.4

0.4

0.151515

0

0.003067

0.208738

412

21

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19.619048

0.806748

0.123786

0

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0.285714

0

0.285714

0

null

0

null

0

1

0

044bdba3231e4ede68a14c679aac47d587768f13

2,800

py

Python

metaci/plan/views.py

sebastianocostanzo/MetaCI

a880a8b1caa7cf1445f220b6c2e4f83fe8d38312

[ "BSD-3-Clause" ]

null

metaci/plan/views.py

sebastianocostanzo/MetaCI

a880a8b1caa7cf1445f220b6c2e4f83fe8d38312

[ "BSD-3-Clause" ]

null

metaci/plan/views.py

sebastianocostanzo/MetaCI

a880a8b1caa7cf1445f220b6c2e4f83fe8d38312

[ "BSD-3-Clause" ]

null

from django.contrib.admin.views.decorators import staff_member_required from django.http import HttpResponseRedirect from django.shortcuts import render from django.shortcuts import get_object_or_404 from metaci.build.utils import view_queryset from metaci.plan.models import Plan, PlanRepository from metaci.plan.forms import RunPlanForm from metaci.repository.models import Repository def plan_list(request): if request.user.is_staff: plans = Plan.objects.all() else: plans = Plan.objects.filter(public=True) context = { 'plans': plans, } return render(request, 'plan/list.html', context=context) def plan_detail(request, plan_id): query = {'id': plan_id} if not request.user.is_staff: query['public'] = True plan = get_object_or_404(Plan, **query) query = {'plan': plan} builds = view_queryset(request, query) context = { 'builds': builds, 'plan': plan, } return render(request, 'plan/detail.html', context=context) def plan_detail_repo(request, plan_id, repo_owner, repo_name): query = {'id': plan_id} if not request.user.is_staff: query['public'] = True plan = get_object_or_404(Plan, **query) repo = get_object_or_404(Repository, owner=repo_owner, name=repo_name) query = {'plan': plan, 'repo': repo} builds = view_queryset(request, query) context = { 'builds': builds, 'plan': plan, } return render(request, 'plan/detail.html', context=context) @staff_member_required def plan_run(request, plan_id): plan = get_object_or_404(Plan, id=plan_id) context = {'plan': plan} return render(request, 'plan/run_select_repo.html', context=context) @staff_member_required def plan_run_repo(request, plan_id, repo_owner, repo_name): plan = get_object_or_404(Plan, id=plan_id) repo = get_object_or_404(Repository, owner=repo_owner, name=repo_name) if request.method == 'POST': form = RunPlanForm(plan, repo, request.user, request.POST) if form.is_valid(): build = form.create_build() return HttpResponseRedirect(build.get_absolute_url()) else: form = RunPlanForm(plan, repo, request.user, request.GET) context = { 'form': form, 'plan': plan, 'repo': repo, } return render(request, 'plan/run.html', context=context) @staff_member_required def new_org_please(request): plans = Plan.objects.filter(public=False, active=True, type='org').prefetch_related('repos') plan_repos = PlanRepository.objects.filter(plan__in=plans).order_by('repo__name','plan__name') context = { 'plans': plans, 'plan_repos': plan_repos, } return render(request, 'plan/new_org_please.html', context=context)

32.941176

98

0.683214

366

2,800

5.010929

0.202186

0.059978

0.041985

0.053435

0.522356

0.476009

0.435115

0.368593

0.331516

0.257361

0

0.009362

0.198929

2,800

85

99

32.941176

0.808292

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0.452055

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0.079971

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0

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0

0.287671

0

null

0

null

0

1

0

044d5417447d12920bf78a08d7241650d58ce648

7,743

py

Python

imdb/spiders/movieCrawler.py

pawanmsr/imdb-scraper

74fa529a19c4965667060b7158d35aabfe197993

[ "MIT" ]

2

2018-12-12T15:54:10.000Z

2019-05-16T03:00:32.000Z

imdb/spiders/movieCrawler.py

pawanmsr/imdb-scraper

74fa529a19c4965667060b7158d35aabfe197993

[ "MIT" ]

null

imdb/spiders/movieCrawler.py

pawanmsr/imdb-scraper

74fa529a19c4965667060b7158d35aabfe197993

[ "MIT" ]

null

import scrapy import json import os import sys import time movies_directory = "movies/" links_directory = "links/" if not os.path.exists(movies_directory): os.makedirs(movies_directory) if not os.path.exists(links_directory): os.makedirs(links_directory) linkfile = "links/imdbLinks.json" links_dict = {} try: with open(linkfile,'r') as f: links_dict = json.load(f) except: print("unable to open links file, movieCrawler will not run") pass links = [] for key in links_dict: links.append("https://www.imdb.com" + links_dict[key]) class movieCrawler(scrapy.Spider): name = "movieCrawler" allowed_domains = ['imdb.com'] start_urls = links people_links = {} detail_fields = ["Taglines:", "Country:", "Language:", "Budget:", "Cumulative Worldwide Gross:", "Production Co:"] director_fields = ["Director:", "Writers:"] illegalChars = {'<':'', '>':'', ':':'', '"':'', '/':' ', '\\':' ', '|':'', '?':'', '*':' '} def parse(self,response): movie = {} self.people_links = {} doNotSave = False saveName = response.request.url.split('/')[4] movie['Id'] = saveName title = response.xpath('//div[@class="title_wrapper"]/h1/text()').extract_first() if title!=None: title = ' '.join(title.split()) ''' for key in self.illegalChars: if key in title: title = title.replace(key,self.illegalChars[key]) ''' movie["Title"] = title else: doNotSave=True film_rating = response.xpath('//div[@class="subtext"]/text()').extract_first() if film_rating!=None: movie["Film_rating"] = ' '.join(film_rating.split()) else: movie["Film_rating"] = 'NA' duration = response.xpath('//div[@class="subtext"]/time/text()').extract_first() if duration!=None: movie["Duration"] = ' '.join(duration.split()) else: movie["Duration"] = 'NA' description = response.xpath('//div[@class="summary_text"]/text()').extract_first() if description!=None: movie["Description"] = ' '.join(description.split()) else: movie["Description"] = "NA" imdb_rating = response.xpath('//span[@itemprop="ratingValue"]/text()').extract_first() if imdb_rating!=None: movie["IMDB_rating"] = ' '.join(imdb_rating.split()) else: doNotSave=True rating_count = response.xpath('//span[@itemprop="ratingCount"]/text()').extract_first() if rating_count!=None: movie["IMDB_rating_count"] = ' '.join(rating_count.split()) else: doNotSave=True movie["Genre"], movie["release_date"] = self.getGenreReleaseDate(response.xpath('//div[@class="subtext"]/a')) movie["Storyline"] = self.getStoryline(response.xpath('//div[@id="titleStoryLine"]/div[1]/p')) directors = self.getDirectors(response.xpath('//div[@class="credit_summary_item"]')) movie['Cast'] = self.getCastList(response.xpath('//table[@class="cast_list"]/tr')) movie['Taglines'] = self.getTagline(response.xpath('//div[@class="txt-block"]')) details = self.getDetails(response.xpath('//div[@id="titleDetails"]')) for key in directors: movie[key] = directors[key] for key in details: movie[key] = details[key] movie['url'] = response.request.url if not doNotSave and not os.path.isfile(movies_directory+saveName+".json"): with open(movies_directory+saveName+".json", 'w') as f: json.dump(movie, f) ''' if not doNotSave and not os.path.isfile(links_directory+saveName+" people"+'.json'): with open(links_directory+saveName+" people"+'.json', 'w') as f: json.dump(self.people_links, f) ''' for anchor in response.xpath('//div[@class="rec-title"]'): url = "https://www.imdb.com" + anchor.xpath('./a/@href').extract_first() if url!=None or url!="": #time.sleep(0.1) yield response.follow(url, callback=self.parse) def getGenreReleaseDate(self,subtext): vals = [] for text in subtext: vals.append(text.xpath('./text()').extract_first()) if vals!=None: release_date = ' '.join(vals[-1].split()) else: release_date = "NA" genre = [] if vals!=None: for val in vals[:-1]: for element in val.split(): genre.append(element) return genre, release_date def getDirectors(self,csis): directors = {"Director:":[], "Writers:":[]} for csi in csis: field = csi.xpath('./h4/text()').extract_first() if field==None: continue field = ' '.join(csi.xpath('./h4/text()').extract_first().split()) if field in self.director_fields: lst = [] for val in csi.xpath('./a'): person = ' '.join(val.xpath('./text()').extract_first().split()) if "credits" not in person and "credit" not in person: lst.append(person) self.people_links[person] = val.xpath('./@href').extract_first() directors[field] = lst return directors def getCastList(self,casts): cast_list = [] for row in casts: link = row.xpath('./td[not(@*)]/a') people = link.xpath('./text()').extract_first() if people != None: people = ' '.join(people.split()) if "credits" not in people and "credit" not in people: cast_list.append(people) self.people_links[people] = link.xpath('./@href').extract_first() return cast_list def getTagline(self,txts): taglines = "" for txt in txts: text = txt.xpath('./h4/text()').extract_first() if text==None: continue text = ' '.join(txt.xpath('./h4/text()').extract_first().split()) if text == "Taglines:": taglines = ' '.join(txt.xpath('./text()').extract()[1].split()) return taglines def getDetails(self,titleDetails): details = {"Budget":"", "Revenue":""} for detail in titleDetails.xpath('./div[@class="txt-block"]'): text = detail.xpath('./h4/text()').extract_first() if text==None: continue text = ' '.join(detail.xpath('./h4/text()').extract_first().split()) if text=="Country:": countryList = [] for country in detail.xpath('./a'): countryList.append(' '.join(country.xpath('./text()').extract_first().split())) details["Country"] = countryList if text=="Language:": languageList = [] for language in detail.xpath('./a'): languageList.append(' '.join(language.xpath('./text()').extract_first().split())) details["Language"] = languageList if text=="Budget:": details["Budget"] = ' '.join(detail.xpath('./text()').extract()[1].split()) if text=="Cumulative Worldwide Gross:": details["Revenue"] = ' '.join(detail.xpath('./text()').extract()[1].split()) return details def getStoryline(self,tsl): texts = tsl.xpath('./span/text()').extract() storyline = "" for text in texts: text = ' '.join(text.split()).replace(" (", "").replace(")","") storyline += text return storyline

41.854054

118

0.546687

826

7,743

5.039952

0.194915

0.055489

0.065338

0.047562

0.199375

0.121307

0.077828

0.055249

0.023541

0

0.002865

0.278703

7,743

185

119

41.854054

0.742525

0.001937

0

0.052632

0

0.163515

0.059992

0

1

0.046053

false

0.006579

0.032895

0

0.171053

0.006579

0

null

0

null

0

1

0

044e65053c5708bfb9b6629befce98fe72cc5f1b

1,748

py

Python

samples-python/logbook/logbook/app.py

bracoe/ctrlx-automation-sdk

6b2e61e146c557488125baf941e4d64c6fa6d0fb

[ "MIT" ]

16

2021-08-23T13:07:12.000Z

2022-02-21T13:29:21.000Z

samples-python/logbook/logbook/app.py

bracoe/ctrlx-automation-sdk

6b2e61e146c557488125baf941e4d64c6fa6d0fb

[ "MIT" ]

null

samples-python/logbook/logbook/app.py

bracoe/ctrlx-automation-sdk

6b2e61e146c557488125baf941e4d64c6fa6d0fb

[ "MIT" ]

10

2021-09-29T09:58:33.000Z

2022-01-13T07:20:00.000Z

# MIT License # # Copyright (c) 2021 Bosch Rexroth AG # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import logging from systemd.journal import JournaldLogHandler def run(): print("Simple Snap for insert different loglevels with Python") log = logging.getLogger() log.setLevel(logging.DEBUG) log.addHandler(JournaldLogHandler()) log.exception("I am an exception message") log.critical("I am a critical message") log.error("I am an error") log.warning("I am a warning") log.info("I am an info message") log.debug("I am a debug message") #<timestamp>|<userId>|<mainDiagnosisCode>|<mainTitle>|<detailedDiagnosisCode>|<detailedTitle>|<entity>|<dynamicSource>|<dynamicDescription>

42.634146

143

0.75

248

1,748

5.28629

0.540323

0.067124

0.011442

0

0.002768

0.173341

1,748

41

144

42.634146

0.904498

0.689931

0

0.325

0

1

0.076923

false

0

0.153846

0

0.230769

0.076923

0

null

0

null

0

1

0

044fa9836068fd9e9ce4671f7fecacdfe22d4b05

4,585

py

Python

SVC-project-with-EDA-&-Feature-Selecction/code.py

Sadique96645/ga-learner-dsmp-repo

155f039083c22755b32c7dead39bfd86aff4c157

[ "MIT" ]

null

SVC-project-with-EDA-&-Feature-Selecction/code.py

Sadique96645/ga-learner-dsmp-repo

155f039083c22755b32c7dead39bfd86aff4c157

[ "MIT" ]

null

SVC-project-with-EDA-&-Feature-Selecction/code.py

Sadique96645/ga-learner-dsmp-repo

155f039083c22755b32c7dead39bfd86aff4c157

[ "MIT" ]

1

2020-09-02T04:25:13.000Z

# -------------- import pandas as pd from collections import Counter # Load dataset data = pd.read_csv(path) data.isnull().sum() # -------------- import seaborn as sns from matplotlib import pyplot as plt sns.set_style(style='darkgrid') # Store the label values label = data.iloc[:,-1] label.head(5) sns.countplot(data=data,x='Activity') # plot the countplot # -------------- import numpy as np # make the copy of dataset data_copy = data.copy() mask = ('WALKING_UPSTAIRS', 'WALKING_DOWNSTAIRS') # Create an empty column data_copy['duration'] = '' # Calculate the duration duration_df = data_copy.groupby([label.mask(label!= 'WALKING_UPSTAIRS', 'WALKING_DOWNSTAIRS'), 'subject'])['duration'].count() * 1.28 duration_df = pd.DataFrame(duration_df) plot_data = duration_df.sort_values(by='duration',ascending= False) plot_data.reset_index(inplace=True) replaced_value = {'WALKING_UPSTAIRS':'Upstairs','WALKING_DOWNSTAIRS':'Downstairs'} plot_data['Activity'] = plot_data['Activity'].map(replaced_value) sns.barplot(data=plot_data,x='subject',y='duration') # Sort the values of duration # -------------- #exclude the Activity column and the subject column feature_cols = data.select_dtypes(exclude=['object','int']).columns #Calculate the correlation values correlated_values = data[feature_cols].corr().stack().reset_index() #stack the data and convert to a dataframe correlated_values = pd.DataFrame(correlated_values) correlated_values.rename(columns = {'level_0':'Feature_1','level_1':'Feature_2',0:'Correlation_score'},inplace=True) #create an abs_correlation column correlated_values['abs_correlation'] = correlated_values['Correlation_score'].abs() #Picking most correlated features without having self correlated pairs s_corr_list = correlated_values.sort_values(by='abs_correlation',ascending=False) top_corr_fields = s_corr_list[(s_corr_list['abs_correlation']>0.8)] top_corr_fields = top_corr_fields[(top_corr_fields['Feature_1'])!=(top_corr_fields['Feature_2'])] print(top_corr_fields.head()) # -------------- # importing neccessary libraries from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from sklearn.svm import SVC from sklearn.metrics import precision_recall_fscore_support as error_metric from sklearn.metrics import confusion_matrix, accuracy_score # Encoding the target variable le = LabelEncoder() le.fit(data['Activity']) data['Activity'] = le.transform(data['Activity']) # split the dataset into train and test X = data.drop('Activity',1) y = data['Activity'] X_train, X_test, y_train ,y_test = train_test_split(X,y,test_size=0.3,random_state=40) classifier = SVC() clf = classifier.fit(X_train,y_train) y_pred = clf.predict(X_test) precision, accuracy , f_score, _ = error_metric(y_test,y_pred,average = 'weighted') model1_score = accuracy_score(y_test,y_pred) print(precision) print(accuracy) print(f_score) print(model1_score) # -------------- # importing libraries from sklearn.svm import LinearSVC from sklearn.feature_selection import SelectFromModel # Feature selection using Linear SVC lsvc = LinearSVC(C=0.01,penalty = 'l1',dual = False,random_state =42) lsvc.fit(X_train,y_train) model_2 = SelectFromModel(lsvc,prefit=True) new_train_features= model_2.transform(X_train) new_test_features = model_2.transform(X_test) classifier_2 = SVC() clf_2 = classifier_2.fit(new_train_features,y_train) y_pred_new = clf_2.predict(new_test_features) model2_score = accuracy_score(y_test,y_pred_new) precision, accuracy , f_score, _ = error_metric(y_test,y_pred_new,average = 'weighted') # model building on reduced set of features # -------------- # Importing Libraries from sklearn.model_selection import GridSearchCV # Set the hyperparmeters parameters = {'kernel':['linear','rbf'],'C':[100, 20, 1, 0.1]} # Usage of grid search to select the best hyperparmeters svc = SVC() selector = GridSearchCV(svc,parameters,scoring='accuracy') selector.fit(new_train_features,y_train) print(selector.best_params_) print(selector.cv_results_) means = selector.cv_results_['mean_test_score'] stds = selector.cv_results_['std_test_score'] params = selector.cv_results_['params'] print(means,stds,params) classifier_3 = SVC(C=20,kernel='rbf') clf_3 = classifier_3.fit(new_train_features,y_train) y_pred_final = clf_3.predict(new_test_features) model3_score = accuracy_score(y_test,y_pred_final) precision,recall,f_score,_ = error_metric(y_test,y_pred_final,average='weighted') print(precision) print(recall) print(f_score) print(model3_score) # Model building after Hyperparameter tuning

31.190476

133

0.770556

665

4,585

5.049624

0.285714

0.013401

0.023228

0.017868

0.142644

0.096188

0.077129

0.052114

0.026206

0

0.012274

0.093784

4,585

146

134

31.40411

0.795909

0.178844

0

0.04878

0

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0

1

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false

0

0.158537

0

0.158537

0.146341

0

null

0

null

0

1

0

04521b79fa9e62c6a912d3c7017abfc3f6883ad5

8,354

py

Python

recipes/Python/580640_Scrolling_frame_mouse_wheel/recipe-580640.py

tdiprima/code

61a74f5f93da087d27c70b2efe779ac6bd2a3b4f

[ "MIT" ]

2,023

2017-07-29T09:34:46.000Z

2022-03-24T08:00:45.000Z

recipes/Python/580640_Scrolling_frame_mouse_wheel/recipe-580640.py

unhacker/code

73b09edc1b9850c557a79296655f140ce5e853db

[ "MIT" ]

32

2017-09-02T17:20:08.000Z

2022-02-11T17:49:37.000Z

recipes/Python/580640_Scrolling_frame_mouse_wheel/recipe-580640.py

unhacker/code

73b09edc1b9850c557a79296655f140ce5e853db

[ "MIT" ]

780

2017-07-28T19:23:28.000Z

2022-03-25T20:39:41.000Z

# Version: 0.22 # Author: Miguel Martinez Lopez # Uncomment the next line to see my email # print("Author's email: ", "61706c69636163696f6e616d656469646140676d61696c2e636f6d".decode("hex")) try: from Tkinter import Canvas, Frame from ttk import Scrollbar from Tkconstants import * except ImportError: from tkinter import Canvas, Frame from tkinter.ttk import Scrollbar from tkinter.constants import * import platform OS = platform.system() class Mousewheel_Support(object): # implemetation of singleton pattern _instance = None def __new__(cls, *args, **kwargs): if not cls._instance: cls._instance = object.__new__(cls) return cls._instance def __init__(self, root, horizontal_factor = 2, vertical_factor=2): self._active_area = None if isinstance(horizontal_factor, int): self.horizontal_factor = horizontal_factor else: raise Exception("Vertical factor must be an integer.") if isinstance(vertical_factor, int): self.vertical_factor = vertical_factor else: raise Exception("Horizontal factor must be an integer.") if OS == "Linux" : root.bind_all('<4>', self._on_mousewheel, add='+') root.bind_all('<5>', self._on_mousewheel, add='+') else: # Windows and MacOS root.bind_all("<MouseWheel>", self._on_mousewheel, add='+') def _on_mousewheel(self,event): if self._active_area: self._active_area.onMouseWheel(event) def _mousewheel_bind(self, widget): self._active_area = widget def _mousewheel_unbind(self): self._active_area = None def add_support_to(self, widget=None, xscrollbar=None, yscrollbar=None, what="units", horizontal_factor=None, vertical_factor=None): if xscrollbar is None and yscrollbar is None: return if xscrollbar is not None: horizontal_factor = horizontal_factor or self.horizontal_factor xscrollbar.onMouseWheel = self._make_mouse_wheel_handler(widget,'x', self.horizontal_factor, what) xscrollbar.bind('<Enter>', lambda event, scrollbar=xscrollbar: self._mousewheel_bind(scrollbar) ) xscrollbar.bind('<Leave>', lambda event: self._mousewheel_unbind()) if yscrollbar is not None: vertical_factor = vertical_factor or self.vertical_factor yscrollbar.onMouseWheel = self._make_mouse_wheel_handler(widget,'y', self.vertical_factor, what) yscrollbar.bind('<Enter>', lambda event, scrollbar=yscrollbar: self._mousewheel_bind(scrollbar) ) yscrollbar.bind('<Leave>', lambda event: self._mousewheel_unbind()) main_scrollbar = yscrollbar if yscrollbar is not None else xscrollbar if widget is not None: if isinstance(widget, list) or isinstance(widget, tuple): list_of_widgets = widget for widget in list_of_widgets: widget.bind('<Enter>',lambda event: self._mousewheel_bind(widget)) widget.bind('<Leave>', lambda event: self._mousewheel_unbind()) widget.onMouseWheel = main_scrollbar.onMouseWheel else: widget.bind('<Enter>',lambda event: self._mousewheel_bind(widget)) widget.bind('<Leave>', lambda event: self._mousewheel_unbind()) widget.onMouseWheel = main_scrollbar.onMouseWheel @staticmethod def _make_mouse_wheel_handler(widget, orient, factor = 1, what="units"): view_command = getattr(widget, orient+'view') if OS == 'Linux': def onMouseWheel(event): if event.num == 4: view_command("scroll",(-1)*factor, what) elif event.num == 5: view_command("scroll",factor, what) elif OS == 'Windows': def onMouseWheel(event): view_command("scroll",(-1)*int((event.delta/120)*factor), what) elif OS == 'Darwin': def onMouseWheel(event): view_command("scroll",event.delta, what) return onMouseWheel class Scrolling_Area(Frame, object): def __init__(self, master, width=None, anchor=N, height=None, mousewheel_speed = 2, scroll_horizontally=True, xscrollbar=None, scroll_vertically=True, yscrollbar=None, background=None, inner_frame=Frame, **kw): Frame.__init__(self, master, class_="Scrolling_Area", background=background) self.grid_columnconfigure(0, weight=1) self.grid_rowconfigure(0, weight=1) self._width = width self._height = height self.canvas = Canvas(self, background=background, highlightthickness=0, width=width, height=height) self.canvas.grid(row=0, column=0, sticky=N+E+W+S) if scroll_vertically: if yscrollbar is not None: self.yscrollbar = yscrollbar else: self.yscrollbar = Scrollbar(self, orient=VERTICAL) self.yscrollbar.grid(row=0, column=1,sticky=N+S) self.canvas.configure(yscrollcommand=self.yscrollbar.set) self.yscrollbar['command']=self.canvas.yview else: self.yscrollbar = None if scroll_horizontally: if xscrollbar is not None: self.xscrollbar = xscrollbar else: self.xscrollbar = Scrollbar(self, orient=HORIZONTAL) self.xscrollbar.grid(row=1, column=0, sticky=E+W) self.canvas.configure(xscrollcommand=self.xscrollbar.set) self.xscrollbar['command']=self.canvas.xview else: self.xscrollbar = None self.rowconfigure(0, weight=1) self.columnconfigure(0, weight=1) self.innerframe = inner_frame(self.canvas, **kw) self.innerframe.pack(anchor=anchor) self.canvas.create_window(0, 0, window=self.innerframe, anchor='nw', tags="inner_frame") self.canvas.bind('<Configure>', self._on_canvas_configure) Mousewheel_Support(self).add_support_to(self.canvas, xscrollbar=self.xscrollbar, yscrollbar=self.yscrollbar) @property def width(self): return self.canvas.winfo_width() @width.setter def width(self, width): self.canvas.configure(width= width) @property def height(self): return self.canvas.winfo_height() @height.setter def height(self, height): self.canvas.configure(height = height) def set_size(self, width, height): self.canvas.configure(width=width, height = height) def _on_canvas_configure(self, event): width = max(self.innerframe.winfo_reqwidth(), event.width) height = max(self.innerframe.winfo_reqheight(), event.height) self.canvas.configure(scrollregion="0 0 %s %s" % (width, height)) self.canvas.itemconfigure("inner_frame", width=width, height=height) def update_viewport(self): self.update() window_width = self.innerframe.winfo_reqwidth() window_height = self.innerframe.winfo_reqheight() if self._width is None: canvas_width = window_width else: canvas_width = min(self._width, window_width) if self._height is None: canvas_height = window_height else: canvas_height = min(self._height, window_height) self.canvas.configure(scrollregion="0 0 %s %s" % (window_width, window_height), width=canvas_width, height=canvas_height) self.canvas.itemconfigure("inner_frame", width=window_width, height=window_height) if __name__== '__main__': try: from Tkinter import Tk, Label except ImportError: from tkinter import Tk, Label root = Tk() scrolling_area = Scrolling_Area(root) scrolling_area.pack(expand=1, fill=BOTH) for i in range(20): rowFrame = Frame(scrolling_area.innerframe) rowFrame.pack() for j in range(8): Label(rowFrame, text="Label %s, %s" % (str(i), str(j))).pack(side="left") root.mainloop()

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null

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045b11de118c2d3ad2b59923763b0a8d674e2677

2,231

py

Python

examples/realtime_1kHz_scope.py

benjie-git/pyFirmata2

b5c0cbd4822db2906b328a989709dd799b1aed3b

[ "MIT" ]

33

2018-11-04T04:03:22.000Z

2022-03-22T00:13:15.000Z

examples/realtime_1kHz_scope.py

benjie-git/pyFirmata2

b5c0cbd4822db2906b328a989709dd799b1aed3b

[ "MIT" ]

8

2021-03-06T23:11:10.000Z

2022-03-17T08:10:14.000Z

examples/realtime_1kHz_scope.py

benjie-git/pyFirmata2

b5c0cbd4822db2906b328a989709dd799b1aed3b

[ "MIT" ]

20

2018-12-04T07:34:04.000Z

2021-10-01T15:50:05.000Z

#!/usr/bin/python3 """ Plots channel zero at 1kHz. Requires pyqtgraph. Copyright (c) 2018-2021, Bernd Porr <mail@berndporr.me.uk> see LICENSE file. """ import sys import pyqtgraph as pg from pyqtgraph.Qt import QtCore, QtGui import numpy as np from pyfirmata2 import Arduino PORT = Arduino.AUTODETECT # create a global QT application object app = QtGui.QApplication(sys.argv) # signals to all threads in endless loops that we'd like to run these running = True class QtPanningPlot: def __init__(self,title): self.win = pg.GraphicsLayoutWidget() self.win.setWindowTitle(title) self.plt = self.win.addPlot() self.plt.setYRange(-1,1) self.plt.setXRange(0,500) self.curve = self.plt.plot() self.data = [] # any additional initalisation code goes here (filters etc) self.timer = QtCore.QTimer() self.timer.timeout.connect(self.update) self.timer.start(100) self.layout = QtGui.QGridLayout() self.win.setLayout(self.layout) self.win.show() def update(self): self.data=self.data[-500:] if self.data: self.curve.setData(np.hstack(self.data)) def addData(self,d): self.data.append(d) # Let's create two instances of plot windows qtPanningPlot1 = QtPanningPlot("Arduino 1st channel") # sampling rate: 100Hz samplingRate = 1000 # called for every new sample at channel 0 which has arrived from the Arduino # "data" contains the new sample def callBack(data): # filter your channel 0 samples here: # data = self.filter_of_channel0.dofilter(data) # send the sample to the plotwindow qtPanningPlot1.addData(data) # Get the Ardunio board. board = Arduino(PORT) # Set the sampling rate in the Arduino board.samplingOn(1000 / samplingRate) # Register the callback which adds the data to the animated plot # The function "callback" (see above) is called when data has # arrived on channel 0. board.analog[0].register_callback(callBack) # Enable the callback board.analog[0].enable_reporting() board.analog[1].enable_reporting() # showing all the windows app.exec_() # needs to be called to close the serial port board.exit() print("Finished")

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null

0

null

0

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045ecad18e64ea527f95697217787aaa821d67c1

5,684

py

Python

timeutil.py

tanupoo/timeutil

9ee236a680db323f416c7af151a257039936ee08

[ "MIT" ]

null

timeutil.py

tanupoo/timeutil

9ee236a680db323f416c7af151a257039936ee08

[ "MIT" ]

null

timeutil.py

tanupoo/timeutil

9ee236a680db323f416c7af151a257039936ee08

[ "MIT" ]

1

2019-06-24T17:27:56.000Z

#!/usr/bin/env python from argparse import ArgumentParser, RawDescriptionHelpFormatter from os import environ from datetime_util import * default_tzname = environ.get("TZ", "GMT") usage = ''' %(prog)s [options] STR This command converts STR into the format specified. STR is a datetime string. %(prog)s [options] STR1 STR2 This command shows the difference of time between STR1 and STR2. STR1 is a datetime string as same as STR2. "epoch" is acceptable as STR2, which means 1970-01-01T00:00:00 %(prog)s [options] STR1 (+|-|/|x) STR2 This command shows the result where the operand is adopted. STR1 is a datetime string. STR2 is a string, which is the arguments of the timedelta object in python. The STR2 format is like below: days[,seconds[,microseconds[,milliseconds[,minutes[,hours[,weeks]]]]]] ''' desc = ''' description: yet another kitchen nife for datetime. if STR is "now", current time is used. "opt" of the -m option may be used to specify the output format. It is one of the following string. iso: iso8601. (default) ctime: ctime(3). e.g. Sat Jul 29 16:37:02 JST 2017 day: days from 1970-01-01T00:00:00 (epoch). hour: hours from epoch. min: minutes from epoch. sec: seconds from epoch. msec: miliseconds from epoch. e.g. 1546919546426 if datetime object is like below: datetime.datetime(2019, 1, 8, 12, 52, 26, 426765) usec: seconds with microseconds from epoch. e.g. 1546919546.426765 if datetime object is like below: datetime.datetime(2019, 1, 8, 12, 52, 26, 426765) hex: microseconds in a hex string of the big endian. if an operand is not specified, "-" is used. e.g. ''' ap = ArgumentParser( formatter_class=RawDescriptionHelpFormatter, description=desc, usage=usage, epilog="") ap.add_argument("args", metavar="ARGs [...]", type=str, nargs="*", help="a datetime string such as iso8601, ctime," " timestamp, etc.") ap.add_argument("-m", action="store", dest="output_format", help="specify the output format. default is 'iso'.") ap.add_argument("-r", action="store_true", dest="output_rounded", help="specify to round the output.") ap.add_argument("--input-tz", action="store", dest="input_tzname", help="specify the timezone name for the input string" " in case the datetime string doesn't have any timezone." f" default is {default_tzname}") ap.add_argument("--replace-tz", action="store_true", dest="replace_tz", help="replace the timezone in the input string" " into the one specified by --input-tz" " even when the datetime has a timezone.") ap.add_argument("--output-tz", action="store", dest="output_tzname", help="specify the timezone to show." " default is same as the one specified by" " the --input-tz option") ap.add_argument("-v", action="store_true", dest="verbose", help="enable verbose mode.") opt = ap.parse_args() if opt.output_tzname is None: opt.output_tzname = opt.input_tzname # if opt.verbose: print("Input Timezone:", opt.input_tzname) print("Output Timezone:", opt.output_tzname) print("Replace Timezone:", opt.replace_tz) # conversion if len(opt.args) == 1: # if opt.output_format is None: opt.output_format = "iso" dt1 = datestr_to_datetime(opt.args[0], default_tzname=opt.input_tzname, replace_tz=opt.replace_tz) if opt.verbose: print("STR1:", dt1) result = datetime_to_datestr(dt1, output_form=opt.output_format, output_rounded=opt.output_rounded, output_tzname=opt.output_tzname) elif len(opt.args) == 2: # if opt.output_format is None: opt.output_format = "sec" dt1 = datestr_to_datetime(opt.args[0], default_tzname=opt.input_tzname, replace_tz=opt.replace_tz) if opt.args[1] in ["epoch", "EPOCH"]: arg2 = "1970-01-01T00:00:00" else: arg2 = opt.args[1] dt2 = datestr_to_datetime(arg2, default_tzname=opt.input_tzname, replace_tz=opt.replace_tz) if opt.verbose: print("STR1:", dt1) print("STR2:", dt2) result = timedelta_to_datestr(dt1 - dt2, output_form=opt.output_format, output_tzname=opt.output_tzname) elif len(opt.args) == 3: # if opt.output_format is None: opt.output_format = "iso" dt1 = datestr_to_datetime(opt.args[0], default_tzname=opt.input_tzname, replace_tz=opt.replace_tz) op = opt.args[1] time_delta = datestr_to_timedelta(opt.args[2]) if opt.verbose: print("STR1:", dt1) print("STR2:", time_delta) if op == "+": result = datetime_to_datestr(dt1 + time_delta, output_form=opt.output_format, output_rounded=opt.output_rounded, output_tzname=opt.output_tzname) elif op == "-": result = datetime_to_datestr(dt1 - time_delta, output_form=opt.output_format, output_rounded=opt.output_rounded, output_tzname=opt.output_tzname) else: ap.print_help() exit(1) else: ap.print_help() exit(1) # print(result)

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null

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null

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1

0

f086d4ed3018b4e65a14abd8edc1f77c15caba1d

545

py

Python

alien_invasion/game_stats.py

faizkhan12/Alien_invasion

fe1b72aabb40ae4b2e61c5d31936f65709df425c

[ "MIT" ]

4

2018-07-15T17:53:19.000Z

2021-07-30T12:40:47.000Z

alien_invasion/game_stats.py

faizkhan12/Alien_invasion

fe1b72aabb40ae4b2e61c5d31936f65709df425c

[ "MIT" ]

null

alien_invasion/game_stats.py

faizkhan12/Alien_invasion

fe1b72aabb40ae4b2e61c5d31936f65709df425c

[ "MIT" ]

1

2018-07-15T17:55:05.000Z

class GameStats(): """Track statistics for Alien Invasion""" def __init__(self,ai_settings): """Initialise statistics""" self.ai_settings=ai_settings self.reset_stats() #High score shoud never be reset self.high_score=0 #Start Alien Invasion in an inactive stat self.game_active=False def reset_stats(self): """Initialise statistics that can change during the game""" self.ship_remaining=self.ai_settings.ship_limit self.score=0 self.level=1

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0.3

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null

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null

0

1

0

f089d2d0cb1571a790213ee4285268f680d729e0

3,448

py

Python

api/licences/tests/test_register_open_general_licence.py

django-doctor/lite-api

1ba278ba22ebcbb977dd7c31dd3701151cd036bf

[ "MIT" ]

3

2019-05-15T09:30:39.000Z

2020-04-22T16:14:23.000Z

api/licences/tests/test_register_open_general_licence.py

django-doctor/lite-api

1ba278ba22ebcbb977dd7c31dd3701151cd036bf

[ "MIT" ]

85

2019-04-24T10:39:35.000Z

2022-03-21T14:52:12.000Z

api/licences/tests/test_register_open_general_licence.py

django-doctor/lite-api

1ba278ba22ebcbb977dd7c31dd3701151cd036bf

[ "MIT" ]

1

2021-01-17T11:12:19.000Z

from django.urls import reverse from django.utils import timezone from parameterized import parameterized from rest_framework import status from api.cases.enums import CaseTypeEnum from api.cases.models import CaseType from api.licences.enums import LicenceStatus from api.licences.models import Licence from api.open_general_licences.enums import OpenGeneralLicenceStatus from api.open_general_licences.models import OpenGeneralLicenceCase from api.open_general_licences.tests.factories import OpenGeneralLicenceFactory, OpenGeneralLicenceCaseFactory from test_helpers.clients import DataTestClient class RegisterOpenGeneralLicenceTests(DataTestClient): def setUp(self): super().setUp() self.url = reverse("licences:open_general_licences") self.open_general_licence = OpenGeneralLicenceFactory(case_type=CaseType.objects.get(id=CaseTypeEnum.OGTCL.id)) self.exporter_user.set_role(self.organisation, self.exporter_super_user_role) def test_register_open_general_licence_success(self): data = { "open_general_licence": str(self.open_general_licence.id), } response = self.client.post(self.url, data, **self.exporter_headers) response_data = response.json() self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqualIgnoreType(response_data["open_general_licence"], self.open_general_licence.id) self.assertEqual(response_data["registrations"], [str(OpenGeneralLicenceCase.objects.get().id)]) self.assertEqual(OpenGeneralLicenceCase.objects.count(), 1) ogl_case = OpenGeneralLicenceCase.objects.get() self.assertTrue( Licence.objects.filter( reference_code=ogl_case.reference_code, case=ogl_case, status=LicenceStatus.ISSUED, start_date=timezone.now().date(), duration__isnull=False, ).exists() ) @parameterized.expand( [ ("status", OpenGeneralLicenceStatus.DEACTIVATED), # Can't register deactivated OGLs ("registration_required", False), # Can't register OGLs that don't require registration ] ) def test_register_open_general_licence_failure(self, param, value): setattr(self.open_general_licence, param, value) self.open_general_licence.save() data = { "open_general_licence": str(self.open_general_licence.id), } response = self.client.post(self.url, data, **self.exporter_headers) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(OpenGeneralLicenceCase.objects.count(), 0) def test_register_existing_open_general_licence_does_nothing(self): OpenGeneralLicenceCaseFactory( open_general_licence=self.open_general_licence, site=self.organisation.primary_site, organisation=self.organisation, ) data = { "open_general_licence": str(self.open_general_licence.id), } response = self.client.post(self.url, data, **self.exporter_headers) response_data = response.json() self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqualIgnoreType(response_data["open_general_licence"], self.open_general_licence.id) self.assertEqual(OpenGeneralLicenceCase.objects.count(), 1)

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0.253731

0

null

0

null

0

1

0

f08a350375764531781d37f668e6c988dd05358e

1,530

py

Python

tests/unit/dataactvalidator/test_c4_award_financial_2.py

brianherman/data-act-broker-backend

80eb055b9d245046192f7ad4fd0be7d0e11d2dec

[ "CC0-1.0" ]

null

tests/unit/dataactvalidator/test_c4_award_financial_2.py

brianherman/data-act-broker-backend

80eb055b9d245046192f7ad4fd0be7d0e11d2dec

[ "CC0-1.0" ]

null

tests/unit/dataactvalidator/test_c4_award_financial_2.py

brianherman/data-act-broker-backend

80eb055b9d245046192f7ad4fd0be7d0e11d2dec

[ "CC0-1.0" ]

1

2020-07-17T23:50:56.000Z

from tests.unit.dataactcore.factories.staging import AwardFinancialFactory from tests.unit.dataactvalidator.utils import number_of_errors, query_columns _FILE = 'c4_award_financial_2' def test_column_headers(database): expected_subset = {'row_number', 'obligations_delivered_orde_fyb', 'ussgl490100_delivered_orde_fyb', 'difference', 'uniqueid_TAS', 'uniqueid_PIID', 'uniqueid_FAIN', 'uniqueid_URI'} actual = set(query_columns(_FILE, database)) assert (actual & expected_subset) == expected_subset def test_success(database): """ ObligationsDeliveredOrdersUnpaidTotal in File C = USSGL 4901 + 4981 in File C for the same date context (FYB) """ af = AwardFinancialFactory(obligations_delivered_orde_fyb=None, ussgl490100_delivered_orde_fyb=None) assert number_of_errors(_FILE, database, models=[af]) == 0 af = AwardFinancialFactory(obligations_delivered_orde_fyb=1, ussgl490100_delivered_orde_fyb=1) assert number_of_errors(_FILE, database, models=[af]) == 0 def test_failure(database): """ ObligationsDeliveredOrdersUnpaidTotal in File C = USSGL 4901 + 4981 in File C for the same date context (FYB) """ af = AwardFinancialFactory(obligations_delivered_orde_fyb=1, ussgl490100_delivered_orde_fyb=None) assert number_of_errors(_FILE, database, models=[af]) == 1 af = AwardFinancialFactory(obligations_delivered_orde_fyb=1, ussgl490100_delivered_orde_fyb=2) assert number_of_errors(_FILE, database, models=[af]) == 1

37.317073

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0

0.277778

0

null

0

null

0

1

0

f08d564fadd2e70edb8c834bad59039c008f1ce7

988

py

Python

main.py

sebastianpikand/docker_demo

5c836547c17485835fdda492dc8978c838eb1b40

[ "MIT" ]

null

main.py

sebastianpikand/docker_demo

5c836547c17485835fdda492dc8978c838eb1b40

[ "MIT" ]

null

main.py

sebastianpikand/docker_demo

5c836547c17485835fdda492dc8978c838eb1b40

[ "MIT" ]

null

from sp_calculator import Calculator def main(): calc = Calculator() allowed_operations = ['*','/','+','-', 'nth_root', 'reset'] while True: operation = input('Choose an operation [*,/,+,-, nth_root, reset] or [q] for quitting: ') if operation == 'q': break if (operation in allowed_operations): if (operation != 'reset'): value = input('Choose a value (int or float): ') match operation: case '*': res = calc.multiply(value) print(res) case '/': res = calc.divide(value) print(res) case '+': res = calc.add(value) print(res) case '-': res = calc.subtract(value) print(res) case 'nth_root': res = calc.nth_root(value) print(res) else: res = calc.reset() print(res) if __name__ == '__main__': main()

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null

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1

0

f08f5526732f9aed9c895a04ad39d7a0ecc751df

6,877

py

Python

Work/Codes/DataAugmentation/main.py

jeevanpuchakay/BTP

33f372bda2859a9b41207766d6e1bc043c6b2aeb

[ "Apache-2.0" ]

null

Work/Codes/DataAugmentation/main.py

jeevanpuchakay/BTP

33f372bda2859a9b41207766d6e1bc043c6b2aeb

[ "Apache-2.0" ]

null

Work/Codes/DataAugmentation/main.py

jeevanpuchakay/BTP

33f372bda2859a9b41207766d6e1bc043c6b2aeb

[ "Apache-2.0" ]

null

import os import json from PIL import Image from pathlib import Path import torch from torchvision import transforms def rotate_images(input_folder, output_folder, rotation_angle, old_annotations, new_annotations): for file_name in os.listdir(input_folder): if file_name.endswith(".jpg") == False: continue img = Image.open(input_folder + file_name) width, height = img.width, img.height rotated_image = img.rotate(rotation_angle, expand=False) rotated_image.save(output_folder + file_name) # , file_name.split('.')[-1].lower()) x_center, y_center = width / 2, height / 2 failed_files = [] try: if rotation_angle == -90: old_annotation = old_annotations["imgs"][file_name.split('.')[0]] new_annotation = old_annotation new_annotation["objects"] = [] for object in old_annotation["objects"]: bbox = object["bbox"] bbox["xmin"] -= x_center bbox["ymin"] -= y_center bbox["xmax"] -= x_center bbox["ymax"] -= y_center bbox["xmin"], bbox["ymin"] = bbox["ymin"], -bbox["xmin"] bbox["xmax"], bbox["ymax"] = bbox["ymax"], -bbox["xmax"] bbox["xmin"], bbox["ymin"] = bbox["xmin"] + x_center, bbox["ymin"] + y_center bbox["xmax"], bbox["ymax"] = bbox["xmax"] + x_center, bbox["ymax"] + y_center new_annotation["objects"].append(bbox) elif rotation_angle == -180: old_annotation = old_annotations["imgs"][file_name.split('.')[0]] new_annotation = old_annotation new_annotation["objects"] = [] for object in old_annotation["objects"]: bbox = object["bbox"] bbox["xmin"] -= x_center bbox["ymin"] -= y_center bbox["xmax"] -= x_center bbox["ymax"] -= y_center bbox["xmin"], bbox["ymin"] = -bbox["xmin"], -bbox["ymin"] bbox["xmax"], bbox["ymax"] = -bbox["xmax"], -bbox["ymax"] bbox["xmin"], bbox["ymin"] = bbox["xmin"] + x_center, bbox["ymin"] + y_center bbox["xmax"], bbox["ymax"] = bbox["xmax"] + x_center, bbox["ymax"] + y_center new_annotation["objects"].append(bbox) elif rotation_angle == -270: old_annotation = old_annotations["imgs"][file_name.split('.')[0]] new_annotation = old_annotation new_annotation["objects"] = [] for object in old_annotation["objects"]: bbox = object["bbox"] bbox["xmin"] -= x_center bbox["ymin"] -= y_center bbox["xmax"] -= x_center bbox["ymax"] -= y_center bbox["xmin"], bbox["ymin"] = bbox["ymin"], -bbox["xmin"] bbox["xmax"], bbox["ymax"] = bbox["ymax"], -bbox["xmax"] bbox["xmin"], bbox["ymin"] = bbox["xmin"] + x_center, bbox["ymin"] + y_center bbox["xmax"], bbox["ymax"] = bbox["xmax"] + x_center, bbox["ymax"] + y_center new_annotation["objects"].append(bbox) except KeyError: print(file_name + " File details not found in source annotation.") failed_files.append(file_name) return new_annotations, failed_files def change_brightness(new_brightness_level, input_folder, output_folder): for file_name in os.listdir(input_folder): if not file_name.endswith(".jpg"): continue img = Image.open(input_folder + file_name) new_image = transforms.ColorJitter(brightness=new_brightness_level)(img) new_image.save(output_folder + file_name) return def change_contrast(new_contrast, input_folder, output_folder): for file_name in os.listdir(input_folder): if not file_name.endswith(".jpg"): continue img = Image.open(input_folder + file_name) new_image = transforms.ColorJitter(contrast=new_contrast)(img) new_image.save(output_folder + file_name) return def change_hue(new_hue, input_folder, output_folder): for file_name in os.listdir(input_folder): if not file_name.endswith(".jpg"): continue img = Image.open(input_folder + file_name) new_image = transforms.ColorJitter(hue=new_hue)(img) new_image.save(output_folder + file_name) return def resize_pictures(new_size, input_folder, output_folder): for file_name in os.listdir(input_folder): if not file_name.endswith(".jpg"): continue img = Image.open(input_folder + file_name) resized_image = img.resize(new_size) resized_image.save(output_folder + file_name) def read_json_file(file_path): with open(file_path, "r") as file: return json.load(file) def makedir(path): try: path = Path(path) path.mkdir(parents=True) print("Directory created") except FileExistsError as e: print("Output directory already exists.") def write_to_json(new_annotations, new_annotation_path): with open(new_annotation_path, 'w') as f: f.write(json.dumps(new_annotations)) def write_to_txt(array, txt_file_path): with open(txt_file_path, 'w') as f: json.dump(array, f) if __name__ == "__main__": base_path = "/mnt/g/Drive/BTP/TSD" input_folder = base_path + "/tt100k_2021/TSD/" dataset_name = "tt100k_2021_hu_0_4" output_folder = base_path + "/HueVariations/" + dataset_name + "/TSD/" annotations_file_path = base_path + "/tt100k_2021/annotations_all.json" new_annotations_file_path = base_path + "/HueVariations/" + dataset_name + "/annotations_all.json" failed_files_list_path = base_path + "/HueVariations/" + dataset_name + "/failed_files.txt" old_annotations = read_json_file(annotations_file_path) makedir(output_folder) new_annotations = {"types": old_annotations["types"], "imgs": {}} # resize_pictures((1024,1024),input_folder=input_folder, output_folder=output_folder) change_hue(new_hue=0.4, input_folder=input_folder, output_folder=output_folder) # change_contrast(new_contrast=2.5, input_folder=input_folder, output_folder=output_folder) # change_brightness(new_brightness_level=3.5, input_folder=input_folder, output_folder=output_folder) # new_annotations, failed_files = rotate_images(input_folder, output_folder, -180, old_annotations, new_annotations) # write_to_json(new_annotations, new_annotations_file_path) # print(failed_files) # write_to_txt(failed_files, failed_files_list_path)

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f091ec957e06a07d8edc7a3b7208748d886e4532

3,148

py

Python

Filters/Points/Testing/Python/TestPointInterpolator2D.py

satya-arjunan/vtk8

ee7ced57de6d382a2d12693c01e2fcdac350b25f

[ "BSD-3-Clause" ]

1,755

2015-01-03T06:55:00.000Z

2022-03-29T05:23:26.000Z

Filters/Points/Testing/Python/TestPointInterpolator2D.py

satya-arjunan/vtk8

ee7ced57de6d382a2d12693c01e2fcdac350b25f

[ "BSD-3-Clause" ]

29

2015-04-23T20:58:30.000Z

2022-03-02T16:16:42.000Z

Filters/Points/Testing/Python/TestPointInterpolator2D.py

satya-arjunan/vtk8

ee7ced57de6d382a2d12693c01e2fcdac350b25f

[ "BSD-3-Clause" ]

1,044

2015-01-05T22:48:27.000Z

2022-03-31T02:38:26.000Z

#!/usr/bin/env python import vtk from vtk.util.misc import vtkGetDataRoot VTK_DATA_ROOT = vtkGetDataRoot() # Parameters for debugging NPts = 1000000 math = vtk.vtkMath() # create pipeline: use terrain dataset # # Read the data: a height field results demReader = vtk.vtkDEMReader() demReader.SetFileName(VTK_DATA_ROOT + "/Data/SainteHelens.dem") demReader.Update() lo = demReader.GetOutput().GetScalarRange()[0] hi = demReader.GetOutput().GetScalarRange()[1] geom = vtk.vtkImageDataGeometryFilter() geom.SetInputConnection(demReader.GetOutputPort()) warp = vtk.vtkWarpScalar() warp.SetInputConnection(geom.GetOutputPort()) warp.SetNormal(0, 0, 1) warp.UseNormalOn() warp.SetScaleFactor(2) warp.Update() bds = warp.GetOutput().GetBounds() center = warp.GetOutput().GetCenter() # A randomized point cloud, whose attributes are set via implicit function points = vtk.vtkPoints() points.SetDataTypeToFloat() points.SetNumberOfPoints(NPts) for i in range(0,NPts): points.SetPoint(i,math.Random(bds[0],bds[1]),math.Random(bds[2],bds[3]),math.Random(bds[4],bds[5])) source = vtk.vtkPolyData() source.SetPoints(points) sphere = vtk.vtkSphere() sphere.SetCenter(center[0],center[1]-7500,center[2]) attr = vtk.vtkSampleImplicitFunctionFilter() attr.SetInputData(source) attr.SetImplicitFunction(sphere) attr.Update() # Gaussian kernel------------------------------------------------------- gaussianKernel = vtk.vtkGaussianKernel() gaussianKernel.SetSharpness(4) gaussianKernel.SetRadius(50) voronoiKernel = vtk.vtkVoronoiKernel() interpolator1 = vtk.vtkPointInterpolator2D() interpolator1.SetInputConnection(warp.GetOutputPort()) interpolator1.SetSourceConnection(attr.GetOutputPort()) #interpolator1.SetKernel(gaussianKernel) interpolator1.SetKernel(voronoiKernel) interpolator1.SetNullPointsStrategyToClosestPoint() # Time execution timer = vtk.vtkTimerLog() timer.StartTimer() interpolator1.Update() timer.StopTimer() time = timer.GetElapsedTime() print("Interpolate Terrain Points (Gaussian): {0}".format(time)) scalarRange = attr.GetOutput().GetScalarRange() intMapper1 = vtk.vtkPolyDataMapper() intMapper1.SetInputConnection(interpolator1.GetOutputPort()) intMapper1.SetScalarRange(scalarRange) intActor1 = vtk.vtkActor() intActor1.SetMapper(intMapper1) # Create an outline outline1 = vtk.vtkOutlineFilter() outline1.SetInputConnection(warp.GetOutputPort()) outlineMapper1 = vtk.vtkPolyDataMapper() outlineMapper1.SetInputConnection(outline1.GetOutputPort()) outlineActor1 = vtk.vtkActor() outlineActor1.SetMapper(outlineMapper1) # Create the RenderWindow, Renderer and both Actors # ren0 = vtk.vtkRenderer() renWin = vtk.vtkRenderWindow() renWin.AddRenderer(ren0) iren = vtk.vtkRenderWindowInteractor() iren.SetRenderWindow(renWin) # Add the actors to the renderer, set the background and size # ren0.AddActor(intActor1) ren0.AddActor(outlineActor1) ren0.SetBackground(0.1, 0.2, 0.4) renWin.SetSize(250, 250) cam = ren0.GetActiveCamera() cam.SetFocalPoint(center) fp = cam.GetFocalPoint() cam.SetPosition(fp[0]+.2,fp[1]+.1,fp[2]+1) ren0.ResetCamera() iren.Initialize() # render the image # renWin.Render() iren.Start()

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null

0

null

0

1

0

f092fdc888832d78dce05a7e9c60b5fe4f8e29cd

1,797

py

Python

quadpy/e2r/stroud.py

gdmcbain/quadpy

c083d500027d7c1b2187ae06ff2b7fbdd360ccc7

[ "MIT" ]

1

2019-01-02T19:04:42.000Z

quadpy/e2r/stroud.py

gdmcbain/quadpy

c083d500027d7c1b2187ae06ff2b7fbdd360ccc7

[ "MIT" ]

null

quadpy/e2r/stroud.py

gdmcbain/quadpy

c083d500027d7c1b2187ae06ff2b7fbdd360ccc7

[ "MIT" ]

null

# -*- coding: utf-8 -*- # """ Arthur Stroud, Approximate Calculation of Multiple Integrals, Prentice Hall, 1971. """ from __future__ import division import numpy import sympy from . import rabinowitz_richter from . import stroud_secrest from ..helpers import untangle def _gen4_1(symbolic): frac = sympy.Rational if symbolic else lambda x, y: x / y sqrt = numpy.vectorize(sympy.sqrt) if symbolic else numpy.sqrt cos = numpy.vectorize(sympy.cos) if symbolic else numpy.cos sin = numpy.vectorize(sympy.sin) if symbolic else numpy.sin pi = sympy.pi if symbolic else numpy.pi pts = ( 2 * sqrt(5) * numpy.array( [ [cos(2 * i * pi / 5) for i in range(5)], [sin(2 * i * pi / 5) for i in range(5)], ] ).T ) data = [(frac(7, 10), numpy.array([[0, 0]])), (frac(3, 50), pts)] return 4, data # The boolean tells whether the factor 2*pi is already in the weights _gen = { "4-1": (_gen4_1, False), "5-1": (stroud_secrest.v, False), "7-1": (stroud_secrest.vi, False), "9-1": (rabinowitz_richter.gen1, True), "11-1": (rabinowitz_richter.gen2, True), "11-2": (rabinowitz_richter.gen3, True), # ERR misprint in Stroud copied from original article # '13-1': (rabinowitz_richter.gen4, "15-1": (rabinowitz_richter.gen5, True), } class Stroud(object): keys = _gen.keys() def __init__(self, key, symbolic=False): self.name = "Stroud_E2r({})".format(key) self.degree, data = _gen[key][0](symbolic) weights_contain_2pi = _gen[key][1] self.points, self.weights = untangle(data) pi = sympy.pi if symbolic else numpy.pi if not weights_contain_2pi: self.weights *= 2 * pi return

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null

0

1

0

f093c60ce6164b4582a2d6f0d5cb7a2a32083d64

5,524

py

Python

build/lib/comptools/similarity.py

pedrofariacomposer/comptools

71f540fa6e109365cb7faf57b354fd0603b4d396

[ "MIT" ]

2

2022-01-12T19:47:53.000Z

2022-02-17T00:39:00.000Z

comptools/similarity.py

pedrofariacomposer/comptools

71f540fa6e109365cb7faf57b354fd0603b4d396

[ "MIT" ]

null

comptools/similarity.py

pedrofariacomposer/comptools

71f540fa6e109365cb7faf57b354fd0603b4d396

[ "MIT" ]

null

""" Module with the similarity tools of the Comp_Tools library. For more information, see: Isaacson - Similarity of Interval-Class Content Between Pitch-Class Sets: The IcVSIM Relation """ from .basic_tools import interval_vector, prime_form from ._all_classes import allClasses from numpy import sqrt, reshape, array from typing import Sequence, List from sklearn.metrics.pairwise import cosine_similarity from sklearn.feature_extraction.text import TfidfVectorizer import pandas as pd def forte( pcset1: Sequence, pcset2: Sequence, ) -> str: """Returns the Forte similarity relation between two pitch class sets. If the sets don't have the same cardinality, the function returns None. """ if len(pcset1) != len(pcset2): forte_relation = None else: v1 = interval_vector(pcset1) v2 = interval_vector(pcset2) common_entries = [i for i in range(6) if v1[i] == v2[i]] if len(common_entries) == 0: forte_relation = "R0" elif len(common_entries) == 4: diff_entries = [i for i in range(6) if i not in common_entries] pair1 = [v1[x] for x in diff_entries] pair2 = [v2[x] for x in diff_entries] if sorted(pair1) == sorted(pair2): forte_relation = "R1" else: forte_relation = "R2" return forte_relation def morris( pcset1: Sequence, pcset2: Sequence, ) -> int: """Returns the Morris similarity relation between two pitch class sets. """ vector1 = interval_vector(pcset1) vector2 = interval_vector(pcset2) result = 0 for i in range(len(vector1)): result += abs(vector1[i]-vector2[i]) return result def lord( pcset1: Sequence, pcset2: Sequence, ) -> float: """Returns the Lord similarity relation between two pitch class sets. """ return morris(pcset1,pcset2) / 2 def rahn( pcset1: Sequence, pcset2: Sequence, ) -> int: """Returns the Rahn similarity relation between two pitch class sets. """ vector1 = interval_vector(pcset1) vector2 = interval_vector(pcset2) result = 0 for i in range(6): if (vector1[i] == 0 or vector2[i] == 0): pass else: result += vector1[i] + vector2[i] return result def rahn_mod( pcset1: Sequence, pcset2: Sequence, ) -> float: """Returns the modified Rahn similarity relation between two pitch class sets. """ original_rahn = rahn(pcset1, pcset2) return (1/2) * original_rahn / ((len(pcset1) * (len(pcset1) - 1)) +(len(pcset2) * (len(pcset2) - 1))) def lewin( pcset1: Sequence, pcset2: Sequence, ) -> float: """Returns the Lewin similarity relation between two pitch class sets. """ vector1 = interval_vector(pcset1) vector2 = interval_vector(pcset2) factor1 = 0 for i in range(6): factor1 += sqrt(vector1[i] * vector2[i]) factor2 = ((len(pcset1) * (len(pcset1) - 1)) *(len(pcset2) * (len(pcset2) - 1))) return (2 * factor1) / sqrt(factor2) def teitelbaum( pcset1: Sequence, pcset2: Sequence, ) -> float: """Returns the Teitelbaum similarity relation between two pitch class sets. """ vector1 = interval_vector(pcset1) vector2 = interval_vector(pcset2) factor1 = 0 for i in range(6): factor1 += (vector1[i]-vector2[i]) ** 2 return sqrt(factor1) def isaacson( pcset1: Sequence, pcset2: Sequence, ) -> float: """Returns the Isaacson similarity relation between two pitch class sets. """ vector1 = interval_vector(pcset1) vector2 = interval_vector(pcset2) idv = [vector2[i]-vector1[i] for i in range(6)] idv_mean = sum(idv) / 6 factor1 = 0 for i in range(6): factor1 += (idv[i] - idv_mean) ** 2 return sqrt(factor1/6) def text_set_class( set_class: Sequence, ) -> str: """Converts a set class into a string representing its interval vector. """ id_dict = {0: "one", 1: "two", 2: "three", 3: "four", 4: "five", 5: "six"} result = "" for i, el in enumerate(interval_vector(set_class)): for _ in range(el): result += id_dict[i] + " " return result.rstrip() def text_sim( sc1: Sequence, sc2: Sequence, ) -> float: """Returns the Text_Sim similarity measure between two pitch class sets. """ sc1 = prime_form(sc1) sc2 = prime_form(sc2) corpus = [text_set_class(x) for x in sorted(allClasses)] vectorizer = TfidfVectorizer() trsfm = vectorizer.fit_transform(corpus) text_similarity = cosine_similarity(trsfm) names = [str(x) for x in sorted(allClasses)] df = pd.DataFrame(text_similarity.round(3), columns=names, index=names) return df[str(sc1)][str(sc2)] def simile_table( pitch_class_sets1: Sequence, pitch_class_sets2: Sequence, simile_function, ) -> array : """Creates a numpy array with the similarities between two sets of pitch-class sets. """ mod = ['X'] + pitch_class_sets2 new = [mod] for i in pitch_class_sets1: new2 = [i] for j in pitch_class_sets2: a = simile_function(i, j) new2.append(a) new.append(new2) k = len(pitch_class_sets1) + 1 n = len(pitch_class_sets2) + 1 m = reshape(new, (k, n)) return m

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null

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1

0

f0949414173bc974ed0bfc16a4d388d9b02e4b7d

8,370

py

Python

sphinxcontrib/openapi.py

diegodelemos/sphinxcontrib-openapi

eba539802bc270c818f2aa2a246a137688a6f2f4

[ "BSD-2-Clause" ]

null

sphinxcontrib/openapi.py

diegodelemos/sphinxcontrib-openapi

eba539802bc270c818f2aa2a246a137688a6f2f4

[ "BSD-2-Clause" ]

null

sphinxcontrib/openapi.py

diegodelemos/sphinxcontrib-openapi

eba539802bc270c818f2aa2a246a137688a6f2f4

[ "BSD-2-Clause" ]

1

2018-10-12T15:11:46.000Z

""" sphinxcontrib.openapi --------------------- The OpenAPI spec renderer for Sphinx. It's a new way to document your RESTful API. Based on ``sphinxcontrib-httpdomain``. :copyright: (c) 2016, Ihor Kalnytskyi. :license: BSD, see LICENSE for details. """ import io import itertools import collections import yaml import jsonschema from docutils import nodes from docutils.parsers.rst import directives from docutils.statemachine import ViewList from sphinx.util.compat import Directive from sphinx.util.nodes import nested_parse_with_titles from sphinxcontrib import httpdomain # Dictionaries do not guarantee to preserve the keys order so when we load # JSON or YAML - we may loose the order. In most cases it's not important # because we're interested in data. However, in case of OpenAPI spec it'd # be really nice to preserve them since, for example, endpoints may be # grouped logically and that improved readability. class _YamlOrderedLoader(yaml.SafeLoader): pass _YamlOrderedLoader.add_constructor( yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG, lambda loader, node: collections.OrderedDict(loader.construct_pairs(node)) ) def _resolve_refs(uri, spec): """Resolve JSON references in a given dictionary. OpenAPI spec may contain JSON references to its nodes or external sources, so any attempt to rely that there's some expected attribute in the spec may fail. So we need to resolve JSON references before we use it (i.e. replace with referenced object). For details see: https://tools.ietf.org/html/draft-pbryan-zyp-json-ref-02 The input spec is modified in-place despite being returned from the function. """ resolver = jsonschema.RefResolver(uri, spec) def _do_resolve(node): if isinstance(node, collections.Mapping) and '$ref' in node: with resolver.resolving(node['$ref']) as resolved: return resolved elif isinstance(node, collections.Mapping): for k, v in node.items(): node[k] = _do_resolve(v) elif isinstance(node, (list, tuple)): for i in range(len(node)): node[i] = _do_resolve(node[i]) return node return _do_resolve(spec) def _httpresource(endpoint, method, properties): parameters = properties.get('parameters', []) responses = properties['responses'] indent = ' ' yield '.. http:{0}:: {1}'.format(method, endpoint) yield ' :synopsis: {0}'.format(properties.get('summary', 'null')) yield '' if 'summary' in properties: for line in properties['summary'].splitlines(): yield '{indent}**{line}**'.format(**locals()) yield '' if 'description' in properties: for line in properties['description'].splitlines(): yield '{indent}{line}'.format(**locals()) yield '' # print request's route params for param in filter(lambda p: p['in'] == 'path', parameters): yield indent + ':param {type} {name}:'.format(**param) for line in param.get('description', '').splitlines(): yield '{indent}{indent}{line}'.format(**locals()) # print request's query params for param in filter(lambda p: p['in'] == 'query', parameters): yield indent + ':query {type} {name}:'.format(**param) for line in param.get('description', '').splitlines(): yield '{indent}{indent}{line}'.format(**locals()) # print response status codes for status, response in responses.items(): yield '{indent}:status {status}:'.format(**locals()) for line in response['description'].splitlines(): yield '{indent}{indent}{line}'.format(**locals()) # print request header params for param in filter(lambda p: p['in'] == 'header', parameters): yield indent + ':reqheader {name}:'.format(**param) for line in param.get('description', '').splitlines(): yield '{indent}{indent}{line}'.format(**locals()) # print response headers for status, response in responses.items(): for headername, header in response.get('headers', {}).items(): yield indent + ':resheader {name}:'.format(name=headername) for line in header['description'].splitlines(): yield '{indent}{indent}{line}'.format(**locals()) yield '' def _normalize_spec(spec, **options): # OpenAPI spec may contain JSON references, so we need resolve them # before we access the actual values trying to build an httpdomain # markup. Since JSON references may be relative, it's crucial to # pass a document URI in order to properly resolve them. spec = _resolve_refs(options.get('uri', ''), spec) # OpenAPI spec may contain common endpoint's parameters top-level. # In order to do not place if-s around the code to handle special # cases, let's normalize the spec and push common parameters inside # endpoints definitions. for endpoint in spec['paths'].values(): parameters = endpoint.pop('parameters', []) for method in endpoint.values(): method.setdefault('parameters', []) method['parameters'].extend(parameters) def openapi2httpdomain(spec, **options): generators = [] # OpenAPI spec may contain JSON references, common properties, etc. # Trying to render the spec "As Is" will require to put multiple # if-s around the code. In order to simplify flow, let's make the # spec to have only one (expected) schema, i.e. normalize it. _normalize_spec(spec, **options) # If 'paths' are passed we've got to ensure they exist within an OpenAPI # spec; otherwise raise error and ask user to fix that. if 'paths' in options: if not set(options['paths']).issubset(spec['paths']): raise ValueError( 'One or more paths are not defined in the spec: %s.' % ( ', '.join(set(options['paths']) - set(spec['paths'])), ) ) for endpoint in options.get('paths', spec['paths']): for method, properties in spec['paths'][endpoint].items(): generators.append(_httpresource(endpoint, method, properties)) return iter(itertools.chain(*generators)) class OpenApi(Directive): required_arguments = 1 # path to openapi spec final_argument_whitespace = True # path may contain whitespaces option_spec = { 'encoding': directives.encoding, # useful for non-ascii cases :) 'paths': lambda s: s.split(), # endpoints to be rendered } def run(self): env = self.state.document.settings.env relpath, abspath = env.relfn2path(directives.path(self.arguments[0])) # Add OpenAPI spec as a dependency to the current document. That means # the document will be rebuilt if the spec is changed. env.note_dependency(relpath) # Read the spec using encoding passed to the directive or fallback to # the one specified in Sphinx's config. encoding = self.options.get('encoding', env.config.source_encoding) with io.open(abspath, 'rt', encoding=encoding) as stream: spec = yaml.load(stream, _YamlOrderedLoader) # URI parameter is crucial for resolving relative references. So # we need to set this option properly as it's used later down the # stack. self.options.setdefault('uri', 'file://%s' % abspath) # reStructuredText DOM manipulation is pretty tricky task. It requires # passing dozen arguments which is not easy without well-documented # internals. So the idea here is to represent OpenAPI spec as # reStructuredText in-memory text and parse it in order to produce a # real DOM. viewlist = ViewList() for line in openapi2httpdomain(spec, **self.options): viewlist.append(line, '<openapi>') # Parse reStructuredText contained in `viewlist` and return produced # DOM nodes. node = nodes.section() node.document = self.state.document nested_parse_with_titles(self.state, viewlist, node) return node.children def setup(app): if 'http' not in app.domains: httpdomain.setup(app) app.add_directive('openapi', OpenApi)

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null

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null

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f095110e765b2827d7fdc2f784891c575cdab197

1,274

py

Python

hypernets/hyperctl/dao.py

lyhue1991/Hypernets

d726bd297869eacb0cba84376fbac30206bbb60a

[ "Apache-2.0" ]

3

2022-03-25T23:27:44.000Z

2022-03-27T01:32:28.000Z

hypernets/hyperctl/dao.py

lyhue1991/Hypernets

d726bd297869eacb0cba84376fbac30206bbb60a

[ "Apache-2.0" ]

null

hypernets/hyperctl/dao.py

lyhue1991/Hypernets

d726bd297869eacb0cba84376fbac30206bbb60a

[ "Apache-2.0" ]

null

import os from pathlib import Path from hypernets.hyperctl import get_context from hypernets.hyperctl.batch import ShellJob def get_job_by_name(job_name): for job in get_context().batch.jobs: if job.name == job_name: return job return None def get_jobs(): return get_context().batch.jobs def change_job_status(job: ShellJob, next_status): current_status = job.status target_status_file = job.status_file_path(next_status) if next_status == job.STATUS_INIT: raise ValueError(f"can not change to {next_status} ") elif next_status == job.STATUS_RUNNING: if current_status != job.STATUS_INIT: raise ValueError(f"only job in {job.STATUS_INIT} can change to {next_status}") elif next_status in job.FINAL_STATUS: if current_status != job.STATUS_RUNNING: raise ValueError(f"only job in {job.STATUS_RUNNING} can change to " f"{next_status} but now is {current_status}") # delete running status file running_status_file = job.status_file_path(job.STATUS_RUNNING) os.remove(running_status_file) else: raise ValueError(f"unknown status {next_status}") with open(target_status_file, 'w') as f: pass

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null

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null

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f0990d409066675bc8254e738cd19e11296ac77d

19,592

py

Python

optbinning/binning/mip.py

jensgk/optbinning

5ccd892fa4ee0a745ab539cee10a2069b35de6da

[ "Apache-2.0" ]

207

2020-01-23T21:32:59.000Z

2022-03-30T06:33:21.000Z

optbinning/binning/mip.py

jensgk/optbinning

5ccd892fa4ee0a745ab539cee10a2069b35de6da

[ "Apache-2.0" ]

133

2020-01-23T22:14:35.000Z

2022-03-29T14:05:04.000Z

optbinning/binning/mip.py

jensgk/optbinning

5ccd892fa4ee0a745ab539cee10a2069b35de6da

[ "Apache-2.0" ]

50

2020-01-27T15:37:08.000Z

2022-03-30T06:33:25.000Z

""" Generalized assigment problem: solve constrained optimal binning problem. Mixed-Integer programming implementation. """ # Guillermo Navas-Palencia <g.navas.palencia@gmail.com> # Copyright (C) 2019 import numpy as np from ortools.linear_solver import pywraplp from .model_data import model_data class BinningMIP: def __init__(self, monotonic_trend, min_n_bins, max_n_bins, min_bin_size, max_bin_size, min_bin_n_event, max_bin_n_event, min_bin_n_nonevent, max_bin_n_nonevent, min_event_rate_diff, max_pvalue, max_pvalue_policy, gamma, user_splits_fixed, mip_solver, time_limit): self.monotonic_trend = monotonic_trend self.min_n_bins = min_n_bins self.max_n_bins = max_n_bins self.min_bin_size = min_bin_size self.max_bin_size = max_bin_size self.min_bin_n_event = min_bin_n_event self.max_bin_n_event = max_bin_n_event self.min_bin_n_nonevent = min_bin_n_nonevent self.max_bin_n_nonevent = max_bin_n_nonevent self.min_event_rate_diff = min_event_rate_diff self.max_pvalue = max_pvalue self.max_pvalue_policy = max_pvalue_policy self.gamma = gamma self.user_splits_fixed = user_splits_fixed self.mip_solver = mip_solver self.time_limit = time_limit self.solver_ = None self._n = None self._x = None def build_model(self, divergence, n_nonevent, n_event, trend_change): # Parameters D, V, pvalue_violation_indices = model_data(divergence, n_nonevent, n_event, self.max_pvalue, self.max_pvalue_policy) n = len(n_nonevent) n_records = n_nonevent + n_event # Initialize solver if self.mip_solver == "bop": solver = pywraplp.Solver( 'BinningMIP', pywraplp.Solver.BOP_INTEGER_PROGRAMMING) elif self.mip_solver == "cbc": solver = pywraplp.Solver( 'BinningMIP', pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING) # Decision variables x, y, t, d, u, bin_size_diff = self.decision_variables(solver, n) # Objective function if self.gamma: total_records = int(n_records.sum()) regularization = self.gamma / total_records pmax = solver.IntVar(0, total_records, "pmax") pmin = solver.IntVar(0, total_records, "pmin") solver.Maximize(solver.Sum([(V[i][i] * x[i, i]) + solver.Sum([(V[i][j] - V[i][j+1]) * x[i, j] for j in range(i)]) for i in range(n)]) - regularization * (pmax - pmin)) else: solver.Maximize(solver.Sum([(V[i][i] * x[i, i]) + solver.Sum([(V[i][j] - V[i][j+1]) * x[i, j] for j in range(i)]) for i in range(n)])) # Constraint: unique assignment self.add_constraint_unique_assignment(solver, n, x) # Constraint: continuity self.add_constraint_continuity(solver, n, x) # Constraint: min / max bins self.add_constraint_min_max_bins(solver, n, x, d) # Constraint: min / max bin size self.add_constraint_min_max_bin_size(solver, n, x, u, n_records, bin_size_diff) # Constraint: min / max n_nonevent per bin if (self.min_bin_n_nonevent is not None or self.max_bin_n_nonevent is not None): for i in range(n): bin_ne_size = solver.Sum([n_nonevent[j] * x[i, j] for j in range(i + 1)]) if self.min_bin_n_nonevent is not None: solver.Add(bin_ne_size >= self.min_bin_n_nonevent*x[i, i]) if self.max_bin_n_nonevent is not None: solver.Add(bin_ne_size <= self.max_bin_n_nonevent*x[i, i]) # Constraint: min / max n_event per bin if (self.min_bin_n_event is not None or self.max_bin_n_event is not None): for i in range(n): bin_e_size = solver.Sum([n_event[j] * x[i, j] for j in range(i + 1)]) if self.min_bin_n_event is not None: solver.Add(bin_e_size >= self.min_bin_n_event * x[i, i]) if self.max_bin_n_event is not None: solver.Add(bin_e_size <= self.max_bin_n_event * x[i, i]) # Constraints: monotonicity if self.monotonic_trend == "ascending": self.add_constraint_monotonic_ascending(solver, n, D, x) elif self.monotonic_trend == "descending": self.add_constraint_monotonic_descending(solver, n, D, x) elif self.monotonic_trend == "concave": self.add_constraint_monotonic_concave(solver, n, D, x) elif self.monotonic_trend == "convex": self.add_constraint_monotonic_convex(solver, n, D, x) elif self.monotonic_trend in ("peak", "valley"): for i in range(n): solver.Add(t >= i - n * (1 - y[i])) solver.Add(t <= i + n * y[i]) if self.monotonic_trend == "peak": self.add_constraint_monotonic_peak(solver, n, D, x, y) else: self.add_constraint_monotonic_valley(solver, n, D, x, y) elif self.monotonic_trend == "peak_heuristic": self.add_constraint_monotonic_peak_heuristic( solver, n, D, x, trend_change) elif self.monotonic_trend == "valley_heuristic": self.add_constraint_monotonic_valley_heuristic( solver, n, D, x, trend_change) # Constraint: reduction of dominating bins if self.gamma: for i in range(n): bin_size = solver.Sum([n_records[j] * x[i, j] for j in range(i + 1)]) solver.Add(pmin <= total_records * (1 - x[i, i]) + bin_size) solver.Add(pmax >= bin_size) solver.Add(pmin <= pmax) # Constraint: max-pvalue self.add_max_pvalue_constraint(solver, x, pvalue_violation_indices) # Constraint: fixed splits self.add_constraint_fixed_splits(solver, n, x) self.solver_ = solver self._n = n self._x = x def solve(self): self.solver_.SetTimeLimit(self.time_limit * 1000) status = self.solver_.Solve() if status in (pywraplp.Solver.OPTIMAL, pywraplp.Solver.FEASIBLE): if status == pywraplp.Solver.OPTIMAL: status_name = "OPTIMAL" else: status_name = "FEASIBLE" solution = np.array([self._x[i, i].solution_value() for i in range(self._n)]).astype(bool) else: if status == pywraplp.Solver.ABNORMAL: status_name = "ABNORMAL" elif status == pywraplp.Solver.INFEASIBLE: status_name = "INFEASIBLE" elif status == pywraplp.Solver.UNBOUNDED: status_name = "UNBOUNDED" else: status_name = "UNKNOWN" solution = np.zeros(self._n).astype(bool) solution[-1] = True return status_name, solution def decision_variables(self, solver, n): x = {} for i in range(n): for j in range(i + 1): x[i, j] = solver.BoolVar("x[{}, {}]".format(i, j)) y = None t = None d = None u = None bin_size_diff = None if self.monotonic_trend in ("peak", "valley"): # Auxiliary binary variables y = {} for i in range(n): y[i] = solver.BoolVar("y[{}]".format(i)) # Change point t = solver.IntVar(0, n, "t") if self.min_n_bins is not None and self.max_n_bins is not None: n_bin_diff = self.max_n_bins - self.min_n_bins # Range constraints auxiliary variables d = solver.IntVar(0, n_bin_diff, "n_bin_diff") if self.min_bin_size is not None and self.max_bin_size is not None: bin_size_diff = self.max_bin_size - self.min_bin_size # Range constraints auxiliary variables u = {} for i in range(n): u[i] = solver.IntVar(0, bin_size_diff, "u[{}]".format(i)) return x, y, t, d, u, bin_size_diff def add_constraint_unique_assignment(self, solver, n, x): for j in range(n): solver.Add(solver.Sum([x[i, j] for i in range(j, n)]) == 1) def add_constraint_continuity(self, solver, n, x): for i in range(n): for j in range(i): solver.Add(x[i, j] - x[i, j+1] <= 0) def add_constraint_min_max_bins(self, solver, n, x, d): if self.min_n_bins is not None or self.max_n_bins is not None: trace = solver.Sum([x[i, i] for i in range(n)]) if self.min_n_bins is not None and self.max_n_bins is not None: solver.Add(d + trace - self.max_n_bins == 0) elif self.min_n_bins is not None: solver.Add(trace >= self.min_n_bins) elif self.max_n_bins is not None: solver.Add(trace <= self.max_n_bins) def add_constraint_min_max_bin_size(self, solver, n, x, u, n_records, bin_size_diff): if self.min_bin_size is not None or self.max_bin_size is not None: for i in range(n): bin_size = solver.Sum([n_records[j] * x[i, j] for j in range(i + 1)]) if (self.min_bin_size is not None and self.max_bin_size is not None): solver.Add(u[i] + bin_size - self.max_bin_size * x[i, i] == 0) solver.Add(u[i] <= bin_size_diff * x[i, i]) elif self.min_bin_size is not None: solver.Add(bin_size >= self.min_bin_size * x[i, i]) elif self.max_bin_size is not None: solver.Add(bin_size <= self.max_bin_size * x[i, i]) def add_constraint_monotonic_ascending(self, solver, n, D, x): for i in range(1, n): for z in range(i): solver.Add( solver.Sum([(D[z][j] - D[z][j+1]) * x[z, j] for j in range(z)]) + D[z][z] * x[z, z] - 1 - (D[i][i] - 1) * x[i, i] - solver.Sum([(D[i][j] - D[i][j + 1]) * x[i, j] for j in range(i)]) + self.min_event_rate_diff * (x[i, i] + x[z, z] - 1) <= 0) # Preprocessing if self.min_event_rate_diff == 0: for i in range(n - 1): if D[i+1][i] - D[i+1][i+1] > 0: solver.Add(x[i, i] == 0) for j in range(n - i - 1): if D[i+1+j][i] - D[i+1+j][i+1+j] > 0: solver.Add(x[i+j, i+j] == 0) def add_constraint_monotonic_descending(self, solver, n, D, x): for i in range(1, n): for z in range(i): solver.Add( solver.Sum([(D[i][j] - D[i][j + 1]) * x[i, j] for j in range(i)]) + D[i][i] * x[i, i] - 1 - (D[z][z] - 1) * x[z, z] - solver.Sum([(D[z][j] - D[z][j+1]) * x[z, j] for j in range(z)]) + self.min_event_rate_diff * (x[i, i] + x[z, z] - 1) <= 0) # Preprocessing if self.min_event_rate_diff == 0: for i in range(n - 1): if D[i+1][i] - D[i+1][i+1] < 0: solver.Add(x[i, i] == 0) for j in range(n - i - 1): if D[i+1+j][i] - D[i+1+j][i+1+j] < 0: solver.Add(x[i+j, i+j] == 0) def add_constraint_monotonic_concave(self, solver, n, D, x): for i in range(2, n): for j in range(1, i): for k in range(j): solver.Add( -(solver.Sum([(D[i][z] - D[i][z+1]) * x[i, z] for z in range(i)]) + D[i][i]*x[i, i]) + 2 * (solver.Sum([(D[j][z] - D[j][z+1]) * x[j, z] for z in range(j)]) + D[j][j] * x[j, j]) - (solver.Sum([(D[k][z] - D[k][z+1]) * x[k, z] for z in range(k)]) + D[k][k] * x[k, k]) >= ( x[i, i] + x[j, j] + x[k, k] - 3)) def add_constraint_monotonic_convex(self, solver, n, D, x): for i in range(2, n): for j in range(1, i): for k in range(j): solver.Add( (solver.Sum([(D[i][z] - D[i][z+1]) * x[i, z] for z in range(i)]) + D[i][i] * x[i, i]) - 2 * (solver.Sum([(D[j][z] - D[j][z+1]) * x[j, z] for z in range(j)]) + D[j][j] * x[j, j]) + (solver.Sum([(D[k][z] - D[k][z+1]) * x[k, z] for z in range(k)]) + D[k][k] * x[k, k]) >= ( x[i, i] + x[j, j] + x[k, k] - 3)) def add_constraint_monotonic_peak(self, solver, n, D, x, y): for i in range(1, n): for z in range(i): solver.Add( y[i] + y[z] + 1 + (D[z][z] - 1) * x[z, z] + solver.Sum([(D[z][j] - D[z][j+1]) * x[z, j] for j in range(z)]) - solver.Sum([(D[i][j] - D[i][j + 1]) * x[i, j] for j in range(i)]) - D[i][i] * x[i, i] >= 0) solver.Add( 2 - y[i] - y[z] + 1 + (D[i][i] - 1) * x[i, i] + solver.Sum([(D[i][j] - D[i][j + 1]) * x[i, j] for j in range(i)]) - solver.Sum([(D[z][j] - D[z][j+1]) * x[z, j] for j in range(z)]) - D[z][z] * x[z, z] >= 0) def add_constraint_monotonic_valley(self, solver, n, D, x, y): for i in range(1, n): for z in range(i): solver.Add( y[i] + y[z] + 1 + (D[i][i] - 1) * x[i, i] + solver.Sum([(D[i][j] - D[i][j + 1]) * x[i, j] for j in range(i)]) - solver.Sum([(D[z][j] - D[z][j+1]) * x[z, j] for j in range(z)]) - D[z][z] * x[z, z] >= 0) solver.Add( 2 - y[i] - y[z] + 1 + (D[z][z] - 1) * x[z, z] + solver.Sum([(D[z][j] - D[z][j+1]) * x[z, j] for j in range(z)]) - solver.Sum([(D[i][j] - D[i][j + 1]) * x[i, j] for j in range(i)]) - D[i][i] * x[i, i] >= 0) def add_constraint_monotonic_peak_heuristic(self, solver, n, D, x, tc): for i in range(1, tc): for z in range(i): solver.Add( solver.Sum([(D[z][j] - D[z][j+1]) * x[z, j] for j in range(z)]) + D[z][z] * x[z, z] - 1 - (D[i][i] - 1) * x[i, i] - solver.Sum([(D[i][j] - D[i][j + 1]) * x[i, j] for j in range(i)]) + self.min_event_rate_diff * (x[i, i] + x[z, z] - 1) <= 0) # Preprocessing if self.min_event_rate_diff == 0: for i in range(tc - 1): if D[i+1][i] - D[i+1][i+1] > 0: solver.Add(x[i, i] == 0) for j in range(tc - i - 1): if D[i+1+j][i] - D[i+1+j][i+1+j] > 0: solver.Add(x[i+j, i+j] == 0) for i in range(tc, n): for z in range(tc, i): solver.Add( solver.Sum([(D[i][j] - D[i][j + 1]) * x[i, j] for j in range(i)]) + D[i][i] * x[i, i] - 1 - (D[z][z] - 1) * x[z, z] - solver.Sum([(D[z][j] - D[z][j+1]) * x[z, j] for j in range(z)]) + self.min_event_rate_diff * (x[i, i] + x[z, z] - 1) <= 0) # Preprocessing if self.min_event_rate_diff == 0: for i in range(tc, n - 1): if D[i+1][i] - D[i+1][i+1] < 0: solver.Add(x[i, i] == 0) for j in range(tc, n - i - 1): if D[i+1+j][i] - D[i+1+j][i+1+j] < 0: solver.Add(x[i+j, i+j] == 0) def add_constraint_monotonic_valley_heuristic(self, solver, n, D, x, tc): for i in range(1, tc): for z in range(i): solver.Add( solver.Sum([(D[i][j] - D[i][j + 1]) * x[i, j] for j in range(i)]) + D[i][i] * x[i, i] - 1 - (D[z][z] - 1) * x[z, z] - solver.Sum([(D[z][j] - D[z][j+1]) * x[z, j] for j in range(z)]) + self.min_event_rate_diff * (x[i, i] + x[z, z] - 1) <= 0) # Preprocessing if self.min_event_rate_diff == 0: for i in range(tc - 1): if D[i+1][i] - D[i+1][i+1] < 0: solver.Add(x[i, i] == 0) for j in range(tc - i - 1): if D[i+1+j][i] - D[i+1+j][i+1+j] < 0: solver.Add(x[i+j, i+j] == 0) for i in range(tc, n): for z in range(tc, i): solver.Add( solver.Sum([(D[z][j] - D[z][j+1]) * x[z, j] for j in range(z)]) + D[z][z] * x[z, z] - 1 - (D[i][i] - 1) * x[i, i] - solver.Sum([(D[i][j] - D[i][j + 1]) * x[i, j] for j in range(i)]) + self.min_event_rate_diff * (x[i, i] + x[z, z] - 1) <= 0) # Preprocessing if self.min_event_rate_diff == 0: for i in range(tc, n - 1): if D[i+1][i] - D[i+1][i+1] > 0: solver.Add(x[i, i] == 0) for j in range(tc, n - i - 1): if D[i+1+j][i] - D[i+1+j][i+1+j] > 0: solver.Add(x[i+j, i+j] == 0) def add_max_pvalue_constraint(self, solver, x, pvalue_violation_indices): for ind1, ind2 in pvalue_violation_indices: solver.Add(x[ind1[0], ind1[1]] + x[ind2[0], ind2[1]] <= 1) def add_constraint_fixed_splits(self, solver, n, x): if self.user_splits_fixed is not None: for i in range(n - 1): if self.user_splits_fixed[i]: solver.Add(x[i, i] == 1)

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0.054112

0.070377

0.014363

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19,592

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0

null

0

null

0

1

0

f09e777349a3fb2b5e4eefb367e93e2d2b1b6306

3,125

py

Python

aws_log_collector/converters/cloudwatch.py

theletterf/aws-log-collector

ac4201d43fde6b1e4631b279c1e5d11019b8488d

[ "Apache-2.0" ]

1

2021-07-09T15:56:23.000Z

aws_log_collector/converters/cloudwatch.py

theletterf/aws-log-collector

ac4201d43fde6b1e4631b279c1e5d11019b8488d

[ "Apache-2.0" ]

15

2021-06-29T09:40:38.000Z

2022-03-29T20:23:43.000Z

aws_log_collector/converters/cloudwatch.py

theletterf/aws-log-collector

ac4201d43fde6b1e4631b279c1e5d11019b8488d

[ "Apache-2.0" ]

2

2021-11-17T14:17:19.000Z

2021-12-20T08:33:17.000Z

# Copyright 2021 Splunk, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import base64 import gzip import json from io import BytesIO, BufferedReader from aws_log_collector.converters.converter import Converter from aws_log_collector.enrichers.cloudwatch import CloudWatchLogsEnricher from aws_log_collector.metric import size_of_json class CloudWatchLogsConverter(Converter): def __init__(self, logs_enricher: CloudWatchLogsEnricher): self._logs_enricher = logs_enricher def supports(self, log_event): try: data = log_event["awslogs"]["data"] return len(data) > 0 except KeyError: return False def _convert_to_hec(self, log_event, context, sfx_metrics): aws_logs_base64 = log_event["awslogs"]["data"] aws_logs_compressed = base64.b64decode(aws_logs_base64) aws_logs = self._read_logs(aws_logs_compressed) metadata = self._logs_enricher.get_metadata(aws_logs, context, sfx_metrics) sfx_metrics.namespace(metadata["source"]) self._send_input_metrics(sfx_metrics, aws_logs_base64, aws_logs_compressed, aws_logs) return self._enriched_logs_to_hec(aws_logs, metadata) @staticmethod def _read_logs(aws_logs): with gzip.GzipFile(fileobj=BytesIO(aws_logs)) as decompress_stream: data = b"".join(BufferedReader(decompress_stream)) return json.loads(data) @staticmethod def _enriched_logs_to_hec(logs, metadata): def _get_fields(): result = dict(metadata) del result["host"] del result["source"] del result["sourcetype"] return result fields = _get_fields() for item in logs["logEvents"]: timestamp_as_string = str(item['timestamp']) hec_item = {"event": item["message"], "fields": fields, "host": metadata["host"], "source": metadata["source"], "sourcetype": metadata["sourcetype"], "time": timestamp_as_string[0:-3] + "." + timestamp_as_string[-3:], } yield hec_item @staticmethod def _send_input_metrics(sfx_metrics, aws_logs_base64, aws_logs_compressed, logs): sfx_metrics.counters( ("sf.org.awsLogCollector.num.inputBase64Bytes", len(aws_logs_base64)), ("sf.org.awsLogCollector.num.inputCompressedBytes", len(aws_logs_compressed)), ("sf.org.awsLogCollector.num.inputUncompressedBytes", size_of_json(logs)) )

37.650602

94

0.66528

369

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5.384824

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3,125

82

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0.05417

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0

null

0

null

0

1

0

f0a187e8bd9b56832ad3e091b3ee4c6bde8bf101

10,099

py

Python

source_document.py

thesecretlab/snippet-expander

70db56a7d45fdec3808ea593f846a7c06841b6b3

[ "MIT" ]

2

2016-11-17T04:41:07.000Z

2018-04-30T06:52:29.000Z

source_document.py

thesecretlab/snippet-expander

70db56a7d45fdec3808ea593f846a7c06841b6b3

[ "MIT" ]

null

source_document.py

thesecretlab/snippet-expander

70db56a7d45fdec3808ea593f846a7c06841b6b3

[ "MIT" ]

null

#!/usr/bin/env python import re import itertools import os import logging from fuzzywuzzy import process SNIP_PREFIX="// snip" SNIP_FILE_PREFIX="// snip-file" TAG_PREFIX="// tag" # The virtual "ref" that represents the current state of the files on disk, # and may not necessarily be stored in the index or in a commit. Uses a # space because these are very uncommon in tags or branch names, and not # seen in commit hashes. WORKSPACE_REF = "working-copy" class SourceDocument(object): """A document, containing snippets that refer to tagged code.""" def __init__ (self, path): self.path = path with open(path, "r") as source_file: self.contents = source_file.read() @property def filename(self): return os.path.splitext(os.path.basename)[0] @staticmethod def find(base_path, extensions): assert isinstance(base_path, str) assert isinstance(extensions, list) documents = [] starting_dir = base_path for (path, dirs, files) in os.walk(starting_dir): for filename in files: for extension in extensions: if filename.endswith("."+extension): file_path = path+os.path.sep+filename if ".git" in file_path: continue documents.append(SourceDocument(file_path)) return documents @property def cleaned_contents(self): """Returns a version of 'text' that has no expanded snippets.""" snip_with_code = re.compile("(//.*snip(\-file)*:?.*\n)(\+\n)?(\[.*\]\n)*----\n(.*\n)*?----\n", flags=re.IGNORECASE) cleaned = re.sub(snip_with_code, r'\1', self.contents) return cleaned @property def snippets(self): """Returns the list of snippets in this document, as a TagQuery.""" queries = [] from tagged_document import TagQuery # start with a version of ourself that has no expanded snippets source_lines = self.cleaned_contents.split("\n") # the list of lines we're working with output_lines = [] # default to working with files at the current state on disk; this # can change to specific refs when a // tag: instruction is # encountered in the document current_ref = WORKSPACE_REF tag_regex = re.compile(r"$\/\/\s*tag:?\s*(.*)^", flags=re.IGNORECASE) snip_regex = re.compile(r"$\/\/\s*tag:?\s*(.*)^", flags=re.IGNORECASE) for line in source_lines: output_lines.append(line) # change which tag we're looking at if we hit an instruction to # do so tag = tag_regex.match(line) if tag: current_ref = tag.groups(1).strip() # is this a snippet? snippet = snip_regex.match(line) if snippet: # figure out what tags we're supposed to be using here query_text = snippet.groups(1) # build the tag query from this query = TagQuery(query_text, ref=current_ref) queries.append(query) return queries @property def tags_used(self): """Returns the set of all tags referred to in this document.""" return set([query.all_referenced_tags for query in self.snippets]) def render_snippet(self, query, tagged_documents): from tagged_document import TagQuery assert isinstance(query, TagQuery) # get the list of documents that actually exist at this point documents_at_current_tag = filter(None, [document[query.ref] for document in tagged_documents]) # get the tagged lines that apply from these documents rendered_content = [document.query(query.query_string) for document in documents_at_current_tag] # any document that produced no lines will have returned None; # remove those rendered_content = filter(None, rendered_content) rendered_content = [content.split("\n") for content in rendered_content] # we now have a list of list of lines; we want to flatten this to a # plain list of lines rendered_lines = list(itertools.chain.from_iterable(rendered_content)) rendered_lines = "\n".join(rendered_lines) # finally, identify and remove any chain of 2 or more empty lines, # replacing it with a single empty line empty_lines = re.compile(r"(\s*?\n){2,}") rendered_lines = re.sub(empty_lines, "\n\n", rendered_lines) return rendered_lines def render(self, tagged_documents, language=None, clean=False, show_query=True, file_getter=None, as_inline_list_items=False): """Returns a tuple of (string,bool): a version of itself after expanding snippets with code found in 'tagged_documents', and True if any snippets were rendered""" assert isinstance(tagged_documents, list) assert isinstance(language, str) or language is None if clean: return self.cleaned_contents, True # start with a version of ourself that has no expanded snippets source_lines = self.cleaned_contents.split("\n") # the list of lines we're working with output_lines = [] # default to working with files at HEAD current_ref = WORKSPACE_REF # true if this file rendered any snippets dirty = False all_tags_at_current_tag = list({tag for doc in tagged_documents for tag in doc[current_ref].tags}) snippet_count = 0 for line in source_lines: output_lines.append(line) # change which tag we're looking at if we hit an instruction to do so if line.startswith(TAG_PREFIX): current_ref = line[len(TAG_PREFIX)+1:].strip() all_valid_docs_at_current_ref = [doc for doc in tagged_documents if doc[current_ref]] all_tags_at_current_tag = list({tag for doc in all_valid_docs_at_current_ref for tag in doc[current_ref].tags}) # expand file snippets as we encounter them if line.startswith(SNIP_FILE_PREFIX): if not file_getter: logging.warn("snip-file command used, but no file getter was provided") continue dirty = True filename = line[len(SNIP_FILE_PREFIX)+1:].strip() file_contents = file_getter(filename) output_lines.append("----") output_lines.append(file_contents) output_lines.append("----") # expand snippets as we encounter them if line.startswith(SNIP_PREFIX): dirty = True # figure out what tags we're supposed to be using here query_text = line[len(SNIP_PREFIX)+1:] # get the list of documents that actually exist at this # point documents_at_current_tag = filter(None, [document[current_ref] for document in tagged_documents]) # get the tagged lines that apply from these documents rendered_content = [document.query(query_text) for document in documents_at_current_tag] # any document that produced no lines will have returned # None; remove those rendered_content = filter(None, rendered_content) rendered_content = [content.split("\n") for content in rendered_content] # we now have a list of list of lines; we want to flatten # this to a plain list of lines rendered_lines = list(itertools.chain.from_iterable(rendered_content)) if show_query: from tagged_document import TagQuery query_obj = TagQuery(query_text) description = "// Snippet: {}-{}\n".format(snippet_count, query_obj.as_filename) rendered_lines = [description] + rendered_lines if not rendered_lines: # if we got no lines, we log a warning and also render # out that warning in the final output (so that a # proofreader can spot it) # try and find some potential tags that could fit from tagged_document import TagQuery query = TagQuery(query_text) bests = [result[0] for result in process.extractBests(query.include[0], all_tags_at_current_tag, score_cutoff=80)] import textwrap warning = "No code found for query '{}' at ref '{}'. Possible replacement tags include: {}".format(query_text, current_ref, ", ".join(bests)) warning = textwrap.fill(warning, 80) logging.warn("%s: %s", self.path, warning) exclamations = "!" * 8 rendered_lines = [exclamations, warning, exclamations] # time to produce our output! if as_inline_list_items: output_lines.append("+") # add the language tag if one was specified if language: output_lines.append("[source,{}]".format(language)) # and output the snippet output_lines.append("----") output_lines += rendered_lines output_lines.append("----") snippet_count += 1 # render the output into a string output = "\n".join(output_lines) # finally, identify and remove any chain of 2 or more empty lines, # replacing it with a single empty line empty_lines = re.compile(r"(\s*?\n){2,}") output = re.sub(empty_lines, "\n\n", output) return output, dirty

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10,099

4.707218

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0.026361

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10,099

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false

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0.201493

0

null

0

null

0

1

0

f0a5c3fd2b17d5ab1a135bbe1cf07f52dd327cd8

4,173

py

Python

middleware/legato/templates/legato_gfx_pda_tm4301b/Support_BSP_PIC32MZ_EF_Starter_Kit_MEB2.py

automaate/gfx3.8

55bf94302f00c8d513c84d910185cef2ca6b5be2

[ "0BSD" ]

null

middleware/legato/templates/legato_gfx_pda_tm4301b/Support_BSP_PIC32MZ_EF_Starter_Kit_MEB2.py

automaate/gfx3.8

55bf94302f00c8d513c84d910185cef2ca6b5be2

[ "0BSD" ]

null

middleware/legato/templates/legato_gfx_pda_tm4301b/Support_BSP_PIC32MZ_EF_Starter_Kit_MEB2.py

automaate/gfx3.8

55bf94302f00c8d513c84d910185cef2ca6b5be2

[ "0BSD" ]

null

# coding: utf-8 ############################################################################## # Copyright (C) 2018 Microchip Technology Inc. and its subsidiaries. # # Subject to your compliance with these terms, you may use Microchip software # and any derivatives exclusively with Microchip products. It is your # responsibility to comply with third party license terms applicable to your # use of third party software (including open source software) that may # accompany Microchip software. # # THIS SOFTWARE IS SUPPLIED BY MICROCHIP "AS IS". NO WARRANTIES, WHETHER # EXPRESS, IMPLIED OR STATUTORY, APPLY TO THIS SOFTWARE, INCLUDING ANY IMPLIED # WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A # PARTICULAR PURPOSE. # # IN NO EVENT WILL MICROCHIP BE LIABLE FOR ANY INDIRECT, SPECIAL, PUNITIVE, # INCIDENTAL OR CONSEQUENTIAL LOSS, DAMAGE, COST OR EXPENSE OF ANY KIND # WHATSOEVER RELATED TO THE SOFTWARE, HOWEVER CAUSED, EVEN IF MICROCHIP HAS # BEEN ADVISED OF THE POSSIBILITY OR THE DAMAGES ARE FORESEEABLE. TO THE # FULLEST EXTENT ALLOWED BY LAW, MICROCHIP'S TOTAL LIABILITY ON ALL CLAIMS IN # ANY WAY RELATED TO THIS SOFTWARE WILL NOT EXCEED THE AMOUNT OF FEES, IF ANY, # THAT YOU HAVE PAID DIRECTLY TO MICROCHIP FOR THIS SOFTWARE. ############################################################################## ############ LCC + TOUCH I2C CONFIG ###################################################### bsp_pic32mzef_sk_meb_ActivateList = ["le_gfx_driver_lcc", "i2c_bb", "tmr2", "drv_i2c", "drv_i2c0", "core_timer", "sys_time", "ebi"] bsp_pic32mzef_sk_meb_AutoConnectList = [["gfx_legato", "gfx_driver", "le_gfx_driver_lcc", "gfx_driver_lcc"], ["le_gfx_driver_lcc", "Graphics Display", "gfx_disp_pdatm4301b_480x272", "gfx_display"], ["drv_i2c_0", "drv_i2c_I2C_dependency", "i2c_bb", "I2C"], ["i2c_bb", "TMR", "tmr2", "TMR2_TMR"], ["gfx_maxtouch_controller", "i2c", "drv_i2c_0", "drv_i2c"], ["sys_time", "sys_time_TMR_dependency", "core_timer", "CORE_TIMER_TMR"], ["le_gfx_driver_lcc", "EBI_CS", "ebi", "ebi_cs0"]] bsp_pic32mzef_sk_meb_PinConfig = [{"pin": 23, "name": "BSP_MAXTOUCH_CHG", "type": "GPIO", "direction": "In", "latch": "", "abcd": ""}, #RE8 {"pin": 26, "name": "GFX_DISP_INTF_PIN_DE", "type": "GPIO", "direction": "Out", "latch": "Low", "abcd": ""}, #RB4 {"pin": 35, "name": "GFX_DISP_INTF_PIN_HSYNC", "type": "GPIO", "direction": "Out", "latch": "Low", "abcd": ""}, #RB1 {"pin": 39, "name": "GFX_DISP_INTF_PIN_VSYNC", "type": "GPIO", "direction": "Out", "latch": "Low", "abcd": ""}, #RA9 {"pin": 57, "name": "GFX_DISP_INTF_PIN_BACKLIGHT", "type": "GPIO", "direction": "Out", "latch": "Low", "abcd": ""}, #RF13 {"pin": 117, "name": "GFX_DISP_INTF_PIN_RESET", "type": "GPIO", "direction": "Out", "latch": "High", "abcd": ""}, {"pin": 95, "name": "I2C_BB_SCL", "type": "GPIO", "direction": "In", "latch": "", "abcd": ""}, #RA14 - GPIO input for I2C BB SCL {"pin": 96, "name": "I2C_BB_SDA", "type": "GPIO", "direction": "In", "latch": "", "abcd": ""}] #RA15 - GPIO input for I2C BB SDA ################################################################################## def bsp_pic32mzef_sk_meb_EventHandler(event): global pinConfigureFxn if (event == "configure"): #Override default pin configur function w/ PIC32M specific one pinConfigureFxn = configurePinsPIC32M try: ### Configure I2C BB driver Database.setSymbolValue("i2c_bb", "I2C_CLOCK_SPEED", 50000, 1) Database.setSymbolValue("i2c_bb", "I2CBB_SCL_PIN", 10, 1) #RA14 Database.setSymbolValue("i2c_bb", "I2CBB_SDA_PIN", 11, 1) #RA15 except: return bsp_pic32mzef_sk_meb_DisplayInterfaceList = ["LCC"] bsp_pic32mzef_sk_meb_obj = bspSupportObj(bsp_pic32mzef_sk_meb_PinConfig, bsp_pic32mzef_sk_meb_ActivateList, None, bsp_pic32mzef_sk_meb_AutoConnectList, bsp_pic32mzef_sk_meb_EventHandler) addDisplayIntfSupport("BSP_PIC32MZ_EF_Starter_Kit", bsp_pic32mzef_sk_meb_DisplayInterfaceList) addBSPSupport("BSP_PIC32MZ_EF_Starter_Kit", "LCC", bsp_pic32mzef_sk_meb_obj)

63.227273

140

0.639588

524

4,173

4.830153

0.410305

0.056895

0.066377

0.080601

0.33465

0.101936

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0

0.034278

0.154086

4,173

66

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63.227273

0.68272

0.319434

0

0.391875

0.099713

0

1

0.029412

false

0

0.058824

0

null

0

null

0

1

0

f0a5fca6a56c6ea38565e62b6423e3ad0179eeac

4,618

py

Python

torchdet3d/losses/regression_losses.py

phi-wol/3d-object-detection.pytorch

9437e289ba878da2dbf03e7e7d4d7ae1eb9da486

[ "MIT" ]

6

2021-06-10T11:53:24.000Z

2022-03-31T19:34:59.000Z

torchdet3d/losses/regression_losses.py

phi-wol/3d-object-detection.pytorch

9437e289ba878da2dbf03e7e7d4d7ae1eb9da486

[ "MIT" ]

6

2021-03-15T11:01:27.000Z

2021-09-25T16:58:16.000Z

torchdet3d/losses/regression_losses.py

phi-wol/3d-object-detection.pytorch

9437e289ba878da2dbf03e7e7d4d7ae1eb9da486

[ "MIT" ]

2

2021-07-29T08:05:54.000Z

2022-02-22T16:14:06.000Z

import math import torch from torch.nn.modules.loss import _Loss __all__ = ['DiagLoss', 'ADD_loss', 'WingLoss', 'LossManager'] class DiagLoss(_Loss): __constants__ = ['reduction'] def __init__(self, size_average=None, reduce=None, reduction: str = 'mean') -> None: super().__init__(size_average, reduce, reduction) self.l1_loss = torch.nn.SmoothL1Loss(beta=.4) def forward(self, input_: torch.Tensor, target: torch.Tensor) -> torch.Tensor: diag_pr = compute_diag(input_) diag_tr = compute_diag(target) diag_diff = self.l1_loss(diag_pr, diag_tr) return diag_diff class ADD_loss(_Loss): def forward(self, input_: torch.Tensor, target: torch.Tensor) -> torch.Tensor: # find distance between each point of the input and target. Sum it for each # instance and mean it over all instances return torch.mean(torch.sum(torch.linalg.norm(input_-target, dim=2), dim=1)) class WingLoss(_Loss): def __init__(self, size_average=None, reduce=None, w=0.05, eps=2, reduction: str = 'mean') -> None: super().__init__(size_average, reduce, reduction) self.w = w self.eps = eps def forward(self, input_: torch.Tensor, target: torch.Tensor) -> torch.Tensor: wing_const = self.w - self.wing_core(self.w, self.w, self.eps) loss = torch.abs(input_ - target) loss[loss < self.w] = self.wing_core(loss[loss < self.w], self.w, self.eps) loss[loss >= self.w] -= wing_const # diag_dist = compute_diag(target) # loss /= diag_dist.view(input_.size(0),1,1) return torch.mean(loss) @staticmethod def wing_core(x, w, eps): """Calculates the wing function from https://arxiv.org/pdf/1711.06753.pdf""" if isinstance(x, float): return w*math.log(1. + x / eps) return w*torch.log(1. + x / eps) def compute_diag(input_: torch.Tensor): x0 = torch.min(input_[:,:,0], dim=1).values y0 = torch.min(input_[:,:,1], dim=1).values x1 = torch.max(input_[:,:,0], dim=1).values y1 = torch.max(input_[:,:,1], dim=1).values diag = torch.sqrt((x1 - x0)**2 + (y1 - y0)**2) return diag class LossManager: def __init__(self, criterions, coefficients, alwa): self.reg_criterions, self.class_criterions = criterions self.reg_coeffs, self.class_coeffs = coefficients assert len(self.reg_coeffs) == len(self.reg_criterions) assert len(self.class_coeffs) == len(self.class_criterions) assert self.reg_criterions self.use_alwa = alwa.use if alwa.use: assert self.class_criterions assert self.reg_coeffs[0] == self.class_coeffs[0] == 1. # init lambdas for alwa algorithm self.lam_cls = alwa.lam_cls self.lam_reg = alwa.lam_reg self.s_cls = list() self.s_reg = list() self.C = alwa.C self.alwa_version = 'ver_1' if alwa.compute_std else 'ver_2' def parse_losses(self, pred_kp, gt_kp, pred_cats, gt_cats, iter_): class_loss = [] regress_loss = [] # compute losses if self.class_criterions: for k, cr in zip(self.class_coeffs, self.class_criterions): class_loss.append(cr(pred_cats, gt_cats) * k) else: class_loss = torch.zeros(1, requires_grad=True) for k, cr in zip(self.reg_coeffs, self.reg_criterions): regress_loss.append(cr(pred_kp, gt_kp) * k) reg_loss = sum(regress_loss) cls_loss = sum(class_loss) # compute alwa algo or just return sum of losses if not self.use_alwa: return sum(regress_loss) + sum(class_loss) self.s_cls.append(self.lam_cls*cls_loss) self.s_reg.append(self.lam_reg*reg_loss) if iter_ % self.C == 0 and iter_ != 0: cls_mean = torch.mean(torch.stack(self.s_cls)) cls_std = torch.std(torch.stack(self.s_cls)) reg_mean = torch.mean(torch.stack(self.s_reg)) reg_std = torch.std(torch.stack(self.s_reg)) self.s_cls.clear() self.s_reg.clear() if self.alwa_version == 'ver_1': cls = cls_mean + cls_std reg = reg_mean + reg_std else: cls = cls_mean reg = reg_mean if cls > reg: self.lam_cls = (1 - (cls - reg)/cls).item() print(f"classification coefficient changed : {self.lam_cls}") return self.lam_reg * sum(regress_loss) + self.lam_cls * sum(class_loss)

39.810345

103

0.61217

652

4,618

4.102761

0.211656

0.041122

0.020187

0.02243

0.288598

0.22729

0.19215

0.136075

0.109159

0

0.01533

0.265483

4,618

115

104

40.156522

0.77329

0.076873

0

0.076923

0

0.027758

0

0.054945

1

0.098901

false

0

0.032967

0.010989

0.274725

0.010989

0

null

0

null

0

1

0

f0aa15f7cbb7355fda96e634f5fd7046680597d8

474

py

Python

example/model/tests/__init__.py

donghak-shin/dp-tornado

095bb293661af35cce5f917d8a2228d273489496

[ "MIT" ]

18

2015-04-07T14:28:39.000Z

2020-02-08T14:03:38.000Z

example/model/tests/__init__.py

donghak-shin/dp-tornado

095bb293661af35cce5f917d8a2228d273489496

[ "MIT" ]

7

2016-10-05T05:14:06.000Z

2021-05-20T02:07:22.000Z

example/model/tests/__init__.py

donghak-shin/dp-tornado

095bb293661af35cce5f917d8a2228d273489496

[ "MIT" ]

11

2015-12-15T09:49:39.000Z

2021-09-06T18:38:21.000Z

# -*- coding: utf-8 -*- from dp_tornado.engine.model import Model as dpModel class TestsModel(dpModel): def assert_tuple(self, a, b, comp=True): if comp is True: if list(a) != list(b): print('A > %s' % list(a)) print('B > %s' % list(b)) assert(list(a) == list(b)) elif comp is False: assert(list(a) == list(b)) else: assert(list(a) == comp and list(b) == comp)

24.947368

55

0.489451

65

474

3.538462

0.461538

0.108696

0.117391

0.130435

0.13913

0

0.003257

0.352321

474

18

56

26.333333

0.745928

0.044304

0

0.166667

0

0.026608

0

0.333333

1

0.083333

false

0

0.083333

0

0.25

0.166667

0

null

0

null

0

1

0

f0ab19e1a358fb99ef04574c61702c57419daa1d

18,964

py

Python

src/clarinet/train_student.py

roberthoenig/VQ-VAE-Speech

3c537c17465bf59855f0b81d9265354f65016563

[ "MIT" ]

241

2019-03-27T09:08:14.000Z

2022-03-12T07:19:01.000Z

src/clarinet/train_student.py

roberthoenig/VQ-VAE-Speech

3c537c17465bf59855f0b81d9265354f65016563

[ "MIT" ]

5

2019-06-29T14:22:31.000Z

2019-11-17T21:24:45.000Z

src/clarinet/train_student.py

roberthoenig/VQ-VAE-Speech

3c537c17465bf59855f0b81d9265354f65016563

[ "MIT" ]

49

2019-05-27T07:43:27.000Z

2022-03-21T16:37:05.000Z

##################################################################################### # MIT License # # # # Copyright (C) 2018 Sungwon Kim # # # # 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. # ##################################################################################### from clarinet.data import LJspeechDataset, collate_fn, collate_fn_synthesize from clarinet.modules import ExponentialMovingAverage, KL_Loss, STFT from clarinet.wavenet import Wavenet from clarinet.wavenet_iaf import Wavenet_Student import torch from torch import optim import torch.nn as nn from torch.utils.data import DataLoader from torch.distributions.normal import Normal import numpy as np import librosa import os import argparse import json import time def build_model(): model_t = Wavenet(out_channels=2, num_blocks=args.num_blocks_t, num_layers=args.num_layers_t, residual_channels=args.residual_channels, gate_channels=args.gate_channels, skip_channels=args.skip_channels, kernel_size=args.kernel_size, cin_channels=args.cin_channels, upsample_scales=[16, 16]) return model_t def build_student(): model_s = Wavenet_Student(num_blocks_student=[1, 1, 1, 4], num_layers=args.num_layers_s) return model_s def clone_as_averaged_model(model_s, ema): assert ema is not None averaged_model = build_student() averaged_model.to(device) averaged_model.load_state_dict(model_s.state_dict()) for name, param in averaged_model.named_parameters(): if name in ema.shadow: param = ema.shadow[name].clone() return averaged_model def train(epoch, model_t, model_s, optimizer, ema): global global_step epoch_loss = 0.0 running_loss = [0.0, 0.0, 0.0, 0.0] model_t.eval() model_s.train() start_time = time.time() display_step = 100 for batch_idx, (x, y, c, _) in enumerate(train_loader): global_step += 1 if global_step == 200000: for param_group in optimizer.param_groups: param_group['learning_rate'] *= 0.5 state['learning_rate'] = param_group['learning_rate'] if global_step == 400000: for param_group in optimizer.param_groups: param_group['learning_rate'] *= 0.5 state['learning_rate'] = param_group['learning_rate'] if global_step == 600000: for param_group in optimizer.param_groups: param_group['learning_rate'] *= 0.5 state['learning_rate'] = param_group['learning_rate'] x, y, c = x.to(device), y.to(device), c.to(device) q_0 = Normal(x.new_zeros(x.size()), x.new_ones(x.size())) z = q_0.sample() optimizer.zero_grad() c_up = model_t.upsample(c) x_student, mu_s, logs_s = model_s(z, c_up) # q_T ~ N(mu_tot, logs_tot.exp_()) mu_logs_t = model_t(x_student, c) if args.KL_type == 'pq': loss_t, loss_KL, loss_reg = criterion_t(mu_logs_t[:, 0:1, :-1], mu_logs_t[:, 1:, :-1], mu_s, logs_s) elif args.KL_type == 'qp': loss_t, loss_KL, loss_reg = criterion_t(mu_s, logs_s, mu_logs_t[:, 0:1, :-1], mu_logs_t[:, 1:, :-1]) stft_student, _ = stft(x_student[:, :, 1:]) stft_truth, _ = stft(x[:, :, 1:]) loss_frame = criterion_frame(stft_student, stft_truth) loss_tot = loss_t + loss_frame loss_tot.backward() nn.utils.clip_grad_norm_(model_s.parameters(), 10) optimizer.step() if ema is not None: for name, param in model_s.named_parameters(): if name in ema.shadow: ema.update(name, param.data) running_loss[0] += loss_tot.item() / display_step running_loss[1] += loss_KL.item() / display_step running_loss[2] += loss_reg.item() / display_step running_loss[3] += loss_frame.item() / display_step epoch_loss += loss_tot.item() if (batch_idx + 1) % display_step == 0: end_time = time.time() print('Global Step : {}, [{}, {}] [Total Loss, KL Loss, Reg Loss, Frame Loss] : {}' .format(global_step, epoch, batch_idx + 1, np.array(running_loss))) print('{} Step Time : {}'.format(display_step, end_time - start_time)) start_time = time.time() running_loss = [0.0, 0.0, 0.0, 0.0] del loss_tot, loss_frame, loss_KL, loss_reg, loss_t, x, y, c, c_up, stft_student, stft_truth, q_0, z del x_student, mu_s, logs_s, mu_logs_t print('{} Epoch Training Loss : {:.4f}'.format(epoch, epoch_loss / (len(train_loader)))) return epoch_loss / len(train_loader) def evaluate(model_t, model_s, ema=None): if ema is not None: model_s_ema = clone_as_averaged_model(model_s, ema) model_t.eval() model_s_ema.eval() running_loss = [0., 0., 0., 0.] epoch_loss = 0. display_step = 100 for batch_idx, (x, y, c, _) in enumerate(test_loader): x, y, c = x.to(device), y.to(device), c.to(device) q_0 = Normal(x.new_zeros(x.size()), x.new_ones(x.size())) z = q_0.sample() c_up = model_t.upsample(c) x_student, mu_s, logs_s = model_s_ema(z, c_up) mu_logs_t = model_t(x_student, c) if args.KL_type == 'pq': loss_t, loss_KL, loss_reg = criterion_t(mu_logs_t[:, 0:1, :-1], mu_logs_t[:, 1:, :-1], mu_s, logs_s) elif args.KL_type == 'qp': loss_t, loss_KL, loss_reg = criterion_t(mu_s, logs_s, mu_logs_t[:, 0:1, :-1], mu_logs_t[:, 1:, :-1]) stft_student, _ = stft(x_student[:, :, 1:]) stft_truth, _ = stft(x[:, :, 1:]) loss_frame = criterion_frame(stft_student, stft_truth.detach()) loss_tot = loss_t + loss_frame running_loss[0] += loss_tot.item() / display_step running_loss[1] += loss_KL.item() / display_step running_loss[2] += loss_reg.item() / display_step running_loss[3] += loss_frame.item() / display_step epoch_loss += loss_tot.item() if (batch_idx + 1) % display_step == 0: print('{} [Total, KL, Reg, Frame Loss] : {}'.format(batch_idx + 1, np.array(running_loss))) running_loss = [0., 0., 0., 0.] del loss_tot, loss_frame, loss_KL, loss_reg, loss_t, x, y, c, c_up, stft_student, stft_truth, q_0, z del x_student, mu_s, logs_s, mu_logs_t epoch_loss /= len(test_loader) print('Evaluation Loss : {:.4f}'.format(epoch_loss)) del model_s_ema return epoch_loss def synthesize(model_t, model_s, ema=None): global global_step if ema is not None: model_s_ema = clone_as_averaged_model(model_s, ema) model_s_ema.eval() for batch_idx, (x, y, c, _) in enumerate(synth_loader): if batch_idx == 0: x, c = x.to(device), c.to(device) q_0 = Normal(x.new_zeros(x.size()), x.new_ones(x.size())) z = q_0.sample() wav_truth_name = '{}/{}/{}/generate_{}_{}_truth.wav'.format(args.sample_path, args.teacher_name, args.model_name, global_step, batch_idx) librosa.output.write_wav(wav_truth_name, y.squeeze().numpy(), sr=22050) print('{} Saved!'.format(wav_truth_name)) torch.cuda.synchronize() start_time = time.time() c_up = model_t.upsample(c) with torch.no_grad(): y_gen = model_s_ema.generate(z, c_up).squeeze() torch.cuda.synchronize() print('{} seconds'.format(time.time() - start_time)) wav = y_gen.to(torch.device("cpu")).data.numpy() wav_name = '{}/{}/{}/generate_{}_{}.wav'.format(args.sample_path, args.teacher_name, args.model_name, global_step, batch_idx) librosa.output.write_wav(wav_name, wav, sr=22050) print('{} Saved!'.format(wav_name)) del y_gen, wav, x, y, c, c_up, z, q_0 del model_s_ema def save_checkpoint(model, optimizer, global_step, global_epoch, ema=None): checkpoint_path = os.path.join(args.save, args.teacher_name, args.model_name, "checkpoint_step{:09d}.pth".format(global_step)) optimizer_state = optimizer.state_dict() torch.save({"state_dict": model.state_dict(), "optimizer": optimizer_state, "global_step": global_step, "global_epoch": global_epoch}, checkpoint_path) if ema is not None: averaged_model = clone_as_averaged_model(model, ema) checkpoint_path = os.path.join(args.save, args.teacher_name, args.model_name, "checkpoint_step{:09d}_ema.pth".format(global_step)) torch.save({"state_dict": averaged_model.state_dict(), "optimizer": optimizer_state, "global_step": global_step, "global_epoch": global_epoch}, checkpoint_path) def load_checkpoint(step, model_s, optimizer, ema=None): global global_step global global_epoch checkpoint_path = os.path.join(args.save, args.teacher_name, args.model_name, "checkpoint_step{:09d}.pth".format(step)) print("Load checkpoint from: {}".format(checkpoint_path)) checkpoint = torch.load(checkpoint_path) model_s.load_state_dict(checkpoint["state_dict"]) optimizer.load_state_dict(checkpoint["optimizer"]) global_step = checkpoint["global_step"] global_epoch = checkpoint["global_epoch"] if ema is not None: checkpoint_path = os.path.join(args.save, args.teacher_name, args.model_name, "checkpoint_step{:09d}_ema.pth".format(step)) checkpoint = torch.load(checkpoint_path) averaged_model = build_student() averaged_model.to(device) averaged_model.load_state_dict(checkpoint["state_dict"]) for name, param in averaged_model.named_parameters(): if param.requires_grad: ema.register(name, param.data) return model_s, optimizer, ema def load_teacher_checkpoint(path, model_t): print("Load checkpoint from: {}".format(path)) checkpoint = torch.load(path, map_location=lambda storage, loc: storage) model_t.load_state_dict(checkpoint["state_dict"]) return model_t if __name__ == "__main__": torch.backends.cudnn.benchmark = True np.set_printoptions(precision=4) parser = argparse.ArgumentParser(description='Train WaveNet of LJSpeech', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--data_path', type=str, default='../DATASETS/ljspeech/', help='Dataset Path') parser.add_argument('--sample_path', type=str, default='../samples', help='Sample Path') parser.add_argument('--save', '-s', type=str, default='../params', help='Folder to save checkpoints.') parser.add_argument('--load', '-l', type=str, default='../params', help='Checkpoint path to resume / test.') parser.add_argument('--loss', type=str, default='../loss', help='Folder to save loss') parser.add_argument('--log', type=str, default='../log', help='Log folder.') parser.add_argument('--teacher_name', type=str, default='wavenet_gaussian_01', help='Model Name') parser.add_argument('--model_name', type=str, default='wavenet_student_gaussian_01', help='Model Name') parser.add_argument('--teacher_load_step', type=int, default=0, help='Teacher Load Step') parser.add_argument('--load_step', type=int, default=0, help='Student Load Step') parser.add_argument('--KL_type', type=str, default='qp', help='KL_pq vs KL_qp') parser.add_argument('--epochs', '-e', type=int, default=1000, help='Number of epochs to train.') parser.add_argument('--batch_size', '-b', type=int, default=4, help='Batch size.') parser.add_argument('--learning_rate', '-lr', type=float, default=1e-3, help='The Learning Rate.') parser.add_argument('--ema_decay', type=float, default=0.9999, help='Exponential Moving Average Decay') parser.add_argument('--num_blocks_t', type=int, default=4, help='Number of blocks (Teacher)') parser.add_argument('--num_layers_t', type=int, default=6, help='Number of layers (Teacher)') parser.add_argument('--num_layers_s', type=int, default=6, help='Number of layers (Student)') parser.add_argument('--residual_channels', type=int, default=128, help='Residual Channels') parser.add_argument('--gate_channels', type=int, default=256, help='Gate Channels') parser.add_argument('--skip_channels', type=int, default=128, help='Skip Channels') parser.add_argument('--kernel_size', type=int, default=3, help='Kernel Size') parser.add_argument('--cin_channels', type=int, default=80, help='Cin Channels') parser.add_argument('--num_workers', type=int, default=3, help='Number of workers') args = parser.parse_args() # Init logger if not os.path.isdir(args.log): os.makedirs(args.log) # Checkpoint dir if not os.path.isdir(args.save): os.makedirs(args.save) if not os.path.isdir(args.loss): os.makedirs(args.loss) if not os.path.isdir(os.path.join(args.save, args.teacher_name)): os.makedirs(os.path.join(args.save, args.teacher_name)) if not os.path.isdir(os.path.join(args.save, args.teacher_name, args.model_name)): os.makedirs(os.path.join(args.save, args.teacher_name, args.model_name)) if not os.path.isdir(os.path.join(args.sample_path, args.teacher_name)): os.makedirs(os.path.join(args.sample_path, args.teacher_name)) if not os.path.isdir(os.path.join(args.sample_path, args.teacher_name, args.model_name)): os.makedirs(os.path.join(args.sample_path, args.teacher_name, args.model_name)) use_cuda = torch.cuda.is_available() device = torch.device("cuda" if use_cuda else "cpu") # LOAD DATASETS train_dataset = LJspeechDataset(args.data_path, True, 0.1) test_dataset = LJspeechDataset(args.data_path, False, 0.1) train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, collate_fn=collate_fn, num_workers=args.num_workers, pin_memory=True) test_loader = DataLoader(test_dataset, batch_size=args.batch_size, collate_fn=collate_fn, num_workers=args.num_workers) synth_loader = DataLoader(test_dataset, batch_size=1, collate_fn=collate_fn_synthesize, num_workers=args.num_workers, pin_memory=True) teacher_step = args.teacher_load_step path = os.path.join(args.load, args.teacher_name, "checkpoint_step{:09d}_ema.pth".format(teacher_step)) model_t = build_model() model_t = load_teacher_checkpoint(path, model_t) model_s = build_student() stft = STFT(filter_length=1024, hop_length=256) model_t.to(device) model_s.to(device) stft.to(device) optimizer = optim.Adam(model_s.parameters(), lr=args.learning_rate) criterion_t = KL_Loss() criterion_frame = nn.MSELoss() ema = ExponentialMovingAverage(args.ema_decay) for name, param in model_s.named_parameters(): if param.requires_grad: ema.register(name, param.data) for name, param in model_t.named_parameters(): if param.requires_grad: param.requires_grad = False global_step, global_epoch = 0, 0 load_step = args.load_step log = open(os.path.join(args.log, '{}.txt'.format(args.model_name)), 'w') state = {k: v for k, v in args._get_kwargs()} if load_step == 0: list_train_loss, list_loss = [], [] log.write(json.dumps(state) + '\n') test_loss = 100.0 else: model_s, optimizer, ema = load_checkpoint(load_step, model_s, optimizer, ema) list_train_loss = np.load('{}/{}_train.npy'.format(args.loss, args.model_name)).tolist() list_loss = np.load('{}/{}.npy'.format(args.loss, args.model_name)).tolist() list_train_loss = list_train_loss[:global_epoch] list_loss = list_loss[:global_epoch] test_loss = np.min(list_loss) for epoch in range(global_epoch + 1, args.epochs + 1): training_epoch_loss = train(epoch, model_t, model_s, optimizer, ema) with torch.no_grad(): test_epoch_loss = evaluate(model_t, model_s, ema) state['training_loss'] = training_epoch_loss state['eval_loss'] = test_epoch_loss state['epoch'] = epoch list_train_loss.append(training_epoch_loss) list_loss.append(test_epoch_loss) if test_loss > test_epoch_loss: test_loss = test_epoch_loss save_checkpoint(model_s, optimizer, global_step, epoch, ema) print('Epoch {} Model Saved! Loss : {:.4f}'.format(epoch, test_loss)) synthesize(model_t, model_s, ema) np.save('{}/{}_train.npy'.format(args.loss, args.model_name), list_train_loss) np.save('{}/{}.npy'.format(args.loss, args.model_name), list_loss) log.write('%s\n' % json.dumps(state)) log.flush() print(state) log.close()

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null

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f0ae863ab9a27628499b3a102924f3d1a73f6ab0

5,647

py

Python

_GTW/_OMP/_SWP/Picture.py

Tapyr/tapyr

4235fba6dce169fe747cce4d17d88dcf4a3f9f1d

[ "BSD-3-Clause" ]

6

2016-12-10T17:51:10.000Z

2021-10-11T07:51:48.000Z

_GTW/_OMP/_SWP/Picture.py

Tapyr/tapyr

4235fba6dce169fe747cce4d17d88dcf4a3f9f1d

[ "BSD-3-Clause" ]

null

_GTW/_OMP/_SWP/Picture.py

Tapyr/tapyr

4235fba6dce169fe747cce4d17d88dcf4a3f9f1d

[ "BSD-3-Clause" ]

3

2020-03-29T07:37:03.000Z

2021-01-21T16:08:40.000Z

# -*- coding: utf-8 -*- # Copyright (C) 2010-2015 Mag. Christian Tanzer All rights reserved # Glasauergasse 32, A--1130 Wien, Austria. tanzer@swing.co.at # **************************************************************************** # This module is part of the package GTW.OMP.SWP. # # This module is licensed under the terms of the BSD 3-Clause License # <http://www.c-tanzer.at/license/bsd_3c.html>. # **************************************************************************** # #++ # Name # GTW.OMP.SWP.Picture # # Purpose # Model a picture that can be displayed on a web page # # Revision Dates # 22-Mar-2010 (CT) Creation # 13-Oct-2010 (CT) `example` added # 5-Sep-2011 (CT) `width.max_value` increased from 1000 to 1200 # 22-Sep-2011 (CT) s/C_Type/P_Type/ for _A_Composite_ attributes # 18-Nov-2011 (CT) Import `unicode_literals` from `__future__` # 30-Jan-2013 (MG) Make `extension` changeable, change min values for # width and height # 31-Jan-2013 (MG) change kind of `extension` to `Optional` # 15-May-2013 (CT) Replace `auto_cache` by `rev_ref_attr_name` # 22-May-2013 (CT) Change `max_value` of `height` and `width` to 1280 # 30-Oct-2013 (CT) Remove unnecessary `Picture.left.rev_ref_attr_name` # 25-Nov-2015 (CT) Change `_Pic_.path` from `A_String` to `A_Text` # * don't want a restrictive `max_length` # ««revision-date»»··· #-- from _MOM.import_MOM import * from _GTW import GTW import _GTW._OMP._SWP.Gallery from _MOM._Attr.A_2D import A_2D_Int, D2_Value_Int from _TFL import sos from _TFL.I18N import _, _T, _Tn _Ancestor_Essence = D2_Value_Int class _Pic_ (_Ancestor_Essence) : """Model a picture""" class _Attributes (_Ancestor_Essence._Attributes) : _Ancestor = _Ancestor_Essence._Attributes class dir (A_String) : """Directory in gallery holding pictures.""" kind = Attr.Const default = "im" # end class dir class extension (A_String) : """Extension of file holding picture.""" kind = Attr.Optional Kind_Mixins = (Attr.Init_Only_Mixin, ) max_length = 10 default = ".jpg" # end class extension class height (_Ancestor.y) : """Height of picture.""" max_value = 1280 min_value = 200 # end class height class path (A_Text) : """Path of file holding picture.""" kind = Attr.Computed def computed (self, obj) : owner = obj.owner if owner : p = sos.path.join \ (owner.gallery.directory, obj.dir, owner.name) return p + obj.extension # end def computed # end class path class width (_Ancestor.x) : """Width of picture.""" max_value = 1280 min_value = 200 # end class width # end class _Attributes # end class _Pic_ _Ancestor_Essence = _Pic_ class _Thumb_ (_Ancestor_Essence) : """Model a thumbnail of a picture.""" class _Attributes (_Ancestor_Essence._Attributes) : _Ancestor = _Ancestor_Essence._Attributes class dir (_Ancestor.dir) : """Directory in gallery holding thumbnails.""" default = "th" example = "th" # end class dir class height (_Ancestor.height) : max_value = 200 min_value = 50 # end class height class width (_Ancestor.width) : max_value = 200 min_value = 50 # end class width # end class _Attributes # end class _Thumb_ _Ancestor_Essence = GTW.OMP.SWP.Link1 class Picture (_Ancestor_Essence) : """Model a picture that can be displayed on a web page.""" class _Attributes (_Ancestor_Essence._Attributes) : _Ancestor = _Ancestor_Essence._Attributes ### Primary attributes class left (_Ancestor.left) : """Gallery to which this picture belongs.""" role_type = GTW.OMP.SWP.Gallery # end class left class number (A_Int) : """Number of picture in gallery.""" kind = Attr.Primary check = ("value >= 0", ) # end class number ### Non-primary attributes class name (A_String) : kind = Attr.Optional Kind_Mixins = (Attr.Computed_Set_Mixin, ) max_length = 100 def computed (self, obj) : if obj.number is not None : return "%4.4d" % obj.number # end def computed # end class name class photo (A_2D_Int) : """Picture.""" kind = Attr.Necessary P_Type = _Pic_ typ = "Picture" # end class photo class thumb (A_2D_Int) : """Thumbnail""" kind = Attr.Necessary P_Type = _Thumb_ typ = "Thumbnail" # end class thumb # end class _Attributes # end class Picture if __name__ != "__main__" : GTW.OMP.SWP._Export ("*") ### __END__ GTW.OMP.SWP.Picture

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null

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f0b371551aa30a140c9ee0200648fe4590ea0606

2,432

py

Python

util/common/readParams.py

xinglun/TestFramework

c262599cd563d1aed4ddf79a1860748fb498fdd4

[ "MIT" ]

null

util/common/readParams.py

xinglun/TestFramework

c262599cd563d1aed4ddf79a1860748fb498fdd4

[ "MIT" ]

null

util/common/readParams.py

xinglun/TestFramework

c262599cd563d1aed4ddf79a1860748fb498fdd4

[ "MIT" ]

null

import re from util.yaml.yaml_util import YamlUtil from util.randomData import choiceData,getTime,randomInt,randomFloat,randomString def read_param(value): # re int_list = re.findall('\\$randomInt\$([0-9]*,[0-9]*?)\$\\$', value) string_list = re.findall('\\$randomString\$([0-9]*?)\$\\$', value) float_list = re.findall("\\$randomFloat\$([0-9]*,[0-9]*,[0-9]*)\$\\$", value) time_list = re.findall("\\$getTime\$time_type=(.*?),layout=(.*?),unit=([0-9],[0-9],[0-9],[0-9],[0-9])\$\\$", value) choice_list = re.findall("\\$choiceData\$((?!\\$Choice\\().*?)\$\\$", value) config_list = re.findall("\\$getConfigData\$(.*?)\$\\$", value) # init var if len(int_list): for i in int_list: pattern = re.compile('\\$randomInt\$' + i + '\$\\$') k = str(randomInt.random_int(i)) value = re.sub(pattern, k, value, count=1) value = read_param(value) elif len(string_list): # 获取字符串替换 for j in string_list: pattern = re.compile('\\$RandomString\$' + j + '\$\\$') k = randomString.random_string(j) value = re.sub(pattern, k, value, count=1) value = read_param(value) elif len(float_list): # 获取浮点数 for n in float_list: if len(n.split(",")) == 3: pattern = re.compile('\\$RandomFloat\$' + n + '\$\\$') k = str(randomFloat.random_float(n)) value = re.sub(pattern, k, value, count=1) value = read_param(value) elif len(time_list): # 获取时间替换 for n in time_list: if len(n[0]) and len(n[1]): pattern = re.compile('\\$GetTime\$time_type='+n[0]+',layout='+n[1]+',unit='+n[2]+'\$\\$') k = str(getTime.get_time(n[0], n[1], n[2])) value = re.sub(pattern, k, value, count=1) value = read_param(value) elif len(choice_list): # 调用choice方法 for n in choice_list: pattern = re.compile('\\$choiceData\$' + n + '\$list\\$') k = str(choiceData.choice_data(n)) value = re.sub(pattern, k, value, count=1) value = read_param(value) else: for n in config_list: pattern = re.compile('\\$getConfigData\$' + n + '\$\\$') k = YamlUtil().read_config_yaml_item(n) print(k) value = k return value

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0.020408

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null

0

null

0

1

0

f0b5158379c142f1483c71cf1930f5a16ca17ba6

9,367

py

Python

rl/augmentations/augmentations.py

Luca96/carla-driving-rl-agent

00ae9ec6dc61f82ecd19e96b6c1a5e1903911e62

[ "MIT" ]

26

2021-01-27T21:42:17.000Z

2022-03-31T08:46:30.000Z

rl/augmentations/augmentations.py

Martje55555/carla-rl-agent

0d38cc3080cab900f4eaa3cd4735918c5868103a

[ "MIT" ]

9

2021-05-21T14:50:57.000Z

2022-03-25T17:50:03.000Z

rl/augmentations/augmentations.py

Martje55555/carla-rl-agent

0d38cc3080cab900f4eaa3cd4735918c5868103a

[ "MIT" ]

10

2021-03-23T14:10:14.000Z

2022-03-24T17:49:12.000Z

"""Data augmentations based on tf's functions""" import tensorflow as tf from typing import Union, List, Tuple from rl import utils Size = Union[List[int], Tuple[int, ...], tf.TensorShape] # ------------------------------------------------------------------------------------------------- # -- Geometric/Spatial Augmentations # ------------------------------------------------------------------------------------------------- def tf_resize(image, size: Size): return tf.image.resize(image, size) def tf_crop(image, size: Size, resize=False, seed=None): cropped = tf.image.random_crop(image, size, seed=seed) if resize: return tf_resize(cropped, size=image.shape[:2]) return cropped def tf_flip(image, horizontal=True, vertical=False, seed=None): if horizontal: image = tf.image.random_flip_left_right(image, seed=seed) if vertical: image = tf.image.random_flip_up_down(image, seed=seed) return image def tf_quality(image, min_quality: int, max_quality: int, seed=None): return tf.image.random_jpeg_quality(image, min_jpeg_quality=min_quality, max_jpeg_quality=max_quality, seed=seed) @tf.function def tf_cutout(image, size=5, seed=None): cut_mask = tf.random.normal(shape=(size, size), seed=seed) cut_mask = tf.where(condition=cut_mask == tf.reduce_max(cut_mask), x=0.0, y=1.0) cut_mask = tf.stack((cut_mask,) * 3, axis=-1) cut_mask = tf.image.resize([cut_mask], size=image.shape[:2], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)[0] return image * cut_mask @tf.function def tf_cutout_batch(images, size=5, seed=None): masks = [] for _ in range(images.shape[0]): cut_mask = tf.random.normal(shape=(size, size), seed=seed) cut_mask = tf.where(condition=cut_mask == tf.reduce_max(cut_mask), x=0.0, y=1.0) cut_mask = tf.stack((cut_mask,) * 3, axis=-1) masks.append(cut_mask) masks = tf.stack(masks, axis=0) masks = tf.image.resize(masks, size=images.shape[1:3], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)[0] return images * masks @tf.function def tf_coarse_dropout(image, size=25, amount=0.1, seed=None): drop_mask = tf.keras.backend.random_binomial((size, size), p=1.0 - amount, seed=seed) drop_mask = tf.stack((drop_mask,) * 3, axis=-1) drop_mask = tf.image.resize([drop_mask], size=image.shape[:2], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)[0] return image * drop_mask @tf.function def tf_coarse_dropout_batch(images, size=25, amount=0.1, seed=None): masks = [] for _ in range(images.shape[0]): drop_mask = tf.keras.backend.random_binomial((size, size), p=1.0 - amount, seed=seed) drop_mask = tf.stack((drop_mask,) * 3, axis=-1) masks.append(drop_mask) masks = tf.stack(masks, axis=0) masks = tf.image.resize(masks, size=images.shape[1:3], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)[0] return images * masks def tf_rotate(image, degrees=90): assert degrees % 90 == 0 return tf.image.rot90(image, k=degrees // 90) # ------------------------------------------------------------------------------------------------- # -- Appearance Augmentations # ------------------------------------------------------------------------------------------------- def tf_saturation(image, lower=0.5, upper=1.5, seed=None): return tf.image.random_saturation(image, lower, upper, seed=seed) def tf_contrast(image, lower=0.4, upper=1.6, seed=None): return tf.image.random_contrast(image, lower, upper, seed=seed) def tf_brightness(image, delta=0.75, seed=None): return tf.image.random_brightness(image, max_delta=delta, seed=seed) def tf_hue(image, delta=0.5, seed=None): return tf.image.random_hue(image, max_delta=delta, seed=seed) def tf_grayscale(rgb_image): return tf.image.rgb_to_grayscale(rgb_image) def tf_rgb(gray_image): return tf.image.grayscale_to_rgb(gray_image) @tf.function def tf_gaussian_noise(image, amount=0.25, std=0.2, seed=None): mask_select = tf.keras.backend.random_binomial(image.shape[:2], p=amount, seed=seed) mask_select = tf.stack((mask_select,) * 3, axis=-1) mask_noise = tf.random.normal(shape=image.shape, stddev=std, seed=seed) mask_noise = tf.clip_by_value(mask_noise, 0.0, 1.0) return image + (mask_select * mask_noise) @tf.function def tf_gaussian_noise_batch(images, amount=0.25, std=0.2, seed=None): masks = [] for _ in range(images.shape[0]): mask_select = tf.keras.backend.random_binomial(images.shape[1:3], p=amount, seed=seed) mask_select = tf.stack((mask_select,) * 3, axis=-1) mask_noise = tf.random.normal(shape=images.shape[1:], stddev=std, seed=seed) masks.append(tf.clip_by_value(mask_select * mask_noise, 0.0, 1.0)) return images + tf.stack(masks, axis=0) @tf.function def tf_salt_and_pepper(image, amount=0.1, prob=0.5, seed=None): # source: https://stackoverflow.com/questions/55653940/how-do-i-implement-salt-pepper-layer-in-keras mask_select = tf.keras.backend.random_binomial(image.shape[:2], p=amount / 10, seed=seed) mask_select = tf.stack((mask_select,) * 3, axis=-1) mask_noise = tf.keras.backend.random_binomial(image.shape[:2], p=prob, seed=seed) mask_noise = tf.stack((mask_noise,) * 3, axis=-1) return image * (1 - mask_select) + mask_noise * mask_select @tf.function def tf_salt_and_pepper_batch(images, amount=0.1, prob=0.5, seed=None): # source: https://stackoverflow.com/questions/55653940/how-do-i-implement-salt-pepper-layer-in-keras masks_select = [] masks_noise = [] for _ in range(images.shape[0]): mask_select = tf.keras.backend.random_binomial(images.shape[1:3], p=amount / 10, seed=seed) mask_select = tf.stack((mask_select,) * 3, axis=-1) masks_select.append(mask_select) mask_noise = tf.keras.backend.random_binomial(images.shape[1:3], p=prob, seed=seed) mask_noise = tf.stack((mask_noise,) * 3, axis=-1) masks_noise.append(mask_noise) mask_select = tf.stack(masks_select, axis=0) mask_noise = tf.stack(masks_noise, axis=0) return images * (1 - mask_select) + mask_noise * mask_select @tf.function def tf_gaussian_blur(image, size=5, std=0.25, seed=None): # source: https://gist.github.com/blzq/c87d42f45a8c5a53f5b393e27b1f5319 gaussian_kernel = tf.random.normal(shape=(size, size, image.shape[-1], 1), mean=1.0, stddev=std, seed=seed) if len(image.shape) == 3: image = tf.expand_dims(image, axis=0) image = tf.nn.depthwise_conv2d(image, gaussian_kernel, [1, 1, 1, 1], padding='SAME', data_format='NHWC')[0] else: image = tf.nn.depthwise_conv2d(image, gaussian_kernel, [1, 1, 1, 1], padding='SAME', data_format='NHWC') return image @tf.function def tf_median_blur(image, size=5): median_kernel = tf.ones((size, size, image.shape[-1], 1)) if len(image.shape) == 3: image = tf.expand_dims(image, axis=0) image = tf.nn.depthwise_conv2d(image, median_kernel, [1, 1, 1, 1], padding='SAME', data_format='NHWC')[0] else: image = tf.nn.depthwise_conv2d(image, median_kernel, [1, 1, 1, 1], padding='SAME', data_format='NHWC') return image @tf.function def tf_multiply_channels(image, strength=1.0, seed=None): """Channel-wise multiplication of given image by random scalars. The scalars sum to one, each scalar multiplies an entire channel """ assert len(image.shape) == 3 logits = tf.random.uniform(shape=(image.shape[2],), minval=-1, maxval=1, seed=seed) alpha = tf.nn.softmax(logits) * strength return tf_normalize(image * alpha) @tf.function def tf_sobel(image, grayscale=False, restore_depth=True, normalize=True): """Applies Sobel filtering""" if grayscale: depth = image.shape[2] image = tf_grayscale(image) image = tf.image.sobel_edges(tf.expand_dims(image, axis=0)) dx, dy = tf.unstack(image[0], axis=-1) result = dx + dy if grayscale and restore_depth: result = tf_repeat_channels(result, n=depth) if normalize: return tf_normalize(result) return result # ------------------------------------------------------------------------------------------------- @tf.function def tf_normalize(image, eps=utils.EPSILON): """Scales the given image in range [0.0, 1.0]""" image -= tf.reduce_min(image) image /= tf.reduce_max(image) + eps return image @tf.function def tf_normalize_batch(images): return tf.map_fn(fn=tf_normalize, elems=images) def tf_chance(seed=None): """Use to get a single random number between 0 and 1""" return tf.random.uniform(shape=(1,), minval=0.0, maxval=1.0, seed=seed) @tf.function def tf_repeat_channels(image, n=3): if len(image.shape) == 2: return tf.stack((image,) * n, axis=-1) return tf.concat((image,) * n, axis=-1) def tf_scale_shape(image, scale: Tuple[float, float]): h, w, d = image.shape return utils.to_int((h * scale[0], w * scale[0], d)) def tf_size(image): return image.shape[:2]

33.33452

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4.210059

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0.034259

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0.569396

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0.421293

0.404076

0.382994

0

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0.185118

9,367

280

118

33.453571

0.715839

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0

0.388235

0

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0

0.011765

1

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false

0

0.017647

0.058824

0.376471

0

null

0

null

0

1

0

f0b6251dcba60014a19dc34f004de3d228f64666

17,771

py

Python

invsolve/measure.py

danassutula/biomech-inverse

4ee415f181e815085660dfe722bd861c99da0cd9

[ "MIT" ]

2

2020-08-09T08:48:28.000Z

2020-10-07T22:05:51.000Z

invsolve/measure.py

danassutula/biomech-inverse

4ee415f181e815085660dfe722bd861c99da0cd9

[ "MIT" ]

null

invsolve/measure.py

danassutula/biomech-inverse

4ee415f181e815085660dfe722bd861c99da0cd9

[ "MIT" ]

1

2019-11-25T17:25:23.000Z

''' For converting a sequence of measurements into a measurement expression. ''' import numpy as np from dolfin import Function from dolfin import UserExpression from matplotlib.tri import Triangulation from matplotlib.tri import LinearTriInterpolator SEQUENCE_TYPES = (tuple, list) def make_measurement_setter_with_time_as_argument(*args): '''Make a measurement setter function for expressions. Parameters ---------- args : MeasurementExpressionBase Measurement expressions. Returns ------- measurement_setter : function(t:float) Measurement setting function. ''' if not all(isinstance(arg, MeasurementExpressionBase) for arg in args): raise TypeError('`args` must have base type `MeasurementExpressionBase`.') def measurement_setter(t:float): '''Set all measurements at time.''' for arg in args: arg.at_time(t) return measurement_setter def make_measurement_setter_with_index_as_argument(*args): '''Make a measurement setter function for expressions. Parameters ---------- args : MeasurementExpressionBase Measurement expressions. Returns ------- measurement_setter : function(i:int) Measurement setting function. ''' if not all(isinstance(arg, MeasurementExpressionBase) for arg in args): raise TypeError('`args` must have base type `MeasurementExpressionBase`.') def measurement_setter(i:int): '''Set all measurements at index.''' for arg in args: arg.at_index(i) return measurement_setter def measurement_expression(f_msr, t_msr=None, degree=None): '''Return a suitable measurement expression for the type of parameters. Parameters ---------- f_msr : sequence of dolfin.Function's or numpy.ndarray's Sequence of measurement snapshots. t_msr : a sequence of ascending values or a single value (optional) Measurement times. Could be a sequence of values for the measurement snapshots, or a sequence of two values for the first time and the last time of the snapshots, or a single value for the last time. ''' if not isinstance(f_msr, (list, tuple, np.ndarray)): raise TypeError('Expecting parameter `f_msr` to be a ' 'sequence of measurement snapshots.') if all(isinstance(f_msr_i, Function) for f_msr_i in f_msr): return MeasurementExpressionFromFunctions(f_msr, t_msr, degree= \ f_msr[0].ufl_element().degree() if degree is None else degree) elif all(isinstance(f_msr_i, np.ndarray) for f_msr_i in f_msr): return MeasurementExpressionFromArrays(f_msr, t_msr, degree=0) elif all(isinstance(f_msr_i, (float, int)) for f_msr_i in f_msr): return MeasurementExpressionFromScalars(f_msr, t_msr, degree=0) else: raise TypeError('Expected parameter `f_msr` to be a sequence of ' 'either `dolfin.Function`s or `numpy.ndarray`s.') class MeasurementExpressionBase(UserExpression): _msr_rtol = 1e-14 def __new__(cls, *args, **kwargs): '''Must be extended by deriving class.''' if 'degree' not in kwargs and 'element' not in kwargs: raise TypeError('Require `degree` or `element` as keyword argument.') self = super().__new__(cls) self._ufl_shape = None return self def __init__(self, f_msr, t_msr=None, **kwargs): '''Must be extended by deriving class. Parameters ---------- f_msr : sequence of object's Sequence of measurement snapshots. t_msr : a sequence of ascending values or a single value (optional) Measurement times. Could be a sequence of values for the measurement snapshots, or a sequence of two values for the first time and the last time of the snapshots, or a single value for the last time. Keyword Parameters ------------------ degree : int The `degree` must be given if no `element` is given. element : dolfin.Element (optional) The `element` must be given if no `degree` is given. ''' # Must initialize base class super().__init__(**kwargs) n_msr = len(f_msr) if t_msr is None: t_msr = tuple(np.linspace(0, 1, n_msr, dtype=float)) elif not hasattr(t_msr, '__getitem__'): t_msr = tuple(np.linspace(0, t_msr, n_msr, dtype=float)) else: if not all(t_i < t_j for t_i, t_j in zip(t_msr[:-1], t_msr[1:])): raise TypeError('Parameter `t_msr` must be an ascending sequence.') if len(t_msr) == 2: t_msr = tuple(np.linspace(t_msr[0], t_msr[1], n_msr, dtype=float)) elif len(t_msr) != n_msr: raise TypeError('Parameter `t_msr` is incompatible with `f_msr`.') if len(t_msr) > 1: self._msr_atol = self._msr_rtol * (t_msr[-1] - t_msr[0]) else: self._msr_atol = self._msr_rtol self._msr_f_msr = f_msr if isinstance(f_msr, tuple) else tuple(f_msr) self._msr_t_msr = t_msr if isinstance(t_msr, tuple) else tuple(t_msr) self._msr_n_msr = n_msr self._msr_f_cur = None self._msr_t_cur = t_msr[0] self._msr_i_cur = 0 def __repr__(self): return f'<{self.__class__.__name__} at {hex(id(self))}>' def _msr_index_from_time(self, t, i_start=0): '''Find the index `i` that corresponds to the left of (or at) time `t`. `i_start` can be specified to start the search around index `i_start`, otherwise `i_start=0` and so the search starts from begining.''' while i_start < 0: i_start += self._msr_n_msr if t >= self._msr_t_msr[i_start]: # search to the right of `i_start` if t >= self._msr_t_msr[-2]: # edge case return self._msr_n_msr-2 # NOTE: t >= self._msr_t_msr[i_start] and t < self._msr_t_msr[-2] # hence, first lesser between `i_start+1` and `end-1` return next(i for i, t_j in enumerate( self._msr_t_msr[i_start+1:-1], i_start) if t < t_j) else: # t < self._msr_t_msr[i_start]: # search to the left of `i_start` if t <= self._msr_t_msr[1]: # edge case return 0 # NOTE: t < self._msr_t_msr[i_start] and t > self._msr_t_msr[1] # hence, first greater between `i_start-1` and `0` return next(i_start-i for i, t_j in enumerate( self._msr_t_msr[i_start-1:0:-1], start=1) if t > t_j) def _msr_index_and_weight_from_time(self, t): '''Index and weight of the adjacent left measurement for time `t`.''' if (t < self._msr_t_msr[0]-self._msr_atol or t > self._msr_t_msr[-1]+self._msr_atol): raise ValueError('Measurement time `t` out of range.') i = self._msr_index_from_time(t, self._msr_i_cur) assert (0 <= i < self._msr_n_msr-1), f'i = {i}' w = (self._msr_t_msr[i+1]-t)/(self._msr_t_msr[i+1]-self._msr_t_msr[i]) assert (-self._msr_rtol < w < 1.0 + self._msr_rtol), f'w = {w}' return i, w @property def n_msr(self): '''Number of measurements.''' return self._msr_n_msr @property def t_msr(self): '''All measurement times.''' return self._msr_t_msr @property def f_msr(self): '''All measurement values.''' return self._msr_f_msr def at_index(self, i): '''Set measurement at index.''' raise NotImplementedError def at_time(self, t): '''Set measurement at time.''' raise NotImplementedError def get_index(self): '''Current measurement index.''' return self._msr_i_cur def get_time(self): '''Current measurement time.''' return self._msr_t_cur def get_value(self, copy=True): '''Current measurement value.''' return NotImplementedError def eval(self, value, x): raise NotImplementedError def value_shape(self): return self._ufl_shape class MeasurementExpressionFromFunctions(MeasurementExpressionBase): def __new__(cls, f_msr, *args, **kwargs): self = super().__new__(cls, **kwargs) if not isinstance(f_msr, SEQUENCE_TYPES) or \ not all(isinstance(f, Function) for f in f_msr): raise TypeError('Parameter `f_msr` must be a ' 'sequence of `dolfin.Function`s.') self._ufl_shape = f_msr[0].ufl_shape return self def __init__(self, f_msr, t_msr=None, **kwargs): ''' Parameters ---------- f_msr : sequence of dolfin.Function's. Sequence of measurement snapshots. t_msr : a sequence of ascending values or a single value (optional) Measurement times. Could be a sequence of values for the measurement snapshots, or a sequence of two values for the first time and the last time of the snapshots, or a single value for the last time. Keyword Parameters ------------------ degree : int The `degree` must be given if no `element` is given. element : dolfin.Element (optional) The `element` must be given if no `degree` is given. ''' super().__init__(f_msr, t_msr, **kwargs) self._msr_f_cur = Function.copy(f_msr[0], deepcopy=True) def at_index(self, i): '''Set measurement at index `i`.''' if i < 0: i += self._msr_n_msr try: self._msr_f_cur.vector()[:] = self._msr_f_msr[i].vector() self._msr_i_cur, self._msr_t_cur = i, self._msr_t_msr[i] except IndexError: raise IndexError('Measurement index `i` out of range.') return self def at_time(self, t): '''Set measurement at time `t`.''' # Adjacent left measurement index and weight i, w = self._msr_index_and_weight_from_time(t) self._msr_f_cur.vector()[:] = self._msr_f_msr[i].vector()*w \ + self._msr_f_msr[i+1].vector()*(1.0-w) self._msr_t_cur = t self._msr_i_cur = i return self def get_value(self, copy=True): '''Current measurement value.''' return self._msr_f_cur.copy(True) if copy else self._msr_f_cur def eval(self, value, x): self._msr_f_cur.eval(value, x) class MeasurementExpressionFromArrays(MeasurementExpressionBase): def __new__(cls, f_msr, *args, **kwargs): self = super().__new__(cls, **kwargs) if not hasattr(f_msr, '__getitem__') or \ not all(isinstance(f, np.ndarray) for f in f_msr): raise TypeError('Parameter `f_msr` must be a ' 'sequence of `numpy.ndarray`s.') self._ufl_shape = f_msr[0].shape return self def __init__(self, f_msr, t_msr=None, **kwargs): ''' Parameters ---------- f_msr : sequence of numpy.ndarray's. Sequence of measurement snapshots. t_msr : a sequence of ascending values or a single value (optional) Measurement times. Could be a sequence of values for the measurement snapshots, or a sequence of two values for the first time and the last time of the snapshots, or a single value for the last time. Keyword Parameters ------------------ degree : int The `degree` must be given if no `element` is given. element : dolfin.Element (optional) The `element` must be given if no `degree` is given. ''' super().__init__(f_msr, t_msr, **kwargs) self._msr_f_cur = np.array(f_msr[0], float) def at_index(self, i): '''Set measurement at index `i`.''' if i < 0: i += self._msr_n_msr try: self._msr_f_cur[:] = self._msr_f_msr[i] self._msr_t_cur = self._msr_t_msr[i] self._msr_i_cur = i except IndexError: raise IndexError('Measurement index `i` out of range.') return self def at_time(self, t): '''Set measurement at time `t` by linear interpolation.''' # Adjacent left measurement index and weight i, w = self._msr_index_and_weight_from_time(t) self._msr_f_cur[:] = self._msr_f_msr[i]*w \ + self._msr_f_msr[i+1]*(1.0-w) self._msr_t_cur = t self._msr_i_cur = i return self def get_value(self, copy=True): '''Current measurement value.''' return self._msr_f_cur.copy() if copy else self._msr_f_cur def eval(self, value, x): value[:] = self._msr_f_cur class MeasurementExpressionFromScalars(MeasurementExpressionBase): def __new__(cls, f_msr, *args, **kwargs): self = super().__new__(cls, **kwargs) if not hasattr(f_msr, '__getitem__') or \ not all(isinstance(f, (float, int)) for f in f_msr): raise TypeError('Parameter `f_msr` must be a ' 'sequence of float\'s or int\'s.') self._ufl_shape = () return self def __init__(self, f_msr, t_msr=None, **kwargs): ''' Parameters ---------- f_msr : sequence of reals. Sequence of measurement snapshots. t_msr : a sequence of ascending values or a single value (optional) Measurement times. Could be a sequence of values for the measurement snapshots, or a sequence of two values for the first time and the last time of the snapshots, or a single value for the last time. Keyword Parameters ------------------ degree : int The `degree` must be given if no `element` is given. element : dolfin.Element (optional) The `element` must be given if no `degree` is given. ''' super().__init__(f_msr, t_msr, **kwargs) self._msr_f_cur = f_msr[0] def at_index(self, i): '''Set measurement at index `i`.''' if i < 0: i += self._msr_n_msr try: self._msr_f_cur = self._msr_f_msr[i] self._msr_t_cur = self._msr_t_msr[i] self._msr_i_cur = i except IndexError: raise IndexError('Measurement index `i` out of range.') return self def at_time(self, t): '''Set measurement at time `t` by linear interpolation.''' # Adjacent left measurement index and weight i, w = self._msr_index_and_weight_from_time(t) self._msr_f_cur = self._msr_f_msr[i]*w \ + self._msr_f_msr[i+1]*(1.0-w) self._msr_t_cur = t self._msr_i_cur = i return self def get_value(self, copy=True): '''Current measurement value.''' return self._msr_f_cur def eval(self, value, x): value[:] = self._msr_f_cur # class MeasurementExpressionFromScatters(MeasurementExpressionFromArrays): # # def __new__(cls, x_msr, f_msr, *args, **kwargs): # self = super().__new__(cls, f_msr, *args, **kwargs) # # if any(f_i.ndim != 2 for f_i in f_msr): # raise TypeError('Parameter `f_msr` must contain 2D `numpy.ndarray`s.') # # self._ufl_shape = f_msr[0][0].shape # # return self # # def __init__(self, x_msr, f_msr, t_msr=None, **kwargs): # ''' # # Parameters # ---------- # x_msr : numpy.ndarray (2D) # Coordinates of measurement points. # f_msr : sequence of numpy.ndarray's. # Sequence of measurement snapshots. # t_msr : a sequence of ascending values or a single value (optional) # Measurement times. Could be a sequence of values for the measurement # snapshots, or a sequence of two values for the first time and the # last time of the snapshots, or a single value for the last time. # # Keyword Parameters # ------------------ # degree : int # The `degree` must be given if no `element` is given. # element : dolfin.Element (optional) # The `element` must be given if no `degree` is given. # # ''' # # super().__init__(f_msr, t_msr, **kwargs) # tri = Triangulation(x_msr[:,0], x_msr[:,1]) # self._msr_z_cur = np.empty((len(f_msr[0]),), float) # self.interp = LinearTriInterpolator(tri, self._msr_z_cur) # # # def __repr__(self): # return f'<{self.__class__.__name__} at {hex(id(self))}>' # # def eval(self, value, x): # for i in range(self._ufl_shape[0]): # self._msr_z_cur[:] = self._msr_f_cur[:,i] # value[i] = self.interp(*x).data # # # def LinearInterpolator2D(self, xk, fk): # # self._xk = np.array(xk, order='C') # self._fk = np.array(fk, order='F') # self._zk = np.empty((len(fk),)) # # tri = Triangulation(self._xk[:,0], self._xk[:,1]) # self.interpolator = LinearTriInterpolator(tri, self._zk) # # mesh = dolfin.Mesh() # editor = dolfin.MeshEditor() # # editor.open(mesh, 'triangle', tdim=xk.shape[1], gdim=xk.shape[1]) # editor.init_vertices(len(self._xk)) # editor.init_cells(len(tri.triangles)) # # for i, v_i in enumerate(self._xk): # editor.add_vertex(i, v_i.tolist()) # # for i, c_i in enumerate(tri.triangles): # editor.add_cell(i, c_i.tolist()) # # editor.close()

31.84767

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0.595127

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0.022093

0.681964

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null

0

null

0

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0

f0b7f2b80875da9580a1f340ffc38e5131c56a51

1,834

py

Python

legacy/legacy_scripts/legacy/.ipynb_checkpoints/clean_all_data-checkpoint.py

tomkimpson/ML4L

ffa8360cb80df25bd6af4fa5cc39b42bd6f405cd

[ "MIT" ]

1

2022-02-23T12:31:56.000Z

legacy/legacy_scripts/legacy/.ipynb_checkpoints/clean_all_data-checkpoint.py

tomkimpson/ML4L

ffa8360cb80df25bd6af4fa5cc39b42bd6f405cd

[ "MIT" ]

null

legacy/legacy_scripts/legacy/.ipynb_checkpoints/clean_all_data-checkpoint.py

tomkimpson/ML4L

ffa8360cb80df25bd6af4fa5cc39b42bd6f405cd

[ "MIT" ]

null

import pandas as pd import xarray as xr import numpy as np import sys from config import * def index_level_dtypes(df): return [f"{df.index.names[i]}: {df.index.get_level_values(n).dtype}" for i, n in enumerate(df.index.names)] def process_x_df(df): #Convert to long1 and set the index df['latitude'] = np.round(df.index.get_level_values('latitude').values,3) df['longitude'] = np.round((df.index.get_level_values('longitude').values +180.0) %360.0 - 180.0,3) df['time'] = df.index.get_level_values('time').values print ('X time:') print (np.unique(df['time'])) df = df.set_index(['latitude', 'longitude','time'], drop=True) return df.dropna() def process_y_df(df): #Reindex dfy via a linear shift #---ATTENTION---!> We add a linear shift of 0.0250 such that the coordinates match between the X and Y data # We need to clarify the proper way to deal with this. Perhaps some interpolation method? df['latitude'] = np.round(df.index.get_level_values('y').values - 0.0250,3) df['longitude'] = np.round(df.index.get_level_values('x').values - 0.0250,3) print ('Y time:') print (np.unique(df['time'])) df = df.set_index(['latitude', 'longitude','time'], drop=True) selected_y_columns = ['LST_Day_CMG'] #only use these columns, drop the others df = df[selected_y_columns] return df.dropna() #Load the data cds_xarray = xr.open_dataset(data_root+"xdata.nc") df_x = cds_xarray.to_dataframe() df_y = pd.read_pickle(data_root+'modis.pkl') #Process and clean the data df_x_clean = process_x_df(df_x) df_y_clean = process_y_df(df_y) #Process the X data df_merged = pd.merge(df_x_clean,df_y_clean,how='inner',left_index=True, right_index=True) df_merged.to_pickle(data_root+"df_clean.pkl") print (df_merged)

22.641975

111

0.685387

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1,834

3.896104

0.340909

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0.17121

1,834

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22.925

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0.024691

0

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false

0

0.151515

0.030303

0.333333

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0

null

0

null

0

1

0

f0b7f2d97fdba8a6a8b62217d5ef8ab4b19d2f17

2,788

py

Python

lib/sphinx_exhibit/_offset_annotator.py

anntzer/sphinx-exhibit

5bdb0c41ef5bde3aea72b48e5aebe292696c53c1

[ "MIT" ]

null

lib/sphinx_exhibit/_offset_annotator.py

anntzer/sphinx-exhibit

5bdb0c41ef5bde3aea72b48e5aebe292696c53c1

[ "MIT" ]

null

lib/sphinx_exhibit/_offset_annotator.py

anntzer/sphinx-exhibit

5bdb0c41ef5bde3aea72b48e5aebe292696c53c1

[ "MIT" ]

null

import ast import bisect import itertools import tokenize def iter_attribute_tokens(fname): with open(fname, "rb") as file: # The call to filter handles cases where an attribute access dot is at # the end of a line and the attribute itself on the next one. tokens = filter(lambda token: token.string != "\n", tokenize.tokenize(file.readline)) for token in tokens: if token.string == ".": yield next(tokens) # Also catches submodule imports :/ def parse(fname, code_line_idxs): attr_tokens = iter_attribute_tokens(fname) with tokenize.open(fname) as file: source = file.read() lines = source.splitlines(keepends=True) skipped_line_idxs = {*range(1, len(lines) + 1)}.difference(code_line_idxs) for idx in skipped_line_idxs: lines[idx - 1] = "" line_start_offsets = [ 0, *itertools.accumulate(len(line) for line in lines)] def to_offset(lineno, col_offset): return line_start_offsets[lineno - 1] + col_offset class OffsetAnnotator(ast.NodeVisitor): def visit_Name(self, node): self.generic_visit(node) # NOTE: For decorators, this will miss the "@" just before. This # is taken into account at the annotation embedding stage. # NOTE: Something funky is going on with whether @foo.bar is # highlighted fully as a decorator or only partially... node.offset = to_offset(node.lineno, node.col_offset) def visit_Attribute(self, node): self.generic_visit(node) while True: # Skip spurious ".foo" coming from submodule imports. token = next(attr_tokens) if node.attr == token.string: break node.offset = to_offset(*token.start) # These are only necessary to handle fields in the order in which they # appear in the source, rather than the order they appear in the node. def visit_FunctionDef(self, node): for expr in node.decorator_list: self.visit(expr) self.visit(node.args) if node.returns: self.visit(node.returns) for stmt in node.body: self.visit(stmt) visit_AsyncFunctionDef = visit_FunctionDef def visit_ClassDef(self, node): for expr in node.decorator_list: self.visit(expr) for expr in node.bases: self.visit(expr) for keyword in node.keywords: self.visit(keyword) for stmt in node.body: self.visit(stmt) mod = ast.parse(source) OffsetAnnotator().visit(mod) return mod

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78

0.601506

347

2,788

4.73487

0.391931

0.043822

0.016433

0.023737

0.161899

0.127815

0.093731

0.057212

0

0.002633

0.318867

2,788

77

79

36.207792

0.862559

0.210187

0

0.2

0

0.002282

0

1

0.127273

false

0

0.072727

0.018182

0.272727

0

null

0

null

0

1

0

f0ba3d67bb844650e0e8a241e604236780948e92

8,242

py

Python

interface/github.py

ubclaunchpad/rocket2

7a4f05f46229d1c9a900aac1694b3d822f9d6b0f

[ "MIT" ]

14

2019-01-20T21:54:36.000Z

2021-10-09T21:06:23.000Z

interface/github.py

ubclaunchpad/rocket2

7a4f05f46229d1c9a900aac1694b3d822f9d6b0f

[ "MIT" ]

510

2018-11-18T20:07:51.000Z

2022-02-01T15:34:03.000Z

interface/github.py

ubclaunchpad/rocket2.0

7a4f05f46229d1c9a900aac1694b3d822f9d6b0f

[ "MIT" ]

9

2019-08-20T16:57:21.000Z

2021-05-04T12:51:47.000Z

"""Utility classes for interacting with Github API via PyGithub.""" from github import Github, GithubException from github.NamedUser import NamedUser from github.Team import Team from interface.exceptions.github import GithubAPIException from interface.github_app import GithubAppInterface, \ DefaultGithubAppAuthFactory from app.model import Team as ModelTeam from typing import cast, List from functools import wraps import logging def handle_github_error(func): """Github error handler that updates Github App API token if necessary.""" @wraps(func) def wrapper(self, *arg, **kwargs): try: return func(self, *arg, **kwargs) except GithubException as e: logging.warning(f"GithubException raised with message {e.data}" f" and error code {e.status}") if e.status == 401: logging.warning( "Attempting to create new instance of pygithub interface") self.github = self.github_factory.create() logging.warning( "Attempting to create new instance of organization object") self.org = self.github.get_organization(self.org_name) try: return func(self, *arg, **kwargs) except GithubException as e: logging.error("Second attempt of using pygithub interface" f" failed with message {e.data} and error " f"code {e.status}") raise GithubAPIException(e.data) else: logging.error(f"Unable to handle error code {e.status}") raise GithubAPIException(e.data) return wrapper class DefaultGithubFactory: """Default factory for creating interface to Github API.""" def __init__(self, app_id: str, private_key: str): """ Init factory. :param app_id: Github Apps ID :param private_key: Private key provided by Github Apps registration """ self.auth = GithubAppInterface( DefaultGithubAppAuthFactory(app_id, private_key)) self.github = Github def create(self) -> Github: """Create instance of pygithub interface with Github Apps API token.""" logging.info("Creating new instance of pygithub interface") return self.github(self.auth.create_api_token()) class GithubInterface: """Utility class for interacting with Github API.""" def __init__(self, github_factory: DefaultGithubFactory, org: str): """Initialize bot by creating Github object and get organization.""" logging.info("Creating rocket's Github interface") self.org_name = org self.github_factory = github_factory self.github = github_factory.create() try: self.org = self.github.get_organization(org) logging.info(f"Successfully fetched {org} Github organization") except GithubException as e: logging.error(f"Failed to fetch {org} Github organization with " f"error message {e.data} and error code {e.status}") raise GithubAPIException(e.data) @handle_github_error def org_add_member(self, username: str) -> str: """ Add/update to member with given username to organization. If the user is already in the organization, don't do anything. """ user = cast(NamedUser, self.github.get_user(username)) if not self.org.has_in_members(user): self.org.add_to_members(user, "member") return str(user.id) @handle_github_error def org_add_admin(self, username: str): """Add member with given username as admin to organization.""" user = cast(NamedUser, self.github.get_user(username)) self.org.add_to_members(user, "admin") @handle_github_error def org_remove_member(self, username: str): """Remove member with given username from organization.""" user = cast(NamedUser, self.github.get_user(username)) self.org.remove_from_membership(user) @handle_github_error def org_has_member(self, username: str) -> bool: """Return true if user with username is member of organization.""" user = cast(NamedUser, self.github.get_user(username)) return cast(bool, self.org.has_in_members(user)) @handle_github_error def org_get_team(self, id: int) -> Team: """Given Github team ID, return team from organization.""" return self.org.get_team(id) @handle_github_error def org_create_team(self, name: str) -> int: """ Create team with given name and add to organization. :param name: name of team :return: Github team ID """ team = self.org.create_team(name, privacy="closed") return cast(int, team.id) @handle_github_error def org_delete_team(self, id: int): """Get team with given ID and delete it from organization.""" team = self.org_get_team(id) team.delete() @handle_github_error def org_edit_team(self, key: int, name: str, description: str = None): """ Get team with given ID and edit name and description. :param key: team's Github ID :param name: new team name :param description: new team description """ team = self.org_get_team(key) if description is not None: team.edit(name, description) else: team.edit(name) @handle_github_error def org_get_teams(self) -> List[ModelTeam]: """Return array of teams associated with organization.""" teams = self.org.get_teams() team_array = [] for team in teams: # convert PaginatedList to List team_model = ModelTeam(str(team.id), team.name, "") team_model.members = set(str(user.id) for user in self.list_team_members(team.id)) team_array.append(team_model) return team_array # --------------------------------------------------------------- # --------------- methods related to team members --------------- # --------------------------------------------------------------- @handle_github_error def list_team_members(self, team_id: str) -> List[NamedUser]: """Return a list of users in the team of id team_id.""" team = self.org.get_team(int(team_id)) return list(team.get_members()) @handle_github_error def get_team_member(self, username: str, team_id: str) -> NamedUser: """Return a team member with a username of username.""" try: team = self.org.get_team(int(team_id)) team_members = team.get_members() return next(member for member in team_members if member.name == username) except StopIteration: raise GithubAPIException( f"User \"{username}\" does not exist in team \"{team_id}\"!") @handle_github_error def add_team_member(self, username: str, team_id: str): """Add user with given username to team with id team_id.""" team = self.org.get_team(int(team_id)) new_member = cast(NamedUser, self.github.get_user(username)) team.add_membership(new_member) @handle_github_error def has_team_member(self, username: str, team_id: str) -> bool: """Check if team with team_id contains user with username.""" team = self.org.get_team(int(team_id)) member = cast(NamedUser, self.github.get_user(username)) return cast(bool, team.has_in_members(member)) @handle_github_error def remove_team_member(self, username: str, team_id: str): """Remove user with given username from team with id team_id.""" team = self.org.get_team(int(team_id)) to_be_removed_member = cast(NamedUser, self.github.get_user(username)) team.remove_membership(to_be_removed_member)

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8,242

4.906344

0.149043

0.027094

0.05234

0.057471

0.385878

0.303161

0.222906

0.202997

0.117611

0.094622

0

0.000508

0.283184

8,242

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false

0

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0

0.304348

0

null

0

null

0

1

0

f0be5283bf687e9f7f7e680dad449395b66740f6

1,612

py

Python

director/commands/celery.py

LiniusAustPty/celery-director

5308c49e1f8502e244765025eb75b45bbe3c2d45

[ "BSD-3-Clause" ]

null

director/commands/celery.py

LiniusAustPty/celery-director

5308c49e1f8502e244765025eb75b45bbe3c2d45

[ "BSD-3-Clause" ]

4

2021-12-07T19:31:20.000Z

2022-03-10T10:17:22.000Z

director/commands/celery.py

LiniusAustPty/celery-director

5308c49e1f8502e244765025eb75b45bbe3c2d45

[ "BSD-3-Clause" ]

null

import os import click from urllib.parse import urlparse from director.context import pass_ctx @click.group() def celery(): """Celery commands""" @celery.command(name="beat", context_settings=dict(ignore_unknown_options=True)) @click.option("--dev", "dev_mode", default=False, is_flag=True, type=bool) @click.argument("beat_args", nargs=-1, type=click.UNPROCESSED) def beat(dev_mode, beat_args): """Start the beat instance""" args = [ "celery", "-A", "director._auto:cel", "beat", ] if dev_mode: args += [ "--loglevel", "INFO", ] args += list(beat_args) os.execvp(args[0], args) @celery.command("worker", context_settings=dict(ignore_unknown_options=True)) @click.option("--dev", "dev_mode", default=False, is_flag=True, type=bool) @click.argument("worker_args", nargs=-1, type=click.UNPROCESSED) def worker(dev_mode, worker_args): """Start a Celery worker instance""" args = [ "celery", "-A", "director._auto:cel", "worker", ] if dev_mode: args += [ "--loglevel", "INFO", ] args += list(worker_args) os.execvp(args[0], args) @celery.command(name="flower", context_settings=dict(ignore_unknown_options=True)) @click.argument("flower_args", nargs=-1, type=click.UNPROCESSED) @pass_ctx def flower(ctx, flower_args): """Start the flower instance""" broker = ctx.app.config["CELERY_CONF"]["broker_url"] args = ["celery", "flower", "-b", broker] + list(flower_args) os.execvp(args[0], args[1:])

26

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202

1,612

4.79703

0.287129

0.043344

0.058824

0.077399

0.599587

0.546956

0.408669

0.220846

0

0.005512

0.212159

1,612

61

83

26.42623

0.75748

0.059553

0

0.425532

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0.132441

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false

0.042553

0.085106

0

0.170213

0

null

0

null

0

1

0

f0c0cafb1abfa54ba047b3d42c073b1ab5bb4969

462

py

Python

PyPoll/main.py

d-jenkins/python-challenge

e45d85d7d4df9543b522718e688784d5bcd1e354

[ "MIT" ]

null

PyPoll/main.py

d-jenkins/python-challenge

e45d85d7d4df9543b522718e688784d5bcd1e354

[ "MIT" ]

null

PyPoll/main.py

d-jenkins/python-challenge

e45d85d7d4df9543b522718e688784d5bcd1e354

[ "MIT" ]

null

#modules import os import csv csvpath = os.path.join('Resources', 'election_data.csv') #Print Total Votes set with separator print('Election Results') print("----------------------------") #open csv with open(csvpath) as csv_file: #call csv reader csv_reader = csv.reader(csv_file,delimiter=',') csv_header = next(csv_reader) total_votes = len(list(csv_reader)) print("Total Votes: ", total_votes)

19.25

57

0.608225

57

462

4.77193

0.473684

0.165441

0.132353

0.110294

0

0.225108

462

23

58

20.086957

0.759777

0.142857

0

0.214286

0.071429

0

1

0

false

0

0.181818

0

0.181818

0.272727

0

null

0

null

0

1

0

f0c23188163e8da685c4778386fca45ff71610b9

1,985

py

Python

raspi_shutdown_daemon.py

NAKKA-K/raspi-shutdown-daemon

df624134f968b5233e5abacca39fc339e1ad076b

[ "MIT" ]

null

raspi_shutdown_daemon.py

NAKKA-K/raspi-shutdown-daemon

df624134f968b5233e5abacca39fc339e1ad076b

[ "MIT" ]

null

raspi_shutdown_daemon.py

NAKKA-K/raspi-shutdown-daemon

df624134f968b5233e5abacca39fc339e1ad076b

[ "MIT" ]

null

#!/usr/bin/env python3 import os import sys import RPi.GPIO as GPIO import time import pygame.mixer import argparse import tempfile # 5秒間スイッチを長押しすると、シャットダウンする # 長押ししている間はLEDが光る # defalt: LED1(PIN21) & SW1(PIN7) def raspi_shutdown_unit(SW1=7, LED1=21): GPIO.setmode(GPIO.BCM) GPIO.setup(SW1, GPIO.IN) GPIO.setup(LED1, GPIO.OUT) cnt = 0 while 1: if GPIO.input(SW1) == 1: cnt += 1 else: cnt = 0 if cnt >= 25: info_shutdown_daemon('!!!!! This raspi shutdown !!!!!') os.system('sudo shutdown -h now') GPIO.output(LED1, cnt) time.sleep(0.2) # このデーモン用log def info_shutdown_daemon(info): print(info) args = get_flags() if args.alert: alert_message_at_shutdown("シャットダウンします") # CL引数のパース def get_flags(): parser = argparse.ArgumentParser() parser.add_argument("--alert", help="Alert message at shutdown.", action="store_true") return parser.parse_args() def alert_message_at_shutdown(message): temp_wav = '/tmp/temp_damemon.wav' try: make_alert_wav(temp_wav, message) pygame.mixer.init() pygame.mixer.music.load(temp_wav) pygame.mixer.music.play() finally: os.remove(temp_wav) def make_alert_wav(file_name, message): jtalk_option="\ -m /usr/share/hts-voice/mei/mei_normal.htsvoice \ -x /var/lib/mecab/dic/open-jtalk/naist-jdic \ -ow {}".format(file_name) os.system("echo {} | open_jtalk {}".format(message, jtalk_option)) # プロセスのフォークと親プロセスの終了 def fork(): pid = os.fork() if pid: write_pid(pid) sys.exit() # pidファイルへ書き込み def write_pid(pid): with open('/var/run/raspi_shutdown_daemon.pid', 'w') as pid_file: pid_file.write(str(pid)+"\n") # deamonプロセスの起動 def daemon(): fork() # 親プロセスを殺し、子プロセスを孤児化させる os.setsid() fork() # セッションリーダーを殺し、プロセスを完全に独立化 raspi_shutdown_unit() if __name__ == '__main__': daemon()

23.081395

90

0.63728

262

1,985

4.645038

0.473282

0.036154

0.034511

0.054232

0

0.016361

0.230227

1,985

85

91

23.352941

0.780105

0.104282

0

0.065574

0

0.109225

0.031126

0

1

0.131148

false

0

0.114754

0

0.262295

0.016393

0

null

0

null

0

1

0

f0c4d0d98821b73c57cf31ce6c154f663684fffa

603

py

Python

scapy_port_scanner.py

corentinmusard/scapy_port_scanner

8c41c1c1f6bb1899222c49548d49eb9e01c41516

[ "MIT" ]

4

2017-10-31T17:39:51.000Z

2018-08-21T18:37:43.000Z

scapy_port_scanner.py

corentinmusard/scapy_port_scanner

8c41c1c1f6bb1899222c49548d49eb9e01c41516

[ "MIT" ]

2

2021-04-20T19:38:54.000Z

2021-06-02T01:11:44.000Z

scapy_port_scanner.py

corentinmusard/scapy_port_scanner

8c41c1c1f6bb1899222c49548d49eb9e01c41516

[ "MIT" ]

1

2018-07-21T21:58:33.000Z

#!/usr/bin/env python """ scapy_port_scanner.py """ from src import ScanFinder def main() -> None: """Main function of scapy_port_scanner""" scan_finder = ScanFinder() scan_name = scan_finder.find_type() if scan_name is None: print("Scan's name not found.") exit() scan = scan_finder.get_scan(scan_name) if scan is None: print("Scan not found.") exit() scan.start() # Start the scan with thread scan.join() # Wait the end of all thread scan.info() # Print some info about the scan if __name__ == "__main__": main()

19.451613

49

0.620232

85

603

4.164706

0.482353

0.084746

0.090395

0.084746

0

0.263682

603

30

50

20.1

0.797297

0.270315

0

0.125

0

0.107143

0

1

0.0625

false

0

0.0625

0

0.125

0

null

0

null

0

1

0

f0c523a76b4f98a35781377d46fb51baa9a01a0f

2,387

py

Python

optonome_V1/optonome.py

dyyyni/fluorometerCodes

8f192ce656ba492e42cb80b6e6a655ea926418a4

[ "MIT" ]

1

2021-12-08T14:12:43.000Z

optonome_V1/optonome.py

dyyyni/fluorometerCodes

8f192ce656ba492e42cb80b6e6a655ea926418a4

[ "MIT" ]

null

optonome_V1/optonome.py

dyyyni/fluorometerCodes

8f192ce656ba492e42cb80b6e6a655ea926418a4

[ "MIT" ]

null

# globals task = None exit_flag = False wrt_file = None counts_prev = None counts_now = None interval = 1 n_measurements = 0 def clear_screen(): import os os.system('cls') return def set_interval(): import sys global interval if sys.argv.__len__() < 2: return interval = float(sys.argv[1]) return def prepare_file(): import os import sys global wrt_file save_path = os.getcwd() + '\\12hsadfTesti' sys.stdout.write('Saving data to \'' + save_path + '\'\nTo save data and exit the program hit Enter \nInitialising...') wrt_file = open(save_path, 'w') return def start_device(): import sys import nidaqmx as ni global task devices = ni.system.system.System.local().devices if devices.__len__() < 1: print('No NI device detected. Aborting program execution.') sys.exit(1) name = devices[0].name + '/ctr1' if devices.__len__() > 1: print('Multiple NI devices detected. Using device/channel \'' + name + '\'') task = ni.Task('digital readout') task.ci_channels.add_ci_count_edges_chan(name) task.start() return def abort_acquisition(): global exit_flag input() exit_flag = True return def enable_user_input_abortion(): import threading thread = threading.Thread(target=abort_acquisition) thread.start() return def read_counts(): import time global n_measurements global counts_now counts_now = task.ci_channels[0].ci_count time.sleep(interval) return def write_counts(): import sys import datetime global n_measurements global counts_prev counts = counts_now - counts_prev n_measurements += 1 sys.stdout.write('\r\033[KCounts at ' + '{:.2f}'.format(n_measurements * interval) + 's: ' + str(counts)) wrt_file.write(str(datetime.datetime.now()) + ' ' + str(counts) + '\n') return def stop_device(): task.stop() task.close() wrt_file.close() return def main(): global counts_prev clear_screen() set_interval() prepare_file() start_device() enable_user_input_abortion() while not exit_flag: read_counts() if counts_prev is not None: write_counts() counts_prev = counts_now stop_device() return if __name__ == '__main__': main()

16.692308

123

0.639715

306

2,387

4.738562

0.339869

0.055862

0.02069

0.017931

0.067586

0

0.00954

0.253456

2,387

143

124

16.692308

0.804153

0.002933

0

0.238636

0

0.118537

0

1

0.113636

false

0

0.113636

0

0.352273

0.022727

0

null

0

null

0

1

0

f0c8cd99962986b595c6a3341057ee8c726d9975

2,083

py

Python

prep_train_data.py

Sinpex-GmbH/pygaggle

9d0c18f5ececdb07e80270afb26d0805503961d3

[ "Apache-2.0" ]

null

prep_train_data.py

Sinpex-GmbH/pygaggle

9d0c18f5ececdb07e80270afb26d0805503961d3

[ "Apache-2.0" ]

null

prep_train_data.py

Sinpex-GmbH/pygaggle

9d0c18f5ececdb07e80270afb26d0805503961d3

[ "Apache-2.0" ]

null

""" Data Prep https://github.com/castorini/pygaggle/blob/master/docs/experiments-monot5-tpu.md Gives example dataset and how to convert it to use with t5 model training """ import json import csv fpath = "/Users/nikolettatoth/T5_ranking/pygaggle/test_files/labels_for_training_q5q6q17/train_ar_tf_q5q6q17_v1.json" with open(fpath, "r") as infile: train_data = json.load(infile) collect_dict = {} for label in train_data["data"]: question = label["paragraphs"][0]["qas"][0]["question"] context = label["paragraphs"][0]["context"] if question in collect_dict: list_pf_context = collect_dict[question] else: list_pf_context = [] list_pf_context.append(context) collect_dict.update({question: list_pf_context}) question_par_pairs = [] for query, context_list in collect_dict.items(): # remove doubled question-paragraph pairs context_list = list(set(context_list)) print(query + " " + str(len(context_list))) for context_i in context_list: question_par_pairs.append([query, context_i]) output_path = fpath.replace(".json", "_pairs.tsv") # finally we have 505 query - context pairs # What is the identification number of the company? 166 # Which commercial register is the company registered in? 124 # How much is the capital share? 215 """ This script creates monoT5 input files for training, Each line in the monoT5 input file follows the format: f'Query: {query} Document: {document} Relevant:\t{label}\n') """ from tqdm import tqdm # input file should be a tsv with the following lines: # NOTE: we should add negative examples !!!!!!!!!!! # <query> \t <positive_document> \t <negative_document>" with open(output_path, 'w') as fout_t5: for item_id, item in enumerate(tqdm(question_par_pairs)): # item = ['query', 'positive_context'] query, positive_document = item[0], item[1] fout_t5.write(f'Query: {query} Document: {positive_document} Relevant:\ttrue\n') #fout_t5.write(f'Query: {query} Document: {negative_document} Relevant:\tfalse\n') print('Done!')

32.546875

117

0.713874

297

2,083

4.838384

0.457912

0.045929

0.036187

0.039666

0.041754

0

0.01954

0.164666

2,083

63

118

33.063492

0.806322

0.324532

0

0.194536

0.088576

0

1

0

false

0

0.107143

0

0.107143

0.071429

0

null

0

null

0

1

0

f0c9f121e87959133c7bed647a70db229bdfd15d

4,324

py

Python

python/ray/serve/tests/storage_tests/test_kv_store.py

linyiyue/ray

90d2456ec70270a1f894ec3ef6f3004533859e03

[ "Apache-2.0" ]

21,382

2016-09-26T23:12:52.000Z

2022-03-31T21:47:45.000Z

python/ray/serve/tests/storage_tests/test_kv_store.py

linyiyue/ray

90d2456ec70270a1f894ec3ef6f3004533859e03

[ "Apache-2.0" ]

19,689

2016-09-17T08:21:25.000Z

2022-03-31T23:59:30.000Z

python/ray/serve/tests/storage_tests/test_kv_store.py

linyiyue/ray

90d2456ec70270a1f894ec3ef6f3004533859e03

[ "Apache-2.0" ]

4,114

2016-09-23T18:54:01.000Z

2022-03-31T15:07:32.000Z

import os import tempfile import sys from typing import Optional import pytest from ray.serve.constants import DEFAULT_CHECKPOINT_PATH from ray.serve.storage.checkpoint_path import make_kv_store from ray.serve.storage.kv_store import (RayInternalKVStore, RayLocalKVStore, RayS3KVStore) from ray.serve.storage.kv_store_base import KVStoreBase def test_ray_internal_kv(serve_instance): # noqa: F811 with pytest.raises(TypeError): RayInternalKVStore(namespace=1) RayInternalKVStore(namespace=b"") kv = RayInternalKVStore() with pytest.raises(TypeError): kv.put(1, b"1") with pytest.raises(TypeError): kv.put("1", 1) with pytest.raises(TypeError): kv.put("1", "1") kv.put("1", b"2") assert kv.get("1") == b"2" kv.put("2", b"4") assert kv.get("2") == b"4" kv.put("1", b"3") assert kv.get("1") == b"3" assert kv.get("2") == b"4" def test_ray_internal_kv_collisions(serve_instance): # noqa: F811 kv1 = RayInternalKVStore() kv1.put("1", b"1") assert kv1.get("1") == b"1" kv2 = RayInternalKVStore("namespace") assert kv2.get("1") is None kv2.put("1", b"-1") assert kv2.get("1") == b"-1" assert kv1.get("1") == b"1" def _test_operations(kv_store): # Trival get & put kv_store.put("1", b"1") assert kv_store.get("1") == b"1" kv_store.put("2", b"2") assert kv_store.get("1") == b"1" assert kv_store.get("2") == b"2" # Overwrite same key kv_store.put("1", b"-1") assert kv_store.get("1") == b"-1" # Get non-existing key assert kv_store.get("3") is None # Delete existing key kv_store.delete("1") kv_store.delete("2") assert kv_store.get("1") is None assert kv_store.get("2") is None # Delete non-existing key kv_store.delete("3") def test_external_kv_local_disk(): kv_store = RayLocalKVStore( "namespace", os.path.join(tempfile.gettempdir(), "test_kv_store.db")) _test_operations(kv_store) @pytest.mark.skip(reason="Need to figure out credentials for testing") def test_external_kv_aws_s3(): kv_store = RayS3KVStore( "namespace", bucket="jiao-test", s3_path="/checkpoint", aws_access_key_id=os.environ.get("AWS_ACCESS_KEY_ID", None), aws_secret_access_key=os.environ.get("AWS_SECRET_ACCESS_KEY", None), aws_session_token=os.environ.get("AWS_SESSION_TOKEN", None), ) _test_operations(kv_store) class MyNonCompliantStoreCls: pass class MyCustomStorageCls(KVStoreBase): def __init__(self, namespace, **kwargs): self.namespace = namespace self.kwargs = kwargs def delete(self, key: str) -> None: return super().delete(key) def get(self, key: str) -> Optional[bytes]: return super().get(key) def get_storage_key(self, key: str) -> str: return super().get_storage_key(key) def put(self, key: str, val: bytes) -> bool: return super().put(key, val) @pytest.mark.skipif(sys.platform == "win32", reason="Using tmp dir.") def test_make_kv_store(serve_instance): namespace = "ns" assert isinstance( make_kv_store(DEFAULT_CHECKPOINT_PATH, namespace), RayInternalKVStore) assert isinstance( make_kv_store("file:///tmp/deep/dir/my_path", namespace), RayLocalKVStore) assert isinstance( make_kv_store("s3://object_store/my_path", namespace), RayS3KVStore) with pytest.raises(ValueError, match="shouldn't be empty"): # Empty path make_kv_store("file://", namespace) with pytest.raises(ValueError, match="must be one of"): # Wrong prefix make_kv_store("s4://some_path", namespace) module_name = "ray.serve.tests.storage_tests.test_kv_store" with pytest.raises(ValueError, match="doesn't inherit"): make_kv_store( f"custom://{module_name}.MyNonCompliantStoreCls", namespace=namespace) store = make_kv_store( f"custom://{module_name}.MyCustomStorageCls?arg1=val1&arg2=val2", namespace=namespace) assert store.namespace == namespace assert store.kwargs == {"arg1": "val1", "arg2": "val2"} if __name__ == "__main__": import sys sys.exit(pytest.main(["-v", "-s", __file__]))

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f0cbf7d65b4f209f86204912736057b7423145a0

7,258

py

Python

lib/sqlalchemy_json_querybuilder/querybuilder/operators.py

suyash248/sqlalchemy-json-querybuilder

3deefd8935ce49c484a4936a751e9ccb5eb574a6

[ "MIT" ]

32

2018-06-21T17:07:57.000Z

2021-11-29T14:04:40.000Z

lib/sqlalchemy_json_querybuilder/querybuilder/operators.py

suyash248/sqlalchemy-json-querybuilder

3deefd8935ce49c484a4936a751e9ccb5eb574a6

[ "MIT" ]

null

lib/sqlalchemy_json_querybuilder/querybuilder/operators.py

suyash248/sqlalchemy-json-querybuilder

3deefd8935ce49c484a4936a751e9ccb5eb574a6

[ "MIT" ]

10

2018-12-11T10:00:16.000Z

2022-02-12T14:07:31.000Z

__author__ = "Suyash Soni" __email__ = "suyash.soni248@gmail.com" from ..commons.error_handlers.exceptions.exceptions import ExceptionBuilder, SqlAlchemyException from ..constants.error_codes import ErrorCode class OperatorEvaluator(object): """Represents an operator""" def __init__(self, model_cls, field_name, field_value): self.model_cls = model_cls self.field_name = field_name self.field_value = field_value @staticmethod def obj(operator_name, model_cls, field_name, field_value): operator_name = operator_name.lower() try: op_eval_cls = __OPERATORS_MAPPING__[operator_name] return op_eval_cls(model_cls, field_name, field_value) except: ExceptionBuilder(SqlAlchemyException).error(ErrorCode.INVALID_OPERATOR, operator_name, message="Invalid operator: {}".format(operator_name)).throw() def expr(self): """Evaluates criterion and returns expression to be used inside `model_cls.query.filter(*expressions)` method. Concrete operator classes must override this method.""" ExceptionBuilder(SqlAlchemyException).error(ErrorCode.INVALID_OPERATOR, self.field_name, message="Invalid operator").throw() @property def model_field(self): try: return getattr(self.model_cls, self.field_name) except: ExceptionBuilder(SqlAlchemyException).error(ErrorCode.INVALID_FIELD, self.field_name, message="Couldn't find {} under {}".format(self.field_name, self.model_cls.__name__)).throw() class __Equals__(OperatorEvaluator): def expr(self): return self.model_field == self.field_value class __NotEquals__(OperatorEvaluator): def expr(self): return self.model_field != self.field_value class __LessThan__(OperatorEvaluator): def expr(self): return self.model_field < self.field_value class __LessThanEq__(OperatorEvaluator): def expr(self): return self.model_field <= self.field_value class __GreaterThan__(OperatorEvaluator): def expr(self): return self.model_field > self.field_value class __GreaterThanEq__(OperatorEvaluator): def expr(self): return self.model_field >= self.field_value class __IN__(OperatorEvaluator): def expr(self): try: iter(self.field_value) except TypeError as te: ExceptionBuilder(SqlAlchemyException).error(ErrorCode.INVALID_DATA_TYPE, self.field_name, message="field_value must be iterable").throw() return self.model_field.in_(self.field_value) class __NotIn__(OperatorEvaluator): def expr(self): try: iter(self.field_value) except TypeError as te: ExceptionBuilder(SqlAlchemyException).error(ErrorCode.INVALID_DATA_TYPE, self.field_name, message="field_value must be iterable").throw() return ~self.model_field.in_(self.field_value) class __IsNull__(OperatorEvaluator): def expr(self): return self.model_field.is_(None) class __IsNotNull__(OperatorEvaluator): def expr(self): return self.model_field.isnot(None) class __Like__(OperatorEvaluator): def expr(self): return self.model_field.like(self.field_value) class __ILike__(OperatorEvaluator): def expr(self): return self.model_field.ilike(self.field_value) class __StartsWith__(__Like__): def expr(self): if type(self.field_value) == str: self.field_value = self.field_value + '%' return super(__StartsWith__, self).expr() ExceptionBuilder(SqlAlchemyException).error(ErrorCode.INVALID_DATA_TYPE, self.field_name, message="field_value must be string").throw() class __IStartsWith__(__ILike__): def expr(self): if type(self.field_value) == str: self.field_value = self.field_value + '%' return super(__IStartsWith__, self).expr() ExceptionBuilder(SqlAlchemyException).error(ErrorCode.INVALID_DATA_TYPE, self.field_name, message="field_value must be string").throw() class __EndsWith__(__Like__): def expr(self): if type(self.field_value) == str: self.field_value = '%' + self.field_value return super(__EndsWith__, self).expr() ExceptionBuilder(SqlAlchemyException).error(ErrorCode.INVALID_DATA_TYPE, self.field_name, message="field_value must be string").throw() class __IEndsWith__(__ILike__): def expr(self): if type(self.field_value) == str: self.field_value = '%' + self.field_value return super(__IEndsWith__, self).expr() ExceptionBuilder(SqlAlchemyException).error(ErrorCode.INVALID_DATA_TYPE, self.field_name, message="field_value must be string").throw() class __Contains__(__Like__): def expr(self): if type(self.field_value) == str: self.field_value = '%' + self.field_value + '%' return super(__Contains__, self).expr() ExceptionBuilder(SqlAlchemyException).error(ErrorCode.INVALID_DATA_TYPE, self.field_name, message="field_value must be string").throw() class __IContains__(__ILike__): def expr(self): if type(self.field_value) == str: self.field_value = '%' + self.field_value + '%' return super(__IContains__, self).expr() ExceptionBuilder(SqlAlchemyException).error(ErrorCode.INVALID_DATA_TYPE, self.field_name, message="field_value must be string").throw() class __Match__(OperatorEvaluator): def expr(self): return self.model_field.match(self.field_value) class __Any__(OperatorEvaluator): def expr(self): return self.model_field.any(self.field_value) class __Has__(OperatorEvaluator): def expr(self): return self.model_field.has(self.field_value) # Maps `operator_name` to corresponding 'operator` class. __OPERATORS_MAPPING__ = { 'equals': __Equals__, 'eq': __Equals__, '==': __Equals__, 'notequals': __NotEquals__, 'not_equals': __NotEquals__, 'ne': __NotEquals__, '!=': __NotEquals__, '~=': __NotEquals__, 'less_than': __LessThan__, 'lt': __LessThan__, '<': __LessThan__, 'less_than_equals': __LessThanEq__, 'lte': __LessThanEq__, '<=': __LessThanEq__, 'greater_than': __GreaterThan__, 'gt': __GreaterThan__, '>': __GreaterThan__, 'greater_than_equals': __GreaterThanEq__, 'gte': __GreaterThanEq__, '>=': __GreaterThanEq__, 'like': __Like__, 'ilike': __ILike__, 'startswith': __StartsWith__, 'istartswith': __IStartsWith__, 'endswith': __EndsWith__, 'iendswith': __IEndsWith__, 'contains': __Contains__, 'icontains': __IContains__, 'match': __Match__, 'in': __IN__, 'notin': __NotIn__, 'isnull': __IsNull__, 'isnotnull': __IsNotNull__, 'any': __Any__, 'has': __Has__ }

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0.444444

0

null

0

null

0

1

0

f0cdfb201fe62710a14b49c8128fd0ea49481d0c

1,018

py

Python

admin.py

ax-rwnd/E-dot

b1b64fcce43c5d6f54dc38959498cdba95e757b1

[ "BSD-3-Clause" ]

null

admin.py

ax-rwnd/E-dot

b1b64fcce43c5d6f54dc38959498cdba95e757b1

[ "BSD-3-Clause" ]

1

2015-12-05T02:04:35.000Z

2015-12-11T02:47:28.000Z

admin.py

ax-rwnd/E-dot

b1b64fcce43c5d6f54dc38959498cdba95e757b1

[ "BSD-3-Clause" ]

null

from flask import abort, g from flask.ext.login import current_user from access_levels import access ## Admin Table # pk(user_id), level # user_id - fk, int(11) # level, unsigned int(4) def test_access (uid, access): db = getattr(g, 'db', None) with db as cursor: query = "select level from tbl_admin where user_id = %s;" if cursor.execute(query, (uid,)) <= 0: #That user has no clearance. return False; elif cursor.fetchone()[0] > access: #That user has insufficient clearance. return False; else: #That user has sufficient clearance. return True #check if current_user actually has access def perimeter_check (access_str): if not test_access(current_user.uid, access[access_str]): abort(403) def admin_config (uid, newaccess): db = getattr(g, 'db', None) with db as cursor: query = "insert into tbl_admin (user_id, level) values (\ (select id from tbl_user where id = %s), %s) on\ duplicate key update level=values(level);" cursor.execute(query, (uid, newaccess))

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0.391304

0

null

0

null

0

1

0

f0ce476bae592fd658b7dfce1f2bf351c00bb04e

4,563

py

Python

index.py

hongfs/python-douyin

2cf63ce71158b03c0f08d873bcad1a5865ca8d03

[ "MIT" ]

165

2018-06-27T08:21:48.000Z

2022-03-18T06:27:41.000Z

index.py

zhutieing/python-douyin

2cf63ce71158b03c0f08d873bcad1a5865ca8d03

[ "MIT" ]

3

2018-09-09T23:30:39.000Z

2019-01-11T12:16:59.000Z

index.py

zhutieing/python-douyin

2cf63ce71158b03c0f08d873bcad1a5865ca8d03

[ "MIT" ]

104

2018-06-27T08:52:32.000Z

2022-03-25T17:28:40.000Z

import requests, json, re, os, sys, time from urllib.parse import urlparse from contextlib import closing class DY(object): def __init__(self): self.headers = { 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8', 'accept-encoding': 'gzip, deflate, br', 'accept-language': 'zh-CN,zh;q=0.9', 'cache-control': 'max-age=0', 'upgrade-insecure-requests': '1', 'user-agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 11_0 like Mac OS X) AppleWebKit/604.1.38 (KHTML, like Gecko) Version/11.0 Mobile/15A372 Safari/604.1' } self.domain = ['www.douyin.com', 'v.douyin.com', 'www.snssdk.com', 'www.amemv.com', 'www.iesdouyin.com', 'aweme.snssdk.com'] def hello(self): print('*' * 60) print('\t\t抖音无水印视频下载') print('\t作者:hongfs(https://github.com/hongfs)') print('*' * 60) self.run() def run(self): self.share_url = input('请输入分享链接：') # self.share_url = 'http://v.douyin.com/7CDeV/' if not self.share_url: return self.run() self.share_url = self.getLocation() share_url_parse = urlparse(self.share_url) if not share_url_parse.scheme in ['http', 'https'] or not share_url_parse.netloc in self.domain: return self.run() uid = re.findall(r'\/share\/user\/(\d*)', share_url_parse.path) if uid: self.uid = uid[0] else: vid = re.findall(r'\/share\/video\/(\d*)', share_url_parse.path) if vid: self.getUid(self.share_url) else: print('链接无法识别，请提交issues') return self.run() self.count = 0 self.getUserData(self.uid) def getLocation(self): response = requests.get(self.share_url, headers=self.headers, allow_redirects=False) if 'Location' in response.headers.keys(): return response.headers['Location'] else: return self.share_url def getUid(self, url): response = requests.get(url, headers=self.headers) if not response.status_code == 200: return False uid = re.findall(r'uid?: \"(\d*)"', response.text) if uid: self.uid = uid[0] else: return False def getUserData(self, uid, cursor = 0): url = 'https://www.douyin.com/aweme/v1/aweme/favorite/?user_id=%s&max_cursor=%s&count=35' % (uid, cursor) response = requests.get(url, headers=self.headers) if not response.status_code == 200: return print('请求失败') data = response.json() if 'status_code' not in data.keys(): return print('获取数据失败') if len(data['aweme_list']) == 0: return print('\n完成') self.nickname = data['aweme_list'][0]['author']['nickname'] if cursor == 0 and self.nickname not in os.listdir(): os.mkdir(self.nickname) for item in data['aweme_list']: if not 'video' in item.keys(): continue if not self.nickname == item['author']['nickname']: return print('\n完成') video_id = item['video']['play_addr']['uri'] video_name = item['desc'] if item['desc'] else video_id for c in r'\/:*?"<>|/': video_name = video_name.replace(c, '') path = os.path.join(self.nickname, video_name) + '.mp4' self.count = self.count + 1 print('第' + str(self.count) + '个：') if os.path.isfile(path): print(video_name + ' -- 已存在') continue print(video_name + ' -- 下载中') self.download(video_id, path) self.getUserData(self.uid, data['max_cursor']) def download(self, vid, path): time.sleep(1) url = 'https://aweme.snssdk.com/aweme/v1/play/?video_id=%s&line=0' % str(vid) with closing(requests.get(url, headers=self.headers, stream=True)) as response: chunk_size = 1024 content_size = int(response.headers['content-length']) if response.status_code == 200: print(' [文件大小]:%0.2f MB\n' % (content_size / chunk_size / 1024)) with open(r'' + path, 'wb') as file: for data in response.iter_content(chunk_size = chunk_size): file.write(data) file.flush() if __name__ == '__main__': dy = DY() dy.hello()

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0

null

0

null

0

1

0

f0cf8fc7ef6e693e4d36f40c69aaf2886cad2131

808

py

Python

base/config.py

joeportela/tinyAPI

f2469c38a605b00519acd0b79af17d0041f5ae7b

[ "MIT" ]

6

2016-11-18T22:32:44.000Z

2021-04-01T17:02:13.000Z

base/config.py

joeportela/tinyAPI

f2469c38a605b00519acd0b79af17d0041f5ae7b

[ "MIT" ]

1

2018-12-20T23:07:52.000Z

base/config.py

joeportela/tinyAPI

f2469c38a605b00519acd0b79af17d0041f5ae7b

[ "MIT" ]

10

2018-02-23T00:08:21.000Z

2020-10-01T03:06:12.000Z

# ----- Info ------------------------------------------------------------------ __author__ = 'Michael Montero <mcmontero@gmail.com>' # ----- Imports --------------------------------------------------------------- from .exception import ConfigurationException import tinyAPI_config __all__ = ['ConfigManager'] # ----- Public Classes -------------------------------------------------------- class ConfigManager(object): '''Handles retrieval and validation of configuration settings.''' @staticmethod def value(key): '''Retrieves the configuration value named by key.''' if key in tinyAPI_config.values: return tinyAPI_config.values[key] else: raise ConfigurationException( '"' + key + '" is not configured in tinyAPI_config')

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0

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808

25

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0

0.416667

0

null

0

null

0

1

0

f0d1a950996390512955a5f285ae00148e3d2ae1

5,735

py

Python

snakewm/wm.py

Krobix/dioware

f9577eba58f26af6c609815b6d0ef706ffab044b

[ "MIT" ]

null

snakewm/wm.py

Krobix/dioware

f9577eba58f26af6c609815b6d0ef706ffab044b

[ "MIT" ]

null

snakewm/wm.py

Krobix/dioware

f9577eba58f26af6c609815b6d0ef706ffab044b

[ "MIT" ]

null

""" Snake Window Manager """ TESTMODE = __name__ == '__main__' import os import sys import importlib import pygame, pygame_gui if TESTMODE: from appmenu.appmenupanel import AppMenuPanel else: from snakewm.appmenu.appmenupanel import AppMenuPanel class SnakeWM: SCREEN = None DIMS = None BG = None MANAGER = None BG_COLOR = (0, 128, 128) # currently focused window FOCUS = None # dict that will contain the apps directory structure APPS = {} # reference to the root app menu object APPMENU = None def __init__(self): # populate the apps tree apps_path = os.path.dirname(os.path.abspath(__file__)) + '/apps' SnakeWM.iter_dir(self.APPS, apps_path) pygame.init() # initialize pygame to framebuffer os.putenv('SDL_FBDEV', '/dev/fb0') pygame.display.init() # get screen dimensions self.DIMS = ( pygame.display.Info().current_w, pygame.display.Info().current_h ) # init screen self.SCREEN = pygame.display.set_mode( self.DIMS, pygame.FULLSCREEN ) # init background self.BG = pygame.Surface((self.DIMS)) self.BG.fill(self.BG_COLOR) # init UI manager self.MANAGER = pygame_gui.UIManager(self.DIMS) pygame.mouse.set_visible(True) pygame.display.update() def iter_dir(tree, path): """ Static function that recursively populates dict 'tree' with the app directory structure starting at 'path'. """ for f in os.listdir(path): if os.path.isfile(path + '/' + f + '/__init__.py'): tree[f] = None elif os.path.isdir(path + '/' + f): tree[f] = {} SnakeWM.iter_dir(tree[f], path + '/' + f) def loadapp(self, app, params=None): """ Load and run a Python module as an app (ie "apps.test.HelloWorld"). Apps are basically just Python packages. The loaded app package must contain an __init__.py with a load() function that accepts a UIManager parameter and a params list parameter. The load() function should create an instance of the app to load and add the app UI to the passed UIManager object. See existing apps for examples. """ if not TESTMODE: app = 'snakewm.' + app _app = importlib.import_module(app) _app.load(self.MANAGER, params) def appmenu_load(self, app): """ This function is passed to AppMenuPanel objects to be called when an app is selected to be opened. The root app menu is destroyed, and the app is loaded. """ if self.APPMENU is not None: self.APPMENU.destroy() self.APPMENU = None self.loadapp(app) def set_bg_color(self, color): """ Set the desktop background to 'color', where color is an RGB tuple. """ self.BG = pygame.Surface((self.DIMS)) self.BG_COLOR = color self.BG.fill(self.BG_COLOR) def set_bg_image(self, file): """ Sets the desktop background to an image. """ filename, file_extension = os.path.splitext(file) if file_extension == ".jpg" or file_extension == ".png": self.BG = pygame.transform.scale(pygame.image.load(file), self.DIMS) def run(self): clock = pygame.time.Clock() running = True while running: delta = clock.tick(60) / 1000.0 pressed = pygame.key.get_pressed() for event in pygame.event.get(): if event.type == pygame.KEYDOWN: if event.key == pygame.K_LSUPER: if self.APPMENU is None: # open app menu self.APPMENU = AppMenuPanel( self.MANAGER, (0, 0), 'apps', self.APPS, self.appmenu_load ) else: # close app menu self.APPMENU.destroy() self.APPMENU = None if pressed[pygame.K_LALT]: if event.key == pygame.K_ESCAPE: running = False return pygame.quit() elif event.type == pygame.USEREVENT: if event.user_type == 'window_selected': # focus selected window if self.FOCUS is not None: self.FOCUS.unfocus() self.FOCUS = event.ui_element self.FOCUS.focus() elif event.user_type == pygame_gui.UI_COLOUR_PICKER_COLOUR_PICKED: if event.ui_object_id == '#desktop_colour_picker': # set desktop background color - no alpha channel self.set_bg_color(event.colour[:-1]) elif event.user_type == pygame_gui.UI_FILE_DIALOG_PATH_PICKED: if event.ui_object_id == '#background_picker': self.set_bg_image(event.text) self.MANAGER.process_events(event) self.MANAGER.update(delta) self.SCREEN.blit(self.BG, (0, 0)) self.MANAGER.draw_ui(self.SCREEN) pygame.display.update() if TESTMODE: wm = SnakeWM() wm.run()

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null

0

null

0

1

0

f0d20e65dbb954b1d329d0b6c224a54a1e459a35

2,877

py

Python

convert_dict_to_json.py

blackCheetah/tvshortcut.herokuapp.com

923b130802351a3c716cc6ef5121cd6cb1b6175d

[ "MIT" ]

6

2017-08-06T08:46:56.000Z

2020-04-07T20:09:51.000Z

convert_dict_to_json.py

blackCheetah/tvshortcut.herokuapp.com

923b130802351a3c716cc6ef5121cd6cb1b6175d

[ "MIT" ]

3

2021-03-19T22:21:02.000Z

2021-12-13T19:44:44.000Z

convert_dict_to_json.py

blackCheetah/tvshortcut.herokuapp.com

923b130802351a3c716cc6ef5121cd6cb1b6175d

[ "MIT" ]

null

""" # Structure { "tvShows": [ { "name": "Arrow", "shorctut": "arrow", "new": "" }, { "name": "something", "shorctut": "something", "new": "" } ] } """ import json import os tv_shows = { "Arrow": "arrow", "Agents of S.H.I.E.L.D.": "agents-of-shield", "Better Call Saul": "better-call-saul", "Daredevil": "daredevil", "Fear the Walking Dead": "fear-the-walking-dead", "Game of Thrones": "game-of-thrones", "Gotham": "gotham", "Iron Fist": "iron-fist", "Jessica Jones": "jessica-jones", "Legends of Tomorrow": "legends-of-tomorrow", "Luke Cage": "luke-cage", "Marco Polo": "marco-polo", "Mr. Robot": "mr-robot", "Supergirl": "supergirl", "Vikings": "vikings", "The Flash": "the-flash", "The Walking Dead": "walking-dead", "Prison break": "prison-break", "American Gods": "american-gods", "Narcos": "narcos", "House of Cards": "house-of-cards", "Peaky Blinders": "peaky-blinders", "West World": "westworld", "Homeland": "homeland", "The 100": "the-hundred", "SouthPark": "south-park", "Defenders": "defenders" } def create_a_json_file(location, file_name, json_data): try: with open(os.path.join(location, file_name), "w", encoding='utf-8') as output_file: output_file.write(json_data) #json.dump(json_data, output_file, indent=4, sort_keys=True, ensure_ascii=False) except FileNotFoundError as fNot: #except IOError as e: print("Jezuz christ!!! File not found!! \n{0}".format(fNot)) new_tv_shows = {'tvShows' : []} default = {"name": "", "shortcut": "", "new" : ""} for iterator in range(0, len(tv_shows.items())): new_tv_shows.get('tvShows').append(default) json_string = json.dumps(new_tv_shows) jdict = json.loads(json_string) iterator = 0 for key, value in tv_shows.items(): jdict["tvShows"][iterator]["name"] = key jdict["tvShows"][iterator]["shortcut"] = value jdict["tvShows"][iterator]["new"] = "" iterator += 1 new_json_string = json.dumps(jdict, indent=4, sort_keys=True, ensure_ascii=False) #rint(new_json_string) create_a_json_file("data", "tvshows.json", new_json_string) def load_json(location, file_name): with open(os.path.join(location + "/", file_name), "r", encoding='utf-8', errors='ignore') as output_file: json_load_file = json.load(output_file) #print(json_load_file) return json_load_file loaded_json = load_json("data", "tvshows.json") for i in range(0, len(loaded_json['tvShows'])): print(loaded_json['tvShows'][i]['name'] + ' ' + loaded_json['tvShows'][i]['shortcut'])

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f0d5ec44af0b7fd99da7def80b7579ceb6efab44

943

py

Python

setup.py

expobrain/json-schema-codegen

e22b386333c6230e5d6f5984fd947fdd7b947e82

[ "MIT" ]

21

2018-06-15T16:08:57.000Z

2022-02-11T16:16:11.000Z

setup.py

expobrain/json-schema-codegen

e22b386333c6230e5d6f5984fd947fdd7b947e82

[ "MIT" ]

14

2018-08-09T18:02:19.000Z

2022-01-24T18:04:17.000Z

setup.py

expobrain/json-schema-codegen

e22b386333c6230e5d6f5984fd947fdd7b947e82

[ "MIT" ]

4

2018-11-30T18:19:10.000Z

2021-11-18T04:04:36.000Z

import setuptools from pkg_resources import parse_version SETUPTOOLS_MIN_VER = "40.1.0" if parse_version(setuptools.__version__) < parse_version(SETUPTOOLS_MIN_VER): raise RuntimeError("setuptools minimum required version: %s" % SETUPTOOLS_MIN_VER) from setuptools import setup, find_packages setup( name="json_codegen", version="0.4.6", keywords="python javascript json-schema codegen", author="Daniele Esposti", author_email="daniele.esposti@gmail.com", url="https://github.com/expobrain/json-schema-codegen", packages=find_packages(exclude=["tests", "tests.*"]), entry_points={"console_scripts": ["json_codegen = json_codegen.cli:main"]}, python_requires=">=3", license="MIT", install_requires=["astor>=0.7.1", "setuptools>={}".format(SETUPTOOLS_MIN_VER)], scripts=["bin/ast_to_js"], long_description=open("README.md").read(), long_description_content_type="text/markdown", )

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null

0

null

0

1

0

f0d7fc7ede4a8bc9347dce8d8fc42b285d73ea63

13,142

py

Python

cogs/musik/listener.py

noaione/naoTimes

39f3f1ae434baf4ff9f3ed4a19cbfd69f76f881d

[ "MIT" ]

5

2019-06-14T01:29:46.000Z

2021-02-08T08:21:24.000Z

cogs/musik/listener.py

naoTimesdev/naoTimes

39f3f1ae434baf4ff9f3ed4a19cbfd69f76f881d

[ "MIT" ]

21

2021-03-26T08:31:45.000Z

2022-03-26T10:15:25.000Z

cogs/musik/listener.py

noaione/naoTimes

39f3f1ae434baf4ff9f3ed4a19cbfd69f76f881d

[ "MIT" ]

4

2019-06-26T14:18:09.000Z

2021-02-08T08:21:39.000Z

import asyncio import logging import random import traceback from typing import TYPE_CHECKING, Dict, List, Optional, Union import arrow import discord import wavelink from discord.backoff import ExponentialBackoff from discord.ext import commands try: from sentry_sdk import push_scope except ImportError: pass from naotimes.bot import naoTimesBot from naotimes.music import TrackEntry, TrackRepeat from naotimes.utils import quote if TYPE_CHECKING: from cogs.botbrain.error import BotBrainErrorHandler VocalChannel = Union[discord.VoiceChannel, discord.StageChannel] class MusikPlayerListener(commands.Cog): def __init__(self, bot: naoTimesBot): self.bot = bot self.logger = logging.getLogger("MusicP.Listener") self.error_backoff: Dict[str, ExponentialBackoff] = {} def delay_next(self, guild_id: str): guild_id = str(guild_id) if guild_id not in self.error_backoff: # Dont delay first try self.error_backoff[guild_id] = ExponentialBackoff() return None delay = self.error_backoff[guild_id].delay() return delay def clean_delay(self, guild_id: int): guild_id = str(guild_id) if guild_id in self.error_backoff: try: del self.error_backoff[guild_id] except KeyError: pass @commands.Cog.listener("on_wavelink_node_ready") async def on_node_ready(self, node: wavelink.Node): self.logger.info(f"Node: <{node.identifier}> [{node.region.name}] is ready!") @commands.Cog.listener("on_wavelink_track_end") async def on_track_end(self, player: wavelink.Player, track: wavelink.Track, reason: str): ctime = self.bot.now().int_timestamp current = self.bot.ntplayer.get(player) current_track = player.source or track if current.current: current_track = current.current.track node = player.node track_title = None if current_track: track_title = current_track.title self.logger.info( f"Player: <{player.guild}> [{node.identifier}] track [{track_title}] has ended with: {reason}" ) # Dispatch task self.bot.loop.create_task( self.bot.ntplayer.play_next(player), name=f"naotimes-track-end-{player.guild.id}_{ctime}_{reason}", ) @commands.Cog.listener("on_wavelink_track_exception") async def on_track_exception(self, player: wavelink.Player, track: wavelink.Track, error: Exception): node = player.node real_track = player.source or track self.logger.warning( f"Player: <{player.guild}> [{node.identifier}] track [{real_track.title}] has exception: {error}" ) vc_player = self.bot.ntplayer.get(player) channel = None determine_announce = True # Determine if we should announce error # If the current position is around 5 seconds before the track end, dont announce it. if vc_player.current: channel = vc_player.current.channel cpos = player.position duration = vc_player.current.track.duration grace_period = duration - 5 if cpos >= grace_period: determine_announce = False await self._push_error_to_sentry(player, vc_player.current, error, "track-exc") if channel and determine_announce: try: await channel.send( f"Terjadi kesalahan ketika menyetel lagu `{track.title}`, mohon kontak Owner Bot!" ) except (discord.Forbidden, discord.HTTPException): pass @commands.Cog.listener("on_wavelink_track_start") async def on_track_start(self, player: wavelink.Player, track: wavelink.Track): instance = self.bot.ntplayer.get(player) self.clean_delay(player.guild.id) # Temporary update the position. last_update = arrow.utcnow().datetime player.last_position = 1 player.last_update = last_update current = instance.current track = current.track track_title = track.title self.logger.info( f"Player: <{player.guild}> [{player.node.identifier}]: <{track_title}> has started playing!" ) embed = self.bot.ntplayer.generate_track_embed(current) try: await current.channel.send(embed=embed) except (discord.Forbidden, discord.HTTPException): pass async def _dispatch_playback_next_later( self, player: wavelink.Player, delay: Optional[float], ctime: int ): self.logger.info(f"Player: Delaying playback of next track by {delay} seconds") if delay: await asyncio.sleep(delay) self.bot.loop.create_task( self.bot.ntplayer.play_next(player), name=f"naotimes-playback-retries-{player.guild.id}_{ctime}_{delay}", ) @commands.Cog.listener("on_naotimes_playback_failed") async def on_playback_failed(self, player: wavelink.Player, entry: TrackEntry, exception: Exception): ctime = self.bot.now().int_timestamp instance = self.bot.ntplayer.get(player) self.logger.warning( f"Player: <{player.guild}> failed to play track: {entry.track}", exc_info=exception ) delay_next = self.delay_next(player.guild.id) if instance.repeat != TrackRepeat.single: delay_next = None # Dispatch play_next agane self.bot.loop.create_task( self._dispatch_playback_next_later(player, delay_next, ctime), name=f"naotimes-playback-retries-delayed-{player.guild.id}_{ctime}_{str(exception)}", ) channel = entry.channel if channel: error_msg_delay = f"Lagu `{entry.track.title}` gagal diputar, bot akan melewati lagu tersebut!" if delay_next: error_msg_delay += ( f"\nBot akan mencoba menyetel lagu selanjutnya dalam {round(delay_next, 2)} detik" ) try: await channel.send(error_msg_delay) except (discord.Forbidden, discord.HTTPException, Exception): pass _do_not_log = (wavelink.errors.LoadTrackError, wavelink.errors.BuildTrackError) if isinstance(exception, _do_not_log): return # Push to log channel embed = discord.Embed( title="🎵 Music Error Log", colour=0xFF253E, description="Terjadi kesalahan ketika ingin memutar musik!", timestamp=self.bot.now().datetime, ) track = entry.track _source = getattr(track, "source", "Unknown") track_info = f"**Judul**: `{track.title}`\n**Artis**: `{track.author}`" track_info += f"\n**Link**: [Link]({track.uri})\n**Source**: `{_source}`" embed.add_field(name="Lagu", value=track_info, inline=False) peladen_info = f"{player.guild.name} ({player.guild.id})" author_info = f"{str(entry.requester)} ({entry.requester.id})" embed.add_field( name="Pemutar", value=f"**Peladen**: {peladen_info}\n**Pemutar**: {author_info}", inline=False ) error_info = [ f"Lagu: {track.author} - {track.title}", f"URL: {track.uri} ({_source})", f"Peladen: {peladen_info}", f"Pemutar: {author_info}", ] tb = traceback.format_exception(type(exception), exception, exception.__traceback__) tb_fmt = "".join(tb).replace("`", "") tb_fmt_quote = quote(tb_fmt, True, "py") full_pesan = "**Terjadi kesalahan pada pemutar musik**\n\n" full_pesan += quote("\n".join(error_info), True, "py") + "\n\n" full_pesan += tb_fmt_quote embed.add_field(name="Traceback", value=tb_fmt_quote, inline=False) error_cog: BotBrainErrorHandler = self.bot.get_cog("BotBrainErrorHandler") await error_cog._push_bot_log_or_cdn(embed, full_pesan) await self._push_error_to_sentry(player, entry, exception) async def _push_error_to_sentry( self, player: wavelink.Player, track: TrackEntry, e: Exception, handler: str = "playback" ): if self.bot._use_sentry: with push_scope() as scope: scope.user = { "id": track.requester.id, "username": str(track.requester), } scope.set_tag("cog", "music-backend") scope.set_tag("command", f"music-{handler}-handler") track_src = getattr(track.track, "source", "Unknown") scope.set_context( "track", { "title": track.track.title, "artist": track.track.author, "source": track_src, "link": track.track.uri, }, ) scope.set_tag("command_type", "music") scope.set_tag("guild_id", str(player.guild.id)) scope.set_tag("channel_id", str(player.channel.id)) self.logger.error( f"Player: <{player.guild}> failed to play track: <{track.track}>", exc_info=e ) def _select_members( self, members: List[discord.Member], id_check: int = None ) -> Optional[discord.Member]: # Select one member # Use priority, so if the member an admin, pick them # then check if they have specific permissions # if none of them match, get random person. administrator = [] moderators = [] normal_members = [] for member in members: if member.bot: continue if member.id == id_check: continue if member.guild_permissions.administrator: administrator.append(member) elif member.guild_permissions.manage_guild: moderators.append(member) else: normal_members.append(member) if administrator: return random.choice(administrator) if moderators: return random.choice(moderators) if not normal_members: # Mark no delegate, if someone joined, mark them as # the new delegation later. return None return random.choice(normal_members) async def _delegate_on_bot_new_channel(self, guild: discord.Guild, after_channel: Optional[VocalChannel]): if after_channel is None: self.logger.info(f"Player: Bot got kicked from <{guild.id}> VC, deleting queue...") self.bot.ntplayer.delete(guild) return vc_members = after_channel.members delegated = self._select_members(vc_members) if delegated is None: self.logger.info(f"Player<{guild.id}>: No delegate found, no one to delegate to.") self.bot.ntplayer.change_dj(guild, None) return self.bot.ntplayer.change_dj(guild, delegated) @commands.Cog.listener("on_voice_state_update") async def _auto_voice_delegation( self, member: discord.Member, before: discord.VoiceState, after: discord.VoiceState, ): """Automatically delegate the DJ of the current music player""" guild = member.guild has_instance = self.bot.ntplayer.has(guild) if not has_instance: return if member.id == self.bot.user.id: return await self._delegate_on_bot_new_channel(guild, after.channel) if member.bot: return vc_check = guild.voice_client if not vc_check: self.bot.ntplayer.delete(guild) return instance = self.bot.ntplayer.get(guild) if instance.host is None: self.logger.info(f"Player: <{guild.id}> no host set, using <{member}> as host") self.bot.ntplayer.change_dj(guild, member) return if instance.host.id != member.id: return if before.channel is not None and before.channel.id == instance.channel.id: if after.channel is None or after.channel.id != instance.channel.id: channel = instance.channel self.logger.info(f"Player: <{guild.id}> host left VC, trying to delegate...") new_host = self._select_members(channel.members, member.id) if new_host is None: # No one to delegate to, mark as none while we wait for a new one. self.logger.info(f"Player: <{guild.id}> no delegate found, marking as None") self.bot.ntplayer.change_dj(guild, None) return self.logger.info(f"Player: <{guild.id}> delegate found, <{new_host}> is the new host.") self.bot.ntplayer.change_dj(guild, new_host) def setup(bot: naoTimesBot): bot.add_cog(MusikPlayerListener(bot))

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0

0.138182

0

null

0

null

0

1

0

f0d85ff21b9583ecc358a110a43fecddd61cee4d

20,658

py

Python

apis_core/apis_entities/forms.py

acdh-oeaw/apis-core

f7ece05eec46c820321fd28d3e947653dcb98ae7

[ "MIT" ]

11

2018-07-11T18:11:40.000Z

2022-03-25T11:07:12.000Z

apis_core/apis_entities/forms.py

acdh-oeaw/apis-core

f7ece05eec46c820321fd28d3e947653dcb98ae7

[ "MIT" ]

309

2018-06-11T08:38:50.000Z

2022-03-31T13:45:22.000Z

apis_core/apis_entities/forms.py

acdh-oeaw/apis-core

f7ece05eec46c820321fd28d3e947653dcb98ae7

[ "MIT" ]

5

2017-08-21T10:37:07.000Z

2021-09-27T19:08:47.000Z

# -*- coding: utf-8 -*- from crispy_forms.bootstrap import Accordion, AccordionGroup from crispy_forms.helper import FormHelper from crispy_forms.layout import Layout, Fieldset from crispy_forms.layout import Submit from dal import autocomplete from django import forms from django.conf import settings from django.contrib.contenttypes.models import ContentType from django.core.validators import URLValidator from django.db.models.fields import BLANK_CHOICE_DASH from django.forms import ModelMultipleChoiceField, ModelChoiceField from django.urls import reverse from apis_core.apis_metainfo.models import Text, Uri, Collection from apis_core.apis_vocabularies.models import TextType from apis_core.helper_functions import DateParser from apis_core.helper_functions.RDFParser import RDFParser from .fields import ListSelect2, Select2Multiple from .models import AbstractEntity if "apis_highlighter" in settings.INSTALLED_APPS: from apis_highlighter.models import AnnotationProject class SearchForm(forms.Form): search = forms.CharField(label="Search") @property def helper(self): helper = FormHelper() helper.form_id = "searchForm" helper.form_tag = False helper.add_input(Submit("fieldn", "search")) helper.form_method = "GET" return helper def get_entities_form(entity): # TODO __sresch__ : consider moving this class outside of the function call to avoid redundant class definitions class GenericEntitiesForm(forms.ModelForm): class Meta: model = AbstractEntity.get_entity_class_of_name(entity) exclude = [ "start_date", "start_start_date", "start_end_date", "start_date_is_exact", "end_date", "end_start_date", "end_end_date", "end_date_is_exact", "text", "source", "published", ] exclude.extend(model.get_related_entity_field_names()) exclude.extend(model.get_related_relationtype_field_names()) def __init__(self, *args, **kwargs): super(GenericEntitiesForm, self).__init__(*args, **kwargs) self.helper = FormHelper() self.helper.form_class = entity.title() + "Form" self.helper.form_tag = False self.helper.help_text_inline = True acc_grp1 = Fieldset("Metadata {}".format(entity.title())) acc_grp2 = AccordionGroup("MetaInfo", "references", "notes", "review") attrs = { "data-placeholder": "Type to get suggestions", "data-minimum-input-length": getattr(settings, "APIS_MIN_CHAR", 3), "data-html": True, } # list to catch all fields that will not be inserted into accordion group acc_grp2 fields_list_unsorted = [] for f in self.fields.keys(): if isinstance( self.fields[f], (ModelMultipleChoiceField, ModelChoiceField) ): v_name_p = str(self.fields[f].queryset.model.__name__) if isinstance(self.fields[f], ModelMultipleChoiceField): widget1 = Select2Multiple else: widget1 = ListSelect2 if ( ContentType.objects.get( app_label__in=[ "apis_entities", "apis_metainfo", "apis_relations", "apis_vocabularies", "apis_labels", ], model=v_name_p.lower(), ).app_label.lower() == "apis_vocabularies" ): self.fields[f].widget = widget1( url=reverse( "apis:apis_vocabularies:generic_vocabularies_autocomplete", kwargs={"vocab": v_name_p.lower(), "direct": "normal"}, ), attrs=attrs, ) if self.instance: res = [] if isinstance(self.fields[f], ModelMultipleChoiceField): try: for x in getattr(self.instance, f).all(): res.append((x.pk, x.label)) except ValueError: pass self.fields[f].initial = res self.fields[f].choices = res else: try: res = getattr(self.instance, f) if res is not None: self.fields[f].initial = (res.pk, res.label) self.fields[f].choices = [ (res.pk, res.label), ] except ValueError: res = "" if f not in acc_grp2: # append to unsorted list, so that it can be sorted and afterwards attached to accordion group acc_grp1 fields_list_unsorted.append(f) def sort_fields_list(list_unsorted, entity_label): """ Sorts a list of model fields according to a defined order. :param list_unsorted: list The unsorted list of fields. :param entity_label: str The string representation of entity type, necessary to find the entity-specific ordering (if it is defined) :return: list The sorted list if entity-specific ordering was defined, the same unordered list if not. """ entity_settings = getattr(settings, 'APIS_ENTITIES', None) if entity_settings is None: return list_unsorted sort_preferences = entity_settings[entity_label].get('form_order', None) sort_preferences_used = [] if sort_preferences is None: return list_unsorted else: # list of tuples to be sorted later field_rank_pair_list = [] for field in list_unsorted: if field in sort_preferences: # if this succeeds, then the field has been given a priorites ordering above ranking_by_index = sort_preferences.index(field) sort_preferences_used.append(field) field_rank_pair = (field, ranking_by_index) else: # if no ordering for the field was found, then give it 'Inf' # so that it will be attached at the end. field_rank_pair = (field, float('Inf')) field_rank_pair_list.append(field_rank_pair) # Make a check if all items of sort_preferences were used. If not, this indicates an out of sync setting # if len(sort_preferences) > 0: if len(sort_preferences_used) != len(sort_preferences): differences = [] for p in sort_preferences_used: if p not in sort_preferences: differences.append(p) for p in sort_preferences: if p not in sort_preferences_used: differences.append(p) raise Exception( "An item of the entity setting 'form_order' list was not used. \n" "This propably indicates that the 'form_order' settings is out of sync with the effective django models.\n" f"The relevant entity is: {entity_label}\n" f"And the differences between used list and settings list are: {differences}" ) # sort the list according to the second element in each tuple # and then take the first elements from it and return as list return [ t[0] for t in sorted(field_rank_pair_list, key=lambda x: x[1]) ] # sort field list, iterate over it and append each element to the accordion group for f in sort_fields_list(fields_list_unsorted, entity): acc_grp1.append(f) self.helper.layout = Layout(Accordion(acc_grp1, acc_grp2)) self.fields["status"].required = False self.fields["collection"].required = False self.fields["start_date_written"].required = False self.fields["end_date_written"].required = False instance = getattr(self, "instance", None) if instance != None: if instance.start_date_written: self.fields[ "start_date_written" ].help_text = DateParser.get_date_help_text_from_dates( single_date=instance.start_date, single_start_date=instance.start_start_date, single_end_date=instance.start_end_date, single_date_written=instance.start_date_written, ) else: self.fields[ "start_date_written" ].help_text = DateParser.get_date_help_text_default() if instance.end_date_written: self.fields[ "end_date_written" ].help_text = DateParser.get_date_help_text_from_dates( single_date=instance.end_date, single_start_date=instance.end_start_date, single_end_date=instance.end_end_date, single_date_written=instance.end_date_written, ) else: self.fields[ "end_date_written" ].help_text = DateParser.get_date_help_text_default() def save(self, *args, **kwargs): obj = super(GenericEntitiesForm, self).save(*args, **kwargs) if obj.collection.all().count() == 0: col_name = getattr( settings, "APIS_DEFAULT_COLLECTION", "manually created entity" ) col, created = Collection.objects.get_or_create(name=col_name) obj.collection.add(col) return obj return GenericEntitiesForm class GenericEntitiesStanbolForm(forms.Form): def save(self, *args, **kwargs): cd = self.cleaned_data entity = RDFParser(cd["entity"], self.entity.title()).get_or_create() return entity def __init__(self, entity, *args, **kwargs): attrs = { "data-placeholder": "Type to get suggestions", "data-minimum-input-length": getattr(settings, "APIS_MIN_CHAR", 3), "data-html": True, "style": "width: auto", } ent_merge_pk = kwargs.pop("ent_merge_pk", False) super(GenericEntitiesStanbolForm, self).__init__(*args, **kwargs) self.entity = entity self.helper = FormHelper() form_kwargs = {"entity": entity} url = reverse( "apis:apis_entities:generic_entities_autocomplete", args=[entity.title(), "remove"], ) label = "Create {} from reference resources".format(entity.title()) button_label = "Create" if ent_merge_pk: form_kwargs["ent_merge_pk"] = ent_merge_pk url = reverse( "apis:apis_entities:generic_entities_autocomplete", args=[entity.title(), ent_merge_pk], ) label = "Search for {0} in reference resources or db".format(entity.title()) button_label = "Merge" self.helper.form_action = reverse( "apis:apis_entities:generic_entities_stanbol_create", kwargs=form_kwargs ) self.helper.add_input(Submit("submit", button_label)) self.fields["entity"] = autocomplete.Select2ListCreateChoiceField( label=label, widget=ListSelect2(url=url, attrs=attrs), validators=[URLValidator], ) class FullTextForm(forms.Form): def save(self, entity): cd = self.cleaned_data text = None for f in cd.keys(): text_type = TextType.objects.get(pk=f.split("_")[1]) text = Text.objects.filter(tempentityclass=entity, kind=text_type) if text.count() == 1: text = text[0] text.text = cd[f] text.save() elif text.count() == 0: text = Text(text=cd[f], kind=text_type) text.save() entity.text.add(text) return text def __init__(self, *args, **kwargs): if "entity" in kwargs.keys(): entity = kwargs.pop("entity", None) else: entity = None if "instance" in kwargs.keys(): instance = kwargs.pop("instance", None) else: instance = None super(FullTextForm, self).__init__(*args, **kwargs) self.helper = FormHelper() self.helper.form_class = "FullTextForm" self.helper.form_tag = False self.helper.help_text_inline = True collections = [] if instance: for i in instance.collection.all(): collections.append(i) try: if len(collections) > 0: q = TextType.objects.filter( entity__iexact=entity, collections__in=collections ) else: q = TextType.objects.filter(entity__iexact=entity) for txt in q: self.fields["text_" + str(txt.pk)] = forms.CharField( label=txt.name, help_text=txt.description, required=False, widget=forms.Textarea, ) if instance: for t in instance.text.all(): if "text_" + str(t.kind.pk) in self.fields.keys(): self.fields["text_" + str(t.kind.pk)].initial = t.text except: pass class PersonResolveUriForm(forms.Form): # person = forms.CharField(label=False, widget=al.TextWidget('PersonAutocomplete')) person = forms.CharField(label=False) person_uri = forms.CharField(required=False, widget=forms.HiddenInput()) def save(self, site_instance, instance=None, commit=True): cd = self.cleaned_data if cd["person"].startswith("http"): uri = Uri.objects.create(uri=cd["person"], entity=site_instance) else: uri = Uri.objects.create(uri=cd["person_uri"], entity=site_instance) return uri def __init__(self, *args, **kwargs): entity_type = kwargs.pop("entity_type", False) self.request = kwargs.pop("request", False) super(PersonResolveUriForm, self).__init__(*args, **kwargs) self.helper = FormHelper() self.helper.form_tag = False def clean(self): cleaned_data = super(PersonResolveUriForm, self).clean() if Uri.objects.filter(uri=cleaned_data["person_uri"]).exists(): self.add_error("person", "This Person has already been added to the DB.") elif cleaned_data["person"].startswith("http"): if Uri.objects.filter(uri=cleaned_data["person"]).exists(): self.add_error("person", "This URI has already been added to the DB.") class NetworkVizFilterForm(forms.Form): ann_include_all = forms.BooleanField( required=False, label="Include general relations", help_text="""Not all relations are connected to an annotation.\ If checked relations that are not attached to an annotation are include.\ This setting is only used when an Annotation project is specified.""", ) start_date = forms.CharField( label="Start date", required=False, widget=forms.TextInput( attrs={"data-provide": "datepicker", "data-date-format": "dd.mm.yyyy"} ), ) end_date = forms.CharField( label="End date", required=False, widget=forms.TextInput( attrs={"data-provide": "datepicker", "data-date-format": "dd.mm.yyyy"} ), ) def __init__(self, *args, **kwargs): rel_attrs = { "data-placeholder": "Type to get suggestions", "data-minimum-input-length": getattr(settings, "APIS_MIN_CHAR", 3), "data-html": True, } attrs = { "data-placeholder": "Type to get suggestions", "data-minimum-input-length": getattr(settings, "APIS_MIN_CHAR", 3), "data-html": True, } super(NetworkVizFilterForm, self).__init__(*args, **kwargs) self.fields["select_relation"] = forms.ChoiceField( label="Relation type", choices=list( ("-".join(x.name.split()), x.name) for x in ContentType.objects.filter(app_label="apis_relations") ), help_text="Include only relations related to this annotation project \ (See the include general relations checkbox)", ) self.fields["select_relation"].initial = ("person-place", "person place") self.fields["search_source"] = autocomplete.Select2ListCreateChoiceField( label="Search source", widget=ListSelect2( url=reverse( "apis:apis_entities:generic_network_entities_autocomplete", kwargs={"entity": "person"}, ), attrs=attrs, ), ) self.fields["search_target"] = autocomplete.Select2ListCreateChoiceField( label="Search target", widget=ListSelect2( url=reverse( "apis:apis_entities:generic_network_entities_autocomplete", kwargs={"entity": "place"}, ), attrs=attrs, ), ) self.fields["select_kind"] = autocomplete.Select2ListCreateChoiceField( label="Select kind", widget=ListSelect2( url=reverse( "apis:apis_vocabularies:generic_vocabularies_autocomplete", kwargs={"vocab": "personplacerelation", "direct": "normal"}, ), attrs=rel_attrs, ), ) if "apis_highlighter" in settings.INSTALLED_APPS: self.fields["annotation_proj"] = forms.ChoiceField( label="Annotation Project", choices=BLANK_CHOICE_DASH + list((x.pk, x.name) for x in AnnotationProject.objects.all()), required=False, help_text="Include only relations related to this annotation project \ (See the include general relations checkbox)", ) self.helper = FormHelper() self.helper.form_class = "FilterNodesForm" self.helper.form_action = reverse("apis:apis_core:NetJson-list") self.helper.add_input(Submit("Submit", "Add nodes")) self.order_fields( ( "select_relation", "ann_include_all", "annotation_proj", "search_source", "select_kind", "search_target", ) ) class GenericFilterFormHelper(FormHelper): def __init__(self, *args, **kwargs): super(GenericFilterFormHelper, self).__init__(*args, **kwargs) self.helper = FormHelper() self.form_class = "genericFilterForm" self.form_method = "GET" self.add_input(Submit("Filter", "Filter"))

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f0d8ddfb1dd3917ee1c7754ff4a656b8aa207cb2

3,154

py

Python

Python Programs/ROCK-o-DRUM-master/ROCK-o-DRUM-master/drum_player.py

Chibi-Shem/Hacktoberfest2020-Expert

324843464aec039e130e85a16e74b76d310f1497

[ "MIT" ]

77

2020-10-01T10:06:59.000Z

2021-11-08T08:57:18.000Z

Python Programs/ROCK-o-DRUM-master/ROCK-o-DRUM-master/drum_player.py

Chibi-Shem/Hacktoberfest2020-Expert

324843464aec039e130e85a16e74b76d310f1497

[ "MIT" ]

46

2020-09-27T04:55:36.000Z

2021-05-14T18:49:06.000Z

Python Programs/ROCK-o-DRUM-master/ROCK-o-DRUM-master/drum_player.py

Chibi-Shem/Hacktoberfest2020-Expert

324843464aec039e130e85a16e74b76d310f1497

[ "MIT" ]

327

2020-09-26T17:06:03.000Z

2021-10-09T06:04:39.000Z

import cv2 #importing modules import numpy as np from drum_styles import draw,drum_press cap=cv2.VideoCapture(0) while True: ret,frame=cap.read() #accessing the frames frame=cv2.flip(frame,1) frame=cv2.GaussianBlur(frame,(9,9),0) #gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #_, binary = cv2.threshold(gray, 225, 255, cv2.THRESH_BINARY_INV) hsv=cv2.cvtColor(frame,cv2.COLOR_BGR2HSV) #converting to Hue_Saturation_Vue format #mask=cv2.inRange(hsv,lower_red,upper_red) #kernel=np.ones((5,5),np.float32)/25 #mask=cv2.filter2D(mask,-1,kernel) #mask=cv2.blur(mask,(3,3)) draw(frame) #creating the rectangular drums kernel1=np.ones((4,4),np.uint8) #kernels for smoothing the frames kernel2=np.ones((15,15),np.uint8) lower_red=np.array([132,90,120]) #creating the mask for red color upper_red=np.array([179,255,255]) mask1=cv2.inRange(hsv, lower_red,upper_red) lower_red=np.array([0,110,100]) upper_red= np.array([3,255,255]) mask2=cv2.inRange(hsv, lower_red,upper_red) mask_r=mask1+mask2 #final red mask mask_r=cv2.erode(mask_r,kernel1,iterations = 1) mask_r=cv2.morphologyEx(mask_r,cv2.MORPH_CLOSE,kernel2) xr,yr,wr,hr=0,0,0,0 contours_r,hierarchy=cv2.findContours(mask_r,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) #getting the contours in the mask try: for i in range (0,10): xr,yr,wr,hr=cv2.boundingRect(contours_r[i]) if(wr*hr)>2000: #checking for a proper area to avoid noisy disturbances break except: pass #passes if no maks are there in the image lower_b=np.array([38,86,0]) #blue color range upper_b= np.array([121,255,255]) mask_b=cv2.inRange(hsv, lower_b,upper_b) #final blue mask mask_b=cv2.erode(mask_b,kernel1,iterations=1) mask_b=cv2.morphologyEx(mask_b,cv2.MORPH_CLOSE,kernel2) xb,yb,wb,hb=0,0,0,0 contours_r,hierarchy=cv2.findContours(mask_b,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) #getting the contours in the mask try: for i in range (0,10): xb,yb,wb,hb=cv2.boundingRect(contours_r[i]) #getting the coordinates of the contour if(wb*hb)>2000: #checking for a proper area to avoid noisy disturbances break except: pass cv2.rectangle(frame,(xr,yr),(xr+wr,yr+hr),(255,255,255),2) #drawing a rectangle around the red object cv2.rectangle(frame,(xb,yb),(xb+wb,yb+hb),(255,255,255),2) drum_press(frame,xr,yr,wr,hr) #checking the drums it hits drum_press(frame,xb,yb,wb,hb) frame=cv2.resize(frame,(800,600)) cv2.imshow('ROCK-o-DRUM',frame) #displaying the frames #cv2.imshow('MASK_red',mask) if cv2.waitKey(1)==ord('q'): break cap.release() cv2.destroyAllWindows()

39.425

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0.603361

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3,154

3.984946

0.329032

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0.055556

0

null

0

null

0

1

0

f0d99f2f6f98885e72a2e5530207ddd70dec3390

5,252

py

Python

client.py

devilcius/funkwhale-predatum

cac9ea671275dd1d66d9ead7a1773047ef4324de

[ "Unlicense" ]

null

client.py

devilcius/funkwhale-predatum

cac9ea671275dd1d66d9ead7a1773047ef4324de

[ "Unlicense" ]

null

client.py

devilcius/funkwhale-predatum

cac9ea671275dd1d66d9ead7a1773047ef4324de

[ "Unlicense" ]

null

import json import ssl import time from http.client import HTTPSConnection import urllib.request import urllib.error import urllib.parse from urllib.error import URLError, HTTPError from http.client import BadStatusLine from .funkwhale_startup import PLUGIN import hashlib LOGIN_SCROBBLER_URL = "https://api.predatum.com/api/login" HOST_NAME = "api.predatum.com" PATH_SUBMIT = "/api/scrobble" SSL_CONTEXT = ssl.create_default_context() class Track: def __init__(self, artist_name, track_title, release_name=None, additional_info={}): self.artist_name = artist_name self.track_title = track_title self.release_name = release_name self.additional_info = additional_info @staticmethod def from_dict(data): return Track( data["artist_name"], data["track_title"], data.get("release_name", None), data.get("additional_info", {}), ) def to_dict(self): return { "artist_name": self.artist_name, "track_title": self.track_title, "release_name": self.release_name, "additional_info": self.additional_info, } def __repr__(self): return "Track(%s, %s)" % (self.artist_name, self.track_title) class PredatumScrobbler: def __init__(self, username, password): self.__next_request_time = 0 self.logger = PLUGIN["logger"] hashedAuth = hashlib.md5( (username + " " + password).encode("utf-8") ).hexdigest() self.token_cache_key = "predatum:sessionkey:{}".format(hashedAuth) self.username = username self.password = password self.setToken() def submit(self, listened_at, track): payload = _get_payload(track, listened_at) return self._submit("single", payload) def _submit(self, listen_type, payload, retry=0): self._wait_for_ratelimit() self.logger.info("ListenPredatum %s: %r", listen_type, payload) headers = { "Authorization": "Bearer %s" % self.token, "Accept": "application/json", "Content-Type": "application/json" } body = json.dumps(payload) conn = HTTPSConnection(HOST_NAME, context=SSL_CONTEXT) conn.request("POST", PATH_SUBMIT, body, headers) response = conn.getresponse() response_text = response.read() try: response_data = json.loads(response_text) except json.decoder.JSONDecodeError: response_data = response_text self._handle_ratelimit(response) log_msg = "Response %s: %r" % (response.status, response_data) if response.status == 429 and retry < 5: # Too Many Requests self.logger.warning(log_msg) return self._submit(listen_type, payload, retry + 1) elif response.status == 401 and retry < 5: self.logger.warning(log_msg) self.setToken() return self._submit(listen_type, payload, retry + 1) elif response.status == 201: self.logger.debug(log_msg) else: self.logger.error(log_msg) return response def _wait_for_ratelimit(self): now = time.time() if self.__next_request_time > now: delay = self.__next_request_time - now self.logger.debug("Rate limit applies, delay %d", delay) time.sleep(delay) def _handle_ratelimit(self, response): remaining = int(response.getheader("X-RateLimit-Remaining", 0)) reset_in = int(response.getheader("X-RateLimit-Reset-In", 0)) self.logger.debug("X-RateLimit-Remaining: %i", remaining) self.logger.debug("X-RateLimit-Reset-In: %i", reset_in) if remaining == 0: self.__next_request_time = time.time() + reset_in def setToken(self, renew = False): token = PLUGIN["cache"].get(self.token_cache_key) if not token or renew: token = self.login() self.token = token def login(self): logger = PLUGIN["logger"] params = dict(username = self.username, password = self.password, remember = '1', submit = 'Submit') data = urllib.parse.urlencode(params).encode('utf-8') try: request = urllib.request.Request(LOGIN_SCROBBLER_URL, data) response = urllib.request.urlopen(request) jsonResponse = json.loads(response.read().decode('utf-8')) return jsonResponse['token'] except HTTPError as e: logger.info('The server couldn\'t fulfill the authentication request.') logger.info('Error code: {}'.format(e.read())) except URLError as e: print('We failed to reach a server.') print(('Reason: ', e.reason)) except BadStatusLine as e: print(("the status line can’t be parsed as a valid HTTP/1.0 or 1.1 status line: ", e.line)) def _get_payload(track, listened_at=None): data = {"track_metadata": track.to_dict()} if listened_at is not None: data["listened_at"] = listened_at return data

35.248322

105

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0.272578

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0.024532

0.148483

0.036798

0

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0.278561

5,252

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0.003237

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0.016434

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false

0.031746

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0.02381

0

null

0

null

0

1

0

f0dc38ecbe730e8453f2a6c493fc69be4e6d68ae

12,654

py

Python

src/panel/panel_props.py

KeithPinson/cityvilleburg

d002645c3fb6738e0406aded338f16efb75532eb

[ "MIT" ]

null

src/panel/panel_props.py

KeithPinson/cityvilleburg

d002645c3fb6738e0406aded338f16efb75532eb

[ "MIT" ]

null

src/panel/panel_props.py

KeithPinson/cityvilleburg

d002645c3fb6738e0406aded338f16efb75532eb

[ "MIT" ]

null

"""Properties of the N-Key-Panel""" # # The properties of the N-Key-Panel. We want to store these # in the Blend file and to facilitate this we need to # put the properties in a class derived from the # PropertyGroup. # # It is not obvious but if we attach the properties to # the bpy.types.Scene object then Blender will keep the # the properties in the bpy.context.scene object and # the properties will be saved in the file. # # Copyright (c) 2021 Keith Pinson from math import isclose import bpy from bpy.types import PropertyGroup from bpy.props import ( PointerProperty, StringProperty, IntProperty, BoolProperty, EnumProperty) # pylint: disable=relative-beyond-top-level from ..terrain.terrain_props import CVB_TerrainProperties from .citysketchname_props import CVB_CityNameProperties, is_sketch_list_empty from ..utils.collection_utils import viewlayer_collections, collection_sibling_names from ..utils.object_utils import object_get, object_get_or_add_empty, object_parent_all from ..utils.fass_grid import fassGrid from ..addon.preferences import cvb_icon, cvb_prefs def _mini_factor(t, n): """For a square scale vector: factor will result in a NxN x, y geometry""" # This is intended for tiles. As the lengths of x and y diverge, the # resulting geometry will approach 2Nx0 f = 1 if len(t) > 1: a = t[0] b = t[1] f = 2*n / (a + b) if (a + b) > 0 else 1 return f class CVB_PanelProperties(PropertyGroup): # pylint: disable=invalid-name, line-too-long """Panel properties saved to the blend file""" _grid = fassGrid() def decode_style(self, coded_style): styles_found = \ [s[0] for s in self.sketch_style_list if s[0].startswith(coded_style)] if styles_found: style = styles_found[0] else: style = self.sketch_style_list[0][0] return style def encode_style(self, style): result = style[0] if style else self.sketch_style_list[0][0][0] return result def get_mini_sketch(self): """Check the size of the Transform empty to determine if mini sketch""" is_full = True cvb = bpy.context.scene.CVB sketch_name = cvb.city_props.get_sketch_name() if \ (not len(cvb.import_name_prop) > 0) and \ (not cvb.city_props.is_get_sketch_name_pending()) \ else "" if sketch_name: # Get the empty transform_object = object_get("/CVB/{0}/{0} Transform".format(sketch_name)) if transform_object and hasattr(transform_object, "scale") and transform_object.scale: is_full = isclose(1.0, transform_object.scale[0], abs_tol=0.0001) return not is_full def mini_sketch_add_or_toggle(self, is_mini=True): """Adds or modifies the Transform empty to change the size of the sketch""" # # Requirements: # 1. Shrink the size of the sketch and related map,terrain,city to # to a footprint factor of 10x10 to 20x~0 depending on aspect ratio # 2. Center sketch to the tile zero position # 3. Hide any other sketches and related maps,terrains,cities # cvb = bpy.context.scene.CVB sketch_name = cvb.city_props.get_sketch_name() if \ (not len(cvb.import_name_prop) > 0) and \ (not cvb.city_props.is_get_sketch_name_pending()) \ else "" if not sketch_name: return # Make sure the view layers are in sync scene = viewlayer_collections("/CVB/{0}".format(sketch_name)) if scene: scene.exclude = False self.sketch_visibility_toggle(cvb.sketch_visible_prop) # # 1. Shrink Sketch # # Use the props for size rather than extracting size from sketch name size = (cvb.sketch_xy_linked_prop, cvb.sketch_xy_linked_prop) if \ cvb.using_tile_id_prop else (cvb.sketch_x_prop, cvb.sketch_y_prop) factor = _mini_factor(size, 10) empty = self.parent_to_sketch(sketch_name) if empty: empty.scale = (factor, factor, factor) if is_mini else (1, 1, 1) # # 2. Center to Tile Zero # tile_position = 0 if is_mini else cvb.tile_id_prop self.move_tile_position(empty, tile_position) # # 3. Hide other Sketches # sketch_path = "/CVB/{0}".format(sketch_name) sibling_sketch_names = collection_sibling_names(sketch_path) for sibling_sketch_name in sibling_sketch_names: scene = viewlayer_collections("/CVB/{0}".format(sibling_sketch_name)) if scene: scene.exclude = is_mini # To keep everything toggling in sync make sure these are not mini empty = self.parent_to_sketch(sibling_sketch_name) if empty: empty.scale = (1, 1, 1) def move_tile_position(self, empty, tile_id): if empty: # TODO: Get position based off tile_id pass def parent_to_sketch(self, sketch_name): sketch_path = "/CVB/{0}".format(sketch_name) empty_name = "{0} Transform".format(sketch_name) empty = object_get_or_add_empty( sketch_path, empty_name, radius=0.12, display_type='CUBE') if empty: object_parent_all(empty, "/CVB/{0}/Sketch ~ {0}".format(sketch_name)) object_parent_all(empty, "/CVB/{0}/Map ~ {0}".format(sketch_name)) object_parent_all(empty, "/CVB/{0}/Terrain ~ {0}".format(sketch_name)) object_parent_all(empty, "/CVB/{0}/City ~ {0}".format(sketch_name)) return empty def set_mini_sketch(self, value): """Toggle the mini sketch""" self.mini_sketch_add_or_toggle(value) def set_seed(self, value): """Keeps the addon preference seed in sync""" if cvb_prefs(bpy.context): if cvb_prefs(bpy.context).cvb_seed: cvb_prefs(bpy.context).cvb_seed = value def sketch_visibility_toggle(self, is_visible=True): """Turns the visibility of the sketch off or on""" cvb = bpy.context.scene.CVB sketch_name = cvb.city_props.get_sketch_name() if \ (not len(cvb.import_name_prop) > 0) and \ (not cvb.city_props.is_get_sketch_name_pending()) \ else "" if sketch_name: scene = viewlayer_collections("/CVB/{0}/Sketch ~ {0}".format(sketch_name)) if scene: scene.exclude = not is_visible def update_seed(self, context): """Seed update""" cvb = context.scene.CVB cvb.city_props.refresh_sketch_list(cvb) self.set_seed(cvb.seed_prop) def update_sketch_style(self, context): """Sketch style update""" cvb = context.scene.CVB cvb.city_props.refresh_sketch_list(cvb) def update_sketch_visibility(self, context): """Toggle visibility of sketch layer""" cvb = context.scene.CVB self.sketch_visibility_toggle(cvb.sketch_visible_prop) def update_sketch_xy_linked(self, context): """Sketch xy linked update""" cvb = context.scene.CVB cvb.city_props.refresh_sketch_list(cvb) def update_sketch_x(self, context): """Sketch x update""" cvb = context.scene.CVB cvb.city_props.refresh_sketch_list(cvb) def update_sketch_y(self, context): """Sketch y update""" cvb = context.scene.CVB cvb.city_props.refresh_sketch_list(cvb) def update_tile_id(self, context): """Impacts the file name """ cvb = context.scene.CVB (x,y) = self._grid.get_tile_xy(cvb.tile_id_prop) # Default font bfont.ttf (DejaVu Sans) use of hyphen represents minus sign poorly coords = "{0:+04d} {1:+04d}".format(x,y) coords = coords.replace("-", "\u2212") # replace hyphen with minus sign cvb.tile_position_prop = coords cvb.city_props.refresh_sketch_list(cvb) # def update_tile_position(self, context): # """Translation of tile id to position""" # cvb = context.scene.CVB # cvb.sketch_xy_linked_prop = # (x,y) = self._grid.get_tile_xy(cvb.tile_id_prop) # print(cvb.tile_id_prop, "{0:+04d} {1:+04d}".format(x,y)) city_props: PointerProperty(type=CVB_CityNameProperties) terrain_props: PointerProperty(type=CVB_TerrainProperties) import_name_prop: StringProperty( name="", description="""Imported Sketch""", default="") seed_prop: IntProperty( name="Seed", description="""Reproducible random sketch id""", default=1, min=1, max=32_767, update=update_seed) sketch_minimized_prop: BoolProperty( name="Mini Sketch Toggle", description="""Toggle Sketch Size""" if not is_sketch_list_empty() else "Inactive until New Sketch", get=get_mini_sketch, set=set_mini_sketch) # First letter of first element must be unique (it is used in city filename) sketch_style_list = [ ('grid', "Grid Plan City", "A city map modeled after the planned grid system"), ('medieval', "Medieval City Style", "A layout from years ago when cities formed inside a defensive wall"), ('skyscrapers', "Skyscraper City Style", "A city map modeled on the if you can't build out, build up"), ('western', "Western City Style", "A town built along a thoroughfare; water, rail, or road") ] sketch_style_prop: EnumProperty( name="", description="""Style hint that affects map sketch""", default='grid', items=sketch_style_list, update=update_sketch_style) sketch_visible_prop: BoolProperty( name="Sketch Visibility", description="""Toggle Sketch Visibility""" if not is_sketch_list_empty() else "Inactive until New Sketch", default=True, update=update_sketch_visibility) sketch_xy_linked_prop: IntProperty( name="Sketch XY", description="""Sketch XY size""", min=1, max=10_000, step=100, default=1000, update=update_sketch_xy_linked) sketch_x_prop: IntProperty( name="Sketch X", description="""Sketch X size""", min=1, max=10_000, step=100, default=1000, update=update_sketch_x) sketch_y_prop: IntProperty( name="Sketch Y", description="""Sketch Y size""", min=1, max=10_000, step=100, default=1000, update=update_sketch_y) tile_id_prop: IntProperty( name="", description="""Unique ID of tile""", default=0, min=0, max=_grid.get_last_tile(), update=update_tile_id) tile_position_prop: StringProperty( name="", description="""Matrix position from central tile""", default="+000 +000") # update=update_tile_position) using_tile_id_prop: BoolProperty( name="Multi-file Renders", description="""Facilitates rendering across multiple files for one single city""", default=False) # Internal number, typically incremented when new sketch is added variant_prop: IntProperty( name="Variant", description="""Sketch variant""", default=0, min=0, max=999) visible_city_sketch_prop: BoolProperty( name="City Sketch Visible", description="""Is City Sketch Visible""", default=False) visible_sketch_settings_prop: BoolProperty( name="Sketch Settings Visible", description="""Are Sketch Settings Visible""", default=True) visible_terrain_editor_prop: BoolProperty( name="Terrain Editor Visible", description="""Is Terrain Editor Visible""", default=False) def cvb_panel_register(): """Panel properties to register""" bpy.utils.register_class(CVB_TerrainProperties) bpy.utils.register_class(CVB_CityNameProperties) bpy.utils.register_class(CVB_PanelProperties) # pylint: disable=assignment-from-no-return bpy.types.Scene.CVB = PointerProperty(name='CVB', type=CVB_PanelProperties) # pylint: enable=assignment-from-no-return def cvb_panel_unregister(): """Panel properties for unregistering""" if bpy.types.Scene.CVB is not None: del bpy.types.Scene.CVB bpy.utils.unregister_class(CVB_PanelProperties) bpy.utils.unregister_class(CVB_CityNameProperties) bpy.utils.unregister_class(CVB_TerrainProperties)

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0.035911

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0.192638

0.178274

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0

0.015963

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12,654

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33.744

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0.264317

0

null

0

null

0

1

0

f0dc9dcef3186a2cff574f5ec1ff43c473d8d93f

218

py

Python

ind1.py

LokiTheGodOfBitchez/Lab_6

bd599514b39cea278b6563c3c3ff0b7cf6c463ad

[ "MIT" ]

null

ind1.py

LokiTheGodOfBitchez/Lab_6

bd599514b39cea278b6563c3c3ff0b7cf6c463ad

[ "MIT" ]

null

ind1.py

LokiTheGodOfBitchez/Lab_6

bd599514b39cea278b6563c3c3ff0b7cf6c463ad

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- if __name__ == '__main__': s = input("Enter the text: ") i = s.count('+') j = s.count('-') counter = i + j print("The number of '-' and '+': ", counter)

19.818182

45

0.509174

30

218

3.433333

0.766667

0.116505

0

0.012195

0.247706

218

10

46

21.8

0.615854

0.197248

0

0.306358

0

1

0

false

0

0.166667

0

null

0

null

0

1

0

f0e000f1d7c3aa0289cff7dbc52536879580c2d3

9,931

py

Python

bocce/responses.py

brianjpetersen/bocce

20a4845400e8759173c5391ce52f18dafbf4c678

[ "MIT" ]

null

bocce/responses.py

brianjpetersen/bocce

20a4845400e8759173c5391ce52f18dafbf4c678

[ "MIT" ]

null

bocce/responses.py

brianjpetersen/bocce

20a4845400e8759173c5391ce52f18dafbf4c678

[ "MIT" ]

null

# standard libraries import os import abc import collections import copy import io import gzip import mimetypes import datetime # third party libraries import numpy import werkzeug # first party libraries from . import (surly, utils, headers, cookies, ) __where__ = os.path.dirname(os.path.abspath(__file__)) mimetypes._winreg = None # do not load mimetypes from windows registry mimetypes.add_type('text/javascript', '.js') # stdlib default is application/x-javascript mimetypes.add_type('image/x-icon', '.ico') # not among defaults class FileIterator: def __init__(self, filename, mode='rb', block_size=None):#, delete_after=False): self.filename = filename if block_size is None: stats = self.stats = os.stat(filename) block_size = getattr(stats, 'st_blksize', 4096) self.file_ = open(filename, mode) self.block_size = block_size #self.delete_after = delete_after def __iter__(self): block_size = self.block_size file_ = self.file_ try: while True: block = file_.read(block_size) if not block: break yield block finally: file_.close() """ if self.delete_after: try: os.remove(self.filename) except: pass """ class BodyIterable: def __init__(self, iterable, content_length, content_type, content_encoding): self.iterable = iterable self.content_length = content_length self.content_type = content_type self.content_encoding = content_encoding @property def wsgi(self): return self.iterable class BodyFile(BodyIterable): def __init__(self, filename, mimetype=None, charset=None, is_compressed=False): self.filename = filename self.iterable = FileIterator(self.filename) self.content_length = getattr(self.iterable.stats, 'st_size', None) if mimetype is None: mimetype, _ = mimetypes.guess_type(self.filename) if mimetype is None: self.content_type = None else: if charset is None: self.content_type = mimetype else: self.content_type = '{}; {}'.format(mimetype, charset) self.is_compressed = is_compressed if self.is_compressed: self.content_encoding = 'gzip' else: self.content_encoding = None """ def compress(self, level=2, threshold=128): if len(self._content) < threshold: return """ @property def wsgi(self): return self.iterable class BodyContent: def __init__(self, content, content_type): self._content = content self.content_type = content_type self.content_encoding = None @property def content(self): if self.content_encoding == 'gzip': return self._compressed_content else: return self._content @property def content_length(self): return len(self.content) def compress(self, level=2, threshold=128): if len(self._content) < threshold: return compressed_content = io.BytesIO() with gzip.GzipFile(fileobj=compressed_content, mode='wb', compresslevel=level) as f: f.write(self._content) self._compressed_content = compressed_content.getvalue() self.content_encoding = 'gzip' @property def wsgi(self): return [self.content, ] class JsonBody(collections.OrderedDict): def __init__(self, charset='utf-8', indent=None, serializers=None): super(JsonBody, self).__init__() if serializers is None: serializers = {} serializers[surly.Url] = lambda url: str(url) serializers[tuple] = lambda tuple: list(tuple) serializers[set] = lambda set: list(set) serializers[datetime.date] = lambda date: date.isoformat() serializers[datetime.datetime] = lambda datetime: datetime.isoformat() serializers[bytes] = lambda bytes: bytes.decode(charset) serializers[numpy.ndarray] = lambda array: array.tolist() self.serializers = serializers self.indent = indent self.charset = charset self.content_type = 'application/json; charset={}'.format(charset) self.content_encoding = None self._cached_content = None self._compression_level = None self._compression_threshold = None self._compression_requested = False def __setitem__(self, *args, **kwargs): self._cached_content = None super(JsonBody, self).__setitem__(*args, **kwargs) def __getitem__(self, *args, **kwargs): self._cached_content = None return super(JsonBody, self).__getitem__(*args, **kwargs) @property def content_length(self): return len(self.content) @property def content(self): if self._cached_content is not None: return self._cached_content encoder = utils.JsonEncoder(self.indent, self.serializers) content = encoder.encode(self).encode(self.charset) # attempt to compress compression_threshold = getattr(self, '_compression_threshold', 128) compression_level = getattr(self, '_compression_level', 2) compression_requested = getattr(self, '_compression_requested', False) if compression_requested and len(content) >= compression_threshold: compressed_content = io.BytesIO() with gzip.GzipFile(fileobj=compressed_content, mode='wb', compresslevel=compression_level) as f: f.write(content) content = compressed_content.getvalue() self.content_encoding = 'gzip' self._cached_content = content return content def compress(self, level=2, threshold=128): self._cached_content = None self._compression_level = level self._compression_threshold = threshold self._compression_requested = True @property def wsgi(self): return [self.content, ] class Body: def __init__(self): self.set_content(bytes()) @property def content_length(self): content_length = getattr(self._iterable, 'content_length', None) if content_length is not None: content_length = str(content_length) return content_length @property def content_type(self): content_type = getattr(self._iterable, 'content_type', None) if content_type is not None: content_type = str(content_type) return content_type @property def content_encoding(self): content_encoding = getattr(self._iterable, 'content_encoding', None) if content_encoding is not None: content_encoding = str(content_encoding) return content_encoding def compress(self, *args): self._iterable.compress(*args) def set_file(self): raise NotImplementedError def get_json(self): if not isinstance(self._iterable, JsonBody): self.set_json({}) return self._iterable def set_json(self, value=None, charset='utf-8', indent=None, serializers=None): previous_json_body = getattr(self, '_iterable', None) if value is None: value = {} self._iterable = JsonBody(charset, indent, serializers) if previous_json_body is not None: if isinstance(previous_json_body, JsonBody): level = previous_json_body._compression_level threshold = previous_json_body._compression_threshold requested = previous_json_body._compression_requested self._iterable._compression_level = level self._iterable._compression_threshold = threshold self._iterable._compression_requested = requested self._iterable.update(value) def set_html(self, html, charset='utf-8'): self.set_text(html, mimetype='text/html', charset=charset) def set_text(self, text, mimetype='text/plain', charset='utf-8'): if mimetype is None: content_type = None else: if charset is None: content_type = mimetype else: content_type = '{}; {}'.format(mimetype, charset) self.set_content(text.encode(charset), content_type) def set_content(self, content, content_type=None): self._iterable = BodyContent(content, content_type) def set_iterable(self, iterable, content_length=None, content_type=None, content_encoding=None): self._iterable = BodyIterable(iterable, content_length, content_type, content_encoding) def __iter__(self): return iter(self._iterable.wsgi) content = property(fset=set_content) text = property(fset=set_text) json = property(fget=get_json, fset=set_json) html = property(fset=set_html) #file = property(fset=set_file) class Response: def __init__(self): self.status_code = 200 self.body = Body() self.cookies = cookies.ResponseCookies() self.headers = headers.DelegatedResponseHeaders( headers.CookiesResponseHeadersView(self.cookies), headers.BodyResponseHeadersView(self.body), ) @property def status(self): status_code = self.status_code return '{} {}'.format( status_code, werkzeug.http.HTTP_STATUS_CODES[status_code] ) def start(self, start_response): start_response(self.status, list(self.headers)) return self.body

32.993355

108

0.628436

1,067

9,931

5.597001

0.159325

0.055258

0.028634

0.014735

0.254521

0.232083

0.198593

0.145345

0.065975

0.0499

0

0.003791

0.282751

9,931

300

109

33.103333

0.83462

0.027389

0

0.263158

0

0.028806

0.004694

0

1

0.149123

false

0

0.048246

0.030702

0.328947

0

null

0

null

0

1

0

f0e3484f5109e28245966fa572c5449b1c193808

3,042

py

Python

jesse/routes/__init__.py

b1nhm1nh/jesse-ctf

98e519ba6a08af5dd8dd5bae09617a6056f3b5e4

[ "MIT" ]

3

2021-09-26T15:55:00.000Z

2022-01-17T08:04:21.000Z

jesse/routes/__init__.py

b1nhm1nh/jesse-ctf

98e519ba6a08af5dd8dd5bae09617a6056f3b5e4

[ "MIT" ]

26

2021-10-31T07:04:04.000Z

2022-03-24T04:24:21.000Z

jesse/routes/__init__.py

b1nhm1nh/jesse

98e519ba6a08af5dd8dd5bae09617a6056f3b5e4

[ "MIT" ]

null

import sys from typing import List, Any import jesse.helpers as jh from jesse import exceptions from jesse.services import logger from jesse.models import Route class RouterClass: def __init__(self) -> None: self.routes = [] self.extra_candles = [] self.market_data = [] def _reset(self) -> None: self.routes = [] self.extra_candles = [] self.market_data = [] @property def formatted_routes(self) -> list: """ Example: [{'exchange': 'Binance', 'strategy': 'A1', 'symbol': 'BTC-USDT', 'timeframe': '1m'}] """ return [ { 'exchange': r.exchange, 'symbol': r.symbol, 'timeframe': r.timeframe, 'strategy': r.strategy_name, } for r in self.routes ] @property def formatted_extra_routes(self) -> list: """ Example: [{'exchange': 'Binance', 'symbol': 'BTC-USD', 'timeframe': '3m'}] """ return [{ 'exchange': r['exchange'], 'symbol': r['symbol'], 'timeframe': r['timeframe'] } for r in self.extra_candles] def initiate(self, routes: list, extra_routes: list = None): if extra_routes is None: extra_routes = [] self.set_routes(routes) self.set_extra_candles(extra_routes) from jesse.store import store store.reset(force_install_routes=jh.is_unit_testing()) def set_routes(self, routes: List[Any]) -> None: self._reset() self.routes = [] for r in routes: # validate strategy that the strategy file exists (if sent as a string) if isinstance(r["strategy"], str): strategy_name = r["strategy"] if jh.is_unit_testing(): path = sys.path[0] # live plugin if path.endswith('jesse-live'): strategies_dir = f'{sys.path[0]}/tests/strategies' # main framework else: strategies_dir = f'{sys.path[0]}/jesse/strategies' exists = jh.file_exists(f"{strategies_dir}/{strategy_name}/__init__.py") else: exists = jh.file_exists(f'strategies/{strategy_name}/__init__.py') else: exists = True if not exists and isinstance(r["strategy"], str): raise exceptions.InvalidRoutes( f'A strategy with the name of "{r["strategy"]}" could not be found.') self.routes.append(Route(r["exchange"], r["symbol"], r["timeframe"], r["strategy"], None)) def set_market_data(self, routes: List[Any]) -> None: self.market_data = [] for r in routes: self.market_data.append(Route(*r)) def set_extra_candles(self, extra_candles: list) -> None: self.extra_candles = extra_candles router: RouterClass = RouterClass()

32.361702

102

0.539119

331

3,042

4.791541

0.271903

0.050441

0.022699

0.302648

0.129887

0

0.002979

0.337936

3,042

93

103

32.709677

0.784508

0.087442

0

0.227273

0

0.128735

0.052379

0

1

0.121212

false

0

0.106061

0

0.272727

0

null

0

null

0

1

0

f0e5fa4bea32ee401cfc25d86926f7410166e17c

2,978

py

Python

LeetCode/contest-2018-11-6/decode_at_index.py

Max-PJB/python-learning2

e8b05bef1574ee9abf8c90497e94ef20a7f4e3bd

[ "MIT" ]

null

LeetCode/contest-2018-11-6/decode_at_index.py

Max-PJB/python-learning2

e8b05bef1574ee9abf8c90497e94ef20a7f4e3bd

[ "MIT" ]

null

LeetCode/contest-2018-11-6/decode_at_index.py

Max-PJB/python-learning2

e8b05bef1574ee9abf8c90497e94ef20a7f4e3bd

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ ------------------------------------------------- @ Author : pengj @ date : 2018/11/6 22:23 @ IDE : PyCharm @ GitHub : https://github.com/JackyPJB @ Contact : pengjianbiao@hotmail.com ------------------------------------------------- Description : 880. 索引处的解码字符串虚拟用户通过次数 7 虚拟用户尝试次数 92 虚拟通过次数 7 虚拟提交次数 92 题目难度 Medium 给定一个编码字符串 S。为了找出解码字符串并将其写入磁带，从编码字符串中每次读取一个字符，并采取以下步骤：如果所读的字符是字母，则将该字母写在磁带上。如果所读的字符是数字（例如 d），则整个当前磁带总共会被重复写 d-1 次。现在，对于给定的编码字符串 S 和索引 K，查找并返回解码字符串中的第 K 个字母。示例 1：输入：S = "leet2code3", K = 10 输出："o" 解释：解码后的字符串为 "leetleetcodeleetleetcodeleetleetcode"。字符串中的第 10 个字母是 "o"。示例 2：输入：S = "ha22", K = 5 输出："h" 解释：解码后的字符串为 "hahahaha"。第 5 个字母是 "h"。示例 3：输入：S = "a2345678999999999999999", K = 1 输出："a" 解释：解码后的字符串为 "a" 重复 8301530446056247680 次。第 1 个字母是 "a"。提示： 2 <= S.length <= 100 S 只包含小写字母与数字 2 到 9 。 S 以字母开头。 1 <= K <= 10^9 解码后的字符串保证少于 2^63 个字母。 ------------------------------------------------- """ import time import re __author__ = 'Max_Pengjb' start = time.time() # 下面写上代码块 def decode_at_index(S, K): if re.match("[2-9]", S[0]): return False record = [] i = 0 count = 0 while count < K and i < K: if not S[i].isdigit(): count += 1 else: count *= int(S[i]) record.append((S[i], count)) i += 1 j = len(record) - 1 K = K % record[j][1] print(record) while K != 0: # if re.match("[2-9]", record[j][0]): # j -= 1 # K %= record[j][1] # else: # j -= 1 # K -= 1 # print(K, j, record[j][1]) while K < record[j][1]: j -= 1 K %= record[j][1] print("while K != 0:的时候", K, j, record[j][1]) print("While K=0 de j ", j) while re.match("[2-9]", record[j][0]): j -= 1 print(record[j][0]) return record # 看看大神的优秀方法 def decodeAtIndex(S, K): size = 0 for i in S: if i.isdigit(): size *= int(i) else: size += 1 for index in reversed(S): K %= size if K == 0 and index.isalpha(): return index if index.isdigit(): size /= int(index) else: size -= 1 return # s_in = "a2345678999999999999999" # k_in = 1 # s_in = "ha22" # k_in = 5 # s_in = "leet2code3" # k_in = 10 # s_in = "y959q969u3hb22odq595" # k_in = 222280369 # s_in = "vk6u5xhq9v" # k_in = 554 s_in = "vzpp636m8y" k_in = 2920 print(decode_at_index(s_in, k_in)) # 判断一个字符是不是数字 # 正则的方法是 # import re # return re.match('\d',x) # python 内置了 isdigit() 和 isalpha() 函数 def is_digit(x): try: x = int(x) return isinstance(x, int) except ValueError: return False # 上面中间写上代码块 end = time.time() print('Running time: %s Seconds' % (end - start))

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null

0

null

0

1

0

f0e71e20f553f75129fec573500727dc91adb11d

6,109

py

Python

Fusion/modules/Fusion/Utils/WinUT.py

roadnarrows-robotics/rnr-sdk

aee20c65b49fb3eedf924c5c2ec9f19f4f1a1b29

[ "MIT" ]

null

Fusion/modules/Fusion/Utils/WinUT.py

roadnarrows-robotics/rnr-sdk

aee20c65b49fb3eedf924c5c2ec9f19f4f1a1b29

[ "MIT" ]

null

Fusion/modules/Fusion/Utils/WinUT.py

roadnarrows-robotics/rnr-sdk

aee20c65b49fb3eedf924c5c2ec9f19f4f1a1b29

[ "MIT" ]

null

################################################################################ # # WinUT.py # """ Unit Test Window Module Simple and Handy Unit Test Window Harness for Fusion Module Unit Testing. Author: Robin D. Knight Email: robin.knight@roadnarrowsrobotics.com URL: http://www.roadnarrowsrobotics.com Date: 2006.12.05 Copyright (C) 2006. RoadNarrows LLC. """ # # All Rights Reserved # # Permission is hereby granted, without written agreement and without # license or royalty fees, to use, copy, modify, and distribute this # software and its documentation for any purpose, provided that # (1) The above copyright notice and the following two paragraphs # appear in all copies of the source code and (2) redistributions # including binaries reproduces these notices in the supporting # documentation. Substantial modifications to this software may be # copyrighted by their authors and need not follow the licensing terms # described here, provided that the new terms are clearly indicated in # all files where they apply. # # IN NO EVENT SHALL THE AUTHOR, ROADNARROWS LLC, OR ANY MEMBERS/EMPLOYEES # OF ROADNARROW LLC OR DISTRIBUTORS OF THIS SOFTWARE BE LIABLE TO ANY # PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL # DAMAGES ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, # EVEN IF THE AUTHORS OR ANY OF THE ABOVE PARTIES HAVE BEEN ADVISED OF # THE POSSIBILITY OF SUCH DAMAGE. # # THE AUTHOR AND ROADNARROWS LLC SPECIFICALLY DISCLAIM ANY WARRANTIES, # INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND # FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS ON AN # "AS IS" BASIS, AND THE AUTHORS AND DISTRIBUTORS HAVE NO OBLIGATION TO # PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. # ################################################################################ import tkinter as tk import Fusion.Utils.IVTimer as IVTimer import Fusion.Gui.GuiTextBar as GuiTextBar #------------------------------------------------------------------------------- # CLASS: WinUT #------------------------------------------------------------------------------- class WinUT: """ Handy and Simple Unit Test Window Harness Class. Derive from this class and add specific UT functions. """ #-- def __init__(self, title="Unit Test Window", ut={}): """ Initialize the Window. Parameters: title - Title of this window. ut - Unit test dictionary: {'Menu name': calback, ... } """ if not ut: ut = {'<dummy test>': self.utDummyStart} root = tk.Tk() root.wm_title(title) mb = tk.Menubutton(root, text="Select a Unit Test", bg="#00cccc", relief=tk.RAISED) mb.grid(row=0, column=0, stick=tk.W) mb.menu = tk.Menu(mb, tearoff=0) for k,v in ut.items(): mb.menu.add_command(label=k, command=v) mb.config(menu=mb.menu) b = tk.Button(root, text="Quit", bg="#990000", fg="#ffffff", command=root.destroy) b.grid(row=0, column=1, stick=tk.E) frame = tk.Frame(root, relief=tk.SUNKEN) frame.grid(row=1, column=0, columnspan=2, padx=3, pady=5, sticky=tk.W+tk.E) self.mStatusPane = GuiTextBar.GuiTextBar(frame, width=100, height=4, maxHistory=1000) self.mStatusPane.TagAdd("blue", foreground='blue') self.mStatusPane.TagAdd("black", foreground='black') self.mStatusPane.TagAdd("red", foreground='red') self.mStatusPane.TagAdd("green", foreground='#009900') self.mStatusPane.TagAdd("orange", foreground='#996600') self.mRoot = root # this Unit Test Window's 'widget' self.mSut = None # System Under Test self.mIvt = None # handy interval timer self.wut_showstatus("Ready", fg='green') #-- def wut_this(self): """ Return this UT window's widget. """ return self.mRoot #-- def wut_mark_sut(self, sut): """ Mark UT window's SUT window. """ self.mSut = sut self.mRoot.tkraise() #-- def wut_cancel(self): """ Cancel any unit test residules. """ if self.mIvt: self.mIvt.cancel() #-- def wut_showstatus(self, msg, fg='black'): """ Show UT status message. """ self.mStatusPane.ShowStatus(msg, tag=fg) #-- def utDummyStart(self): """ Dummy UT Start .""" self.wut_showstatus("Started dummy UT.") self.mIvt = IVTimer.IVTimer(0.5, 0.5, self.utDummyIter, cnt=0) self.mIvt.start() #-- def utDummyIter(self, ivt): """ Dummy UT Iterator. """ self.wut_showstatus("Dummy UT: pass %d" % ivt.cnt) ivt.cnt += 1 #------------------------------------------------------------------------------- # Unit Test Code #------------------------------------------------------------------------------- if __name__ == '__main__': import Fusion.Gui.GuiWinText as GuiWinText class MyWinUT(WinUT): def __init__(self): ut = { 'My Test 1': self.utTest1Start, 'My Test 2': self.utTest2Start } WinUT.__init__(self, title="My Unit Test Window", ut=ut) def utTest1Start(self): self.wut_showstatus("Nothing to run for my test 1", fg='red') def utTest2Start(self): self.wut_showstatus("Started my test 2.") self.mIvt = IVTimer.IVTimer(0.5, 0.5, self.utTest2Iter, firsttime=True, cnt=0) self.mIvt.start() def utTest2Iter(self, ivt): if ivt.firsttime: msg = "First time for my test 2." ivt.firsttime = False else: msg = "my test 2, pass #%d" % ivt.cnt self.wut_showstatus(msg) if self.mSut: self.mSut.TextAdd(msg+'\n') ivt.cnt += 1 #-- def main(how='base'): """ WinUT Unit Test Main. """ if how == 'sut': winUT = MyWinUT() winSut = GuiWinText.GuiWinText(winUT.wut_this(), title="Text Window UT") winUT.wut_mark_sut(winSut) elif how == 'derived': winUT = MyWinUT() else: # 'base': winUT = WinUT("WinUT Unit Test Window") winUT.wut_this().mainloop() winUT.wut_cancel() # run unit test #main(how='base') #main(how='derived') main(how='sut')

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0.215909

0

null

0

null

0

1

0

f0e8efa9862a958716652bef31005c1dfcb312ee

2,241

py

Python

src/main/interfaces/ui_ppt.py

NLGS2907/Alg1-Lector-de-Ejercicios

bb7e44bd8e5fd7420a61108e5ecb246b510b396b

[ "MIT" ]

4

2021-09-23T16:06:18.000Z

2021-09-23T23:17:32.000Z

src/main/interfaces/ui_ppt.py

NLGS2907/Alg1-Lector-de-Ejercicios

bb7e44bd8e5fd7420a61108e5ecb246b510b396b

[ "MIT" ]

null

src/main/interfaces/ui_ppt.py

NLGS2907/Alg1-Lector-de-Ejercicios

bb7e44bd8e5fd7420a61108e5ecb246b510b396b

[ "MIT" ]

null

""" Interfaz para un juego de \"Piedras, Papel o Tijeras\". """ from discord import Interaction from discord import PartialEmoji as Emoji from discord.enums import ButtonStyle from discord.ui import Button, button from ..archivos import DiccionarioStats from ..ppt import jugar_partida_ppt from .ui_general import VistaGeneral class JuegoPPT(VistaGeneral): """ Interfaz con la que jugar Piedra, Papel, o Tijeras. """ def __init__(self, stats: DiccionarioStats) -> None: """ Crea uan instancia de 'JuegoPPT'. """ super().__init__() self.stats_juego = stats @button(style=ButtonStyle.blurple, custom_id="rock", label="Piedra", emoji=Emoji.from_str("\N{rock}")) async def elegir_piedra(self, interaccion: Interaction, _boton: Button) -> None: """ El usuario ha elegido 'Piedra' en una partida de 'Piedra, Papel o Tijeras'. """ await jugar_partida_ppt("PIEDRA", str(interaccion.user.id), self.stats_juego, interaccion) @button(style=ButtonStyle.blurple, custom_id="paper", label="Papel", emoji=Emoji.from_str("\N{roll of paper}")) async def elegir_papel(self, interaccion: Interaction, _boton: Button) -> None: """ El usuario ha elegido 'Piedra' en una partida de 'Piedra, Papel o Tijeras'. """ await jugar_partida_ppt("PAPEL", str(interaccion.user.id), self.stats_juego, interaccion) @button(style=ButtonStyle.blurple, custom_id="scissors", label="Tijeras", emoji=Emoji.from_str("\N{Black Scissors}")) async def elegir_tijeras(self, interaccion: Interaction, _boton: Button) -> None: """ El usuario ha elegido 'Piedra' en una partida de 'Piedra, Papel o Tijeras'. """ await jugar_partida_ppt("TIJERAS", str(interaccion.user.id), self.stats_juego, interaccion)

32.478261

85

0.562249

230

2,241

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0.533007

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0.458843

0.422168

0

0.338242

2,241

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false

0

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0

0.236842

0

null

0

null

0

1

0

f0e9c98fae6ec582844793ea4f404f630174755a

3,626

py

Python

coramin/utils/mpi_utils.py

dilr/Coramin

22187e5f9e1631867c29f981ff6dc035341bd23d

[ "BSD-3-Clause" ]

11

2019-04-03T21:33:29.000Z

2022-02-28T06:07:03.000Z

coramin/utils/mpi_utils.py

dilr/Coramin

22187e5f9e1631867c29f981ff6dc035341bd23d

[ "BSD-3-Clause" ]

50

2019-04-01T18:48:14.000Z

2022-03-04T21:51:27.000Z

coramin/utils/mpi_utils.py

dilr/Coramin

22187e5f9e1631867c29f981ff6dc035341bd23d

[ "BSD-3-Clause" ]

9

2019-03-31T21:29:35.000Z

2021-09-02T02:33:40.000Z

from mpi4py import MPI import numpy as np import sys import os class MPISyncError(Exception): pass class MPIInterface: def __init__(self): self._comm = MPI.COMM_WORLD self._size = self._comm.Get_size() self._rank = self._comm.Get_rank() @property def comm(self): return self._comm @property def rank(self): return self._rank @property def size(self): return self._size class MPIAllocationMap: def __init__(self, mpi_interface, global_N): self._mpi_interface = mpi_interface self._global_N = global_N rank = self._mpi_interface.rank size = self._mpi_interface.size # there must be a better way to do this # find which entries in global correspond # to this process (want them to be contiguous # for the MPI Allgather calls later local_N = [0 for i in range(self._mpi_interface.size)] for i in range(global_N): process_i = i % size local_N[process_i] += 1 start = 0 end = None for i,v in enumerate(local_N): if i == self._mpi_interface.rank: end = start + v break else: start += v self._local_map = list(range(start, end)) def local_allocation_map(self): return list(self._local_map) def local_list(self, global_data): local_data = list() assert(len(global_data) == self._global_N) for i in self._local_map: local_data.append(global_data[i]) return local_data def global_list_float64(self, local_data_float64): assert(len(local_data_float64) == len(self._local_map)) global_data_numpy = np.zeros(self._global_N, dtype='d')*np.nan local_data_numpy = np.asarray(local_data_float64, dtype='d') comm = self._mpi_interface.comm comm.Allgatherv([local_data_numpy, MPI.DOUBLE], [global_data_numpy, MPI.DOUBLE]) return global_data_numpy.tolist() def activate_mpi_printing(style='rank-0-console', rank_0_filename='output_rank_0.txt'): """ Redirect standard output based on process rank. Parameters ---------- style: str Can be set to one of: * 'ignore-all': ignore all printing (actually, redirect all printing to os.devnull) * 'rank-0-console': printing from rank 0 will go to the console, printing from other processes will be ignored * 'rank-0-console-x-files': printing from rank 0 will go to the console, printing from other processes will go to a separate file ('output_rank_x.txt') * 'rank-0-file': printing from rank 0 will go to 'output_rank_0.txt' * 'separate-files': printing from each processor will be redirected to a separate file for each process ('output_rank_x.txt') """ rank = MPIInterface().rank if style == 'ignore-all': sys.stdout = open(os.devnull, 'w') elif style == 'rank-0-console': if rank != 0: sys.stdout = open(os.devnull, 'w') elif style == 'rank-0-file': if rank == 0: sys.stdout = open(rank_0_filename, 'w') else: sys.stdout = open(os.devnull, 'w') elif style == 'rank-0-console-x-files': if rank != 0: sys.stdout = open('output_rank_{0}.txt'.format(str(MPIInterface().rank)), 'w') elif style == 'separate-files': sys.stdout = open('output_rank_{0}.txt'.format(str(MPIInterface().rank)), 'w')

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0.229117

0.200477

0.184248

0.172315

0

0.012062

0.29123

3,626

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0

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0

0.027778

1

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false

0.013889

0.055556

0.305556

0.013889

0

null

0

null

0

1

0

f0ed48db9dedc8fe3209aa95d92dad8965be38ea

6,281

py

Python

ai/src/guest_identification.py

huonguy/dataspire-lite

e6953c5e3ece41373b66f50c8908eff60c1d3d66

[ "MIT" ]

12

2021-03-31T14:32:39.000Z

2022-02-14T01:49:49.000Z

ai/src/guest_identification.py

huonguy/dataspire-lite

e6953c5e3ece41373b66f50c8908eff60c1d3d66

[ "MIT" ]

1

2021-09-11T06:02:12.000Z

ai/src/guest_identification.py

huonguy/dataspire-lite

e6953c5e3ece41373b66f50c8908eff60c1d3d66

[ "MIT" ]

8

2021-04-26T07:05:12.000Z

2021-12-31T17:42:30.000Z

import numpy as np import pandas as pd from pandas import DataFrame import fuzzymatcher import traceback from datetime import datetime def load_data_file(file_path:str, nrows:int = 10e10): """ Load datafile from directory and return output as Dataframe Pandas for processing Parameters ---------- file_path : str Path to load the data file """ print(f"Start Loading Data File") df_input = pd.DataFrame() try: df_input = pd.read_csv(file_path, nrows = nrows) except Exception as error_sum: print("___") print("Error summary: \n", error_sum) error_log = traceback.format_exc() print("Error Details: \n", str(error_log)) print("___") print(f"Loading File With Total {len(df_input)} Observations") print(f"__________________________") return df_input def preprocess_data_file(df_input: DataFrame): """ Preprocess the dataframe input for fuzzy matching algorihtm in next step. Output of the function is the processed dataframe Parameters ---------- df_input : DataFrame Input DataFrame """ print(f"Start Preprocess Data File") start_time = datetime.now() df_input_processed = df_input.copy() df_input_processed['RoomNo'] = df_input_processed['RoomNo'].fillna("") df_input_processed['Children'] = df_input_processed['Children'].fillna(0) try: df_input_processed["orderid"] = df_input_processed.index df_input_processed["LastName"] = df_input_processed["LastName"].fillna("") df_input_processed["LastName"] = df_input_processed["LastName"].str.lower() df_input_processed["LastName"] = df_input_processed["LastName"].str.strip() df_input_processed["FirstName"] = df_input_processed["FirstName"].fillna("") df_input_processed["FirstName"] = df_input_processed["FirstName"].str.lower() df_input_processed["FirstName"] = df_input_processed["FirstName"].str.strip() df_input_processed["tmp_name"] = df_input_processed["FirstName"] + " " + df_input_processed["LastName"] df_input_processed["Email"] = df_input_processed["Email"].fillna("") df_input_processed["Email"] = df_input_processed["Email"].str.lower() df_input_processed["Email"] = df_input_processed["Email"].str.strip() except Exception as error_sum: print("___") print("Error summary: \n", error_sum) error_log = traceback.format_exc() print("Error Details: \n", str(error_log)) print("___") end_time = datetime.now() process_time = str(end_time - start_time) print(f"Preprocess Data File Time Consuming: {process_time}") print(f"__________________________") return df_input_processed def fuzzy_matching_algorithm(df_input_processed: DataFrame): """ Run the fuzzy matching algorithm to return dataframe with guest id Parameters ---------- df_input_processed : DataFrame Input DataFrame """ cols_on_matching = ['tmp_name', 'Email'] print(f"Start Apply Fuzzy Matching Algorithm") start_time = datetime.now() df_output = pd.DataFrame() try: DF = dict() id_features = cols_on_matching + ['orderid'] DF['guest_id_left'] = df_input_processed[id_features] DF['guest_id_right'] = df_input_processed[id_features] matched_results = fuzzymatcher.fuzzy_left_join(DF['guest_id_left'], DF['guest_id_right'], cols_on_matching, cols_on_matching, left_id_col='orderid', right_id_col='orderid') print(f"Guest Identification Output") print(matched_results.sort_values(by="best_match_score", ascending=False).head(10)) print(f"__________________________") df_matched = matched_results[matched_results["best_match_score"]>=0.05].copy().sort_values(by="best_match_score", ascending=True) df_matched = df_matched[["__id_left", "__id_right"]] df_output = pd.merge(df_input_processed, df_matched, how="left", left_on="orderid", right_on="__id_left") df_output['__id_right'] = df_output['__id_right'].mask(pd.isnull, df_output['orderid']) df_output = df_output.drop(columns = ["__id_left", "tmp_name", "orderid"]) df_output = df_output.rename(columns={"__id_right": "GuestID"}) df_output['GuestID'] = df_output['GuestID'].astype(int).astype(str) except Exception as error_sum: print("___") print("Error summary: \n", error_sum) error_log = traceback.format_exc() print("Error Details: \n", str(error_log)) print("___") end_time = datetime.now() process_time = str(end_time - start_time) print(f"Fuzzy Matching Algorithm Time Consuming: {process_time}") print(f"__________________________") return df_output def guest_identification_process(data_input: str, folder_path: str, force_run: int=0): try: df_input = pd.read_json(data_input) except: df_input = pd.DataFrame(data_input) print(f"\nInput DataFrame with total {len(df_input)} observations") print(df_input.columns) print(df_input.head(10)) print(f"__________________________") if len(df_input) < 1000 and force_run == 0: return df_input elif len(df_input) > 75000 and force_run == 0: return df_input elif len(df_input) < 300: return df_input df_input_processed = preprocess_data_file(df_input) df_output = fuzzy_matching_algorithm(df_input_processed) df_output.to_csv(str(folder_path) + "data_input_with_guest_id.csv", index = False) return df_output if __name__ == "__main__": file_path = "../sincq-dataset/SINCQ_merged_without_id.csv" df_input = load_data_file(file_path, 10000) df_input_processed = preprocess_data_file(df_input) df_output = fuzzy_matching_algorithm(df_input_processed) print(df_output.head(10))

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0.647827

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6,281

4.833773

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0.112718

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0.045852

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0.193231

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0.240885

6,281

164

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0.261261

0

null

0

null

0

1

0

f0ed79fac549ead768c886ae922c556e689cb9a0

1,297

py

Python

journal/views/utilities.py

kevinlee12/cas

1284d5a05731e441d523a4894a28e8a194c491f0

[ "Apache-2.0" ]

null

journal/views/utilities.py

kevinlee12/cas

1284d5a05731e441d523a4894a28e8a194c491f0

[ "Apache-2.0" ]

3

2015-04-19T03:00:57.000Z

2015-04-19T03:02:20.000Z

journal/views/utilities.py

kevinlee12/cas

1284d5a05731e441d523a4894a28e8a194c491f0

[ "Apache-2.0" ]

null

# coding: utf-8 # # Copyright 2015 The iU 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. from actstream.models import user_stream from django.http import HttpResponseRedirect from django.shortcuts import render import itertools def unread_notifications_count(request): """Returns the number of unseen notifications""" count = len(list(itertools.filterfalse(lambda x: x.data['seen'], user_stream(request.user)))) return render(request, 'journal/unread_count.html', {'unread_count': count}) def reset_notifications_count(request): def set_seen(action): action.data['seen'] = True action.save() list(map(set_seen, user_stream(request.user))) return HttpResponseRedirect('/activities')

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74

0.728604

176

1,297

5.306818

0.596591

0.06424

0.027837

0.034261

0.066381

0

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0.182729

1,297

37

75

35.054054

0.872642

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0.03876

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0.2

false

0

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0

0.6

0

null

0

null

0

1

0

f0eda1c7981350260ce730649b40e3ee0edd88c1

4,691

py

Python

Maze.py

SheffieldCao/MazeSolver

78dbeb93d414e0cc821be638ed0c4776e3ef0274

[ "MIT" ]

null

Maze.py

SheffieldCao/MazeSolver

78dbeb93d414e0cc821be638ed0c4776e3ef0274

[ "MIT" ]

3

2021-09-08T03:41:18.000Z

2022-03-12T01:00:58.000Z

Maze.py

SheffieldCao/MazeSolver

78dbeb93d414e0cc821be638ed0c4776e3ef0274

[ "MIT" ]

null

import cv2 import numpy as np import utils class MazeMap: '''define Pixel Point class''' class Point: def __init__(self, position): # Coordinate of Point self.Position = position # 4 neighbours, by default go straight self.Neighbours = [None, None, None, None] def __gt__(self, node): # By default, the priority of the compared nodes is lower than self return True def __init__(self, im): if np.any(im[0,:]) == True: self.rotate = False # don't rotate the img elif np.any(im[:,0]) == True: self.rotate = True im = utils.rotate(im,-90) width = im.shape[1] height = im.shape[0] data = list(im.ravel().astype(np.int)/255) self.start = None self.end = None # initialize start Point toppoints = [None] * width count = 0 for x in range (1, width - 1): # border of maze is wall, grayscale value:0 ,x from 1 to width-2 if data[x] > 0: self.start = MazeMap.Point((0,x)) toppoints[x] = self.start count += 1 break for y in range (1, height - 1): # border of maze is wall, grayscale value:0 ,y from 1 to height-2 row_offset = y * width rowup_offset = row_offset - width rowdown_offset = row_offset + width # Initialize previous, current and next values prv = False cur = False nxt = data[row_offset + 1] > 0 # initialize nxt = data[i*weight+1] for y = i leftnode = None for x in range (1, width - 1): # Step by step, Move prev, current and next towards right. # read all internal points of row_y prv = cur cur = nxt nxt = data[row_offset + x + 1] > 0 n = None if cur == False: # cur is on wall, do nothing continue move continue if prv == True: if nxt == True: # prv, cur, nxt = road, road, road # Create node only if paths above or below if data[rowup_offset + x] > 0 or data[rowdown_offset + x] > 0: n = MazeMap.Point((y,x)) leftnode.Neighbours[1] = n n.Neighbours[3] = leftnode leftnode = n else: # prv, cur, nxt = road, road, wall # Create path at end of corridor n = MazeMap.Point((y,x)) leftnode.Neighbours[1] = n n.Neighbours[3] = leftnode leftnode = None else: if nxt == True: # prv, cur, nxt = wall, road, road # Create path at start of corridor n = MazeMap.Point((y,x)) leftnode = n else: # prv, cur, nxt = road, wall, road # Create node only if in dead end if (data[rowup_offset + x] == 0) or (data[rowdown_offset + x] == 0): #print ("Create Node in dead end") n = MazeMap.Point((y,x)) # If node isn't none, we can assume we can connect N-S somewhere if n != None: # Clear above, connect to waiting top node if (data[rowup_offset + x] > 0): t = toppoints[x] t.Neighbours[2] = n n.Neighbours[0] = t # If clear below, put this new node in the top row for the next connection if (data[rowdown_offset + x] > 0): toppoints[x] = n else: toppoints[x] = None count += 1 # Last row row_offset = (height - 1) * width for x in range (1, width - 1): if data[row_offset + x] > 0: self.end = MazeMap.Point((height - 1,x)) t = toppoints[x] t.Neighbours[2] = self.end self.end.Neighbours[0] = t count += 1 break self.count = count self.width = width self.height = height

37.528

94

0.433596

525

4,691

3.820952

0.253333

0.007976

0.027916

0.317547

0.274676

0.224327

0.158026

0.13659

0.102692

0

0.021892

0.483905

4,691

125

95

37.528

0.806691

0.219996

0

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0

1

0.035294

false

0

0.035294

0.011765

0.105882

0

null

0

null

0

1

0

f0eece2bd0d0d30e357612d5e1a6f584f087d50f

71,193

py

Python

lib/ugrid_checks/check.py

bjlittle/ugrid-checks

c9247a3ab2412c6e2eaaf0fd1401e9c35a530595

[ "BSD-3-Clause" ]

null

lib/ugrid_checks/check.py

bjlittle/ugrid-checks

c9247a3ab2412c6e2eaaf0fd1401e9c35a530595

[ "BSD-3-Clause" ]

2

2022-02-28T19:47:47.000Z

2022-03-01T19:39:38.000Z

lib/ugrid_checks/check.py

bjlittle/ugrid-checks

c9247a3ab2412c6e2eaaf0fd1401e9c35a530595

[ "BSD-3-Clause" ]

null

from pathlib import Path import re from typing import AnyStr, Dict, List, Set, Tuple, Union import numpy as np from .nc_dataset_scan import NcFileSummary, NcVariableSummary, scan_dataset from .scan_utils import ( property_as_single_name, property_namelist, vars_w_props, ) from .ugrid_logger import CheckLoggingInterface __all__ = ["Checker", "check_dataset"] _VALID_UGRID_LOCATIONS = [ "node", "edge", "face", # Not supporting 'volume' at present ] _VALID_CONNECTIVITY_ROLES = [ "edge_node_connectivity", "face_node_connectivity", "face_edge_connectivity", "edge_face_connectivity", "face_face_connectivity", "boundary_node_connectivity", ] _VALID_UGRID_CF_ROLES = [ "mesh_topology", "location_index_set", ] + _VALID_CONNECTIVITY_ROLES _VALID_MESHCOORD_ATTRS = [ f"{location}_coordinates" for location in _VALID_UGRID_LOCATIONS ] _VALID_CF_CF_ROLES = [ "timeseries_id", "profile_id", "trajectory_id", ] # Valid cf varname regex : copied from iris.common.metadata code. _VALID_NAME_REGEX = re.compile(r"""^[a-zA-Z][a-zA-Z0-9]*[\w.+\-@]*$""") class Checker: """ Object to perform UGRID checking on a file. Scans a file on creation, and records the checking messages on its 'self.logger', which is a :class:`CheckLoggingInterface`. Can produce text reports for a checking summary, and a file structure summary. Could also be used programmatically to aid file analysis, but the way the information is stored is not currently designed with external use in mind. """ def __init__( self, file_scan: NcFileSummary, logger: CheckLoggingInterface = None, do_data_checks: bool = False, ignore_warnings=False, ignore_codes: Union[List[str], None] = None, ): self.file_scan = file_scan if logger is None: logger = CheckLoggingInterface() self.logger = logger self.do_data_checks = do_data_checks if ignore_codes is None: ignore_codes = [] self.ignore_codes = ignore_codes self.ignore_warnings = ignore_warnings # A shortcut for all the variables self._all_vars = file_scan.variables # Note: the following are filled in by 'dataset_identify_containers' self._meshdata_vars: Dict[str, NcVariableSummary] = {} self._mesh_vars: Dict[str, NcVariableSummary] = {} self._lis_vars: Dict[str, NcVariableSummary] = {} self._mesh_referrers: Dict[str, str] = {} self._lis_referrers: Dict[str, str] = {} # Note: these are filled by 'dataset_check_containers_and_map_dims' self._all_mesh_dims: Dict[str, Dict[str, Union[None, str]]] = {} self._allowed_cfrole_varnames: List[str] self._orphan_connectivities: Dict[str, NcVariableSummary] = {} # Initialise self.check_dataset() def state(self, errcode: str, vartype: str, varname: str, msg: str): """ Log a checking statement. Interface as for :meth:`CheckLoggingInterface.state`. """ if errcode not in self.ignore_codes: if not self.ignore_warnings or not errcode.startswith("A"): self.logger.state(errcode, vartype, varname, msg) def check_mesh_attr_is_varlist( self, meshvar: NcVariableSummary, attrname: str ): """ Check that a mesh-var attribute, if it exists, is a valid varlist. Parameters ---------- meshvar : class:`NcVariableSummary` mesh variable attrname : str name of the attribute of 'meshvar' to check Returns ------- ok : bool True iff no problems were found """ value = meshvar.attributes.get(attrname) if value is None: # Missing is ok. But NB *not* an empty string (see below). success = True else: success = value.dtype.kind == "U" if not success: msg = ( f"attribute '{attrname}' has type \"{value.dtype}\", " "which is not a string type." ) self.state("R105", "Mesh", meshvar.name, msg) if success: varnames = property_namelist(value) if not varnames: # Empty is *not* a valid content. # N.B. this includes non-string contents. self.state( "R105", "Mesh", meshvar.name, f'has {attrname}="{value}", ' "which is not a valid list of netcdf variable names.", ) success = False if success: for varname in varnames: if not varname: # skip any extra blanks continue if not _VALID_NAME_REGEX.match(varname): self.state( "R105", "Mesh", meshvar.name, f'has {attrname}="{varname}", ' "which is not a valid netcdf variable name.", ) success = False elif varname not in self._all_vars: self.state( "R106", "Mesh", meshvar.name, f"attribute '{attrname}' refers to a variable " f'"{varname}", but there is no such variable ' "in the dataset.", ) success = False return success def var_ref_problem(self, attr_value: np.ndarray) -> str: """ Make a text description of any problems of a single-variable reference. Check that the input contains a single, valid name, referring to an existing variable. If no problem, returns an empty string. """ succeed = True if attr_value.dtype.kind != "U": result = "is not a string value" succeed = False if succeed: names = property_namelist(attr_value) if len(names) != 1: result = "is not a single variable name" succeed = False if succeed: boundsvar_name = property_as_single_name(attr_value) if not _VALID_NAME_REGEX.match(boundsvar_name): result = "is not a valid netcdf variable name" succeed = False if succeed: bounds_var = self._all_vars.get(boundsvar_name) if bounds_var is None: result = "is not a variable in the dataset" succeed = False if succeed: result = "" return result def check_coord_bounds(self, coord: NcVariableSummary) -> List[Tuple[str]]: """ Validity-check the bounds of a coordinate (if any). Ok for _no_ bounds-attribute, but not if it is an empty string. Check: existence, n-dims, parent dimension, standard-name and units. Note: this method does not log messages directly, but returns results for the caller to log them with added context. Returns codes_and_messages : List[tuple(str, str)] a list of codes and messages, to be logged in the context of the parent coordinate variable. """ bounds_name = coord.attributes.get("bounds") result_codes_and_messages = [] def log_bounds_statement(code, msg): msg = f'has bounds="{bounds_name}", which {msg}' result_codes_and_messages.append((code, msg)) has_bounds = bounds_name is not None if has_bounds: msg = self.var_ref_problem(bounds_name) if msg != "": log_bounds_statement("R203", f"{msg}.") # NB full stop ! has_bounds = False if has_bounds: # NB from the above check, we do have a bounds variable. bounds_var = self._all_vars[str(bounds_name)] bounds_dims = bounds_var.dimensions (coord_dim,) = coord.dimensions # NB always has exactly 1 if coord_dim not in bounds_dims: msg = ( f"has dimensions {bounds_dims!r}, which does not include " f'the parent variable dimension, "{coord_dim}".' ) log_bounds_statement("R203", msg) n_bounds_dims = len(bounds_dims) if n_bounds_dims != 2: msg = ( f"has dimensions {bounds_dims!r}, of which there should " f"be 2, instead of {n_bounds_dims}." ) log_bounds_statement("R203", msg) # # Advisory checks # def check_attr_mismatch(attr_name): coord_attr, bounds_attr = [ var.attributes.get(attr_name) for var in (coord, bounds_var) ] if bounds_attr is not None and bounds_attr != coord_attr: if coord_attr is None: coord_attr = "<none>" msg = ( f'has {attr_name}="{bounds_attr}", which does not ' f"match the parent '{attr_name}' of \"{coord_attr}\"." ) log_bounds_statement("R203", msg) check_attr_mismatch("standard_name") check_attr_mismatch("units") # Do the data-values check. This is potentially costly. if self.do_data_checks: # TODO: enable data-value checks by attaching lazy data arrays # to scan variables. assert bounds_var.data is not None raise ValueError("Not ready for data-value checks.") log_bounds_statement("A205", "???") return result_codes_and_messages def check_mesh_coordinates( self, meshvar: NcVariableSummary, attr_name: str, ): """Validity-check a coordinate attribute of a mesh-variable.""" # Note: the content of the coords attribute was already checked # Elements which change as we scan the various coords. coord = None common_msg_prefix = "" # Function to emit a statement message, adding context as to the # specific coord variable. def log_coord(code, msg): self.state( code, "Mesh coordinate", coord.name, common_msg_prefix + msg ) coord_names = property_namelist(meshvar.attributes.get(attr_name)) for coord_name in coord_names: if coord_name not in self._all_vars: # This problem will already have been detected + logged. continue coord = self._all_vars[coord_name] common_msg_prefix = f"within {meshvar.name}:{attr_name} " coord_ndims = len(coord.dimensions) if coord_ndims != 1: msg = ( f"should have exactly one dimension, but has " f"{coord_ndims} dimensions : {coord.dimensions!r}." ) log_coord("R201", msg) else: # Check the dimension is the correct one according to location. (coord_dim,) = coord.dimensions location = attr_name.split("_")[0] mesh_dim = self._all_mesh_dims[meshvar.name][location] if coord_dim != mesh_dim: msg = ( f'has dimension "{coord_dim}", but the parent mesh ' f'{location} dimension is "{mesh_dim}".' ) log_coord("R202", msg) # Check coord bounds (if any) # N.B. this *also* assumes a single dim for the primary var codes_and_messages = self.check_coord_bounds(coord) for code, msg in codes_and_messages: log_coord(code, msg) # # Advisory notes.. # # A201 should have 1-and-only-1 parent mesh : this is handled by # 'check_dataset', as it involves multiple meshes. # A202 floating-point type dtype = coord.dtype if dtype.kind != "f": log_coord( "A202", f'has type "{dtype}", which is not a floating-point type.', ) # A203 standard-name : has+valid (can't handle fully ??) stdname = coord.attributes.get("standard_name") if not stdname: log_coord("A203", "has no 'standard_name' attribute.") # A204 units : has+valid (can't handle fully ??) stdname = coord.attributes.get("units") if not stdname: log_coord("A204", "has no 'units' attribute.") # A205 bounds data values match derived ones # - did this already above, within "check_coord_bounds" def check_connectivity( self, conn_var: NcVariableSummary, meshvar: Union[NcVariableSummary, None] = None, role_name: Union[str, None] = None, ): """ Validity-check a connectivity variable. This is either in the context of a containing 'meshvar', **or** with no containing mesh (so-called "orphan connectivity"). In the 'orphan' case, both meshvar and role_name are None. """ # Add to our list of variables 'allowed' to have a UGRID cf-role. conn_name = conn_var.name self._allowed_cfrole_varnames.append(conn_name) if meshvar: msg_prefix = f'of mesh "{meshvar.name}" ' else: msg_prefix = "" def log_conn(errcode, msg): self.state( errcode, "Mesh connectivity", conn_name, msg_prefix + msg ) cf_role = conn_var.attributes.get("cf_role") if cf_role is None: log_conn("R301", "has no 'cf_role' attribute.") elif cf_role not in _VALID_CONNECTIVITY_ROLES: msg = ( f'has cf_role="{cf_role}", ' "which is not a valid UGRID connectivity attribute." ) log_conn("R302", msg) elif role_name and cf_role != role_name: msg = ( f'has cf_role="{cf_role}", which is different from its ' f'role in the parent mesh, which is "{role_name}".' ) log_conn("R303", msg) if meshvar: # In the context of a meshvar, take 'role_name' as the definition. # -- we will then check the 'cf_role' attribute against that. assert role_name else: # With no meshvar, use the 'cf_role' attribute as our role # definition -- if there is one. role_name = str(cf_role) if cf_role else None conn_dims = conn_var.dimensions dims_msg = f"has dimensions {conn_dims!r}" if len(conn_dims) != 2: msg = ( f"{dims_msg}, of which there are " f"{len(conn_dims)}, instead of 2." ) log_conn("R304", msg) if meshvar: # Check dims : can only be checked against a parent mesh mesh_dims = self._all_mesh_dims[meshvar.name] is_parent_dim = [dim in mesh_dims.values() for dim in conn_dims] n_parent_dims = sum(is_parent_dim) if n_parent_dims == 0: msg = ( f"{dims_msg}, which does not contain any element " f"dimension of the parent mesh." ) log_conn("R305", msg) elif n_parent_dims == len(conn_dims): msg = ( f"{dims_msg}, which does not contain any dimension " f"which is not an element dimension of the parent mesh." ) log_conn("R306", msg) else: # Some are parent mesh-dims, and some not. # Just check that the *expected* mesh-dim is there. location = role_name.split("_")[0] parent_dim = mesh_dims[location] if parent_dim not in conn_dims: msg = ( f"{dims_msg}, which does not include the expected " f"{location} dimension of the parent mesh, " f'"{parent_dim}".' ) log_conn("R307", msg) edgelike_conns = ( "edge_node_connectivity", "boundary_node_connectivity", ) if role_name in edgelike_conns and n_parent_dims == 1: (conn_nonmesh_dim,) = ( dim for dim, in_parent in zip(conn_dims, is_parent_dim) if not in_parent ) nonmesh_dim = self.file_scan.dimensions[conn_nonmesh_dim] nonmesh_length = nonmesh_dim.length if nonmesh_length != 2: msg = ( f"{dims_msg}, which contains the non-mesh " f'dimension "{conn_nonmesh_dim}", but this has ' f"length {nonmesh_length} instead of 2." ) log_conn("R308", msg) index_value = conn_var.attributes.get("start_index") if index_value is not None: # Note: check value, converted to int. # This avoids an extra warning for strings like "0", "1", # since a non-integral type triggers an A302 warning anyway. if int(index_value) not in (0, 1): msg = ( f'has start_index="{index_value}", which is not ' "either 0 or 1." ) log_conn("R309", msg) if role_name and self.do_data_checks: if role_name.endswith("_node_connectivity"): # Check for missing values msg = "may have missing indices (NOT YET CHECKED)." log_conn("R310", msg) # # Advisory checks # # A301 1-and-only-1 parent mesh # In 'dataset_detect_multiple_refs', since it involves multiple meshes if conn_var.dtype.kind != "i": msg = ( f'has type "{conn_var.dtype}", ' "which is not an integer type." ) log_conn("A302", msg) if index_value is not None and index_value.dtype != conn_var.dtype: msg = ( f"has a 'start_index' of type \"{index_value.dtype}\", " "which is different from the variable type, " f'"{conn_var.dtype}".' ) log_conn("A303", msg) fill_value = conn_var.attributes.get("_FillValue") if ( role_name and role_name.endswith("_node_connectivity") and fill_value is not None ): msg = ( f"has a '_FillValue' attribute, which should not be present " f'on a "{role_name}" connectivity.' ) log_conn("A304", msg) if self.do_data_checks: # check for missing indices msg = "may have missing indices (NOT YET CHECKED)." log_conn("A305", msg) if fill_value is not None and fill_value.dtype != conn_var.dtype: msg = ( f"has a '_FillValue' of type \"{fill_value.dtype}\", " "which is different from the variable type, " f'"{conn_var.dtype}".' ) log_conn("A306", msg) if fill_value is not None and fill_value >= 0: msg = f'has _FillValue="{fill_value}", which is not negative.' log_conn("A307", msg) if meshvar and self.do_data_checks: # check for missing indices msg = ( "may have indices which exceed the length of the element " "dimension (NOT YET CHECKED)." ) log_conn("A308", msg) def check_mesh_connectivity( self, meshvar: NcVariableSummary, attr_name: str, ): """Validity-check a connectivity attribute of a mesh-variable.""" attr_value = meshvar.attributes.get(attr_name) ok = attr_value is not None if ok: conn_name = property_as_single_name(attr_value) ok = conn_name is not None if ok: conn_var = self._all_vars.get(conn_name) ok = conn_var is not None if ok: # Remove from the orphan list self._orphan_connectivities.pop(conn_name, None) # Check it, in the context of the containing mesh self.check_connectivity(conn_var, meshvar, attr_name) def check_mesh_var(self, meshvar: NcVariableSummary) -> Dict[str, str]: """ Validity-check a mesh variable. Parameters ---------- meshvar : :class:`NcVariableSummary` meshvar to check """ def log_meshvar(code, msg): self.state(code, "Mesh", meshvar.name, msg) # First check for bad 'cf_role' : # if wrong, meshvar can only have been identified by reference. cfrole_prop = meshvar.attributes.get("cf_role", None) if cfrole_prop != "mesh_topology": # This variable does not have the expected 'cf_role', so if we are # checking it, it must be referred to as 'mesh' by some variable. referring_var_name = self._mesh_referrers[meshvar.name] # Either there is no 'cf_role', or it is "wrong". msg = ( f"appears to be a mesh, " f'since it is the value of "{referring_var_name}:mesh". ' "But it has " ) if cfrole_prop is None: msg += "no 'cf_role' property," errcode = "R101" else: msg += f'cf_role="{cfrole_prop}",' errcode = "R102" msg += ' which should be "mesh_topology".' # N.B. do not identify as a Mesh, statement just says "variable" self.state(errcode, "", meshvar.name, msg) # Also, if the 'cf_role' was something else, then check it is a # valid option + emit an additional message if needed. if ( cfrole_prop is not None and cfrole_prop not in _VALID_UGRID_CF_ROLES ): msg = ( f'has cf_role="{cfrole_prop}", ' "which is not a valid UGRID cf_role." ) log_meshvar("A905", msg) topology_dimension = meshvar.attributes.get("topology_dimension") if topology_dimension is None: log_meshvar("R103", "has no 'topology_dimension' attribute.") else: # Check the topology dimension. # In principle, this controls which other connectivity properties # may appear : In practice, it is better to parse those # independently, and then cross-check. if topology_dimension not in (0, 1, 2): msg = ( f'has topology_dimension="{topology_dimension}", ' "which is not 0, 1 or 2." ) log_meshvar("R104", msg) # Handle this subsequently as if it was missing topology_dimension = None # Work out what topology-dimension is implied by the available mesh # properties, which we will use *instead* of the declared one in # subsequent tests (and check the declared one against it). highest_connectivity = None appropriate_dim = 0 if "face_node_connectivity" in meshvar.attributes: appropriate_dim = 2 highest_connectivity = "face_node_connectivity" elif "edge_node_connectivity" in meshvar.attributes: appropriate_dim = 1 highest_connectivity = "edge_node_connectivity" if topology_dimension is not None: # Emit an error if the attributes present don't match the stated # topology-dimension. If *no* topology-dimension, skip this : we # already flagged that it was missing, above. if topology_dimension != appropriate_dim: if topology_dimension == 0: if appropriate_dim == 1: errcode = "R111" # unexpected edge-node else: assert appropriate_dim == 2 errcode = "R113" # unexpected face-node elif topology_dimension == 1: if appropriate_dim == 0: errcode = "R112" # missing edge-node else: assert appropriate_dim == 2 errcode = "R113" # unexpected face-node else: assert topology_dimension == 2 errcode = "R113" # missing face-node if topology_dimension < appropriate_dim: # something is extra msg = ( f'has topology_dimension="{topology_dimension}", ' f"but the presence of a '{highest_connectivity}' " f"attribute implies it should be {appropriate_dim}." ) else: # something is missing topology_required_attribute = { 0: "face_node", 1: "edge_node_connectivity", 2: "face_node_connectivity", }[int(topology_dimension)] msg = ( f'has topology_dimension="{topology_dimension}", ' f"but it has no '{topology_required_attribute}' " f"attribute." ) log_meshvar(errcode, msg) # Check all coordinate and connectivity attributes are valid "varlists" varlist_names = _VALID_MESHCOORD_ATTRS + _VALID_CONNECTIVITY_ROLES for attr in varlist_names: is_conn = attr in _VALID_CONNECTIVITY_ROLES attr_value = meshvar.attributes.get(attr) if attr_value is not None: ok = self.check_mesh_attr_is_varlist(meshvar, attr) var_names = property_namelist(attr_value) if not ok: errcode = "R109" if is_conn else "R108" msg = ( f'has {attr}="{attr_value}", which is not ' "a list of variables in the dataset." ) log_meshvar(errcode, msg) elif is_conn and len(var_names) != 1: msg = ( f'has {attr}="{attr_value}", which contains ' f"{len(var_names)} names, instead of 1." ) log_meshvar("R107", msg) # Work out the actual mesh dimensions. mesh_dims = { name: None for name in ("face", "edge", "node", "boundary") } self._all_mesh_dims[meshvar.name] = mesh_dims if "node_coordinates" not in meshvar.attributes: log_meshvar( "R110", "does not have a 'node_coordinates' attribute." ) else: # Note: if a 'node_coordinates' attribute exists, then we already # checked that it is a valid varlist. # So don't re-raise any problems here, just press on. coord_names = property_namelist( meshvar.attributes["node_coordinates"] ) if coord_names: coord_var = self._all_vars.get(coord_names[0]) if coord_var: # Answer is the first dimension, if any. if len(coord_var.dimensions) > 0: mesh_dims["node"] = coord_var.dimensions[0] def deduce_element_dim(location): # Identify the dim, and check consistency of relevant attributes. # If found, set it in 'mesh_dims' dimattr_name = f"{location}_dimension" connattr_name = f"{location}_node_connectivity" dimension_name = property_as_single_name( meshvar.attributes.get(dimattr_name) ) if location in ("boundary", "node"): # No 'boundary_dimension' attribute is supported. if dimension_name: dimension_name = None msg = ( f"has an attribute '{dimattr_name}', which is not " "a valid UGRID term, and may be a mistake." ) log_meshvar("A106", msg) if dimension_name: # There is an explicit 'xxx_dimension' property. if connattr_name not in meshvar.attributes: errcode = {"edge": "R123", "face": "R122"}[location] msg = ( f"has an attribute '{dimattr_name}', " "which is not valid " f"since there is no '{connattr_name}'." ) log_meshvar(errcode, msg) elif dimension_name in self.file_scan.dimensions: mesh_dims[location] = dimension_name else: errcode = {"edge": "R115", "face": "R117"}[location] msg = ( f'has {dimattr_name}="{dimension_name}", which is not ' "a dimension in the dataset." ) log_meshvar(errcode, msg) elif connattr_name in meshvar.attributes: # No "xxx_dimension" attribute, but we *do* have # "xxx_node_connectivity", so mesh does _have_ this location. connvar_name = property_as_single_name( meshvar.attributes[connattr_name] ) conn_var = self._all_vars.get(connvar_name) if conn_var: # Answer is the first dimension, if any. if len(conn_var.dimensions) > 0: mesh_dims[location] = conn_var.dimensions[0] deduce_element_dim("node") deduce_element_dim("boundary") deduce_element_dim("edge") deduce_element_dim("face") # Check that, if any connectivities have non-standard dim order, then a # dimension attribute exists. def var_has_nonfirst_dim(varname, dimname): conn_var = self._all_vars.get(varname) result = conn_var is not None if result: result = dimname in conn_var.dimensions if result: result = conn_var.dimensions[0] != dimname return result location_altordered_conns = {} for attr in _VALID_CONNECTIVITY_ROLES: maindim_location = attr.split("_")[0] assert maindim_location != "node" # no such connectivities maindim_name = mesh_dims[maindim_location] for conn_name in property_namelist(meshvar.attributes.get(attr)): if var_has_nonfirst_dim(conn_name, maindim_name): # We found a connectivity with a nonstandard dim order dim_attr = f"{maindim_location}_dimension" if dim_attr not in meshvar.attributes: # There is no corresponding 'xxx_dimension', so warn. conns = location_altordered_conns.get( maindim_location, set() ) conns.add(conn_name) location_altordered_conns[maindim_location] = conns for location, conns in location_altordered_conns.items(): # Found connectivities with a nonstandard dim order for this dim. assert location in ("face", "edge") errcode = {"edge": "R116", "face": "R118"}[location] conn_names = [f'"{name}"' for name in conns] conn_names_str = ", ".join(conn_names) msg = ( f"has no '{dim_attr}' attribute, but there are " f"{location} connectivities " f"with non-standard dimension order : {conn_names_str}." ) log_meshvar(errcode, msg) # Check that all existing coordinates are valid. for coords_name in _VALID_MESHCOORD_ATTRS: location = coords_name.split("_")[0] # Only check coords of locations present in the mesh. # This avoids complaints about coords dis-connected by problems # with the topology identification. if mesh_dims[location]: self.check_mesh_coordinates(meshvar, coords_name) # Check that all existing connectivities are valid. for attr in _VALID_CONNECTIVITY_ROLES: self.check_mesh_connectivity(meshvar, attr) # deal with the optional elements (connectivities) def check_requires(errcode, attrname, location_1, location_2=None): exist = attrname in meshvar.attributes if exist: elems = [location_1] if location_2: elems.append(location_2) required_elements = [ f"{name}_node_connectivity" for name in elems ] missing_elements = [ f"'{name}'" for name in required_elements if name not in meshvar.attributes ] if missing_elements: err_msg = ( f"has a '{attrname}' attribute, which is not valid " f"since there is no " ) err_msg += "or ".join(missing_elements) err_msg += " attribute present." log_meshvar(errcode, err_msg) check_requires("R114", "boundary_node_connectivity", "face") check_requires("R119", "face_face_connectivity", "face") check_requires("R120", "face_edge_connectivity", "face", "edge") check_requires("R121", "edge_face_connectivity", "face", "edge") # Advisory checks. if meshvar.dimensions: log_meshvar("A101", "has dimensions.") if "standard_name" in meshvar.attributes: log_meshvar("A102", "has a 'standard_name' attribute.") if "units" in meshvar.attributes: log_meshvar("A103", "has a 'units' attribute.") # NOTE: "A104" relates to multiple meshvars, so is handled in caller. return mesh_dims def check_meshdata_var(self, datavar: NcVariableSummary): """Validity-check a mesh data variable.""" def log_meshdata(errcode, msg): self.state(errcode, "Mesh data", datavar.name, msg) lis_name = datavar.attributes.get("location_index_set") mesh_name = datavar.attributes.get("mesh") location = datavar.attributes.get("location") # At least one of these is true, or we would not have identified this # as a mesh-data var. assert mesh_name is not None or lis_name is not None # Decide whether to check this as a lis-datavar or a mesh-datavar # This is designed to produce 3 possible "clash" errors: # lis & mesh & ~location --> R506 # lis & location & ~mesh --> R507 # mesh & lis --> R501 treat_as_lis = lis_name is not None and ( mesh_name is None or location is None ) # Initialise reference used for the generic parent dimension check parent_varname = None # Can be either a meshvar or a lis parent_location = None if treat_as_lis: # Treat the datavar as a 'lis-datavar' # --> has "location_index_set", but no "mesh" or "location" ref_msg = self.var_ref_problem(lis_name) if ref_msg: # Invalid 'location_index_set' reference msg = f'has location_index_set="{lis_name}", which {ref_msg}.' log_meshdata("R508", msg) else: # We have a valid lis var. Take this as the 'parent' for # the generic dimension test R510 parent_varname = str(lis_name) lis_var = self._lis_vars[parent_varname] # Also set the parent-location. # NOTE: we are not checking the lis-var here, only the datavar, # so just get a value that works if the lis is valid. parent_location = str(lis_var.attributes.get("location", "")) if parent_location not in _VALID_UGRID_LOCATIONS: parent_location = None if mesh_name is not None: msg = ( "has a 'mesh' attribute, which is invalid since it is " "based on a 'location_index_set' attribute." ) log_meshdata("R506", msg) if location is not None: msg = ( "has a 'location' attribute, which is invalid since it is " "based on a 'location_index_set' attribute." ) log_meshdata("R507", msg) else: # Treat the datavar as a 'mesh-datavar' # --> has "mesh" and "location", but no "location_index_set" ref_msg = self.var_ref_problem(mesh_name) if ref_msg: # Invalid 'mesh' reference msg = f'has mesh="{mesh_name}", which {ref_msg}.' log_meshdata("R502", msg) if lis_name is not None: msg = ( "has a 'location_index_set' attribute, which is invalid " "since it is based on a 'mesh' attribute." ) log_meshdata("R501", msg) if location is None: log_meshdata("R503", "has no 'location' attribute.") elif str(location) not in _VALID_UGRID_LOCATIONS: msg = ( f'has location="{location}", which is not one of ' f'"face", "edge" or "node".' ) log_meshdata("R504", msg) else: # Given a valid location, check that it exists in the parent if not ref_msg: parent_varname = str(mesh_name) parent_location = str(location) assert parent_varname in self._all_mesh_dims mesh_dims = self._all_mesh_dims[parent_varname] parent_dim = mesh_dims.get(parent_location) if parent_dim is None: msg = ( f'has location="{location}", which is a location ' "that does not exist in the parent mesh, " f'"{parent_varname}".' ) log_meshdata("R505", msg) # Generic dimension testing, for either lis- or mesh-type datavars # First check there is only 1 mesh-dim data_dims = datavar.dimensions data_mesh_dims = [ dim for dim in data_dims if any( dim in self._all_mesh_dims[some_mesh_name].values() for some_mesh_name in self._all_mesh_dims ) ] n_data_mesh_dims = len(data_mesh_dims) if n_data_mesh_dims != 1: msg = ( f"has dimensions {data_dims}, of which {n_data_mesh_dims} " "are mesh dimensions, instead of 1." ) log_meshdata("R509", msg) data_meshdim = None # cannot check against parent else: # We have a single element-dim : check against a parent mesh or lis (data_meshdim,) = data_mesh_dims if parent_varname and parent_location and data_meshdim: # If we have a valid parent ref, and single mesh dimension of the # datavar, check that they match mesh_dims = self._all_mesh_dims[parent_varname] parent_dim = mesh_dims[parent_location] if parent_dim is not None and data_meshdim != parent_dim: # Warn only if the parent_dim *exists*, but does not match # N.B. missing parent dim is checked elsewhere : R505 or R404 if parent_varname in self._lis_vars: typename = "location_index_set" else: typename = "mesh" msg = ( f'has the element dimension "{data_meshdim}", which does ' f"not match the {parent_location} dimension of the " f'"{parent_varname}" {typename}, which is "{parent_dim}".' ) log_meshdata("R510", msg) def check_lis_var(self, lis_var: NcVariableSummary): """Validity-check a location-index-set variable.""" # Add the lis element dimension into self._all_mesh_dims dims = lis_var.dimensions if len(dims) == 1: # Lis has a valid location and single dim # So we can record 'our' dim as an element-dim (lis_dim,) = dims # Note: record this under **all** locations. # Since we want to recognise this as a 'mesh dim', even if the lis # has an invalid mesh or location, and we don't use this to check # it against the parent element dim. self._all_mesh_dims[lis_var.name] = { name: lis_dim for name in _VALID_UGRID_LOCATIONS } def log_lis(errcode, msg): self.state(errcode, "location-index-set", lis_var.name, msg) cf_role = lis_var.attributes.get("cf_role") if cf_role is None: log_lis("R401", "has no 'cf_role' attribute.") elif cf_role != "location_index_set": msg = f'has cf_role="{cf_role}", instead of "location_index_set".' log_lis("R401", msg) mesh_var = None # Used to skip additional checks when mesh is bad mesh_name = lis_var.attributes.get("mesh") if mesh_name is None: log_lis("R402", "has no 'mesh' attribute.") else: msg_ref = self.var_ref_problem(mesh_name) if msg_ref: msg = f'has mesh="{mesh_name}", which {msg_ref}.' log_lis("R402", msg) else: mesh_name = str(mesh_name) mesh_var = self._mesh_vars.get(mesh_name) if mesh_var is None: msg = ( f'has mesh="{mesh_name}", ' "which is not a valid mesh variable." ) log_lis("R402", msg) location = lis_var.attributes.get("location") parent_dim = None if location is None: log_lis("R403", "has no 'location' attribute.") elif str(location) not in _VALID_UGRID_LOCATIONS: msg = ( f'has location="{location}", which is not one of ' '"face", "edge" or "node".' ) log_lis("R403", msg) elif mesh_var: # check the location exists in the parent mesh location = str(location) mesh_dims = self._all_mesh_dims[mesh_name] parent_dim = mesh_dims[location] if parent_dim is None: msg = ( f'has location="{location}", which is a location ' "that does not exist in the parent mesh, " f'"{mesh_name}".' ) log_lis("R404", msg) # Don't attempt any further checks against the mesh mesh_var = None lis_dims = lis_var.dimensions n_lis_dims = len(lis_dims) if n_lis_dims != 1: msg = ( f"has dimensions {lis_dims!r}, of which there are " f"{n_lis_dims} instead of 1." ) log_lis("R405", msg) lis_dim = None else: (lis_dim,) = lis_dims index_value = lis_var.attributes.get("start_index") if index_value is not None: # Note: check value, converted to int. # This avoids an extra warning for strings like "0", "1", # since a non-integral type triggers an A407 warning anyway. if int(index_value) not in (0, 1): msg = ( f'has start_index="{index_value}", which is not ' "either 0 or 1." ) log_lis("R406", msg) # # Advisory checks # if lis_var.dtype.kind != "i": msg = f'has type "{lis_var.dtype}", which is not an integer type.' log_lis("A401", msg) if self.do_data_checks: # TODO: data checks log_lis("A402", "contains missing indices.") if "_FillValue" in lis_var.attributes: msg = ( "has a '_FillValue' attribute, which should not be present " "on a location-index-set." ) log_lis("A403", msg) if mesh_var and lis_dim and parent_dim: len_lis = self.file_scan.dimensions[lis_dim].length len_parent = self.file_scan.dimensions[parent_dim].length if len_lis >= len_parent: msg = ( f'has dimension "{lis_dim}", length {len_lis}, which is ' f"longer than the {location} dimension of the parent " f'mesh "{mesh_name}" : ' f'"{parent_dim}", length {len_parent}.' ) log_lis("A404", msg) if self.do_data_checks: # TODO: data checks msg = "contains repeated index values." log_lis( "A405", ) if mesh_var: msg = ( "contains index values which are outside the range of the " f'parent mesh "{mesh_name}" {location} dimension, ' f' : "{parent_dim}", range 1..{len_parent}.' ) log_lis( "A406", ) if index_value is not None and index_value.dtype != lis_var.dtype: msg = ( f"has a 'start_index' of type \"{index_value.dtype}\", " "which is different from the variable type, " f'"{lis_var.dtype}".' ) log_lis("A407", msg) def dataset_identify_containers(self): """ Find "mesh" , "mesh data", and "location index set" variables, Also include possibles due to mesh/lis references from data variables. Results set as self properties : self._meshdata_vars self._mesh_vars self._lis_vars self._mesh_referrers self._lis_referrers """ # Location index sets are those with a cf_role of 'location_index_set' self._lis_vars = vars_w_props( self._all_vars, cf_role="location_index_set" ) # Mesh data variables are those with either a 'mesh' or # 'location_index_set' attribute, but excluding the lis-vars. self._meshdata_vars = { varname: var for varname, var in self._all_vars.items() if ( varname not in self._lis_vars and ( "mesh" in var.attributes or "location_index_set" in var.attributes ) ) } # Mesh vars are those with cf_role="mesh_topology". self._mesh_vars = vars_w_props(self._all_vars, cf_role="mesh_topology") # Scan for any meshvars referred to by 'mesh' or 'location_index_set' # properties in mesh-data vars. # These are included among potential meshdata- and lis- variables # (so they are detected + checked even without the correct cf_role) self._mesh_referrers = {} self._lis_referrers = {} for referrer_name, referrer_var in list(self._meshdata_vars.items()): # Note: taking a copy as we may modify _meshdata_vars in the loop meshprop = referrer_var.attributes.get("mesh") meshvar_name = property_as_single_name(meshprop) if ( meshvar_name is not None and meshvar_name in self._all_vars and meshvar_name not in self._mesh_vars ): # Add this reference to our list of all meshvars self._mesh_vars[meshvar_name] = self._all_vars[meshvar_name] # Record name of referring var. # N.B. potentially this can overwrite a previous referrer, # but "any one of several" will be OK for our purpose. self._mesh_referrers[meshvar_name] = referrer_name # Do something similar with lis references. meshprop = referrer_var.attributes.get("location_index_set") lisvar_name = property_as_single_name(meshprop) if ( lisvar_name is not None and lisvar_name in self._all_vars and lisvar_name not in self._lis_vars ): # Add this reference to our list of all meshvars self._lis_vars[lisvar_name] = self._all_vars[lisvar_name] # Also remove it from the meshdata-vars if it was there # N.B. this could only happen if it has a wrong cf_role, but # that is just the kind of error we dealing with here. self._meshdata_vars.pop(lisvar_name, None) # Record name of referring var. self._lis_referrers[lisvar_name] = referrer_name def dataset_check_containers_and_map_dims(self): """ Check all putative mesh + lis variables and collect dimension maps. Writes self._all_mesh_dims: {<mesh or lis name>: {location: dim-name}} Note: in checking the individual mesh variables, we also check all the coordinates and connectivities. This routine also sets self._allowed_cfrole_varnames """ # Build a map of the dimensions of all the meshes, # all_meshes_dims: {meshname: {location: dimname}} self._all_mesh_dims = {} # This list of "UGRID variables" is used by 'dataset_global_checks' to # find any vars with a UGRID-style 'cf_role' that should not have one. # N.B. we don't include meshdata-variables, or coordinate variables, # which should *not* have a 'cf_role' anyway. # After this, all connectivities will be added by 'check_connectivity'. self._allowed_cfrole_varnames = list(self._mesh_vars.keys()) + list( self._lis_vars.keys() ) # Find all connectivity variables and, initially, put them all on the # "orphan connectivities" list : Those attached to meshes will be # removed when we check the meshes (next). self._orphan_connectivities = { var_name: var for var_name, var in self._all_vars.items() if ( "cf_role" in var.attributes and ( str(var.attributes.get("cf_role")) in _VALID_CONNECTIVITY_ROLES ) ) } # Check all mesh vars # Note: this call also fills in 'self._all_mesh_dims', and checks all # the attached coordinates and connectivites for each mesh. for meshvar in self._mesh_vars.values(): self.check_mesh_var(meshvar) # Check all lis-vars # Note: this call also fills in 'self._all_mesh_dims'. for lis_var in self._lis_vars.values(): self.check_lis_var(lis_var) def dataset_detect_shared_dims(self): """ Check for any dimensions shared between meshes - an advisory warning. """ # Convert all_meshes_dims: {meshname: {location: dimname}} # .. to dim_meshes: {dimname: [meshnames]} dim_meshes = {} for mesh, location_dims in self._all_mesh_dims.items(): for location, dim in location_dims.items(): # Fetch list meshnames = dim_meshes.get(dim, set()) if dim: # TODO: what if a dim is used by 2 different locations of # of the same mesh ? meshnames.add(mesh) # Write list back dim_meshes[dim] = meshnames # Check for any dims which are used more than once. for dim, meshnames in dim_meshes.items(): if len(meshnames) > 1: # TODO: what if a dim is used by 2 different locations of # of the same mesh ? # We would get a repeated meshname here... meshnames = sorted(meshnames) other_meshes, last_mesh = meshnames[:-1], meshnames[-1] if len(other_meshes) == 1: other_mesh = other_meshes[0] msg = ( f'Dimension "{dim}" is mapped by both ' f'mesh "{other_mesh}" and mesh "{last_mesh}".' ) else: msg = f'Dimension "{dim}" is mapped by multiple meshes : ' msg += ", ".join(f'"{mesh}"' for mesh in other_meshes) msg += f' and "{last_mesh}".' self.state("A104", None, None, msg) def dataset_detect_multiple_refs(self): """ Check for any coords and conns referenced by multiple meshes. N.B. relevant errors are : * A201 coord should have 1-and-only-1 parent mesh * A301 connectivity should have 1-and-only-1 parent mesh """ var_refs_meshes_attrs = {} all_ref_attrs = _VALID_MESHCOORD_ATTRS + _VALID_CONNECTIVITY_ROLES for some_meshname in sorted(self._mesh_vars): some_meshvar = self._mesh_vars[some_meshname] for some_refattr in all_ref_attrs: is_coord = some_refattr in _VALID_MESHCOORD_ATTRS attrval = some_meshvar.attributes.get(some_refattr, None) somevar_names = property_namelist(attrval) for somevar_name in somevar_names: # NB only collect valid refs (to real variables) if somevar_name in self._all_vars: meshes = var_refs_meshes_attrs.get(somevar_name, set()) meshes.add((some_meshname, some_refattr)) var_refs_meshes_attrs[somevar_name] = meshes for some_varname, meshes_and_attrs in var_refs_meshes_attrs.items(): some_var = self._all_vars[some_varname] # NB have only 'real' refs if len(meshes_and_attrs) > 1: meshes_and_attrs = sorted( meshes_and_attrs, key=lambda pair: pair[0] ) refs_msg = ", ".join( [ f"{some_mesh}:{attr_name}" for some_mesh, attr_name in meshes_and_attrs ] ) msg = f"is referenced by multiple mesh variables : {refs_msg}." # Structurally, a var *could* be referenced as both a coord # *and* a connectivity. But they have different required # numbers of dims, so we use that to decide what to call it. is_coord = len(some_var.dimensions) == 1 if is_coord: vartype = "Mesh coordinate" code = "A201" else: vartype = "Mesh connectivity" code = "A301" self.state(code, vartype, some_varname, msg) def dataset_global_checks(self): """Do file-level checks not based on any particular variable type.""" def log_dataset(errcode, msg): self.state(errcode, "", "", msg) # A901 "dataset contents should also be CF compliant" -- not checkable, # unless we integrate this code with cf-checker. # Check the global Conventions attribute for a UGRID version. conventions = self.file_scan.attributes.get("Conventions") if conventions is None: msg = "" log_dataset("A902", "dataset has no 'Conventions' attribute.") else: conventions = str(conventions) re_conventions = re.compile(r"UGRID-[0-9]+\.[0-9]+") if not re_conventions.search(conventions): # NOTE: just search. Don't attempt to split, as usage of # comma/space/semicolon might be inconsistent, and we don't # need to care about that here. msg = ( f'dataset has Conventions="{conventions}", which does not ' "contain a UGRID convention statement of the form " '"UGRID-<major>.<minor>".' ) log_dataset("A903", msg) # Check for any unexpected 'cf_role' usages. # N.B. the logic here is that # 1) if it has a UGRID-type cf-role, then *either* it was already # identified (and checked), *or* it generates a A904 warning # 2) if it has a CF cf-role, we don't comment # 3) if it has some other cf-role, this is unrecognised -> A905 for var_name, var in self._all_vars.items(): if ( "cf_role" in var.attributes and var_name not in self._allowed_cfrole_varnames ): cf_role = str(var.attributes["cf_role"]) if cf_role in _VALID_UGRID_CF_ROLES: msg = ( f'has cf_role="{cf_role}", which is a UGRID defined ' "cf_role term, but the variable is not recognised as " "a UGRID mesh, location-index-set or connectivity " "variable." ) self.state("A904", "netcdf", var_name, msg) elif cf_role not in _VALID_CF_CF_ROLES: msg = ( f'has cf_role="{cf_role}", which is not a recognised ' "cf-role value defined by either CF or UGRID." ) self.state("A905", "netcdf", var_name, msg) def check_dataset(self): """ Run all conformance checks on the contained file scan. All results logged via `self.state`. """ self.dataset_identify_containers() self.dataset_check_containers_and_map_dims() # Check any orphan connectivities. for var_name, var in self._orphan_connectivities.items(): self.check_connectivity(var) # Always flag these as a possible problem. self.state("A301", "connectivity", var_name, "has no parent mesh.") # Check all the mesh-data vars for meshdata_var in self._meshdata_vars.values(): self.check_meshdata_var(meshdata_var) # Do the checks which cut across different meshes self.dataset_detect_shared_dims() self.dataset_detect_multiple_refs() # Do the miscellaneous dataset-level checks self.dataset_global_checks() def checking_report(self) -> str: """Produce a text summary of the checking results.""" report_lines = [] def line(msg: str): report_lines.append(msg) log = self.logger logs = log.report_statement_logrecords() line("") line("UGRID conformance checks complete.") line("") if log.N_FAILURES + log.N_WARNINGS == 0: line("No problems found.") else: if logs: line("List of checker messages :") for log_record in logs: line(" " + log_record.msg) line("") line( f"Total of {log.N_WARNINGS + log.N_FAILURES} " "problems logged :" ) line(f" {log.N_FAILURES} Rxxx requirement failures") line(f" {log.N_WARNINGS} Axxx advisory recommendation warnings") line("") line("Done.") return "\n".join(report_lines) def structure_report(self, include_nonmesh: bool = False) -> str: """ Produce a text summary of the dataset UGRID structure. Parameters ---------- include_nonmesh : bool, default False If set, also output a list of file dimensions and variables *not* relating to the UGRID meshes contained. """ result_lines = [] indent = " " def line(msg, n_indent=0): result_lines.append(indent * n_indent + msg) def varlist_str(var: NcVariableSummary, attr_name: str) -> str: names_attr = var.attributes.get(attr_name) if not names_attr: result = "<none>" else: names = str(names_attr).split(" ") result = ", ".join(f'"{name}"' for name in names) return result if not self._mesh_vars: line("Meshes : <none>") else: line("Meshes") for mesh_name, mesh_var in self._mesh_vars.items(): line(f'"{mesh_name}"', 1) dims = self._all_mesh_dims[mesh_name] # Nodes is a bit 'special' dim = dims["node"] if not dim: line("<? no node coordinates or dimension ?>", 2) else: line(f'node("{dim}")', 2) coords = varlist_str(mesh_var, "node_coordinates") line(f"coordinates : {coords}", 3) # Other dims all reported in the same way for location in ("edge", "face", "boundary"): dim = dims[location] if dim: line(f'{location}("{dim}")', 2) attr_name = f"{location}_node_connectivity" conn_str = varlist_str(mesh_var, attr_name) line(f"{attr_name} : {conn_str}", 3) coord_name = f"{location}_coordinates" if coord_name in mesh_var.attributes: coords = varlist_str(mesh_var, coord_name) line(f"coordinates : {coords}", 3) if self._lis_vars: line("") line("Location Index Sets") for lis_name, lis_var in self._lis_vars.items(): dim = self._all_mesh_dims[lis_name] dim = varlist_str(dim) line(f"{lis_name}({dim})", 2) mesh = varlist_str(lis_var, "mesh") line(f"mesh : {mesh}", 3) loc = varlist_str(lis_var, "location") line(f"location : {loc}", 3) if self._orphan_connectivities: line("") line("?? Connectivities with no mesh ??") for conn_name, conn_var in self._orphan_connectivities.items(): dims = ", ".join(f'"{dim}"' for dim in conn_var.dimensions) line(f'"{conn_name}" ( {dims} )', 1) cf_role = varlist_str(conn_var, "cf_role") line(f"cf_role = {cf_role}", 2) if self._meshdata_vars: line("") line("Mesh Data Variables") for var_name, var in self._meshdata_vars.items(): line(f'"{var_name}"', 1) attrs = { attr_name: var.attributes.get(attr_name) for attr_name in ("mesh", "location", "location_index_set") } # 'treat as' mirrors logic in 'check_meshdata_var' treat_as_lis = attrs["location_index_set"] and ( not attrs["mesh"] or not attrs["location"] ) if treat_as_lis: order_and_expected = [ ("location_index_set", True), ("mesh", False), ("location", False), ] else: order_and_expected = [ ("mesh", True), ("location", True), ("location_index_set", False), ] for attr_name, expected in order_and_expected: attr = attrs[attr_name] value = None if attr: value = varlist_str(var, attr_name) if not expected: value = f"? {value}" elif expected: value = "? <none>" if value: line(f"{attr_name} : {value}", 2) if include_nonmesh: # A non-mesh var is one that isn't one that isn't referred to # by any UGRID mesh components. def var_names_set(vars: List[NcVariableSummary]) -> Set[str]: return set([var.name for var in vars]) all_mesh_varnames = ( var_names_set(self._mesh_vars.values()) | var_names_set(self._lis_vars.values()) | var_names_set(self._meshdata_vars.values()) | var_names_set(self._orphan_connectivities.values()) ) nonmesh_vars = set(self._all_vars.keys()) - all_mesh_varnames # A mesh dimension is one that is a location dim of any # mesh, or any connectivity (e.g. includes dims used for # nodes of a face). nonmesh_dims = set(self.file_scan.dimensions.keys()) # Exclude from 'nonmesh' : all dims and vars of each mesh. for meshvar in self._mesh_vars.values(): # Exclude all mesh location dims. mesh_dims = self._all_mesh_dims[meshvar.name] nonmesh_dims -= set(mesh_dims.values()) # Exclude all location coordinates, and their bounds vars. for location in _VALID_UGRID_LOCATIONS: attrname = f"{location}_coordinates" attr = meshvar.attributes.get(attrname) location_coord_names = property_namelist(attr) nonmesh_vars -= set(location_coord_names) for coord_name in location_coord_names: coord_var = self._all_vars.get(coord_name) bounds_attr = coord_var.attributes.get("bounds") bounds_varname = property_as_single_name(bounds_attr) if bounds_varname: nonmesh_vars.discard(bounds_varname) # Exclude all connectivities, and all their dims. for attrname in _VALID_CONNECTIVITY_ROLES: conn_attr = meshvar.attributes.get(attrname) conn_name = property_as_single_name(conn_attr) if conn_name: nonmesh_vars.discard(conn_name) conn_var = self._all_vars.get(conn_name) if conn_var: nonmesh_dims -= set(conn_var.dimensions) # Also exclude all dimensions of 'orphan' connectivities. for conn_var in self._orphan_connectivities.values(): nonmesh_dims -= set(conn_var.dimensions) # Add report section, if any nonmesh found. if nonmesh_dims or nonmesh_vars: line("") line("Non-mesh variables and/or dimensions") if nonmesh_dims: line("dimensions:", 1) for dim in sorted(nonmesh_dims): line(f'"{dim}"', 2) if nonmesh_vars: line("variables:", 1) for var in sorted(nonmesh_vars): line(f'"{var}"', 2) return "\n".join(result_lines) def check_dataset( file: Union[NcFileSummary, AnyStr, Path], print_summary: bool = True, omit_advisories: bool = False, ignore_codes: Union[List[str], None] = None, ) -> Checker: """ Run UGRID conformance checks on a file. Optionally print a result summary. Optionally ignore errors below a logging level. Returns a checker object with a file analysis and checking log records. Parameters ---------- file : string, Path or :class:`NcFileSummary` path to, or representation of a netcdf input file print_summary : bool, default=True print a results summary at the end omit_advisories : bool, default False If set, log only 'requirements' Rxxx statements, and ignore the advisory 'Axxx' ones. ignore_codes : list(str) or None, default None A list of error codes to ignore. Returns ------- checker : Checker A checker for the file. """ if isinstance(file, str): file_path = Path(file) elif isinstance(file, Path): file_path = file if isinstance(file, NcFileSummary): file_scan = file else: file_scan = scan_dataset(file_path) checker = Checker( file_scan, ignore_codes=ignore_codes, ignore_warnings=omit_advisories ) if print_summary: # Print the results : this is the default action print(checker.checking_report()) return checker

40.939045

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0.533999

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71,193

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0.273299

0.196991

0.146823

0.114476

0.100253

0.074826

0

0.010424

0.384195

71,193

1,738

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40.962601

0.82026

0.205399

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0.165444

0.025409

0

0.001726

0.007494

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0.029142

false

0

0.005828

0.000833

0.04413

0.002498

0

null

0

null

0

1

0

f0ef62d4a86e0b113ee61b8371810694544d577c

731

py

Python

Tests/Comms/TestRxChars.py

Simulators/PiBusRaider

ec091f3c74ea25c3287d26d990ff5d1b90e97e92

[ "MIT" ]

7

2021-01-23T04:37:18.000Z

2022-01-08T04:44:00.000Z

Tests/Comms/TestRxChars.py

Simulators/PiBusRaider

ec091f3c74ea25c3287d26d990ff5d1b90e97e92

[ "MIT" ]

3

2021-04-01T11:28:31.000Z

2021-05-10T09:56:05.000Z

Tests/Comms/TestRxChars.py

robdobsn/BusRaider

691e7882a06408208ca2abece5e7c4bcb4b4fa45

[ "MIT" ]

null

import serial import threading import keyboard import time # Read data from serial port and echo def serialRead(): global serialIsClosing, serPort while True: if serialIsClosing: break if serPort.isOpen(): val = serPort.read() if len(val) == 0: continue for v in val: print("{:02x} ".format(v), end="") print() serPort = serial.Serial('COM5', 115200) serialIsClosing = False # Thread for reading from port thread = threading.Thread(target=serialRead, args=()) thread.start() while True: if keyboard.is_pressed(' '): serialIsClosing = True time.sleep(1.0) break serPort.close()

20.885714

53

0.589603

81

731

5.308642

0.555556

0.04186

0.051163

0

0.02381

0.310534

731

34

54

21.5

0.829365

0.087551

0

0.153846

0

0.018072

0

1

0.038462

false

0

0.153846

0

0.192308

0.076923

0

null

0

null

0

1

0

f0f2f34a9ad73740c5bb4f118b59d33e045bc0bb

1,150

py

Python

apps/diana-cli/diana_cli/mock.py

thomasyi17/diana2

2167053dfe15b782d96cb1e695047433f302d4dd

[ "MIT" ]

15

2019-02-12T23:26:09.000Z

2021-12-21T08:53:58.000Z

apps/diana-cli/diana_cli/mock.py

thomasyi17/diana2

2167053dfe15b782d96cb1e695047433f302d4dd

[ "MIT" ]

2

2019-01-23T21:13:12.000Z

2019-06-28T15:45:51.000Z

apps/diana-cli/diana_cli/mock.py

thomasyi17/diana2

2167053dfe15b782d96cb1e695047433f302d4dd

[ "MIT" ]

6

2019-01-23T20:22:50.000Z

2022-02-03T03:27:04.000Z

import click import yaml from diana.apis import Orthanc from diana.daemons import MockSite from diana.daemons.mock_site import sample_site_desc epilog = """ DESC must be a mock-site description in yaml format. \b --- - name: Example Hospital services: - name: Main CT modality: CT devices: 3 studies_per_hour: 15 - name: Main MR modality: MR devices: 2 studies_per_hour: 4 ... """ @click.command(epilog=epilog, short_help="Generate mock DICOM traffic") @click.argument('desc', required=False) @click.option('--dest', help="Destination DICOM service") @click.pass_context def mock(ctx, desc, dest): """Generate synthetic studies on a schedule according to a site description DESC. Studies are optionally forwarded to an endpoint DEST.""" services = ctx.obj.get('services') click.echo(click.style('Generate mock DICOM data', underline=True, bold=True)) if not desc: desc = sample_site_desc desc = yaml.load(desc) H = MockSite.Factory.create(desc=desc) O = None if dest: _desc = services[dest] O = Orthanc(**_desc) for h in H: h.run(pacs=O)

23

82

0.682609

163

1,150

4.736196

0.509202

0.034974

0.041451

0

0.005507

0.210435

1,150

49

83

23.469388

0.844714

0.115652

0

0.343595

0

1

0.026316

false

0.026316

0.131579

0

0.157895

0

null

0

null

0

1

0

f0f3345ae4e7d0fc3d248e4e59a1c69d38a3932d

3,415

py

Python

alphafold_components/cls_components/jackhmmer.py

aburdenko/alphafold-inference-pipeline

b48dc5dba162d02450ce111fe9d52a09d03a0236

[ "Apache-2.0" ]

4

2022-02-14T17:54:18.000Z

2022-02-25T12:58:58.000Z

alphafold_components/cls_components/jackhmmer.py

jarokaz/alphafold-inference-pipeline

846c90fa05f3f1b2f0ac03c4a43a34a0142987e9

[ "Apache-2.0" ]

1

2022-03-18T18:23:42.000Z

alphafold_components/cls_components/jackhmmer.py

aburdenko/alphafold-inference-pipeline

b48dc5dba162d02450ce111fe9d52a09d03a0236

[ "Apache-2.0" ]

1

2022-03-05T22:54:24.000Z

# Copyright 2021 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from kfp.v2 import dsl from kfp.v2.dsl import Output, Input, Artifact, Dataset import config @dsl.component( base_image=config.CLS_WRAPPERS_IMAGE, output_component_file='component_msa_search.yaml' ) def jackhmmer( project: str, region: str, database: str, reference_databases: Input[Dataset], sequence: Input[Dataset], msa: Output[Dataset], cls_logging: Output[Artifact], maxseq:int=10_000, machine_type:str='n1-standard-8', boot_disk_size:int=100, n_cpu:int=8, ): """Searches the specified database using jackhmmer. This is a simple prototype using dsub to submit a Cloud Life Sciences pipeline. We are using CLS as KFP does not support attaching pre-populated disks or premtible VMs. GCSFuse does not perform well with genetic database search tools . The prototype also lacks job control. If a pipeline step fails, the CLS job can get orphaned """ import logging import os import sys import time from alphafold.data import parsers from dsub_wrapper import run_dsub_job _SUPPORTED_DATABASES = ['uniref90', 'mgnify'] _DSUB_PROVIDER = 'google-cls-v2' _LOG_INTERVAL = '30s' _ALPHAFOLD_RUNNER_IMAGE = 'gcr.io/jk-mlops-dev/alphafold' _SCRIPT = '/scripts/alphafold_runners/jackhmmer_runner.py' logging.basicConfig(format='%(asctime)s - %(message)s', level=logging.INFO, datefmt='%d-%m-%y %H:%M:%S', stream=sys.stdout) if not (str(database) in _SUPPORTED_DATABASES): raise RuntimeError(f'Jackhmmer cannot be used with {database} database.') job_params = [ '--machine-type', machine_type, '--boot-disk-size', str(boot_disk_size), '--logging', cls_logging.uri, '--log-interval', _LOG_INTERVAL, '--image', _ALPHAFOLD_RUNNER_IMAGE, '--env', f'PYTHONPATH=/app/alphafold', '--mount', f'DB_ROOT={reference_databases.metadata["disk_image"]}', '--input', f'INPUT_PATH={sequence.uri}', '--output', f'OUTPUT_PATH={msa.uri}', '--env', f'DB_PATH={reference_databases.metadata[database]}', '--env', f'N_CPU={n_cpu}', '--env', f'MAXSEQ={maxseq}', '--script', _SCRIPT ] t0 = time.time() logging.info('Starting database search...') result = run_dsub_job( provider=_DSUB_PROVIDER, project=project, regions=region, params=job_params, ) t1 = time.time() logging.info(f'Search completed. Elapsed time: {t1-t0}') with open(msa.path) as f: msa_str = f.read() parsed_msa = parsers.parse_stockholm(msa_str) msa.metadata['data_format'] = 'sto' msa.metadata['num of sequences'] = len(parsed_msa.sequences)

29.95614

92

0.658565

448

3,415

4.881696

0.475446

0.027435

0.016461

0.014632

0

0.011398

0.229283

3,415

113

93

30.221239

0.819529

0.272328

0

0.029412

0

0.262188

0.11102

0

1

0.014706

false

0

0.147059

0

0.161765

0

null

0

null

0

1

0

f0f3af4c9714fab190218b538b99ee23af39c663

1,169

py

Python

reservas/utils/models.py

hello-alf/reservas

b5569fd92da62ecd2e26c6756c170de69b8afa0b

[ "MIT" ]

null

reservas/utils/models.py

hello-alf/reservas

b5569fd92da62ecd2e26c6756c170de69b8afa0b

[ "MIT" ]

null

reservas/utils/models.py

hello-alf/reservas

b5569fd92da62ecd2e26c6756c170de69b8afa0b

[ "MIT" ]

null

"""Django models.utilities""" from django.db import models class BookingAudit(models.Model): """Comparte Ride base model, molde de atributos BookingAudit Model acts as an abstract base class from which every other model in the project will inherit. This class provides every table with the following attributes: + created (DateTime): Store the datetime the objects was created + modified (DateTime): Store the last datetime the objects was modified """ created = models.DateTimeField( 'created at', auto_now_add=True, help_text='Date time on which the object was created.') modified = models.DateTimeField( 'modified at', auto_now=True, help_text='Date time on which the object was modified.') class Meta: """Meta option. we define the abstract attribute for the database not consider this class and model physically """ abstract = True #Para adicionar funcionalidad extra al objeto, no para mapearla en BD proxy = true #proxy = True get_latest_by = 'created' ordering = ['-created', '-modified']

32.472222

106

0.663815

145

1,169

5.303448

0.537931

0.058518

0.041612

0.054616

0.10143

0

0.266039

1,169

35

107

33.4

0.89627

0.502139

0

0.250482

0

1

0

false

0

0.071429

0

0.357143

0

null

0

null

0

1

0

f0f4971c45d20418a287e24a19c39a4e09365b02

310

py

Python

Python/13 - Regex and Parsing/Validating phone numbers.py

sohammanjrekar/HackerRank

1f5010133a1ac1e765e855a086053c97d9e958be

[ "MIT" ]

null

Python/13 - Regex and Parsing/Validating phone numbers.py

sohammanjrekar/HackerRank

1f5010133a1ac1e765e855a086053c97d9e958be

[ "MIT" ]

null

Python/13 - Regex and Parsing/Validating phone numbers.py

sohammanjrekar/HackerRank

1f5010133a1ac1e765e855a086053c97d9e958be

[ "MIT" ]

null

import re N = int(input()) for i in range(N): number = input() if 2 <= len(number) <= 15 and number.isdigit(): output = re.findall(r"^[789]\d{9}$", number) if len(output) == 1: print("YES") else: print("NO") else: print("NO")

20.666667

53

0.448387

40

310

3.475

0.675

0.129496

0.158273

0

0.041451

0.377419

310

14

54

22.142857

0.678756

0

0.333333

0

0.064189

0

1

0

false

0

0.083333

0

0.083333

0.25

0

null

0

null

0

1

0

f0f8fcb01b70468adeeb63a76de6b7573aecfbe3

434

py

Python

exam_at_home/1/bracket_matching.py

jamie-jjd/110_spring_IDS

7f15c0c73b9d663373b791b9ddcc836957dcc3d2

[ "MIT" ]

2

2022-02-21T10:37:22.000Z

2022-03-02T01:43:30.000Z

exam_at_home/1/bracket_matching.py

jamie-jjd/110_spring_IDS

7f15c0c73b9d663373b791b9ddcc836957dcc3d2

[ "MIT" ]

null

exam_at_home/1/bracket_matching.py

jamie-jjd/110_spring_IDS

7f15c0c73b9d663373b791b9ddcc836957dcc3d2

[ "MIT" ]

3

2022-02-21T05:06:19.000Z

2022-03-27T07:58:11.000Z

# # author: wang-yang # email: tnst92002@gmail.com # N = int(input()) def check(s: str): st = [] for c in s: if c == '(': st.append(')') elif c == '[': st.append(']') else: if len(st) == 0 or st[-1] != c: return False st.pop() return len(st) == 0 for _ in range(N): s = input() st = [] print("Yes" if check(s) else "No")

18.869565

43

0.410138

58

434

3.051724

0.568966

0.067797

0.101695

0

0.030888

0.403226

434

23

44

18.869565

0.65251

0.101382

0

0.117647

0

0.023316

0

1

0.058824

false

0

0.176471

0.058824

0

null

0

null

0

1

0

f0fbc18a3420f689083c5d29cf438daccff37a32

1,327

py

Python

deepaugment/examples/run_full_model_on_pawprint_images.py

abcp4/deepaugment

dd45cdcbd00ca7dfb7c8035252e45ecaed05bfbd

[ "MIT" ]

221

2019-02-22T06:48:41.000Z

2022-03-30T11:34:03.000Z

deepaugment/examples/run_full_model_on_pawprint_images.py

abcp4/deepaugment

dd45cdcbd00ca7dfb7c8035252e45ecaed05bfbd

[ "MIT" ]

16

2019-04-02T11:33:05.000Z

2021-05-13T07:47:28.000Z

deepaugment/examples/run_full_model_on_pawprint_images.py

abcp4/deepaugment

dd45cdcbd00ca7dfb7c8035252e45ecaed05bfbd

[ "MIT" ]

43

2019-02-14T00:53:06.000Z

2022-03-23T10:25:52.000Z

import numpy as np import os from keras.preprocessing import image from sklearn.model_selection import train_test_split import sys from os.path import dirname, realpath file_path = realpath(__file__) dir_of_file = dirname(file_path) parent_dir_of_file = dirname(dir_of_file) sys.path.insert(0, parent_dir_of_file) from run_full_model import run_full_model def load_images(image_dir_path): subfolders = next(os.walk(image_dir_path))[1] img_class = 0 X_list = [] y_list = [] for subfolder in subfolders: subfolder_path = os.path.join(image_dir_path, subfolder) print(subfolder_path) for f in os.listdir(subfolder_path): if f.startswith("."): # dont look .DS_store print (f) continue img = image.load_img(os.path.join(subfolder_path,f), target_size=(100, 100)) img_arr = image.img_to_array(img) X_list.append(img_arr) y_list.append(img_class) img_class+=1 X = np.array(X_list) y = np.array(y_list) return X, y X, y = load_images("../../data/raw/pawprints/images") # policies_path = "../../reports/experiments/pawprints_02-14_19-22/top20_policies.csv" policies_path = "random" run_full_model(X, y, epochs=200, batch_size=32, policies_path=policies_path)

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null

0

null

0

1

0

f0fcc6e422c29a91a03c1fc40f3b97ab36d8a918

4,373

py

Python

phylotoast/test/test_otu_calc.py

bhawan1/phylotoast

87d4b00f5da30855b9eb05398f2f605dcf61de38

[ "MIT" ]

19

2015-07-11T18:22:45.000Z

2022-02-05T19:57:49.000Z

phylotoast/test/test_otu_calc.py

bhawan1/phylotoast

87d4b00f5da30855b9eb05398f2f605dcf61de38

[ "MIT" ]

16

2015-12-24T22:11:54.000Z

2021-12-18T20:26:17.000Z

phylotoast/test/test_otu_calc.py

bhawan1/phylotoast

87d4b00f5da30855b9eb05398f2f605dcf61de38

[ "MIT" ]

7

2016-01-07T02:34:26.000Z

2019-10-24T22:03:54.000Z

#!/usr/bin/env python """ :Author: Akshay Paropkari :Date Created: 10/22/2014 :Abstract: Automated Tests for OTU calculations. """ import biom import unittest from phylotoast import otu_calc as oc class otu_calc_Test(unittest.TestCase): def setUp(self): """ Setting up the test module. Initializing BIOM format file. """ pass def test_otu_name(self): """ Testing the otu_name() function of otu_calc.py. :return: Returns OK if the test goals were achieved, otherwise raises error. """ self.taxa = { "Unclassified_Methanosarcinales": ["k__Archaea", "p__Euryarchaeota", "c__Methanomicrobia", "o__Methanosarcinales", "f__", "g__", "s__concilii"], "Campylobacter_gracilis": ["k__Bacteria", "p__Proteobacteria", "c__Epsilonproteobacteria", "o__Campylobacterales", "f__Campylobacteraceae", "g__Campylobacter", "s__gracilis"], "Escherichia_spp.": ["k__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacteriales", "f__Enterobacteriaceae", "g__Escherichia", "s__"], "Fusobacterium_nucleatum": ["k__Bacteria", "p__Fusobacteria", "c__Fusobacteria", "o__", "f__", "g__Fusobacterium", "s__nucleatum"], "Fusobacterium_spp.": ["k__Bacteria", "p__Fusobacteria", "c__Fusobacteria", "o__", "f__", "g__Fusobacterium", "s__"] } for expected, test in self.taxa.items(): self.result = oc.otu_name(test) # Testing the validity of the otu_name() function self.assertEqual( self.result, expected, msg="Error!\nExpected result: {}.\notu_name() result: {}". format(expected, self.result) ) def test_load_core_file(self): """ Testing the load_core_file() function of otu_calc.py :return: Returns OK if the test goals were achieved, otherwise raises error. """ result = oc.load_core_file("phylotoast/test/test_core.txt") hand_calc = {"Actinomyces_spp.", "Campylobacter_spp.", "Capnocytophaga_spp.", "Catonella_spp.", "Corynebacterium_spp.", "Dialister_spp.", "Eikenella_spp.", "Filifactor_spp.", "Fusobacterium_spp.", "Gemella_spp.", "Granulicatella_spp.", "Kingella_spp.", "Leptotrichia_spp.", "Megasphaera_spp.", "Parvimonas_spp.", "Prevotella_melaninogenica", "Prevotella_spp.", "Selenomonas_noxia", "Selenomonas_spp.", "Streptococcus_anginosus", "Streptococcus_equi", "Streptococcus_infantis", "Streptococcus_spp.", "Unclassified_Lachnospiraceae", "Unclassified_TM7-3", "Unclassified_[Mogibacteriaceae]", "Veillonella_dispar", "Veillonella_parvula", "Veillonella_spp."} # Testing if all core OTU's samples were in the output. self.assertSetEqual( result, hand_calc, msg="Error! Genus-species names not calculated as expected." ) def test_assign_otu_membership(self): """ Testing assign_otu_membership() function of otu_calc.py. :return: Returns OK if the test goals were achieved, otherwise raises error. """ self.biomf = biom.load_table("phylotoast/test/test.biom") self.result = oc.assign_otu_membership(self.biomf) # Obtaining the values to be tested hand_calc = {"S9": ["GG_OTU_2", "GG_OTU_3", "GG_OTU_5"], "S3": ["GG_OTU_1", "GG_OTU_2", "GG_OTU_4", "GG_OTU_5"], "S6": ["GG_OTU_1", "GG_OTU_2", "GG_OTU_3", "GG_OTU_4", "GG_OTU_5"]} # Testing the validity of assign_otu_membership() function for sid in ["S3", "S6", "S9"]: self.assertListEqual( sorted(hand_calc[sid]), sorted(self.result[sid]), msg="Error! OTU membership calculations are inaccurate!" ) def tearDown(self): """ Tearing down of this unittest framework. """ pass if __name__ == "__main__": unittest.main()

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null

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null

0

1

0

f0fd958fcbe4a518387af5bdb0b4ac10d866d7d0

4,086

py

Python

old_python_scripts/pagelinks-creator.py

wikitools/wikigraph-controller

9ceba7ee49259a7f65001b1c8e76cc5aaa087ea5

[ "MIT" ]

null

old_python_scripts/pagelinks-creator.py

wikitools/wikigraph-controller

9ceba7ee49259a7f65001b1c8e76cc5aaa087ea5

[ "MIT" ]

null

old_python_scripts/pagelinks-creator.py

wikitools/wikigraph-controller

9ceba7ee49259a7f65001b1c8e76cc5aaa087ea5

[ "MIT" ]

null

import json import time import re import pickle import sys import os DUMPS_PATH = sys.argv[1] if len(sys.argv) >= 2 else '/' if not DUMPS_PATH.endswith('/'): DUMPS_PATH = DUMPS_PATH + '/' DATA_FILES_FOLDER = 'data-files/' DUMPS_VERSION = sys.argv[2] if len(sys.argv) >= 3 else 'latest' TITLE_ID_MAP_FILE_NAME = 'title_to_id.map' TITLE_ID_MAP_PATH = DATA_FILES_FOLDER + TITLE_ID_MAP_FILE_NAME OUTFILE = 'links.map' def create_title_to_id_map(): title_to_id = {} start = time.time() with open(DUMPS_PATH + 'enwiki-' + DUMPS_VERSION + '-pages-articles-multistream-index.txt', encoding="UTF-8") as f: for line in f: line = line[:-1] parts = line.split(':', maxsplit=2) if not parts[2].startswith('Category') and parts[2].__contains__(':'): continue title_to_id[parts[2]] = int(parts[1]) print('Article title to id map created in: ' + str(time.time() - start) + 's.') return title_to_id def save_title_to_id_map(): title_to_id = create_title_to_id_map() with open(TITLE_ID_MAP_PATH, 'wb+') as map: pickle.dump(title_to_id, map) def load_title_to_id_map(): start = time.time() with open(TITLE_ID_MAP_PATH, 'rb') as map: title_to_id = pickle.load(map) print('Article title to id map loaded in: ' + str(time.time() - start) + 's.') return title_to_id def create_page_links_map(lines_to_proccess=-1, inserts_per_line_to_proccess=-1): start = time.time() links = {} with open(DUMPS_PATH + 'enwiki-' + DUMPS_VERSION + '-pagelinks.sql', encoding="UTF-8", errors='ignore') as f: # opening pagelinks file - encoding errors line_no = 0 for line in f: temp_pageid = 0 # used for print every x lines if not line.startswith('INSERT INTO'): # ignoring create and headers continue if len(line.split(' ')) != 5: # line has to have minimum 5 parts when it's an insert print('Inserts line ' + str(line_no) + ' has unusual number of spaces ' + str(len(line.split(' ')))) inserts = line.split(' ')[4:] # extracting part with values to insert in one string inserts = ''.join(inserts) value_list = inserts.split('),(') # extracting string with 4 values, separated by comma and backslashes for i in range(len(value_list)): # if 0 <= inserts_per_line_to_proccess < i: # break values = value_list[i].split('\'') # splittig each of 4 values separately if (len(values) == 3): try: title = values[1] # trying to make title look like in indexes file except: title = 'Wrong splitting of values!!!' print('bad title! - Line number' + str(line_no) + ', value: ' + value_list[i]) id_getter = values[0].split(',') if i == 0: page_id = int(id_getter[0][1:]) # deleting bracket in first occurance else: try: page_id = int(id_getter[0]) except: page_id = 0 print('bad id! - Line number' + str(line_no) + ', inserted data number: ' + str(i)) temp_pageid = page_id if not page_id in links: links[page_id] = [] # if occurs for the first time, create empty value if title in title_to_id: links[page_id].append( title_to_id[title]) # if managed to find id based on title, append it as child to page if line_no % 100 == 0: print(str(line_no)) line_no += 1 # if 0 <= lines_to_proccess < line_no: # break print('Article links map created in: ' + str(time.time() - start) + 's.') return links def createJSON(links): print('Creation of map started...') json_object = {} json_object['pagelinks'] = [] for parent, child in links.items(): pagelinks = str(parent) for el in child: pagelinks += "," + str(el) # creating long string consisted of ID and children IDs based on links dict json_object['pagelinks'].append({ 'pl': pagelinks }) with open(DUMPS_PATH + OUTFILE, 'w') as outfile: json.dump(json_object, outfile) if not os.path.isfile(TITLE_ID_MAP_PATH): if not os.path.exists("data-files/"): os.makedirs(os.path.dirname(TITLE_ID_MAP_PATH)) save_title_to_id_map() title_to_id = load_title_to_id_map() createJSON(create_page_links_map(400)) print('Completed.')

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null

0

null

0

1

0

0b00751199a21103bbd2d9de1bbc3315e858f87a

2,476

py

Python

switch_inputs/bioenergy_clean.py

Switch-Mexico/switch-inputs

e2afa96c40b516435c350d525119e4594f1b7eca

[ "MIT" ]

1

2020-07-14T21:50:28.000Z

switch_inputs/bioenergy_clean.py

Switch-Mexico/switch-inputs

e2afa96c40b516435c350d525119e4594f1b7eca

[ "MIT" ]

14

2018-12-14T23:21:09.000Z

2019-05-10T21:42:36.000Z

switch_inputs/bioenergy_clean.py

Switch-Mexico/switch-inputs

e2afa96c40b516435c350d525119e4594f1b7eca

[ "MIT" ]

1

2020-07-14T21:50:37.000Z

""" Clean bioenergy data from AZEL """ import os import json import itertools import geopandas as gpd import pandas as pd os.makedirs('data', exist_ok=True) projection = 'epsg:4326' name = ['pecuarios', 'forestales', 'industriales', 'urbanos'] scenario = ['E3', 'E1'] for scenario, name in itertools.product(scenario, name): # Load bioenergy shape file print ('Reading file: {}_R{}.shp'.format(scenario, name)) df = gpd.read_file('../data/interim/shapes/FBio_{0}_R{1}.shp'.format(scenario, name)) df = df[df.geometry.notnull()].to_crs({'init': projection}) # Load transmission region dictionary with open(os.path.join('../data/interim/', 'trans-regions.json'), 'r') as fp: trans_regions = json.load(fp) # Load transmission region shapefiles lz = gpd.read_file('../data/interim/shapes/Mask_T.shp') lz = lz.to_crs({'init': projection}) lz.loc[:, 'trans-region'] = (lz['ID'].astype(int) .map('{0:02}'.format) .map(trans_regions)) assert lz.crs == df.crs if not 'forestal' in name: join = gpd.sjoin(df, lz, op='within') else: join = gpd.overlay(lz, df, how='intersection') # Get specific columns for output data try: columns = ['trans-region', 'X', 'Y', 'CLASIFICAC', 'TIPO', 'PROCESO', 'GENE_GWha', 'CAPINST_MW', 'FP'] bio = join[columns].copy(); except KeyError: columns = ['trans-region', 'CLASIFICAC', 'TIPO', 'PROCESO', 'GENE_GWha', 'CAPINST_MW', 'FP'] bio = join[columns].copy(); bio['CLASIFICAC'] = bio.CLASIFICAC.map(str.lower).str.replace(' ', '_') bio['TIPO'] = bio.TIPO.map(str.lower).str.replace(' ', '_') bio['PROCESO'] = bio.PROCESO.map(str.lower).str.replace(' ', '_') if 'E3' in scenario: scenario = 'high' else: scenario = 'low' bio.loc[:, 'scenario'] = scenario bio.loc[:, 'id'] = name bio = bio.rename(columns={'X': 'lng', 'Y': 'lat', 'CLASIFICAC': 'source', 'TIPO': 'category', 'FP': 'cf', 'GENE_GWha': 'gen_GWha', 'CAPINST_MW':'cap_MW', 'PROCESO': 'fuel_type'}) print ('Saving data: {0}_{1}'.format(scenario, name)) bio.to_csv('data/bioenergy_{0}_{1}.csv'.format(scenario, name), index=False)

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0.040816

0

null

0

null

0

1

0

0b01eb37721be4134c94f63340c71ce5f1825746

11,010

py

Python

src/preprocess.py

hongsups/blog

01e416fa040ed021d85cd89beff69d72c8972e32

[ "Apache-2.0" ]

null

src/preprocess.py

hongsups/blog

01e416fa040ed021d85cd89beff69d72c8972e32

[ "Apache-2.0" ]

3

2021-02-13T23:21:48.000Z

2021-11-04T13:04:54.000Z

src/preprocess.py

hongsups/blog

01e416fa040ed021d85cd89beff69d72c8972e32

[ "Apache-2.0" ]

null

import pandas as pd import numpy as np incident_causes_list = ['Traffic Stop', 'Emergency/Request for Assistance', 'Execution of a Warrant', 'Hostage/Barricade/Other Emergency', 'Other'] age_names = ['1-4' '5-14' '15-24' '25-34' '35-44' '45-54' '55-64' '65-74' '75+'] # report delay report_delay_days_bins = [0, 7, 14, 30, 60, 90, 180, 360, 720] report_delay_days_binnames = ['Same Day'] + ['{} to {} Days'.format(report_delay_days_bins[i]+1, report_delay_days_bins[i+1]) for i in range(len(report_delay_days_bins)-1)] + ['More than 720 Days'] def convert_date_cols(df, col_date='date'): """ Convert string format of date to numpy datetime (replace the old columns) :param pd.DataFrame df: :param str col_date: substring to identify the columns to convert :return: dataframe with new replaced columns """ cols_date = df.columns[df.columns.str.contains(col_date)] for col in cols_date: df[col] = pd.to_datetime(df[col]) return df def get_duplicates_from_cols(df, cols_to_use, what_to_keep='first'): """ Check duplicated rows by using the combination of multiple columns This is a workaround in the case where one doesn't have unique identifiers for a dataset :param pd.DataFrame df: :param list cols_to_use: columns to use to create unique combination :param str what_to_keep: arguments needed for the duplicated function of pandas, decide which instance to keep :return: """ # drop na to avoid confusion df_non_na = df[cols_to_use].dropna().copy() inds_duplicates_to_drop = df_non_na[df_non_na[cols_to_use].duplicated(keep=what_to_keep)].index df_duplicates = df.loc[inds_duplicates_to_drop, cols_to_use] df_unique = df.drop(index=inds_duplicates_to_drop) return df_unique, df_duplicates def crosstab_by_topN_cities(df, col_interest, col_incident_loc='incident_county', N=5, ratio=False): """ Select top N location(county, city, etc.) based on the total number of incidents Sort them by total number (descending) and then compute the crosstab (pd.crosstab) for the column of interest (col_interest). df_pop_county has index as county names and a single column that shows population of each county :param pd.DataFrame df: :param str col_interest: column names to visualize :param col_incident_loc: colum names that have county (location) information :param int N: no. of top counties to compute :param bool ratio: if True, normalize the data and return the ratio. Otherwise, integer counts :return: """ # get the index of the locations based on its total counts topN_loc_indices = df.groupby(col_incident_loc)[col_interest].count().sort_values( ascending=False)[:N].index # transpose so that our interest becomes columns df_crosstab = pd.crosstab(df[col_interest], df.loc[df[col_incident_loc].isin(topN_loc_indices), col_incident_loc]).T df_crosstab['TOTAL'] = df_crosstab.sum(axis=1) if 'race' in col_interest: col_list = ['WHITE', 'BLACK', 'HISPANIC', 'OTHER'] # some category might be missing if df_crosstab.shape[1] < len(col_list)+1: missing_cols = list(set(col_list) - set(df_crosstab.columns)) for col in missing_cols: df_crosstab[col] = 0 else: col_list = list(np.sort(df[col_interest].unique())) df_crosstab = df_crosstab.loc[:, col_list + ['TOTAL']] if ratio: df_crosstab = df_crosstab.sort_values(by='TOTAL', ascending=False) df_crosstab_ratio = df_crosstab.apply(lambda x: x/x['TOTAL'], axis=1).drop('TOTAL', axis=1) return df_crosstab_ratio else: return df_crosstab.sort_values(by='TOTAL', ascending=False) def pct(df, axis): """ Compute percentage by normalizing based on the total sum :param pd.DataFrame df: :param int axis: 0 for rows, 1 for columns :return: normalized dataframe """ if axis == 1: return df.apply(lambda x: x/df.sum(axis=axis))*100 if axis == 0: return df.apply(lambda x: x/df.sum(axis=axis), axis=1)*100 def count_agencies_by_year_type(df, agency_names, N=5): """ Count the number of agencies by agency type (police, sheriff, and others) by year and county. :param pd.DataFrame df: officer or civilian dataset :param list or np.array agency_names: list of columns that have agency names, e.g., 'agency_name_1' :param int N: number of counties to visualize :return: """ # select the agency names and remove empty values df_agency_names = df[agency_names].values.ravel() df_agency_names = df_agency_names[~pd.isnull(df_agency_names)] # categorize agency names based on substring dict_agency_names_all = dict() dict_agency_names_all['police'] = [s for s in df_agency_names if 'POLICE' in s] dict_agency_names_all['sheriff'] = [s for s in df_agency_names if 'SHER' in s] dict_agency_names_all['other'] = [s for s in df_agency_names if 'POLICE' not in s and 'SHER' not in s] # select the top N agencies dict_agency_topN = dict() for key, val in dict_agency_names_all.items(): dict_agency_topN[key] = pd.Series(val).value_counts()[:N].index # count the agency names by year and focus on the top N agencies years = sorted(df['year'].unique()) df_agency_count = dict() for year in years: df_year = df[df['year']==year] df_agency_names = df_year[agency_names].values.ravel() df_agency_names = df_agency_names[pd.isnull(df_agency_names) == False] dict_agency_names = dict() dict_agency_names['police'] = [s for s in df_agency_names if 'POLICE' in s] dict_agency_names['sheriff'] = [s for s in df_agency_names if 'SHER' in s] dict_agency_names['other'] = [s for s in df_agency_names if 'POLICE' not in s and 'SHER' not in s] dict_results = dict() for key, val in dict_agency_names.items(): temp = pd.Series(val).value_counts() temp_topN = temp[temp.index.isin(dict_agency_topN[key])] dict_results['n_' + key] = len(np.unique(dict_agency_names[key])) dict_results[key + '_top'] = temp_topN df_agency_count[year] = dict_results df_agency_count = pd.DataFrame(df_agency_count).T # Using this information, create a dataframe for plotting df_agency_count_plot = dict() for key in dict_agency_names.keys(): # agency types temp = pd.concat(df_agency_count[key + '_top'].values, axis=1).fillna(0).T temp.index = years if key is 'police': temp.columns = [s.split('POLICE')[0].strip() for s in temp.columns] elif key is 'sheriff': temp.columns = [s.split('SHER')[0].strip() for s in temp.columns] df_agency_count_plot[key] = temp return df_agency_count, df_agency_count_plot def clean_incident_causes(s): if 'EMERGENCY' in s: return 'Emergency/Request for Assistance' elif 'HOSTAGE' in s: return 'Hostage/Barricade/Other Emergency' elif 'OTHER' in s: return 'Other' elif 'TRAFFIC STOP' in s: return 'Traffic Stop' elif 'WARRANT' in s: return 'Execution of a Warrant' else: raise ValueError('Double check the string from incident causes.') class Preprocess: """Preprocess the raw OIS data (csv file from the TJI website) and return a preprocessed dataframe """ def __init__( self, df, correct_county_names, years = [2016, 2017, 2018, 2019, 2020] ): self.df = df self.correct_county_names = correct_county_names self.years = years def add_date_cols(self): self.df = convert_date_cols(self.df, 'date') self.df.loc[:, 'year'] = self.df['date_incident'].dt.year.values self.df.loc[:, 'month'] = self.df['date_incident'].dt.month.values return self.df def select_rows_by_year(self): self.df = self.df.loc[self.df['year'].isin(self.years)] def check_county_names(self): non_existent_counties = set(self.df['incident_county']) - set(self.correct_county_names) if len(non_existent_counties) > 0: raise ValueError("Incorrect county names exist: {}".format(non_existent_counties)) def remove_duplicates(self): df_civilian_unique, _ = get_duplicates_from_cols( self.df, ['civilian_name_full', 'date_incident'], what_to_keep='first' ) self.df = df_civilian_unique def add_death_indicator_col(self, death_injury_col_name): self.df['died'] = self.df[death_injury_col_name]=='DEATH' def clean_incident_cause_str(self): self.df.loc[self.df['incident_result_of']=='EMERGENCY', 'incident_result_of'] = 'EMERGENCY CALL OR REQUEST FOR ASSISTANCE' self.df.loc[self.df['incident_result_of']=='EMERGENCY CALL OR REQUEST FOR ASSISTANCE, TRAFFIC STOP', 'incident_result_of'] = \ 'EMERGENCY CALL OR REQUEST FOR ASSISTANCE; TRAFFIC STOP' self.df['incident_result_of'] = self.df['incident_result_of'].str.strip() df_causes_list = self.df['incident_result_of'].str.split(';') df_causes_list_clean = df_causes_list.apply(lambda x: [clean_incident_causes(s) for s in x]).apply(pd.Series) df_causes_list_clean_separated = df_causes_list_clean.stack().str.get_dummies().sum(level=0)[incident_causes_list] self.df = pd.concat([self.df, df_causes_list_clean_separated], axis=1) def add_age_groups(self): bins = [5, 15, 25, 35, 45, 55, 65, 75, 100] self.df['civilian_age_binned'] = np.digitize(self.df['civilian_age'], bins) def compute_report_delay(self): self.df.loc[:, 'delay_days'] = (self.df['date_ag_received'] - self.df['date_incident']).dt.days self.df.loc[self.df['delay_days']<0, 'delay_days'] = np.nan # bin the report deplay bins = [0, 7, 14, 30, 60, 90, 180, 360, 720] delay_bins = np.digitize(self.df['delay_days'].values, bins, right=True) nan_inds = np.argwhere(pd.isnull(self.df['delay_days']).values).ravel() delay_bins[nan_inds] = -1 self.df.loc[:, 'delay_bin_label'] = delay_bins def get_civilian_data(self): self.check_county_names() self.add_date_cols() self.select_rows_by_year() self.remove_duplicates() self.add_death_indicator_col(death_injury_col_name='civilian_died') self.clean_incident_cause_str() self.add_age_groups() self.compute_report_delay() return self.df def get_officer_data(self): self.check_county_names() self.add_date_cols() self.select_rows_by_year() self.add_death_indicator_col(death_injury_col_name='officer_harm') self.compute_report_delay() return self.df

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0

null

0

null

0

1

0

0b070a6623cb430f75477957caa002003f205d14

1,745

py

Python

spotify.py

themoat/spotify-dl

67af7b25ed847c8432d37d5230ea93715409242b

[ "MIT" ]

1

2020-06-28T23:05:44.000Z

spotify.py

themoat/spotify-dl

67af7b25ed847c8432d37d5230ea93715409242b

[ "MIT" ]

null

spotify.py

themoat/spotify-dl

67af7b25ed847c8432d37d5230ea93715409242b

[ "MIT" ]

null

from __future__ import unicode_literals import spotipy.util as util from scaffold import * from tokens import * import youtube_dl def authenticate(): return util.prompt_for_user_token(username,scope, CLIENT_ID, CLIENT_SECRET, REDIRECT_URL) def fetch_saved_tracks(sp): log.debug('Fetching saved tracks') offset = 0 songs = [] while True: results = sp.current_user_saved_tracks(limit=50, offset=offset) log.debug('Got result json {}'.format(results)) for item in results['items']: track = item['track'] log.debug('Appending {} to songs list'.format(track['name'] + ' - ' + track['artists'][0]['name'])) songs.append(track['name'] + ' - ' + track['artists'][0]['name']) offset += 1 if results.get('next') is None: log.info('All pages fetched, time to leave. Added {} songs in total'.format(offset)) break return songs def save_songs_to_file(songs): with open('songs.txt', 'w') as f: f.write('\n'.join(songs)) f.close() def download_songs(songs,download_directory): ydl_opts = { 'format': 'bestaudio/best', 'download_archive': 'downloaded_songs.txt', 'outtmpl': download_directory+'%(title)s.%(ext)s', 'postprocessors': [{ 'key': 'FFmpegExtractAudio', 'preferredcodec': 'mp3', 'preferredquality': '192', }], } log.debug('Songs to download: {}'.format(songs)) with youtube_dl.YoutubeDL(ydl_opts) as ydl: for item in songs: try: ydl.download([item]) except Exception: print('Failed to download: {}'.format(item)) continue

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1,745

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false

0

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null

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null

0

1

0

0b0806474d34ea185831d946d0369770520ca74c

8,585

py

Python

22_GScan/lib/plugins/File_Check.py

hemuke/python

bc99f2b5aee997083ae31f59a2b33db48c8255f3

[ "Apache-2.0" ]

null

22_GScan/lib/plugins/File_Check.py

hemuke/python

bc99f2b5aee997083ae31f59a2b33db48c8255f3

[ "Apache-2.0" ]

null

22_GScan/lib/plugins/File_Check.py

hemuke/python

bc99f2b5aee997083ae31f59a2b33db48c8255f3

[ "Apache-2.0" ]

null

# -*- coding: utf8 -*- # author：咚咚呛 # 对系统重要文件夹进行监控，并把修改、创建的文件进行日志打印， # 排除prelink服务对二进制文件修改对结果进行干扰，每次排查都会排除prelink的操作 from __future__ import print_function import os, sys, hashlib from lib.core.globalvar import * from lib.core.common import * class File_Check: def __init__(self): # 异常文件列表 self.file_malware = [] self.CHECK_DIR = ['/bin/', '/sbin/', '/usr/bin/', '/usr/sbin/', '/usr/local/sbin/', '/usr/local/bin/'] # 是否只针对特定文件进行监控 self.HIGH_FILTER = True # 监控文件内容列表 self.HEIGH_FILE_ALARM = ["depmod", "fsck", "fuser", "ifconfig", "ifdown", "ifup", "init", "insmod", "ip", "lsmod", "modinfo", "modprobe", "nologin", "rmmod", "route", "rsyslogd", "runlevel", "sulogin", "sysctl", "awk", "basename", "bash", "cat", "chmod", "chown", "cp", "cut", "date", "df", "dmesg", "echo", "egrep", "env", "fgrep", "find", "grep", "kill", "logger", "login", "ls", "mail", "mktemp", "more", "mount", "mv", "netstat", "ping", "ps", "pwd", "readlink", "rpm", "sed", "sh", "sort", "su", "touch", "uname", "gawk", "mailx", "adduser", "chroot", "groupadd", "groupdel", "groupmod", "grpck", "lsof", "pwck", "sestatus", "sshd", "useradd", "userdel", "usermod", "vipw", "chattr", "curl", "diff", "dirname", "du", "file", "groups", "head", "id", "ipcs", "killall", "last", "lastlog", "ldd", "less", "lsattr", "md5sum", "newgrp", "passwd", "perl", "pgrep", "pkill", "pstree", "runcon", "sha1sum", "sha224sum", "sha256sum", "sha384sum", "sha512sum", "size", "ssh", "stat", "strace", "strings", "sudo", "tail", "test", "top", "tr", "uniq", "users", "vmstat", "w", "watch", "wc", "wget", "whereis", "which", "who", "whoami", "test"] # 系统执行路径 self.SYS_PATH = get_value('SYS_PATH') self.HASH_DB = get_value('SYS_PATH') + '/db/hash_db.txt' # prelink服务会修改二进制文件，此处保存prelink服务的相关日志路径 self.PRELINK_LOG_PATH = ['/var/log/prelink/prelink.log', '/var/log/prelink.log'] # 开始进行扫描 self.check_dir_hash() # 计算一个文件的hash值 # 返回hash值字符串 def file_hash(self, file_path): try: md5obj = hashlib.md5() size = 102400 fp = open(file_path, 'rb') while True: content = fp.read(size) if not content: break md5obj.update(content) fp.close() return md5obj.hexdigest() except: return "error" # 获取一个目录下的所有文件HASH值 # 返回内容hash_list_content，包含[[文件路径，hash值],[文件路径，hash值]] def dir_hash(self, path): hash_list_content = [] for root, dirs, files in os.walk(path, topdown=True): for filename in files: # 如果只监控重要名称文件，则其他文件抛弃不创建hash if self.HIGH_FILTER: if filename in self.HEIGH_FILE_ALARM: # 存在软链指向真实文件不存在现象 if os.path.exists(os.path.join(root, filename)): hash_list = [] hash_list.append(os.path.join(root, filename)) # 保存文件绝对路径 if 'error' == self.file_hash(os.path.join(root, filename)): continue hash_list.append(self.file_hash(os.path.join(root, filename))) # 保存文件hash hash_list_content.append(hash_list) else: # 存在软链指向真实文件不存在现象 if os.path.exists(os.path.join(root, filename)): hash_list = [] hash_list.append(os.path.join(root, filename)) # 保存文件绝对路径 hash_list.append(self.file_hash(os.path.join(root, filename))) # 保存文件hash hash_list_content.append(hash_list) return hash_list_content # 获取存储的hash值文件 # 返回内容history_hash_list_content，包含[[],[]] def get_history_hash_list(self): if not os.path.exists(self.HASH_DB): self.write_hash_db("Initialization") return "", "" if os.path.getsize(self.HASH_DB) == 0: self.write_hash_db("Initialization") return "", "" # 获取hash文件内容到数据组中 history_hash_list_content = [] # 获取文件路绝对路径到数组中 history_file_path_list = [] for line in open(self.HASH_DB): if line != "" or line != None: tmp_hash = [] tmp_hash.append(line.split('||')[0].split('\n')[0]) # 文件绝对路径 tmp_hash.append(line.split('||')[1].split('\n')[0]) # 文件hash history_hash_list_content.append(tmp_hash) history_file_path_list.append(line.split('||')[0].split('\n')[0]) return history_hash_list_content, history_file_path_list # 写hash数据文件 # 传入参数为操作类型， # Initialization为初始化hash文件， # Coverage为文件变动时，覆盖原hash文件 def write_hash_db(self, type): time_string = time.time() if type == "Initialization": if not os.path.exists(self.HASH_DB): f = open(self.HASH_DB, "w") f.truncate() f.close() if os.path.getsize(self.HASH_DB) == 0: f = open(self.HASH_DB, 'w') for check_dir in self.CHECK_DIR: for hash_list in self.dir_hash(check_dir): f.write(hash_list[0] + "||" + hash_list[1] + "||" + str(time_string) + "\n") f.close() if type == "Coverage": if os.path.exists(self.HASH_DB): os.remove(self.HASH_DB) f = open(self.HASH_DB, 'w') for check_dir in self.CHECK_DIR: for hash_list in self.dir_hash(check_dir): f.write(hash_list[0] + "||" + hash_list[1] + "||" + str(time_string) + "\n") f.close() # 检测操作类型，判断出现文件变动时，是修改还是创建 # True为修改 # Flase为创建 def check_operation_type(self, file_path, history_file_path_list): return True if file_path in history_file_path_list else False # 检测是否存在prelink服务 # 返回服务真假，和日志内容 def check_prelink_server(self): for path in self.PRELINK_LOG_PATH: if os.path.exists(path): file_object = open(path) try: all_the_text = file_object.read() finally: file_object.close() return True, all_the_text return False, "" # 检测相对应目录的hash是否进行了变化 def check_dir_hash(self): # 判断是否出现文件变动 HASH_FILE_TYPE = False # 最新hash文件列表 current_hash_list_content = [] # 获取HASH库文件列表 history_hash_list_content, history_file_path_list = self.get_history_hash_list() if len(history_hash_list_content) == 0 or len(history_file_path_list) == 0: return # 判断是否存在prelink服务，并返回内容 PRELINK_SERVER, prelingk_log = self.check_prelink_server() # 开始针对监控目录进行检测 for check_dir in self.CHECK_DIR: try: current_hash_list_content = self.dir_hash(check_dir) for hash_list in current_hash_list_content: # 判断是否存在hash记录 if not hash_list in history_hash_list_content: HASH_FILE_TYPE = True # 判断是否是prelink服务更新 if PRELINK_SERVER: if len(prelingk_log) > 0: # 判断是否存在prelink此条日志 if prelingk_log.find(hash_list[0]) > 0: continue # 记录变动文件结果 self.file_malware.append({'file': hash_list[0], 'action': 'Edit' if self.check_operation_type(hash_list[0], history_file_path_list) else 'Create', 'newMD5': hash_list[1]}) except: continue # 存在文件修改，hash进行覆盖 if HASH_FILE_TYPE: self.write_hash_db("Coverage") if __name__ == '__main__': info = File_Check().file_malware for i in info: print(i)

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134

0.508561

886

8,585

4.69526

0.328442

0.069231

0.050481

0.036538

0.271154

0.250481

0.223317

0.20625

0.155288

0.146635

0

0.008442

0.365288

8,585

193

135

44.481865

0.755001

0.091322

0

0.255474

0

0.114407

0.003612

0

1

0.058394

false

0.007299

0.029197

0.007299

0.167883

0.014599

0

null

0

null

0

1

0

0b087fc92a5c2ccca05a7e240d35465e0d13304d

2,960

py

Python

src/lyxnotebook/entry_points.py

abarker/lyxNotebook

c458b21e1b183b94172414e14dea671e1f3d4b22

[ "MIT" ]

12

2015-07-16T13:39:04.000Z

2022-02-14T15:36:10.000Z

src/lyxnotebook/entry_points.py

abarker/lyxNotebook

c458b21e1b183b94172414e14dea671e1f3d4b22

[ "MIT" ]

4

2020-03-11T00:33:50.000Z

2020-05-21T22:05:13.000Z

src/lyxnotebook/entry_points.py

abarker/lyxNotebook

c458b21e1b183b94172414e14dea671e1f3d4b22

[ "MIT" ]

3

2015-07-16T13:39:06.000Z

2020-04-15T19:17:45.000Z

""" These are the entry points to run LyxNotebook. They are set up in `setup.py` to become command-line commands. """ import os import sys import argparse from . import config_file_processing script_path = os.path.abspath(__file__) script_dir = os.path.dirname(script_path) def parse_args(): """Parse the command-line arguments.""" parser = argparse.ArgumentParser() parser.add_argument("--install", action="store_true", help= "Install files in the LyX user directory specified by the '--user-dir'" " argument, if one is given. By default files are installed in '~/.lyx'.") parser.add_argument("--no-editable-insets", action="store_true", help= "Whether or not the LyX version has editable insets (4.0 or greater)." " Needed to generate the layout module files since a new, incompatible," " property was added (EditExternal). Only meaningful with '--install';" " otherwise use the setting in the 'lyxnotebook.cfg' file.") parser.add_argument("--user-dir", nargs=1, help= "The LyX user directory in which to find or install LyX Notebook layout and" " binding files and the 'lyxnotebook.cfg' config file. The default is" " '~/.lyx'.") parser.add_argument("--ensure-tty", action="store_true", help= "Passed to run LyX Notebook from a LyX LFUN, or any other situation where " "there is no obvious tty to associate with LyX Notebook. This checks first, " "and opens a new tty if necessary. (Running the interpreters requires a tty " "to be associated with them, and the LyX needs a place to write its stdout.)") args = parser.parse_args() return args def run_lyxnotebook(): """Run LyxNotebook in the ordinary way from a terminal.""" args = parse_args() lyx_user_dir = args.user_dir[0] if args.user_dir else "~/.lyx" lyx_user_dir = os.path.abspath(os.path.expanduser(lyx_user_dir)) if args.install: from . import install # Pass lfun script command to set up a key binding. install.run_setup(lyx_user_dir, "\\\"lyxnotebook --ensure-tty --user-dir {}\\\"".format(lyx_user_dir), has_editable_insets=not args.no_editable_insets) return from . import config_file_processing config_file_processing.initialize_config_data(lyx_user_dir) if args.ensure_tty: cmd_string = "lyxnotebook " + " ".join(sys.argv[1:]) cmd_string = cmd_string.replace(" --ensure-tty", "") # Avoid recursive call. print("\nCommand to start lyxnotebook, with terminal output:\n ", cmd_string) from . import run_lyxnotebook_from_LFUN run_lyxnotebook_from_LFUN.main(cmd_string) # Pass regular script name to call after setup. else: config_file_processing.initialize_config_data(lyx_user_dir) from . import run_lyxnotebook run_lyxnotebook.main()

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Python

_kaggle/_render/heart-diseases-modeling/nb.py

jramirez857/soorgeon

54ab679f72be38731f5b43c6835f9a14921c396d

[ "Apache-2.0" ]

26

2021-12-01T10:00:31.000Z

2022-03-24T18:21:58.000Z

_kaggle/_render/heart-diseases-modeling/nb.py

jramirez857/soorgeon

54ab679f72be38731f5b43c6835f9a14921c396d

[ "Apache-2.0" ]

31

2021-12-20T03:20:37.000Z

2022-03-15T01:14:40.000Z

_kaggle/_render/heart-diseases-modeling/nb.py

jramirez857/soorgeon

54ab679f72be38731f5b43c6835f9a14921c396d

[ "Apache-2.0" ]

4

2022-02-03T21:40:55.000Z

2022-03-26T21:55:33.000Z

# -*- coding: utf-8 -*- # --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: percent # format_version: '1.3' # jupytext_version: 1.13.1 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # %% _uuid="8f2839f25d086af736a60e9eeb907d3b93b6e0e5" _cell_guid="b1076dfc-b9ad-4769-8c92-a6c4dae69d19" # This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) # Input data files are available in the read-only "../input/" directory # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory import os for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename)) # You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session # %% import seaborn as sns import matplotlib.pyplot as plt import pandas as pd import numpy as np import numpy as np import pandas as pd import statsmodels.api as sm import seaborn as sns import matplotlib.pyplot as plt from sklearn.preprocessing import scale, StandardScaler from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score from sklearn.metrics import confusion_matrix, accuracy_score, mean_squared_error, r2_score, roc_auc_score, roc_curve, classification_report from sklearn.linear_model import LogisticRegression from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.neural_network import MLPClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import GradientBoostingClassifier from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.metrics import mean_squared_error, r2_score import matplotlib.pyplot as plt from sklearn.model_selection import cross_val_score from sklearn.preprocessing import scale from sklearn.preprocessing import StandardScaler from sklearn import model_selection from sklearn.linear_model import LinearRegression from sklearn.tree import DecisionTreeRegressor from sklearn.neighbors import KNeighborsRegressor from sklearn.neural_network import MLPRegressor from sklearn.ensemble import RandomForestRegressor from sklearn.ensemble import GradientBoostingRegressor import numpy as np from sklearn.neighbors import LocalOutlierFactor from sklearn import neighbors from sklearn.svm import SVR from sklearn.metrics import mean_squared_error, r2_score from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV from xgboost import XGBRegressor from lightgbm import LGBMRegressor from sklearn import preprocessing from sklearn.preprocessing import scale from sklearn.metrics import mean_squared_log_error from sklearn.preprocessing import OrdinalEncoder from xgboost import XGBClassifier from lightgbm import LGBMClassifier from catboost import CatBoostClassifier import random import warnings warnings.filterwarnings("ignore", category=DeprecationWarning) warnings.filterwarnings("ignore", category=FutureWarning) models = [ LogisticRegression, KNeighborsClassifier, SVC, MLPClassifier, DecisionTreeClassifier, RandomForestClassifier, GradientBoostingClassifier, XGBClassifier, LGBMClassifier ] #,CatBoostClassifier pd.set_option('display.max_columns', None) pd.set_option('display.max_rows', 10) pd.set_option('display.float_format', lambda x: '%.5f' % x) # %% [markdown] # ## Adding Functions # %% def degisken_tiplerine_ayirma(data, cat_th, car_th): """ Veri:data parametresi ili fonksiyona girilen verinin değişkenlerin sınıflandırılması. Parameters ---------- data: pandas.DataFrame İşlem yapılacak veri seti cat_th:int categoric değişken threshold değeri car_th:int Cardinal değişkenler için threshold değeri Returns ------- cat_deg:list categorik değişken listesi num_deg:list numeric değişken listesi car_deg:list categoric ama cardinal değişken listesi Examples ------- df = dataset_yukle("breast_cancer") cat,num,car=degisken_tiplerine_ayirma(df,10,20) Notes ------- cat_deg + num_deg + car_deg = toplam değişken sayısı """ num_but_cat = [ i for i in data.columns if data[i].dtypes != "O" and data[i].nunique() < cat_th ] car_deg = [ i for i in data.columns if data[i].dtypes == "O" and data[i].nunique() > car_th ] num_deg = [ i for i in data.columns if data[i].dtypes != "O" and i not in num_but_cat ] cat_deg = [ i for i in data.columns if data[i].dtypes == "O" and i not in car_deg ] cat_deg = cat_deg + num_but_cat print(f"Dataset kolon/değişken sayısı: {data.shape[1]}") print(f"Dataset satır/veri sayısı: {data.shape[0]}") print("********************************************") print(f"Datasetin numeric değişken sayısı: {len(num_deg)}") print(f"Datasetin numeric değişkenler: {num_deg}") print("********************************************") print(f"Datasetin categoric değişken sayısı: {len(cat_deg)}") print(f"Datasetin categoric değişkenler: {cat_deg}") print("********************************************") print(f"Datasetin cardinal değişken sayısı: {len(car_deg)}") print(f"Datasetin cardinal değişkenler: {car_deg}") print("********************************************") return cat_deg, num_deg, car_deg def categoric_ozet(data, degisken, plot=False, null_control=False): """ Task ---------- Datasetinde bulunan categoric değişkenlerin değişken tiplerinin sayısını ve totale karşı oranını bulur. Ayrıca isteğe bağlı olarak değişken dağılımının grafiğini ve değişken içinde bulunan null sayısını çıkartır. Parameters ---------- data:pandas.DataFrame categoric değişkenin bulunduğu dataset. degisken:String Categoric değişken ismi. plot:bool Fonksiyonda categoric değişken dağılımının grafiğini çizdirmek için opsiyonel özellik. null_control:bool Fonksiyonda değişken içinde null değer kontolü için opsiyonel özellik Returns ------- tablo:pandas.DataFrame Unique değişkenlerin ratio olarak oran tablosu Examples ------- df=dataset_yukle("titanic") cat_deg,num_deg,car_deg=degisken_tiplerine_ayirma(df,10,20) for i in cat_deg: tablo=categoric_ozet(df,i,True,True) """ print( pd.DataFrame({ degisken: data[degisken].value_counts(), "Ratio": 100 * data[degisken].value_counts() / len(data) })) tablo = pd.DataFrame({ degisken: data[degisken].value_counts(), "Ratio": 100 * data[degisken].value_counts() / len(data) }) print("##########################################") if plot: sns.countplot(x=data[degisken], data=data) plt.show(block=True) if null_control: print(f"Null veri sayısı: {data[degisken].isnull().sum()}") return tablo def dataset_ozet(data, head=5): print("##################### Shape #####################") print(f"Satır sayısı: {data.shape[0]}") print(f"Kolon sayısı: {data.shape[1]}") print("##################### Types #####################") print(data.dtypes) print("##################### Head #####################") print(data.head(head)) print("##################### Tail #####################") print(data.tail(head)) print("##################### NA Kontrolü #####################") print(data.isnull().sum()) print("##################### Quantiles #####################") print(data.quantile([0, 0.05, 0.50, 0.95, 0.99, 1]).T) print("##################### Describe Tablosu #####################") print(data.describe().T) def outlier_threshold(data, degisken): Q1 = data[degisken].quantile(0.01) Q3 = data[degisken].quantile(0.99) Q_Inter_Range = Q3 - Q1 alt_limit = Q1 - 1.5 * Q_Inter_Range ust_limit = Q3 + 1.5 * Q_Inter_Range return alt_limit, ust_limit def threshold_degisimi(data, degisken): alt_limit, ust_limit = outlier_threshold(data, degisken) data.loc[(data[degisken] < alt_limit), degisken] = alt_limit data.loc[(data[degisken] > ust_limit), degisken] = ust_limit #data[data[degisken]<alt_limit][degisken]=alt_limit #data[data[degisken]>ust_limit][degisken]=ust_limit return data def numeric_ozet(data, degisken, plot=False, null_control=False): """ Task ---------- Datasetinde bulunan numeric değişkenlerin değişken tiplerinin sayısını ve totale karşı oranını bulur. Ayrıca isteğe bağlı olarak değişken dağılımının grafiğini ve değişken içinde bulunan null sayısını çıkartır. Parameters ---------- data:pandas.DataFrame categoric değişkenin bulunduğu dataset. degisken:String Categoric değişken ismi. plot:bool Fonksiyonda categoric değişken dağılımının grafiğini çizdirmek için opsiyonel özellik. null_control:bool Fonksiyonda değişken içinde null değer kontolü için opsiyonel özellik Returns ------- tablo:pandas.DataFrame Unique değişkenlerin ratio olarak oran tablosu Examples ------- df=dataset_yukle("titanic") cat_deg,num_deg,car_deg=degisken_tiplerine_ayirma(df,10,20) for i in cat_deg: tablo=categoric_ozet(df,i,True,True) """ quantiles = [ 0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95, 0.99 ] print(data[degisken].describe(quantiles).T) if plot: data[degisken].hist(bins=20) plt.xlabel(degisken) plt.title(degisken) plt.show(block=True) print("##########################################") if null_control: print(f"Null veri sayısı: {data[degisken].isnull().sum()}") def missing_values_table(dataframe, na_name=False): na_columns = [ col for col in dataframe.columns if dataframe[col].isnull().sum() > 0 ] n_miss = dataframe[na_columns].isnull().sum().sort_values(ascending=False) ratio = (dataframe[na_columns].isnull().sum() / dataframe.shape[0] * 100).sort_values(ascending=False) missing_df = pd.concat([n_miss, np.round(ratio, 2)], axis=1, keys=['n_miss', 'ratio']) print(missing_df, end="\n") if na_name: return na_columns def one_hot_encoder(dataframe, categorical_cols, drop_first=True): dataframe = pd.get_dummies(dataframe, columns=categorical_cols, drop_first=drop_first) return dataframe def model_karsilastirma(df, model, target): X = df.drop(columns=target) y = df[target] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42) model_fit = model().fit(X_train, y_train) y_pred = model_fit.predict(X_test) acc = accuracy_score(y_test, y_pred) print(model, "için sonuç doğruluk değeri:", acc) return acc def target_analyser(dataframe, target, num_deg, cat_deg): for degisken in dataframe.columns: if degisken in cat_deg: print(degisken, ":", len(dataframe[degisken].value_counts())) print(pd.DataFrame({ "COUNT": dataframe[degisken].value_counts(), "RATIO": dataframe[degisken].value_counts() / len(dataframe), "TARGET_MEAN": dataframe.groupby(degisken)[target].mean() }), end="\n\n\n") if degisken in num_deg: print(pd.DataFrame( {"TARGET_MEAN": dataframe.groupby(target)[degisken].mean()}), end="\n\n\n") # %% [markdown] # ## Some image # ![This is an image](https://www.sbbs-soc.com/wp-content/uploads/2020/09/Heart-Disease.jpg) # %% #loading dataset df = pd.read_csv("../input/heart-disease-uci/heart.csv") df.head() # %% [markdown] # ## Some info # * age: The person's age in years # * sex: The person's sex (1 = male, 0 = female) # * cp: The chest pain experienced (Value 1: typical angina, Value 2: atypical angina, Value 3: non-anginal pain, Value 4: asymptomatic) # * trestbps: The person's resting blood pressure (mm Hg on admission to the hospital) # * chol: The person's cholesterol measurement in mg/dl # * fbs: The person's fasting blood sugar (> 120 mg/dl, 1 = true; 0 = false) # * restecg: Resting electrocardiographic measurement (0 = normal, 1 = having ST-T wave abnormality, 2 = showing probable or definite left ventricular hypertrophy by Estes' criteria) # * thalach: The person's maximum heart rate achieved # * exang: Exercise induced angina (1 = yes; 0 = no) # * oldpeak: ST depression induced by exercise relative to rest ('ST' relates to positions on the ECG plot. See more here) # * slope: the slope of the peak exercise ST segment (Value 1: upsloping, Value 2: flat, Value 3: downsloping) # * ca: The number of major vessels (0-3) # * thal: A blood disorder called thalassemia (3 = normal; 6 = fixed defect; 7 = reversable defect) # * target: Heart disease (0 = no, 1 = yes) # %% #Analysis of Dataset dataset_ozet(df) cat_deg, num_deg, car_deg = degisken_tiplerine_ayirma(df, 10, 20) # %% #EDA of Dataset for i in cat_deg: categoric_ozet(df, i, True, True) for i in num_deg: numeric_ozet(df, i, True, True) # %% #All columns analaysis based on target column target_analyser(df, "target", num_deg, cat_deg) # %% #Filling missing values null_cols = missing_values_table(df, True) for i in null_cols: df[i].fillna(df[i].transform("mean"), inplace=True) #There is no missing values # %% #Outlier processing for i in num_deg: df = threshold_degisimi(df, i) # %% #Data Extraction df.age.describe() df.loc[(df["age"] < 40), 'NEW_AGE_CAT'] = 'Young' df.loc[(df["age"] >= 40) & (df["age"] < 50), 'NEW_AGE_CAT'] = 'Middle Age' df.loc[(df["age"] >= 50) & (df["age"] < 60), 'NEW_AGE_CAT'] = 'Pre-Old' df.loc[(df["age"] >= 60), 'NEW_AGE_CAT'] = 'Old' df.groupby('NEW_AGE_CAT')["target"].mean() # %% df.trestbps.describe() df.loc[(df["trestbps"] < 90), 'NEW_RBP_CAT'] = 'Low' df.loc[(df["trestbps"] >= 90) & (df["trestbps"] < 120), 'NEW_RBP_CAT'] = 'Ideal' df.loc[(df["trestbps"] >= 120) & (df["trestbps"] < 140), 'NEW_RBP_CAT'] = 'Pre-HIGH' df.loc[(df["trestbps"] >= 140), 'NEW_RBP_CAT'] = 'Hypertension' df.groupby('NEW_RBP_CAT')["target"].mean() # %% df.chol.describe() df.loc[(df["chol"] < 200), 'NEW_CHOL_CAT'] = 'Ideal' df.loc[(df["chol"] >= 200) & (df["chol"] < 240), 'NEW_CHOL_CAT'] = 'HIGH' df.loc[(df["chol"] >= 240), 'NEW_CHOL_CAT'] = 'Very Risky' df.groupby('NEW_CHOL_CAT')["target"].mean() # %% #Encoding of categoric columns cat_deg, num_deg, car_deg = degisken_tiplerine_ayirma(df, 10, 20) cat_deg = [i for i in cat_deg if i != "target"] df = one_hot_encoder(df, cat_deg) df.head() # %% #Scaling of numeric columns scaler = StandardScaler() df[num_deg] = scaler.fit_transform(df[num_deg]) # %% #Comparing of all models for mod in models: model_karsilastirma(df, mod, "target") # %% [markdown] # ## SVM Tuning # %% X = df.drop(columns="target") y = df["target"] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42) svm = SVC() svm_tuned = SVC(C=1, kernel="linear").fit(X_train, y_train) y_pred = svm_tuned.predict(X_test) acc = accuracy_score(y_test, y_pred) print("SVM accuracy: ", acc) # %% [markdown] # ## Logistic Regression Tuning # %% X = df.drop(columns="target") y = df["target"] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42) loj_model = LogisticRegression(solver="liblinear").fit(X_train, y_train) y_pred = loj_model.predict(X_test) acc = accuracy_score(y_test, y_pred) print("Lojistic_model accuracy: ", acc) # %% [markdown] # ## Light GBM Model Tuning # %% X = df.drop(columns="target") y = df["target"] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42) lgbm_tuned = LGBMClassifier(learning_rate=0.01, max_depth=5, n_estimators=250).fit(X_train, y_train) y_pred = lgbm_tuned.predict(X_test) acc = accuracy_score(y_test, y_pred) print("LGBM accuracy: ", acc)

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Python

light_field_neural_rendering/src/utils/config_utils.py

ParikhKadam/google-research

00a282388e389e09ce29109eb050491c96cfab85

[ "Apache-2.0" ]

2

2022-01-21T18:15:34.000Z

2022-01-25T15:21:34.000Z

light_field_neural_rendering/src/utils/config_utils.py

ParikhKadam/google-research

00a282388e389e09ce29109eb050491c96cfab85

[ "Apache-2.0" ]

110

2021-10-01T18:22:38.000Z

2021-12-27T22:08:31.000Z

light_field_neural_rendering/src/utils/config_utils.py

admariner/google-research

7cee4b22b925581d912e8d993625c180da2a5a4f

[ "Apache-2.0" ]

1

2022-02-10T10:43:10.000Z

# coding=utf-8 # Copyright 2021 The Google Research Authors. # # 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. """Config utilities.""" import dataclasses from typing import Any, Callable, Optional from jax import lax #------------------------------------------------------- # MLP parameters #------------------------------------------------------- @dataclasses.dataclass class MLPParams: """Parameters for NeRF MLP.""" net_depth: int net_width: int net_activation: Callable[Ellipsis, Any] num_rgb_channels: int skip_layer: int def get_mlp_config(config, net_activation): return MLPParams( net_depth=config.model.net_depth, net_width=config.model.net_width, net_activation=net_activation, num_rgb_channels=config.model.num_rgb_channels, skip_layer=config.model.skip_layer, ) #------------------------------------------------------- # Rendering parameters #------------------------------------------------------- @dataclasses.dataclass class RenderParams: """Parameters related to rendering""" near: float far: float lindisp: bool white_bkgd: bool num_coarse_samples: int num_fine_samples: int use_viewdirs: bool noise_std: float num_rgb_channels: int rgb_activation: Callable sigma_activation: Optional[Callable] = None def get_render_params(config, rgb_activation, sigma_activation=None): return RenderParams( near=config.model.near, far=config.model.far, white_bkgd=config.model.white_bkgd, lindisp=config.model.lindisp, num_coarse_samples=config.model.num_coarse_samples, num_fine_samples=config.model.num_fine_samples, use_viewdirs=config.model.use_viewdirs, noise_std=config.model.noise_std, rgb_activation=rgb_activation, sigma_activation=sigma_activation, num_rgb_channels=config.model.num_rgb_channels, ) #------------------------------------------------------- # Position Encoding parameters #------------------------------------------------------- @dataclasses.dataclass class EncodingParams: """Parameters for poisitonal encoding""" name: str min_deg_point: int max_deg_point: int deg_view: int def get_encoding_params(config): return EncodingParams( name=config.model.mapping_type, min_deg_point=config.model.min_deg_point, max_deg_point=config.model.max_deg_point, deg_view=config.model.deg_view, ) #------------------------------------------------------- # LightField parameters #------------------------------------------------------- @dataclasses.dataclass class LightFieldParams: """Parameter of lightfield representation""" name: str # Light Slab parameters st_plane: float uv_plane: float # Encoding parameters encoding_name: bool min_deg_point: int max_deg_point: int def get_lightfield_params(config): config.lightfield.st_plane = config.model.near config.lightfield.uv_plane = config.model.far return LightFieldParams( name=config.lightfield.name, st_plane=config.lightfield.st_plane, uv_plane=config.lightfield.uv_plane, encoding_name=config.lightfield.encoding_name, min_deg_point=config.lightfield.min_deg_point, max_deg_point=config.lightfield.max_deg_point, ) #------------------------------------------------------- # Transformer parameters #------------------------------------------------------- @dataclasses.dataclass class TransformerParams: """Parameters for Transformer.""" num_layers: int attention_heads: int qkv_params: Optional[int] = None mlp_params: Optional[int] = None dropout_rate: float = 0. def __post_init__(self): assert self.dropout_rate == 0, "Dropout not supported yet." def get_epipolar_transformer_params(config): return TransformerParams( num_layers=config.model.transformer_layers, attention_heads=config.model.transformer_heads, qkv_params=config.model.qkv_dim, mlp_params=config.model.transformer_mlp_dim, dropout_rate=0.) def get_view_transformer_params(config): return TransformerParams( num_layers=config.model.transformer_layers, attention_heads=config.model.transformer_heads, qkv_params=config.model.qkv_dim, mlp_params=config.model.transformer_mlp_dim, dropout_rate=0.) #------------------------------------------------------- # Epipolar Projection parameters #------------------------------------------------------- @dataclasses.dataclass class EpipolarParams: """Parameters for epipolar projection""" use_pixel_centers: bool min_depth: int max_depth: int image_height: int image_width: int num_projections: int num_train_views: int use_learned_embedding: bool learned_embedding_mode: str mask_invalid_projection: bool use_conv_features: bool conv_feature_dim: int ksize1: int ksize2: int interpolation_type: str precision: lax.Precision def __post_init__(self): if self.interpolation_type == "linear": assert (self.use_pixel_centers == False ), "Cannot use pixel center with linear interpolation" def get_epipolar_params(config): assert config.dataset.image_height != -1, ("Image height for dataset was not " "set") assert config.dataset.image_width != -1, "Image width for dataset was not set" assert config.model.near != 0, "0 depth projections can lead to error" assert config.dataset.num_train_views != -1, ("Number of train views should " "be set") return EpipolarParams( use_pixel_centers=config.dataset.use_pixel_centers, min_depth=config.model.near, max_depth=config.model.far, image_height=config.dataset.image_height, image_width=config.dataset.image_width, num_projections=config.model.num_projections, num_train_views=config.dataset.num_train_views, use_learned_embedding=config.model.use_learned_embedding, learned_embedding_mode=config.model.learned_embedding_mode, mask_invalid_projection=config.model.mask_invalid_projection, use_conv_features=config.model.use_conv_features, conv_feature_dim=config.model.conv_feature_dim, ksize1=config.model.ksize1, ksize2=config.model.ksize2, interpolation_type=config.model.interpolation_type, precision=getattr(lax.Precision, config.model.init_final_precision), )

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null

0

null

0

1

0

0b0adb19e81258e1fbdcea69959d95afd67c7522

758

py

Python

tests/test_xp_style.py

TNThieding/exif

2e59701aec7416fbb3b2db76e7d090f166f1f132

[ "MIT" ]

51

2018-12-28T19:48:40.000Z

2021-12-10T00:35:41.000Z

tests/test_xp_style.py

TNThieding/exif

2e59701aec7416fbb3b2db76e7d090f166f1f132

[ "MIT" ]

33

2019-02-08T10:15:25.000Z

2022-02-11T18:37:45.000Z

tests/test_xp_style.py

TNThieding/exif

2e59701aec7416fbb3b2db76e7d090f166f1f132

[ "MIT" ]

11

2019-10-24T14:03:02.000Z

2020-12-10T04:07:20.000Z

"""Test special behavior for accessing Windows XP style EXIF attribute.""" import os import pytest from exif import Image read_attributes = [ ("xp_author", "XP-Style Author"), ("xp_comment", "XP-Style Comment ⛷"), ("xp_keywords", "XP-Style Keywords"), ("xp_subject", "XP-Style Subject 🤓"), ("xp_title", "XP-Style Title"), ] @pytest.mark.parametrize( "attribute, value", read_attributes, ids=[params[0] for params in read_attributes] ) def test_read(attribute, value): """Test reading tags and compare to known baseline values.""" with open( os.path.join(os.path.dirname(__file__), "windows_xp_tags.jpg"), "rb" ) as image_file: image = Image(image_file) assert getattr(image, attribute) == value

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null

0

null

0

1

0

0b0ebdaa54e0ceda206e818639c1c7b8395d9b82

3,713

py

Python

tornado/modules/api/api.py

maqg/wcrobot

7d026c1a34362c5434105c27c5bd25f08c6fabe2

[ "MIT" ]

null

tornado/modules/api/api.py

maqg/wcrobot

7d026c1a34362c5434105c27c5bd25f08c6fabe2

[ "MIT" ]

null

tornado/modules/api/api.py

maqg/wcrobot

7d026c1a34362c5434105c27c5bd25f08c6fabe2

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- from conf.dbconfig import TB_APITRACE from core import dbmysql from core.err_code import SEGMENT_NOT_EXIST, OCT_SUCCESS, err_desc_ch from core.log import ERROR, WARNING from models.Api import Api, API_STATE_NEW, API_STATE_FINISHED from utils.commonUtil import CRC32 from models.Common import DEFAULT_ACCOUNT_ID def getApiCount(db, cond=""): return db.rowcount(TB_APITRACE, cond=cond) def addTask(db, arg, taskParas): api = Api(db) api.accountId = arg["paras"].get("accountId") api.user = arg["env"].get("USERNAME") api.state = API_STATE_NEW api.apiId = arg["api"] api.type = "task" api.name = arg.get("apiName") or "" if (taskParas.get("object")): api.name = api.name + "[%s]" % str(taskParas.get("object")) api.request = taskParas ret = api.add() return (ret, api.myId) def addApi(db, arg, taskParas): api = Api(db) api.accountId = arg["paras"].get("accountId") api.user = arg["session"].get("username") api.state = API_STATE_NEW api.apiId = arg["api"] api.name = arg.get("apiName") or "" if (taskParas.get("object")): api.name = api.name + "[%s]" % str(taskParas.get("object")) api.request = arg ret = api.add() return (ret, api.simpleObj()) def buildApiResult(res): errorNo = res["RetCode"] return { "errorObj": { "errorNo": errorNo, "errorMsg": err_desc_ch.get(errorNo), }, "data": res["RetObj"] } def addApiResult(db, env, arg, result=None): api = Api(db) api.accountId = arg["paras"].get("accountId") api.user = env["USERNAME"] api.state = API_STATE_FINISHED api.apiId = arg["api"] api.name = arg.get("apiName") or "" api.request = arg api.reply = buildApiResult(result) ret = api.add() return (ret, api.simpleObj()) def deleteApi(db, arg): apiId = arg["paras"].get("id") api = getApi(db, apiId=apiId) if (not api): WARNING("api %s not exist" % apiId) return SEGMENT_NOT_EXIST return api.delete() def updateApiReply(db, arg): apiId = arg.get("id") api = getApi(db, apiId=apiId) if (not api): WARNING("api %s not exist" % apiId) return SEGMENT_NOT_EXIST # TBD return api.updateReply() def getApis(db, arg): listObj = { "data": [], "total": 0 } cond = "WHERE 1=1 " accountId = arg["paras"].get("accountId") start = arg["paras"].get("start") or 0 limit = arg["paras"].get("limit") or 10 keyword = arg["paras"].get("keyword") or "" type = arg["paras"].get("type") apiName = arg["paras"].get("apiName") serverTaskId = arg["paras"].get("serverTaskId") if (accountId and accountId != DEFAULT_ACCOUNT_ID): cond += "AND AT_AccountId='%s' " % (accountId) if (type): cond += "AND AT_Type='%s' " % type if (apiName): cond += "AND AT_Name LIKE '%%%s%%' " % (apiName) if (keyword): cond += "AND AT_ApiId LIKE '%%%s%%' " % (keyword) if (serverTaskId): cond += "AND AT_ServerTaskId='%s' " % serverTaskId cond += "ORDER BY AT_StartTime DESC" ret = db.select(TB_APITRACE, cond=cond, limit=int(limit), offset=int(start)) if ret == -1: ERROR("get modules list error") return (OCT_SUCCESS, listObj) hashStr = "" for dur in db.cur: obj = dbmysql.row_to_dict(TB_APITRACE, dur) api = Api(db, dbObj=obj) api.loadFromObj() obj = api.toObj() hashStr += api.myId hashStr += api.state listObj["data"].append(obj) listObj["total"] = getApiCount(db, cond=cond) listObj["hashValue"] = CRC32(hashStr) return (OCT_SUCCESS, listObj) def getApi(db, apiId=None, apiName=None, submoduleId=None): if (not apiId): return None cond = "WHERE ID='%s'" % (apiId) dbObj = db.fetchone(TB_APITRACE, cond=cond) if (not dbObj): WARNING("module %s not exist" % cond) return None api = Api(db, dbObj=dbObj) api.loadFromObj() return api

22.233533

77

0.660382

540

3,713

4.464815

0.235185

0.036499

0.050187

0.033181

0.321858

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0.263791

0

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0.16348

3,713

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0.252101

0

null

0

null

0

1

0

0b0fef11f33991a8404426d2f39ce4db0fc17bdf

1,157

py

Python

app/httprpc.py

zhangpanyi/btsmonitor

d61599300f929753fcf8385cc37f6ed99726732c

[ "MIT" ]

5

2018-02-18T14:35:50.000Z

2019-07-10T13:53:33.000Z

app/httprpc.py

zhangpanyi/btsmonitor

d61599300f929753fcf8385cc37f6ed99726732c

[ "MIT" ]

4

2018-02-18T23:37:59.000Z

2021-11-26T14:23:24.000Z

app/httprpc.py

zhangpanyi/btsmonitor

d61599300f929753fcf8385cc37f6ed99726732c

[ "MIT" ]

4

2018-02-18T14:35:10.000Z

2019-05-17T10:35:58.000Z

# -*- coding:utf-8 -*- import json import aiohttp from .asyncrpc import RPCError from bitsharesbase.chains import known_chains class HttpRPC(object): ''' 短链接RPC客户端 ''' def __init__(self, access, loop=None): self._url = 'https://' + access self._loop = loop async def _rpc(self, method, params): ''' 远程过程调用 ''' # 生成请求内容 request = {'id': 1, 'method': method, 'params': params} # 异步执行请求 async with aiohttp.ClientSession() as session: async with session.post(self._url, json=request) as resp: # 格式化返回结果 ret = json.loads(await resp.text()) if 'error' in ret: if 'detail' in ret['error']: raise RPCError(ret['error']['detail']) else: raise RPCError(ret['error']['message']) return ret['result'] def __getattr__(self, name): ''' 简化方法调用 ''' async def method(*args, **kwargs): return await self._rpc(name, [*args]) return method

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1,157

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false

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0

0.434783

0

null

0

null

0

1

0

0b1070cbf18c29a6ad2654bf66152a86ebfd2a4a

1,199

py

Python

checkbook/loader.py

monospacesoftware/checkbook

b8e3ea498dc6b4d0c3fddedabfb208de5b516361

[ "MIT" ]

null

checkbook/loader.py

monospacesoftware/checkbook

b8e3ea498dc6b4d0c3fddedabfb208de5b516361

[ "MIT" ]

null

checkbook/loader.py

monospacesoftware/checkbook

b8e3ea498dc6b4d0c3fddedabfb208de5b516361

[ "MIT" ]

null

import re from os import listdir from os.path import isfile from checkbook.chase import Chase from checkbook.database import Database from checkbook.psecu import Psecu from checkbook.transaction_source import TransactionSource class Loader: @classmethod def load_incoming(cls, db: Database): chase = Chase() psecu = Psecu() for path in cls.list_incoming_files(): if re.match(".*chase.*", path, re.IGNORECASE): cls.load_trans(path, chase, db, "2_Amazon Credit Card") elif re.match(".*psecu.*", path, re.IGNORECASE): cls.load_trans(path, psecu, db, "1_PSECU Joint Checking") else: print(f"Skipping unrecognized file {path}") @classmethod def load_trans(cls, path: str, source: TransactionSource, db: Database, acct_name: str): for tran in source.load(path, acct_name): db.add(tran) @classmethod def list_incoming_files(cls): paths = [] for file_name in listdir('incoming'): path = f"incoming/{file_name}" if not isfile(path): continue paths.append(path) return paths

30.74359

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5

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false

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0

0.375

0.03125

0

null

0

null

0

1

0

0b119d780ac562813940597b495ba869ce5283d0

1,281

py

Python

deprecated/pelicanconf.py

IEEEComputerSocietyUNB/ieee-computer-society-unb

49b4226a95450c359c4ddd1266b9af1fa7fe6bda

[ "MIT" ]

1

2019-10-01T01:56:48.000Z

deprecated/pelicanconf.py

IEEEComputerSocietyUNB/ieee-computer-society-unb

49b4226a95450c359c4ddd1266b9af1fa7fe6bda

[ "MIT" ]

31

2019-09-02T12:53:30.000Z

2019-10-19T20:34:14.000Z

deprecated/pelicanconf.py

IEEEComputerSocietyUNB/ieee-computer-society-unb

49b4226a95450c359c4ddd1266b9af1fa7fe6bda

[ "MIT" ]

2

2019-09-13T15:47:46.000Z

2019-09-28T04:50:43.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # from __future__ import unicode_literals AUTHOR = 'IEEE Computer Society UnB' SITENAME = 'IEEE Computer Society UnB' SITEURL = '' # Customized settings THEME = 'bulrush/bulrush' LOAD_CONTENT_CACHE = False PATH = 'content' TIMEZONE = 'America/Sao_Paulo' DEFAULT_LANG = 'pt-br' ARTICLE_PATHS = ['articles', ] ARTICLE_URL = 'articles/{slug}.html' ARTICLE_SAVE_AS = 'articles/{slug}.html' PAGE_URL = 'pages/{slug}/' PAGE_SAVE_AS = 'pages/{slug}.html' # Feed generation is usually not desired when developing FEED_ALL_ATOM = None CATEGORY_FEED_ATOM = None TRANSLATION_FEED_ATOM = None AUTHOR_FEED_ATOM = None AUTHOR_FEED_RSS = None # Social GITHUB_URL = 'http://getpelican.com/' TWITTER_URL = 'http://getpelican.com/' FACEBOOK_URL = 'http://getpelican.com/' # Blogroll LINKS = (('Facebook', 'http://getpelican.com/'), ('Github', 'http://python.org/'), ('Jinja2', 'http://jinja.pocoo.org/'), ('You can modify those links in your config file', '#'),) # Social widget SOCIAL = (('You can add links in your config file', '#'), ('Facebook', 'http://getpelican.com/'),) DEFAULT_PAGINATION = 10 # Uncomment following line if you want document-relative URLs when developing # RELATIVE_URLS = True

24.634615

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5.180723

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0

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1,281

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0

0.424951

0

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false

0

0.034483

0

0.034483

0

null

0

null

0

1

0

0b15d62e42779dc542f62eb75c22bdace6503fd8

7,690

py

Python

vispy/ext/_bundled/husl.py

hmaarrfk/vispy

7f3f6f60c8462bb8a3a8fa03344a2e6990b86eb2

[ "BSD-3-Clause" ]

3

2019-02-28T16:05:33.000Z

2020-05-03T21:29:03.000Z

vispy/ext/_bundled/husl.py

hmaarrfk/vispy

7f3f6f60c8462bb8a3a8fa03344a2e6990b86eb2

[ "BSD-3-Clause" ]

1

2021-06-04T13:48:46.000Z

2021-06-05T10:57:33.000Z

vispy/ext/_bundled/husl.py

hmaarrfk/vispy

7f3f6f60c8462bb8a3a8fa03344a2e6990b86eb2

[ "BSD-3-Clause" ]

1

2019-04-03T12:49:18.000Z

""" HUSL colors python implementation. Source: https://github.com/husl-colors/husl.py Copyright (c) 2015 Alexei Boronine Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import operator import math __version__ = "4.0.2" m = [ [3.240969941904521, -1.537383177570093, -0.498610760293], [-0.96924363628087, 1.87596750150772, 0.041555057407175], [0.055630079696993, -0.20397695888897, 1.056971514242878], ] m_inv = [ [0.41239079926595, 0.35758433938387, 0.18048078840183], [0.21263900587151, 0.71516867876775, 0.072192315360733], [0.019330818715591, 0.11919477979462, 0.95053215224966], ] refX = 0.95045592705167 refY = 1.0 refZ = 1.089057750759878 refU = 0.19783000664283 refV = 0.46831999493879 kappa = 903.2962962 epsilon = 0.0088564516 # Public API def husl_to_rgb(h, s, l): return lch_to_rgb(*husl_to_lch([h, s, l])) def husl_to_hex(h, s, l): return rgb_to_hex(husl_to_rgb(h, s, l)) def rgb_to_husl(r, g, b): return lch_to_husl(rgb_to_lch(r, g, b)) def hex_to_husl(hex): return rgb_to_husl(*hex_to_rgb(hex)) def huslp_to_rgb(h, s, l): return lch_to_rgb(*huslp_to_lch([h, s, l])) def huslp_to_hex(h, s, l): return rgb_to_hex(huslp_to_rgb(h, s, l)) def rgb_to_huslp(r, g, b): return lch_to_huslp(rgb_to_lch(r, g, b)) def hex_to_huslp(hex): return rgb_to_huslp(*hex_to_rgb(hex)) def lch_to_rgb(l, c, h): return xyz_to_rgb(luv_to_xyz(lch_to_luv([l, c, h]))) def rgb_to_lch(r, g, b): return luv_to_lch(xyz_to_luv(rgb_to_xyz([r, g, b]))) def get_bounds(L): sub1 = ((L + 16.0) ** 3.0) / 1560896.0 sub2 = sub1 if sub1 > epsilon else L / kappa ret = [] for [m1, m2, m3] in m: for t in [0, 1]: top1 = (284517.0 * m1 - 94839.0 * m3) * sub2 top2 = ((838422.0 * m3 + 769860.0 * m2 + 731718.0 * m1) * L * sub2 - 769860.0 * t * L) bottom = (632260.0 * m3 - 126452.0 * m2) * sub2 + 126452.0 * t ret.append((top1 / bottom, top2 / bottom)) return ret def intersect_line_line(line1, line2): return (line1[1] - line2[1]) / (line2[0] - line1[0]) def distance_from_pole(point): return math.sqrt(point[0] ** 2 + point[1] ** 2) def length_of_ray_until_intersect(theta, line): m1, b1 = line length = b1 / (math.sin(theta) - m1 * math.cos(theta)) if length < 0: return None return length def max_safe_chroma_for_L(L): lengths = [] for [m1, b1] in get_bounds(L): x = intersect_line_line((m1, b1), (-1.0 / m1, 0.0)) lengths.append(distance_from_pole((x, b1 + x * m1))) return min(lengths) def max_chroma_for_LH(L, H): hrad = H / 360.0 * math.pi * 2.0 lengths = [] for line in get_bounds(L): ray_length = length_of_ray_until_intersect(hrad, line) if ray_length is not None: lengths.append(ray_length) return min(lengths) def dot_product(a, b): return sum(map(operator.mul, a, b)) def f(t): if t > epsilon: return 116 * math.pow((t / refY), 1.0 / 3.0) - 16.0 else: return (t / refY) * kappa def f_inv(t): if t > 8: return refY * math.pow((t + 16.0) / 116.0, 3.0) else: return refY * t / kappa def from_linear(c): if c <= 0.0031308: return 12.92 * c else: return (1.055 * math.pow(c, 1.0 / 2.4) - 0.055) def to_linear(c): a = 0.055 if c > 0.04045: return (math.pow((c + a) / (1.0 + a), 2.4)) else: return (c / 12.92) def rgb_prepare(triple): ret = [] for ch in triple: ch = round(ch, 3) if ch < -0.0001 or ch > 1.0001: raise Exception("Illegal RGB value %f" % ch) if ch < 0: ch = 0 if ch > 1: ch = 1 # Fix for Python 3 which by default rounds 4.5 down to 4.0 # instead of Python 2 which is rounded to 5.0 which caused # a couple off by one errors in the tests. Tests now all pass # in Python 2 and Python 3 ret.append(round(ch * 255 + 0.001, 0)) return ret def hex_to_rgb(hex): if hex.startswith('#'): hex = hex[1:] r = int(hex[0:2], 16) / 255.0 g = int(hex[2:4], 16) / 255.0 b = int(hex[4:6], 16) / 255.0 return [r, g, b] def rgb_to_hex(triple): [r, g, b] = triple return '#%02x%02x%02x' % tuple(rgb_prepare([r, g, b])) def xyz_to_rgb(triple): xyz = map(lambda row: dot_product(row, triple), m) return list(map(from_linear, xyz)) def rgb_to_xyz(triple): rgbl = list(map(to_linear, triple)) return list(map(lambda row: dot_product(row, rgbl), m_inv)) def xyz_to_luv(triple): X, Y, Z = triple if X == Y == Z == 0.0: return [0.0, 0.0, 0.0] varU = (4.0 * X) / (X + (15.0 * Y) + (3.0 * Z)) varV = (9.0 * Y) / (X + (15.0 * Y) + (3.0 * Z)) L = f(Y) # Black will create a divide-by-zero error if L == 0.0: return [0.0, 0.0, 0.0] U = 13.0 * L * (varU - refU) V = 13.0 * L * (varV - refV) return [L, U, V] def luv_to_xyz(triple): L, U, V = triple if L == 0: return [0.0, 0.0, 0.0] varY = f_inv(L) varU = U / (13.0 * L) + refU varV = V / (13.0 * L) + refV Y = varY * refY X = 0.0 - (9.0 * Y * varU) / ((varU - 4.0) * varV - varU * varV) Z = (9.0 * Y - (15.0 * varV * Y) - (varV * X)) / (3.0 * varV) return [X, Y, Z] def luv_to_lch(triple): L, U, V = triple C = (math.pow(math.pow(U, 2) + math.pow(V, 2), (1.0 / 2.0))) hrad = (math.atan2(V, U)) H = math.degrees(hrad) if H < 0.0: H = 360.0 + H return [L, C, H] def lch_to_luv(triple): L, C, H = triple Hrad = math.radians(H) U = (math.cos(Hrad) * C) V = (math.sin(Hrad) * C) return [L, U, V] def husl_to_lch(triple): H, S, L = triple if L > 99.9999999: return [100, 0.0, H] if L < 0.00000001: return [0.0, 0.0, H] mx = max_chroma_for_LH(L, H) C = mx / 100.0 * S return [L, C, H] def lch_to_husl(triple): L, C, H = triple if L > 99.9999999: return [H, 0.0, 100.0] if L < 0.00000001: return [H, 0.0, 0.0] mx = max_chroma_for_LH(L, H) S = C / mx * 100.0 return [H, S, L] def huslp_to_lch(triple): H, S, L = triple if L > 99.9999999: return [100, 0.0, H] if L < 0.00000001: return [0.0, 0.0, H] mx = max_safe_chroma_for_L(L) C = mx / 100.0 * S return [L, C, H] def lch_to_huslp(triple): L, C, H = triple if L > 99.9999999: return [H, 0.0, 100.0] if L < 0.00000001: return [H, 0.0, 0.0] mx = max_safe_chroma_for_L(L) S = C / mx * 100.0 return [H, S, L]

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0b16807157db29b3659c0af09e72504b46a2a4be

3,330

py

Python

src/incorrect_disambiguation_detection/concatenate_subgraph_entity_embedding_with_cluster_connected_kg_embedding.py

mainuliitkgp/AR-BERT

d6d5e8542a3a1c76edac49cec9e99ebda6395725

[ "MIT" ]

4

2022-03-06T17:41:57.000Z

2022-03-22T08:42:58.000Z

src/incorrect_disambiguation_detection/concatenate_subgraph_entity_embedding_with_cluster_connected_kg_embedding.py

mainuliitkgp/AR-BERT

d6d5e8542a3a1c76edac49cec9e99ebda6395725

[ "MIT" ]

null

src/incorrect_disambiguation_detection/concatenate_subgraph_entity_embedding_with_cluster_connected_kg_embedding.py

mainuliitkgp/AR-BERT

d6d5e8542a3a1c76edac49cec9e99ebda6395725

[ "MIT" ]

1

2022-03-19T14:04:42.000Z

import sys import numpy as np import json import pickle unique_entity_fp = sys.argv[1] sub_graph_embedding_fp = sys.argv[2] sub_graph_embedding_indices_fp = sys.argv[3] connected_entity_to_idx_dict_fp = sys.argv[4] node_to_cluster_id_dict_fp = sys.argv[5] cluster_kg_embedding_fp = sys.argv[6] cluster_kg_embedding_indices_fp = sys.argv[7] ds_name = sys.argv[8] # prepare unique entity list unique_entity_list = [] with open(unique_entity_fp) as fp: for line in fp: unique_entity_list.append(line.strip()) # read sub-graph entity embedding and corresponding indices sub_graph_entity_embedding = np.load(sub_graph_embedding_fp) sub_graph_entity_embedding_indices = [] with open(sub_graph_embedding_indices_fp) as fp: for line in fp: sub_graph_entity_embedding_indices.append(int(line.strip())) # re-arrange sub-graph entity embedding wrt. unique entities rearranged_sub_graph_entity_embedding = [] for i, entity in enumerate(unique_entity_list): idx = sub_graph_entity_embedding_indices.index(i) emd = sub_graph_entity_embedding[idx] rearranged_sub_graph_entity_embedding.append(emd) rearranged_sub_graph_entity_embedding = np.array(rearranged_sub_graph_entity_embedding, dtype = np.float32) # read connected KG entity to id dictionary and map sub-graph entity index to connected KG entity index connected_entity_to_idx_dict = json.load(open(connected_entity_to_idx_dict_fp)) sub_graph_entity_idx_to_connected_kg_idx = {} for i, entity in enumerate(unique_entity_list): try: sub_graph_entity_idx_to_connected_kg_idx[int(i)] = connected_entity_to_idx_dict[entity.strip()] except: sub_graph_entity_idx_to_connected_kg_idx[int(i)] = -1 # for not mapped sub-graph entities in connected KG # map sub-graph entity index to cluster id with open(node_to_cluster_id_dict_fp, "rb") as fp: node_to_cluster_id_dict = pickle.load(fp) sub_graph_entity_idx_to_cluster_id_dict = {} for i, entity in enumerate(unique_entity_list): node_id_in_kg = sub_graph_entity_idx_to_connected_kg_idx[int(i)] if node_id_in_kg != -1: sub_graph_entity_idx_to_cluster_id_dict[int(i)] = node_to_cluster_id_dict[int(node_id_in_kg)] else: sub_graph_entity_idx_to_cluster_id_dict[int(i)] = -1 # no cluster membership # read cluster KG embedding and corresponding indices cluster_kg_embedding = np.load(cluster_kg_embedding_fp) cluster_kg_embedding_indices = [] with open(cluster_kg_embedding_indices_fp) as fp: for line in fp: cluster_kg_embedding_indices.append(int(line.strip())) # re-arrange cluster KG embedding wrt. unique entities rearranged_cluster_kg_entity_embedding = np.zeros((len(unique_entity_list), 50), dtype = np.float32) for i, entity in enumerate(unique_entity_list): cluster_id = sub_graph_entity_idx_to_cluster_id_dict[int(i)] if cluster_id != -1: idx = cluster_kg_embedding_indices.index(cluster_id) emd = cluster_kg_embedding[idx] rearranged_cluster_kg_entity_embedding[i] = emd # concatenate sub-graph entity embedding and cluster KG embedding concatenated_embedding = np.concatenate((rearranged_sub_graph_entity_embedding, rearranged_cluster_kg_entity_embedding), axis = 1) # save concatenated embedding np.save(concatenated_embedding, 'concatenated_embedding_'+ds_name+'.npy')

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0.092677

0.133223

0.123707

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0.090608

0

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3,330

89

131

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0.829184

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0

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0

null

0

null

0

1

0

0b16ad70e459e73dd2b22da02fbb9c36e130e787

20,678

py

Python

metattack/utils.py

AndreamApp/gnn-meta-attack

8391a5a477a0d19be9755237f8e236d854a9811c

[ "MIT" ]

null

metattack/utils.py

AndreamApp/gnn-meta-attack

8391a5a477a0d19be9755237f8e236d854a9811c

[ "MIT" ]

null

metattack/utils.py

AndreamApp/gnn-meta-attack

8391a5a477a0d19be9755237f8e236d854a9811c

[ "MIT" ]

null

""" Implementation of the method proposed in the paper: 'Adversarial Attacks on Graph Neural Networks via Meta Learning' by Daniel Zügner, Stephan Günnemann Published at ICLR 2019 in New Orleans, USA. Copyright (C) 2019 Daniel Zügner Technical University of Munich """ import tensorflow.compat.v1 as tf tf.disable_v2_behavior() import numpy as np from sklearn.model_selection import train_test_split import scipy.sparse as sp from scipy.sparse.csgraph import connected_components def load_npz(file_name): """Load a SparseGraph from a Numpy binary file. Parameters ---------- file_name : str Name of the file to load. Returns ------- sparse_graph : gust.SparseGraph Graph in sparse matrix format. """ if not file_name.endswith('.npz'): file_name += '.npz' with np.load(file_name, allow_pickle=True) as loader: loader = dict(loader) adj_matrix = sp.csr_matrix((loader['adj_data'], loader['adj_indices'], loader['adj_indptr']), shape=loader['adj_shape']) if 'attr_data' in loader: attr_matrix = sp.csr_matrix((loader['attr_data'], loader['attr_indices'], loader['attr_indptr']), shape=loader['attr_shape']) else: attr_matrix = None labels = loader.get('labels') return adj_matrix, attr_matrix, labels def largest_connected_components(adj, n_components=1): """Select the largest connected components in the graph. Parameters ---------- adj : gust.SparseGraph Input graph. n_components : int, default 1 Number of largest connected components to keep. Returns ------- sparse_graph : gust.SparseGraph Subgraph of the input graph where only the nodes in largest n_components are kept. """ _, component_indices = connected_components(adj) component_sizes = np.bincount(component_indices) components_to_keep = np.argsort(component_sizes)[::-1][:n_components] # reverse order to sort descending nodes_to_keep = [ idx for (idx, component) in enumerate(component_indices) if component in components_to_keep ] print("Selecting {0} largest connected components".format(n_components)) return nodes_to_keep def train_val_test_split_tabular(*arrays, train_size=0.5, val_size=0.3, test_size=0.2, stratify=None, random_state=None): """ Split the arrays or matrices into random train, validation and test subsets. Parameters ---------- *arrays : sequence of indexables with same length / shape[0] Allowed inputs are lists, numpy arrays or scipy-sparse matrices. train_size : float, default 0.5 Proportion of the dataset included in the train split. val_size : float, default 0.3 Proportion of the dataset included in the validation split. test_size : float, default 0.2 Proportion of the dataset included in the test split. stratify : array-like or None, default None If not None, data is split in a stratified fashion, using this as the class labels. random_state : int or None, default None Random_state is the seed used by the random number generator; Returns ------- splitting : list, length=3 * len(arrays) List containing train-validation-test split of inputs. """ if len(set(array.shape[0] for array in arrays)) != 1: raise ValueError("Arrays must have equal first dimension.") idx = np.arange(arrays[0].shape[0]) idx_train_and_val, idx_test = train_test_split(idx, random_state=random_state, train_size=(train_size + val_size), test_size=test_size, stratify=stratify) if stratify is not None: stratify = stratify[idx_train_and_val] idx_train, idx_val = train_test_split(idx_train_and_val, random_state=random_state, train_size=(train_size / (train_size + val_size)), test_size=(val_size / (train_size + val_size)), stratify=stratify) result = [] for X in arrays: result.append(X[idx_train]) result.append(X[idx_val]) result.append(X[idx_test]) return result def preprocess_graph(adj): """ Perform the processing of the adjacency matrix proposed by Kipf et al. 2017. Parameters ---------- adj: sp.spmatrix Input adjacency matrix. Returns ------- The matrix (D+1)^(-0.5) (adj + I) (D+1)^(-0.5) """ adj_ = adj + sp.eye(adj.shape[0]) rowsum = adj_.sum(1).A1 degree_mat_inv_sqrt = sp.diags(np.power(rowsum, -0.5)) adj_normalized = adj_.dot(degree_mat_inv_sqrt).T.dot(degree_mat_inv_sqrt).tocsr() return adj_normalized def unravel_index_tf(ix, shape): """ Unravels the input index similar to np.unravel_index. That is, given the "flat" (i.e. between 0 and shape[0] * shape[1] - 1) input index and a 2D shape computes the 2D index corresponding to the input index. Parameters ---------- ix: tf.int32 The input index. shape: tuple or list of ints with length 2 2D shape (e.g. adjacency matrix dimensions). Returns ------- tf.Tensor, dtype int, shape (2,) The index in the 2D shape corresponding to the "flat" input index ix. """ output_list = [] output_list.append(ix // (shape[1])) output_list.append(ix % (shape[1])) return tf.stack(output_list) def ravel_index(ix, shape): """ "Flattens" the 2D input index into a single index on the flattened matrix, similar to np.ravel_multi_index. Parameters ---------- ix: array or list of ints of shape (2,) The 2D input index. shape: list or tuple of ints of length 2 The shape of the corresponding matrix. Returns ------- int between 0 and shape[0]*shape[1]-1 The index on the flattened matrix corresponding to the 2D input index. """ return ix[0]*shape[1] + ix[1] def ravel_multiple_indices(ixs, shape): """ "Flattens" multiple 2D input indices into indices on the flattened matrix, similar to np.ravel_multi_index. Does the same as ravel_index but for multiple indices at once. Parameters ---------- ixs: array of ints shape (n, 2) The array of n indices that will be flattened. shape: list or tuple of ints of length 2 The shape of the corresponding matrix. Returns ------- array of n ints between 0 and shape[0]*shape[1]-1 The indices on the flattened matrix corresponding to the 2D input indices. """ return ixs[:, 0] * shape[1] + ixs[:, 1] def compute_log_likelihood(n, alpha, sum_log_degrees, d_min): """ Computes thelog likelihood of the observed Powerlaw distribution given the Powerlaw exponent alpha. Parameters ---------- n: int The number of samples in the observed distribution whose value is >= d_min. alpha: float The Powerlaw exponent for which the log likelihood is to be computed. sum_log_degrees: float The sum of the logs of samples in the observed distribution whose values are >= d_min. d_min: int The minimum degree to be considered in the Powerlaw computation. Returns ------- float The log likelihood of the given observed Powerlaw distribution and exponend alpha. """ return n * tf.log(alpha) + n * alpha * tf.log(d_min) + (alpha + 1) * sum_log_degrees def update_sum_log_degrees(sum_log_degrees_before, n_old, d_old, d_new, d_min): """ Compute the sum of the logs of samples in the observed distribution whose values are >= d_min for a single edge changing in the graph. That is, given that two degrees in the graph change from d_old to d_new respectively (resulting from adding or removing a single edge), compute the updated sum of log degrees >= d_min. Parameters ---------- sum_log_degrees_before: tf.Tensor of floats of length n The sum of log degrees >= d_min before the change. n_old: tf.Tensor of ints of length n The number of degrees >= d_min before the change. d_old: tf.Tensor of ints, shape [n, 2] The old (i.e. before change) degrees of the two nodes affected by an edge to be inserted/removed. n corresponds to the number of edges for which this will be computed in a vectorized fashion. d_new: tf.Tensor of ints, shape [n,2] The new (i.e. after the change) degrees of the two nodes affected by an edge to be inserted/removed. n corresponds to the number of edges for which this will be computed in a vectorized fashion. d_min: int The minimum degree considered in the Powerlaw distribution. Returns ------- sum_log_degrees_after: tf.Tensor of floats shape (n,) The updated sum of log degrees whose values are >= d_min after a potential edge being added/removed. new_n: tf.Tensor dtype int shape (n,) The updated number of degrees which are >= d_min after a potential edge being added/removed. """ # Find out whether the degrees before and after the change are above the threshold d_min. old_in_range = d_old >= d_min new_in_range = d_new >= d_min # Mask out the degrees whose values are below d_min by multiplying them by 0. d_old_in_range = tf.multiply(d_old, tf.cast(old_in_range, tf.float32)) d_new_in_range = tf.multiply(d_new, tf.cast(new_in_range, tf.float32)) # Update the sum by subtracting the old values and then adding the updated logs of the degrees. sum_log_degrees_after = sum_log_degrees_before - tf.reduce_sum(tf.log(tf.maximum(d_old_in_range, 1)), axis=1) + tf.reduce_sum( tf.log(tf.maximum(d_new_in_range, 1)), axis=1) # Update the number of degrees >= d_min new_n = tf.cast(n_old, tf.int64) - tf.count_nonzero(old_in_range, axis=1) + tf.count_nonzero(new_in_range, axis=1) return sum_log_degrees_after, new_n def compute_alpha(n, sum_log_degrees, d_min): """ Compute the maximum likelihood value of the Powerlaw exponent alpha of the degree distribution. Parameters ---------- n: int The number of degrees >= d_min sum_log_degrees: float The sum of log degrees >= d_min d_min: int The minimum degree considered in the Powerlaw distribution. Returns ------- alpha: float The maximum likelihood estimate of the Powerlaw exponent alpha. """ return n / (sum_log_degrees - n * tf.log(d_min - 0.5)) + 1 def degree_sequence_log_likelihood(degree_sequence, d_min): """ Compute the (maximum) log likelihood of the Powerlaw distribution fit on a degree distribution. Parameters ---------- degree_sequence: tf.Tensor dtype int shape (N,) Observed degree distribution. d_min: int The minimum degree considered in the Powerlaw distribution. Returns ------- ll: tf.Tensor dtype float, (scalar) The log likelihood under the maximum likelihood estimate of the Powerlaw exponent alpha. alpha: tf.Tensor dtype float (scalar) The maximum likelihood estimate of the Powerlaw exponent. n: int The number of degrees in the degree sequence that are >= d_min. sum_log_degrees: tf.Tensor dtype float (scalar) The sum of the log of degrees in the distribution which are >= d_min. """ # Determine which degrees are to be considered, i.e. >= d_min. in_range = tf.greater_equal(degree_sequence, d_min) # Sum the log of the degrees to be considered sum_log_degrees = tf.reduce_sum(tf.log(tf.boolean_mask(degree_sequence, in_range))) # Number of degrees >= d_min n = tf.cast(tf.count_nonzero(in_range), tf.float32) # Maximum likelihood estimate of the Powerlaw exponent alpha = compute_alpha(n, sum_log_degrees, d_min) # Log likelihood under alpha ll = compute_log_likelihood(n, alpha, sum_log_degrees, d_min) return ll, alpha, n, sum_log_degrees def updated_log_likelihood_for_edge_changes(node_pairs, adjacency_matrix, d_min): """ Compute the change of the log likelihood of the Powerlaw distribution fit on the input adjacency matrix's degree distribution that results when adding/removing edges for the input node pairs. Assumes an undirected unweighted graph. Parameters ---------- node_pairs: tf.Tensor, shape (e, 2) dtype int The e node pairs to consider, where each node pair consists of the two indices of the nodes. adjacency_matrix: tf.Tensor shape (N,N) dtype int The input adjacency matrix. Assumed to be unweighted and symmetric. d_min: int The minimum degree considered in the Powerlaw distribution. Returns ------- new_ll: tf.Tensor of shape (e,) and dtype float The log likelihoods for node pair in node_pairs obtained when adding/removing the edge for that node pair. new_alpha: tf.Tensor of shape (e,) and dtype float For each node pair, contains the maximum likelihood estimates of the Powerlaw distributions obtained when adding/removing the edge for that node pair. new_n: tf.Tensor of shape (e,) and dtype float The updated number of degrees which are >= d_min for each potential edge being added/removed. sum_log_degrees_after: tf.Tensor of floats shape (e,) The updated sum of log degrees whose values are >= d_min for each of the e potential edges being added/removed. """ # For each node pair find out whether there is an edge or not in the input adjacency matrix. edge_entries_before = tf.cast(tf.gather_nd(adjacency_matrix, tf.cast(node_pairs, tf.int32)), tf.float32) # Compute the degree for each node degree_seq = tf.reduce_sum(adjacency_matrix, 1) # Determine which degrees are to be considered, i.e. >= d_min. in_range = tf.greater_equal(degree_seq, d_min) # Sum the log of the degrees to be considered sum_log_degrees = tf.reduce_sum(tf.log(tf.boolean_mask(degree_seq, in_range))) # Number of degrees >= d_min n = tf.cast(tf.count_nonzero(in_range), tf.float32) # The changes to the edge entries to add an edge if none was present and remove it otherwise. # i.e., deltas[ix] = -1 if edge_entries[ix] == 1 else 1 deltas = -2 * edge_entries_before + 1 # The degrees of the nodes in the input node pairs d_edges_before = tf.gather(degree_seq, tf.cast(node_pairs, tf.int32)) # The degrees of the nodes in the input node pairs after performing the change (i.e. adding the respective value of # delta. d_edges_after = tf.gather(degree_seq, tf.cast(node_pairs, tf.int32)) + deltas[:, None] # Sum the log of the degrees after the potential changes which are >= d_min sum_log_degrees_after, new_n = update_sum_log_degrees(sum_log_degrees, n, d_edges_before, d_edges_after, d_min) # Update the number of degrees >= d_min new_n = tf.cast(new_n, tf.float32) # Updated estimates of the Powerlaw exponents new_alpha = compute_alpha(new_n, sum_log_degrees_after, d_min) # Updated log likelihood values for the Powerlaw distributions new_ll = compute_log_likelihood(new_n, new_alpha, sum_log_degrees_after, d_min) return new_ll, new_alpha, new_n, sum_log_degrees_after def likelihood_ratio_filter(node_pairs, modified_adjacency, original_adjacency, d_min, threshold=0.004): """ Filter the input node pairs based on the likelihood ratio test proposed by Zügner et al. 2018, see https://dl.acm.org/citation.cfm?id=3220078. In essence, for each node pair return 1 if adding/removing the edge between the two nodes does not violate the unnoticeability constraint, and return 0 otherwise. Assumes unweighted and undirected graphs. Parameters ---------- node_pairs: tf.Tensor, shape (e, 2) dtype int The e node pairs to consider, where each node pair consists of the two indices of the nodes. modified_adjacency: tf.Tensor shape (N,N) dtype int The input (modified) adjacency matrix. Assumed to be unweighted and symmetric. original_adjacency: tf.Tensor shape (N,N) dtype int The input (original) adjacency matrix. Assumed to be unweighted and symmetric. d_min: int The minimum degree considered in the Powerlaw distribution. threshold: float, default 0.004 Cutoff value for the unnoticeability constraint. Smaller means stricter constraint. 0.004 corresponds to a p-value of 0.95 in the Chi-square distribution with one degree of freedom. Returns ------- allowed_mask: tf.Tensor, shape (e,), dtype bool For each node pair p return True if adding/removing the edge p does not violate the cutoff value, False otherwise. current_ratio: tf.Tensor, shape (), dtype float The current value of the log likelihood ratio. """ N = int(modified_adjacency.shape[0]) original_degree_sequence = tf.cast(tf.reduce_sum(original_adjacency, axis=1), tf.float32) current_degree_sequence = tf.cast(tf.reduce_sum(modified_adjacency, axis=1), tf.float32) # Concatenate the degree sequences concat_degree_sequence = tf.concat((current_degree_sequence[None, :], original_degree_sequence[None, :]), axis=1) # Compute the log likelihood values of the original, modified, and combined degree sequences. ll_orig, alpha_orig, n_orig, sum_log_degrees_original = degree_sequence_log_likelihood(original_degree_sequence, d_min) ll_current, alpha_current, n_current, sum_log_degrees_current = degree_sequence_log_likelihood( current_degree_sequence, d_min) ll_comb, alpha_comb, n_comb, sum_log_degrees_combined = degree_sequence_log_likelihood(concat_degree_sequence, d_min) # Compute the log likelihood ratio current_ratio = -2 * ll_comb + 2 * (ll_orig + ll_current) # Compute new log likelihood values that would arise if we add/remove the edges corresponding to each node pair. new_lls, new_alphas, new_ns, new_sum_log_degrees = updated_log_likelihood_for_edge_changes(node_pairs, tf.cast( modified_adjacency, tf.float32), d_min) # Combination of the original degree distribution with the distributions corresponding to each node pair. n_combined = n_orig + new_ns new_sum_log_degrees_combined = sum_log_degrees_original + new_sum_log_degrees alpha_combined = compute_alpha(n_combined, new_sum_log_degrees_combined, d_min) new_ll_combined = compute_log_likelihood(n_combined, alpha_combined, new_sum_log_degrees_combined, d_min) new_ratios = -2 * new_ll_combined + 2 * (new_lls + ll_orig) # Allowed edges are only those for which the resulting likelihood ratio measure is < than the threshold allowed_edges = new_ratios < threshold filtered_edges = tf.boolean_mask(node_pairs, allowed_edges) # Get the flattened indices for the allowed edges [e,2] -> [e,], similar to np.ravel_multi_index flat_ixs = ravel_multiple_indices(tf.cast(filtered_edges, tf.int32), modified_adjacency.shape) # Also for the reverse direction (we assume unweighted graphs). flat_ixs_reverse = ravel_multiple_indices(tf.reverse(tf.cast(filtered_edges, tf.int32), [1]), modified_adjacency.shape) # Construct a [N * N] array with ones at the admissible node pair locations and 0 everywhere else. indices_1 = tf.scatter_nd(flat_ixs[:, None], tf.ones_like(flat_ixs, dtype=tf.float32), shape=[N * N]) indices_2 = tf.scatter_nd(flat_ixs_reverse[:, None], tf.ones_like(flat_ixs_reverse, dtype=tf.float32), shape=[N * N]) # Add both directions allowed_mask = tf.clip_by_value(indices_1 + indices_2, 0, 1) return allowed_mask, current_ratio

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null

0

null

0

1

0

0b17ab164469ffccaa291796f82605247060ae56

4,714

py

Python

Embedded/test/test_readcsv.py

omnisci/mapd-core

cde582ebc3edba3fb86bacefa5bd9b3418a367b4

[ "Apache-2.0" ]

266

2018-09-27T06:11:36.000Z

2019-05-10T15:03:55.000Z

Embedded/test/test_readcsv.py

omnisci/mapd-core

cde582ebc3edba3fb86bacefa5bd9b3418a367b4

[ "Apache-2.0" ]

96

2018-10-01T18:30:31.000Z

2019-05-13T14:41:11.000Z

Embedded/test/test_readcsv.py

omnisci/mapd-core

cde582ebc3edba3fb86bacefa5bd9b3418a367b4

[ "Apache-2.0" ]

38

2018-10-04T01:02:54.000Z

2019-05-09T04:23:35.000Z

import os import io import datetime import pytest import pyarrow as pa from pyarrow import csv import heavydbe as dbe import ctypes ctypes._dlopen('libDBEngine.so', ctypes.RTLD_GLOBAL) root = os.path.join( os.path.dirname( os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "Tests/Import/datafiles" ) def test_init(): global engine engine = dbe.PyDbEngine( enable_union=1, enable_columnar_output=1, enable_lazy_fetch=0, null_div_by_zero=1, ) assert bool(engine.closed) == False engine = None def test_santander(): table = csv.read_csv(root + "/santander_top1000.csv") assert table engine.importArrowTable("santander", table) assert bool(engine.closed) == False r = engine.executeDML("select * from santander") assert r assert r.colCount() == 202 assert r.rowCount() == 999 def test_usecols_csv(): target = { 'a': [1, 2, 3, 4, 5, 6], 'b': [2, 3, 4, 5, 6, 7], 'c': [3, 4, 5, 6, 7, 8], 'd': [4, 5, 6, 7, 8, 9], 'e': ['5', '6', '7', '8', '9', '0'] } fp = io.BytesIO( b'a,b,c,d,e\n1,2,3,4,5\n2,3,4,5,6\n3,4,5,6,7\n4,5,6,7,8\n5,6,7,8,9\n6,7,8,9,0' ) fp.seek(0) table = csv.read_csv( fp, convert_options=csv.ConvertOptions( column_types={ 'a': pa.int32(), 'b': pa.int64(), 'c': pa.int64(), 'd': pa.int64(), 'e': pa.string(), } ) ) assert table engine.importArrowTable("usecols", table) assert bool(engine.closed) == False cursor = engine.executeDML("select * from usecols") assert cursor batch = cursor.getArrowRecordBatch() assert batch assert batch.to_pydict() == target def test_time_parsing(): target = { 'timestamp': [datetime.datetime(2010, 4, 1, 0, 0), datetime.datetime(2010, 4, 1, 0, 30), datetime.datetime(2010, 4, 1, 1, 0)], 'symbol': ['USD/JPY', 'USD/JPY', 'USD/JPY'], 'high': [93.526, 93.475, 93.421], 'low': [93.361, 93.352, 93.326], 'open': [93.518, 93.385, 93.391], 'close': [93.382, 93.391, 93.384], 'spread': [0.005, 0.006, 0.006], 'volume': [3049, 2251, 1577] } fp = io.BytesIO( b'timestamp,symbol,high,low,open,close,spread,volume\n' b'2010-04-01 00:00:00,USD/JPY,93.52600,93.36100,93.51800,93.38200,0.00500,3049\n' b'2010-04-01 00:30:00,USD/JPY,93.47500,93.35200,93.38500,93.39100,0.00600,2251\n' b'2010-04-01 01:00:00,USD/JPY,93.42100,93.32600,93.39100,93.38400,0.00600,1577\n' ) fp.seek(0) table = csv.read_csv(fp) assert table engine.importArrowTable("time_parsing", table) assert bool(engine.closed) == False cursor = engine.executeDML("select * from time_parsing") assert cursor batch = cursor.getArrowRecordBatch() assert batch assert batch.to_pydict() == target def test_csv_fillna(): target = { 'CRIM': [0.00632], 'ZN': [18.0], 'INDUS': [2.31], 'CHAS': [0.0], 'NOX': [0.538], 'RM': [6.575], 'AGE': [65.2], 'DIS': [4.09], 'RAD': [1.0], 'TAX': [296.0], 'PTRATIO': [15.3], 'B': [396.9], 'LSTAT': [4.98], 'PRICE': [24.0] } fp = io.BytesIO( b',CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,PRICE\n' b'0,0.00632,18.0,2.31,0.0,0.538,6.575,65.2,4.09,1.0,296.0,15.3,396.9,4.98,24.0\n' ) fp.seek(0) table = csv.read_csv(fp) assert table engine.importArrowTable("csv_fillna", table) assert bool(engine.closed) == False cursor = engine.executeDML("select CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,PRICE from csv_fillna") assert cursor batch = cursor.getArrowRecordBatch() assert batch assert batch.to_pydict() == target def test_null_col(): target = {'a': [1, 2, 3], 'b': [1, 2, 3], 'c': [None, None, None]} fp = io.BytesIO(b'a,b,c\n1,1,\n2,2,\n3,3,\n') fp.seek(0) table = csv.read_csv( fp, convert_options=csv.ConvertOptions( column_types={ 'a': pa.int32(), 'b': pa.int64(), 'c': pa.int64(), } ) ) assert table engine.importArrowTable("test_null_col", table) assert bool(engine.closed) == False cursor = engine.executeDML("select * from test_null_col") assert cursor batch = cursor.getArrowRecordBatch() assert batch assert batch.to_pydict() == target if __name__ == "__main__": pytest.main(["-v", __file__])

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null

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null

0

1

0

0b18f94f71d17a6b43b3acd30defa7fd83e9b6e1

317

py

Python

app/healthcheck/healthcheck.py

biancarosa/todo-list

9602c14b1f5d60b6010921b4918131d495b9ba69

[ "MIT" ]

1

2022-02-21T14:17:21.000Z

app/healthcheck/healthcheck.py

biancarosa/todo-list

9602c14b1f5d60b6010921b4918131d495b9ba69

[ "MIT" ]

1

2022-02-21T13:34:49.000Z

app/healthcheck/healthcheck.py

biancarosa/todo-list

9602c14b1f5d60b6010921b4918131d495b9ba69

[ "MIT" ]

null

"""app.healthcheck.healthcheck Module that deals with HealthCheck route.""" from flask import jsonify import logging logger = logging.getLogger(__name__) def healthcheck(): """Returns health information""" logging.info("Info endpoint hit") return jsonify({ "message": "I feel good." })

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0.5

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null

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null

0

1

0

0b194325430617ab5cb1adcd99354c592ca983d4

4,364

py

Python

lib/utils_files.py

minimada/openbmc-test-automation

1982e7b88a80690202d0f68f4bab978c1675e37f

[ "Apache-2.0" ]

67

2016-12-06T17:52:06.000Z

2022-01-17T22:12:37.000Z

lib/utils_files.py

minimada/openbmc-test-automation

1982e7b88a80690202d0f68f4bab978c1675e37f

[ "Apache-2.0" ]

2,181

2016-01-12T05:14:25.000Z

2022-03-31T17:29:12.000Z

lib/utils_files.py

minimada/openbmc-test-automation

1982e7b88a80690202d0f68f4bab978c1675e37f

[ "Apache-2.0" ]

75

2015-12-21T06:23:46.000Z

2021-12-31T15:05:53.000Z

#!/usr/bin/env python3 r""" This module contains file functions such as file_diff. """ import time import os import re from gen_cmd import cmd_fnc_u robot_env = 1 try: from robot.libraries.BuiltIn import BuiltIn from robot.libraries import DateTime except ImportError: robot_env = 0 def file_diff(file1_path, file2_path, diff_file_path, skip_string): r""" Compare the contents of two text files. The comparison uses the Unix 'diff' command. Differences can be selectively ignored by use of the skip_string parameter. The output of diff command is written to a user-specified file and is also written (logged) to the console. Description of arguments: file1_path File containing text data. file2_path Text file to compare to file1. diff_file_path Text file which will contain the diff output. skip_string To allow for differences which may expected or immaterial, skip_string parameter is a word or a string of comma separated words which specify what should be ignored. For example, "size,speed". Any line containing the word size or the word speed will be ignored when the diff is performed. This parameter is optional. Returns: 0 if both files contain the same information or they differ only in items specified by the skip_string. 2 if FILES_DO_NOT_MATCH. 3 if INPUT_FILE_DOES_NOT_EXIST. 4 if IO_EXCEPTION_READING_FILE. 5 if IO_EXCEPTION_WRITING_FILE. 6 if INPUT_FILE_MALFORMED """ FILES_MATCH = 0 FILES_DO_NOT_MATCH = 2 INPUT_FILE_DOES_NOT_EXIST = 3 IO_EXCEPTION_READING_FILE = 4 IO_EXCEPTION_WRITING_FILE = 5 INPUT_FILE_MALFORMED = 6 # The minimum size in bytes a file must be. min_file_byte_size = 1 now = time.strftime("%Y-%m-%d %H:%M:%S") if (not os.path.exists(file1_path) or (not os.path.exists(file2_path))): return INPUT_FILE_DOES_NOT_EXIST try: with open(file1_path, 'r') as file: initial = file.readlines() with open(file2_path, 'r') as file: final = file.readlines() except IOError: file.close() return IO_EXCEPTION_READING_FILE except ValueError: file.close() return INPUT_FILE_MALFORMED else: file.close() # Must have more than a trivial number of bytes. if len(initial) < min_file_byte_size: return INPUT_FILE_MALFORMED if (initial == final): try: file = open(diff_file_path, 'w') except IOError: file.close() line_to_print = "Specified skip (ignore) string = " + \ skip_string + "\n\n" file.write(line_to_print) line_to_print = now + " found no difference between file " + \ file1_path + " and " + \ file2_path + "\n" file.write(line_to_print) file.close() return FILES_MATCH # Find the differences and write difference report to diff_file_path file try: file = open(diff_file_path, 'w') except IOError: file.close() return IO_EXCEPTION_WRITING_FILE # Form a UNIX diff command and its parameters as a string. For example, # if skip_string="size,capacity", command = 'diff -I "size" # -I "capacity" file1_path file2_path'. skip_list = filter(None, re.split(r"[ ]*,[ ]*", skip_string)) ignore_string = ' '.join([("-I " + '"' + x + '"') for x in skip_list]) command = ' '.join(filter(None, ["diff", ignore_string, file1_path, file2_path])) line_to_print = now + " " + command + "\n" file.write(line_to_print) # Run the command and get the differences rc, out_buf = cmd_fnc_u(command, quiet=0, print_output=0, show_err=0) # Write the differences to the specified diff_file and console. if robot_env == 1: BuiltIn().log_to_console("DIFF:\n" + out_buf) else: print("DIFF:\n", out_buf) file.write(out_buf) file.close() if rc == 0: # Any differences found were on the skip_string. return FILES_MATCH else: # We have at least one difference not in the skip_string. return FILES_DO_NOT_MATCH

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null

0

null

0

1

0

0b1bd0979218d8a0b24010e5cf993177e766314c

1,055

py

Python

examples/blocking/block_io_operations.py

ckxz105/rltk

2d08269002c00c0218421c8c2dc0cc7c4f677131

[ "MIT" ]

98

2017-03-07T22:59:41.000Z

2022-02-02T16:10:40.000Z

examples/blocking/block_io_operations.py

ckxz105/rltk

2d08269002c00c0218421c8c2dc0cc7c4f677131

[ "MIT" ]

26

2017-04-25T17:25:22.000Z

2021-09-10T16:57:05.000Z

examples/blocking/block_io_operations.py

ckxz105/rltk

2d08269002c00c0218421c8c2dc0cc7c4f677131

[ "MIT" ]

31

2017-03-09T22:40:40.000Z

2022-03-11T16:28:23.000Z

import rltk b1 = rltk.Block() b1.add('001', '1', '1') b1.add('001', '2', 'a') b1.add('002', '1', '2') b1.add('002', '2', 'b') b1.add('002', '2', 'c') print('--- block1 ---') for bb in b1: print(bb) b2 = rltk.Block() b2.add('001', '1', '1') b2.add('001', '2', 'a') b2.add('001', '2', 'd') b2.add('002', '1', '1') b2.add('002', '2', 'c') b2.add('002', '3', 'k') print('--- block2 (pairwise) ---') for bb in b2.pairwise('1', '2'): print(bb) print('--- block2 (pairwise, single dataset) ---') for bb in b2.pairwise('2'): print(bb) b1_inverted = rltk.BlockingHelper.generate_inverted_indices(b1) b2_inverted = rltk.BlockingHelper.generate_inverted_indices(b2) b3 = rltk.BlockingHelper.union(b1, b1_inverted, b2, b2_inverted) print('--- union ---') for bb in b3: print(bb) print('--- union raw ---') for rr in b3.key_set_adapter: print(rr) b4 = rltk.BlockingHelper.intersect(b1, b1_inverted, b2, b2_inverted) print('--- intersect --') for bb in b4: print(bb) print('--- intersect raw --') for rr in b4.key_set_adapter: print(rr)

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null

0

null

0

1

0

0b1d82735f58ce19af7bfb48924b511d6c453095

5,058

py

Python

posts/views.py

promokk/hw05_final

a8e31e9dde904e3e396740c2c9edf773a54865df

[ "BSD-3-Clause" ]

null

posts/views.py

promokk/hw05_final

a8e31e9dde904e3e396740c2c9edf773a54865df

[ "BSD-3-Clause" ]

null

posts/views.py

promokk/hw05_final

a8e31e9dde904e3e396740c2c9edf773a54865df

[ "BSD-3-Clause" ]

null

from django.shortcuts import render, get_object_or_404 from django.shortcuts import redirect from django.views.generic import CreateView from django.urls import reverse_lazy from django.contrib.auth.decorators import login_required from django.core.paginator import Paginator from .models import Post, Group, User, Comment, Follow from .forms import PostForm, CommentForm def index(request): post_list = Post.objects.order_by("-pub_date").all() paginator = Paginator(post_list, 10) page_number = request.GET.get("page") page = paginator.get_page(page_number) return render( request, "index.html", {"page": page, "paginator": paginator} ) def group_posts(request, slug): group = get_object_or_404(Group, slug=slug) post_list = group.posts.all() paginator = Paginator(post_list, 10) page_number = request.GET.get('page') page = paginator.get_page(page_number) return render( request, "group.html", {"group": group, 'page': page, 'paginator': paginator} ) @login_required def new_post(request): form = PostForm(request.POST or None) if request.method == "POST": if form.is_valid(): post = form.save(commit=False) post.author = request.user form.save() return redirect("index") return render(request, "new_post.html", { "form": form, "flag": True }) def profile(request, username): author = get_object_or_404(User, username=username) post_list = author.posts.all() paginator = Paginator(post_list, 10) page_number = request.GET.get('page') page = paginator.get_page(page_number) is_follow = author.following.filter(user=request.user.id).exists() return render( request, "profile.html", { "author": author, "user": request.user, "page": page, "paginator": paginator, "following": is_follow } ) def post_view(request, username, post_id): post = get_object_or_404(Post, author__username=username, id=post_id) comments = post.comments.all() form = CommentForm(request.POST or None) return render( request, "post.html", { "author": post.author, "post": post, "comments": comments, "form": form } ) def post_edit(request, username, post_id): post = get_object_or_404(Post, author__username=username, id=post_id) form = PostForm(request.POST or None, files=request.FILES or None, instance=post) if request.method == "POST": if form.is_valid(): post.text = form.cleaned_data["text"] post.group = form.cleaned_data["group"] post.save() return redirect( "post", username=username, post_id=post_id ) return render( request, "new_post.html", { "form": form, "post": post, "flag": False } ) @login_required def add_comment(request, username, post_id): post = get_object_or_404(Post, author__username=username, id=post_id) form = CommentForm(request.POST or None) if request.method == "POST": if form.is_valid(): comment = form.save(commit=False) comment.post = post comment.author = request.user comment.save() return redirect( "post", username=username, post_id=post_id ) return render( request, "post.html", { "form": form, "post": post } ) def page_not_found(request, exception): return render( request, "misc/404.html", {"path": request.path}, status=404 ) def server_error(request): return render(request, "misc/500.html", status=500) @login_required def follow_index(request): post_list = Post.objects.filter(author__following__user=request.user) paginator = Paginator(post_list, 10) page_number = request.GET.get("page") page = paginator.get_page(page_number) return render( request, "follow.html", { "page": page, "paginator": paginator } ) @login_required def profile_follow(request, username): author = get_object_or_404(User, username=username) if author != request.user: Follow.objects.get_or_create(user=request.user, author=author) return redirect("profile", username=username) @login_required def profile_unfollow(request, username): author = get_object_or_404(User, username=username) Follow.objects.filter(user=request.user, author=author).delete() return redirect("profile", username=username)

27.639344

73

0.589363

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5,058

5.144385

0.158645

0.033264

0.065835

0.038808

0.550243

0.484754

0.424463

0.395357

0.375606

0.361746

0

0.012479

0.302887

5,058

182

74

27.791209

0.806012

0

0.425806

0

0.062475

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0.077419

false

0

0.051613

0.012903

0.225806

0

null

0

null

0

1

0

0b1edafe645cabe70b17100f13c91eeb991c939b

1,694

py

Python

gubernator/update_config.py

rodtreweek/test-infra

5f2755189ee01b13ff4da26cbf70d0582545868a

[ "Apache-2.0" ]

6

2018-01-31T07:36:42.000Z

2019-06-17T21:47:39.000Z

gubernator/update_config.py

rodtreweek/test-infra

5f2755189ee01b13ff4da26cbf70d0582545868a

[ "Apache-2.0" ]

67

2017-07-14T08:18:28.000Z

2020-11-23T08:59:51.000Z

gubernator/update_config.py

Acidburn0zzz/test-infra

ad19d04798049201a82c70639900bba593e740d6

[ "Apache-2.0" ]

8

2017-08-15T12:37:14.000Z

2021-08-23T17:52:37.000Z

#!/usr/bin/env python # Copyright 2017 The Kubernetes Authors. # # 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. """Updates the Gubernator configuration from the Prow configuration.""" import argparse import yaml def main(prow_config, gubernator_config): with open(prow_config) as prow_file: prow_data = yaml.load(prow_file) default_presubmits = [] for job in prow_data['presubmits']['kubernetes/kubernetes']: if job.get('always_run'): default_presubmits.append(job['name']) with open(gubernator_config) as gubernator_file: gubernator_data = yaml.load(gubernator_file) gubernator_data['jobs']['kubernetes-jenkins/pr-logs/directory/'] = default_presubmits with open(gubernator_config, 'w+') as gubernator_file: yaml.dump(gubernator_data, gubernator_file, default_flow_style=False, explicit_start=True) if __name__ == '__main__': PARSER = argparse.ArgumentParser() PARSER.add_argument('prow_config', help="Path to Prow configuration YAML.") PARSER.add_argument('gubernator_config', help="Path to Gubernator configuration YAML.") ARGS = PARSER.parse_args() main(ARGS.prow_config, ARGS.gubernator_config)

37.644444

98

0.743211

229

1,694

5.331878

0.484716

0.04914

0.021294

0.026208

0

0.005634

0.161747

1,694

44

99

38.5

0.854225

0.381936

0

0.188716

0.05642

0

1

0.05

false

0

0.1

0

0.15

0

null

0

null

0

1

0

0b1fa845a5312447d4861e8c9f56fdff7415fd4e

3,865

py

Python

rlcard/envs/tienlen.py

xiviu123/rlcard

2a5273dff6c9dd49a3d4ab84a952fed9a387955b

[ "MIT" ]

null

rlcard/envs/tienlen.py

xiviu123/rlcard

2a5273dff6c9dd49a3d4ab84a952fed9a387955b

[ "MIT" ]

null

rlcard/envs/tienlen.py

xiviu123/rlcard

2a5273dff6c9dd49a3d4ab84a952fed9a387955b

[ "MIT" ]

null

from collections import OrderedDict from rlcard.envs import Env import numpy as np from rlcard.games.tienlen.game import TienlenGame as Game from rlcard.games.tienlen.utils import encode_cards, encode_players_round_active, get_one_hot_array class TienlenEnv(Env): def __init__(self, config): self.name = 'tienlen' self.game = Game() super().__init__(config) self.state_shape = [[100] for _ in range(self.num_players)] self.action_shape = [[54] for _ in range(self.num_players)] def _extract_state(self, state): ''' current_hand current_played_cards players_round_active unknown_cards up_opponent_played_cards up_opponent_num_cards_left down_opponent_played_cards down_opponent_num_cards_left op_opponent_played_cards op_opponent_num_cards_left ''' player_id = state['player_id'] current_hand = encode_cards(state['current_hand']) players_round_active = encode_players_round_active(state['players_round_active']) unknown_cards = encode_cards(state['unknown_cards']) num_cards_left = state['num_cards_left'] played_cards = state['played_cards'] current_played_cards = encode_cards(played_cards[player_id]) up_opponent_id = ( player_id + 1) % self.game.num_players up_opponent_played_cards = encode_cards(played_cards[up_opponent_id]) up_opponent_num_cards_left = get_one_hot_array(num_cards_left[up_opponent_id], 13) if self.game.num_players >= 2: down_opponent_id = self.game.num_players - player_id - 1 down_opponent_played_cards = encode_cards(played_cards[down_opponent_id]) down_opponent_num_cards_left = get_one_hot_array(num_cards_left[down_opponent_id], 13) if self.game.num_players == 4: op_opponent_id = ( player_id + 2) % self.game.num_players op_opponent_played_cards = encode_cards(played_cards[op_opponent_id]) op_opponent_num_cards_left = get_one_hot_array(num_cards_left[op_opponent_id], 13) if self.game.num_players == 2: obs = np.concatenate((current_hand, current_played_cards, players_round_active, unknown_cards, up_opponent_played_cards, up_opponent_num_cards_left)) elif self.game.num_players == 3: obs = np.concatenate((current_hand, current_played_cards, players_round_active, unknown_cards, up_opponent_played_cards, up_opponent_num_cards_left, down_opponent_played_cards, down_opponent_num_cards_left)) elif self.game.num_players == 4: obs = np.concatenate((current_hand, current_played_cards, players_round_active, unknown_cards, up_opponent_played_cards, up_opponent_num_cards_left, down_opponent_played_cards, down_opponent_num_cards_left, op_opponent_played_cards, op_opponent_num_cards_left)) legal_actions = OrderedDict({action_id: None for action_id in state['actions']}) extracted_state = OrderedDict({'obs': obs, 'legal_actions': legal_actions}) extracted_state['raw_obs'] = state extracted_state['raw_legal_actions'] = list(legal_actions.keys()) return extracted_state def _decode_action(self, action): return action def get_payoffs(self): ''' Get the payoffs of players. Must be implemented in the child class. Returns: payoffs (list): a list of payoffs for each player ''' return self.game.judger.get_payoffs()

37.163462

99

0.656662

478

3,865

4.841004

0.171548

0.109334

0.088159

0.103717

0.524201

0.504322

0.469317

0.416162

0.402334

0.354797

0

0.006754

0.272186

3,865

103

100

37.524272

0.815855

0.093402

0

0.283582

0

0.040156

0

1

0.059701

false

0

0.074627

0.014925

0.19403

0

null

0

null

0

1

0

9bc2141debdb3afcc0f8f3217d1f2d229c76d00a

50,869

py

Python

vbox/src/VBox/ValidationKit/testmanager/webui/wuitestresult.py

Nurzamal/rest_api_docker

a9cc01dfc235467d490d9663755b33ef6990bdd8

[ "MIT" ]

null

vbox/src/VBox/ValidationKit/testmanager/webui/wuitestresult.py

Nurzamal/rest_api_docker

a9cc01dfc235467d490d9663755b33ef6990bdd8

[ "MIT" ]

null

vbox/src/VBox/ValidationKit/testmanager/webui/wuitestresult.py

Nurzamal/rest_api_docker

a9cc01dfc235467d490d9663755b33ef6990bdd8

[ "MIT" ]

null

# -*- coding: utf-8 -*- # $Id: wuitestresult.py 69111 2017-10-17 14:26:02Z vboxsync $ """ Test Manager WUI - Test Results. """ __copyright__ = \ """ Copyright (C) 2012-2017 Oracle Corporation This file is part of VirtualBox Open Source Edition (OSE), as available from http://www.virtualbox.org. This file is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (GPL) as published by the Free Software Foundation, in version 2 as it comes in the "COPYING" file of the VirtualBox OSE distribution. VirtualBox OSE is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY of any kind. The contents of this file may alternatively be used under the terms of the Common Development and Distribution License Version 1.0 (CDDL) only, as it comes in the "COPYING.CDDL" file of the VirtualBox OSE distribution, in which case the provisions of the CDDL are applicable instead of those of the GPL. You may elect to license modified versions of this file under the terms and conditions of either the GPL or the CDDL or both. """ __version__ = "$Revision: 69111 $" # Python imports. import datetime; # Validation Kit imports. from testmanager.webui.wuicontentbase import WuiContentBase, WuiListContentBase, WuiHtmlBase, WuiTmLink, WuiLinkBase, \ WuiSvnLink, WuiSvnLinkWithTooltip, WuiBuildLogLink, WuiRawHtml, \ WuiHtmlKeeper; from testmanager.webui.wuimain import WuiMain; from testmanager.webui.wuihlpform import WuiHlpForm; from testmanager.webui.wuiadminfailurereason import WuiFailureReasonAddLink, WuiFailureReasonDetailsLink; from testmanager.webui.wuitestresultfailure import WuiTestResultFailureDetailsLink; from testmanager.core.failurereason import FailureReasonData, FailureReasonLogic; from testmanager.core.report import ReportGraphModel, ReportModelBase; from testmanager.core.testbox import TestBoxData; from testmanager.core.testcase import TestCaseData; from testmanager.core.testset import TestSetData; from testmanager.core.testgroup import TestGroupData; from testmanager.core.testresultfailures import TestResultFailureData; from testmanager.core.build import BuildData; from testmanager.core import db; from testmanager import config; from common import webutils, utils; class WuiTestSetLink(WuiTmLink): """ Test set link. """ def __init__(self, idTestSet, sName = WuiContentBase.ksShortDetailsLink, fBracketed = False): WuiTmLink.__init__(self, sName, WuiMain.ksScriptName, { WuiMain.ksParamAction: WuiMain.ksActionTestResultDetails, TestSetData.ksParam_idTestSet: idTestSet, }, fBracketed = fBracketed); self.idTestSet = idTestSet; class WuiTestResult(WuiContentBase): """Display test case result""" def __init__(self, fnDPrint = None, oDisp = None): WuiContentBase.__init__(self, fnDPrint = fnDPrint, oDisp = oDisp); # Cyclic import hacks. from testmanager.webui.wuiadmin import WuiAdmin; self.oWuiAdmin = WuiAdmin; def _toHtml(self, oObject): """Translate some object to HTML.""" if isinstance(oObject, WuiHtmlBase): return oObject.toHtml(); if db.isDbTimestamp(oObject): return webutils.escapeElem(self.formatTsShort(oObject)); if db.isDbInterval(oObject): return webutils.escapeElem(self.formatIntervalShort(oObject)); if utils.isString(oObject): return webutils.escapeElem(oObject); return webutils.escapeElem(str(oObject)); def _htmlTable(self, aoTableContent): """Generate HTML code for table""" sHtml = u' <table class="tmtbl-testresult-details" width="100%%">\n'; for aoSubRows in aoTableContent: if not aoSubRows: continue; # Can happen if there is no testsuit. oCaption = aoSubRows[0]; sHtml += u' \n' \ u' <tr class="tmtbl-result-details-caption">\n' \ u' <td colspan="2">%s</td>\n' \ u' </tr>\n' \ % (self._toHtml(oCaption),); iRow = 0; for aoRow in aoSubRows[1:]: iRow += 1; sHtml += u' <tr class="%s">\n' % ('tmodd' if iRow & 1 else 'tmeven',); if len(aoRow) == 1: sHtml += u' <td class="tmtbl-result-details-subcaption" colspan="2">%s</td>\n' \ % (self._toHtml(aoRow[0]),); else: sHtml += u' <th scope="row">%s</th>\n' % (webutils.escapeElem(aoRow[0]),); if len(aoRow) > 2: sHtml += u' <td>%s</td>\n' % (aoRow[2](aoRow[1]),); else: sHtml += u' <td>%s</td>\n' % (self._toHtml(aoRow[1]),); sHtml += u' </tr>\n'; sHtml += u' </table>\n'; return sHtml def _highlightStatus(self, sStatus): """Return sStatus string surrounded by HTML highlight code """ sTmp = '<font color=%s><b>%s</b></font>' \ % ('red' if sStatus == 'failure' else 'green', webutils.escapeElem(sStatus.upper())) return sTmp def _anchorAndAppendBinaries(self, sBinaries, aoRows): """ Formats each binary (if any) into a row with a download link. """ if sBinaries is not None: for sBinary in sBinaries.split(','): if not webutils.hasSchema(sBinary): sBinary = config.g_ksBuildBinUrlPrefix + sBinary; aoRows.append([WuiLinkBase(webutils.getFilename(sBinary), sBinary, fBracketed = False),]); return aoRows; def _formatEventTimestampHtml(self, tsEvent, tsLog, idEvent, oTestSet): """ Formats an event timestamp with a main log link. """ tsEvent = db.dbTimestampToZuluDatetime(tsEvent); #sFormattedTimestamp = u'%04u\u2011%02u\u2011%02u\u00a0%02u:%02u:%02uZ' \ # % ( tsEvent.year, tsEvent.month, tsEvent.day, # tsEvent.hour, tsEvent.minute, tsEvent.second,); sFormattedTimestamp = u'%02u:%02u:%02uZ' \ % ( tsEvent.hour, tsEvent.minute, tsEvent.second,); sTitle = u'#%u - %04u\u2011%02u\u2011%02u\u00a0%02u:%02u:%02u.%06uZ' \ % ( idEvent, tsEvent.year, tsEvent.month, tsEvent.day, tsEvent.hour, tsEvent.minute, tsEvent.second, tsEvent.microsecond, ); tsLog = db.dbTimestampToZuluDatetime(tsLog); sFragment = u'%02u_%02u_%02u_%06u' % ( tsLog.hour, tsLog.minute, tsLog.second, tsLog.microsecond); return WuiTmLink(sFormattedTimestamp, '', { WuiMain.ksParamAction: WuiMain.ksActionViewLog, WuiMain.ksParamLogSetId: oTestSet.idTestSet, }, sFragmentId = sFragment, sTitle = sTitle, fBracketed = False, ).toHtml(); def _recursivelyGenerateEvents(self, oTestResult, sParentName, sLineage, iRow, iFailure, oTestSet, iDepth): # pylint: disable=R0914 """ Recursively generate event table rows for the result set. oTestResult is an object of the type TestResultDataEx. """ # Hack: Replace empty outer test result name with (pretty) command line. if iRow == 1: sName = ''; sDisplayName = sParentName; else: sName = oTestResult.sName if sParentName == '' else '%s, %s' % (sParentName, oTestResult.sName,); sDisplayName = webutils.escapeElem(sName); # Format error count. sErrCnt = ''; if oTestResult.cErrors > 0: sErrCnt = ' (1 error)' if oTestResult.cErrors == 1 else ' (%d errors)' % oTestResult.cErrors; # Format bits for adding or editing the failure reason. Level 0 is handled at the top of the page. sChangeReason = ''; if oTestResult.cErrors > 0 and iDepth > 0 and self._oDisp is not None and not self._oDisp.isReadOnlyUser(): dTmp = { self._oDisp.ksParamAction: self._oDisp.ksActionTestResultFailureAdd if oTestResult.oReason is None else self._oDisp.ksActionTestResultFailureEdit, TestResultFailureData.ksParam_idTestResult: oTestResult.idTestResult, }; sChangeReason = ' <a href="?%s" class="tmtbl-edit-reason" onclick="addRedirectToAnchorHref(this)">%s</a> ' \ % ( webutils.encodeUrlParams(dTmp), WuiContentBase.ksShortEditLinkHtml ); # Format the include in graph checkboxes. sLineage += ':%u' % (oTestResult.idStrName,); sResultGraph = '<input type="checkbox" name="%s" value="%s%s" title="Include result in graph."/>' \ % (WuiMain.ksParamReportSubjectIds, ReportGraphModel.ksTypeResult, sLineage,); sElapsedGraph = ''; if oTestResult.tsElapsed is not None: sElapsedGraph = '<input type="checkbox" name="%s" value="%s%s" title="Include elapsed time in graph."/>' \ % ( WuiMain.ksParamReportSubjectIds, ReportGraphModel.ksTypeElapsed, sLineage); if not oTestResult.aoChildren \ and len(oTestResult.aoValues) + len(oTestResult.aoMsgs) + len(oTestResult.aoFiles) == 0: # Leaf - single row. tsEvent = oTestResult.tsCreated; if oTestResult.tsElapsed is not None: tsEvent += oTestResult.tsElapsed; sHtml = ' <tr class="%s tmtbl-events-leaf tmtbl-events-lvl%s tmstatusrow-%s" id="S%u">\n' \ ' <td id="E%u">%s</td>\n' \ ' <td>%s</td>\n' \ ' <td>%s</td>\n' \ ' <td>%s</td>\n' \ ' <td colspan="2"%s>%s%s%s</td>\n' \ ' <td>%s</td>\n' \ ' </tr>\n' \ % ( 'tmodd' if iRow & 1 else 'tmeven', iDepth, oTestResult.enmStatus, oTestResult.idTestResult, oTestResult.idTestResult, self._formatEventTimestampHtml(tsEvent, oTestResult.tsCreated, oTestResult.idTestResult, oTestSet), sElapsedGraph, webutils.escapeElem(self.formatIntervalShort(oTestResult.tsElapsed)) if oTestResult.tsElapsed is not None else '', sDisplayName, ' id="failure-%u"' % (iFailure,) if oTestResult.isFailure() else '', webutils.escapeElem(oTestResult.enmStatus), webutils.escapeElem(sErrCnt), sChangeReason if oTestResult.oReason is None else '', sResultGraph ); iRow += 1; else: # Multiple rows. sHtml = ' <tr class="%s tmtbl-events-first tmtbl-events-lvl%s ">\n' \ ' <td>%s</td>\n' \ ' <td></td>\n' \ ' <td></td>\n' \ ' <td>%s</td>\n' \ ' <td colspan="2">%s</td>\n' \ ' <td></td>\n' \ ' </tr>\n' \ % ( 'tmodd' if iRow & 1 else 'tmeven', iDepth, self._formatEventTimestampHtml(oTestResult.tsCreated, oTestResult.tsCreated, oTestResult.idTestResult, oTestSet), sDisplayName, 'running' if oTestResult.tsElapsed is None else '', ); iRow += 1; # Depth. Check if our error count is just reflecting the one of our children. cErrorsBelow = 0; for oChild in oTestResult.aoChildren: (sChildHtml, iRow, iFailure) = self._recursivelyGenerateEvents(oChild, sName, sLineage, iRow, iFailure, oTestSet, iDepth + 1); sHtml += sChildHtml; cErrorsBelow += oChild.cErrors; # Messages. for oMsg in oTestResult.aoMsgs: sHtml += ' <tr class="%s tmtbl-events-message tmtbl-events-lvl%s">\n' \ ' <td>%s</td>\n' \ ' <td></td>\n' \ ' <td></td>\n' \ ' <td colspan="3">%s: %s</td>\n' \ ' <td></td>\n' \ ' </tr>\n' \ % ( 'tmodd' if iRow & 1 else 'tmeven', iDepth, self._formatEventTimestampHtml(oMsg.tsCreated, oMsg.tsCreated, oMsg.idTestResultMsg, oTestSet), webutils.escapeElem(oMsg.enmLevel), webutils.escapeElem(oMsg.sMsg), ); iRow += 1; # Values. for oValue in oTestResult.aoValues: sHtml += ' <tr class="%s tmtbl-events-value tmtbl-events-lvl%s">\n' \ ' <td>%s</td>\n' \ ' <td></td>\n' \ ' <td></td>\n' \ ' <td>%s</td>\n' \ ' <td class="tmtbl-events-number">%s</td>\n' \ ' <td class="tmtbl-events-unit">%s</td>\n' \ ' <td><input type="checkbox" name="%s" value="%s%s:%u" title="Include value in graph."></td>\n' \ ' </tr>\n' \ % ( 'tmodd' if iRow & 1 else 'tmeven', iDepth, self._formatEventTimestampHtml(oValue.tsCreated, oValue.tsCreated, oValue.idTestResultValue, oTestSet), webutils.escapeElem(oValue.sName), utils.formatNumber(oValue.lValue).replace(' ', ' '), webutils.escapeElem(oValue.sUnit), WuiMain.ksParamReportSubjectIds, ReportGraphModel.ksTypeValue, sLineage, oValue.idStrName, ); iRow += 1; # Files. for oFile in oTestResult.aoFiles: if oFile.sMime in [ 'text/plain', ]: aoLinks = [ WuiTmLink('%s (%s)' % (oFile.sFile, oFile.sKind), '', { self._oDisp.ksParamAction: self._oDisp.ksActionViewLog, self._oDisp.ksParamLogSetId: oTestSet.idTestSet, self._oDisp.ksParamLogFileId: oFile.idTestResultFile, }, sTitle = oFile.sDescription), WuiTmLink('View Raw', '', { self._oDisp.ksParamAction: self._oDisp.ksActionGetFile, self._oDisp.ksParamGetFileSetId: oTestSet.idTestSet, self._oDisp.ksParamGetFileId: oFile.idTestResultFile, self._oDisp.ksParamGetFileDownloadIt: False, }, sTitle = oFile.sDescription), ] else: aoLinks = [ WuiTmLink('%s (%s)' % (oFile.sFile, oFile.sKind), '', { self._oDisp.ksParamAction: self._oDisp.ksActionGetFile, self._oDisp.ksParamGetFileSetId: oTestSet.idTestSet, self._oDisp.ksParamGetFileId: oFile.idTestResultFile, self._oDisp.ksParamGetFileDownloadIt: False, }, sTitle = oFile.sDescription), ] aoLinks.append(WuiTmLink('Download', '', { self._oDisp.ksParamAction: self._oDisp.ksActionGetFile, self._oDisp.ksParamGetFileSetId: oTestSet.idTestSet, self._oDisp.ksParamGetFileId: oFile.idTestResultFile, self._oDisp.ksParamGetFileDownloadIt: True, }, sTitle = oFile.sDescription)); sHtml += ' <tr class="%s tmtbl-events-file tmtbl-events-lvl%s">\n' \ ' <td>%s</td>\n' \ ' <td></td>\n' \ ' <td></td>\n' \ ' <td>%s</td>\n' \ ' <td></td>\n' \ ' <td></td>\n' \ ' <td></td>\n' \ ' </tr>\n' \ % ( 'tmodd' if iRow & 1 else 'tmeven', iDepth, self._formatEventTimestampHtml(oFile.tsCreated, oFile.tsCreated, oFile.idTestResultFile, oTestSet), '\n'.join(oLink.toHtml() for oLink in aoLinks),); iRow += 1; # Done? if oTestResult.tsElapsed is not None: tsEvent = oTestResult.tsCreated + oTestResult.tsElapsed; sHtml += ' <tr class="%s tmtbl-events-final tmtbl-events-lvl%s tmstatusrow-%s" id="E%d">\n' \ ' <td>%s</td>\n' \ ' <td>%s</td>\n' \ ' <td>%s</td>\n' \ ' <td>%s</td>\n' \ ' <td colspan="2"%s>%s%s%s</td>\n' \ ' <td>%s</td>\n' \ ' </tr>\n' \ % ( 'tmodd' if iRow & 1 else 'tmeven', iDepth, oTestResult.enmStatus, oTestResult.idTestResult, self._formatEventTimestampHtml(tsEvent, tsEvent, oTestResult.idTestResult, oTestSet), sElapsedGraph, webutils.escapeElem(self.formatIntervalShort(oTestResult.tsElapsed)), sDisplayName, ' id="failure-%u"' % (iFailure,) if oTestResult.isFailure() else '', webutils.escapeElem(oTestResult.enmStatus), webutils.escapeElem(sErrCnt), sChangeReason if cErrorsBelow < oTestResult.cErrors and oTestResult.oReason is None else '', sResultGraph); iRow += 1; # Failure reason. if oTestResult.oReason is not None: sReasonText = '%s / %s' % ( oTestResult.oReason.oFailureReason.oCategory.sShort, oTestResult.oReason.oFailureReason.sShort, ); sCommentHtml = ''; if oTestResult.oReason.sComment and oTestResult.oReason.sComment.strip(): sCommentHtml = '<br>' + webutils.escapeElem(oTestResult.oReason.sComment.strip()); sCommentHtml = sCommentHtml.replace('\n', '<br>'); sDetailedReason = ' <a href="?%s" class="tmtbl-show-reason">%s</a>' \ % ( webutils.encodeUrlParams({ self._oDisp.ksParamAction: self._oDisp.ksActionTestResultFailureDetails, TestResultFailureData.ksParam_idTestResult: oTestResult.idTestResult,}), WuiContentBase.ksShortDetailsLinkHtml,); sHtml += ' <tr class="%s tmtbl-events-reason tmtbl-events-lvl%s">\n' \ ' <td>%s</td>\n' \ ' <td colspan="2">%s</td>\n' \ ' <td colspan="3">%s%s%s%s</td>\n' \ ' <td>%s</td>\n' \ ' </tr>\n' \ % ( 'tmodd' if iRow & 1 else 'tmeven', iDepth, webutils.escapeElem(self.formatTsShort(oTestResult.oReason.tsEffective)), oTestResult.oReason.oAuthor.sUsername, webutils.escapeElem(sReasonText), sDetailedReason, sChangeReason, sCommentHtml, 'todo'); iRow += 1; if oTestResult.isFailure(): iFailure += 1; return (sHtml, iRow, iFailure); def _generateMainReason(self, oTestResultTree, oTestSet): """ Generates the form for displaying and updating the main failure reason. oTestResultTree is an instance TestResultDataEx. oTestSet is an instance of TestSetData. """ _ = oTestSet; sHtml = ' '; if oTestResultTree.isFailure() or oTestResultTree.cErrors > 0: sHtml += ' <h2>Failure Reason:</h2>\n'; oData = oTestResultTree.oReason; # We need the failure reasons for the combobox. aoFailureReasons = FailureReasonLogic(self._oDisp.getDb()).fetchForCombo('Test Sheriff, you figure out why!'); assert aoFailureReasons; # For now we'll use the standard form helper. sFormActionUrl = '%s?%s=%s' % ( self._oDisp.ksScriptName, self._oDisp.ksParamAction, WuiMain.ksActionTestResultFailureAddPost if oData is None else WuiMain.ksActionTestResultFailureEditPost ) fReadOnly = not self._oDisp or self._oDisp.isReadOnlyUser(); oForm = WuiHlpForm('failure-reason', sFormActionUrl, sOnSubmit = WuiHlpForm.ksOnSubmit_AddReturnToFieldWithCurrentUrl, fReadOnly = fReadOnly); oForm.addTextHidden(TestResultFailureData.ksParam_idTestResult, oTestResultTree.idTestResult); oForm.addTextHidden(TestResultFailureData.ksParam_idTestSet, oTestSet.idTestSet); if oData is not None: oForm.addComboBox(TestResultFailureData.ksParam_idFailureReason, oData.idFailureReason, 'Reason', aoFailureReasons, sPostHtml = u' ' + WuiFailureReasonDetailsLink(oData.idFailureReason).toHtml() + (u' ' + WuiFailureReasonAddLink('New', fBracketed = False).toHtml() if not fReadOnly else u'')); oForm.addMultilineText(TestResultFailureData.ksParam_sComment, oData.sComment, 'Comment') oForm.addNonText(u'%s (%s), %s' % ( oData.oAuthor.sUsername, oData.oAuthor.sUsername, self.formatTsShort(oData.tsEffective),), 'Sheriff', sPostHtml = ' ' + WuiTestResultFailureDetailsLink(oData.idTestResult, "Show Details").toHtml() ) oForm.addTextHidden(TestResultFailureData.ksParam_tsEffective, oData.tsEffective); oForm.addTextHidden(TestResultFailureData.ksParam_tsExpire, oData.tsExpire); oForm.addTextHidden(TestResultFailureData.ksParam_uidAuthor, oData.uidAuthor); oForm.addSubmit('Change Reason'); else: oForm.addComboBox(TestResultFailureData.ksParam_idFailureReason, -1, 'Reason', aoFailureReasons, sPostHtml = ' ' + WuiFailureReasonAddLink('New').toHtml() if not fReadOnly else ''); oForm.addMultilineText(TestResultFailureData.ksParam_sComment, '', 'Comment'); oForm.addTextHidden(TestResultFailureData.ksParam_tsEffective, ''); oForm.addTextHidden(TestResultFailureData.ksParam_tsExpire, ''); oForm.addTextHidden(TestResultFailureData.ksParam_uidAuthor, ''); oForm.addSubmit('Add Reason'); sHtml += oForm.finalize(); return sHtml; def showTestCaseResultDetails(self, # pylint: disable=R0914,R0915 oTestResultTree, oTestSet, oBuildEx, oValidationKitEx, oTestBox, oTestGroup, oTestCaseEx, oTestVarEx): """Show detailed result""" def getTcDepsHtmlList(aoTestCaseData): """Get HTML <ul> list of Test Case name items""" if aoTestCaseData: sTmp = '<ul>' for oTestCaseData in aoTestCaseData: sTmp += '<li>%s</li>' % (webutils.escapeElem(oTestCaseData.sName),); sTmp += '</ul>' else: sTmp = 'No items' return sTmp def getGrDepsHtmlList(aoGlobalResourceData): """Get HTML <ul> list of Global Resource name items""" if aoGlobalResourceData: sTmp = '<ul>' for oGlobalResourceData in aoGlobalResourceData: sTmp += '<li>%s</li>' % (webutils.escapeElem(oGlobalResourceData.sName),); sTmp += '</ul>' else: sTmp = 'No items' return sTmp asHtml = [] from testmanager.webui.wuireport import WuiReportSummaryLink; tsReportEffectiveDate = None; if oTestSet.tsDone is not None: tsReportEffectiveDate = oTestSet.tsDone + datetime.timedelta(days = 4); if tsReportEffectiveDate >= self.getNowTs(): tsReportEffectiveDate = None; # Test result + test set details. aoResultRows = [ WuiHtmlKeeper([ WuiTmLink(oTestCaseEx.sName, self.oWuiAdmin.ksScriptName, { self.oWuiAdmin.ksParamAction: self.oWuiAdmin.ksActionTestCaseDetails, TestCaseData.ksParam_idTestCase: oTestCaseEx.idTestCase, self.oWuiAdmin.ksParamEffectiveDate: oTestSet.tsConfig, }, fBracketed = False), WuiReportSummaryLink(ReportModelBase.ksSubTestCase, oTestCaseEx.idTestCase, tsNow = tsReportEffectiveDate, fBracketed = False), ]), ]; if oTestCaseEx.sDescription: aoResultRows.append([oTestCaseEx.sDescription,]); aoResultRows.append([ 'Status:', WuiRawHtml('<span class="tmspan-status-%s">%s</span>' % (oTestResultTree.enmStatus, oTestResultTree.enmStatus,))]); if oTestResultTree.cErrors > 0: aoResultRows.append(( 'Errors:', oTestResultTree.cErrors )); aoResultRows.append([ 'Elapsed:', oTestResultTree.tsElapsed ]); cSecCfgTimeout = oTestCaseEx.cSecTimeout if oTestVarEx.cSecTimeout is None else oTestVarEx.cSecTimeout; cSecEffTimeout = cSecCfgTimeout * oTestBox.pctScaleTimeout / 100; aoResultRows.append([ 'Timeout:', '%s (%s sec)' % (utils.formatIntervalSeconds(cSecEffTimeout), cSecEffTimeout,) ]); if cSecEffTimeout != cSecCfgTimeout: aoResultRows.append([ 'Cfg Timeout:', '%s (%s sec)' % (utils.formatIntervalSeconds(cSecCfgTimeout), cSecCfgTimeout,) ]); aoResultRows += [ ( 'Started:', WuiTmLink(self.formatTsShort(oTestSet.tsCreated), WuiMain.ksScriptName, { WuiMain.ksParamAction: WuiMain.ksActionResultsUnGrouped, WuiMain.ksParamEffectiveDate: oTestSet.tsCreated, }, fBracketed = False) ), ]; if oTestSet.tsDone is not None: aoResultRows += [ ( 'Done:', WuiTmLink(self.formatTsShort(oTestSet.tsDone), WuiMain.ksScriptName, { WuiMain.ksParamAction: WuiMain.ksActionResultsUnGrouped, WuiMain.ksParamEffectiveDate: oTestSet.tsDone, }, fBracketed = False) ) ]; else: aoResultRows += [( 'Done:', 'Still running...')]; aoResultRows += [( 'Config:', oTestSet.tsConfig )]; if oTestVarEx.cGangMembers > 1: aoResultRows.append([ 'Member No:', '#%s (of %s)' % (oTestSet.iGangMemberNo, oTestVarEx.cGangMembers) ]); aoResultRows += [ ( 'Test Group:', WuiHtmlKeeper([ WuiTmLink(oTestGroup.sName, self.oWuiAdmin.ksScriptName, { self.oWuiAdmin.ksParamAction: self.oWuiAdmin.ksActionTestGroupDetails, TestGroupData.ksParam_idTestGroup: oTestGroup.idTestGroup, self.oWuiAdmin.ksParamEffectiveDate: oTestSet.tsConfig, }, fBracketed = False), WuiReportSummaryLink(ReportModelBase.ksSubTestGroup, oTestGroup.idTestGroup, tsNow = tsReportEffectiveDate, fBracketed = False), ]), ), ]; if oTestVarEx.sTestBoxReqExpr is not None: aoResultRows.append([ 'TestBox reqs:', oTestVarEx.sTestBoxReqExpr ]); elif oTestCaseEx.sTestBoxReqExpr is not None or oTestVarEx.sTestBoxReqExpr is not None: aoResultRows.append([ 'TestBox reqs:', oTestCaseEx.sTestBoxReqExpr ]); if oTestVarEx.sBuildReqExpr is not None: aoResultRows.append([ 'Build reqs:', oTestVarEx.sBuildReqExpr ]); elif oTestCaseEx.sBuildReqExpr is not None or oTestVarEx.sBuildReqExpr is not None: aoResultRows.append([ 'Build reqs:', oTestCaseEx.sBuildReqExpr ]); if oTestCaseEx.sValidationKitZips is not None and oTestCaseEx.sValidationKitZips != '@VALIDATIONKIT_ZIP@': aoResultRows.append([ 'Validation Kit:', oTestCaseEx.sValidationKitZips ]); if oTestCaseEx.aoDepTestCases: aoResultRows.append([ 'Prereq. Test Cases:', oTestCaseEx.aoDepTestCases, getTcDepsHtmlList ]); if oTestCaseEx.aoDepGlobalResources: aoResultRows.append([ 'Global Resources:', oTestCaseEx.aoDepGlobalResources, getGrDepsHtmlList ]); # Builds. aoBuildRows = []; if oBuildEx is not None: aoBuildRows += [ WuiHtmlKeeper([ WuiTmLink('Build', self.oWuiAdmin.ksScriptName, { self.oWuiAdmin.ksParamAction: self.oWuiAdmin.ksActionBuildDetails, BuildData.ksParam_idBuild: oBuildEx.idBuild, self.oWuiAdmin.ksParamEffectiveDate: oTestSet.tsCreated, }, fBracketed = False), WuiReportSummaryLink(ReportModelBase.ksSubBuild, oBuildEx.idBuild, tsNow = tsReportEffectiveDate, fBracketed = False), ]), ]; self._anchorAndAppendBinaries(oBuildEx.sBinaries, aoBuildRows); aoBuildRows += [ ( 'Revision:', WuiSvnLinkWithTooltip(oBuildEx.iRevision, oBuildEx.oCat.sRepository, fBracketed = False) ), ( 'Product:', oBuildEx.oCat.sProduct ), ( 'Branch:', oBuildEx.oCat.sBranch ), ( 'Type:', oBuildEx.oCat.sType ), ( 'Version:', oBuildEx.sVersion ), ( 'Created:', oBuildEx.tsCreated ), ]; if oBuildEx.uidAuthor is not None: aoBuildRows += [ ( 'Author ID:', oBuildEx.uidAuthor ), ]; if oBuildEx.sLogUrl is not None: aoBuildRows += [ ( 'Log:', WuiBuildLogLink(oBuildEx.sLogUrl, fBracketed = False) ), ]; aoValidationKitRows = []; if oValidationKitEx is not None: aoValidationKitRows += [ WuiTmLink('Validation Kit', self.oWuiAdmin.ksScriptName, { self.oWuiAdmin.ksParamAction: self.oWuiAdmin.ksActionBuildDetails, BuildData.ksParam_idBuild: oValidationKitEx.idBuild, self.oWuiAdmin.ksParamEffectiveDate: oTestSet.tsCreated, }, fBracketed = False), ]; self._anchorAndAppendBinaries(oValidationKitEx.sBinaries, aoValidationKitRows); aoValidationKitRows += [ ( 'Revision:', WuiSvnLink(oValidationKitEx.iRevision, fBracketed = False) ) ]; if oValidationKitEx.oCat.sProduct != 'VBox TestSuite': aoValidationKitRows += [ ( 'Product:', oValidationKitEx.oCat.sProduct ), ]; if oValidationKitEx.oCat.sBranch != 'trunk': aoValidationKitRows += [ ( 'Product:', oValidationKitEx.oCat.sBranch ), ]; if oValidationKitEx.oCat.sType != 'release': aoValidationKitRows += [ ( 'Type:', oValidationKitEx.oCat.sType), ]; if oValidationKitEx.sVersion != '0.0.0': aoValidationKitRows += [ ( 'Version:', oValidationKitEx.sVersion ), ]; aoValidationKitRows += [ ( 'Created:', oValidationKitEx.tsCreated ), ]; if oValidationKitEx.uidAuthor is not None: aoValidationKitRows += [ ( 'Author ID:', oValidationKitEx.uidAuthor ), ]; if oValidationKitEx.sLogUrl is not None: aoValidationKitRows += [ ( 'Log:', WuiBuildLogLink(oValidationKitEx.sLogUrl, fBracketed = False) ), ]; # TestBox. aoTestBoxRows = [ WuiHtmlKeeper([ WuiTmLink(oTestBox.sName, self.oWuiAdmin.ksScriptName, { self.oWuiAdmin.ksParamAction: self.oWuiAdmin.ksActionTestBoxDetails, TestBoxData.ksParam_idGenTestBox: oTestSet.idGenTestBox, }, fBracketed = False), WuiReportSummaryLink(ReportModelBase.ksSubTestBox, oTestSet.idTestBox, tsNow = tsReportEffectiveDate, fBracketed = False), ]), ]; if oTestBox.sDescription: aoTestBoxRows.append([oTestBox.sDescription, ]); aoTestBoxRows += [ ( 'IP:', oTestBox.ip ), #( 'UUID:', oTestBox.uuidSystem ), #( 'Enabled:', oTestBox.fEnabled ), #( 'Lom Kind:', oTestBox.enmLomKind ), #( 'Lom IP:', oTestBox.ipLom ), ( 'OS/Arch:', '%s.%s' % (oTestBox.sOs, oTestBox.sCpuArch) ), ( 'OS Version:', oTestBox.sOsVersion ), ( 'CPUs:', oTestBox.cCpus ), ]; if oTestBox.sCpuName is not None: aoTestBoxRows.append(['CPU Name', oTestBox.sCpuName.replace(' ', ' ')]); if oTestBox.lCpuRevision is not None: sMarch = oTestBox.queryCpuMicroarch(); if sMarch is not None: aoTestBoxRows.append( ('CPU Microarch', sMarch) ); uFamily = oTestBox.getCpuFamily(); uModel = oTestBox.getCpuModel(); uStepping = oTestBox.getCpuStepping(); aoTestBoxRows += [ ( 'CPU Family', '%u (%#x)' % ( uFamily, uFamily, ) ), ( 'CPU Model', '%u (%#x)' % ( uModel, uModel, ) ), ( 'CPU Stepping', '%u (%#x)' % ( uStepping, uStepping, ) ), ]; asFeatures = [ oTestBox.sCpuVendor, ]; if oTestBox.fCpuHwVirt is True: asFeatures.append(u'HW\u2011Virt'); if oTestBox.fCpuNestedPaging is True: asFeatures.append(u'Nested\u2011Paging'); if oTestBox.fCpu64BitGuest is True: asFeatures.append(u'64\u2011bit\u2011Guest'); if oTestBox.fChipsetIoMmu is True: asFeatures.append(u'I/O\u2011MMU'); aoTestBoxRows += [ ( 'Features:', u' '.join(asFeatures) ), ( 'RAM size:', '%s MB' % (oTestBox.cMbMemory,) ), ( 'Scratch Size:', '%s MB' % (oTestBox.cMbScratch,) ), ( 'Scale Timeout:', '%s%%' % (oTestBox.pctScaleTimeout,) ), ( 'Script Rev:', WuiSvnLink(oTestBox.iTestBoxScriptRev, fBracketed = False) ), ( 'Python:', oTestBox.formatPythonVersion() ), ( 'Pending Command:', oTestBox.enmPendingCmd ), ]; aoRows = [ aoResultRows, aoBuildRows, aoValidationKitRows, aoTestBoxRows, ]; asHtml.append(self._htmlTable(aoRows)); # # Convert the tree to a list of events, values, message and files. # sHtmlEvents = ''; sHtmlEvents += '<table class="tmtbl-events" id="tmtbl-events" width="100%">\n'; sHtmlEvents += ' <tr class="tmheader">\n' \ ' <th>When</th>\n' \ ' <th></th>\n' \ ' <th>Elapsed</th>\n' \ ' <th>Event name</th>\n' \ ' <th colspan="2">Value (status)</th>' \ ' <th></th>\n' \ ' </tr>\n'; sPrettyCmdLine = ' \\<br>    \n'.join(webutils.escapeElem(oTestCaseEx.sBaseCmd + ' ' + oTestVarEx.sArgs).split() ); (sTmp, _, cFailures) = self._recursivelyGenerateEvents(oTestResultTree, sPrettyCmdLine, '', 1, 0, oTestSet, 0); sHtmlEvents += sTmp; sHtmlEvents += '</table>\n' # # Put it all together. # sHtml = '<table class="tmtbl-testresult-details-base" width="100%">\n'; sHtml += ' <tr>\n' sHtml += ' <td valign="top" width="20%%">\n%s\n</td>\n' % ' <br>\n'.join(asHtml); sHtml += ' <td valign="top" width="80%" style="padding-left:6px">\n'; sHtml += self._generateMainReason(oTestResultTree, oTestSet); sHtml += ' <h2>Events:</h2>\n'; sHtml += ' <form action="#" method="get" id="graph-form">\n' \ ' <input type="hidden" name="%s" value="%s"/>\n' \ ' <input type="hidden" name="%s" value="%u"/>\n' \ ' <input type="hidden" name="%s" value="%u"/>\n' \ ' <input type="hidden" name="%s" value="%u"/>\n' \ ' <input type="hidden" name="%s" value="%u"/>\n' \ % ( WuiMain.ksParamAction, WuiMain.ksActionGraphWiz, WuiMain.ksParamGraphWizTestBoxIds, oTestBox.idTestBox, WuiMain.ksParamGraphWizBuildCatIds, oBuildEx.idBuildCategory, WuiMain.ksParamGraphWizTestCaseIds, oTestSet.idTestCase, WuiMain.ksParamGraphWizSrcTestSetId, oTestSet.idTestSet, ); if oTestSet.tsDone is not None: sHtml += ' <input type="hidden" name="%s" value="%s"/>\n' \ % ( WuiMain.ksParamEffectiveDate, oTestSet.tsDone, ); sHtml += ' <p>\n'; sFormButton = '<button type="submit" onclick="%s">Show graphs</button>' \ % ( webutils.escapeAttr('addDynamicGraphInputs("graph-form", "main", "%s", "%s");' % (WuiMain.ksParamGraphWizWidth, WuiMain.ksParamGraphWizDpi, )) ); sHtml += ' ' + sFormButton + '\n'; sHtml += ' %s %s %s\n' \ % ( WuiTmLink('Log File', '', { WuiMain.ksParamAction: WuiMain.ksActionViewLog, WuiMain.ksParamLogSetId: oTestSet.idTestSet, }), WuiTmLink('Raw Log', '', { WuiMain.ksParamAction: WuiMain.ksActionGetFile, WuiMain.ksParamGetFileSetId: oTestSet.idTestSet, WuiMain.ksParamGetFileDownloadIt: False, }), WuiTmLink('Download Log', '', { WuiMain.ksParamAction: WuiMain.ksActionGetFile, WuiMain.ksParamGetFileSetId: oTestSet.idTestSet, WuiMain.ksParamGetFileDownloadIt: True, }), ); sHtml += ' </p>\n'; if cFailures == 1: sHtml += ' <p>%s</p>\n' % ( WuiTmLink('Jump to failure', '#failure-0'), ) elif cFailures > 1: sHtml += ' <p>Jump to failure: '; if cFailures <= 13: for iFailure in range(0, cFailures): sHtml += ' ' + WuiTmLink('#%u' % (iFailure,), '#failure-%u' % (iFailure,)).toHtml(); else: for iFailure in range(0, 6): sHtml += ' ' + WuiTmLink('#%u' % (iFailure,), '#failure-%u' % (iFailure,)).toHtml(); sHtml += ' ... '; for iFailure in range(cFailures - 6, cFailures): sHtml += ' ' + WuiTmLink('#%u' % (iFailure,), '#failure-%u' % (iFailure,)).toHtml(); sHtml += ' </p>\n'; sHtml += sHtmlEvents; sHtml += ' <p>' + sFormButton + '</p>\n'; sHtml += ' </form>\n'; sHtml += ' </td>\n'; sHtml += ' </tr>\n'; sHtml += '</table>\n'; return ('Test Case result details', sHtml) class WuiGroupedResultList(WuiListContentBase): """ WUI results content generator. """ def __init__(self, aoEntries, cEntriesCount, iPage, cItemsPerPage, tsEffective, fnDPrint, oDisp, aiSelectedSortColumns = None): """Override initialization""" WuiListContentBase.__init__(self, aoEntries, iPage, cItemsPerPage, tsEffective, sTitle = 'Ungrouped (%d)' % cEntriesCount, sId = 'results', fnDPrint = fnDPrint, oDisp = oDisp, aiSelectedSortColumns = aiSelectedSortColumns); self._cEntriesCount = cEntriesCount self._asColumnHeaders = [ 'Start', 'Product Build', 'Kit', 'Box', 'OS.Arch', 'Test Case', 'Elapsed', 'Result', 'Reason', ]; self._asColumnAttribs = ['align="center"', 'align="center"', 'align="center"', 'align="center"', 'align="center"', 'align="center"', 'align="center"', 'align="center"', 'align="center"', 'align="center"', 'align="center"', 'align="center"', 'align="center"', ]; # Prepare parameter lists. self._dTestBoxLinkParams = self._oDisp.getParameters(); self._dTestBoxLinkParams[WuiMain.ksParamAction] = WuiMain.ksActionResultsGroupedByTestBox; self._dTestCaseLinkParams = self._oDisp.getParameters(); self._dTestCaseLinkParams[WuiMain.ksParamAction] = WuiMain.ksActionResultsGroupedByTestCase; self._dRevLinkParams = self._oDisp.getParameters(); self._dRevLinkParams[WuiMain.ksParamAction] = WuiMain.ksActionResultsGroupedByBuildRev; def _formatListEntry(self, iEntry): """ Format *show all* table entry """ oEntry = self._aoEntries[iEntry]; from testmanager.webui.wuiadmin import WuiAdmin; from testmanager.webui.wuireport import WuiReportSummaryLink; oValidationKit = None; if oEntry.idBuildTestSuite is not None: oValidationKit = WuiTmLink('r%s' % (oEntry.iRevisionTestSuite,), WuiAdmin.ksScriptName, { WuiAdmin.ksParamAction: WuiAdmin.ksActionBuildDetails, BuildData.ksParam_idBuild: oEntry.idBuildTestSuite }, fBracketed = False); aoTestSetLinks = []; aoTestSetLinks.append(WuiTmLink(oEntry.enmStatus, WuiMain.ksScriptName, { WuiMain.ksParamAction: WuiMain.ksActionTestResultDetails, TestSetData.ksParam_idTestSet: oEntry.idTestSet }, fBracketed = False)); if oEntry.cErrors > 0: aoTestSetLinks.append(WuiRawHtml('-')); aoTestSetLinks.append(WuiTmLink('%d error%s' % (oEntry.cErrors, '' if oEntry.cErrors == 1 else 's', ), WuiMain.ksScriptName, { WuiMain.ksParamAction: WuiMain.ksActionTestResultDetails, TestSetData.ksParam_idTestSet: oEntry.idTestSet }, sFragmentId = 'failure-0', fBracketed = False)); self._dTestBoxLinkParams[WuiMain.ksParamGroupMemberId] = oEntry.idTestBox; self._dTestCaseLinkParams[WuiMain.ksParamGroupMemberId] = oEntry.idTestCase; self._dRevLinkParams[WuiMain.ksParamGroupMemberId] = oEntry.iRevision; sTestBoxTitle = u''; if oEntry.sCpuVendor is not None: sTestBoxTitle += 'CPU vendor:\t%s\n' % ( oEntry.sCpuVendor, ); if oEntry.sCpuName is not None: sTestBoxTitle += 'CPU name:\t%s\n' % ( ' '.join(oEntry.sCpuName.split()), ); if oEntry.sOsVersion is not None: sTestBoxTitle += 'OS version:\t%s\n' % ( oEntry.sOsVersion, ); asFeatures = []; if oEntry.fCpuHwVirt is True: asFeatures.append(u'HW\u2011Virt'); if oEntry.fCpuNestedPaging is True: asFeatures.append(u'Nested\u2011Paging'); if oEntry.fCpu64BitGuest is True: asFeatures.append(u'64\u2011bit\u2011Guest'); #if oEntry.fChipsetIoMmu is True: asFeatures.append(u'I/O\u2011MMU'); sTestBoxTitle += u'CPU features:\t' + u', '.join(asFeatures); # Testcase if oEntry.sSubName: sTestCaseName = '%s / %s' % (oEntry.sTestCaseName, oEntry.sSubName,); else: sTestCaseName = oEntry.sTestCaseName; # Reason: aoReasons = []; for oIt in oEntry.aoFailureReasons: sReasonTitle = 'Reason: \t%s\n' % ( oIt.oFailureReason.sShort, ); sReasonTitle += 'Category:\t%s\n' % ( oIt.oFailureReason.oCategory.sShort, ); sReasonTitle += 'Assigned:\t%s\n' % ( self.formatTsShort(oIt.tsFailureReasonAssigned), ); sReasonTitle += 'By User: \t%s\n' % ( oIt.oFailureReasonAssigner.sUsername, ); if oIt.sFailureReasonComment: sReasonTitle += 'Comment: \t%s\n' % ( oIt.sFailureReasonComment, ); if oIt.oFailureReason.iTicket is not None and oIt.oFailureReason.iTicket > 0: sReasonTitle += 'xTracker:\t#%s\n' % ( oIt.oFailureReason.iTicket, ); for i, sUrl in enumerate(oIt.oFailureReason.asUrls): sUrl = sUrl.strip(); if sUrl: sReasonTitle += 'URL#%u: \t%s\n' % ( i, sUrl, ); aoReasons.append(WuiTmLink(oIt.oFailureReason.sShort, WuiAdmin.ksScriptName, { WuiAdmin.ksParamAction: WuiAdmin.ksActionFailureReasonDetails, FailureReasonData.ksParam_idFailureReason: oIt.oFailureReason.idFailureReason }, sTitle = sReasonTitle)); return [ oEntry.tsCreated, [ WuiTmLink('%s %s (%s)' % (oEntry.sProduct, oEntry.sVersion, oEntry.sType,), WuiMain.ksScriptName, self._dRevLinkParams, sTitle = '%s' % (oEntry.sBranch,), fBracketed = False), WuiSvnLinkWithTooltip(oEntry.iRevision, 'vbox'), ## @todo add sRepository TestResultListingData WuiTmLink(self.ksShortDetailsLink, WuiAdmin.ksScriptName, { WuiAdmin.ksParamAction: WuiAdmin.ksActionBuildDetails, BuildData.ksParam_idBuild: oEntry.idBuild }, fBracketed = False), ], oValidationKit, [ WuiTmLink(oEntry.sTestBoxName, WuiMain.ksScriptName, self._dTestBoxLinkParams, fBracketed = False, sTitle = sTestBoxTitle), WuiTmLink(self.ksShortDetailsLink, WuiAdmin.ksScriptName, { WuiAdmin.ksParamAction: WuiAdmin.ksActionTestBoxDetails, TestBoxData.ksParam_idTestBox: oEntry.idTestBox }, fBracketed = False), WuiReportSummaryLink(ReportModelBase.ksSubTestBox, oEntry.idTestBox, fBracketed = False), ], '%s.%s' % (oEntry.sOs, oEntry.sArch), [ WuiTmLink(sTestCaseName, WuiMain.ksScriptName, self._dTestCaseLinkParams, fBracketed = False, sTitle = (oEntry.sBaseCmd + ' ' + oEntry.sArgs) if oEntry.sArgs else oEntry.sBaseCmd), WuiTmLink(self.ksShortDetailsLink, WuiAdmin.ksScriptName, { WuiAdmin.ksParamAction: WuiAdmin.ksActionTestCaseDetails, TestCaseData.ksParam_idTestCase: oEntry.idTestCase }, fBracketed = False), WuiReportSummaryLink(ReportModelBase.ksSubTestCase, oEntry.idTestCase, fBracketed = False), ], oEntry.tsElapsed, aoTestSetLinks, aoReasons ];

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null

0

null

0

1

0

9bc514a61a5a549beb8528bf4d495c4be510163e

1,246

py

Python

tests/tests.py

willmycroft/po10-api

2b7839ed8404d2a482995cf251b5dd9dd5e7ceb7

[ "MIT" ]

null

tests/tests.py

willmycroft/po10-api

2b7839ed8404d2a482995cf251b5dd9dd5e7ceb7

[ "MIT" ]

null

tests/tests.py

willmycroft/po10-api

2b7839ed8404d2a482995cf251b5dd9dd5e7ceb7

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Wed Jun 6 18:32:47 2018 @author: pmp13wm """ import unittest from poweroften import PowerOfTen class TestStringMethods(unittest.TestCase): def test_search(self): po10 = PowerOfTen() df = po10.search('Will', 'Mycroft') self.assertTrue(7172 in df.index) def test_get_athlete(self): po10 = PowerOfTen() athlete_info, yearly_info, seasons_bests, results = po10.get_athlete(7172) self.assertEqual(athlete_info['Name'], 'William Mycroft') self.assertEqual(athlete_info['Gender'], 'Male') self.assertTrue('Oxford Uni' in yearly_info[2010]['clubs']) self.assertEqual(seasons_bests.loc['3000SC', '2017'], '9:01.89') self.assertEqual(results[results.MeetingId == 199052].iloc[0].Event, '3000SC') def test_get_rankings(self): po10 = PowerOfTen() df = po10.get_rankings('3000SC', 'SEN', 'M', 2017) self.assertEqual(df.loc[7172].Rank, 17) def test_get_results(self): po10 = PowerOfTen() df = po10.get_results(199052)['3000SC B'] self.assertEqual(df[df.AthleteId == 7172].iloc[0].Perf, '9:01.89') if __name__ == '__main__': unittest.main()

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false

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0.28

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null

0

null

0

1

0

9bc6f4f3184c06b4722c241c5be229e97e002b91

5,297

py

Python

utils/io_module.py

cioppaanthony/context-aware-loss

cc42187d49794d63845c4a277398cba094a52268

[ "Apache-2.0" ]

27

2020-03-19T16:09:44.000Z

2022-01-03T07:26:44.000Z

utils/io_module.py

cioppaanthony/context-aware-loss

cc42187d49794d63845c4a277398cba094a52268

[ "Apache-2.0" ]

4

2020-03-20T06:01:17.000Z

2021-03-26T14:37:04.000Z

utils/io_module.py

cioppaanthony/context-aware-loss

cc42187d49794d63845c4a277398cba094a52268

[ "Apache-2.0" ]

7

2020-04-24T03:24:25.000Z

2021-02-08T07:24:32.000Z

""" ---------------------------------------------------------------------------------------- Copyright (c) 2020 - see AUTHORS file Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ---------------------------------------------------------------------------------------- """ import numpy as np import json import sys import os from tqdm import tqdm import h5py import time import utils.constants as C import utils.preprocessing import random import cv2 from utils.argument_parser import args def readLabels(game_folder, sequence_length_first_half, sequence_length_second_half, framerate=2, num_classes=3): json_data = json.load(open(game_folder + C.LABEL_NAME)) return utils.preprocessing.labelToCategorical(json_data, sequence_length_first_half, sequence_length_second_half, framerate, num_classes) def readFeatures(game_folder, feature_type): feature_1 = None feature_2 = None if os.path.exists(game_folder+ "/1_" + feature_type) and os.path.exists(game_folder+ "/2_" + feature_type): feature_1 = np.load(game_folder+ "/1_" + feature_type) feature_2 = np.load(game_folder+ "/2_" + feature_type) else: print("Warning... missing at least one half of the game: ", game_folder) return feature_1, feature_2 class Dataset: """ Dataset class This class deals with the loading of the dataset to be able to access the different elements. The code loads the features extracted from args.featurestype and returns it. It then creates the labels from the json files based on the number of frames where the features were extracted. If the preprocessed features are passed as argument, then it will simply load them. Otherwise, it loads everything from the original SoccerNet dataset. """ def __init__(self, dataset_path, set_type, feature_type = None, framerate=2, num_classes=3): # Get the list of folders to read self.dataset_path = dataset_path self.datatype = set_type self.num_classes = num_classes self.input_shape=None self.framerate = framerate self.set_path = None self.game_list = None self.max_index = None self.next_index = 0 self.max_index = 0 self.features = list() self.labels = list() self.feature_type = feature_type def randomize(self): random.shuffle(self.game_list) self.next_index = 0 def nextFeatures(self): # Get the features feature_1, feature_2 = readFeatures(self.dataset_path + self.game_list[self.next_index], self.feature_type) # Get the labels if the feature for this game exist label_1, label_2 = (None, None) if feature_1 is not None and feature_2 is not None: label1, label2 = readLabels(self.dataset_path + self.game_list[self.next_index], feature_1.shape[0], feature_2.shape[0], self.framerate, self.num_classes) # Transform the labels to the Time Shift Encodings label_1 = utils.preprocessing.oneHotToShifts(label1, C.K_MATRIX) label_2 = utils.preprocessing.oneHotToShifts(label2, C.K_MATRIX) # Reading order management self.next_index += 1 if self.next_index >= self.max_index: self.randomize() return feature_1, feature_2, label_1, label_2, False return feature_1, feature_2, label_1, label_2, True def storeFeatures(self): # Loading from the preprocessed .npy files if available (faster) file_path_features = self.dataset_path + self.datatype[0:-4] + "_" + args.featuretype[0:-4] + "_features.npy" file_path_labels = self.dataset_path + self.datatype[0:-4] + "_" + args.featuretype[0:-4] + "_labels.npy" if os.path.exists(file_path_features) and os.path.exists(file_path_labels): self.features = np.load(file_path_features, allow_pickle=True) self.labels = np.load(file_path_labels, allow_pickle=True) self.input_shape = (args.chunksize*args.framerate, self.features[0].shape[1],1) return self.set_path = os.path.join(self.dataset_path + self.datatype) self.game_list = np.load(self.set_path) self.max_index = len(self.game_list) # Otherwise, load the dataset from the original SoccerNet ret = True pbar = tqdm(total=len(self.game_list)) while ret: feature_1, feature_2, label_1, label_2, ret = self.nextFeatures() if feature_1 is not None: self.features.append(feature_1) self.labels.append(label_1) if feature_2 is not None: self.features.append(feature_2) self.labels.append(label_2) pbar.update(1) pbar.close() self.input_shape = (args.chunksize*args.framerate, self.features[0].shape[1],1) self.features = np.array(self.features) self.labels = np.array(self.labels) # Save the preregistered features for faster loading next time # Check if the folder exist, otherwise create it #if not os.path.isdir(self.dataset_path + "/preregistered/"): # os.mkdir(self.dataset_path + "/preregistered/") #np.save(file_path_features, self.features) #np.save(file_path_labels, self.labels)

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0.0125

0

null

0

null

0

1

0

9bc71507643f71645ffd0790bda8c2ca6b565c64

5,007

py

Python

backend/app.py

trunghc97/fa

66ebf8c43c6023b5e0a4da2debc61f8d04b7ad5f

[ "MIT" ]

null

backend/app.py

trunghc97/fa

66ebf8c43c6023b5e0a4da2debc61f8d04b7ad5f

[ "MIT" ]

11

2021-03-10T00:56:18.000Z

2022-03-31T00:15:29.000Z

backend/app.py

hct97/fa

66ebf8c43c6023b5e0a4da2debc61f8d04b7ad5f

[ "MIT" ]

null

from __future__ import absolute_import from __future__ import division from __future__ import print_function from flask import Flask from flask import render_template, request, json from flask_cors import CORS, cross_origin from flask_sqlalchemy import SQLAlchemy import tensorflow.compat.v1 as tf import src.facenet import pickle import src.align.detect_face import src.align_dataset_mtcnn import src.classifier import numpy as np import cv2 import base64 import os import pdb; tf.disable_v2_behavior() MINSIZE = 20 THRESHOLD = [0.6, 0.7, 0.7] FACTOR = 0.709 IMAGE_SIZE = 182 INPUT_IMAGE_SIZE = 160 CLASSIFIER_PATH = 'Models/facemodel.pkl' FACENET_MODEL_PATH = 'Models/20180402-114759.pb' # Load The Custom Classifier with open(CLASSIFIER_PATH, 'rb') as file: model, class_names = pickle.load(file) print("Custom Classifier, Successfully loaded") tf.Graph().as_default() gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6) sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False)) # Load the model print('Loading feature extraction model') src.facenet.load_model(FACENET_MODEL_PATH) # Get input and output tensors images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0") embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0") phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0") embedding_size = embeddings.get_shape()[1] pnet, rnet, onet = src.align.detect_face.create_mtcnn(sess, "src/align") app = Flask(__name__) CORS(app) @app.route('/') @cross_origin() def index(): return "OK!" @app.route('/attendances', methods=['POST']) @cross_origin() def upload_img_file(): if request.method == 'POST': # base 64 if 'image' in request.files: f = request.files['image'].read() else: f = request.form.get('image').split(',')[1] f = base64.b64decode(f) # w = int(request.form.get('w')) # h = int(request.form.get('h')) # decoded_string = base64.b64decode(f) frame = np.fromstring(f, dtype=np.uint8) # frame = frame.reshape(,,3) frame = cv2.imdecode(frame, cv2.IMREAD_ANYCOLOR) # cv2.IMREAD_COLOR in OpenCV 3.1 bounding_boxes, _ = src.align.detect_face.detect_face(frame, MINSIZE, pnet, rnet, onet, THRESHOLD, FACTOR) faces_found = bounding_boxes.shape[0] name = [] if faces_found > 0: det = bounding_boxes[:, 0:4] bb = np.zeros((faces_found, 4), dtype=np.int32) for i in range(faces_found): bb[i][0] = det[i][0] bb[i][1] = det[i][1] bb[i][2] = det[i][2] bb[i][3] = det[i][3] # cropped = frame cropped = frame[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :] if cropped.any(): scaled = cv2.resize(cropped, (INPUT_IMAGE_SIZE, INPUT_IMAGE_SIZE), interpolation=cv2.INTER_CUBIC) scaled = src.facenet.prewhiten(scaled) scaled_reshape = scaled.reshape(-1, INPUT_IMAGE_SIZE, INPUT_IMAGE_SIZE, 3) feed_dict = {images_placeholder: scaled_reshape, phase_train_placeholder: False} emb_array = sess.run(embeddings, feed_dict=feed_dict) predictions = model.predict_proba(emb_array) best_class_indices = np.argmax(predictions, axis=1) best_class_probabilities = predictions[ np.arange(len(best_class_indices)), best_class_indices] best_name = class_names[best_class_indices[0]] print("Name: {}, Probability: {}".format(best_name, best_class_probabilities)) if best_class_probabilities > 0.6: # name = class_names[best_class_indices[0]] name.append(class_names[best_class_indices[0]]) else: # name = "Unknown" name.append("Unknown") return json_response(name) @app.route('/train', methods=['GET']) @cross_origin() def train_model(): result = src.align_dataset_mtcnn.main() src.classifier.main() return json_response(result) @app.route('/upload-images', methods=['POST']) @cross_origin() def upload_images(): if request.method == "POST": files = request.files.getlist("image") userID = request.form.get('userID') folder = "Dataset/FaceData/raw/" + userID if not os.path.exists(folder): os.mkdir(folder) for file in files: file.save(os.path.join(folder, file.filename)) return json_response("Upload success %d images" %(len(files))) def json_response(payload, status=200): return json.dumps(payload), status, {'content-type': 'application/json'} if __name__ == '__main__': app.run(debug=True)

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0

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0.24286

5,007

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false

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