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#!/usr/bin/env python3 # Copyright (c) 2014-2016 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # # Helpful routines for regression testing # import os import sys from binascii import hexlify, unhexlify from base64 import b64encode from decimal import Decimal, ROUND_DOWN import json import http.client import random import shutil import subprocess import time import re import errno from . import coverage from .authproxy import AuthServiceProxy, JSONRPCException COVERAGE_DIR = None # The maximum number of nodes a single test can spawn MAX_NODES = 8 # Don't assign rpc or p2p ports lower than this PORT_MIN = 11000 # The number of ports to "reserve" for p2p and rpc, each PORT_RANGE = 5000 BITCOIND_PROC_WAIT_TIMEOUT = 60 class PortSeed: # Must be initialized with a unique integer for each process n = None #Set Mocktime default to OFF. #MOCKTIME is only needed for scripts that use the #cached version of the blockchain. If the cached #version of the blockchain is used without MOCKTIME #then the mempools will not sync due to IBD. MOCKTIME = 0 def enable_mocktime(): #For backwared compatibility of the python scripts #with previous versions of the cache, set MOCKTIME #to Jan 1, 2014 + (201 * 10 * 60) global MOCKTIME MOCKTIME = 1388534400 + (201 * 10 * 60) def disable_mocktime(): global MOCKTIME MOCKTIME = 0 def get_mocktime(): return MOCKTIME def enable_coverage(dirname): """Maintain a log of which RPC calls are made during testing.""" global COVERAGE_DIR COVERAGE_DIR = dirname def get_rpc_proxy(url, node_number, timeout=None): """ Args: url (str): URL of the RPC server to call node_number (int): the node number (or id) that this calls to Kwargs: timeout (int): HTTP timeout in seconds Returns: AuthServiceProxy. convenience object for making RPC calls. """ proxy_kwargs = {} if timeout is not None: proxy_kwargs['timeout'] = timeout proxy = AuthServiceProxy(url, **proxy_kwargs) proxy.url = url # store URL on proxy for info coverage_logfile = coverage.get_filename( COVERAGE_DIR, node_number) if COVERAGE_DIR else None return coverage.AuthServiceProxyWrapper(proxy, coverage_logfile) def p2p_port(n): assert(n <= MAX_NODES) return PORT_MIN + n + (MAX_NODES * PortSeed.n) % (PORT_RANGE - 1 - MAX_NODES) def rpc_port(n): return PORT_MIN + PORT_RANGE + n + (MAX_NODES * PortSeed.n) % (PORT_RANGE - 1 - MAX_NODES) def check_json_precision(): """Make sure json library being used does not lose precision converting BTC values""" n = Decimal("20000000.00000003") satoshis = int(json.loads(json.dumps(float(n)))*1.0e8) if satoshis != 2000000000000003: raise RuntimeError("JSON encode/decode loses precision") def count_bytes(hex_string): return len(bytearray.fromhex(hex_string)) def bytes_to_hex_str(byte_str): return hexlify(byte_str).decode('ascii') def hex_str_to_bytes(hex_str): return unhexlify(hex_str.encode('ascii')) def str_to_b64str(string): return b64encode(string.encode('utf-8')).decode('ascii') def sync_blocks(rpc_connections, *, wait=1, timeout=60): """ Wait until everybody has the same tip. sync_blocks needs to be called with an rpc_connections set that has least one node already synced to the latest, stable tip, otherwise there's a chance it might return before all nodes are stably synced. """ # Use getblockcount() instead of waitforblockheight() to determine the # initial max height because the two RPCs look at different internal global # variables (chainActive vs latestBlock) and the former gets updated # earlier. maxheight = max(x.getblockcount() for x in rpc_connections) start_time = cur_time = time.time() while cur_time <= start_time + timeout: tips = [r.waitforblockheight(maxheight, int(wait * 1000)) for r in rpc_connections] if all(t["height"] == maxheight for t in tips): if all(t["hash"] == tips[0]["hash"] for t in tips): return raise AssertionError("Block sync failed, mismatched block hashes:{}".format( "".join("\n {!r}".format(tip) for tip in tips))) cur_time = time.time() raise AssertionError("Block sync to height {} timed out:{}".format( maxheight, "".join("\n {!r}".format(tip) for tip in tips))) def sync_chain(rpc_connections, *, wait=1, timeout=60): """ Wait until everybody has the same best block """ while timeout > 0: best_hash = [x.getbestblockhash() for x in rpc_connections] if best_hash == [best_hash[0]]*len(best_hash): return time.sleep(wait) timeout -= wait raise AssertionError("Chain sync failed: Best block hashes don't match") def sync_mempools(rpc_connections, *, wait=1, timeout=60): """ Wait until everybody has the same transactions in their memory pools """ while timeout > 0: pool = set(rpc_connections[0].getrawmempool()) num_match = 1 for i in range(1, len(rpc_connections)): if set(rpc_connections[i].getrawmempool()) == pool: num_match = num_match+1 if num_match == len(rpc_connections): return time.sleep(wait) timeout -= wait raise AssertionError("Mempool sync failed") bitcoind_processes = {} def initialize_datadir(dirname, n): datadir = os.path.join(dirname, "node"+str(n)) if not os.path.isdir(datadir): os.makedirs(datadir) rpc_u, rpc_p = rpc_auth_pair(n) with open(os.path.join(datadir, "ethros.conf"), 'w', encoding='utf8') as f: f.write("regtest=1\n") f.write("rpcuser=" + rpc_u + "\n") f.write("rpcpassword=" + rpc_p + "\n") f.write("port="+str(p2p_port(n))+"\n") f.write("rpcport="+str(rpc_port(n))+"\n") f.write("listenonion=0\n") return datadir def rpc_auth_pair(n): return 'rpcuser💻' + str(n), 'rpcpass🔑' + str(n) def rpc_url(i, rpchost=None): rpc_u, rpc_p = rpc_auth_pair(i) host = '127.0.0.1' port = rpc_port(i) if rpchost: parts = rpchost.split(':') if len(parts) == 2: host, port = parts else: host = rpchost return "http://%s:%s@%s:%d" % (rpc_u, rpc_p, host, int(port)) def wait_for_bitcoind_start(process, url, i): ''' Wait for bitcoind to start. This means that RPC is accessible and fully initialized. Raise an exception if bitcoind exits during initialization. ''' while True: if process.poll() is not None: raise Exception('ethrosd exited with status %i during initialization' % process.returncode) try: rpc = get_rpc_proxy(url, i) blocks = rpc.getblockcount() break # break out of loop on success except IOError as e: if e.errno != errno.ECONNREFUSED: # Port not yet open? raise # unknown IO error except JSONRPCException as e: # Initialization phase if e.error['code'] != -28: # RPC in warmup? raise # unknown JSON RPC exception time.sleep(0.25) def initialize_chain(test_dir, num_nodes, cachedir): """ Create a cache of a 200-block-long chain (with wallet) for MAX_NODES Afterward, create num_nodes copies from the cache """ assert num_nodes <= MAX_NODES create_cache = False for i in range(MAX_NODES): if not os.path.isdir(os.path.join(cachedir, 'node'+str(i))): create_cache = True break if create_cache: #find and delete old cache directories if any exist for i in range(MAX_NODES): if os.path.isdir(os.path.join(cachedir,"node"+str(i))): shutil.rmtree(os.path.join(cachedir,"node"+str(i))) # Create cache directories, run bitcoinds: for i in range(MAX_NODES): datadir=initialize_datadir(cachedir, i) args = [ os.getenv("BITCOIND", "ethrosd"), "-server", "-keypool=1", "-datadir="+datadir, "-discover=0" ] if i > 0: args.append("-connect=127.0.0.1:"+str(p2p_port(0))) bitcoind_processes[i] = subprocess.Popen(args) if os.getenv("PYTHON_DEBUG", ""): print("initialize_chain: ethrosd started, waiting for RPC to come up") wait_for_bitcoind_start(bitcoind_processes[i], rpc_url(i), i) if os.getenv("PYTHON_DEBUG", ""): print("initialize_chain: RPC successfully started") rpcs = [] for i in range(MAX_NODES): try: rpcs.append(get_rpc_proxy(rpc_url(i), i)) except: sys.stderr.write("Error connecting to "+url+"\n") sys.exit(1) # Create a 200-block-long chain; each of the 4 first nodes # gets 25 mature blocks and 25 immature. # Note: To preserve compatibility with older versions of # initialize_chain, only 4 nodes will generate coins. # # blocks are created with timestamps 10 minutes apart # starting from 2010 minutes in the past enable_mocktime() block_time = get_mocktime() - (201 * 10 * 60) for i in range(2): for peer in range(4): for j in range(25): set_node_times(rpcs, block_time) rpcs[peer].generate(1) block_time += 10*60 # Must sync before next peer starts generating blocks sync_blocks(rpcs) # Shut them down, and clean up cache directories: stop_nodes(rpcs) disable_mocktime() for i in range(MAX_NODES): os.remove(log_filename(cachedir, i, "debug.log")) os.remove(log_filename(cachedir, i, "db.log")) os.remove(log_filename(cachedir, i, "peers.dat")) os.remove(log_filename(cachedir, i, "fee_estimates.dat")) for i in range(num_nodes): from_dir = os.path.join(cachedir, "node"+str(i)) to_dir = os.path.join(test_dir, "node"+str(i)) shutil.copytree(from_dir, to_dir) initialize_datadir(test_dir, i) # Overwrite port/rpcport in bitcoin.conf def initialize_chain_clean(test_dir, num_nodes): """ Create an empty blockchain and num_nodes wallets. Useful if a test case wants complete control over initialization. """ for i in range(num_nodes): datadir=initialize_datadir(test_dir, i) def _rpchost_to_args(rpchost): '''Convert optional IP:port spec to rpcconnect/rpcport args''' if rpchost is None: return [] match = re.match('(\[[0-9a-fA-f:]+\]|[^:]+)(?::([0-9]+))?$', rpchost) if not match: raise ValueError('Invalid RPC host spec ' + rpchost) rpcconnect = match.group(1) rpcport = match.group(2) if rpcconnect.startswith('['): # remove IPv6 [...] wrapping rpcconnect = rpcconnect[1:-1] rv = ['-rpcconnect=' + rpcconnect] if rpcport: rv += ['-rpcport=' + rpcport] return rv def start_node(i, dirname, extra_args=None, rpchost=None, timewait=None, binary=None): """ Start a bitcoind and return RPC connection to it """ datadir = os.path.join(dirname, "node"+str(i)) if binary is None: binary = os.getenv("LITECOIND", "ethrosd") args = [ binary, "-datadir="+datadir, "-server", "-keypool=1", "-discover=0", "-rest", "-mocktime="+str(get_mocktime()) ] if extra_args is not None: args.extend(extra_args) bitcoind_processes[i] = subprocess.Popen(args) if os.getenv("PYTHON_DEBUG", ""): print("start_node: ethrosd started, waiting for RPC to come up") url = rpc_url(i, rpchost) wait_for_bitcoind_start(bitcoind_processes[i], url, i) if os.getenv("PYTHON_DEBUG", ""): print("start_node: RPC successfully started") proxy = get_rpc_proxy(url, i, timeout=timewait) if COVERAGE_DIR: coverage.write_all_rpc_commands(COVERAGE_DIR, proxy) return proxy def start_nodes(num_nodes, dirname, extra_args=None, rpchost=None, timewait=None, binary=None): """ Start multiple bitcoinds, return RPC connections to them """ if extra_args is None: extra_args = [ None for _ in range(num_nodes) ] if binary is None: binary = [ None for _ in range(num_nodes) ] rpcs = [] try: for i in range(num_nodes): rpcs.append(start_node(i, dirname, extra_args[i], rpchost, timewait=timewait, binary=binary[i])) except: # If one node failed to start, stop the others stop_nodes(rpcs) raise return rpcs def log_filename(dirname, n_node, logname): return os.path.join(dirname, "node"+str(n_node), "regtest", logname) def stop_node(node, i): try: node.stop() except http.client.CannotSendRequest as e: print("WARN: Unable to stop node: " + repr(e)) return_code = bitcoind_processes[i].wait(timeout=BITCOIND_PROC_WAIT_TIMEOUT) assert_equal(return_code, 0) del bitcoind_processes[i] def stop_nodes(nodes): for i, node in enumerate(nodes): stop_node(node, i) assert not bitcoind_processes.values() # All connections must be gone now def set_node_times(nodes, t): for node in nodes: node.setmocktime(t) def connect_nodes(from_connection, node_num): ip_port = "127.0.0.1:"+str(p2p_port(node_num)) from_connection.addnode(ip_port, "onetry") # poll until version handshake complete to avoid race conditions # with transaction relaying while any(peer['version'] == 0 for peer in from_connection.getpeerinfo()): time.sleep(0.1) def connect_nodes_bi(nodes, a, b): connect_nodes(nodes[a], b) connect_nodes(nodes[b], a) def find_output(node, txid, amount): """ Return index to output of txid with value amount Raises exception if there is none. """ txdata = node.getrawtransaction(txid, 1) for i in range(len(txdata["vout"])): if txdata["vout"][i]["value"] == amount: return i raise RuntimeError("find_output txid %s : %s not found"%(txid,str(amount))) def gather_inputs(from_node, amount_needed, confirmations_required=1): """ Return a random set of unspent txouts that are enough to pay amount_needed """ assert(confirmations_required >=0) utxo = from_node.listunspent(confirmations_required) random.shuffle(utxo) inputs = [] total_in = Decimal("0.00000000") while total_in < amount_needed and len(utxo) > 0: t = utxo.pop() total_in += t["amount"] inputs.append({ "txid" : t["txid"], "vout" : t["vout"], "address" : t["address"] } ) if total_in < amount_needed: raise RuntimeError("Insufficient funds: need %d, have %d"%(amount_needed, total_in)) return (total_in, inputs) def make_change(from_node, amount_in, amount_out, fee): """ Create change output(s), return them """ outputs = {} amount = amount_out+fee change = amount_in - amount if change > amount*2: # Create an extra change output to break up big inputs change_address = from_node.getnewaddress() # Split change in two, being careful of rounding: outputs[change_address] = Decimal(change/2).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) change = amount_in - amount - outputs[change_address] if change > 0: outputs[from_node.getnewaddress()] = change return outputs def send_zeropri_transaction(from_node, to_node, amount, fee): """ Create&broadcast a zero-priority transaction. Returns (txid, hex-encoded-txdata) Ensures transaction is zero-priority by first creating a send-to-self, then using its output """ # Create a send-to-self with confirmed inputs: self_address = from_node.getnewaddress() (total_in, inputs) = gather_inputs(from_node, amount+fee*2) outputs = make_change(from_node, total_in, amount+fee, fee) outputs[self_address] = float(amount+fee) self_rawtx = from_node.createrawtransaction(inputs, outputs) self_signresult = from_node.signrawtransaction(self_rawtx) self_txid = from_node.sendrawtransaction(self_signresult["hex"], True) vout = find_output(from_node, self_txid, amount+fee) # Now immediately spend the output to create a 1-input, 1-output # zero-priority transaction: inputs = [ { "txid" : self_txid, "vout" : vout } ] outputs = { to_node.getnewaddress() : float(amount) } rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"]) def random_zeropri_transaction(nodes, amount, min_fee, fee_increment, fee_variants): """ Create a random zero-priority transaction. Returns (txid, hex-encoded-transaction-data, fee) """ from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (txid, txhex) = send_zeropri_transaction(from_node, to_node, amount, fee) return (txid, txhex, fee) def random_transaction(nodes, amount, min_fee, fee_increment, fee_variants): """ Create a random transaction. Returns (txid, hex-encoded-transaction-data, fee) """ from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (total_in, inputs) = gather_inputs(from_node, amount+fee) outputs = make_change(from_node, total_in, amount, fee) outputs[to_node.getnewaddress()] = float(amount) rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"], fee) def assert_fee_amount(fee, tx_size, fee_per_kB): """Assert the fee was in range""" target_fee = tx_size * fee_per_kB / 1000 if fee < target_fee: raise AssertionError("Fee of %s LTC too low! (Should be %s LTC)"%(str(fee), str(target_fee))) # allow the wallet's estimation to be at most 2 bytes off if fee > (tx_size + 2) * fee_per_kB / 1000: raise AssertionError("Fee of %s LTC too high! (Should be %s LTC)"%(str(fee), str(target_fee))) def assert_equal(thing1, thing2, *args): if thing1 != thing2 or any(thing1 != arg for arg in args): raise AssertionError("not(%s)" % " == ".join(str(arg) for arg in (thing1, thing2) + args)) def assert_greater_than(thing1, thing2): if thing1 <= thing2: raise AssertionError("%s <= %s"%(str(thing1),str(thing2))) def assert_greater_than_or_equal(thing1, thing2): if thing1 < thing2: raise AssertionError("%s < %s"%(str(thing1),str(thing2))) def assert_raises(exc, fun, *args, **kwds): assert_raises_message(exc, None, fun, *args, **kwds) def assert_raises_message(exc, message, fun, *args, **kwds): try: fun(*args, **kwds) except exc as e: if message is not None and message not in e.error['message']: raise AssertionError("Expected substring not found:"+e.error['message']) except Exception as e: raise AssertionError("Unexpected exception raised: "+type(e).__name__) else: raise AssertionError("No exception raised") def assert_raises_jsonrpc(code, message, fun, *args, **kwds): """Run an RPC and verify that a specific JSONRPC exception code and message is raised. Calls function `fun` with arguments `args` and `kwds`. Catches a JSONRPCException and verifies that the error code and message are as expected. Throws AssertionError if no JSONRPCException was returned or if the error code/message are not as expected. Args: code (int), optional: the error code returned by the RPC call (defined in src/rpc/protocol.h). Set to None if checking the error code is not required. message (string), optional: [a substring of] the error string returned by the RPC call. Set to None if checking the error string is not required fun (function): the function to call. This should be the name of an RPC. args*: positional arguments for the function. kwds**: named arguments for the function. """ try: fun(*args, **kwds) except JSONRPCException as e: # JSONRPCException was thrown as expected. Check the code and message values are correct. if (code is not None) and (code != e.error["code"]): raise AssertionError("Unexpected JSONRPC error code %i" % e.error["code"]) if (message is not None) and (message not in e.error['message']): raise AssertionError("Expected substring not found:"+e.error['message']) except Exception as e: raise AssertionError("Unexpected exception raised: "+type(e).__name__) else: raise AssertionError("No exception raised") def assert_is_hex_string(string): try: int(string, 16) except Exception as e: raise AssertionError( "Couldn't interpret %r as hexadecimal; raised: %s" % (string, e)) def assert_is_hash_string(string, length=64): if not isinstance(string, str): raise AssertionError("Expected a string, got type %r" % type(string)) elif length and len(string) != length: raise AssertionError( "String of length %d expected; got %d" % (length, len(string))) elif not re.match('[abcdef0-9]+$', string): raise AssertionError( "String %r contains invalid characters for a hash." % string) def assert_array_result(object_array, to_match, expected, should_not_find = False): """ Pass in array of JSON objects, a dictionary with key/value pairs to match against, and another dictionary with expected key/value pairs. If the should_not_find flag is true, to_match should not be found in object_array """ if should_not_find == True: assert_equal(expected, { }) num_matched = 0 for item in object_array: all_match = True for key,value in to_match.items(): if item[key] != value: all_match = False if not all_match: continue elif should_not_find == True: num_matched = num_matched+1 for key,value in expected.items(): if item[key] != value: raise AssertionError("%s : expected %s=%s"%(str(item), str(key), str(value))) num_matched = num_matched+1 if num_matched == 0 and should_not_find != True: raise AssertionError("No objects matched %s"%(str(to_match))) if num_matched > 0 and should_not_find == True: raise AssertionError("Objects were found %s"%(str(to_match))) def satoshi_round(amount): return Decimal(amount).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) # Helper to create at least "count" utxos # Pass in a fee that is sufficient for relay and mining new transactions. def create_confirmed_utxos(fee, node, count): node.generate(int(0.5*count)+101) utxos = node.listunspent() iterations = count - len(utxos) addr1 = node.getnewaddress() addr2 = node.getnewaddress() if iterations <= 0: return utxos for i in range(iterations): t = utxos.pop() inputs = [] inputs.append({ "txid" : t["txid"], "vout" : t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr1] = satoshi_round(send_value/2) outputs[addr2] = satoshi_round(send_value/2) raw_tx = node.createrawtransaction(inputs, outputs) signed_tx = node.signrawtransaction(raw_tx)["hex"] txid = node.sendrawtransaction(signed_tx) while (node.getmempoolinfo()['size'] > 0): node.generate(1) utxos = node.listunspent() assert(len(utxos) >= count) return utxos # Create large OP_RETURN txouts that can be appended to a transaction # to make it large (helper for constructing large transactions). def gen_return_txouts(): # Some pre-processing to create a bunch of OP_RETURN txouts to insert into transactions we create # So we have big transactions (and therefore can't fit very many into each block) # create one script_pubkey script_pubkey = "6a4d0200" #OP_RETURN OP_PUSH2 512 bytes for i in range (512): script_pubkey = script_pubkey + "01" # concatenate 128 txouts of above script_pubkey which we'll insert before the txout for change txouts = "81" for k in range(128): # add txout value txouts = txouts + "0000000000000000" # add length of script_pubkey txouts = txouts + "fd0402" # add script_pubkey txouts = txouts + script_pubkey return txouts def create_tx(node, coinbase, to_address, amount): inputs = [{ "txid" : coinbase, "vout" : 0}] outputs = { to_address : amount } rawtx = node.createrawtransaction(inputs, outputs) signresult = node.signrawtransaction(rawtx) assert_equal(signresult["complete"], True) return signresult["hex"] # Create a spend of each passed-in utxo, splicing in "txouts" to each raw # transaction to make it large. See gen_return_txouts() above. def create_lots_of_big_transactions(node, txouts, utxos, num, fee): addr = node.getnewaddress() txids = [] for _ in range(num): t = utxos.pop() inputs=[{ "txid" : t["txid"], "vout" : t["vout"]}] outputs = {} change = t['amount'] - fee outputs[addr] = satoshi_round(change) rawtx = node.createrawtransaction(inputs, outputs) newtx = rawtx[0:92] newtx = newtx + txouts newtx = newtx + rawtx[94:] signresult = node.signrawtransaction(newtx, None, None, "NONE") txid = node.sendrawtransaction(signresult["hex"], True) txids.append(txid) return txids def mine_large_block(node, utxos=None): # generate a 66k transaction, # and 14 of them is close to the 1MB block limit num = 14 txouts = gen_return_txouts() utxos = utxos if utxos is not None else [] if len(utxos) < num: utxos.clear() utxos.extend(node.listunspent()) fee = 100 * node.getnetworkinfo()["relayfee"] create_lots_of_big_transactions(node, txouts, utxos, num, fee=fee) node.generate(1) def get_bip9_status(node, key): info = node.getblockchaininfo() return info['bip9_softforks'][key]
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from django import template from django.utils.safestring import mark_safe from webpack_loader.templatetags.webpack_loader import _get_bundle register = template.Library() def render_as_tags(bundle, defer): tags = [] for chunk in bundle: url = chunk['url'] if chunk['name'].endswith('.js'): tags.append(( '<script{1} type="text/javascript" src="{0}"></script>' ).format(url, ' defer' if defer else '',)) elif chunk['name'].endswith('.css'): tags.append(( '<link type="text/css" href="{0}" rel="stylesheet"/>' ).format(url)) return mark_safe('\n'.join(tags)) @register.simple_tag def render_bundle(bundle_name, extension=None, config='DEFAULT', defer=False): return render_as_tags(_get_bundle(bundle_name, extension, config), defer)
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# -*- coding: utf-8 -*- # This file is a part of DDT (https://github.com/txels/ddt) # Copyright 2012-2015 Carles Barrobés and DDT contributors # For the exact contribution history, see the git revision log. # DDT is licensed under the MIT License, included in # https://github.com/txels/ddt/blob/master/LICENSE.md import inspect import json import os import re import codecs from functools import wraps try: import yaml except ImportError: # pragma: no cover _have_yaml = False else: _have_yaml = True __version__ = '1.1.2' # These attributes will not conflict with any real python attribute # They are added to the decorated test method and processed later # by the `ddt` class decorator. DATA_ATTR = '%values' # store the data the test must run with FILE_ATTR = '%file_path' # store the path to JSON file UNPACK_ATTR = '%unpack' # remember that we have to unpack values index_len = 5 # default max length of case index try: trivial_types = (type(None), bool, int, float, basestring) except NameError: trivial_types = (type(None), bool, int, float, str) def is_trivial(value): if isinstance(value, trivial_types): return True elif isinstance(value, (list, tuple)): return all(map(is_trivial, value)) return False def unpack(func): """ Method decorator to add unpack feature. """ setattr(func, UNPACK_ATTR, True) return func def data(*values): """ Method decorator to add to your test methods. Should be added to methods of instances of ``unittest.TestCase``. """ global index_len index_len = len(str(len(values))) return idata(values) def idata(iterable): """ Method decorator to add to your test methods. Should be added to methods of instances of ``unittest.TestCase``. """ def wrapper(func): setattr(func, DATA_ATTR, iterable) return func return wrapper def file_data(value): """ Method decorator to add to your test methods. Should be added to methods of instances of ``unittest.TestCase``. ``value`` should be a path relative to the directory of the file containing the decorated ``unittest.TestCase``. The file should contain JSON encoded data, that can either be a list or a dict. In case of a list, each value in the list will correspond to one test case, and the value will be concatenated to the test method name. In case of a dict, keys will be used as suffixes to the name of the test case, and values will be fed as test data. """ def wrapper(func): setattr(func, FILE_ATTR, value) return func return wrapper # # bak # def mk_test_name(name, value, index=0): # """ # Generate a new name for a test case. # # It will take the original test name and append an ordinal index and a # string representation of the value, and convert the result into a valid # python identifier by replacing extraneous characters with ``_``. # # We avoid doing str(value) if dealing with non-trivial values. # The problem is possible different names with different runs, e.g. # different order of dictionary keys (see PYTHONHASHSEED) or dealing # with mock objects. # Trivial scalar values are passed as is. # # A "trivial" value is a plain scalar, or a tuple or list consisting # only of trivial values. # """ # # Add zeros before index to keep order # index = "{0:0{1}}".format(index + 1, index_len) # if not is_trivial(value): # return "{0}_{1}".format(name, index) # try: # value = str(value) # except UnicodeEncodeError: # # fallback for python2 # value = value.encode('ascii', 'backslashreplace') # test_name = "{0}_{1}_{2}".format(name, index, value) # return re.sub(r'\W|^(?=\d)', '_', test_name) # 自定义用例标题 def mk_test_name(name, value, index=0): """ Generate a new name for a test case. It will take the original test name and append an ordinal index and a string representation of the value, and convert the result into a valid python identifier by replacing extraneous characters with ``_``. We avoid doing str(value) if dealing with non-trivial values. The problem is possible different names with different runs, e.g. different order of dictionary keys (see PYTHONHASHSEED) or dealing with mock objects. Trivial scalar values are passed as is. A "trivial" value is a plain scalar, or a tuple or list consisting only of trivial values. """ # Add zeros before index to keep order # index = "{0:0{1}}".format(index + 1, index_len) index = "{0:0{1}}".format(index + 1, 4)#将序列号强制改成3位 try: test_name = name+'_'+str(index)+':\t'+str(value[0]) return test_name except: if not is_trivial(value): return "{0}_{1}".format(name, index) try: value = str(value) except UnicodeEncodeError: # fallback for python2 value = value.encode('ascii', 'backslashreplace') test_name = "{0}_{1}_{2}".format(name, index, value) return re.sub(r'\W|^(?=\d)', '_', test_name) def feed_data(func, new_name, *args, **kwargs): """ This internal method decorator feeds the test data item to the test. """ @wraps(func) def wrapper(self): return func(self, *args, **kwargs) wrapper.__name__ = new_name wrapper.__wrapped__ = func # Try to call format on the docstring if func.__doc__: try: wrapper.__doc__ = func.__doc__.format(*args, **kwargs) except (IndexError, KeyError): # Maybe the user has added some of the formating strings # unintentionally in the docstring. Do not raise an exception as it # could be that he is not aware of the formating feature. pass return wrapper def add_test(cls, test_name, func, *args, **kwargs): """ Add a test case to this class. The test will be based on an existing function but will give it a new name. """ setattr(cls, test_name, feed_data(func, test_name, *args, **kwargs)) def process_file_data(cls, name, func, file_attr): """ Process the parameter in the `file_data` decorator. """ cls_path = os.path.abspath(inspect.getsourcefile(cls)) data_file_path = os.path.join(os.path.dirname(cls_path), file_attr) def create_error_func(message): # pylint: disable-msg=W0613 def func(*args): raise ValueError(message % file_attr) return func # If file does not exist, provide an error function instead if not os.path.exists(data_file_path): test_name = mk_test_name(name, "error") add_test(cls, test_name, create_error_func("%s does not exist"), None) return _is_yaml_file = data_file_path.endswith((".yml", ".yaml")) # Don't have YAML but want to use YAML file. if _is_yaml_file and not _have_yaml: test_name = mk_test_name(name, "error") add_test( cls, test_name, create_error_func("%s is a YAML file, please install PyYAML"), None ) return with codecs.open(data_file_path, 'r', 'utf-8') as f: # Load the data from YAML or JSON if _is_yaml_file: data = yaml.safe_load(f) else: data = json.load(f) _add_tests_from_data(cls, name, func, data) def _add_tests_from_data(cls, name, func, data): """ Add tests from data loaded from the data file into the class """ for i, elem in enumerate(data): if isinstance(data, dict): key, value = elem, data[elem] test_name = mk_test_name(name, key, i) elif isinstance(data, list): value = elem test_name = mk_test_name(name, value, i) if isinstance(value, dict): add_test(cls, test_name, func, **value) else: add_test(cls, test_name, func, value) def ddt(cls): """ Class decorator for subclasses of ``unittest.TestCase``. Apply this decorator to the test case class, and then decorate test methods with ``@data``. For each method decorated with ``@data``, this will effectively create as many methods as data items are passed as parameters to ``@data``. The names of the test methods follow the pattern ``original_test_name_{ordinal}_{data}``. ``ordinal`` is the position of the data argument, starting with 1. For data we use a string representation of the data value converted into a valid python identifier. If ``data.__name__`` exists, we use that instead. For each method decorated with ``@file_data('test_data.json')``, the decorator will try to load the test_data.json file located relative to the python file containing the method that is decorated. It will, for each ``test_name`` key create as many methods in the list of values from the ``data`` key. """ for name, func in list(cls.__dict__.items()): if hasattr(func, DATA_ATTR): for i, v in enumerate(getattr(func, DATA_ATTR)): test_name = mk_test_name(name, getattr(v, "__name__", v), i) if hasattr(func, UNPACK_ATTR): if isinstance(v, tuple) or isinstance(v, list): add_test(cls, test_name, func, *v) else: # unpack dictionary add_test(cls, test_name, func, **v) else: add_test(cls, test_name, func, v) delattr(cls, name) elif hasattr(func, FILE_ATTR): file_attr = getattr(func, FILE_ATTR) process_file_data(cls, name, func, file_attr) delattr(cls, name) return cls
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class C: x = 1 y = x x = 2 print y print C.y C.x = 10 print C.y
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import sys import os import zipfile try: import tarfile except ImportError: pass try: from urllib import request except ImportError: import urllib as request from requests import Session as HTTPSession from selenium import webdriver from selenium.webdriver.chrome.options import Options as ChromeOptions from .constants import CONSTANTS, CHROME_CONSTANTS, IE_CONSTANTS, FIREFOX_CONSTANTS from .exceptions import InvalidArgumentError, OperationFailedException from .common_utils import (get_user_home_dir, is_valid_url, copy_file, hash_, rm_files, guess_filename_from_url, add_to_path, find_patterns_in_str, os_name, make_dir, file_exists) from .htmlm import HTML from .log_adapter import get_logger from .decorators import task_runner #TODO: abstract away all usage of anything dealing with file systems. eg: os, platform, ntpath etc #TODO: user provided hash comparsion and security def download_driver(driver_name, version=None, download_filename=None, add_to_ospath=True, overwrite_existing=True): if driver_name == CONSTANTS.CHROME_DRIVER: webdriver_downloader.download_chrome(version=version, download_filename=download_filename, add_to_ospath=add_to_ospath, overwrite_existing=overwrite_existing) elif driver_name == CONSTANTS.IE_DRIVER: webdriver_downloader.download_ie(version=version, download_filename=download_filename, add_to_ospath=add_to_ospath, overwrite_existing=overwrite_existing) elif driver_name == CONSTANTS.FIREFOX_DRIVER: webdriver_downloader.download_firefox(version=version, download_filename=download_filename, add_to_ospath=add_to_ospath, overwrite_existing=overwrite_existing) else: get_logger().error(f"Unable to download {driver_name} driver at this point") def download_url(url, to_dir=None, download_filename=None, overwrite_existing=True, asynch=True, unzip=False, del_zipfile=False, add_to_ospath=False, callback=None): d = Downloader(from_url=url, to_dir=to_dir, download_filename=download_filename, overwrite_existing=overwrite_existing, asynch=asynch, unzip=unzip, del_zipfile=del_zipfile, add_to_ospath=add_to_ospath, callback=callback) d_files = d.download() return d_files class Downloader(object): @staticmethod def any_file_exists(files): if not isinstance(files, list): files = [files] for f in files: if file_exists(f): return True return False def __init__(self, from_url=None, to_dir=None, download_filename=None, unzip_filename=None, overwrite_existing=True, asynch=False, unzip=True, del_zipfile=True, add_to_ospath=False, callback=None): if not is_valid_url(from_url): get_logger().error(f"{__class__.__name__}: from_url is mandatory") raise InvalidArgumentError("from_url is mandatory and should be a valid url") self.from_url = from_url self.to_dir = to_dir or get_user_home_dir() self.overwrite_existing = overwrite_existing self.download_ok = False self.download_fullfilename = None if not download_filename: download_filename = guess_filename_from_url(from_url) if download_filename: self.download_fullfilename = os.path.join(self.to_dir, download_filename) if unzip_filename: self.unzip_fullfilename = os.path.join(self.to_dir, unzip_filename) self.unzip = unzip self.del_zipfile = del_zipfile self.add_to_ospath = add_to_ospath self.filehash = None self.downloaded_files = None self.asynch = asynch self.callback = callback def _downloadhook(self, blocknum, blocksize, totalsize): readsofar = blocknum * blocksize if totalsize > 0: percent = 100.0 if readsofar > totalsize else (readsofar * 100.0 / totalsize) s = f"\rdownloading...[{percent:.0f}%]" sys.stdout.write(s) if readsofar >= totalsize: # near the end sys.stdout.write("\n") self.download_ok = True else: # total size is unknown sys.stderr.write("read %d\n" % (readsofar,)) def _can_proceed(self): if not self.overwrite_existing: #print("self.unzip_fullfilename - " + self.unzip_fullfilename) if Downloader.any_file_exists([self.unzip_fullfilename, self.download_fullfilename]): return False return True def _unzip(self): f = self.download_fullfilename if not f: return extracted_names = [] extracted = False if zipfile.is_zipfile(f): with zipfile.ZipFile(f) as zf: zf.extractall(path=self.to_dir) extracted_names = zf.namelist() extracted = True elif f.endswith("tar.gz"): with tarfile.open(f, "r:gz") as tar: tar.extractall(path=self.to_dir) extracted_names = tar.getnames() extracted = True elif f.endswith("tar"): with tarfile.open(f, "r:") as tar: tar.extractall(path=self.to_dir) extracted_names = tar.getnames() extracted = True if extracted: self.downloaded_files = [os.path.join(self.to_dir, fl) for fl in extracted_names] if self.del_zipfile: rm_files(f) def _add_to_path(self): if not self.to_dir: return add_to_path(self.to_dir) #decorate download function for ability to run in background @task_runner def download(self): if self._can_proceed(): filename, headers = None, None try: hook = None if not self.asynch: hook = self._downloadhook filename, headers = request.urlretrieve(self.from_url, filename=self.download_fullfilename, reporthook=hook) self.download_ok = True except Exception as e: raise OperationFailedException(f"Download from {self.from_url} failed. Details - \n{str(e)}") self.filehash = hash_(filename) if (not self.download_fullfilename) and filename: bn = os.path.basename(filename) copy_file(filename, self.to_dir, overwrite=self.overwrite_existing) rm_files(filename) self.download_fullfilename = os.path.join(self.to_dir, bn) if self.download_ok: self.downloaded_files = [self.download_fullfilename] if self.unzip: #print("unzipping") self._unzip() else: raise OperationFailedException(f"Download from {self.from_url} failed") if self.add_to_ospath: self._add_to_path() if self.callback and callable(self.callback): self.callback(self.downloaded_files) return self.downloaded_files class WebdriverDownloader(Downloader): _OSMAP_CHROME = {'windows':"win32", 'mac':"mac64", 'linux':"linux64"} _OSMAP_FIREFOX = {'windows':"win64", 'mac':"macos", 'linux':"linux64"} _ZIPEXTMAP_FIREFOX = {'windows':"zip", 'mac':"tar.gz", 'linux':"tar.gz"} WEBDRIVERNAMES = {CONSTANTS.CHROME_DRIVER : "chromedriver", CONSTANTS.IE_DRIVER : "IEDriverServer", CONSTANTS.FIREFOX_DRIVER : "geckodriver" } def __init__(self, url=None, to_dir=None, overwrite_existing=True, download_filename=None, unzip_filename=None, asynch=False, unzip=True, add_to_ospath=False): self.to_dir = to_dir or self.default_download_directory() super(WebdriverDownloader, self).__init__(from_url=url, to_dir=self.to_dir, download_filename=download_filename, unzip_filename=unzip_filename, overwrite_existing=overwrite_existing, asynch=asynch, unzip=unzip, add_to_ospath=add_to_ospath) @staticmethod def default_download_directory(): dir_name = CONSTANTS.DOWNLOAD_DIR_NAME start_dir = CONSTANTS.DIR_PATH or get_user_home_dir() #home_dir = get_user_home_dir() default_dir = os.path.join(start_dir, CONSTANTS.DIR_NAME, dir_name) make_dir(default_dir) return default_dir @staticmethod def latest_chrome_version(use_default=True): #TODO: this needs to change #get from chromium website LATEST_VERSION_PATTERN = r'Latest Release:.*ChromeDriver ([\d+\.+]+)' plain_text = "" with HTTPSession() as session: h = session.get(CHROME_CONSTANTS.HOME_URL) r = HTML(h.text) r.render() plain_text = str(r.text).encode('ascii', errors='ignore').decode() v = find_patterns_in_str(LATEST_VERSION_PATTERN, plain_text, first=True) if (not v) and use_default: v = CHROME_CONSTANTS.DEFAULT_VERSION if not v: message = """ Unable to pull latest available Chromedriver version. Check, 1. Your internet connection 2. If internet is fine, contact implementor. Perhaps requires logic change """ raise OperationFailedException(message) return str(v).strip() @staticmethod def ie_download_url(version, filename): home_url = IE_CONSTANTS.DOWNLOAD_URL.format(version, filename) return home_url @classmethod def download_chrome(cls, to_dir=None, version=None, download_filename=None, overwrite_existing=False, asynch=False, unzip=True, add_to_ospath=True): unzip_filename = WebdriverDownloader.WEBDRIVERNAMES[CONSTANTS.CHROME_DRIVER] #TODO: de-duplication start_dir = to_dir or WebdriverDownloader.default_download_directory() f_dir = os.path.join(start_dir, unzip_filename) if (not overwrite_existing) and file_exists(f_dir): if add_to_ospath: add_to_path(start_dir) return download_url = CHROME_CONSTANTS.DOWNLOAD_URL if download_url: url = download_url else: #determine download_url if version: version = str(version) version = version or CHROME_CONSTANTS.VERSION or WebdriverDownloader.latest_chrome_version() filename = CHROME_CONSTANTS.FILENAME_TEMPLATE.format(WebdriverDownloader._OSMAP_CHROME[os_name()]) if not download_filename: download_filename = filename url = CHROME_CONSTANTS.DOWNLOAD_URL_TEMPLATE.format(version, filename) wd = WebdriverDownloader(url=url, to_dir=to_dir, download_filename=download_filename, unzip_filename=unzip_filename, overwrite_existing=overwrite_existing, asynch=asynch, unzip=unzip, add_to_ospath=add_to_ospath) wd.download() #TODO: automatically determine version @classmethod def download_ie(cls, to_dir=None, version=None, download_filename=None, overwrite_existing=False, asynch=False, unzip=True, add_to_ospath=True): unzip_filename = WebdriverDownloader.WEBDRIVERNAMES[CONSTANTS.IE_DRIVER] #TODO: de-duplication start_dir = to_dir or WebdriverDownloader.default_download_directory() f_dir = os.path.join(start_dir, unzip_filename) if (not overwrite_existing) and file_exists(f_dir): if add_to_ospath: add_to_path(start_dir) return download_url = IE_CONSTANTS.DOWNLOAD_URL if download_url: url = download_url else: #determine download_url version = version or IE_CONSTANTS.VERSION version = str(version) filename = IE_CONSTANTS.FILENAME_TEMPLATE.format(version) if not download_filename: download_filename = filename url = IE_CONSTANTS.DOWNLOAD_URL_TEMPLATE.format(version, filename) wd = WebdriverDownloader(url=url, to_dir=to_dir, download_filename=download_filename, unzip_filename=unzip_filename, overwrite_existing=overwrite_existing, asynch=asynch, unzip=unzip, add_to_ospath=add_to_ospath) wd.download() #TODO: logic to get latest version from Website @classmethod def download_firefox(cls, to_dir=None, version=None, download_filename=None, overwrite_existing=False, asynch=False, unzip=True, add_to_ospath=True): unzip_filename = WebdriverDownloader.WEBDRIVERNAMES[CONSTANTS.FIREFOX_DRIVER] #TODO: de-duplication start_dir = to_dir or WebdriverDownloader.default_download_directory() f_dir = os.path.join(start_dir, unzip_filename) if (not overwrite_existing) and file_exists(f_dir): if add_to_ospath: add_to_path(start_dir) return download_url = FIREFOX_CONSTANTS.DOWNLOAD_URL if download_url: url = download_url else: #determine download_url version = version or FIREFOX_CONSTANTS.VERSION or FIREFOX_CONSTANTS.DEFAULT_VERSION version = str(version) ospart = WebdriverDownloader._OSMAP_FIREFOX[os_name()] extpart = WebdriverDownloader._ZIPEXTMAP_FIREFOX[os_name()] filename = FIREFOX_CONSTANTS.FILENAME_TEMPLATE.format(version, ospart, extpart) if not download_filename: download_filename = filename url = FIREFOX_CONSTANTS.DOWNLOAD_URL_TEMPLATE.format(version, filename) wd = WebdriverDownloader(url=url, to_dir=to_dir, download_filename=download_filename, unzip_filename=unzip_filename, overwrite_existing=overwrite_existing, asynch=asynch, unzip=unzip, add_to_ospath=add_to_ospath) wd.download() webdriver_downloader = WebdriverDownloader
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# Space Invaders # Created by Raysa Dutra # -*- coding: utf-8 -*- #!/usr/bin/env python from estados_orm import StateOrm class State(object): def __init__(self): self.dao = StateOrm() def save_state(self, player_position, player_life, enemy_type): self.dao.create(player_position=player_position, player_life=player_life, enemy_type=enemy_type)
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# Generated by Django 3.0.8 on 2020-11-15 23:26 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('blog', '0045_auto_20201115_1516'), ] operations = [ migrations.AlterField( model_name='comment', name='post_connected', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='blog.Post'), ), migrations.AlterField( model_name='preference', name='post', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='blog.Post'), ), ]
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Jan 11 18:56:02 2021 @author: lferiani """ from pathlib import Path base_path = Path(__file__).parent BINARY_MODEL_PATH = base_path / 'trained_models' / 'v_06_60_best.pth' MULTICLASS_MODEL_PATH = ( base_path / 'trained_models' / 'v_12_63_20201218_213041.pth')
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/day09/mygui3.py
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#!/usr/bin/env python3 # -*- coding:utf-8 -*- import tkinter from functools import partial def say_hi(world): def welcome(): label.config(text='Hello %s' % world) return welcome root = tkinter.Tk() label = tkinter.Label(text='Hello world', font="15px") b1 = tkinter.Button(root, bg='red', fg='white', text='button1', comand=say_hi('sss')) MyButton = partial(tkinter.Button(root, bg='red', fg='white')) # 偏函数 b2 = MyButton(text='button2', command=say_hi('chine')) b3 = MyButton(text='quit', command=root.quit()) label.pack() # ? b1.pack() b2.pack() b3.pack() root.mainloop() # ?
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from django.conf.urls import url from . import views urlpatterns = [ url(r'^$', views.index, name='index') ]
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import requests from .google.geolocation import Geolocation from .yelp.business import BusinessSearch class Client: def __init__(self): self.session = requests.session() self.yelp = BusinessSearch(self) self.geolocation = Geolocation(self)
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#calss header class _CLAMBAKE(): def __init__(self,): self.name = "CLAMBAKE" self.definitions = [u'an event in which seafood is cooked and eaten outside, usually near the sea'] self.parents = [] self.childen = [] self.properties = [] self.jsondata = {} self.specie = 'nouns' def run(self, obj1 = [], obj2 = []): return self.jsondata
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# Generated with SMOP 0.41 from libsmop import * # /workspace/MOT/cmot-v1/Common/CenterToLeft.m @function def CenterToLeft(x=None,y=None,height=None,width=None,*args,**kwargs): varargin = CenterToLeft.varargin nargin = CenterToLeft.nargin ## Copyright (C) 2014 Seung-Hwan Bae ## All rights reserved. # (x,y): Center position h_height=height / 2 # /workspace/MOT/cmot-v1/Common/CenterToLeft.m:7 h_width=width / 2 # /workspace/MOT/cmot-v1/Common/CenterToLeft.m:8 L_x=x - round(h_width) # /workspace/MOT/cmot-v1/Common/CenterToLeft.m:10 L_y=y - round(h_height) # /workspace/MOT/cmot-v1/Common/CenterToLeft.m:11 return L_x,L_y if __name__ == '__main__': pass
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from django.db import models class ShirtStyles(models.Model): ShirtStyleNumber = models.CharField('Style Number', max_length=20) ShirtStyleDescription = models.CharField('Description', max_length=200) def __unicode__(self): return self.ShirtStyleNumber + ' ' + self.ShirtStyleDescription class StyleColorCategories(models.Model): StyleColorCategoryName = models.CharField('Color Category', max_length=20) class ShirtStyleSKUs(models.Model): ShirtStyle = models.ForeignKey(ShirtStyles) StyleColorCategory = models.ForeignKey(StyleColorCategories) ShirtStyleSize = models.CharField('Size', max_length=2) FabricRollYield = models.IntegerField('Fabric Roll Yield') KnitSize = models.FloatField('Knit Size') SizePrice = models.FloatField('Size Price') Active = models.BooleanField() def __unicode__(self): return self.ShirtStyleSize class Customers(models.Model): CustomerName = models.CharField('Customer Name', max_length=40) def __unicode__(self): return self.CustomerName class ShirtOrders(models.Model): Customer = models.ForeignKey(Customers) PONumber = models.CharField('PO#', max_length=20) Complete = models.BooleanField() def __unicode__(self): return self.PONumber class ShirtOrderSKUs(models.Model): ShirtOrder = models.ForeignKey(ShirtOrders) ShirtStyleSKU = models.ForeignKey(ShirtStyleSKUs) OrderQuantity = models.IntegerField('Quantity') class Shipments(models.Model): DateShipped = models.DateTimeField('Date Shipped') class ShirtSKUInventory(models.Model): CutOrder = models.CharField('Cut Order', max_length=20) Pieces = models.IntegerField() Add = models.BooleanField() class ShipmentSKUs(models.Model): Shipment = models.ForeignKey(Shipments) ShirtOrderSKU = models.ForeignKey(ShirtOrderSKUs) CutOrder = models.CharField('Cut Order', max_length=20)
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""" Django settings for heavywaterapp project. Generated by 'django-admin startproject' using Django 2.0.4. For more information on this file, see https://docs.djangoproject.com/en/2.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.0/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/ # SECURITY WARNING: don't run with debug turned on in production! DEBUG = False SECRET_KEY = os.environ.get('SECRET_KEY') ALLOWED_HOSTS = [ 'django-env4.p3r3dh3xti.us-east-1.elasticbeanstalk.com', 'localhost', '127.0.0.1' ] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'docclf' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'heavywaterapp.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': ['docclf/templates'], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'heavywaterapp.wsgi.application' # Database # https://docs.djangoproject.com/en/2.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } CACHES = { 'default': { 'BACKEND': 'django.core.cache.backends.locmem.LocMemCache', } } # Password validation # https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'US/Eastern' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.0/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = 'static'
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import threading from utils.Checker import Checker class Helper(): def url_is_internal(url,compare): # url is the param needed to be compared to compare if ".".join(extract(url)) == ".".join(extract(compare)) or (url[0:4] != "http" and url[0] != "#"): return True else: return False def embed_url(url): features_size = 30 threads = [None]*features_size arr_threads_result = [] arr = [] try: threads[0] = threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.having_IP_Address(arg1),0)), args=(url,)) threads[1] = threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.URL_Length(arg1),1)), args=(url,)) threads[2] = threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.Shortining_Service(arg1),2)), args=(url,)) threads[3] = threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.having_At_Symbol(arg1),3)), args=(url,)) threads[4] = threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.double_slash_redirecting(arg1),4)), args=(url,)) threads[5] = threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.Prefix_Suffix(arg1),5)), args=(url,)) threads[6] = threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.having_Sub_Domain(arg1),6)), args=(url,)) threads[7] = threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.SSLfinal_State(arg1),7)), args=(url,)) threads[8] = threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.Domain_registeration_length(arg1),8)), args=(url,)) threads[9] = threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.Favicon(arg1),9)), args=(url,)) threads[10]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.port(arg1),10)), args=(url,)) threads[11]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.HTTPS_token(arg1),11)), args=(url,)) threads[12]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.Request_URL(arg1),12)), args=(url,)) threads[13]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.URL_of_Anchor(arg1),13)), args=(url,)) threads[14]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.Links_in_tags(arg1),14)), args=(url,)) threads[15]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.SFH(arg1),15)), args=(url,)) threads[16]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.Submitting_to_email(arg1),16)), args=(url,)) threads[17]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.Abnormal_URL(arg1),17)), args=(url,)) threads[18]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.Redirect(arg1),18)), args=(url,)) threads[19]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.on_mouseover (arg1),19)), args=(url,)) threads[20]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.RightClick (arg1),20)), args=(url,)) threads[21]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.popUpWidnow (arg1),21)), args=(url,)) threads[22]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.Iframe(arg1),22)), args=(url,)) threads[23]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.age_of_domain(arg1),23)), args=(url,)) threads[24]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.DNSRecord(arg1),24)), args=(url,)) threads[25]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.web_traffic(arg1),25)), args=(url,)) threads[26]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.Page_Rank(arg1),26)), args=(url,)) threads[27]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.Google_Index(arg1),27)), args=(url,)) threads[28]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.Links_pointing_to_page(arg1),28)), args=(url,)) threads[29]= threading.Thread(target=lambda arg1: arr_threads_result.append((Checker.Statistical_report(arg1),29)), args=(url,)) for i in range(features_size): threads[i].start() for i in range(features_size): threads[i].join() arr_threads_result.sort(key=lambda tup: tup[1]) for elem in arr_threads_result: arr.append(elem[0]) return arr except Exception as e: return e
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ preprocessing_yesno.py Created on Thu May 3 00:15:48 2018 This code saves: - apply zero padding to the first 48,000 samples - [mu-law encoded audio] to <out_filedir>/enc - [mfcc] to <out_filedir>/mfcc - NOT IMPLEMENTED YET ([f0] to <out_filedir>/f0 *) @author: sungkyun """ import argparse import numpy as np import pandas as pd # required for generating .csv files import librosa # required for audio pre-processing, loading mp3 (sudo apt-get install libav-tools) import glob, os # required for obtaining test file ID from util.utils import mu_law_encode #%% Argument Parser parser = argparse.ArgumentParser(description='Audio Preprocessing for yesno dataset') parser.add_argument('-sr', '--sr', type=int, default=16000, metavar='N', help='target sampling rate, default 16000') parser.add_argument('-zp', '--zero_pad', type=int, default=48000, metavar='N', help='target sampling rate, default 48000') parser.add_argument('-i', '--input_filedir', type=str, default='data/waves_yesno/', metavar='N', help='input source dataset directory, default=data/waves_yesno/') parser.add_argument('-o', '--out_filedir', type=str, default='data/processed_yesno/', metavar='N', help='output file directory(root of .wav subdirectories and .csv file), default=data/processed_yesno/') args = parser.parse_args() input_file_dir = args.input_filedir output_file_dir = args.out_filedir #%% Function def. def displayFeat(x_spec=np.ndarray): import matplotlib.pyplot as plt import librosa.display plt.figure(figsize=(10, 4)) librosa.display.specshow(x_spec, x_axis='time') return 0 #%% Preprocessing --> save <u-enc> <mfcc> as .npy input_file_paths = sorted(glob.glob(input_file_dir + '*.wav')) file_ids = [path.split('/')[-1][:-4] for path in input_file_paths] # Load audio -> mono -> resample -> mfcc -> save if not os.path.exists(output_file_dir): os.makedirs(output_file_dir) if not os.path.exists(output_file_dir + 'mulaw/'): os.makedirs(output_file_dir + 'mulaw/') if not os.path.exists(output_file_dir + 'mfcc/'): os.makedirs(output_file_dir + 'mfcc/') total_input_files = len(input_file_paths) for i in range(total_input_files): x_raw, sr = librosa.load(input_file_paths[i], sr=args.sr, mono=True) # Normalize? x_raw = np.pad(x_raw, (args.zero_pad,0), mode='constant') # padding first 48,000 samples with zeros #x_spec = librosa.feature.melspectrogram(y=x_raw, sr=sr, power=2.0, n_fft = 400, hop_length=160, n_mels=128) x_spec = librosa.feature.melspectrogram(y=x_raw, sr=sr, power=2.0, n_fft = 400, hop_length=1, n_mels=128) x_mfcc = librosa.feature.mfcc(S=librosa.power_to_db(x_spec), sr=args.sr, n_mfcc=25) # displayFeat(x_spec); displayFeat(x_mfcc) if x_mfcc.shape[1] > len(x_raw): x_mfcc = x_mfcc[:,0:len(x_raw)] elif x_mfcc.shape[1] < len(x_raw): x_raw = x_raw[0:x_mfcc.shape[1]] x_mulaw = mu_law_encode(x_raw) # Save mulaw save_file_path_mulaw = output_file_dir + 'mulaw/' + file_ids[i] + '.npy' np.save(save_file_path_mulaw, x_mulaw.astype('uint8')) # Save mfcc save_file_path_mfcc = output_file_dir + 'mfcc/' + file_ids[i] + '.npy' np.save(save_file_path_mfcc, x_mfcc) print('Preprocessing: {} files completed.'.format(total_input_files)) #%% Train/test split --> generate .csv # Train/test split : 54 files for train, 6 files for test test_id_sel = [5,11,22,38,43,55] train_id_sel = list(set(range(60)).difference(set(test_id_sel))) # Prepare pandas dataframes df_test = pd.DataFrame(columns=('file_id', 'mulaw_filepath', 'mfcc_filepath')) df_train = pd.DataFrame(columns=('file_id', 'mulaw_filepath', 'mfcc_filepath')) for idx in test_id_sel: save_file_path_mulaw = output_file_dir + 'mulaw/' + file_ids[idx] + '.npy' save_file_path_mfcc = output_file_dir + 'mfcc/' + file_ids[idx] + '.npy' df_test.loc[len(df_test)] = [file_ids[idx], save_file_path_mulaw, save_file_path_mfcc] # add a new row into DataFrame for idx in train_id_sel: save_file_path_mulaw = output_file_dir + 'mulaw/' + file_ids[idx] + '.npy' save_file_path_mfcc = output_file_dir + 'mfcc/' + file_ids[idx] + '.npy' df_train.loc[len(df_train)] = [file_ids[idx], save_file_path_mulaw, save_file_path_mfcc] # add a new row into DataFrame # Save .csv df_test.to_csv(output_file_dir + 'test.csv', encoding='utf-8') df_train.to_csv(output_file_dir + 'train.csv', encoding='utf-8') print('Preprocessing: generated test.csv and train.csv files in {}.'.format(output_file_dir))
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#/user/bin/env python "this is a test module for getting unlock challenge" import sys import os from hashlib import sha256 def challenge_verify(): if (len(sys.argv) != 3) : print "Usage: rkpublickey.py [challenge_file] [product_id_file]" return if ((sys.argv[1] == "-h") or (sys.argv[1] == "--h")): print "Usage: rkpublickey.py [challenge_file] [product_id_file]" return try: challenge_file = open(sys.argv[1], 'rb') product_id_file = open(sys.argv[2], 'rb') challenge_random_file = open('unlock_challenge.bin', 'wb') challenge_data = challenge_file.read(52) product_id_data = product_id_file.read(16) product_id_hash = sha256(product_id_data).digest() print("The challege version is %d" %ord(challenge_data[0])) if (product_id_hash != challenge_data[4:36]) : print("Product id verify error!") return challenge_random_file.write(challenge_data[36:52]) print("Success!") finally: if challenge_file: challenge_file.close() if product_id_file: product_id_file.close() if challenge_random_file: challenge_random_file.close() if __name__ == '__main__': challenge_verify()
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/Luck_check.py
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[]
no_license
szyymek/Python
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refs/heads/master
2020-07-24T01:20:53.418696
2019-10-04T08:05:06
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def luck_check(string): srodek=len(string) if srodek%2==1: string=string[:int(srodek/2)]+string[(int(srodek/2))+1:] lewa=prawa=0 for x in string[:int(srodek/2)]: lewa+=int(x) for y in string[int(srodek/2):]: prawa+=int(y) return prawa==lewa
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/rt-rank/script/get_nfeat.py
30ac703502cab975b5f3396f7d5d370110d342a9
[]
no_license
fancyspeed/solution_of_kaggle_merck
571e4d008141bf6f161ce76af0bf2439dddfb3e9
f1da95bd69529395b8b370831c854f16ba1d90a3
refs/heads/master
2016-09-05T15:03:59.762071
2014-07-23T16:35:07
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def get_head_train(line): arr = line.strip().split(',')[2:] #head = [ele.split('_')[1] for ele in arr] head = [str(i+1) for i in range(len(arr))] return head def trans_train(p_in): n_cur = 0 for line in open(p_in): n_cur += 1 if n_cur == 1: head = get_head_train(line) print head[-1] if __name__ == '__main__': import sys if len(sys.argv) < 2: print '<usage> in' exit(-1) trans_train(sys.argv[1])
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/emotion-program-pi-version.py
0cea550ccba505b69dfebe8c3071874a0e5092a7
[]
no_license
vanstorm9/Emotion-Recognition-DOF
ced912158e45636b53469b3dc0645bb4c5ab69f8
0d6e395bf950388a37065cb9ccf1bba44171c35f
refs/heads/master
2021-01-10T02:14:11.917489
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# Program implemented in the Raspberry Pi (with camera module) #from matplotlib import pyplot as plt #from sklearn.naive_bayes import GaussianNB import numpy as np import math import cv2 import os import os.path import io from time import time import picamera import smtplib #camera = picamera.PiCamera() from time import sleep #import pyttsx # Libraries to preform machine learning import sys from sklearn.svm import SVC from sklearn.metrics import accuracy_score,accuracy_score, confusion_matrix from sklearn.decomposition import PCA, RandomizedPCA # from mpl_toolkits.mplot3d import Axes3D from sklearn.externals import joblib from sklearn import cross_validation from sklearn.linear_model import Ridge from sklearn.learning_curve import validation_curve, learning_curve from sklearn.externals import joblib def emotion_to_text(pred): smtpUser= "(ENTER YOUR EMAIL ADDRESS)" smtpPass= "(ENTER YOUR EMAIL ACCOUNT'S PASSWORD)" toAdd = "[email protected]" fromAdd = smtpUser if pred == "Neutral": subject = "How are you doing?" body = "Hey! Just checking in, I was just wondering how you are doing today. \n \n - Rapiro" elif pred == "Angry": subject = "Are you okay? You look mad" body = "I noticed that you are a bit red. Did something annoy or aggrivate you? /n -Rapiro" elif pred == "Shocked": subject = "Did something scare or surprised you?" body = "What's wrong, you look like you have seen a ghost. . . \n Rapiro" else: subject = "You seem happy today" body = "Hey there! I am very happy that you are happy ^_^ \n \n -Rapiro" header = "To: " + toAdd + "\n" + "From: " + fromAdd + "\n" + "Subject: " + subject #print header + "\n" + body s = smtplib.SMTP('smtp.gmail.com', 587) s.ehlo() s.starttls() s.ehlo() s.login(smtpUser, smtpPass) s.sendmail(fromAdd, toAdd, header + "\n" + body) s.quit() # Cannot use due to memory error def pca_calc(main): n_components = 90000 print '----------------------' print main.shape pca = RandomizedPCA(n_components=n_components, whiten=True).fit(main) main = pca.transform(main) print main.shape return main def motor_emotion_response(pred): if pred == 'Smiling': print 'Activating command. . .' os.system("./rapirocommands 6") sleep(5) os.system("./rapirocommands 0") print 'Command finished' elif pred == 'Neutral': print 'Activating neutral command. . .' os.system("./hellobash") sleep(5) os.system("./rapirocommands 5") sleep(5) os.system("./rapirocommands 0") print 'End command' elif pred == 'Angry': print 'Activating angry command. . .' os.system("./rapirocommands 4") sleep(2) os.system("./rapirocommands 0") print 'Command ended' elif pred == 'Shocked': print 'Activating shocked command' os.system("./rapiro-commands 2") sleep(2) os.system("./rapiro-commands 0") print 'Command ended' def draw_flow(im,flow,step=16): h,w = im.shape[:2] y,x = np.mgrid[step/2:h:step,step/2:w:step].reshape(2,-1) fx,fy = flow[y,x].T # create line endpoints lines = np.vstack([x,y,x+fx,y+fy]).T.reshape(-1,2,2) lines = np.int32(lines) # create image and draw vis = cv2.cvtColor(im,cv2.COLOR_GRAY2BGR) for (x1,y1),(x2,y2) in lines: cv2.line(vis,(x1,y1),(x2,y2),(0,255,0),1) cv2.circle(vis,(x1,y1),1,(0,255,0), -1) return vis def catch_first_frame(): ret, frame_f = capf.read() prev_gray = cv2.cvtColor(frame_f,cv2.COLOR_BGR2GRAY) prev_gray = cv2.resize(prev_gray, (0,0), fx=0.27, fy=0.27) face = face_classifier.detectMultiScale(prev_gray, 1.2, 4) if len(face) == 0: print 'No face was detected' print prev_gray.shape exit() else: print 'Face detected' for (x,y,w,h) in face: prev_gray = prev_gray[y: y+h, x: x+w] capf.release() return (x,y,w,h, prev_gray) def sensitive_override_check(prob_s, pred): if pred == 'Neutral': override_arr = [prob_s[0,3], prob_s[0,2], prob_s[0,0]] max_comp = max(override_arr) max_ind = [i for i, j in enumerate(override_arr) if j == max_comp][0] qualified_override = False if max_comp > 30: qualified_override = True if qualified_override: if max_ind == 0: pred = 'Smiling' elif max_ind == 1: pred = 'Shocked' else: pred = 'Angry' #print 'Sensitive Override triggered. . .' return pred def emotion_to_speech(pred): engine = pyttsx.init() rate = engine.getProperty('rate') engine.setProperty('rate', rate) if pred == 'Neutral': speech = 'Hello, you seem fine today' elif pred == 'Smiling': speech = 'You seem happy. I am very happy that you are happy!' elif pred == 'Shocked': speech = 'What is wrong? You look like you seen a ghost.' elif pred == 'Angry': speech = 'Why are you angry? Did something annoy or frustrate you?' print speech engine.say(speech) engine.runAndWait() motor_emotion_response("Smiling") slash = '/' folder_trans = np.array([]) target = np.array([]) label_trans = np.array([]) folder = '' choice = '' face_classifier = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml') #print 'Load datasets [l] from file or create a new one [n]' loading = 'l' if loading == 'l': #print 'Press [p] to predict test dataset, or else press any key' predict_start = 'n' else: predict_start = 'p' if loading=='l': # load dataset matrix from npy file t0 = time() t1 = time() if predict_start == 'p': print 'Loading the main matrix. . .' main = np.load('optical-main-mini.npy') diff = diff = time() - t1 print 'Loaded main matrix in ', diff, 's of size ', main.shape t2 = time() print 'Loading the target vector. . .' target = np.load('optical-target-mini.npy') diff = time() - t2 print 'Loaded target in ', diff, 's of size ', target.shape print 'Finished' total_time = time() - t0 print total_time, 's' t0 = time() if loading == 'l': print 'Now loading trained model. . .' model = joblib.load('Optical-Model-Mini/optical-model-mini.pkl') t1 = time() print 'Loading time: ', round(time()-t0, 3), 's' else: features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(main, target, test_size = 0.2) print 'Now training. . .' model = SVC(probability=True) ''' #model = SVC(kernel='poly') #model = GaussianNB() ''' model.fit(features_train, labels_train) print 'training time: ', round(time()-t0, 3), 's' print 'Saving model. . .' t1 = time() joblib.dump(model, 'Optical-Model-Mini/optical-model-mini.pkl') t3 = time() print 'model saving time: ', round(time()-t0, 3), 's' print 'Now predicting. . .' if predict_start == 'p': if loading == 'l': features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(main, target, test_size = 0.2) # Determine amount of time to train t1 = time() pred = model.predict(features_test) print 'predicting time: ', round(time()-t1, 3), 's' accuracy = accuracy_score(labels_test, pred) print 'Confusion Matrix: ' print confusion_matrix(labels_test, pred) # Accuracy in the 0.9333, 9.6667, 1.0 range print accuracy # --------------------------------- while True: # Test with another video while True: print 'Press [n] to go into normal mode or [s] to go into sensitive mode' sensitive_out = raw_input() if sensitive_out == 'n' or sensitive_out == 's': break # Manually setting x, y, w, h values in order make more consistant test # and training videos x = 63 y = 35 w = 64 h = 64 #prev_gray = frame_f.copy() #prev_gray = cv2.cvtColor(prev_gray, cv2.COLOR_BGR2GRAY) # Start video to record the user #cap to record user for 15 frames cap = cv2.VideoCapture(0) # Name of the video file path = 'test.h264' # Starting video fourcc = cv2.VideoWriter_fourcc(*'XVID') out = cv2.VideoWriter(path,fourcc, 20.0, (640,480)) print 'Press any key to start recording' go = raw_input() # New recording feature for the Raspberry Pi with picamera.PiCamera() as camera: print 'Starting recording. . .' camera.vflip = True camera.start_recording(path) print 'Before sleep' sleep(5) print 'After sleep' print 'Stopping the camera from recording. . .' camera.stop_recording() print 'Finished recording' # To get a # Cap3 cap3 = cv2.VideoCapture(path) ret, prev_gray = cap3.read() prev_gray = cv2.cvtColor(prev_gray,cv2.COLOR_BGR2GRAY) prev_gray = cv2.resize(prev_gray, (0,0), fx=0.27, fy=0.27) prev_gray = prev_gray[y: y+h, x: x+w] cap3.release() #face = face_classifier.detectMultiScale(prev_gray, 1.2, 4) j = 0 # To analyze the recording and make an emotion prediction cap4 = cv2.VideoCapture(path) max_frame = 36 while True: print 'j: ', j ret, frame = cap4.read() if frame == None: print 'Frame failure, trying again. . .' cap4.release() cap4 = cv2.VideoCapture(path) continue if j > max_frame + 1: cap4.release() break frame = cv2.resize(frame, (0,0), fx=0.35, fy=0.35) frame = frame[y: y+h, x: x+w] #cv2.imshow('To test with', frame) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) flow = cv2.calcOpticalFlowFarneback(prev_gray,gray,None, 0.5, 3, 15, 3, 5, 1.2, 0) # Working with the flow matrix flow_mat = flow.flatten() if j == 1: sub_main = flow_mat elif j != 0: sub_main = np.concatenate((sub_main, flow_mat)) prev_gray = gray # To show us visually each video #cv2.imshow('Optical flow',draw_flow(gray,flow)) if cv2.waitKey(1) & 0xFF == ord('q'): break j = j + 1 cap4.release() #cv2.destroyAllWindows() print 'Now predicting. . .' ### Sliding window ### k_start = 0 k_end = 15 * flow_mat.shape[0] max_frame = 36 * flow_mat.shape[0] while k_end < max_frame: count = float(k_end/max_frame) count = np.around(count, decimals=2) print count, '%' model.predict(sub_main[k_start:k_end]) prob = model.predict_proba(sub_main[k_start:k_end]) prob_s = np.around(prob, decimals=5) prob_s = prob_s* 100 # Determine amount of time to predict t1 = time() pred = model.predict(sub_main[k_start:k_end]) if sensitive_out == 's': pred = sensitive_override_check(prob_s, pred) if pred != 'Neutral': break k_start = k_start + (7 * flow_mat.shape[0]) k_end = k_end + (7 * flow_mat.shape[0]) ###################### print 'predicting time: ', round(time()-t1, 3), 's' print '' print 'Prediction: ' print pred print 'Probability: ' print 'Neutral: ', prob_s[0,1] print 'Smiling: ', prob_s[0,3] print 'Shocked: ', prob_s[0,2] print 'Angry: ', prob_s[0,0] print 'Start hello 2' os.system("./hellobash") print 'End hello 2' emotion_to_text(pred) print 'Starting robot motion response' motor_emotion_response(pred) print 'Motion ended'
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/hello/public/deploy/chal.py
3cdcfdcec0ecf9a88f3f75665084382c0d2855d2
[]
no_license
kendricktan/paradigm-ctf
96768eb6a3ee76867b873e96e2f623796803361c
21ba8273f858d1af24d0abdb841bb019e8fa0965
refs/heads/main
2023-06-26T07:50:39.179665
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import eth_sandbox from web3 import Web3 eth_sandbox.run_launcher([ eth_sandbox.new_launch_instance_action(deploy_value=Web3.toWei(0, 'ether')), eth_sandbox.new_get_flag_action() ])
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/authentication/admin.py
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[]
no_license
sahadeb1234/Ecommerce-Site-using-Django-Framework
f16b4c28756eea087859d099cb68504b1839c950
9ee09c323ed99c185a88ae3327907f27c53b3dbe
refs/heads/master
2023-08-11T11:33:54.353876
2021-09-27T15:45:51
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410,944,658
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from django.contrib import admin from django.contrib.admin.decorators import display from .models import PreRegistration # Register your models here. @admin.register(PreRegistration) class PreRegistrationAdmin(admin.ModelAdmin): list_display=['first_name','last_name']
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/src/UpdateWeather.py
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[]
no_license
shema102/AddressableLedClock
535cfc207d7380e92b3a57bfcbf7cfbcc169858d
e22703544c2254034187a68dbc0e8678b56cc3f0
refs/heads/master
2021-02-15T21:32:12.902520
2020-04-02T17:03:31
2020-04-02T17:03:31
244,934,071
0
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py
import math import urequests apiKey = "&appid=a988988f9b8811b3d5b23b888af9a7ca" url = "api.openweathermap.org/data/2.5/weather?q=" city = "Kyiv" url = "https://" + url + city + "&units=metric" + apiKey response = urequests.get(url) parsed = response.json() if parsed["cod"] == 200: weather = { "temperature": math.ceil(parsed["main"]["temp"]), "pressure": math.ceil(parsed["main"]["pressure"] * 0.75006375541921), "humidity": parsed["main"]["humidity"], "clouds": parsed["clouds"]["all"], "wind_speed": parsed["wind"]["speed"], "wind_direction": parsed["wind"]["deg"], } else: print("Error {}".format(parsed["cod"])) response.close()
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/1266/1266.minimum-time-visiting-all-points.289659185.Accepted.leetcode.python3.py
76eb95f002965918c8ee1bffff4858d8a5a97364
[]
no_license
huangyingw/submissions
7a610613bdb03f1223cdec5f6ccc4391149ca618
bfac1238ecef8b03e54842b852f6fec111abedfa
refs/heads/master
2023-07-25T09:56:46.814504
2023-07-16T07:38:36
2023-07-16T07:38:36
143,352,065
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py
class Solution(object): def minTimeToVisitAllPoints(self, points): x1, y1 = points[0] time = 0 for x2, y2 in points[1:]: dx, dy = abs(x1 - x2), abs(y1 - y2) time += max(dx, dy) x1, y1 = x2, y2 return time
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/bestgroup/bestgroup/wsgi.py
549c220ab1fb16bdcdf73adb8f5c07c9eda1a411
[]
no_license
Mumalo/BestGroups
e78d6b4967169f88bdec6f74c8881c7d200886cd
a2e4d1c7d60813e9a52554291f66f07b66fdbbea
refs/heads/master
2021-01-19T20:43:51.854174
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2017-04-18T21:34:39
88,540,777
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""" WSGI config for bestgroup project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/1.10/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application from mezzanine.utils.conf import real_project_name os.environ.setdefault("DJANGO_SETTINGS_MODULE", "%s.settings" % real_project_name("bestgroup")) application = get_wsgi_application()
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/readfiles.py
9c0300bb83848b8231570bcef6216b1d447617f6
[]
no_license
Miguelmargar/file-io
cc2790b109187dbeec87788c662aaf52d8e96c02
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refs/heads/master
2020-03-17T17:23:49.493484
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#to read a file's data # ONE WAY ----------------------------------------- f = open("data.txt", "r") # opens file to read it with "r" lines = f.readlines() # stores the file info in lines variable therefore in memory f.close() # closes the file but it is still stored in memory print(lines) # OTHER WAY --------------------------------------- f = open("data.txt", "r") #opens file lines = f.read().split("\n") #stores file in varible but as it has .split it gives each line a list without the .split it would all be one string together f.close() #closes file but it is still stored in memory print(lines) # find most common word in a text file------------- import re # imports regular expresions from a python library which is native to python - another library would be "random" import collections text = open("1155-0.txt").read().lower() # opens the file in question .read() reads it and .lower() makes it all lower case and converts it into a string in a variable called text words = re.findall("\w+", text) # this line converts the string into a line - "\w+", text = finds all the words in text long_words = [] for word in words: # this loop takes the words that are bigger than 5 characters if len(word) > 5: long_words.append(word) most_common = collections.Counter(long_words).most_common(10) # this prints out the top 10 words from the list created by the loop above print(most_common)
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/voice/figures_fpa/parse_new.py
eb98cbd05b1055d49523937cfd5453794d041656
[]
no_license
YoungofNUAA/Deep-Reinforcement-Learning-for-5G-Networks
7e13fa6cfbf2d5f0f85829dc09189cb8cd08c3e7
1e8fb6f0407814b7251e443ef1626f6408d85df1
refs/heads/master
2020-09-11T12:31:22.499797
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222,065,219
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2019-11-16T07:47:47
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#!/usr/local/bin/python3 # Note, if any output has NAN in it, we drop the entire episode from the calculation. import glob import re import numpy as np import pandas as pd files = glob.glob('measurements*.txt') f1 = open('ue_1_sinr.txt', 'a') f2 = open('ue_2_sinr.txt', 'a') f3 = open('ue_1_power.txt', 'a') f4 = open('ue_2_power.txt', 'a') episodes = [] pattern = '_([0-9]+)_' for filename in files: episode = re.findall(pattern, filename) episodes.append(episode[0]) episodes = np.array(episodes).astype(int) pd.DataFrame(episodes).to_csv("convergence.txt", index=False, header=False) pattern = re.compile('[\[\]_ \':a-z]+') # get rid of [], colons, and words. for file in files: f = open(file, 'r') lines = f.read() sinr1 = lines.split(':')[1] sinr2 = lines.split(':')[2] tx1 = lines.split(':')[3] tx2 = lines.split(':')[4] # Clean up sinr1, 2 by replacing pattern with '' f1.write('{},'.format(re.sub(pattern, '', sinr1))) f2.write('{},'.format(re.sub(pattern, '', sinr2))) f3.write('{},'.format(re.sub(pattern, '', tx1))) f4.write('{},'.format(re.sub(pattern, '', tx2))) f1.close() f2.close() f3.close() f4.close() f.close()
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/unique_in_order_7.6.18.py
1a4ed2556a8f0b6ac6da205b9dc392af10d25673
[]
no_license
royceduong/codewars
0c7d3ff0ef382b8b8eabd59807b5d495dc49d035
488410017873355d2dad3b125f9185a6c17a79e7
refs/heads/master
2020-03-23T00:45:49.941106
2018-07-21T06:16:11
2018-07-21T06:16:11
140,884,203
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# def unique_in_order(iterable): # array = [] # for v in iterable: # array.append(v) # print(array) # return array # def unique_in_order(iterable): # a = list(iterable) # i = 0 # b = [] # n = len(a) # while i < n: # j = 1 # while j < n: # if j != n-1: # if a[i] == a[j]: # j += 1 # else: # b.append(a[i]) # i = j # j += 1 # else: # if a[i] == a[j]: # b.append(a[i]) # else: # b.append(a[i]) # b.append(a[j]) # j += 1 # i = n # print(b) # def unique_in_order(iterable): # a = list(iterable) # n = len(a) # i = 0 # b = [] # while i < n: # j = i + 1 # while j < n and a[i] == a[j]: # j += 1 # b.append(a[i]) # i = j # print(b) # return b # unique_in_order('AAAABBBCCDAABBB') == ['A', 'B', 'C', 'D', 'A', 'B'] # unique_in_order('ABBCcAD') == ['A', 'B', 'C', 'c', 'A', 'D'] # unique_in_order([1,2,2,3,3]) == [1,2,3] def unique_in_order(iterable): a = list(iterable) i = 0 b = [] n = len(a) while i < n: j = i + 1 while j < n and a[i] == a[j]: j += 1 b.append(a[i]) i = j print(b) return b unique_in_order('AAAABBBCCDAABBB') unique_in_order('ABBCcAD') unique_in_order([1,2,2,3,3])
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/pets.py
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color=input("What's your favorite color? ") animal=input("What's your favorite animal? ") number=input("What's your favorite number? ") print("Ok, I guess your ideal pet would be a "+color,animal+" with "+str(number)+" legs!")
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#!/usr/bin/python3.6 """ Sequence processing pipeline used to analyze packaged viral genomes """ from argparse import ArgumentParser from csv import DictReader, DictWriter import logging import os from pathlib import Path from subprocess import run import sys # Argument parsing setup parser = ArgumentParser(description='Process sequencing files ' 'and collect stats') parser.add_argument('base_dir', type=str, help='Base of where processing takes place. All paths ' 'in the csv are assumed to be relative to this path ' 'and results will be placed in "Output" directory ' 'within this path.') parser.add_argument('csv_file', type=str, help='CSV file detailing samples, and where the relevant ' 'files for those samples can be found, all paths ' 'are relative to the base_dir.') # Logging setup log = logging.getLogger("fastseq") log.addHandler(logging.StreamHandler(sys.stdout)) logfile_formatter = logging.Formatter('%(asctime)s - %(name)s - ' '%(levelname)s - %(message)s') logfile_handler = logging.FileHandler('fastseq.log') logfile_handler.setFormatter(logfile_formatter) log.addHandler(logfile_handler) log.setLevel(logging.INFO) args = parser.parse_args() BASE_DIR = Path(args.base_dir) CSV_PATH = Path(args.csv_file) STATS_OUTPUT_PATH = BASE_DIR / "Output" / "final_stats.tsv.txt" # ---------------------------------- # Configuration Variable Definitions # ---------------------------------- # For simplicity, all program configurations + paths are treated as global # These are designed to work with the corresponding docker image, can be # tweaked to work in different contexts. # Tool Paths TRIMMOMATIC = "/tools/trimmomatic/trimmomatic-0.38.jar" BWA = "/tools/bwa/bwa" SAMTOOLS = "/tools/samtools/bin/samtools" BCFTOOLS = "/tools/samtools/bin/bcftools" PICARD = "/tools/picard/picard.jar" # Configuration for Trimmomatic LEAD_SCORE = 3 TRAIL_SCORE = 3 MIN_LEN = 50 WINDOW_SIZE = 4 WINDOW_QUALITY = 20 # Configuration for bcftools VCF_QUAL = 20 VCF_DP = 10 # Configuration for Picard PICARD_COVERAGE_CAP = 100000 PICARD_FAST_ALG = True PICARD_SAMPLE_SIZE = 5000 # ---------------------------------- # Function Definitions # ---------------------------------- def configure_paths(sample, fwd_read, rev_read, adapter_pth, ref_pth): """ Create all derived paths based on fwd/rev read, adapter, reference Also sets up an output directory with sample name to output files Args: sample (str): Sample name fwd_read (str): Path to forward read rel. to docker base in .fastq.gz rev_read (str): Path to forward read rel. to docker base in .fastq.gz adapter_pth (str): Path to adapter rel. to docker base in .fasta see trimmomatic documentation for what to name the sequences in the .fasta file ref_pth (str): Path to reference rel. to docker base in .fasta Returns: dict: A dictionary with keys of type str, values of type Path, See function for what keys map to what. """ sample_base = BASE_DIR / "Output" / sample os.makedirs(sample_base) return { "output_base": sample_base, "fwd_read": BASE_DIR / fwd_read, "rev_read": BASE_DIR / rev_read, "adapter_pth": BASE_DIR / adapter_pth, "ref_pth": BASE_DIR / ref_pth, # Derived Sample Paths "fwd_trimmed": BASE_DIR / f"{fwd_read}.trimmed.fastq", "rev_trimmed": BASE_DIR / f"{rev_read}.trimmed.fastq", "fwd_unpaired": BASE_DIR / f"{fwd_read}.unpaired.fastq", "rev_unpaired": BASE_DIR / f"{rev_read}.unpaired.fastq", "sam_file": sample_base / f"{sample}.sam", "bam_file": sample_base / f"{sample}.bam", "mpileup_file": sample_base / f"{sample}.mpileup", "vcf_file": sample_base / f"{sample}.vcf", "vcf_stats_file": sample_base / f"{sample}.vcf.stats.txt", "wgs_metrics_file": sample_base / f"{sample}.picard_wgs.txt", "size_metrics_file": sample_base / f"{sample}.picard_size.txt", "size_histogram_file": sample_base / f"{sample}.picard_size_hist.pdf" } def trimmomatic(sample, paths): """ Simple wrapper for applying trimmomatic, trims adapters and cleans sequence ends. Uses phred33 quality threshold. Args: sample (str): Name of sample paths (dict): Paths collection Returns: None """ log.info(f"Starting trimmomatic for {sample}...") run(["java", "-jar", TRIMMOMATIC, "PE", "-phred33", paths["fwd_read"], paths["rev_read"], # Input Files # Output Files paths["fwd_trimmed"], paths["fwd_unpaired"], paths["rev_trimmed"], paths["rev_unpaired"], f"ILLUMINACLIP:{paths['adapter_pth']}:4:20:10", f"LEADING:{LEAD_SCORE}", f"TRAILING:{TRAIL_SCORE}", f"SLIDINGWINDOW:{WINDOW_SIZE}:{WINDOW_QUALITY}", f"MINLEN:{MIN_LEN}"]) log.info(f"...end trimmomatic for {sample}.") def bwa(sample, paths): """ Simple wrapper for applying BWA. First indexes then applys mem algorithm Args: sample (str): Name of sample paths (dict): Paths collection Returns: None """ # index reference log.info(f"Starting BWA Index for {sample}...") run([BWA, "index", paths["ref_pth"]]) log.info(f"...end BWA Index for {sample}.") # mem algorithm to align reads + generate .sam file log.info(f"Starting BWA mem for {sample}...") with open(paths["sam_file"], "w") as f: run([BWA, "mem", paths["ref_pth"], paths["fwd_trimmed"], paths["rev_trimmed"]], stdout=f) # output to SAMPLE_SAM_PTH for samtools log.info(f"...end BWA mem for {sample}.") def samtools(sample, paths): """ Wrapper for applying samtools/bcftools. First converts BAM file to SAM format, then generates a read pileup. Finally creates a VCF file and filters it (though filtering may not be working properly). Args: sample (str): Name of sample paths (dict): Paths collection Returns: None """ # convert .sam to .bam log.info(f"Starting samtools indexing for {sample}...") with open(paths["bam_file"], "w") as f: run([SAMTOOLS, "sort", paths["sam_file"]], stdout=f) # output to SAMPLE_BAM_PTH run([SAMTOOLS, "index", paths["bam_file"]]) log.info(f"...end samtools indexing for {sample}.") # generate read pileup log.info(f"Starting mpileup for {sample}...") with open(paths["mpileup_file"], "w") as f: run([BCFTOOLS, "mpileup", "-f", paths["ref_pth"], paths["bam_file"]], stdout=f) # output to SAMPLE_MPILEUP_PTH log.info(f"...end mpileup for {sample}.") # generate variant calling file (.vcf) for calling SNPs and indels log.info(f"Starting VCF generation for {sample}...") with open(paths["vcf_file"], "w") as f: run([BCFTOOLS, "call", "-c", paths["mpileup_file"]], stdout=f) # output to SAMPLE_VCF_PTH log.info(f"...end VCF generation for {sample}.") # filter .vcf file by quality thresholds log.info(f"Starting VCF filter for {sample}...") run([BCFTOOLS, "filter", "-i", f"QUAL>{VCF_QUAL} && DP>{VCF_DP}", paths["vcf_file"]]) log.info(f"...end VCF filter for {sample}.") def generate_stats(sample, paths): """ Wrapper to compute stats from bcf tools and from picard. Gets VCF stats from BCF tools then collects WGS and Size metrics using Picard. Args: sample (str): Name of sample paths (dict): Paths collection Returns: None """ # BCFTOOLS stats log.info(f"Starting VCF stats for {sample}...") with open(paths["vcf_stats_file"], "w") as f: run([BCFTOOLS, "stats", paths["vcf_file"]], stdout=f) # output to SAMPLE_VCF_STATS_PTH log.info(f"...end VCF stats for {sample}.") # Picard CollectWgsMetrics (library alignment stats) log.info(f"Starting picard WGS stats for {sample}...") run(["java", "-Xmx2048m", "-jar", PICARD, "CollectWgsMetrics", f"COVERAGE_CAP={PICARD_COVERAGE_CAP}", f"USE_FAST_ALGORITHM={PICARD_FAST_ALG}", f"SAMPLE_SIZE={PICARD_SAMPLE_SIZE}", f"I={paths['bam_file']}", # Input file f"R={paths['ref_pth']}", # Reference file f"O={paths['wgs_metrics_file']}"]) # Output file log.info(f"...end picard WGS stats for {sample}.") # Picard CollectInsertSizeMetrics (fragment size stats) log.info(f"Starting picard size stats for {sample}...") run(["java", "-Xmx2048m", "-jar", PICARD, "CollectInsertSizeMetrics", f"I={paths['bam_file']}", f"H={paths['size_histogram_file']}", f"O={paths['size_metrics_file']}"]) log.info(f"...end picard size stats for {sample}.") def extract_bcf_stats(path): """ Extract relevant information from BCF Stats file for a single sample Specifically extract SNPs, MNPs, indels, "others", multiallelic sites, and multiallelic SNPsites. No effort is made to convert strings to numbers for the stat values. Args: path (str): path to the BCF stats file Returns: dict: keys as stat names and the values as stat values """ # Not ideal to hardcode here nor below, but gets the job done stats_of_interest = {"number of SNPs:", "number of MNPs:", "number of indels:", "number of others:", "number of multiallelic sites:", "number of multiallelic SNP sites:"} stats = {} with open(path) as statsf: for line in statsf: if line.startswith("SN"): parts = line.strip().split("\t") stat = parts[-2] num = parts[-1] if stat in stats_of_interest: stats[stat.strip(":")] = num return stats def extract_picard_stats(path): """ Extract relevant information from picard wgs or size stats file. This is assumed to be for a single sample and that there will only be two lines in the "METRICS CLASS" section, which is the only section we'll extract. No effort is made to convert strings to numbers for the stat values. Args: path (str): path to the picard wgs stats file Returns: dict: keys as stat names and the values as stat values """ with open(path) as statsf: split_lines = [] keep_line = False for line in statsf: if keep_line: split_lines.append(line.strip().split("\t")) # if we see metrics label, set flag to start collecting data if line.startswith("## METRICS CLASS"): keep_line = True # stop at first empty line, though in practice we expect this # to happen after exactly 2 lines read if keep_line and not line.strip(): break # expecting only 2 lines, header row and values row stats = dict(zip(split_lines[0], split_lines[1])) return stats # ---------------------------------- # Main Code Execution # ---------------------------------- with open(CSV_PATH) as csvfile: reader = DictReader(csvfile) final_stats = [] for entry in reader: sample_name = entry["Sample"] fwd_pth = entry["Forward Read Path"] rev_pth = entry["Reverse Read Path"] ad_pth = entry["Adapter Path"] rf_pth = entry["Reference Path"] path_dict = configure_paths(sample_name, fwd_pth, rev_pth, ad_pth, rf_pth) # 1. Trimmomatic (trim adapters and filter by quality threshold) PE # (paired end algorithm) with -phred33 (quality threshold) trimmomatic(sample_name, path_dict) # 2. BWA (align to reference) bwa(sample_name, path_dict) # 3. SAMTOOLS/BCFTOOLS (call SNPS/indels) samtools(sample_name, path_dict) # 4. Generate statistics generate_stats(sample_name, path_dict) # 5. Extract statistics and collate into a single row vcf_st = extract_bcf_stats(path_dict["vcf_stats_file"]) picard_wgs_st = extract_picard_stats(path_dict["wgs_metrics_file"]) picard_size_st = extract_picard_stats(path_dict["size_metrics_file"]) # Assuming no overlap in stat names vcf_st.update(picard_wgs_st) vcf_st.update(picard_size_st) vcf_st["Sample Name"] = sample_name final_stats.append(vcf_st) log.info(f"Starting writing final stats...") with open(STATS_OUTPUT_PATH, "w") as statsout: # Assumes all stat entries will have exactly the same headers writer = DictWriter(statsout, final_stats[0].keys(), delimiter="\t") writer.writeheader() writer.writerows(final_stats) log.info(f"...end writing stats.")
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from .base_page import BasePage from .locators import MainPageLocators class MainPage(BasePage): def __init__(self, *args, **kwargs): super(MainPage, self).__init__(*args, **kwargs) def go_to_login_page(self): link = self.browser.find_element(*MainPageLocators.LOGIN_LINK) link.click() def should_be_login_link(self): assert self.is_element_present(*MainPageLocators.LOGIN_LINK), "Login link is not presented" def go_to_see_basket(self): button = self.browser.find_element(*MainPageLocators.LINK_SEE_BASKET) button.click()
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#Program to print Fibonacci numbers n1=0 n2=1 i=0 n=int(input("Enter the number of terms ")) if n<=0: print("Enter positive numbers") elif n==1: print("Fibonacci series:\n") print(n1) else: print("Fibonacci series:\n") while i<n: print(n1) s=n1+n2 n1=n2 n2=s i=i+1
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#import math #import hashlib print("Good evening. Advent of Code 2017, Day 1.") f = open("/home/smacgil/Development/Advent/input2017_1.txt", "r") inputfile = f.read() f.close() totalone = 0 totalhalf = 0 maxval = len(inputfile) - 1 # EOL is a thing. halfval = int(maxval / 2) # Convenience. for digit in range(0, maxval): nextone = (digit + 1) % maxval nexthalf = (digit + halfval) % maxval # Compare chars, and convert if adding. if(inputfile[digit] == inputfile[nextone]): totalone = totalone + int(inputfile[digit]) if(inputfile[digit] == inputfile[nexthalf]): totalhalf = totalhalf + int(inputfile[digit]) print("I am not a human. Proof one: %d" % totalone) print(" Proof half: %d" % totalhalf)
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from __future__ import division from datetime import datetime, timedelta import pandas as pd import numpy as np import numpy.testing as npt import pytest from mizani.breaks import (mpl_breaks, log_breaks, minor_breaks, trans_minor_breaks, date_breaks, timedelta_breaks, extended_breaks) from mizani.transforms import trans def test_mpl_breaks(): x = np.arange(100) limits = min(x), max(x) for nbins in (5, 7, 10, 13, 31): breaks = mpl_breaks(nbins=nbins) assert len(breaks(limits)) <= nbins+1 limits = float('-inf'), float('inf') breaks = mpl_breaks(n=5) assert len(breaks(limits)) == 0 # Zero range discrete limits = [1, 1] assert len(breaks(limits)) == 1 assert breaks(limits)[0] == limits[1] # Zero range continuous limits = [np.pi, np.pi] assert len(breaks(limits)) == 1 assert breaks(limits)[0] == limits[1] def test_log_breaks(): x = [2, 20, 2000] limits = min(x), max(x) breaks = log_breaks()(limits) npt.assert_array_equal(breaks, [1, 10, 100, 1000, 10000]) breaks = log_breaks(3)(limits) npt.assert_array_equal(breaks, [1, 100, 10000]) breaks = log_breaks()((10000, 10000)) npt.assert_array_equal(breaks, [10000]) breaks = log_breaks()((float('-inf'), float('inf'))) assert len(breaks) == 0 def test_minor_breaks(): # equidistant breaks major = [1, 2, 3, 4] limits = [0, 5] breaks = minor_breaks()(major, limits) npt.assert_array_equal(breaks, [.5, 1.5, 2.5, 3.5, 4.5]) minor = minor_breaks(3)(major, [2, 3]) npt.assert_array_equal(minor, [2.25, 2.5, 2.75]) # non-equidistant breaks major = [1, 2, 4, 8] limits = [0, 10] minor = minor_breaks()(major, limits) npt.assert_array_equal(minor, [1.5, 3, 6]) # single major break minor = minor_breaks()([2], limits) assert len(minor) == 0 def test_trans_minor_breaks(): class identity_trans(trans): minor_breaks = trans_minor_breaks() class square_trans(trans): transform = staticmethod(np.square) inverse = staticmethod(np.sqrt) minor_breaks = trans_minor_breaks() class weird_trans(trans): dataspace_is_numerical = False minor_breaks = trans_minor_breaks() major = [1, 2, 3, 4] limits = [0, 5] regular_minors = trans.minor_breaks(major, limits) npt.assert_allclose( regular_minors, identity_trans.minor_breaks(major, limits)) # Transform the input major breaks and check against # the inverse of the output minor breaks squared_input_minors = square_trans.minor_breaks( np.square(major), np.square(limits)) npt.assert_allclose(regular_minors, np.sqrt(squared_input_minors)) t = weird_trans() with pytest.raises(TypeError): t.minor_breaks(major) def test_date_breaks(): # cpython x = [datetime(year, 1, 1) for year in [2010, 2026, 2015]] limits = min(x), max(x) breaks = date_breaks('5 Years') years = [d.year for d in breaks(limits)] npt.assert_array_equal( years, [2010, 2015, 2020, 2025, 2030]) breaks = date_breaks('10 Years') years = [d.year for d in breaks(limits)] npt.assert_array_equal(years, [2010, 2020, 2030]) # numpy x = [np.datetime64(i*10, 'D') for i in range(1, 10)] breaks = date_breaks('10 Years') limits = min(x), max(x) with pytest.raises(AttributeError): breaks(limits) # NaT limits = np.datetime64('NaT'), datetime(2017, 1, 1) breaks = date_breaks('10 Years') assert len(breaks(limits)) == 0 def test_timedelta_breaks(): breaks = timedelta_breaks() # cpython x = [timedelta(days=i*365) for i in range(25)] limits = min(x), max(x) major = breaks(limits) years = [val.total_seconds()/(365*24*60*60)for val in major] npt.assert_array_equal( years, [0, 5, 10, 15, 20, 25]) x = [timedelta(microseconds=i) for i in range(25)] limits = min(x), max(x) major = breaks(limits) mseconds = [val.total_seconds()*10**6 for val in major] npt.assert_array_equal( mseconds, [0, 5, 10, 15, 20, 25]) # pandas x = [pd.Timedelta(seconds=i*60) for i in range(10)] limits = min(x), max(x) major = breaks(limits) minutes = [val.total_seconds()/60 for val in major] npt.assert_allclose( minutes, [0, 2, 4, 6, 8]) # numpy x = [np.timedelta64(i*10, unit='D') for i in range(1, 10)] limits = min(x), max(x) with pytest.raises(ValueError): breaks(limits) # NaT limits = pd.NaT, pd.Timedelta(seconds=9*60) assert len(breaks(limits)) == 0 def test_extended_breaks(): x = np.arange(100) limits = min(x), max(x) for n in (5, 7, 10, 13, 31): breaks = extended_breaks(n=n) assert len(breaks(limits)) <= n+1 # Reverse limits breaks = extended_breaks(n=7) npt.assert_array_equal(breaks((0, 6)), breaks((6, 0))) # Infinite limits limits = float('-inf'), float('inf') breaks = extended_breaks(n=5) assert len(breaks(limits)) == 0 # Zero range discrete limits = [1, 1] assert len(breaks(limits)) == 1 assert breaks(limits)[0] == limits[1] # Zero range continuous limits = [np.pi, np.pi] assert len(breaks(limits)) == 1 assert breaks(limits)[0] == limits[1]
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/pylons__shootout/shootout/tests/test_views.py
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import unittest from pyramid import testing def init_db(): from shootout.models import DBSession from shootout.models import Base from sqlalchemy import create_engine engine = create_engine('sqlite://') DBSession.configure(bind=engine) Base.metadata.bind = engine Base.metadata.create_all(engine) session = DBSession() return session def register_templates(config): config.testing_add_renderer('templates/login.pt') config.testing_add_renderer('templates/toolbar.pt') config.testing_add_renderer('templates/cloud.pt') config.testing_add_renderer('templates/latest.pt') class ViewTests(unittest.TestCase): def setUp(self): self.session = init_db() self.config = testing.setUp() def tearDown(self): import transaction from shootout.models import DBSession transaction.abort() DBSession.remove() testing.tearDown() def _addUser(self, username=u'username'): from shootout.models import User user = User(username=username, password=u'password', name=u'name', email=u'email') self.session.add(user) self.session.flush() return user def _addIdea(self, target=None, user=None): from shootout.models import Idea if not user: user = self._addUser() idea = Idea(target=target, author=user, title=u'title', text=u'text') self.session.add(idea) self.session.flush() return idea def test_main_view(self): from shootout.views import main_view self.config.testing_securitypolicy(u'username') self.config.include(register_templates) request = testing.DummyRequest() result = main_view(request) self.assertEqual(result['username'], u'username') self.assertEqual(len(result['toplists']), 4) def test_idea_add_nosubmit_idea(self): from shootout.views import idea_add self.config.testing_securitypolicy(u'username') self.config.include(register_templates) request = testing.DummyRequest() result = idea_add(request) self.assertEqual(result['target'], None) self.assertEqual(result['kind'], 'idea') def test_idea_add_nosubmit_comment(self): from shootout.views import idea_add self.config.testing_securitypolicy(u'username') self.config.include(register_templates) idea = self._addIdea() request = testing.DummyRequest(params={'target': idea.idea_id}) result = idea_add(request) self.assertEqual(result['target'], idea) self.assertEqual(result['kind'], 'comment') def test_idea_add_not_existing_target(self): from shootout.views import idea_add self.config.testing_securitypolicy(u'username') self.config.include(register_templates) request = testing.DummyRequest(params={'target': 100}) result = idea_add(request) self.assertEqual(result.code, 404) def test_idea_add_submit_schema_fail_empty_params(self): from shootout.views import idea_add self.config.testing_securitypolicy(u'username') self.config.include(register_templates) self.config.include('shootout.addroutes') request = testing.DummyRequest(post={'form.submitted': 'Shoot'}) result = idea_add(request) self.assertEqual( result['form'].form.errors, { 'text': u'Missing value', 'tags': u'Missing value', 'title': u'Missing value' } ) def test_idea_add_submit_schema_succeed(self): from shootout.views import idea_add from shootout.models import Idea self.config.testing_securitypolicy(u'username') self.config.include('shootout.addroutes') request = testing.DummyRequest( post={ 'form.submitted': u'Shoot', 'tags': u'abc def, bar', 'text': u'My idea is cool', 'title': u'My idea', } ) user = self._addUser(u'username') result = idea_add(request) self.assertEqual(result.location, 'http://example.com/ideas/1') ideas = self.session.query(Idea).all() self.assertEqual(len(ideas), 1) idea = ideas[0] self.assertEqual(idea.idea_id, 1) self.assertEqual(idea.text, u'My idea is cool') self.assertEqual(idea.title, u'My idea') self.assertEqual(idea.author, user) self.assertEqual(len(idea.tags), 3) self.assertEqual(idea.tags[0].name, u'abc') self.assertEqual(idea.tags[1].name, u'bar') self.assertEqual(idea.tags[2].name, u'def') def test_comment_add_submit_schema_succeed(self): from shootout.views import idea_add from shootout.models import Idea idea = self._addIdea() self.config.testing_securitypolicy(u'commentator') self.config.include('shootout.addroutes') request = testing.DummyRequest( params={ 'form.submitted': u'Shoot', 'tags': u'abc def, bar', 'text': u'My comment is cool', 'title': u'My comment', 'target': unicode(idea.idea_id), } ) request.method = 'POST' user = self._addUser(u'commentator') result = idea_add(request) self.assertEqual(result.location, 'http://example.com/ideas/2') ideas = self.session.query(Idea).all() self.assertEqual(len(ideas), 2) comment = ideas[1] self.assertEqual(comment.idea_id, 2) self.assertEqual(comment.target_id, 1) self.assertEqual(comment.text, u'My comment is cool') self.assertEqual(comment.title, u'My comment') self.assertEqual(comment.author, user) self.assertEqual(len(comment.tags), 3) self.assertEqual(comment.tags[0].name, u'abc') self.assertEqual(comment.tags[1].name, u'bar') self.assertEqual(comment.tags[2].name, u'def') def test_vote_on_own_idea(self): from shootout.views import idea_vote from shootout.models import User self.config.include('shootout.addroutes') idea = self._addIdea() self.session.query(User).one() self.assertEqual(idea.user_voted(u'username'), False) self.config.testing_securitypolicy(u'username') post_data = { 'form.vote_hit': u'Hit', 'target': 1, } request = testing.DummyRequest(post=post_data) idea_vote(request) self.assertEqual(idea.hits, 0) self.assertEqual(idea.misses, 0) self.assertEqual(idea.hit_percentage, 0) self.assertEqual(idea.total_votes, 0) self.assertEqual(idea.vote_differential, 0) self.assertEqual(idea.author.hits, 0) self.assertEqual(len(idea.voted_users.all()), 0) self.assertEqual(idea.user_voted(u'username'), False) def test_positive_idea_voting(self): from shootout.views import idea_vote self.config.include('shootout.addroutes') user = self._addUser() idea = self._addIdea(user=user) voter = self._addUser(u'votername') self.assertEqual(idea.user_voted(u'votername'), False) self.config.testing_securitypolicy(u'votername') post_data = { 'form.vote_hit': u'Hit', 'target': 1, } request = testing.DummyRequest(post=post_data) idea_vote(request) self.assertEqual(idea.hits, 1) self.assertEqual(idea.misses, 0) self.assertEqual(idea.hit_percentage, 100) self.assertEqual(idea.total_votes, 1) self.assertEqual(idea.vote_differential, 1) self.assertEqual(idea.author.hits, 1) self.assertEqual(len(idea.voted_users.all()), 1) self.assertEqual(idea.voted_users.one(), voter) self.assertTrue(idea.user_voted(u'votername')) def test_negative_idea_voting(self): from shootout.views import idea_vote self.config.include('shootout.addroutes') user = self._addUser() idea = self._addIdea(user=user) voter = self._addUser(u'votername') self.assertEqual(idea.user_voted(u'votername'), False) self.config.testing_securitypolicy(u'votername') post_data = { 'form.vote_miss': u'Miss', 'target': 1, } request = testing.DummyRequest(post=post_data) idea_vote(request) self.assertEqual(idea.hits, 0) self.assertEqual(idea.misses, 1) self.assertEqual(idea.hit_percentage, 0) self.assertEqual(idea.total_votes, 1) self.assertEqual(idea.vote_differential, -1) self.assertEqual(idea.author.hits, 0) self.assertEqual(len(idea.voted_users.all()), 1) self.assertEqual(idea.voted_users.one(), voter) self.assertTrue(idea.user_voted(u'votername')) def test_registration_nosubmit(self): from shootout.views import user_add self.config.include(register_templates) request = testing.DummyRequest() result = user_add(request) self.assertTrue('form' in result) def test_registration_submit_empty(self): from shootout.views import user_add self.config.include(register_templates) request = testing.DummyRequest() result = user_add(request) self.assertTrue('form' in result) request = testing.DummyRequest(post={'form.submitted': 'Shoot'}) result = user_add(request) self.assertEqual( result['form'].form.errors, { 'username': u'Missing value', 'confirm_password': u'Missing value', 'password': u'Missing value', 'email': u'Missing value', 'name': u'Missing value' } ) def test_registration_submit_schema_succeed(self): from shootout.views import user_add from shootout.models import User self.config.include('shootout.addroutes') request = testing.DummyRequest( post={ 'form.submitted': u'Register', 'username': u'username', 'password': u'secret', 'confirm_password': u'secret', 'email': u'[email protected]', 'name': u'John Doe', } ) user_add(request) users = self.session.query(User).all() self.assertEqual(len(users), 1) user = users[0] self.assertEqual(user.username, u'username') self.assertEqual(user.name, u'John Doe') self.assertEqual(user.email, u'[email protected]') self.assertEqual(user.hits, 0) self.assertEqual(user.misses, 0) self.assertEqual(user.delivered_hits, 0) self.assertEqual(user.delivered_misses, 0) self.assertEqual(user.ideas, []) self.assertEqual(user.voted_ideas, []) def test_user_view(self): from shootout.views import user_view self.config.testing_securitypolicy(u'username') self.config.include('shootout.addroutes') self.config.include(register_templates) request = testing.DummyRequest() request.matchdict = {'username': u'username'} self._addUser() result = user_view(request) self.assertEqual(result['user'].username, u'username') self.assertEqual(result['user'].user_id, 1) def test_idea_view(self): from shootout.views import idea_view self.config.testing_securitypolicy(u'username') self.config.include('shootout.addroutes') self.config.include(register_templates) self._addIdea() request = testing.DummyRequest() request.matchdict = {'idea_id': 1} result = idea_view(request) self.assertEqual(result['idea'].title, u'title') self.assertEqual(result['idea'].idea_id, 1) self.assertEqual(result['viewer_username'], u'username') def test_tag_view(self): from shootout.views import tag_view from shootout.models import Tag self.config.testing_securitypolicy(u'username') self.config.include('shootout.addroutes') self.config.include(register_templates) user = self._addUser() tag1 = Tag(u'bar') tag2 = Tag(u'foo') self.session.add_all([tag1, tag2]) idea1 = self._addIdea(user=user) idea1.tags.append(tag1) idea2 = self._addIdea(user=user) idea2.tags.append(tag1) idea3 = self._addIdea(user=user) idea3.tags.append(tag2) self.session.flush() request = testing.DummyRequest() request.matchdict = {'tag_name': u'bar'} result = tag_view(request) ideas = result['ideas'].all() self.assertEqual(ideas[0].idea_id, idea1.idea_id) self.assertEqual(ideas[1].idea_id, idea2.idea_id) self.assertEqual(result['tag'], u'bar') request = testing.DummyRequest() request.matchdict = {'tag_name': u'foo'} result = tag_view(request) self.assertEqual(result['ideas'].one().idea_id, idea3.idea_id) self.assertEqual(result['tag'], u'foo') def test_about_view(self): from shootout.views import about_view self.config.include(register_templates) request = testing.DummyRequest() about_view(request) def test_login_view_submit_fail(self): from shootout.views import login_view self.config.include('shootout.addroutes') self._addUser() request = testing.DummyRequest( post={ 'submit': u'Login', 'login': u'username', 'password': u'wrongpassword', } ) login_view(request) messages = request.session.peek_flash() self.assertEqual(messages, [u'Failed to login.']) def test_login_view_submit_success(self): from shootout.views import login_view self.config.include('shootout.addroutes') self._addUser() request = testing.DummyRequest( post={ 'submit': u'Login', 'login': u'username', 'password': u'password', } ) login_view(request) messages = request.session.peek_flash() self.assertEqual(messages, [u'Logged in successfully.']) def test_logout_view(self): from shootout.views import logout_view self.config.include('shootout.addroutes') request = testing.DummyRequest() logout_view(request) messages = request.session.peek_flash() self.assertEqual(messages, [u'Logged out successfully.'])
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/calameo-downloader.py
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quatrejuin/Calameo-SVG-Downloader
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# -------------------------------------------------------------------------------------------------- # Calameoassets Downloader - # Copyright (c) 2020. Dr Watthanasak Jeamwatthanachai - All Rights Reserved - # -------------------------------------------------------------------------------------------------- import time from pathlib import Path import requests from selenium import webdriver from selenium.webdriver.common.by import By from selenium.webdriver.support.wait import WebDriverWait BASE_DIR = Path(__file__).parent.absolute() driver = webdriver.Chrome(f"{BASE_DIR}/data/drivers/chromedriver") driver.implicitly_wait(5) wait = WebDriverWait(driver, 5) calameoassets_url = 'https://p.calameoassets.com/' header_curl = { 'user-agent': driver.execute_script("return navigator.userAgent;") } driver.get('https://global.oup.com/education/support-learning-anywhere/key-resources-online/?region=uk') book_tables = driver.find_elements(By.XPATH, '//div[@class="content_block full_width"]/div/table/tbody') print(''' ************************************ Collect list of books on the website ************************************ ''') books_list = [] for table in book_tables: tr = table.find_elements(By.TAG_NAME, 'tr') books = tr[-1].find_elements(By.TAG_NAME, 'a') for book in books: url = book.get_attribute('href') name = book.text books_list.append({'name': name, 'url': url}) print(f'> {name} - {url}') # In cases you want to download some particular books, you can manually define books_list # books_list = [ # {'name': 'Book A', 'url': 'https://www.calameo.com/read/00077772151d39c98fbab?authid=5OmdpYZALnKk&region=uk'}, # {'name': 'Book B', 'url': 'https://www.calameo.com/read/00077772164411330cf35?authid=K0Yqvcafhmlu&region=uk'}, # ] print(''' ************************************ Download all books ************************************ ''') for book in books_list: print(f'> Go to {book["url"]}') driver.get(book['url']) iframe = driver.find_element_by_tag_name("iframe") driver.switch_to.frame(iframe) imgs = [] counter = 0 while len(imgs) == 0: imgs = driver.find_elements(By.XPATH, '//img[@class="page"]') time.sleep(1) counter += 1 if counter > 20: raise Exception("Book ID is unreachable") imgs = driver.find_elements(By.XPATH, '//img[@class="page"]') book_id = imgs[0].get_attribute('src').replace(calameoassets_url, '').split('/')[0] print(f'\t* Book ID: {book_id}') Path(f'{BASE_DIR}/books/{book["name"]}').mkdir(parents=True, exist_ok=True) for page in range(1, 9999): filename = f'p{page}.svgz' url = f'{calameoassets_url}{book_id}/{filename}' response = requests.get(url, allow_redirects=True, headers=header_curl) if response.status_code != 200: break print(f'\t* {url}', end='\t...\t') open(f'{BASE_DIR}/books/{book["name"]}/{filename}', 'wb').write(response.content) print('saved') driver.close() driver.quit()
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/Praktikum-Protek-05/Latihan 2/latihan06.2.py
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itsme-dazainaki/Praktikum-Protek-05
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#intro print("Hai.. nama saya Mr. Lappie, saya telah memilih sebuat bilangan bulat secara acak anatara 0 - 100. Silakan tebak ya !!!") #mari mendeklarasikan variabel buat nampung skor skor = 0 #pengecekan menggunakan perulangan while True: skor +=1 bil=int(input("Tebakan Anda : ")) #ini buat menampilkan tulisan, nanti buat nginput juga if(bil<0) or (bil>100): print("error") exit elif(bil<10): print("Hehehe...Bilangan tebakan anda terlalu kecil") elif(bil>10): print("Hehehe...Bilangan tebakan anda terlalu besar") elif(bil==10): print("Yee...Bilangan tebakan anda benar :)") break #ini buat menghentikan perulangan #ini rumusnya buat cetak hasil Skor dari jumlah perulangan print(" ") print("Score anda : ", 100-(skor-1)*2)
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/src/py/var_int_to_var_ubin.py
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[]
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arq5x/gqt
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#!/usr/bin/env python import sys import numpy as np import array from optparse import OptionParser parser = OptionParser() parser.add_option("-v", "--var_int_file", dest="var_int_file", help="Variant int file") parser.add_option("-b", "--ubin_file", dest="ubin_file", help="Uncompressed variant binary output file") parser.add_option("-p", "--print", action="store_true", default=False, dest="print_to_screen", help="Print ints to screen") (options, args) = parser.parse_args() if not options.var_int_file: parser.error('Variant int file not given') if not options.print_to_screen and not options.ubin_file: parser.error('Uncompressed varaint binary output file not given') f = open(options.var_int_file,'r') if options.ubin_file: f_out = open(options.ubin_file, 'wb') tot_vars = -1 if options.ubin_file: data = array.array('I') data.append(1) for l in f: output_int = 0 num_vars = 0 A = l.rstrip().split(' ') if tot_vars == -1: tot_vars = len(A) if options.print_to_screen: print tot_vars else: data.append(tot_vars) for a in A: output_int |= int(a) << (30 - num_vars * 2) num_vars += 1 if num_vars == 16: if options.print_to_screen: print output_int, else: data.append(output_int) output_int = 0 num_vars = 0 if num_vars > 0: if options.print_to_screen: print output_int, else: data.append(output_int) if options.print_to_screen: print else: data.tofile(f_out) data = array.array('I') f.close() if not options.print_to_screen: f_out.close()
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/final/Python-3.10.0/Lib/ensurepip/__init__.py
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Nuitka/Nuitka-references
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refs/heads/main
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import collections import os import os.path import subprocess import sys import sysconfig import tempfile from importlib import resources __all__ = ["version", "bootstrap"] _PACKAGE_NAMES = ('setuptools', 'pip') _SETUPTOOLS_VERSION = "57.4.0" _PIP_VERSION = "21.2.3" _PROJECTS = [ ("setuptools", _SETUPTOOLS_VERSION, "py3"), ("pip", _PIP_VERSION, "py3"), ] # Packages bundled in ensurepip._bundled have wheel_name set. # Packages from WHEEL_PKG_DIR have wheel_path set. _Package = collections.namedtuple('Package', ('version', 'wheel_name', 'wheel_path')) # Directory of system wheel packages. Some Linux distribution packaging # policies recommend against bundling dependencies. For example, Fedora # installs wheel packages in the /usr/share/python-wheels/ directory and don't # install the ensurepip._bundled package. _WHEEL_PKG_DIR = sysconfig.get_config_var('WHEEL_PKG_DIR') def _find_packages(path): packages = {} try: filenames = os.listdir(path) except OSError: # Ignore: path doesn't exist or permission error filenames = () # Make the code deterministic if a directory contains multiple wheel files # of the same package, but don't attempt to implement correct version # comparison since this case should not happen. filenames = sorted(filenames) for filename in filenames: # filename is like 'pip-20.2.3-py2.py3-none-any.whl' if not filename.endswith(".whl"): continue for name in _PACKAGE_NAMES: prefix = name + '-' if filename.startswith(prefix): break else: continue # Extract '20.2.2' from 'pip-20.2.2-py2.py3-none-any.whl' version = filename.removeprefix(prefix).partition('-')[0] wheel_path = os.path.join(path, filename) packages[name] = _Package(version, None, wheel_path) return packages def _get_packages(): global _PACKAGES, _WHEEL_PKG_DIR if _PACKAGES is not None: return _PACKAGES packages = {} for name, version, py_tag in _PROJECTS: wheel_name = f"{name}-{version}-{py_tag}-none-any.whl" packages[name] = _Package(version, wheel_name, None) if _WHEEL_PKG_DIR: dir_packages = _find_packages(_WHEEL_PKG_DIR) # only used the wheel package directory if all packages are found there if all(name in dir_packages for name in _PACKAGE_NAMES): packages = dir_packages _PACKAGES = packages return packages _PACKAGES = None def _run_pip(args, additional_paths=None): # Run the bootstraping in a subprocess to avoid leaking any state that happens # after pip has executed. Particulary, this avoids the case when pip holds onto # the files in *additional_paths*, preventing us to remove them at the end of the # invocation. code = f""" import runpy import sys sys.path = {additional_paths or []} + sys.path sys.argv[1:] = {args} runpy.run_module("pip", run_name="__main__", alter_sys=True) """ return subprocess.run([sys.executable, '-W', 'ignore::DeprecationWarning', "-c", code], check=True).returncode def version(): """ Returns a string specifying the bundled version of pip. """ return _get_packages()['pip'].version def _disable_pip_configuration_settings(): # We deliberately ignore all pip environment variables # when invoking pip # See http://bugs.python.org/issue19734 for details keys_to_remove = [k for k in os.environ if k.startswith("PIP_")] for k in keys_to_remove: del os.environ[k] # We also ignore the settings in the default pip configuration file # See http://bugs.python.org/issue20053 for details os.environ['PIP_CONFIG_FILE'] = os.devnull def bootstrap(*, root=None, upgrade=False, user=False, altinstall=False, default_pip=False, verbosity=0): """ Bootstrap pip into the current Python installation (or the given root directory). Note that calling this function will alter both sys.path and os.environ. """ # Discard the return value _bootstrap(root=root, upgrade=upgrade, user=user, altinstall=altinstall, default_pip=default_pip, verbosity=verbosity) def _bootstrap(*, root=None, upgrade=False, user=False, altinstall=False, default_pip=False, verbosity=0): """ Bootstrap pip into the current Python installation (or the given root directory). Returns pip command status code. Note that calling this function will alter both sys.path and os.environ. """ if altinstall and default_pip: raise ValueError("Cannot use altinstall and default_pip together") sys.audit("ensurepip.bootstrap", root) _disable_pip_configuration_settings() # By default, installing pip and setuptools installs all of the # following scripts (X.Y == running Python version): # # pip, pipX, pipX.Y, easy_install, easy_install-X.Y # # pip 1.5+ allows ensurepip to request that some of those be left out if altinstall: # omit pip, pipX and easy_install os.environ["ENSUREPIP_OPTIONS"] = "altinstall" elif not default_pip: # omit pip and easy_install os.environ["ENSUREPIP_OPTIONS"] = "install" with tempfile.TemporaryDirectory() as tmpdir: # Put our bundled wheels into a temporary directory and construct the # additional paths that need added to sys.path additional_paths = [] for name, package in _get_packages().items(): if package.wheel_name: # Use bundled wheel package from ensurepip import _bundled wheel_name = package.wheel_name whl = resources.read_binary(_bundled, wheel_name) else: # Use the wheel package directory with open(package.wheel_path, "rb") as fp: whl = fp.read() wheel_name = os.path.basename(package.wheel_path) filename = os.path.join(tmpdir, wheel_name) with open(filename, "wb") as fp: fp.write(whl) additional_paths.append(filename) # Construct the arguments to be passed to the pip command args = ["install", "--no-cache-dir", "--no-index", "--find-links", tmpdir] if root: args += ["--root", root] if upgrade: args += ["--upgrade"] if user: args += ["--user"] if verbosity: args += ["-" + "v" * verbosity] return _run_pip([*args, *_PACKAGE_NAMES], additional_paths) def _uninstall_helper(*, verbosity=0): """Helper to support a clean default uninstall process on Windows Note that calling this function may alter os.environ. """ # Nothing to do if pip was never installed, or has been removed try: import pip except ImportError: return # If the installed pip version doesn't match the available one, # leave it alone available_version = version() if pip.__version__ != available_version: print(f"ensurepip will only uninstall a matching version " f"({pip.__version__!r} installed, " f"{available_version!r} available)", file=sys.stderr) return _disable_pip_configuration_settings() # Construct the arguments to be passed to the pip command args = ["uninstall", "-y", "--disable-pip-version-check"] if verbosity: args += ["-" + "v" * verbosity] return _run_pip([*args, *reversed(_PACKAGE_NAMES)]) def _main(argv=None): import argparse parser = argparse.ArgumentParser(prog="python -m ensurepip") parser.add_argument( "--version", action="version", version="pip {}".format(version()), help="Show the version of pip that is bundled with this Python.", ) parser.add_argument( "-v", "--verbose", action="count", default=0, dest="verbosity", help=("Give more output. Option is additive, and can be used up to 3 " "times."), ) parser.add_argument( "-U", "--upgrade", action="store_true", default=False, help="Upgrade pip and dependencies, even if already installed.", ) parser.add_argument( "--user", action="store_true", default=False, help="Install using the user scheme.", ) parser.add_argument( "--root", default=None, help="Install everything relative to this alternate root directory.", ) parser.add_argument( "--altinstall", action="store_true", default=False, help=("Make an alternate install, installing only the X.Y versioned " "scripts (Default: pipX, pipX.Y, easy_install-X.Y)."), ) parser.add_argument( "--default-pip", action="store_true", default=False, help=("Make a default pip install, installing the unqualified pip " "and easy_install in addition to the versioned scripts."), ) args = parser.parse_args(argv) return _bootstrap( root=args.root, upgrade=args.upgrade, user=args.user, verbosity=args.verbosity, altinstall=args.altinstall, default_pip=args.default_pip, )
62c0360071a15ade3e6a6b3f38a577416759847b
7160e632d88bf49492616f8152c91cb9f1d40d8d
/testcases/statistical_form2/test_case_166_statistical_form_alarm_detail.py
53c29adc336cc3d9a149c60941a9e7a5f1d2954e
[]
no_license
huangqiming123/tuqiangol_test1
ad5ddf22ce61b5b6daad55f684be5da160a64e59
75722812260590480320910c4ad6f6c1251a2def
refs/heads/master
2021-03-30T23:29:08.478494
2018-03-12T03:45:11
2018-03-12T03:45:11
124,832,890
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import unittest from time import sleep from automate_driver.automate_driver import AutomateDriver from pages.alarm_info.alarm_info_page import AlarmInfoPage from pages.base.base_page import BasePage from pages.base.lon_in_base import LogInBase from pages.statistical_form.statistical_form_page import StatisticalFormPage from pages.statistical_form.statistical_form_page_read_csv import StatisticalFormPageReadCsv class TestCase166StatisticalFormAlarmDetail(unittest.TestCase): def setUp(self): # 前置条件 # 实例化对象 self.driver = AutomateDriver() self.base_url = self.driver.base_url self.base_page = BasePage(self.driver, self.base_url) self.alarm_info_page = AlarmInfoPage(self.driver, self.base_url) self.statistical_form_page_read_csv = StatisticalFormPageReadCsv() self.log_in_base = LogInBase(self.driver, self.base_url) self.statistical_form_page = StatisticalFormPage(self.driver, self.base_url) # 打开页面,填写用户名、密码、点击登录 self.base_page.open_page() self.driver.set_window_max() self.driver.implicitly_wait(5) self.log_in_base.log_in_jimitest() # 登录之后点击控制台,然后点击指令管理 self.statistical_form_page.click_control_after_click_statistical_form_page() sleep(3) def tearDown(self): self.driver.quit_browser() def test_case_statistical_form_alarm_detail(self): # 断言url expect_url = self.base_url + '/deviceReport/statisticalReport' self.assertEqual(expect_url, self.alarm_info_page.actual_url_click_alarm()) # 点击告警详情 self.alarm_info_page.click_alarm_detail_list() for n in range(5): self.statistical_form_page.click_customer_in_alarm_detail_form(n) # 点击搜索设备按钮 self.statistical_form_page.click_search_dev_button_in_alarm_detail() # 获取有多少组 number = self.statistical_form_page.get_group_number_in_alarm_detail_form() if number == 0: pass else: for m in range(number): # 收起默认组 self.statistical_form_page.click_defalut_group_in_alarm_detail_form() # 获取每个组设备的数量 dev_number = self.statistical_form_page.get_dev_number_in_alarm_detail_form(m) # 点开每一个分组 self.statistical_form_page.click_per_group_in_alarm_detail_form(m) dev_number_list = self.statistical_form_page.get_dev_number_list_in_alarm_detail_form(m) self.assertEqual(str(dev_number_list), dev_number)
fe955cbfd83504fea6956a13b9d40c1f5a88b5a8
9051a89f6e849adf68225d862483f8bd218a54a6
/sql/convert.py
582757cb652b80ba4be06ddc40d55899812a5dc8
[]
no_license
witalosk/chemical
9deb2741db7358ddb163239e9372f64c554f1950
f6c1e69fb50344a8cf3e54d5f638de1c6c21afc1
refs/heads/master
2021-04-18T18:52:02.307949
2018-06-12T15:00:19
2018-06-12T15:00:19
126,440,023
0
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null
null
null
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UTF-8
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py
#! /usr/bin/env python import sys def main(): print("SET sql_mode='NO_BACKSLASH_ESCAPES';") lines = sys.stdin.read().splitlines() for line in lines: processLine(line) def processLine(line): if ( line.startswith("PRAGMA") or line.startswith("BEGIN TRANSACTION;") or line.startswith("COMMIT;") or line.startswith("DELETE FROM sqlite_sequence;") or line.startswith("INSERT INTO \"sqlite_sequence\"") ): return line = line.replace("AUTOINCREMENT", "AUTO_INCREMENT") line = line.replace("DEFAULT 't'", "DEFAULT '1'") line = line.replace("DEFAULT 'f'", "DEFAULT '0'") line = line.replace(",'t'", ",'1'") line = line.replace(",'f'", ",'0'") in_string = False newLine = '' for c in line: if not in_string: if c == "'": in_string = True elif c == '"': newLine = newLine + '`' continue elif c == "'": in_string = False newLine = newLine + c print(newLine) if __name__ == "__main__": main()
dcdddf53bab181771e04c9a9bf3252384665391b
3330426fe8ccd5d313e12fbf53e4423b0428560b
/crossover6.spec
30f91a58e9b76774020dfbcd5cc457e9b95b46b2
[]
no_license
ryanjhbae/ics3u-culminating
75ed5f660a594e412e00f34bf9a3ea0dd7079ccf
c3baa8f87c7d90e040991b2a176abbbf2b44c828
refs/heads/main
2023-04-24T01:27:05.333864
2021-05-06T15:52:50
2021-05-06T15:52:50
364,963,721
0
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null
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spec
# -*- mode: python -*- block_cipher = None a = Analysis(['crossover6.py'], pathex=['E:\\Python\\Culminating5'], binaries=[], datas=[], hiddenimports=[], hookspath=[], runtime_hooks=[], excludes=[], win_no_prefer_redirects=False, win_private_assemblies=False, cipher=block_cipher, noarchive=False) pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher) exe = EXE(pyz, a.scripts, a.binaries, a.zipfiles, a.datas, [], name='crossover6', debug=False, bootloader_ignore_signals=False, strip=False, upx=True, runtime_tmpdir=None, console=False , icon='E:\\Python\\Culminating5\\crossover_logo.ico')
7a74a41d8122d6130dccc0e194bd291246fac564
749f096d2146bdb37f11e5face2b7a1b51fc7beb
/centerloss.py
3b18997efd86c12cdb4bde3e54665af5ad78c892
[]
no_license
WangBenHui/fgcmr
2129eca7d3ea4f059b70710c458891967d0496bf
1568d67817a8bbcb537b556f34285f17654601fa
refs/heads/master
2022-04-24T17:23:58.564203
2020-04-30T14:25:30
2020-04-30T14:25:30
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UTF-8
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py
import torch import torch.nn as nn import scipy.spatial class CenterLoss(nn.Module): """Center loss. Reference: Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016. Args: num_classes (int): number of classes. feat_dim (int): feature dimension.#特征维度 """ def __init__(self, num_classes=10, feat_dim=2, use_gpu=True): super(CenterLoss, self).__init__() self.num_classes = num_classes self.feat_dim = feat_dim self.use_gpu = use_gpu if self.use_gpu: self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim).cuda()) else: self.centers = nn.Parameter(torch.randn(self.num_classes, self.feat_dim))#生成10行2列的向量 def forward(self, x, labels): """ Args: x: feature matrix with shape (batch_size, feat_dim). labels: ground truth labels with shape (batch_size). """ batch_size = x.size(0)#x的size为(16,200) ###dismat为x和centers的欧氏距离。 # .expand()返回tensor的一个新视图,单个维度扩大为更大的尺寸。.t()是转置 #前面是把特征维度总和扩大为(batch_size,类别) distmat = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(batch_size, self.num_classes) + \ torch.pow(self.centers, 2).sum(dim=1, keepdim=True).expand(self.num_classes, batch_size).t() distmat.addmm_(1, -2, x, self.centers.t()) #dismat是[16,200] classes = torch.arange(self.num_classes).long() if self.use_gpu: classes = classes.cuda() labels = labels.unsqueeze(1).expand(batch_size, self.num_classes)#原先的labels是16,现在的是[16,200] mask = labels.eq(classes.expand(batch_size, self.num_classes)) #mask为每个标签的类别向量张量,即一行为一个[0,0,...,1,....]这样的类别向量。 dist = distmat * mask.float()#保留下对应类的dismat中每行对应类别列,其他为0 loss = dist.clamp(min=1e-12, max=1e+12).sum() / batch_size #loss = dist中大于0的元素的和 / batch_size,想离中心越近越好 return loss
bc852715d8fa163594392bc2797185b171e26502
c91100ba78470286ec305a790b7a73747b0cc5c8
/WeatherHarvester.py
e0b31ebe6cb2acf626028400fcd91c0b6d985054
[]
no_license
oze4/oPyn_weather_map
d70ef4707f174f84a0579238d7e145fe5667f63f
353ccef78438b2947a8d1d958c996d41873fdfa0
refs/heads/master
2020-03-27T14:22:08.299948
2018-08-30T03:29:25
2018-08-30T03:29:25
146,658,630
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# TODO: put classes to work
ed86654502c4aba575c6e6a700d207d96b6f6a5e
acdf28f00143f4b9f21494fe08abf2b32a04e2c5
/comment/migrations/0002_auto_20181214_0037.py
8b845764eeedb1286d64a0274b74fa27210adcea
[]
no_license
ITuDous/myblog
5341ff8afba0b624a83c7874b8a6a8791314cc53
c3a84ec34647a4907434c94c80739ee99c11f78d
refs/heads/master
2020-04-11T12:26:03.355313
2018-12-17T16:15:14
2018-12-17T16:15:14
161,780,084
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# -*- coding: utf-8 -*- # Generated by Django 1.11.16 on 2018-12-13 16:37 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('comment', '0001_initial'), ] operations = [ migrations.AlterField( model_name='comment', name='created_time', field=models.DateTimeField(auto_now_add=True, verbose_name='创建日期'), ), ]
ae973368e63b0ad138b2cd102e4f343956afe41d
d6cc3073f85f962e717e706abf83091be3bf44dd
/easemyshopping/urls.py
5ffcc63648d72102c4b8dda700e0f0b2c0115c68
[]
no_license
pranav-katlana-56/Ease-My-Shopping
358d9cfdd5ed9818477a96c80409101b021276eb
480bd69466d06aef56b8331c1f47ca67ceb07704
refs/heads/main
2023-06-23T03:47:33.490227
2021-07-24T18:54:51
2021-07-24T18:54:51
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0
null
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"""easemyshopping URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.1/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include urlpatterns = [ path('admin/', admin.site.urls), path("" , include('shop.urls')) ]
068777999b31a9511eaf61a997af405208261bad
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/Sprint_Challenge/northwind.py
f27555c2382acafb5ef149173936c82e6b8d5d1c
[ "MIT" ]
permissive
albert-h-wong/DS-Unit-3-Sprint-2-SQL-and-Databases
d911ddabce11533a326268048af708ad82ad0f02
4220f5ced48080da338e99d4fa9da3fb878b6daf
refs/heads/master
2020-04-24T11:37:30.227299
2019-02-28T00:47:05
2019-02-28T00:47:05
171,931,476
0
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MIT
2019-02-21T19:25:04
2019-02-21T19:25:04
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#!/usr/bin/env python """ Unit3 Sprint2 Challenge - Northwind """ import sqlite3 conn = sqlite3.connect('northwind_small.sqlite3') curs = conn.cursor() curs.execute("""SELECT ProductName, UnitPrice FROM Product ORDER BY UnitPrice DESC LIMIT 10;""") results1 = curs.fetchall() print(results1) # [('Côte de Blaye', 263.5), ('Thüringer Rostbratwurst', 123.79), # ('Mishi Kobe Niku', 97), ("Sir Rodney's Marmalade", 81), # ('Carnarvon Tigers', 62.5), ('Raclette Courdavault', 55), # ('Manjimup Dried Apples', 53), ('Tarte au sucre', 49.3), # ('Ipoh Coffee', 46), ('Rössle Sauerkraut', 45.6)] curs.execute("""SELECT avg(HireDate - BirthDate) AS AverageAge FROM Employee;""") results2 = curs.fetchall() print(results2) # [(37.22222222222222,)] Average age of employees at the hire date curs.execute("""SELECT City, avg(HireDate - BirthDate) AS AverageAGE FROM Employee GROUP BY City ORDER BY AverageAGE;""") results3 = curs.fetchall() print(results3) # [('Kirkland', 29.0), ('London', 32.5), ('Seattle', 40.0), # ('Tacoma', 40.0), ('Redmond', 56.0)] curs.execute("""SELECT CompanyName,ProductName, UnitPrice FROM Product AS p, Supplier AS sup WHERE p.SupplierId = sup.Id ORDER BY UnitPrice DESC LIMIT 10;""") results4 = curs.fetchall() print(results4) # [('Aux joyeux ecclésiastiques', 'Côte de Blaye', 263.5), # ('Plutzer Lebensmittelgroßmärkte AG', 'Thüringer Rostbratwurst', 123.79), # ('Tokyo Traders', 'Mishi Kobe Niku', 97), ('Specialty Biscuits, Ltd.', # "Sir Rodney's Marmalade", 81), ('Pavlova, Ltd.', 'Carnarvon Tigers', 62.5), # ('Gai pâturage', 'Raclette Courdavault', 55), ("G'day, Mate", # 'Manjimup Dried Apples', 53), ("Forêts d'érables", 'Tarte au sucre', 49.3), # ('Leka Trading', 'Ipoh Coffee', 46), ('Plutzer Lebensmittelgroßmärkte AG', # 'Rössle Sauerkraut', 45.6)] curs.execute("""SELECT cat.CategoryName, Count(p.Id)as NumberOfProducts FROM Product AS p, Category AS cat WHERE cat.Id = p.CategoryId GROUP BY cat.CategoryName ORDER BY NumberOfProducts DESC LIMIT 1;""") results5 = curs.fetchall() print(results5) # [('Confections', 13)] The max category is Confections which has 13 products curs.execute("""SELECT FirstName, LastName, et.EmployeeId, Count(TerritoryId) AS NumberOfTerritories FROM Employee AS emp, EmployeeTerritory AS et WHERE emp.Id = et.EmployeeId GROUP BY et.EmployeeId ORDER BY NumberOfTerritories DESC LIMIT 1;""") results6 = curs.fetchall() print(results6) # [('Robert', 'King', 7, 10)] max territories is 10 by employeeID 7 Robert King curs.close() CONN.close()
[ "albe.h.wong.com" ]
albe.h.wong.com
8482e6db69da923cddef863c3d9bd5a5fa84fe6c
660d24268591ecb54e9f7885a195bb7f37f6836a
/DataStructure/Set/Prob6Union.py
999c8e253f2fd0b02e7af0d0d6a0ecbbdb8704f5
[]
no_license
mandar-degvekar/DataEngineeringGCP
3e924c3438fcdb3db7aa3fe14ab5b60a9a796554
086a82d4fb66c4ed70337d7715dc9ad32ddd46df
refs/heads/master
2022-07-15T19:37:53.052570
2019-06-25T09:59:44
2019-06-25T09:59:44
192,692,729
0
0
null
2022-06-21T22:11:24
2019-06-19T08:37:21
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Python
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s=set(['abc','xyz','ss','bb','ss','dd']) print('Set1:',s) m=set(['abc','xyz','coco','mocha']) print('Set2:',m) print('union:',s.union(m))
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/sage/src/sage/combinat/species/functorial_composition_species.py
6c84368ba4dfa192538a5c7946a0850b4b801bd3
[]
no_license
bopopescu/geosci
28792bda1ec1f06e23ba8dcb313769b98f793dad
0d9eacbf74e2acffefde93e39f8bcbec745cdaba
refs/heads/master
2021-09-22T17:47:20.194233
2018-09-12T22:19:36
2018-09-12T22:19:36
null
0
0
null
null
null
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UTF-8
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""" Functorial composition species """ from __future__ import absolute_import #***************************************************************************** # Copyright (C) 2008 Mike Hansen <[email protected]>, # # Distributed under the terms of the GNU General Public License (GPL) # # This code 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. # # The full text of the GPL is available at: # # http://www.gnu.org/licenses/ #***************************************************************************** from .species import GenericCombinatorialSpecies from .structure import GenericSpeciesStructure from sage.misc.cachefunc import cached_function from sage.structure.unique_representation import UniqueRepresentation class FunctorialCompositionStructure(GenericSpeciesStructure): pass class FunctorialCompositionSpecies(GenericCombinatorialSpecies): def __init__(self, F, G, min=None, max=None, weight=None): """ Returns the functorial composition of two species. EXAMPLES:: sage: E = species.SetSpecies() sage: E2 = species.SetSpecies(size=2) sage: WP = species.SubsetSpecies() sage: P2 = E2*E sage: G = WP.functorial_composition(P2) sage: G.isotype_generating_series().coefficients(5) [1, 1, 2, 4, 11] sage: G = species.SimpleGraphSpecies() sage: c = G.generating_series().coefficients(2) sage: type(G) <class 'sage.combinat.species.functorial_composition_species.FunctorialCompositionSpecies'> sage: G == loads(dumps(G)) True sage: G._check() #False due to isomorphism types not being implemented False """ self._F = F self._G = G self._state_info = [F, G] self._name = "Functorial composition of (%s) and (%s)"%(F, G) GenericCombinatorialSpecies.__init__(self, min=None, max=None, weight=None) _default_structure_class = FunctorialCompositionStructure def _structures(self, structure_class, s): """ EXAMPLES:: sage: G = species.SimpleGraphSpecies() sage: G.structures([1,2,3]).list() [{}, {{1, 2}*{3}}, {{1, 3}*{2}}, {{2, 3}*{1}}, {{1, 2}*{3}, {1, 3}*{2}}, {{1, 2}*{3}, {2, 3}*{1}}, {{1, 3}*{2}, {2, 3}*{1}}, {{1, 2}*{3}, {1, 3}*{2}, {2, 3}*{1}}] """ gs = self._G.structures(s).list() for f in self._F.structures(gs): yield f def _isotypes(self, structure_class, s): """ There is no known algorithm for efficiently generating the isomorphism types of the functorial composition of two species. EXAMPLES:: sage: G = species.SimpleGraphSpecies() sage: G.isotypes([1,2,3]).list() Traceback (most recent call last): ... NotImplementedError """ raise NotImplementedError def _gs(self, series_ring, base_ring): """ EXAMPLES:: sage: G = species.SimpleGraphSpecies() sage: G.generating_series().coefficients(5) [1, 1, 1, 4/3, 8/3] """ return self._F.generating_series(base_ring).functorial_composition(self._G.generating_series(base_ring)) def _itgs(self, series_ring, base_ring): """ EXAMPLES:: sage: G = species.SimpleGraphSpecies() sage: G.isotype_generating_series().coefficients(5) [1, 1, 2, 4, 11] """ return self.cycle_index_series(base_ring).isotype_generating_series() def _cis(self, series_ring, base_ring): """ EXAMPLES:: sage: G = species.SimpleGraphSpecies() sage: G.cycle_index_series().coefficients(5) [p[], p[1], p[1, 1] + p[2], 4/3*p[1, 1, 1] + 2*p[2, 1] + 2/3*p[3], 8/3*p[1, 1, 1, 1] + 4*p[2, 1, 1] + 2*p[2, 2] + 4/3*p[3, 1] + p[4]] """ return self._F.cycle_index_series(base_ring).functorial_composition(self._G.cycle_index_series(base_ring)) def weight_ring(self): """ Returns the weight ring for this species. This is determined by asking Sage's coercion model what the result is when you multiply (and add) elements of the weight rings for each of the operands. EXAMPLES:: sage: G = species.SimpleGraphSpecies() sage: G.weight_ring() Rational Field """ from sage.structure.element import get_coercion_model cm = get_coercion_model() f_weights = self._F.weight_ring() g_weights = self._G.weight_ring() return cm.explain(f_weights, g_weights, verbosity=0) #Backward compatibility FunctorialCompositionSpecies_class = FunctorialCompositionSpecies
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import sklearn.discriminant_analysis from autotabular.pipeline.components.classification.lda import LDA from .test_base import BaseClassificationComponentTest class LDAComponentTest(BaseClassificationComponentTest): __test__ = True res = dict() res['default_iris'] = 1.0 res['default_iris_iterative'] = -1 res['default_iris_proba'] = 0.5614481896257509 res['default_iris_sparse'] = -1 res['default_digits'] = 0.88585306618093507 res['default_digits_iterative'] = -1 res['default_digits_binary'] = 0.9811778992106861 res['default_digits_multilabel'] = 0.82204896441795205 res['default_digits_multilabel_proba'] = 0.9833070018235553 sk_mod = sklearn.discriminant_analysis.LinearDiscriminantAnalysis module = LDA
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# -*- coding: utf-8 -*- # Generated by Django 1.10.5 on 2017-11-12 05:10 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('blog', '0008_auto_20171104_0938'), ] operations = [ migrations.AddField( model_name='post', name='photo', field=models.ImageField(blank=True, upload_to=''), ), ]
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from flask import Flask from flask import render_template, request import time import os app = Flask(__name__) @app.route('/') def function(): humidity = 0 temperature = 2 wateranalog = 3 print ("something for testing") return render_template("Data.html", humidity=humidity, temperature=temperature, wateranalog=wateranalog)
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#!/usr/bin/env python # # This would be what Varnish does. # import struct import socket from time import sleep, time # no empty ending lines. req = ["""xid: 12345 vcl_method: 1 client_ip: 127.0.0.1 t_open: %s http_method: 1 URL: / proto: HTTP/1.1 """ % time(), """Host: localhost Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8 Accept-Language: nb-NO,nb;q=0.8,no;q=0.6,nn;q=0.4,en-US;q=0.2,en;q=0.2 Accept-Charset: ISO-8859-1,utf-8;q=0.7,*;q=0.3 User-Agent: curl 1.2 Cache-Control: no-cache Cookie: __utma=253898641.2098534993.1348749499.1374491627.1374580772.70; __utmz=2538986 41.1348749499.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none) """, "this is the post body"] class ServerError(Exception): pass if __name__ == "__main__": sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) sock.connect("/tmp/foo.sock") # sock.setsockopt(socket.SOL_SOCKET, socket.SO_RCVTIMEO, 2) # sock.setsockopt(socket.SOL_SOCKET, socket.SO_RCVTIMEO, 2) # sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) headercontent = (len(req[0]), len(req[1]), len(req[2])) # print headercontent header = "VPOL01" + struct.pack("!III", *headercontent) # print len(header) sock.send(header) sock.send(req[0]) sock.send(req[1]) sock.send(req[2]) response = '' waited = 0.0 while True: try: r = sock.recv(1500, socket.MSG_DONTWAIT) except Exception as e: if e.errno == 11: # not yet waited += 0.01 sleep(0.01) else: print dir(e) print str(e) else: if len(r) == 0: waited += 0.01 sleep(0.01) else: #print "got %i bytes" % len(r) #print r response += r if len(r) >= 3: break if waited >= 2: raise ServerError("timeout after %ss" % waited) print "response: %s" % response.strip()
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"""Utilities for color conversion, color labeling, and color difference calculations. """ from .colorconv import (convert_colorspace, xyz_tristimulus_values, rgba2rgb, rgb2hsv, hsv2rgb, rgb2xyz, xyz2rgb, rgb2rgbcie, rgbcie2rgb, rgb2gray, gray2rgb, gray2rgba, xyz2lab, lab2xyz, lab2rgb, rgb2lab, xyz2luv, luv2xyz, luv2rgb, rgb2luv, rgb2hed, hed2rgb, lab2lch, lch2lab, rgb2yuv, yuv2rgb, rgb2yiq, yiq2rgb, rgb2ypbpr, ypbpr2rgb, rgb2ycbcr, ycbcr2rgb, rgb2ydbdr, ydbdr2rgb, separate_stains, combine_stains, rgb_from_hed, hed_from_rgb, rgb_from_hdx, hdx_from_rgb, rgb_from_fgx, fgx_from_rgb, rgb_from_bex, bex_from_rgb, rgb_from_rbd, rbd_from_rgb, rgb_from_gdx, gdx_from_rgb, rgb_from_hax, hax_from_rgb, rgb_from_bro, bro_from_rgb, rgb_from_bpx, bpx_from_rgb, rgb_from_ahx, ahx_from_rgb, rgb_from_hpx, hpx_from_rgb) from .colorlabel import color_dict, label2rgb from .delta_e import (deltaE_cie76, deltaE_ciede94, deltaE_ciede2000, deltaE_cmc, ) __all__ = ['convert_colorspace', 'xyz_tristimulus_values', 'rgba2rgb', 'rgb2hsv', 'hsv2rgb', 'rgb2xyz', 'xyz2rgb', 'rgb2rgbcie', 'rgbcie2rgb', 'rgb2gray', 'gray2rgb', 'gray2rgba', 'xyz2lab', 'lab2xyz', 'lab2rgb', 'rgb2lab', 'rgb2hed', 'hed2rgb', 'lab2lch', 'lch2lab', 'rgb2yuv', 'yuv2rgb', 'rgb2yiq', 'yiq2rgb', 'rgb2ypbpr', 'ypbpr2rgb', 'rgb2ycbcr', 'ycbcr2rgb', 'rgb2ydbdr', 'ydbdr2rgb', 'separate_stains', 'combine_stains', 'rgb_from_hed', 'hed_from_rgb', 'rgb_from_hdx', 'hdx_from_rgb', 'rgb_from_fgx', 'fgx_from_rgb', 'rgb_from_bex', 'bex_from_rgb', 'rgb_from_rbd', 'rbd_from_rgb', 'rgb_from_gdx', 'gdx_from_rgb', 'rgb_from_hax', 'hax_from_rgb', 'rgb_from_bro', 'bro_from_rgb', 'rgb_from_bpx', 'bpx_from_rgb', 'rgb_from_ahx', 'ahx_from_rgb', 'rgb_from_hpx', 'hpx_from_rgb', 'color_dict', 'label2rgb', 'deltaE_cie76', 'deltaE_ciede94', 'deltaE_ciede2000', 'deltaE_cmc', ]
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# Generated by Django 3.1.3 on 2020-11-26 02:00 import datetime from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Guestbook', fields=[ ('idx', models.AutoField(primary_key=True, serialize=False)), ('name', models.CharField(max_length=50)), ('email', models.CharField(max_length=50)), ('passwd', models.CharField(max_length=50)), ('content', models.TextField()), ('post_date', models.DateTimeField(blank=True, default=datetime.datetime.now)), ], ), ]
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""" Copyright 2021 The Magma Authors. This source code is licensed under the BSD-style license found in the LICENSE file in the root directory of this source tree. 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 import os import subprocess irq_utility = '/usr/local/bin/set_irq_affinity' ethtool_utility = '/usr/sbin/ethtool' ''' Following function sets various tuning parameters related interface queue. 1. RX queue size 2. TX queue size 3. queue CPU assignment ''' def tune_datapath(config_dict): # TODO move this to mconfig if 'dp_irq' not in config_dict: logging.info("DP Tuning not enabled.") return if _check_util_failed(irq_utility): return if _check_util_failed(ethtool_utility): return tune_dp_irqs = config_dict['dp_irq'] logging.info("set tuning params: %s", tune_dp_irqs) # stop irq-balance stop_irq_balance = ['service', 'irqbalance', 'stop'] logging.debug("cmd: %s", stop_irq_balance) try: subprocess.check_call(stop_irq_balance) except subprocess.CalledProcessError as ex: logging.debug('%s failed with: %s', stop_irq_balance, ex) # set_irq_affinity -X 1-2 eth1 s1_interface = config_dict['enodeb_iface'] s1_cpu = tune_dp_irqs['S1_cpu'] set_s1_cpu_command = [irq_utility, '-X', s1_cpu, s1_interface] logging.debug("cmd: %s", set_s1_cpu_command) try: subprocess.check_call(set_s1_cpu_command) except subprocess.CalledProcessError as ex: logging.debug('%s failed with: %s', set_s1_cpu_command, ex) sgi_interface = config_dict['nat_iface'] sgi_cpu = tune_dp_irqs['SGi_cpu'] set_sgi_cpu_command = [irq_utility, '-X', sgi_cpu, sgi_interface] logging.debug("cmd: %s", set_sgi_cpu_command) try: subprocess.check_call(set_sgi_cpu_command) except subprocess.CalledProcessError as ex: logging.debug('%s failed with: %s', set_sgi_cpu_command, ex) # ethtool -G eth1 rx 1024 tx 1024 s1_queue_size = tune_dp_irqs['S1_queue_size'] set_s1_queue_sz = [ ethtool_utility, '-G', s1_interface, 'rx', str(s1_queue_size), 'tx', str(s1_queue_size), ] logging.debug("cmd: %s", set_s1_queue_sz) try: subprocess.check_call(set_s1_queue_sz) except subprocess.CalledProcessError as ex: logging.debug('%s failed with: %s', set_s1_queue_sz, ex) sgi_queue_size = tune_dp_irqs['SGi_queue_size'] set_sgi_queue_sz = [ ethtool_utility, '-G', sgi_interface, 'rx', str(sgi_queue_size), 'tx', str(sgi_queue_size), ] logging.debug("cmd: %s", set_sgi_queue_sz) try: subprocess.check_call(set_sgi_queue_sz) except subprocess.CalledProcessError as ex: logging.debug('%s failed with: %s', set_sgi_queue_sz, ex) def _check_util_failed(path: str): if not os.path.isfile(path) or not os.access(path, os.X_OK): logging.info( "missing %s: path: %s perm: %s", path, os.path.isfile(path), os.access(path, os.X_OK), ) return True return False
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""" Implementation of the curious variant of the Advantage Actor-Critic algorithm. """ import time import numpy as np import tensorflow as tf from tensorflow.contrib import slim from algorithms.algo_utils import RunningMeanStd, EPS, extract_keys from algorithms.baselines.a2c.agent_a2c import AgentA2C from algorithms.env_wrappers import has_image_observations from algorithms.multi_env import MultiEnv from algorithms.tf_utils import dense, count_total_parameters, conv from algorithms.utils import summaries_dir from utils.distributions import CategoricalProbabilityDistribution from utils.utils import log, AttrDict, put_kernels_on_grid class CuriousA2CPolicy: """A class that represents both the actor's policy and the value estimator.""" def __init__(self, env, observations, timer, params): self.regularizer = tf.contrib.layers.l2_regularizer(scale=1e-10) img_model_name = params.image_model_name fc_layers = params.fc_layers fc_size = params.fc_size lowdim_model_name = params.lowdim_model_name past_frames = params.stack_past_frames image_obs = has_image_observations(env.observation_space.spaces['obs']) num_actions = env.action_space.n if image_obs: # convolutions if img_model_name == 'convnet_simple': conv_filters = self._convnet_simple(observations, [(32, 3, 2)] * 4) else: raise Exception('Unknown model name') encoded_input = tf.contrib.layers.flatten(conv_filters) else: # low-dimensional input if lowdim_model_name == 'simple_fc': frames = tf.split(observations, past_frames, axis=1) fc_encoder = tf.make_template('fc_encoder', self._fc_frame_encoder, create_scope_now_=True) encoded_frames = [fc_encoder(frame) for frame in frames] encoded_input = tf.concat(encoded_frames, axis=1) else: raise Exception('Unknown lowdim model name') if params.ignore_timer: timer = tf.multiply(timer, 0.0) encoded_input_with_timer = tf.concat([encoded_input, tf.expand_dims(timer, 1)], axis=1) fc = encoded_input_with_timer for _ in range(fc_layers - 1): fc = dense(fc, fc_size, self.regularizer) # fully-connected layers to generate actions actions_fc = dense(fc, fc_size // 2, self.regularizer) self.actions = tf.contrib.layers.fully_connected(actions_fc, num_actions, activation_fn=None) self.best_action_deterministic = tf.argmax(self.actions, axis=1) self.actions_prob_distribution = CategoricalProbabilityDistribution(self.actions) self.act = self.actions_prob_distribution.sample() value_fc = dense(fc, fc_size // 2, self.regularizer) self.value = tf.squeeze(tf.contrib.layers.fully_connected(value_fc, 1, activation_fn=None), axis=[1]) if image_obs: # summaries with tf.variable_scope('conv1', reuse=True): weights = tf.get_variable('weights') with tf.name_scope('a2c_agent_summary_conv'): if weights.shape[2].value in [1, 3, 4]: tf.summary.image('conv1/kernels', put_kernels_on_grid(weights), max_outputs=1) log.info('Total parameters in the model: %d', count_total_parameters()) def _fc_frame_encoder(self, x): return dense(x, 128, self.regularizer) def _conv(self, x, filters, kernel, stride, scope=None): return conv(x, filters, kernel, stride=stride, regularizer=self.regularizer, scope=scope) def _convnet_simple(self, x, convs): """Basic stacked convnet.""" layer = x layer_idx = 1 for filters, kernel, stride in convs: layer = self._conv(layer, filters, kernel, stride, 'conv' + str(layer_idx)) layer_idx += 1 return layer class Model: """Single class for inverse and forward dynamics model.""" def __init__(self, env, obs, next_obs, actions, past_frames, forward_fc): """ :param obs - placeholder for observations :param actions - placeholder for selected actions """ self.regularizer = tf.contrib.layers.l2_regularizer(scale=1e-10) image_obs = has_image_observations(env.observation_space.spaces['obs']) num_actions = env.action_space.n if image_obs: # convolutions conv_encoder = tf.make_template( 'conv_encoder', self._convnet_simple, create_scope_now_=True, convs=[(32, 3, 2)] * 4, ) encoded_obs = conv_encoder(obs=obs) encoded_obs = tf.contrib.layers.flatten(encoded_obs) encoded_next_obs = conv_encoder(obs=next_obs) self.encoded_next_obs = tf.contrib.layers.flatten(encoded_next_obs) else: # low-dimensional input lowdim_encoder = tf.make_template( 'lowdim_encoder', self._lowdim_encoder, create_scope_now_=True, past_frames=past_frames, ) encoded_obs = lowdim_encoder(obs=obs) self.encoded_next_obs = lowdim_encoder(obs=next_obs) self.feature_vector_size = encoded_obs.get_shape().as_list()[-1] log.info('Feature vector size in ICM: %d', self.feature_vector_size) actions_one_hot = tf.one_hot(actions, num_actions) # forward model forward_model_input = tf.concat( [encoded_obs, actions_one_hot], axis=1, ) forward_model_hidden = dense(forward_model_input, forward_fc, self.regularizer) forward_model_hidden = dense(forward_model_hidden, forward_fc, self.regularizer) forward_model_output = tf.contrib.layers.fully_connected( forward_model_hidden, self.feature_vector_size, activation_fn=None, ) self.predicted_obs = forward_model_output # inverse model inverse_model_input = tf.concat([encoded_obs, self.encoded_next_obs], axis=1) inverse_model_hidden = dense(inverse_model_input, 256, self.regularizer) inverse_model_output = tf.contrib.layers.fully_connected( inverse_model_hidden, num_actions, activation_fn=None, ) self.predicted_actions = inverse_model_output log.info('Total parameters in the model: %d', count_total_parameters()) def _fc_frame_encoder(self, x): return dense(x, 128, self.regularizer) def _lowdim_encoder(self, obs, past_frames): frames = tf.split(obs, past_frames, axis=1) fc_encoder = tf.make_template('fc_encoder', self._fc_frame_encoder, create_scope_now_=True) encoded_frames = [fc_encoder(frame) for frame in frames] encoded_input = tf.concat(encoded_frames, axis=1) return encoded_input def _conv(self, x, filters, kernel, stride, scope=None): return conv(x, filters, kernel, stride=stride, regularizer=self.regularizer, scope=scope) def _convnet_simple(self, convs, obs): """Basic stacked convnet.""" layer = obs layer_idx = 1 for filters, kernel, stride in convs: layer = self._conv(layer, filters, kernel, stride, 'conv' + str(layer_idx)) layer_idx += 1 return layer class AgentCuriousA2C(AgentA2C): """Agent based on A2C algorithm.""" class Params(AgentA2C.Params): """Hyperparams for the algorithm and the training process.""" def __init__(self, experiment_name): """Default parameter values set in ctor.""" super(AgentCuriousA2C.Params, self).__init__(experiment_name) self.icm_beta = 0.5 # in ICM, importance of training forward model vs inverse model self.model_lr_scale = 10.0 # in ICM, importance of model loss vs actor-critic loss self.prediction_bonus_coeff = 0.05 # scaling factor for prediction bonus vs env rewards self.clip_bonus = 0.1 self.clip_advantage = 10 self.ignore_timer = False # whether or not policy uses the remaining episode time self.forward_fc = 512 self.train_for_env_steps = 10 * 1000 * 1000 * 1000 # noinspection PyMethodMayBeStatic def filename_prefix(self): return 'curious_a2c_' def __init__(self, make_env_func, params): """Initialize A2C computation graph and some auxiliary tensors.""" super(AgentA2C, self).__init__(params) # calling grandparent ctor, skipping parent global_step = tf.train.get_or_create_global_step() self.make_env_func = make_env_func self.selected_actions = tf.placeholder(tf.int32, [None]) # action selected by the policy self.value_estimates = tf.placeholder(tf.float32, [None]) self.discounted_rewards = tf.placeholder(tf.float32, [None]) # estimate of total reward (rollout + value) self.advantages = tf.placeholder(tf.float32, [None]) env = make_env_func() # we need it to query observation shape, number of actions, etc. obs_shape = list(env.observation_space.spaces['obs'].shape) input_shape = [None] + obs_shape # add batch dimension self.observations = tf.placeholder(tf.float32, shape=input_shape) self.next_obs = tf.placeholder(tf.float32, shape=input_shape) self.timer = tf.placeholder(tf.float32, shape=[None]) self.policy = CuriousA2CPolicy(env, self.observations, self.timer, params) self.model = Model( env, self.observations, self.next_obs, self.selected_actions, params.stack_past_frames, params.forward_fc, ) env.close() # negative logarithm of the probabilities of actions neglogp_actions = tf.nn.sparse_softmax_cross_entropy_with_logits( logits=self.policy.actions, labels=self.selected_actions, ) # maximize probabilities of actions that give high advantage action_losses = tf.clip_by_value(self.advantages * neglogp_actions, -20.0, 20.0) action_loss = tf.reduce_mean(action_losses) # penalize for inaccurate value estimation value_losses = tf.square(self.discounted_rewards - self.policy.value) value_losses = tf.clip_by_value(value_losses, -20.0, 20.0) value_loss = self.params.value_loss_coeff * tf.reduce_mean(value_losses) # penalize the agent for being "too sure" about it's actions (to prevent converging to the suboptimal local # minimum too soon) entropy_loss = -tf.reduce_mean(self.policy.actions_prob_distribution.entropy()) entropy_loss_coeff = tf.train.exponential_decay( self.params.initial_entropy_loss_coeff, tf.cast(global_step, tf.float32), 20.0, 0.95, staircase=True, ) entropy_loss_coeff = tf.maximum(entropy_loss_coeff, self.params.min_entropy_loss_coeff) entropy_loss = entropy_loss_coeff * entropy_loss # total actor-critic loss a2c_loss = action_loss + entropy_loss + value_loss # model losses forward_loss_batch = 0.5 * tf.square(self.model.encoded_next_obs - self.model.predicted_obs) forward_loss_batch = tf.reduce_mean(forward_loss_batch, axis=1) * self.model.feature_vector_size forward_loss = tf.reduce_mean(forward_loss_batch) inverse_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( logits=self.model.predicted_actions, labels=self.selected_actions, )) icm_beta = self.params.icm_beta model_loss = forward_loss * icm_beta + inverse_loss * (1.0 - icm_beta) model_loss = self.params.model_lr_scale * model_loss # regularization regularization_loss = tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) # total loss loss = a2c_loss + model_loss + regularization_loss # training self.train = tf.contrib.layers.optimize_loss( loss=loss, global_step=global_step, learning_rate=self.params.learning_rate, optimizer=tf.train.AdamOptimizer, clip_gradients=self.params.clip_gradients, ) bonus = self.params.prediction_bonus_coeff * forward_loss_batch self.prediction_curiosity_bonus = tf.clip_by_value(bonus, -self.params.clip_bonus, self.params.clip_bonus) # summaries for the agent and the training process with tf.name_scope('a2c_agent_summary'): if len(self.observations.shape) >= 4: tf.summary.image( 'observations', self.observations[:, :, :, :3], # first three channels max_outputs=8, ) # output also last channel if self.observations.shape[-1].value > 4: tf.summary.image('observations_last_channel', self.observations[:, :, :, -1:]) tf.summary.scalar('disc_rewards_avg', tf.reduce_mean(self.discounted_rewards)) tf.summary.scalar('disc_rewards_max', tf.reduce_max(self.discounted_rewards)) tf.summary.scalar('disc_rewards_min', tf.reduce_min(self.discounted_rewards)) tf.summary.scalar('bonus_avg', tf.reduce_mean(self.prediction_curiosity_bonus)) tf.summary.scalar('bonus_max', tf.reduce_max(self.prediction_curiosity_bonus)) tf.summary.scalar('bonus_min', tf.reduce_min(self.prediction_curiosity_bonus)) tf.summary.scalar('value', tf.reduce_mean(self.policy.value)) tf.summary.scalar('adv_avg_abs', tf.reduce_mean(tf.abs(self.advantages))) tf.summary.scalar('adv_max', tf.reduce_max(self.advantages)) tf.summary.scalar('adv_min', tf.reduce_min(self.advantages)) tf.summary.scalar('selected_action_avg', tf.reduce_mean(tf.to_float(self.selected_actions))) tf.summary.scalar('policy_entropy', tf.reduce_mean(self.policy.actions_prob_distribution.entropy())) tf.summary.scalar('entropy_coeff', entropy_loss_coeff) with tf.name_scope('a2c_losses'): tf.summary.scalar('action_loss', action_loss) tf.summary.scalar('max_action_loss', tf.reduce_max(action_losses)) tf.summary.scalar('value_loss', value_loss) tf.summary.scalar('max_value_loss', tf.reduce_max(value_losses)) tf.summary.scalar('entropy_loss', entropy_loss) tf.summary.scalar('a2c_loss', a2c_loss) tf.summary.scalar('forward_loss', forward_loss) tf.summary.scalar('inverse_loss', inverse_loss) tf.summary.scalar('model_loss', model_loss) tf.summary.scalar('regularization_loss', regularization_loss) tf.summary.scalar('loss', loss) summary_dir = summaries_dir(self.params.experiment_dir()) self.summary_writer = tf.summary.FileWriter(summary_dir) self.all_summaries = tf.summary.merge_all() with tf.name_scope('a2c_aux_summary'): tf.summary.scalar('training_steps', global_step, collections=['aux']) # if it's not "initialized" yet, just report 0 to preserve tensorboard plot scale best_reward_report = tf.cond( tf.equal(self.best_avg_reward, self.initial_best_avg_reward), true_fn=lambda: 0.0, false_fn=lambda: self.best_avg_reward, ) tf.summary.scalar('best_reward_ever', best_reward_report, collections=['aux']) tf.summary.scalar('avg_reward', self.avg_reward_placeholder, collections=['aux']) self.avg_length_placeholder = tf.placeholder(tf.float32, []) tf.summary.scalar('avg_lenght', self.avg_length_placeholder, collections=['aux']) self.aux_summaries = tf.summary.merge_all(key='aux') self.saver = tf.train.Saver(max_to_keep=3) all_vars = tf.trainable_variables() log.warn('curious a2c variables:') slim.model_analyzer.analyze_vars(all_vars, print_info=True) def best_action(self, observation, deterministic=False): obs, timer = extract_keys([observation], 'obs', 'timer') actions, _ = self._policy_step_timer(obs, timer, deterministic) return actions[0] def _policy_step_timer(self, observations, timer, deterministic=False): """ Select the best action by sampling from the distribution generated by the policy. Also estimate the value for the currently observed environment state. """ ops = [ self.policy.best_action_deterministic if deterministic else self.policy.act, self.policy.value, ] actions, values = self.session.run( ops, feed_dict={ self.observations: observations, self.timer: timer, }, ) return actions, values def _estimate_values_timer(self, observations, timer): values = self.session.run( self.policy.value, feed_dict={ self.observations: observations, self.timer: timer, }, ) return values def _prediction_curiosity_bonus(self, observations, actions, next_obs): bonuses = self.session.run( self.prediction_curiosity_bonus, feed_dict={ self.selected_actions: actions, self.observations: observations, self.next_obs: next_obs, } ) return bonuses def _curious_train_step( self, step, env_steps, observations, timer, actions, values, discounted_rewards, advantages, next_obs ): """ Actually do a single iteration of training. See the computational graph in the ctor to figure out the details. """ with_summaries = self._should_write_summaries(step) summaries = [self.all_summaries] if with_summaries else [] result = self.session.run( [self.train] + summaries, feed_dict={ self.observations: observations, self.timer: timer, self.selected_actions: actions, self.value_estimates: values, self.discounted_rewards: discounted_rewards, self.advantages: advantages, self.next_obs: next_obs, }, ) step = tf.train.global_step(self.session, tf.train.get_global_step()) if with_summaries: summary = result[1] self.summary_writer.add_summary(summary, global_step=env_steps) return step def _learn_loop(self, multi_env, step_callback=None): """ Main training loop. :param step_callback: a hacky callback that takes a dictionary with all local variables as an argument. Allows you too look inside the training process. """ step = initial_step = tf.train.global_step(self.session, tf.train.get_global_step()) env_steps = self.total_env_steps.eval(session=self.session) batch_size = self.params.rollout * self.params.num_envs img_obs, timer_obs = extract_keys(multi_env.initial_obs(), 'obs', 'timer') adv_running_mean_std = RunningMeanStd(max_past_samples=10000) def end_of_training(s, es): return s >= self.params.train_for_steps or es > self.params.train_for_env_steps while not end_of_training(step, env_steps): timing = AttrDict({'experience': time.time(), 'batch': time.time()}) experience_start = time.time() env_steps_before_batch = env_steps batch_obs, batch_timer = [img_obs], [timer_obs] env_steps += len(img_obs) batch_actions, batch_values, batch_rewards, batch_dones, batch_next_obs = [], [], [], [], [] for rollout_step in range(self.params.rollout): actions, values = self._policy_step_timer(img_obs, timer_obs) batch_actions.append(actions) batch_values.append(values) # wait for all the workers to complete an environment step next_obs, rewards, dones, infos = multi_env.step(actions) next_img_obs, next_timer = extract_keys(next_obs, 'obs', 'timer') # calculate curiosity bonus bonuses = self._prediction_curiosity_bonus(img_obs, actions, next_img_obs) rewards += bonuses batch_rewards.append(rewards) batch_dones.append(dones) batch_next_obs.append(next_img_obs) img_obs = next_img_obs timer_obs = next_timer if infos is not None and 'num_frames' in infos[0]: env_steps += sum((info['num_frames'] for info in infos)) else: env_steps += multi_env.num_envs if rollout_step != self.params.rollout - 1: # we don't need the newest observation in the training batch, already have enough batch_obs.append(img_obs) batch_timer.append(timer_obs) assert len(batch_obs) == len(batch_rewards) assert len(batch_obs) == len(batch_next_obs) batch_rewards = np.asarray(batch_rewards, np.float32).swapaxes(0, 1) batch_dones = np.asarray(batch_dones, np.bool).swapaxes(0, 1) batch_values = np.asarray(batch_values, np.float32).swapaxes(0, 1) # Last value won't be valid for envs with done=True (because env automatically resets and shows 1st # observation of the next episode. But that's okay, because we should never use last_value in this case. last_values = self._estimate_values_timer(img_obs, timer_obs) gamma = self.params.gamma disc_rewards = [] for i in range(len(batch_rewards)): env_rewards = self._calc_discounted_rewards(gamma, batch_rewards[i], batch_dones[i], last_values[i]) disc_rewards.extend(env_rewards) disc_rewards = np.asarray(disc_rewards, np.float32) # convert observations and estimations to meaningful n-step batches batch_obs_shape = (self.params.rollout * multi_env.num_envs,) + img_obs[0].shape batch_obs = np.asarray(batch_obs, np.float32).swapaxes(0, 1).reshape(batch_obs_shape) batch_next_obs = np.asarray(batch_next_obs, np.float32).swapaxes(0, 1).reshape(batch_obs_shape) batch_actions = np.asarray(batch_actions, np.int32).swapaxes(0, 1).flatten() batch_timer = np.asarray(batch_timer, np.float32).swapaxes(0, 1).flatten() batch_values = batch_values.flatten() advantages = disc_rewards - batch_values if self.params.normalize_adv: adv_running_mean_std.update(advantages) advantages = (advantages - adv_running_mean_std.mean) / (np.sqrt(adv_running_mean_std.var) + EPS) advantages = np.clip(advantages, -self.params.clip_advantage, self.params.clip_advantage) timing.experience = time.time() - timing.experience timing.train = time.time() step = self._curious_train_step( step, env_steps, batch_obs, batch_timer, batch_actions, batch_values, disc_rewards, advantages, batch_next_obs, ) self._maybe_save(step, env_steps) timing.train = time.time() - timing.train avg_reward = multi_env.calc_avg_rewards(n=self.params.stats_episodes) avg_length = multi_env.calc_avg_episode_lengths(n=self.params.stats_episodes) fps = (env_steps - env_steps_before_batch) / (time.time() - timing.batch) self._maybe_print(step, avg_reward, avg_length, fps, timing) self._maybe_aux_summaries(step, env_steps, avg_reward, avg_length) self._maybe_update_avg_reward(avg_reward, multi_env.stats_num_episodes()) if step_callback is not None: step_callback(locals(), globals()) def learn(self, step_callback=None): try: multi_env = MultiEnv( self.params.num_envs, self.params.num_workers, make_env_func=self.make_env_func, stats_episodes=self.params.stats_episodes, ) self._learn_loop(multi_env, step_callback) except Exception as exc: log.exception(exc) finally: log.info('Closing env...') multi_env.close()
2489b3cb627d4f223f765feef5d1da637afe9945
dcd83aeb799143b58956612fb0bfc0258d30f229
/util/InjectProduction.py
080e7bd5f38e06ae65983f3e5b91a4e0a5d453bf
[]
no_license
giffels/PRODAGENT
67e3e841cfca7421caa505d03417b663a62d321b
c99608e3e349397fdd1b0b5c011bf4f33a1c3aad
refs/heads/master
2021-01-01T05:51:52.200716
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#!/usr/bin/env python """ Generate jobs for the workflow provided """ from MessageService.MessageService import MessageService from ProdAgentCore.Configuration import loadProdAgentConfiguration import sys,os,getopt,time usage="\n Usage: python InjectTest.py <options> \n Options: \n --workflow=<workflow.xml> \t\t workflow file \n --nevts=<NumberofEvent> \t\t number of events per job \n --njobs=<NumberofEvent> \t\t number of jobs \n --plugin=<Submission type> \t type of creation/submission plugin \n --site-pref=<StorageElement name> storage element name \n [ --run=<firstRun> \t\t\t first run number effective only for New Workflow]\n\n *Note* that the run number option is effective only when a New workflow is created and it overwrites the FirstRun default in $PRODAGENT_CONFIG if set" valid = ['workflow=', 'run=', 'nevts=' , 'njobs=', 'site-pref=','plugin='] admitted_vals = ['LCGAdvanced', 'LCG','GliteBulk','T0LSF','GLITE', 'GLiteBulkResCon', 'BossLite'] try: opts, args = getopt.getopt(sys.argv[1:], "", valid) except getopt.GetoptError, ex: print usage print str(ex) sys.exit(1) workflow = None run = "None" nevts = None njobs = None sitePref = None submissiontype = "LCG" for opt, arg in opts: if opt == "--workflow": workflow = arg if opt == "--run": run = int(arg) if opt == "--nevts": nevts = int(arg) if opt == "--njobs": njobs = int(arg) if opt == "--site-pref": sitePref = arg if opt == "--plugin": submissiontype = arg if submissiontype not in admitted_vals : print "Submission plugin: %s is not supported \nSupported values are: %s" % (submissiontype, admitted_vals) sys.exit(1) if workflow == None: print "--workflow option not provided" print usage sys.exit(1) workflow=os.path.expandvars(os.path.expanduser(workflow)) if not os.path.exists(workflow): print "Workflow not found: %s" % workflow sys.exit(1) if nevts == None: print "--nevts option not provided." print usage sys.exit(1) if njobs == None: print "--njobs option not provided." print usage sys.exit(1) if submissiontype == "GliteBulk": if int(njobs) <= 1 : print "--njobs need to be greater than 1 for GliteBulk submission" sys.exit(1) def getRequestInjectorConfig(): """ get the RequestInjector Component dir and the optional FirstRun """ try: config = loadProdAgentConfiguration() except StandardError, ex: msg = "Error: error reading configuration:\n" msg += str(ex) print msg sys.exit(1) if not config.has_key("RequestInjector"): msg = "Error: Configuration block RequestInjector is missing from $PRODAGENT_CONFIG" print msg sys.exit(1) ReqInjConfig = config.getConfig("RequestInjector") #if not ReqInjConfig.has_key("ComponentDir"): # msg = "Error: Configuration block RequestInjector is missing ComponentDir in $PRODAGENT_CONFIG" # print msg # sys.exit(1) return ReqInjConfig.get("ComponentDir", None),ReqInjConfig.get("FirstRun", "None"),ReqInjConfig.get("QueueJobMode", "None") def checkWorkflow(workflow): """ Check if the provided workflow already exists in WorkflowCache area """ WorkflowExists=False workflowBase = os.path.basename(workflow) RequestDir,firstrun,queuemode = getRequestInjectorConfig() workflowCache="%s/WorkflowCache"%RequestDir if not os.path.exists(workflowCache): msg = "Error: there is no WorkflowCache area ==> %s"%workflowCache print msg sys.exit(1) workflowCacheFile = os.path.join(workflowCache, "%s"%workflowBase) if os.path.exists(workflowCacheFile): WorkflowExists=True msg=" Workflow %s already exists"%(workflowBase,) print msg else: msg=" Workflow %s is NEW since the %s doesn't exist"%(workflowBase,workflowCacheFile) print msg return WorkflowExists,firstrun,queuemode ## use MessageService ms = MessageService() ## register message service instance as "Test" ms.registerAs("Test") ## Debug level ms.publish("RequestInjector:StartDebug","none") ms.publish("JobCreator:StartDebug","none") ms.publish("JobSubmitter:StartDebug","none") ms.commit() ms.publish("TrackingComponent:StartDebug","none") ms.commit() ## Set Creator/Submitter if submissiontype == "LCG": ms.publish("JobCreator:SetCreator","LCGCreator") ms.publish("JobSubmitter:SetSubmitter","LCGSubmitter") if submissiontype == "GLITE": ms.publish("JobCreator:SetCreator","LCGCreator") ms.publish("JobSubmitter:SetSubmitter","GLITESubmitter") if submissiontype == "BossLite": ms.publish("JobCreator:SetGenerator","Bulk") ms.commit() time.sleep(0.1) ms.publish("JobCreator:SetCreator","LCGBulkCreator") ms.publish("JobSubmitter:SetSubmitter","BlGLiteBulkSubmitter") ms.publish("RequestInjector:SetBulkMode",'') if submissiontype == "GliteBulk": ms.publish("JobCreator:SetGenerator","Bulk") ms.commit() time.sleep(0.1) ms.publish("JobCreator:SetCreator","LCGBulkCreator") ms.publish("JobSubmitter:SetSubmitter","GLiteBulkSubmitter") ms.publish("RequestInjector:SetBulkMode",'') if submissiontype == "T0LSF": ms.publish("JobCreator:SetCreator","T0LSFCreator") ms.publish("JobSubmitter:SetSubmitter","T0LSFSubmitter") if submissiontype == 'GLiteBulkResCon': ms.publish("JobCreator:SetGenerator","Bulk") ms.commit() time.sleep(0.1) ms.publish("JobCreator:SetCreator","LCGBulkCreator") ms.publish("JobSubmitter:SetSubmitter","GLiteBulkResConSubmitter") ms.publish("RequestInjector:SetBulkMode",'') ms.commit() ## Set Workflow and run WorkflowExists,firstrun,queuemode=checkWorkflow(workflow) if str(queuemode).lower() in ("true", "yes"): queuemode = True ms.publish("JobQueue:StartDebug","none") ms.commit() else: queuemode = False if WorkflowExists: ## reload the Workflow and start from last run run="None" ms.publish("RequestInjector:LoadWorkflows",'') ms.commit() time.sleep(0.1) workflowBase=os.path.basename(workflow) ms.publish("RequestInjector:SelectWorkflow", workflowBase) ms.commit() else: ## set the workflow for the first time and set the compulsory the initial run if run == "None": run = firstrun if run == "None": msg="Error: This is a NEW Workflow so it's compulsory to provide an initial Run number! You can: \n a) use the --run option \n b) set FirstRun in the RequestInjector configuration block in $PRODAGENT_CONFIG" print msg sys.exit(1) ms.publish("RequestInjector:SetWorkflow", workflow) ms.commit() time.sleep(0.1) ms.publish("RequestInjector:SetInitialRun", str(run)) ms.commit() if sitePref != None: ms.publish("RequestInjector:SetSitePref", sitePref) ms.commit() time.sleep(0.1) ms.publish("RequestInjector:SetEventsPerJob", str(nevts)) ms.commit() time.sleep(2) ## Set New Dataset ms.publish("RequestInjector:NewDataset",'') ms.commit() ## Loop over jobs if run != "None": runcomment=" run %s"%str(run) else: runcomment=" last run for %s "%workflowBase if queuemode: print " Trying to insert in JobQueue %s jobs with %s events each starting from %s "%(njobs,str(nevts),runcomment) else: print " Trying to submit %s jobs with %s events each starting from %s"%(njobs,str(nevts),runcomment) if submissiontype == "GliteBulk" or submissiontype == "BossLite": ms.publish("RequestInjector:ResourcesAvailable", str(njobs) ) ms.commit() else: njobs=njobs+1 for i in range(1, njobs): ms.publish("RequestInjector:ResourcesAvailable","none") ms.commit()
[ "" ]
9db249d2ef36520a60d54f9e60ae144e9e92038e
932ce227a7b641cf5243a61dc97ea7c40455ef89
/api/celery.py
d0261c6928ed44368604cc9382ac64e711fa5492
[]
no_license
aserguie/SeelkCoinAPI
d1d3b8a8bc3f86f2680753ecca88c265a795d363
084007153c3614e830874fc8ceade30975b4927c
refs/heads/master
2022-12-10T05:53:25.158661
2019-11-19T10:40:42
2019-11-19T10:40:42
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2022-04-22T22:41:55
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import os from celery import Celery # set the default Django settings module for the 'celery' program. os.environ.setdefault("DJANGO_SETTINGS_MODULE", "api.settings") app = Celery("api", broker="redis://", backend="redis://", include=["api.tasks"]) # Using a string here means the worker doesn't have to serialize # the configuration object to child processes. # - namespace='CELERY' means all celery-related configuration keys # should have a `CELERY_` prefix. app.config_from_object("django.conf:settings", namespace="CELERY") # Load task modules from all registered Django app configs. app.autodiscover_tasks() @app.task(bind=True) def debug_task(self): print("Request: {0!r}".format(self.request))
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# -*- coding: utf-8 -*- from datetime import date, datetime def ddmmyy(value): if isinstance(value, (date, datetime)): return value.strftime('%d.%m.%y') else: return u'--'
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import Observer import random # This class represents a generic monster class Monster(Observer.Observable): def __init__(self, house): super().__init__() self.house = house # set the label to something memorable so we can # catch weird bugs self.label = "__MON__" self.setObser(house) # Called to attack the player, in accordance with the monster's # attack range def attack(self, player): damage = random.uniform(self.attrange[0], self.attrange[1]) player.getHit(damage) return damage # Called when the player attacks the monster def getHit(self, damage, weapon): # Apply any vulnerabilities if weapon in self.vuln: damage = damage * self.vuln[weapon] self.health = self.health - damage # If we die, let the house know if self.health <= 0: self.show() return damage
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#!/usr/bin/python # -*- coding: utf-8 -*- from __future__ import print_function #################################################### # # Uebung: # Erstellen Sie ein Programm, welches drei Threads startet # Der erste Thread läuft 8 Sekunden, der zweite 4 Sekunden und der dritte 6 Sekunden # Nehmen Sie als Vorlage , die vorhergehenden Folie. # #################################################### #### Lösung: #### import time import _thread t = [] def show(c, s): t.append(c) print("Starte Thread", c, "mit", s, "Sek.") time.sleep(s) t.remove(c) _thread.start_new_thread(show, (1, 12,)) time.sleep(0.5) _thread.start_new_thread(show, (2, 22,)) time.sleep(0.5) _thread.start_new_thread(show, (3, 18,)) time.sleep(0.5) _thread.start_new_thread(show, (4, 14,)) time.sleep(0.5) _thread.start_new_thread(show, (5, 21,)) time.sleep(0.5) _thread.start_new_thread(show, (6, 19,)) time.sleep(0.5) _thread.start_new_thread(show, (7, 15,)) time.sleep(0.5) _thread.start_new_thread(show, (8, 18,)) time.sleep(0.5) _thread.start_new_thread(show, (9, 13,)) time.sleep(0.5) _thread.start_new_thread(show, (10, 14,)) time.sleep(0.5) while t: print("Warte auf Ende der Threads", t) time.sleep(1) print("Ende der Threads")
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class BaseHeap: def upheap(self, p): pass def downheap(self, p): pass def
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from websocket import create_connection import json ws = create_connection("ws://localhost:8080/event") msg = "" while msg != "sair": print("Teste de funções (wey): 1 - jogar 2 - ingame 3 - end") msg = input("Digita o função >> ") username = input("Digita o username >> ") if msg == "sair": quit() if msg == "1": ws.send(json.dumps({"function":"jogar","username":username})) if msg == "2": ws.send(json.dumps({"function":"ingame","username":username})) if msg == "3": ws.send(json.dumps({"function":"end","username":username})) else quit() result = ws.recv() print("Reposta do server >> '%s'" % result) ws.close()
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"""Network Authentication Helpers Contains interface (MultiDomainBasicAuth) and associated glue code for providing credentials in the context of network requests. """ import urllib.parse from typing import Any, Dict, List, Optional, Tuple from pip._vendor.requests.auth import AuthBase, HTTPBasicAuth from pip._vendor.requests.models import Request, Response from pip._vendor.requests.utils import get_netrc_auth from pip._internal.utils.logging import getLogger from pip._internal.utils.misc import ( ask, ask_input, ask_password, remove_auth_from_url, split_auth_netloc_from_url, ) from pip._internal.vcs.versioncontrol import AuthInfo logger = getLogger(__name__) Credentials = Tuple[str, str, str] try: import keyring except ImportError: keyring = None except Exception as exc: logger.warning( "Keyring is skipped due to an exception: %s", str(exc), ) keyring = None def get_keyring_auth(url: Optional[str], username: Optional[str]) -> Optional[AuthInfo]: """Return the tuple auth for a given url from keyring.""" global keyring if not url or not keyring: return None try: try: get_credential = keyring.get_credential except AttributeError: pass else: logger.debug("Getting credentials from keyring for %s", url) cred = get_credential(url, username) if cred is not None: return cred.username, cred.password return None if username: logger.debug("Getting password from keyring for %s", url) password = keyring.get_password(url, username) if password: return username, password except Exception as exc: logger.warning( "Keyring is skipped due to an exception: %s", str(exc), ) keyring = None return None class MultiDomainBasicAuth(AuthBase): def __init__( self, prompting: bool = True, index_urls: Optional[List[str]] = None ) -> None: self.prompting = prompting self.index_urls = index_urls self.passwords: Dict[str, AuthInfo] = {} # When the user is prompted to enter credentials and keyring is # available, we will offer to save them. If the user accepts, # this value is set to the credentials they entered. After the # request authenticates, the caller should call # ``save_credentials`` to save these. self._credentials_to_save: Optional[Credentials] = None def _get_index_url(self, url: str) -> Optional[str]: """Return the original index URL matching the requested URL. Cached or dynamically generated credentials may work against the original index URL rather than just the netloc. The provided url should have had its username and password removed already. If the original index url had credentials then they will be included in the return value. Returns None if no matching index was found, or if --no-index was specified by the user. """ if not url or not self.index_urls: return None for u in self.index_urls: prefix = remove_auth_from_url(u).rstrip("/") + "/" if url.startswith(prefix): return u return None def _get_new_credentials( self, original_url: str, allow_netrc: bool = True, allow_keyring: bool = False, ) -> AuthInfo: """Find and return credentials for the specified URL.""" # Split the credentials and netloc from the url. url, netloc, url_user_password = split_auth_netloc_from_url( original_url, ) # Start with the credentials embedded in the url username, password = url_user_password if username is not None and password is not None: logger.debug("Found credentials in url for %s", netloc) return url_user_password # Find a matching index url for this request index_url = self._get_index_url(url) if index_url: # Split the credentials from the url. index_info = split_auth_netloc_from_url(index_url) if index_info: index_url, _, index_url_user_password = index_info logger.debug("Found index url %s", index_url) # If an index URL was found, try its embedded credentials if index_url and index_url_user_password[0] is not None: username, password = index_url_user_password if username is not None and password is not None: logger.debug("Found credentials in index url for %s", netloc) return index_url_user_password # Get creds from netrc if we still don't have them if allow_netrc: netrc_auth = get_netrc_auth(original_url) if netrc_auth: logger.debug("Found credentials in netrc for %s", netloc) return netrc_auth # If we don't have a password and keyring is available, use it. if allow_keyring: # The index url is more specific than the netloc, so try it first # fmt: off kr_auth = ( get_keyring_auth(index_url, username) or get_keyring_auth(netloc, username) ) # fmt: on if kr_auth: logger.debug("Found credentials in keyring for %s", netloc) return kr_auth return username, password def _get_url_and_credentials( self, original_url: str ) -> Tuple[str, Optional[str], Optional[str]]: """Return the credentials to use for the provided URL. If allowed, netrc and keyring may be used to obtain the correct credentials. Returns (url_without_credentials, username, password). Note that even if the original URL contains credentials, this function may return a different username and password. """ url, netloc, _ = split_auth_netloc_from_url(original_url) # Try to get credentials from original url username, password = self._get_new_credentials(original_url) # If credentials not found, use any stored credentials for this netloc if username is None and password is None: username, password = self.passwords.get(netloc, (None, None)) if username is not None or password is not None: # Convert the username and password if they're None, so that # this netloc will show up as "cached" in the conditional above. # Further, HTTPBasicAuth doesn't accept None, so it makes sense to # cache the value that is going to be used. username = username or "" password = password or "" # Store any acquired credentials. self.passwords[netloc] = (username, password) assert ( # Credentials were found (username is not None and password is not None) # Credentials were not found or (username is None and password is None) ), f"Could not load credentials from url: {original_url}" return url, username, password def __call__(self, req: Request) -> Request: # Get credentials for this request url, username, password = self._get_url_and_credentials(req.url) # Set the url of the request to the url without any credentials req.url = url if username is not None and password is not None: # Send the basic auth with this request req = HTTPBasicAuth(username, password)(req) # Attach a hook to handle 401 responses req.register_hook("response", self.handle_401) return req # Factored out to allow for easy patching in tests def _prompt_for_password( self, netloc: str ) -> Tuple[Optional[str], Optional[str], bool]: username = ask_input(f"User for {netloc}: ") if not username: return None, None, False auth = get_keyring_auth(netloc, username) if auth and auth[0] is not None and auth[1] is not None: return auth[0], auth[1], False password = ask_password("Password: ") return username, password, True # Factored out to allow for easy patching in tests def _should_save_password_to_keyring(self) -> bool: if not keyring: return False return ask("Save credentials to keyring [y/N]: ", ["y", "n"]) == "y" def handle_401(self, resp: Response, **kwargs: Any) -> Response: # We only care about 401 responses, anything else we want to just # pass through the actual response if resp.status_code != 401: return resp # We are not able to prompt the user so simply return the response if not self.prompting: return resp parsed = urllib.parse.urlparse(resp.url) # Query the keyring for credentials: username, password = self._get_new_credentials( resp.url, allow_netrc=False, allow_keyring=True, ) # Prompt the user for a new username and password save = False if not username and not password: username, password, save = self._prompt_for_password(parsed.netloc) # Store the new username and password to use for future requests self._credentials_to_save = None if username is not None and password is not None: self.passwords[parsed.netloc] = (username, password) # Prompt to save the password to keyring if save and self._should_save_password_to_keyring(): self._credentials_to_save = (parsed.netloc, username, password) # Consume content and release the original connection to allow our new # request to reuse the same one. resp.content resp.raw.release_conn() # Add our new username and password to the request req = HTTPBasicAuth(username or "", password or "")(resp.request) req.register_hook("response", self.warn_on_401) # On successful request, save the credentials that were used to # keyring. (Note that if the user responded "no" above, this member # is not set and nothing will be saved.) if self._credentials_to_save: req.register_hook("response", self.save_credentials) # Send our new request new_resp = resp.connection.send(req, **kwargs) new_resp.history.append(resp) return new_resp def warn_on_401(self, resp: Response, **kwargs: Any) -> None: """Response callback to warn about incorrect credentials.""" if resp.status_code == 401: logger.warning( "401 Error, Credentials not correct for %s", resp.request.url, ) def save_credentials(self, resp: Response, **kwargs: Any) -> None: """Response callback to save credentials on success.""" assert keyring is not None, "should never reach here without keyring" if not keyring: return creds = self._credentials_to_save self._credentials_to_save = None if creds and resp.status_code < 400: try: logger.info("Saving credentials to keyring") keyring.set_password(*creds) except Exception: logger.exception("Failed to save credentials")
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#!/usr/bin/env python3 from Bio import SeqIO ########### # GLOBALS # ########### fa = snakemake.input['fa'] output_fa = snakemake.output['fa'] target_chr = ['Group1.4', 'Group3.5'] ######## # MAIN # ######## scaffolds = [x for x in SeqIO.parse(fa, 'fasta')] kept_scaffolds = [x for x in scaffolds if x.id in target_chr] #kept_scaffolds = scaffolds SeqIO.write(kept_scaffolds, output_fa, 'fasta')
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''' 4. На входе имеем список строк разной длины. Необходимо написать функцию all_eq(lst), которая вернет новый список из строк одинаковой длины. Длину итоговой строки определяем исходя из самой большой из них. Если конкретная строка короче самой длинной, дополнить ее нижними подчеркиваниями с правого края до требуемого количества символов. Расположение элементов начального списка не менять. ''' def all_eq(lst): max_len = len(max(lst, key=len)) for i in range(len(lst)): cur_len = len(lst[i]) if cur_len < max_len: lst[i] += ("_" * (max_len - cur_len)) return lst lst_ = ["Андрей", "Александр", "Константин", "Владислав", "Даниил", "Роман", "Лев", "Ян", "Синхрофазотрон"] print(all_eq(lst_))
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map_maze = { 'A': [('B', 5)], 'B': [('A', 5), ('C', 7), ('F', 2)], 'C': [('B', 7), ('L', 8)], 'D': [('E', 3)], 'E': [('D', 3), ('I', 6)], 'F': [('B', 2), ('G', 5), ('J', 6)], 'G': [('F', 5), ('K', 6)], 'H': [('I', 3)], 'I': [('E', 6), ('J', 2), ('H', 3)], 'J': [('F', 6), ('I', 2), ('K', 5), ('O', 2)], 'K': [('G', 6), ('J', 5), ('L', 2), ('T', 9)], 'L': [('C', 8), ('K', 2), ('U', 9)], 'M': [('N', 3)], 'N': [('M', 3), ('O', 2), ('R', 7)], 'O': [('J', 2), ('N', 2), ('P', 3)], 'P': [('O', 3), ('S', 7)], 'Q': [('R', 3)], 'R': [('N', 7), ('Q', 3), ('S', 5)], 'S': [('P', 7), ('R', 5), ('T', 2)], 'T': [('K', 9), ('S', 2), ('U', 2)], 'U': [('L', 9), ('T', 2)] } first_state = 'A' objective_state = 'Q' state = first_state way = [] count_cost = 0 parametro = False visited = [first_state] def get_adjacent_not_visited(state): global visited global map_maze states = map_maze[state] return_ = [] for s in states: if s[0] not in visited: return_.append(s) return return_ def get_min_way(state): global vertice global count_cost global map_maze list_vertice = get_adjacent_not_visited(state) if (len(list_vertice) == 0): aux1 = way[len(way) - 1] way.remove(way[len(way) - 1]) aux2 = way[len(way) - 1] states = map_maze[aux2] for s in states: if aux1.__eq__(s[0]): h = s[1] count_cost = count_cost - h return aux2 aux = True for x in list_vertice: if(aux): vertice = x aux = False else: if (x[1] < vertice[1]): vertice = x return vertice way.append(first_state) y = get_min_way(state) way.append(y[0]) count_cost = count_cost + y[1] visited.append(y[0]) state = y[0] while parametro is not True: if not (y[0].__eq__(way[len(way) -1])): way.append(y[0]) count_cost = count_cost + y[1] visited.append(y[0]) state = y[0] else: state = y[0] if(objective_state.__eq__(y[0])): parametro = True break; else: y = get_min_way(state) print("Caminho resultante: %s" % way) print("Custo do caminho: %s" % count_cost)
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# -*- coding: utf-8 -*- """ Created on Sat May 4 11:07:21 2019 @author: Aarav """ class calculator: a=5 b=10 def __init__(self): self.a=10 self.b=20 def addition(self): return self.a+self.b def subtraction(self): return self.a - self.b def multiplication(self): return self.a*self.b def divison(self): try: return self.a/self.b except: print("Exception occured") def all(self): add=self.a+self.b sub=self.a-self.b mul=self.a*self.b div=self.a/+self.b return add,sub,mul,div def printall(self): print("Arithematic opertion on the numbers: {0} {1}" .format(self.a,self.b)) print("Addition : {} " . format(self.addition())) print("Subtraction is :" + str(self.subtraction())) print("Multiplication is :" + str(self.multiplication())) print("Division is :" + str(self.divison())) print("Addition,subtraction,multiplication,division"+ str(self.all())) c=calculator() c.printall() print(c.__module__) print(__name__)
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from django.db import models from django.contrib.auth.models import User SKILL_CHOICES = ( ("No prior experience", "No prior experience"), ("Novice", "Novice"), ("Intermediate", "Intermediate"), ("Skilled", "Skilled"), ("Expert", "Expert") ) class Games(models.Model):#all max_length and other limiters subject to change name = models.TextField(default="No name avaliable") description = models.TextField(default= "No description avaliable") maker = models.CharField(max_length=50, default=" ") category = models.CharField(max_length=50, default="No data") mechanics = models.TextField(default="No data") date_published = models.CharField(max_length=15, default=" ")#should I just change this to a string(charfield)? amount_owned = models.PositiveIntegerField(default="0") average_rating = models.DecimalField(max_digits=5, decimal_places=1, default=0.00000) comments = models.TextField(default="This game has no comments yet.") playtime = models.PositiveIntegerField(default="0") optimal_age = models.PositiveIntegerField(default="0") image_path = models.TextField(default="/static/mtg.jpeg") class Groups(models.Model): name = models.CharField(max_length=90, verbose_name="group name") region = models.CharField(max_length=30, blank=True) address = models.CharField(max_length=60, default="Placeholder address") games = models.TextField(default=" ", verbose_name="Games we play") special_rules = models.TextField(blank=True, verbose_name="house rules") skill_level = models.CharField(default="No skill level selected", max_length=30) #members = models.ManyToManyField(User, related_name="memberlist") email = models.EmailField(blank=True) phone = models.TextField(blank=True) schedule_date = models.DateField(auto_now=False, default='2013-10-13', verbose_name="event date") schedule_time = models.TimeField(auto_now=False, verbose_name="event time", default='00:00:00') schedule_event = models.TextField(default="Please check back soon for our first scheduled event!", verbose_name="event") image_path = models.CharField(default="/static/mtg.jpg", max_length=70, verbose_name="group picture", blank=True) private_group = models.BooleanField(default=False) games_link = models.ManyToManyField(Games, related_name="group games") admin_id = models.IntegerField(default=0) class UserExtension(models.Model): user_link = models.OneToOneField(User, related_name="extension") friends = models.ManyToManyField('self') group_link = models.ManyToManyField(Groups, related_name="members") games_link = models.ManyToManyField(Games, related_name="link_to_games") city = models.CharField(max_length=30, blank=True) characteristics = models.TextField(max_length=255, blank=True) game_pref1 = models.CharField(max_length=30, verbose_name="Game preference") game_pref2 = models.CharField(max_length=30, blank=True, verbose_name="Second preference") game_pref3 = models.CharField(max_length=30, blank=True, verbose_name="Third preference") skill = models.CharField(blank=True, max_length=30, choices=SKILL_CHOICES, verbose_name="Experience") phone = models.CharField(blank=True, max_length=10) facebook = models.URLField(blank=True) image_path = models.CharField(default="/static/mtg.jpg", max_length=100, verbose_name="Profile picture") #class User(models.Model) #extension = link back to all extended user fields #DON'T FORGET ABOUT MANY-TO-MANY BETWEEN GROUPS AND GAMES #DON'T FORGET ABOUT POSITIVE INTERGER FIELDS FOR LATER
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from django.contrib import admin # Register your models here. from .models import Color admin.site.register(Color)
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#encoding:UTF-8 import math import numpy as np import pandas as pd import datetime class IndicatorCalculation(object): @staticmethod def calculateMaxDD(ytdYieldList): #highestYield = -float("inf") highestYield = ytdYieldList[0] maxDD = 0.0 currentDD = 0.0 winDays = 0.0 lossDays = 0.0 avgWinPnl = 0.0 avgLossPnl = 0.0 sumWinPnl = 0.0 sumLossPnl = 0.0 yesterdayPnl = 0.0 winRatio = 0.0 lossRatio = 0.0 for ytd in ytdYieldList: if(ytd > highestYield): highestYield = ytd currentDD = (1 + ytd)/(1 + highestYield) - 1 if(currentDD < maxDD): maxDD = currentDD todayPnl = ytd - yesterdayPnl if(todayPnl > 0): sumWinPnl += todayPnl winDays += 1 elif(todayPnl < 0): sumLossPnl += todayPnl lossDays += 1 yesterdayPnl = ytd if(winDays > 0): avgWinPnl = sumWinPnl/winDays if(lossDays > 0): avgLossPnl = sumLossPnl/lossDays if(len(ytdYieldList) >= 2): dtdYield = ytdYieldList[-1] - ytdYieldList[-2] elif(len(ytdYieldList) == 1): dtdYield = ytdYieldList[0] else: dtdYield = 0.0 if(len(ytdYieldList) > 0): winRatio = winDays/len(ytdYieldList) lossRatio = lossDays/len(ytdYieldList) return (dtdYield, highestYield, maxDD, currentDD, avgWinPnl, winRatio, avgLossPnl, lossRatio) @staticmethod def calculateRecovery(bookYtdGrossReturnDataframe,fundId,bookId): if bookId == 'None': bookId=0 fundAndBookData = bookYtdGrossReturnDataframe[(bookYtdGrossReturnDataframe['FundId'] == int(fundId)) & (bookYtdGrossReturnDataframe['BookId'] == int(bookId))].copy() recovered = 'Not Yet' fundAndBookData.index = pd.to_datetime(fundAndBookData['Date']) fundAndBookData['Date'] = pd.to_datetime(fundAndBookData['Date']) fundAndBookData.sort_index(ascending=True, inplace=True) firstDate = fundAndBookData['Date'].iloc[0] previousDateData = pd.DataFrame([0], columns={'YtdGrossReturn'}, index=[firstDate - datetime.timedelta(days=1)]) #离散return 公式 : (p_t / p_t - 1) - 1 #离散累计return: (1 + 离散return).cumprod() fundAndBookData = pd.concat([fundAndBookData, previousDateData], axis=0) fundAndBookData.sort_index(ascending=True, inplace=True) fundAndBookData.dropna(subset=['YtdGrossReturn'], how='all', inplace=True) #fundAndBookData['YtdGrossReturn'] = fundAndBookData['YtdGrossReturn'].fillna(0) fundAndBookData['PCT_CHG'] = (fundAndBookData['YtdGrossReturn'] - fundAndBookData['YtdGrossReturn'].shift(1)) / (1 + fundAndBookData['YtdGrossReturn'].shift(1)) fundAndBookData['CUM_RET'] = (1+fundAndBookData['PCT_CHG']).astype(float).cumprod() fundAndBookData.dropna(subset=['CUM_RET'], how='all', inplace=True) #连续的cumulative return,但由于PCT change不是连续的,故不适用 #fundAndBookData['CUM_RET'] = fundAndBookData['PCT_CHG'].astype(float).cumsum().apply(np.exp) if not fundAndBookData.empty: fundAndBookData['CUM_MAX'] = fundAndBookData['CUM_RET'].cummax() fundAndBookData['CurrentDD'] = (fundAndBookData['CUM_RET'] /fundAndBookData['CUM_MAX']) -1 maxDD = fundAndBookData['CurrentDD'].min() maxDDDate = fundAndBookData[fundAndBookData['CurrentDD'] == maxDD].index[0] CumReturnBeforeMaxDD = fundAndBookData[fundAndBookData['Date'] <= maxDDDate]['CUM_RET'].max() CumReturnAfterMaxDD = fundAndBookData[fundAndBookData['Date'] > maxDDDate]['CUM_RET'].max() if CumReturnAfterMaxDD > CumReturnBeforeMaxDD: recovered = 'Recovered' maxDDPeriodData = fundAndBookData[fundAndBookData['Date'] <= maxDDDate] duplicated_test = maxDDPeriodData.duplicated(subset=['Date'],keep=False) duplicated_data = maxDDPeriodData[duplicated_test] if not duplicated_data.empty: ##如有重复,只保留Marking source validData = duplicated_data[duplicated_data['Source'] == 'Marking'] if validData.shape[0] ==1: maxDDPeriodData.drop_duplicates(subset='Date', inplace=True, keep=False) maxDDPeriodData = pd.concat([maxDDPeriodData, validData], axis=0) maxDDPeriodData.sort_index(ascending=True, inplace=True) else: raise Exception('duplicate data for fundid:'+str(fundId)+', and bookId:'+str(bookId)+', pls check Nav table') maxDDStartDate = maxDDPeriodData.ix[maxDDPeriodData['CUM_RET'].idxmax(),'Date'] maxDDStartDateStr = maxDDStartDate.strftime('%Y-%m-%d') maxDDDateStr = maxDDDate.strftime('%Y-%m-%d') another_maxDD = maxDD return (another_maxDD, maxDDStartDateStr, maxDDDateStr, recovered) return (0, None, None, None) @staticmethod def calculateRecoveryWithPct(bookYtdGrossReturnDataframe, fundId, bookId): if bookId == 'None': bookId = 0 fundAndBookData = bookYtdGrossReturnDataframe[(bookYtdGrossReturnDataframe['FundId'] == int(fundId)) & ( bookYtdGrossReturnDataframe['BookId'] == int(bookId))].copy() recovered = 'Not Yet' fundAndBookData.index = pd.to_datetime(fundAndBookData['Date']) fundAndBookData['Date'] = pd.to_datetime(fundAndBookData['Date']) fundAndBookData.sort_index(ascending=True, inplace=True) firstDate = fundAndBookData['Date'].iloc[0] # 离散return 公式 : (p_t / p_t - 1) - 1 # 离散累计return: (1 + 离散return).cumprod() fundAndBookData['CUM_RET'] = (1 + fundAndBookData['PCT_CHG']).astype(float).cumprod() fundAndBookData.dropna(subset=['CUM_RET'], how='all', inplace=True) # 连续的cumulative return,但由于PCT change不是连续的,故不适用 # fundAndBookData['CUM_RET'] = fundAndBookData['PCT_CHG'].astype(float).cumsum().apply(np.exp) if not fundAndBookData.empty: fundAndBookData['YTD'] = (1 + fundAndBookData['PCT_CHG']).astype(float).cumprod() - 1 ytdYieldList = fundAndBookData['YTD'].tolist() annualRtn = fundAndBookData['YTD'].iloc[-1] / len(ytdYieldList) * 250 (annualVol, annualRtn, annualSharpe) = IndicatorCalculation.calculateAnnualVolatilitySharpe(ytdYieldList,tradeDays=250) fundAndBookData['CUM_MAX'] = fundAndBookData['CUM_RET'].cummax() fundAndBookData['CurrentDD'] = (fundAndBookData['CUM_RET'] / fundAndBookData['CUM_MAX']) - 1 currentDD = fundAndBookData['CurrentDD'].iloc[-1] maxDD = fundAndBookData['CurrentDD'].min() maxDDDate = fundAndBookData[fundAndBookData['CurrentDD'] == maxDD].index[0] CumReturnBeforeMaxDD = fundAndBookData[fundAndBookData['Date'] <= maxDDDate]['CUM_RET'].max() CumReturnAfterMaxDD = fundAndBookData[fundAndBookData['Date'] > maxDDDate]['CUM_RET'].max() if CumReturnAfterMaxDD > CumReturnBeforeMaxDD: recovered = 'Recovered' maxDDPeriodData = fundAndBookData[fundAndBookData['Date'] <= maxDDDate] duplicated_test = maxDDPeriodData.duplicated(subset=['Date'], keep=False) duplicated_data = maxDDPeriodData[duplicated_test] if not duplicated_data.empty: ##如有重复,只保留Marking source validData = duplicated_data[duplicated_data['Source'] == 'Marking'] if validData.shape[0] == 1: maxDDPeriodData.drop_duplicates(subset='Date', inplace=True, keep=False) maxDDPeriodData = pd.concat([maxDDPeriodData, validData], axis=0) maxDDPeriodData.sort_index(ascending=True, inplace=True) else: raise Exception('duplicate data for fundid:' + str(fundId) + ', and bookId:' + str( bookId) + ', pls check Nav table') maxDDStartDate = maxDDPeriodData.ix[maxDDPeriodData['CUM_RET'].idxmax(), 'Date'] maxDDStartDateStr = maxDDStartDate.strftime('%Y-%m-%d') maxDDDateStr = maxDDDate.strftime('%Y-%m-%d') another_maxDD = maxDD return (another_maxDD, maxDDStartDateStr, maxDDDateStr, recovered, annualRtn, currentDD, annualVol, annualSharpe) return (0, None, None, None,0,0,0,0) @staticmethod def calculateAnnualVolatilitySharpe(ytdYieldList, tradeDays = 252): try: dtdYieldList = [] i = 0 for ytd in ytdYieldList: if(i == 0): dtdYieldList.append(ytd) else: dtdYieldList.append((ytdYieldList[i] - ytdYieldList[i-1])/(1+ytdYieldList[i-1])) i += 1 sumYtd = 0.0 avgYtd = 0.0 for ytd in dtdYieldList: sumYtd += ytd avgYtd = sumYtd/len(dtdYieldList) squareSum = 0.0 for ytd in dtdYieldList: squareSum += (ytd - avgYtd) * (ytd - avgYtd) annualRtn = ytdYieldList[-1]/len(ytdYieldList) * tradeDays annualVol = math.sqrt(squareSum/(len(dtdYieldList) - 1)) * math.sqrt(tradeDays - 2) #why minus 2 annualSharpe = annualRtn/annualVol return (annualVol, annualRtn, annualSharpe) except Exception,e: return (0.0, 0.0, 0.0) if __name__ == '__main__': ytdYieldList = [0.98, 0.99, 0.97, 1, 1.02, 1.01, 1.05, 0.9, 0.98, 1.02] print IndicatorCalculation.calculateMaxDD(ytdYieldList) print IndicatorCalculation.calculateAnnualVolatilitySharpe(ytdYieldList)
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/Part 1 - Getting Started/2 - Key Types/PublishSubject.py
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from rx.subjects import Subject # In RxPY Subject instead of PublishSubject as in RxJava subject = Subject() subject.on_next(1) subject.subscribe(on_next=print) subject.on_next(2) subject.on_next(3) subject.on_next(4)
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/sender/tesla.py
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import traceback import time import json import random import logging import threading import click import requests import teslapy LOG_FORMAT = '%(asctime)s %(levelname)s %(module)s(%(lineno)d) - %(message)s' DATE_FORMAT = '%m/%d %H:%M:%S' logging.basicConfig(format=LOG_FORMAT, datefmt=DATE_FORMAT, level=logging.INFO) logger = logging.getLogger('omw_tesla_sender') logger.setLevel(logging.DEBUG) start_time = int(time.time()) class MockTesla: def __init__(self): with open('tests/fixtures/tesla.json') as f: self._data = json.load(f) logger.info('cache data loaded') def get_vehicle_data(self): self._data['drive_state']['latitude'] += random.random() / 100 self._data['drive_state']['longitude'] += random.random() / 100 logger.debug('getting vehicle data') return self._data def send_data(session, interval, duration, host, vehicle): session_left = duration - (int(time.time()) - start_time) logger.info(f'sending location data... Session time left: {session_left}s') if session_left < 0: exit(0) try: drive_state = vehicle.get_vehicle_data()['drive_state'] now = int(time.time()) payload = { 'next_refresh': now + interval, 'vehicle': drive_state, 'timestamp': now } requests.post(f'{host}/location?session={session}', json=payload) except Exception as err: logger.error('failed to send location data') print(traceback.format_exc()) return @click.command() @click.option('--email', '-e', help='the email address of your Tesla account', envvar='TESLA_EMAIL') @click.option('--password', '-p', help='the password of your Tesla account', envvar='TESLA_PASSWORD') @click.option('--session', '-s', help='name of the session', required=True) @click.option('--interval', '-i', help='sending interval in seconds', default=10) @click.option('--duration', '-d', help='total session duration in minutes', default=60 * 60 * 24) @click.option('--host', '-h', default='http://localhost:5000') @click.option('--debug', is_flag=True, default=False) def tesla(email, password, session, interval, duration, host, debug): logger.info(f'sending location to {host} with interval {interval}s. Session duration {int(duration / 60)} minutes') if debug: logger.info('debug mode on, loading from fixture') vehicle = MockTesla() else: logger.info('connecting to Tesla server...') with teslapy.Tesla(email, password) as tesla: tesla.fetch_token() vehicles = tesla.vehicle_list() if len(vehicles) != 1: logger.error(f'unexpected number of vehicle found ({len(vehicles)})') exit(1) vehicle = vehicles[0] e = threading.Event() while not e.wait(interval): send_data(session, interval, duration, host, vehicle) if __name__ == '__main__': tesla()
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#!/Users/RGero13/Desktop/rgero215_PY1-10-2017/Multiple_Apps/djangoEnv/bin/python from django.core import management if __name__ == "__main__": management.execute_from_command_line()
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#!/home/sonic/Documents/latihan/flaskPACKT/env/bin/python3 # -*- coding: utf-8 -*- import re import sys from IPython import start_ipython if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(start_ipython())
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#!/usr/bin/python #-*- coding: utf-8 -*- import telegram #from telegram.ext import Updater from telegram.ext import Updater, CommandHandler import os myTocken ="354690332:AAFB8Dgjae9nLjbOlwbJHDSczCOXFKP-ybo" def ajuda(bot, update): text = "/quemtaai - Lista todos os membros da agência presentes\n/ajuda - exibe este menu\n/info - exibe informações sobre o desenvolvedor" bot.sendMessage(update.message.chat_id, text) def quemtaai(bot , update): os.system("./buscaNomes.sh") arq = open("listaNomes.txt","r") texto = arq.read(); arq.close() bot.sendMessage(update.message.chat_id, texto) def info(bot , update): text = "Erik Ferreira - [email protected] \n @ekdespe \n 71 9.8277-6545 " bot.sendMessage(update.message.chat_id, text) updater = Updater(myTocken) updater.dispatcher.add_handler(CommandHandler('ajuda', ajuda)) updater.dispatcher.add_handler(CommandHandler('info', info)) updater.dispatcher.add_handler(CommandHandler('quemtaai', quemtaai)) updater.start_polling() updater.idle()
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#!/usr/bin/env python3.5 import logging import os import sys import random import json HERE = os.path.abspath(os.path.dirname(os.path.realpath(__file__))) STATIC_PATH = os.path.join(HERE, 'static') from tornado import gen from tornado.websocket import WebSocketHandler from tornado.web import Application, RequestHandler, StaticFileHandler from tornado.ioloop import IOLoop class Offer: resp_cb = None def __init__(self, key=None, offer_payload=None): self.key = key self.offer_payload = offer_payload self.candidates = [] def __repr__(self): return '<Offer candidates=%d %r>' % (len(self.candidates), self.offer_payload) def wait_resp(self, callback=None): self.resp_cb = callback class Offers: def __init__(self): self.offers = {} @classmethod def mkkey(cls): return str(random.randint(10000, 99999)) # Just to be simple. def add(self, offer): self.offers[offer.key] = offer return offer def find(self, key): return self.offers[key] def pop(self, key): return self.offers.pop(key) offers = Offers() class OfferListingHandler(RequestHandler): def get(self): self.write({ 'offers': [{'key': key, 'resp_cb': repr(resp_cb)} for (key, resp_cb) in offers.offers.items()], }) class OfferHandler(WebSocketHandler): offer = None key = None def open(self): self.key = Offers.mkkey() def _ensure_offer(self): if self.offer is None: self.offer = Offer(key=self.key) return self.offer @gen.coroutine def on_message(self, s): msg = json.loads(s) print('msg', type(msg), repr(msg)) if msg['type'] == 'allocKey': self.write_message({ 'type': 'allocKeyResp', 'key': self.key, }) elif msg['type'] == 'offer': offer = offers.add(self._ensure_offer()) offer.offer_payload = msg self.write_message(json.dumps({ 'type': 'offer-created', })) resp = yield gen.Task(offer.wait_resp) self.write_message(json.dumps({ 'type': 'offer-accepted', 'resp': resp, })) elif msg['type'] == 'take-offer': offer = offers.find(msg['key']) self.write_message(offer.offer_payload) for c in offer.candidates: self.write_message(c) elif msg['type'] == 'answer': key = msg.pop('forKey') offer = offers.pop(key) offer.resp_cb(msg) elif msg['type'] == 'candidate': self._ensure_offer().candidates.append(msg) class NoCacheStaticFileHandler(StaticFileHandler): def set_extra_headers(self, path): self.set_header('Cache-Control', 'no-store, no-cache, must-revalidate, max-age=0') def mkapp(): return Application([ (r'/offer', OfferHandler), (r'/offers/list', OfferListingHandler), (r'/(.*)', NoCacheStaticFileHandler, { 'path': STATIC_PATH, }), ], gzip=True) def main(): port = 17080 mkapp().listen(port) print('Listening on :%d' % port) IOLoop.current().start() if __name__ == '__main__': main()
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/config/conf.py
aa2247f1303787036d7ea5fca1e3fa1d81a42f4c
[]
no_license
wmm0165/PytestAuto
d1bb40dcc5760439658c15af653953646119af44
42846b12ed7aefaa4e5890529ec71a76d27f245d
refs/heads/master
2020-07-16T16:35:19.962864
2019-09-16T10:08:37
2019-09-16T10:08:37
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py
from datetime import datetime import os # 项目根目录 ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # 报告目录 REPORT_DIR = os.path.join(ROOT_DIR, 'report') # ui对象库config.ini文件所在目录 CONF_PATH = os.path.join(ROOT_DIR, 'config', 'config.ini') # 测试数据所在目录 DATA_Path = os.path.join(ROOT_DIR, 'data', 'tcData.xlsx') # 当前时间 CURRENT_TIME = datetime.now().strftime('%H_%M_%S') # 邮件配置信息 # 邮件服务器 SMTP_SERVER = 'smtp.qq.com' # 发送者 FROM_USER = '[email protected]' # 发送者密码 FROM_PASSWORD = 'mhxvqpewblldbjhf' # 接收者 TO_USER = ['账号@qq.com'] # 可以同时发送给多人,追加到列表中 # 邮件标题 SUBJECT = 'xx项目自动化测试报告' # 邮件正文 CONTENTS = '测试报告正文' # 报告名称 HTML_NAME = 'testReport{}.html'.format(CURRENT_TIME) print(HTML_NAME)
8b92035f4b34e0556c903e155ba9a8618bf17529
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/test/test_bad_request_error_code.py
670128cb3664339498ad0e2fe8a03b0977a7c7ff
[]
no_license
cascadiarc/cyclos-python-client
8029ce07174f2fe92350a92dda9a60976b2bb6c2
a2e22a30e22944587293d51be2b8268bce808d70
refs/heads/main
2023-04-03T16:52:01.618444
2021-04-04T00:00:52
2021-04-04T00:00:52
354,419,532
0
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py
# coding: utf-8 """ Cyclos 4.11.5 API The REST API for Cyclos 4.11.5 # noqa: E501 OpenAPI spec version: 4.11.5 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import swagger_client from swagger_client.models.bad_request_error_code import BadRequestErrorCode # noqa: E501 from swagger_client.rest import ApiException class TestBadRequestErrorCode(unittest.TestCase): """BadRequestErrorCode unit test stubs""" def setUp(self): pass def tearDown(self): pass def testBadRequestErrorCode(self): """Test BadRequestErrorCode""" # FIXME: construct object with mandatory attributes with example values # model = swagger_client.models.bad_request_error_code.BadRequestErrorCode() # noqa: E501 pass if __name__ == '__main__': unittest.main()
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/算法入门/查找排序习题/习题3.py
62fdf650386650a471df10ed601c64ac166482f4
[]
no_license
forest-data/luffy_py_algorithm
ab10ca070cfbf17a08a61f88bfd9c3b3cb07f382
f6bec726ee98176b56b5ea556c1a521b693a80fb
refs/heads/master
2023-04-02T21:48:28.372136
2020-09-10T01:46:57
2020-09-10T01:46:57
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""" 3. 给 nums =【1,2,5,4】 target = 3 结果 返回 (0,1) """ from 算法入门.cal_time import cal_time class Solution: @cal_time def twoSum1(self, nums, target): for ind, val in enumerate(nums): if target - val in nums and ind != nums.index(target-val): return (ind, nums.index(target-val)) # 前一个数 和 后面的数比 @cal_time def twoSum2(self, nums, target): n = len(nums) for i in range(n): for j in range(i+1, n): # 跟后面的比 # 跟前面的比 for j in range(i) if nums[i] + nums[j] == target: return sorted([i, j]) def binary_search(self, li, left, right, val): # left = 0 # right = len(li)-1 while left <= right: mid = (left + right) // 2 if li[mid] == val: return mid elif li[mid] > val: right = mid - 1 else: left = mid + 1 else: return None # 假如列表是有序的, 查找 val - target 可采用二分查找 @cal_time def twoSum3(self, nums, target): for i in range(len(nums)): a = nums[i] b = target - a if b>=a: # j = self.binary_search(nums[i+1:], b) # 列表切片复杂度O(n) j = self.binary_search(nums, i+1, len(nums)-1, b) else: j = self.binary_search(nums, 0, i-1, b) if j: break return sorted([i,j]) def binary_search2(self, li, left, right, val): # left = 0 # right = len(li)-1 while left <= right: mid = (left + right) // 2 if li[mid][0] == val: return mid elif li[mid][0] > val: right = mid - 1 else: left = mid + 1 else: return None @cal_time def twoSum4(self, nums, target): new_nums = [[num, i] for i, num in enumerate(nums)] new_nums.sort(key= lambda x:x[0]) for i in range(len(new_nums)): a = new_nums[i][0] b = target - a if b >= a: # j = self.binary_search(nums[i+1:], b) # 列表切片复杂度O(n) j = self.binary_search2(new_nums, i + 1, len(new_nums) - 1, b) else: j = self.binary_search2(new_nums, 0, i - 1, b) if j: break return sorted([new_nums[i][1], new_nums[j][1]]) nums = [1,2,4,5] target = 3 print(Solution().twoSum1(nums, target)) print(Solution().twoSum2(nums, target)) print(Solution().twoSum3(nums, target)) print(Solution().twoSum4(nums, target))
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/Easy/599_minimumIndexSumOfTwoLists.py
d92b6e8b5c30aa160a4ed09faac635a69b9d9ca6
[]
no_license
Yucheng7713/CodingPracticeByYuch
505d18095d4b9a35c1f3b23632a90a76d811b64a
1461b10b8910fa90a311939c6df9082a8526f9b1
refs/heads/master
2022-05-01T11:51:00.612603
2022-04-18T09:46:55
2022-04-18T09:46:55
198,961,132
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class Solution: def findRestaurant(self, list1: List[str], list2: List[str]) -> List[str]: r_set = set(list1 + list2) map_1 = {res : i for i, res in enumerate(list1)} map_2 = {res : i for i, res in enumerate(list2)} common_res = [] min_sum = float('inf') for r in r_set: if r in map_1 and r in map_2: k = map_1[r] + map_2[r] if min_sum > k: common_res = [r] min_sum = k elif min_sum == k: common_resI += [r] return common_res
b4d37ac1239c80ecc49ed220ce9f6f19b293de14
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/功能说明文档/提取文本.py
eef2dbaea6b9c8983c213223f86a1d4861cdcd05
[]
no_license
xiaohaiz1/dxkStickIDE
8027d4962f23ccf48c5fa3c5448ff6cd28d29979
27cbdc9ae4bacc2361e53ebc8c05026f9d58719f
refs/heads/master
2021-03-27T00:07:48.456668
2019-11-17T03:46:57
2019-11-17T03:46:57
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UTF-8
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import os, sys, re from os import path func_name = re.compile(r'(?<=def )([A-Za-z0-9_]+)\((.*)\)') code_dir = '../plugins_setup/microbit_dxk/dxk_ext' output = open('Document_raw.md', 'w', encoding='utf-8') fprint = lambda *args, **kwargs: print(*args, **kwargs, file=output) fprint('# dxkStick 开发者文档') for filename in os.listdir(code_dir): if not filename.endswith('.py'): continue fprint(f'## 模块:{path.splitext(filename)[0]}') with open(path.join(code_dir, filename), encoding='utf-8') as file: for name, args in func_name.findall(file.read()): fprint(f"1. ### {name}({args.replace(',',', ')})") if args: fprint(f' #### 参数:') args = args.split(',') for arg in args: if '=' in arg: a, b = arg.split('=') fprint(f' - {a}(默认为{b}): ') else: fprint(f' - {arg}: ') else: fprint(f' #### 参数: 无') fprint(f' #### 返回值: ') fprint('---') output.close()
8449b0ad34f7cd388b0f486ff3029c4e45215abc
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/art_gallery/gallery/migrations/0012_remove_gallery_g_id.py
270601a74289d7b6ba8a3de57dbdd10be3ec2b2a
[]
no_license
MrunalKotkar/Art-Gallery
b6f1301f236d03af77ee4b09af069d00a3fb1b64
91936a076d34515c7efb858c8f5891b9a4d35689
refs/heads/main
2023-01-07T12:57:54.223138
2020-11-06T16:45:18
2020-11-06T16:45:18
309,106,380
2
3
null
2020-11-06T15:57:31
2020-11-01T13:51:23
HTML
UTF-8
Python
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py
# Generated by Django 3.1.2 on 2020-10-27 11:54 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('gallery', '0011_profile'), ] operations = [ migrations.RemoveField( model_name='gallery', name='g_id', ), ]
34aee97e06e7bc1273527c6d8c5021b48c57f2a5
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/Python Data Structure/Assignment/assignment5.py
d055e0670f996a2223eb32a8bf2757f1111596c2
[]
no_license
shahbazkhan22/Python_for_Everybody_Specialization
23d5b15e9688471fc31a61d8c18e08f9cf4f0028
0d098ccaab64eae484416fed4105aea727393d5e
refs/heads/master
2020-04-29T18:16:48.702294
2019-04-14T07:30:50
2019-04-14T07:30:50
176,318,793
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file = open('mbox-short.txt') count = 0 lst = [] for line in file: if line.startswith('From') and not line.startswith('From:'): #lst = line.split() print(line.rstrip().split()[1]) count = count+1 print('There were',count,'lines in the file with From as the first word')
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/stubhub.py
1350e96fe6d240a1adc6d8f4eefda851bc56ad6d
[]
no_license
jboenawan/Facebook_Chat
3c0c9371d0b46726997de0fc0054ad93b3360785
eb966001c7d1eded9106222187126aaf9bf4c6ec
refs/heads/master
2021-08-23T23:29:25.166366
2017-12-07T02:48:37
2017-12-07T02:48:37
113,389,937
0
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# application token: b0c594ce-9b1d-3796-8dbe-f4b55d7600f6 # iPwnVBEYbRgOswSyn2kTqZq0qcsa:HM3QaC1nnoRbdqJM91UD70gSvnMa # aVB3blZCRVliUmdPc3dTeW4ya1RxWnEwcWNzYTpITTNRYUMxbm5vUmJkcUpNOTFVRDcwZ1N2bk1hDQo= # Content-Type: application/x-www-form-urlencoded # Authorization: Basic basicAuthorizationToken import requests import base64 import json import pprint import pandas as pd import datetime ## Enter user's API key, secret, and Stubhub login app_token = 'b0c594ce-9b1d-3796-8dbe-f4b55d7600f6' consumer_key = 'iPwnVBEYbRgOswSyn2kTqZq0qcsa' consumer_secret = 'HM3QaC1nnoRbdqJM91UD70gSvnMa' stubhub_username = '[email protected]' stubhub_password = 'September13!' combo = consumer_key + ':' + consumer_secret basic_authorization_token = base64.b64encode(bytes(combo, 'utf-8')) print(basic_authorization_token) headers = { 'Content-Type':'application/x-www-form-urlencoded', 'Authorization':'Basic '+basic_authorization_token.decode('utf-8'),} body = { 'grant_type':'password', 'username':stubhub_username, 'password':stubhub_password, 'scope':'PRODUCTION'} url = 'https://api.stubhub.com/login' r = requests.post(url, headers=headers, data=body) token_response = r.json() access_token = token_response['access_token'] user_GUID = r.headers['X-StubHub-User-GUID'] print(r) print(r.text) print(token_response) # inventory_url = 'https://api.stubhub.com/search/inventory/v2' # eventid = '9670859' # data = {'eventid':eventid, 'rows':200} headers['Authorization'] = 'Bearer ' + access_token headers['Accept'] = 'application/json' headers['Accept-Encoding'] = 'application/json' # inventory = requests.get(inventory_url, headers=headers, params=data) # info_url = 'https://api.stubhub.com/catalog/events/v2/' + eventid city = 'New York' Testing_URL = "https://api.stubhub.com/search/catalog/events/v3?city={city_name}".format(city_name= city) info = requests.get(Testing_URL, headers=headers) pprint.pprint(info.json()) # print(info)
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/settings.py
e9e146d84c7c6c40a9d2bb27c732aab7ab72dccd
[]
no_license
dhanushraj2508/products
346f73317f5ee61ad0f0464ef3f25d7663a4ff27
57d7d242ef0f269ea0cf0269781988c73d44c4e7
refs/heads/master
2022-12-25T20:45:13.571309
2020-10-06T08:43:00
2020-10-06T08:43:00
301,618,655
0
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null
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""" Django settings for productdetails project. Generated by 'django-admin startproject' using Django 3.1.1. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ from pathlib import Path import os # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '*fu!^6kof7gp-%p%+$1bl4flk8c$8f#j0w81b29@6!n128m%45' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'productdetails' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'productdetails.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR,'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'productdetails.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/'
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/lambda_function.py
fb2c2809897ab79eca8116d17e19f0dd74416880
[]
no_license
jscaria/meetup-auto-rsvp
caaf422a88314e07e2ce9090100493395417b551
e1b84f827277a02e70f4a8300344ac74d9d127d5
refs/heads/master
2020-03-29T12:35:01.974103
2019-09-01T19:52:03
2019-09-01T19:52:03
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0
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import json from botocore.vendored import requests import logging import os import boto3 import datetime import pytz logger = logging.getLogger() logger.setLevel(logging.INFO) ses = boto3.client('ses') def lambda_handler(event, context): events_url = "https://api.meetup.com/{0!s}/events?sign=true&photo-host=public&page=10".format(os.environ['GROUP_NAME']) rsvp_url = "https://api.meetup.com/2/rsvp/" headers = { "Authorization": "Bearer {0!s}".format(os.environ["OAUTH_ACCESS_TOKEN"]) } events_request = requests.get(events_url, headers=headers) events_response = events_request.json() logger.debug("events_response: " + json.dumps(events_response, indent=4)) ''' [ { "local_time": "11:00", "local_date": "2018-09-09", "link": "https://www.meetup.com/<group_name>/events/<event_id>/", "visibility": "public_limited", "group": { "created": 1373082291000, "name": "<group_nane>", "id": 0, "join_mode": "approval", "lat": 37.31, "lon": -122, "urlname": "<group_url>", "who": "<people>", "localized_location": "<location>", "region": "en_US", "timezone": "US/Pacific" }, "waitlist_count": 0, "yes_rsvp_count": 23, "duration": 7200000, "time": 1536516000000, "utc_offset": -25200000, "name": "<name>", "id": "<id>" } ] ''' rsvp = "YES" responses = [] successful_rsvp = False for entry in events_response: event_id = entry["id"] logger.debug("event: event_id={0!s}&rsvp={1!s}".format(event_id, rsvp)) data = { "event_id": event_id, "rsvp": rsvp } rsvp_request = requests.post(rsvp_url, headers=headers, data=data) rsvp_response = rsvp_request.json() local_response = { "statusCode": rsvp_request.status_code, "body": rsvp_response } responses.append(local_response) if "code" not in rsvp_response or rsvp_response["code"] != "event_past": subject = 'Lambda function - ' + str(rsvp_request.status_code) data = json.dumps(rsvp_response, indent=4) if rsvp_request.status_code == 201: ts = int(rsvp_response["event"]["time"]/1000) tz = pytz.timezone("US/Pacific") event_time = datetime.datetime.utcfromtimestamp(ts).replace(tzinfo=pytz.utc) # in UTC event_time_in_local = event_time.astimezone(tz) # converted to US/Pacific event_time_string = event_time_in_local.strftime('%m-%d-%Y @ %H:%M') subject = 'RSVP {0!s} to {1!s} on {2!s}'.format(rsvp_response["response"], rsvp_response["event"]["name"], event_time_string) response = ses.send_email( Source = os.environ['EMAIL_FROM'], Destination={ 'ToAddresses': [ os.environ['EMAIL_TO'], ], }, Message={ 'Subject': { 'Data': subject }, 'Body': { 'Text': { 'Data': data } } } ) if rsvp_request.status_code == 201: successful_rsvp = True return responses return responses
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/Solutions/350_Intersection_Of_Two_Array_II.py
939ada7741d4713f7c111422eacc9c88f1e45d0d
[]
no_license
xtymichael/Leetcode_python
91ef24adf174cb8d2a3e99f2784995a7439987a1
be47b1e0d22174043a2596eae6f141912573c67f
refs/heads/master
2021-06-15T23:46:57.335941
2017-04-24T05:46:28
2017-04-24T05:46:28
39,522,606
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py
class Solution(object): def intersect(self, nums1, nums2): """ :type nums1: List[int] :type nums2: List[int] :rtype: List[int] """ c1 = collections.Counter(nums1) c2 = collections.Counter(nums2) result = [] for num in c1: if num in c2: result += [num] * min(c1[num], c2[num]) return result
f93ba8788473687f41c7a3b09f3253b2ad98a371
2662da5c82071f4fa03ee0fa1ce3fd8f4e78096a
/Algorithms/Implementation/Lisa's Workbook/solution.py
60e15530cb4e33aea6d950351d5453c92f3b55c2
[]
no_license
perrinod/hacker-rank-solutions
8e64122f15c87932059d7dec5a87bc2f64b23e9e
088e94a4d74516c25781be5fd85a50f09b7cbda2
refs/heads/master
2020-08-02T11:48:48.550299
2020-05-29T10:51:33
2020-05-29T10:51:33
211,340,553
2
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UTF-8
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#https://www.hackerrank.com/challenges/lisa-workbook/problem #!/bin/python3 import math import os import random import re import sys def workbook(n, k, arr): count, page = 0, 1 for problems in range(0, len(arr)): for i in range (1, arr[problems] + 1): if(i == page): count += 1 if(i % k == 0): page += 1 if(arr[problems] % k != 0): page += 1 return count if __name__ == '__main__': fptr = open(os.environ['OUTPUT_PATH'], 'w') nk = input().split() n = int(nk[0]) k = int(nk[1]) arr = list(map(int, input().rstrip().split())) result = workbook(n, k, arr) fptr.write(str(result) + '\n') fptr.close()
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7175d25162eaa96c9d5860563b9d134185a3e706
/input/topics.py
66e5b9d0a8005a2ce62f52067155a7e9925874fd
[]
no_license
wimboes/SpeechHub
3612b8d9b528162b86d84b2a9d822740eab032cb
808f360a804173c1d118b88ab252982133841097
refs/heads/master
2023-03-04T09:22:52.289028
2017-05-26T09:30:21
2017-05-26T09:30:21
69,444,761
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null
2022-10-22T00:56:11
2016-09-28T08:53:34
null
UTF-8
Python
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py
from gensim import models, corpora lda = models.LdaModel.load('lda_512_10.ds') dic = corpora.Dictionary.load('dictionary.ds') #word_to_id = dict() #for (wordid,word) in dic.iteritems(): # word_to_id[word] = wordid nb_topics = lda.num_topics for i in range(nb_topics): lst = lda.get_topic_terms(i,topn=10) lal = [dic[tup[0]] for tup in lst] print('topic ' + str(i)) print(lal)
be5e0b741dc6ad841c668031edaee115bfe5314e
36059411cedfeec7478fd725c43f2120ab5ad38d
/boulder_ftp.py
a055a456e089b72f19569b633bd3059a4fd66cd2
[]
no_license
tytechortz/Arctic-Ice
a8345746bdd2d73559941ea71efe06601212a7f1
83aac39c00027cca6bd85fd2709fcfe86cf3ef31
refs/heads/master
2022-07-09T13:38:41.735808
2020-01-02T16:22:52
2020-01-02T16:22:52
167,096,158
1
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2022-06-21T23:10:16
2019-01-23T01:35:54
Jupyter Notebook
UTF-8
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from ftplib import FTP import os import pandas as pd # Log into ftp site. ftp = FTP('sidads.colorado.edu') ftp.login(user='anonymous', passwd='ICE_PSWD') ftp.login() # Read file. ftp.cwd('/pub/DATASETS/NOAA/G02135/north/daily/data/') ftp.retrbinary('RETR N_seaice_extent_daily_v3.0.csv', open('N_seaice_extent_daily_v3.0.csv', 'wb').write) ftp.quit() # Read data. df = pd.read_csv('N_seaice_extent_daily_v3.0.csv',skiprows=[i for i in range(1,2436)]) # df.columns = [] pd.options.display.float_format = '{:,}'.format print(df.head())
44c37a6d6deff849d4ab0e9f88cf61dcde7a8413
07697d48b35e964a7d851a26c93508eb8abb1569
/xabr/urls.py
96462404368d1dead587bd1ad82d903db2fcce87
[]
no_license
Solntseva24/NewProjectXabr
58decc7961378ed1dca4a7c10744e2d1a0f9740b
20bd62c04a8ae2949ec999795ce45d57cefd090e
refs/heads/main
2023-08-10T18:29:55.137693
2021-09-30T21:07:21
2021-09-30T21:07:21
412,218,218
0
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from django.contrib import admin from django.urls import path, include from django.conf.urls.static import static from xabr import settings urlpatterns = [ path('', include('mainapp.urls', namespace='main')), path('auth/', include('authapp.urls', namespace='auth')), path('blog/', include('blogapp.urls', namespace='blogapp')), path('admin/', admin.site.urls), path('admin/doc/', include('django.contrib.admindocs.urls')), ] if settings.DEBUG: urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
51834523099e3ca59c889ea40d94cfd03ffdbb05
f31fda8014ecadf6af7d4e3392fb917c49e0352a
/HeavyIonsAnalysis/JetAnalysis/python/jets/akPu5CaloJetSequence_pp_data_cff.py
ee3d9695514ad371c28857cd6d3dce622b1ebe50
[]
no_license
jniedzie/lightbylight
acea5051f053c49824a49a0b78bac3a2247ee75f
f5a4661fcf3fd3c0e9ccd8893a46a238e30c2aa8
refs/heads/master
2020-03-18T12:24:31.970468
2018-02-09T15:50:00
2018-02-09T15:50:00
134,724,759
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2018-05-24T14:11:12
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py
import FWCore.ParameterSet.Config as cms from HeavyIonsAnalysis.JetAnalysis.patHeavyIonSequences_cff import patJetGenJetMatch, patJetPartonMatch, patJetCorrFactors, patJets from HeavyIonsAnalysis.JetAnalysis.inclusiveJetAnalyzer_cff import * from HeavyIonsAnalysis.JetAnalysis.bTaggers_cff import * from RecoJets.JetProducers.JetIDParams_cfi import * from RecoJets.JetProducers.nJettinessAdder_cfi import Njettiness akPu5Calomatch = patJetGenJetMatch.clone( src = cms.InputTag("akPu5CaloJets"), matched = cms.InputTag("ak5GenJets"), resolveByMatchQuality = cms.bool(False), maxDeltaR = 0.5 ) akPu5CalomatchGroomed = patJetGenJetMatch.clone( src = cms.InputTag("ak5GenJets"), matched = cms.InputTag("ak5GenJets"), resolveByMatchQuality = cms.bool(False), maxDeltaR = 0.5 ) akPu5Caloparton = patJetPartonMatch.clone(src = cms.InputTag("akPu5CaloJets") ) akPu5Calocorr = patJetCorrFactors.clone( useNPV = cms.bool(False), useRho = cms.bool(False), # primaryVertices = cms.InputTag("hiSelectedVertex"), levels = cms.vstring('L2Relative','L3Absolute'), src = cms.InputTag("akPu5CaloJets"), payload = "AKPu5Calo_offline" ) akPu5CaloJetID= cms.EDProducer('JetIDProducer', JetIDParams, src = cms.InputTag('akPu5CaloJets')) #akPu5Caloclean = heavyIonCleanedGenJets.clone(src = cms.InputTag('ak5GenJets')) akPu5CalobTagger = bTaggers("akPu5Calo",0.5) #create objects locally since they dont load properly otherwise #akPu5Calomatch = akPu5CalobTagger.match akPu5Caloparton = patJetPartonMatch.clone(src = cms.InputTag("akPu5CaloJets"), matched = cms.InputTag("genParticles")) akPu5CaloPatJetFlavourAssociationLegacy = akPu5CalobTagger.PatJetFlavourAssociationLegacy akPu5CaloPatJetPartons = akPu5CalobTagger.PatJetPartons akPu5CaloJetTracksAssociatorAtVertex = akPu5CalobTagger.JetTracksAssociatorAtVertex akPu5CaloJetTracksAssociatorAtVertex.tracks = cms.InputTag("highPurityTracks") akPu5CaloSimpleSecondaryVertexHighEffBJetTags = akPu5CalobTagger.SimpleSecondaryVertexHighEffBJetTags akPu5CaloSimpleSecondaryVertexHighPurBJetTags = akPu5CalobTagger.SimpleSecondaryVertexHighPurBJetTags akPu5CaloCombinedSecondaryVertexBJetTags = akPu5CalobTagger.CombinedSecondaryVertexBJetTags akPu5CaloCombinedSecondaryVertexV2BJetTags = akPu5CalobTagger.CombinedSecondaryVertexV2BJetTags akPu5CaloJetBProbabilityBJetTags = akPu5CalobTagger.JetBProbabilityBJetTags akPu5CaloSoftPFMuonByPtBJetTags = akPu5CalobTagger.SoftPFMuonByPtBJetTags akPu5CaloSoftPFMuonByIP3dBJetTags = akPu5CalobTagger.SoftPFMuonByIP3dBJetTags akPu5CaloTrackCountingHighEffBJetTags = akPu5CalobTagger.TrackCountingHighEffBJetTags akPu5CaloTrackCountingHighPurBJetTags = akPu5CalobTagger.TrackCountingHighPurBJetTags akPu5CaloPatJetPartonAssociationLegacy = akPu5CalobTagger.PatJetPartonAssociationLegacy akPu5CaloImpactParameterTagInfos = akPu5CalobTagger.ImpactParameterTagInfos akPu5CaloImpactParameterTagInfos.primaryVertex = cms.InputTag("offlinePrimaryVertices") akPu5CaloJetProbabilityBJetTags = akPu5CalobTagger.JetProbabilityBJetTags akPu5CaloSecondaryVertexTagInfos = akPu5CalobTagger.SecondaryVertexTagInfos akPu5CaloSimpleSecondaryVertexHighEffBJetTags = akPu5CalobTagger.SimpleSecondaryVertexHighEffBJetTags akPu5CaloSimpleSecondaryVertexHighPurBJetTags = akPu5CalobTagger.SimpleSecondaryVertexHighPurBJetTags akPu5CaloCombinedSecondaryVertexBJetTags = akPu5CalobTagger.CombinedSecondaryVertexBJetTags akPu5CaloCombinedSecondaryVertexV2BJetTags = akPu5CalobTagger.CombinedSecondaryVertexV2BJetTags akPu5CaloSecondaryVertexNegativeTagInfos = akPu5CalobTagger.SecondaryVertexNegativeTagInfos akPu5CaloNegativeSimpleSecondaryVertexHighEffBJetTags = akPu5CalobTagger.NegativeSimpleSecondaryVertexHighEffBJetTags akPu5CaloNegativeSimpleSecondaryVertexHighPurBJetTags = akPu5CalobTagger.NegativeSimpleSecondaryVertexHighPurBJetTags akPu5CaloNegativeCombinedSecondaryVertexBJetTags = akPu5CalobTagger.NegativeCombinedSecondaryVertexBJetTags akPu5CaloPositiveCombinedSecondaryVertexBJetTags = akPu5CalobTagger.PositiveCombinedSecondaryVertexBJetTags akPu5CaloNegativeCombinedSecondaryVertexV2BJetTags = akPu5CalobTagger.NegativeCombinedSecondaryVertexV2BJetTags akPu5CaloPositiveCombinedSecondaryVertexV2BJetTags = akPu5CalobTagger.PositiveCombinedSecondaryVertexV2BJetTags akPu5CaloSoftPFMuonsTagInfos = akPu5CalobTagger.SoftPFMuonsTagInfos akPu5CaloSoftPFMuonsTagInfos.primaryVertex = cms.InputTag("offlinePrimaryVertices") akPu5CaloSoftPFMuonBJetTags = akPu5CalobTagger.SoftPFMuonBJetTags akPu5CaloSoftPFMuonByIP3dBJetTags = akPu5CalobTagger.SoftPFMuonByIP3dBJetTags akPu5CaloSoftPFMuonByPtBJetTags = akPu5CalobTagger.SoftPFMuonByPtBJetTags akPu5CaloNegativeSoftPFMuonByPtBJetTags = akPu5CalobTagger.NegativeSoftPFMuonByPtBJetTags akPu5CaloPositiveSoftPFMuonByPtBJetTags = akPu5CalobTagger.PositiveSoftPFMuonByPtBJetTags akPu5CaloPatJetFlavourIdLegacy = cms.Sequence(akPu5CaloPatJetPartonAssociationLegacy*akPu5CaloPatJetFlavourAssociationLegacy) #Not working with our PU sub, but keep it here for reference #akPu5CaloPatJetFlavourAssociation = akPu5CalobTagger.PatJetFlavourAssociation #akPu5CaloPatJetFlavourId = cms.Sequence(akPu5CaloPatJetPartons*akPu5CaloPatJetFlavourAssociation) akPu5CaloJetBtaggingIP = cms.Sequence(akPu5CaloImpactParameterTagInfos * (akPu5CaloTrackCountingHighEffBJetTags + akPu5CaloTrackCountingHighPurBJetTags + akPu5CaloJetProbabilityBJetTags + akPu5CaloJetBProbabilityBJetTags ) ) akPu5CaloJetBtaggingSV = cms.Sequence(akPu5CaloImpactParameterTagInfos * akPu5CaloSecondaryVertexTagInfos * (akPu5CaloSimpleSecondaryVertexHighEffBJetTags+ akPu5CaloSimpleSecondaryVertexHighPurBJetTags+ akPu5CaloCombinedSecondaryVertexBJetTags+ akPu5CaloCombinedSecondaryVertexV2BJetTags ) ) akPu5CaloJetBtaggingNegSV = cms.Sequence(akPu5CaloImpactParameterTagInfos * akPu5CaloSecondaryVertexNegativeTagInfos * (akPu5CaloNegativeSimpleSecondaryVertexHighEffBJetTags+ akPu5CaloNegativeSimpleSecondaryVertexHighPurBJetTags+ akPu5CaloNegativeCombinedSecondaryVertexBJetTags+ akPu5CaloPositiveCombinedSecondaryVertexBJetTags+ akPu5CaloNegativeCombinedSecondaryVertexV2BJetTags+ akPu5CaloPositiveCombinedSecondaryVertexV2BJetTags ) ) akPu5CaloJetBtaggingMu = cms.Sequence(akPu5CaloSoftPFMuonsTagInfos * (akPu5CaloSoftPFMuonBJetTags + akPu5CaloSoftPFMuonByIP3dBJetTags + akPu5CaloSoftPFMuonByPtBJetTags + akPu5CaloNegativeSoftPFMuonByPtBJetTags + akPu5CaloPositiveSoftPFMuonByPtBJetTags ) ) akPu5CaloJetBtagging = cms.Sequence(akPu5CaloJetBtaggingIP *akPu5CaloJetBtaggingSV *akPu5CaloJetBtaggingNegSV # *akPu5CaloJetBtaggingMu ) akPu5CalopatJetsWithBtagging = patJets.clone(jetSource = cms.InputTag("akPu5CaloJets"), genJetMatch = cms.InputTag("akPu5Calomatch"), genPartonMatch = cms.InputTag("akPu5Caloparton"), jetCorrFactorsSource = cms.VInputTag(cms.InputTag("akPu5Calocorr")), JetPartonMapSource = cms.InputTag("akPu5CaloPatJetFlavourAssociationLegacy"), JetFlavourInfoSource = cms.InputTag("akPu5CaloPatJetFlavourAssociation"), trackAssociationSource = cms.InputTag("akPu5CaloJetTracksAssociatorAtVertex"), useLegacyJetMCFlavour = True, discriminatorSources = cms.VInputTag(cms.InputTag("akPu5CaloSimpleSecondaryVertexHighEffBJetTags"), cms.InputTag("akPu5CaloSimpleSecondaryVertexHighPurBJetTags"), cms.InputTag("akPu5CaloCombinedSecondaryVertexBJetTags"), cms.InputTag("akPu5CaloCombinedSecondaryVertexV2BJetTags"), cms.InputTag("akPu5CaloJetBProbabilityBJetTags"), cms.InputTag("akPu5CaloJetProbabilityBJetTags"), #cms.InputTag("akPu5CaloSoftPFMuonByPtBJetTags"), #cms.InputTag("akPu5CaloSoftPFMuonByIP3dBJetTags"), cms.InputTag("akPu5CaloTrackCountingHighEffBJetTags"), cms.InputTag("akPu5CaloTrackCountingHighPurBJetTags"), ), jetIDMap = cms.InputTag("akPu5CaloJetID"), addBTagInfo = True, addTagInfos = True, addDiscriminators = True, addAssociatedTracks = True, addJetCharge = False, addJetID = False, getJetMCFlavour = False, addGenPartonMatch = False, addGenJetMatch = False, embedGenJetMatch = False, embedGenPartonMatch = False, # embedCaloTowers = False, # embedPFCandidates = True ) akPu5CaloNjettiness = Njettiness.clone( src = cms.InputTag("akPu5CaloJets"), R0 = cms.double( 0.5) ) akPu5CalopatJetsWithBtagging.userData.userFloats.src += ['akPu5CaloNjettiness:tau1','akPu5CaloNjettiness:tau2','akPu5CaloNjettiness:tau3'] akPu5CaloJetAnalyzer = inclusiveJetAnalyzer.clone(jetTag = cms.InputTag("akPu5CalopatJetsWithBtagging"), genjetTag = 'ak5GenJets', rParam = 0.5, matchJets = cms.untracked.bool(False), matchTag = 'patJetsWithBtagging', pfCandidateLabel = cms.untracked.InputTag('particleFlow'), trackTag = cms.InputTag("generalTracks"), fillGenJets = False, isMC = False, doSubEvent = False, useHepMC = cms.untracked.bool(False), genParticles = cms.untracked.InputTag("genParticles"), eventInfoTag = cms.InputTag("generator"), doLifeTimeTagging = cms.untracked.bool(True), doLifeTimeTaggingExtras = cms.untracked.bool(False), bTagJetName = cms.untracked.string("akPu5Calo"), jetName = cms.untracked.string("akPu5Calo"), genPtMin = cms.untracked.double(5), hltTrgResults = cms.untracked.string('TriggerResults::'+'HISIGNAL'), doTower = cms.untracked.bool(False), doSubJets = cms.untracked.bool(False), doGenSubJets = cms.untracked.bool(False), subjetGenTag = cms.untracked.InputTag("ak5GenJets"), doGenTaus = False ) akPu5CaloJetSequence_mc = cms.Sequence( #akPu5Caloclean #* akPu5Calomatch #* #akPu5CalomatchGroomed * akPu5Caloparton * akPu5Calocorr * #akPu5CaloJetID #* akPu5CaloPatJetFlavourIdLegacy #* #akPu5CaloPatJetFlavourId # Use legacy algo till PU implemented * akPu5CaloJetTracksAssociatorAtVertex * akPu5CaloJetBtagging * akPu5CaloNjettiness * akPu5CalopatJetsWithBtagging * akPu5CaloJetAnalyzer ) akPu5CaloJetSequence_data = cms.Sequence(akPu5Calocorr * #akPu5CaloJetID #* akPu5CaloJetTracksAssociatorAtVertex * akPu5CaloJetBtagging * akPu5CaloNjettiness * akPu5CalopatJetsWithBtagging * akPu5CaloJetAnalyzer ) akPu5CaloJetSequence_jec = cms.Sequence(akPu5CaloJetSequence_mc) akPu5CaloJetSequence_mb = cms.Sequence(akPu5CaloJetSequence_mc) akPu5CaloJetSequence = cms.Sequence(akPu5CaloJetSequence_data)
d0f6ae52cfa302a69d163e0abc0b1bd64badc931
ca3da680541003e604947f0f454e11846b0841da
/IEA_Policy_Scrapper_Multi.py
254d52681db3cbb2d5172429f0dc15c3f6551ecf
[]
no_license
Mejinha/IEAPolicyScrapper
06b9c469261d9f849260007e604f40c380d070e6
7e1cc10dcb468cdc3afb586ab651321914e76a69
refs/heads/main
2023-05-05T12:12:44.452081
2021-05-28T13:47:25
2021-05-28T13:47:25
371,115,638
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# -*- coding: utf-8 -*- """ Created on Tue May 25 15:49:06 2021 @author: amejd """ import requests from bs4 import BeautifulSoup import pandas as pd from multiprocessing import Pool import numpy as np def GetData(page): '''Access main page and search data in each row''' url = 'https://www.iea.org/policies' page_data = [] raw = requests.get(f'{url}?page={page}') soup = BeautifulSoup(raw.content, 'html.parser') rows = soup.findAll(class_ = 'm-policy-listing-item__row') try: for row in rows: page_data.append(Scrape(row)) return page_data except: pass def Scrape(row): '''Access each policy and gather useful information''' temp_dict = { 'Country': row.findAll(class_ = 'm-policy-listing-item__col m-policy-listing-item__col--country')[0]['data-sortable-value'], 'Year': row.findAll(class_ = 'm-policy-listing-item__col m-policy-listing-item__col--year')[0]['data-sortable-value'], 'Policy': row.findAll(class_ = 'm-policy-listing-item__link')[0].text.strip() } policy_url = 'https://www.iea.org{}'.format(row.findAll(class_ = 'm-policy-listing-item__link')[0]['href']) raw_internal = requests.get(policy_url) soup_internal = BeautifulSoup(raw_internal.text, 'html.parser') categories = soup_internal.findAll('div', {'class': 'o-policy-content__list'}) for category in categories: catname = category.find(class_ = 'o-policy-content-list__title').text temp_dict[catname] = [item.text for item in category.findAll(class_ = 'a-tag__label')] return temp_dict if __name__ == '__main__': pages = range(1, 186) records = [] # Process the function GetData in batches of 10 parallel functions with Pool(10) as p: records = p.map(GetData, pages) # Execute GetData p.close() # Closes the application to avoid it from keep running as a background task p.join() # Wait for tasks before start a new batch # Organize data df = pd.concat([pd.DataFrame(i) for i in records]) df = df.reset_index() # Get Unique values for each column Topics = [] Policies = [] Sectors = [] Technologies = [] for row in df.iterrows(): try: for topic in row[1]['Topics']: Topics.append(topic) except: pass try: for policy in row[1]['Policy types']: Policies.append(policy) except: pass try: for sector in row[1]['Sectors']: Sectors.append(sector) except: pass try: for techs in row[1]['Technologies']: Technologies.append(techs) except: pass Topics = np.unique(Topics) Policies = np.unique(Policies) Sectors = np.unique(Sectors) Technologies = np.unique(Technologies) # Convert each value into unique boolean columns # 1 Check if the variable is a list, otherwise return False # 2 Check if the list contains the key, if so, return True for key in Topics: df['Topic_'+key] = df['Topics'].apply(lambda x: key in x if type(x) is list else False) for key in Policies: df['Policy_'+key] = df['Policy types'].apply(lambda x: key in x if type(x) is list else False) for key in Sectors: df['Sector_'+key] = df['Sectors'].apply(lambda x: key in x if type(x) is list else False) for key in Technologies: df['Tech_'+key] = df['Technologies'].apply(lambda x: key in x if type(x) is list else False) # Export to excel df.to_excel('IEA_Policies.xlsx')
0d8675623ee25e5eed9509e8c2626383d0468b6c
461670493b15f5e9e471ddcc76732261cf948b37
/challenge_189/challenge.py
7a6537928e7136b6d38472ac3259c8f68390cc46
[]
no_license
narenaryan/CodeEval-Solutions
d3d5f6bbecf74642d83b1e33c9e41f8ac2518924
bd63c78b5fac28439beefd747e272383cd839a89
refs/heads/master
2021-01-12T06:36:54.665037
2016-12-31T16:54:07
2016-12-31T16:54:07
77,394,649
0
0
null
null
null
null
UTF-8
Python
false
false
711
py
import sys with open(sys.argv[1], 'r') as test_cases: for test in test_cases: if test.strip() != '': # Format the input inputList = test.split(" ") noOfFriends = int(inputList[0]) friendHouses = [int(i) for i in inputList[1:]] # This list stores distances between Alice home and friend's minDistanceFromFriendHouses = [] for shiftingHouseDistance in friendHouses: tempDistances = [abs((friendHouseDistance - shiftingHouseDistance)) for friendHouseDistance in friendHouses] minDistanceFromFriendHouses.append(sum(tempDistances)) print min(minDistanceFromFriendHouses)
ffcfc74ce4c1bbce06d1f958565e8f9d7d31fafe
abebd304a9bc3ff1b90db09eba66c003f51a74d5
/sug/preprocess/main/dump_main_db.py
52cee76fa23f547cb7454b3abffa9857895b8aa6
[]
no_license
jiakechong1991/search
817715c58a1b117d177a7b49f443cb2411ee3c6f
86c644e9d26f2eba25d4cf50821ffcc8e14e7953
refs/heads/master
2021-04-15T17:42:01.074581
2018-04-10T14:04:17
2018-04-10T14:04:17
126,668,534
0
0
null
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UTF-8
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# coding: utf8 import argparse import codecs import copy import json import logging import re from search_service.indexing import Searchable from config.conf import LOGGING_FORMAT, MAX_WORD_LEN from utils.connection import with_mysql_cursor class WordGetter(): def __init__(self): pass @with_mysql_cursor('edxapp') def get_course_ids(self, cursor): sql = 'select course_id from course_meta_course where status >= 0' cursor.execute(sql) course_ids = [x['course_id'] for x in cursor.fetchall()] return course_ids def get_clean_text(self, text): special_chars = ['\t', '\n', u'\u2028', u'\u0085'] for c in special_chars: text = text.replace(c, '') return text def dump_file(self, file_ot): course_ids = self.get_course_ids() course_num = len(course_ids) wf = codecs.open(file_ot, 'w', encoding='utf8') search = Searchable() for no, course_id in enumerate(course_ids): try: course_info = search.get_course(course_id.encode('utf8')) except Exception, e: logging.error('%s: %s', course_id, e) continue if not course_info: continue if no % 100 == 0: logging.info('finished: %s/%s', no, course_num) base_info = { 'course_id': course_id, } # course_name row = copy.deepcopy(base_info) row.update({ 'category': 'course_name', 'value': course_info.course_name }) wf.write(json.dumps(row, sort_keys=True, ensure_ascii=False) + '\n') # course_about row = copy.deepcopy(base_info) row.update({ 'category': 'course_about', 'value': self.get_clean_text(course_info.get_about_searchable_text()) }) wf.write(json.dumps(row, sort_keys=True, ensure_ascii=False) + '\n') # children for child in course_info.children: if child.searchable_text: row = copy.deepcopy(base_info) row.update({ 'category': child.category, 'value': self.get_clean_text(child.searchable_text) }) wf.write(json.dumps(row, sort_keys=True, ensure_ascii=False) + '\n') wf.close() if __name__ == '__main__': logging.basicConfig(format=LOGGING_FORMAT, level=logging.INFO) parser = argparse.ArgumentParser() parser.add_argument('--file_ot', required=True) args = parser.parse_args() obj = WordGetter() obj.dump_file(args.file_ot)
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/models/__init__.py
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# from models.seq2seq import *
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/GO4StructuralPatterns/BridgePattern/MessageSenderBase.py
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sumeshsg/GO4DesignPattern
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from abc import abstractmethod class MessageSenderBase(object): @abstractmethod def send_message(self, title, details, importance): pass